pydl-0.6.0/0000775000111400017500000000000013160273564013274 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/0000775000111400017500000000000013160273564014244 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/file_lines.py0000664000111400017500000000337012775255301016732 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- def file_lines(path, compress=False): """Replicates the IDL ``FILE_LINES()`` function. Given a path to a file name or a list of such paths, returns the number of lines in the file(s). Parameters ---------- path : :class:`str` or :class:`list` of :class:`str` Path to a file. Can be a list of paths. compress : :class:`bool`, optional If set to ``True``, assumes that all files in `path` are GZIP compressed. Returns ------- :class:`int` or :class:`list` of :class:`int` The number of lines in `path`. Returns a list of lengths if a list of files is supplied. Notes ----- The ``/NOEXPAND_PATH`` option in IDL's ``FILE_LINES()`` is not implemented. References ---------- http://www.exelisvis.com/docs/FILE_LINES.html Examples -------- >>> from pydl import file_lines >>> from os.path import dirname, join >>> file_lines(join(dirname(__file__),'tests','t','this-file-contains-42-lines.txt')) 42 """ from astropy.extern.six import string_types scalar = False if isinstance(path, string_types): working_path = [path] scalar = True else: working_path = path lines = list() for filename in working_path: if compress: # # gzip in Python 2.6 can't use a context manager. # import gzip f = gzip.open(filename) lines.append(len(f.readlines())) f.close() else: with open(filename) as f: lines.append(len(f.readlines())) if scalar: return lines[0] else: return lines pydl-0.6.0/pydl/rebin.py0000664000111400017500000000763612775255301015731 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- from __future__ import division def rebin(x, d, sample=False): """Resize `x` to new dimensions given by `d`. The new dimensions must be integer multiples or factors of the original dimensions. Although there are some elegant solutions out there for rebinning, this function is intended to replace the IDL ``REBIN()`` function, which has a number of special properties: * It refuses to perform extrapolation when rebinning to a larger size in a particular dimension. * It can simultaneously rebin to a larger size in one dimension while rebinning to a smaller size in another dimension. Parameters ---------- x : :class:`~numpy.ndarray` The array to resample. d : :func:`tuple` The new shape of the array. sample : :class:`bool`, optional If ``True``, nearest-neighbor techniques will be used instead of interpolation. Returns ------- :class:`~numpy.ndarray` The resampled array. Raises ------ ValueError If the new dimensions are incompatible with the algorithm. References ---------- http://www.exelisvis.com/docs/REBIN.html Examples -------- >>> from numpy import arange, float >>> from pydl import rebin >>> rebin(arange(10, dtype=float), (5,)) array([ 0.5, 2.5, 4.5, 6.5, 8.5]) >>> rebin(arange(5, dtype=float), (10,)) array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4. ]) """ from numpy import floor, zeros d0 = x.shape if len(d0) != len(d): raise ValueError(("The new shape is incompatible with the " + "original array.!")) for k in range(len(d0)): if d[k] > d0[k]: if d[k] % d0[k] != 0: raise ValueError(("{0:d} is not a multiple " + "of {1:d}!").format(d[k], d0[k])) elif d[k] == d0[k]: pass else: if d0[k] % d[k] != 0: raise ValueError(("{0:d} is not a multiple " + "of {1:d}!").format(d0[k], d[k])) xx = x.copy() new_shape = list(d0) for k in range(len(d0)): new_shape[k] = d[k] r = zeros(new_shape, dtype=xx.dtype) sliceobj0 = [slice(None)]*len(d0) sliceobj1 = [slice(None)]*len(d0) sliceobj = [slice(None)]*len(d) f = d0[k]/d[k] if d[k] > d0[k]: for i in range(d[k]): p = f*i fp = int(floor(p)) sliceobj0[k] = slice(fp, fp + 1) sliceobj[k] = slice(i, i + 1) if sample: r[sliceobj] = xx[sliceobj0] else: if p < d0[k] - 1: sliceobj1[k] = slice(fp + 1, fp + 2) rshape = r[sliceobj].shape r[sliceobj] = (xx[sliceobj0].reshape(rshape) + (p - fp)*(xx[sliceobj1] - xx[sliceobj0]).reshape(rshape) ) else: r[sliceobj] = xx[sliceobj0] elif d[k] == d0[k]: for i in range(d[k]): sliceobj0[k] = slice(i, i + 1) sliceobj[k] = slice(i, i + 1) r[sliceobj] = xx[sliceobj0] else: for i in range(d[k]): sliceobj[k] = slice(i, i + 1) if sample: fp = int(floor(f*i)) sliceobj0[k] = slice(fp, fp + 1) r[sliceobj] = xx[sliceobj0] else: sliceobj0[k] = slice(int(f*i), int(f*(i+1))) rshape = r[sliceobj].shape r[sliceobj] = xx[sliceobj0].sum(k).reshape(rshape)/f xx = r return r pydl-0.6.0/pydl/smooth.py0000664000111400017500000000303712775255301016132 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- def smooth(signal, owidth, edge_truncate=False): """Replicates the IDL ``SMOOTH()`` function. Parameters ---------- signal : array-like The array to be smoothed. owidth : :class:`int` or array-like Width of the smoothing window. Can be a scalar or an array with length equal to the number of dimensions of `signal`. edge_truncate : :class:`bool`, optional Set `edge_truncate` to ``True`` to apply smoothing to all points. Points near the edge are normally excluded from smoothing. Returns ------- array-like A smoothed array with the same dimesions and type as `signal`. Notes ----- References ---------- http://www.exelisvis.com/docs/SMOOTH.html Examples -------- """ if owidth % 2 == 0: width = owidth + 1 else: width = owidth if width < 3: return signal n = signal.size istart = int((width-1)/2) iend = n - int((width+1)/2) w2 = int(width/2) s = signal.copy() for i in range(n): if i < istart: if edge_truncate: s[i] = (signal[0:istart+i+1].sum() + (istart-i)*signal[0])/float(width) elif i > iend: if edge_truncate: s[i] = (signal[i-istart:n].sum() + (i-iend)*signal[n-1])/float(width) else: s[i] = signal[i-w2:i+w2+1].sum()/float(width) return s pydl-0.6.0/pydl/photoop/0000775000111400017500000000000013160273564015734 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/photoop/tests/0000775000111400017500000000000013160273564017076 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/photoop/tests/test_sdssio.py0000664000111400017500000001343212775255301022016 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import os import numpy as np from astropy.tests.helper import raises from ..sdssio import (filtername, filternum, sdss_calib, sdss_name, sdss_path, sdssflux2ab) class TestSDSSio(object): """Test the functions in pydl.photoop.sdssio. """ # # Set up environment variables for testing sdss_path and sdss_name. # path_envvars = ('PHOTO_CALIB', 'PHOTO_DATA', 'BOSS_PHOTOOBJ', 'PHOTO_REDUX', 'PHOTO_RESOLVE', 'PHOTO_SKY', 'PHOTO_SWEEP') env_cache = {} path_data = { 'apObj': "/PHOTO_REDUX/301/137/objcs/4", 'calibMatch': "/PHOTO_REDUX/301/137/nfcalib", 'calibPhotom': "/PHOTO_REDUX/301/137/nfcalib", 'calibPhotomGlobal': "/PHOTO_CALIB/301/137/nfcalib", 'fakeIdR': "/PHOTO_DATA/137/fake_fields/4", 'fpAtlas': "/PHOTO_REDUX/301/137/objcs/4", 'fpBIN': "/PHOTO_REDUX/301/137/objcs/4", 'fpC': "/PHOTO_REDUX/301/137/objcs/4", 'fpFieldStat': "/PHOTO_REDUX/301/137/objcs/4", 'fpM': "/PHOTO_REDUX/301/137/objcs/4", 'fpObjc': "/PHOTO_REDUX/301/137/objcs/4", 'hoggObj': "/PHOTO_REDUX/301/137/objcs/4", 'idFF': "/PHOTO_REDUX/301/137/objcs/4", 'idR': "/PHOTO_DATA/137/fields/4", 'idRR': "/PHOTO_DATA/137/fields/4", 'psBB': "/PHOTO_REDUX/301/137/objcs/4", 'psFF': "/PHOTO_REDUX/301/137/objcs/4", 'psField': "/PHOTO_REDUX/301/137/objcs/4", 'reObjGlobal': "/PHOTO_RESOLVE/301/137/resolve/4", 'reObjRun': "/PHOTO_REDUX/301/137/resolve/4", 'reObjTmp': "/PHOTO_RESOLVE/301/137/resolve/4", 'tsField': "/PHOTO_REDUX/301/137/calibChunks/4", } name_data = { 'apObj': "apObj-000137-r4-0042.fit", 'calibMatch': "calibMatch-000137-4.fits", 'calibPhotom': "calibPhotom-000137-4.fits", 'calibPhotomGlobal': "calibPhotomGlobal-000137-4.fits", 'fakeIdR': "idR-000137-r4-0042.fit", 'fpAtlas': "fpAtlas-000137-4-0042.fit", 'fpBIN': "fpBIN-000137-r4-0042.fit", 'fpC': "fpC-000137-r4-0042.fit", 'fpFieldStat': "fpFieldStat-000137-4-0042.fit", 'fpM': "fpM-000137-r4-0042.fit", 'fpObjc': "fpObjc-000137-4-0042.fit", 'hoggObj': "hoggObj-000137-4-0042.fits", 'idFF': "idFF-000137-r4.fit", 'idR': "idR-000137-r4-0042.fit", 'idRR': "idRR-000137-r4-0042.fit", 'psBB': "psBB-000137-r4-0042.fit", 'psFF': "psFF-000137-r4.fit", 'psField': "psField-000137-4-0042.fit", 'reObjGlobal': "reObjGlobal-000137-4-0042.fits", 'reObjRun': "reObjRun-000137-4-0042.fits", 'reObjTmp': "reObjTmp-000137-4-0042.fits", 'tsField': "tsField-000137-4-301-0042.fit", } def setup(self): for e in self.path_envvars: try: actual_env = os.environ[e] except KeyError: actual_env = None self.env_cache[e] = actual_env os.environ[e] = '/' + e return def teardown(self): for e in self.env_cache: if self.env_cache[e] is None: del os.environ[e] else: os.environ[e] = self.env_cache[e] return def test_filtername(self): assert filtername(0) == 'u' assert filtername(1) == 'g' assert filtername(2) == 'r' assert filtername(3) == 'i' assert filtername(4) == 'z' # # filtername should return its argument if it's not # integer-like # assert filtername('r') == 'r' def test_filternum(self): assert filternum('u') == 0 assert filternum('g') == 1 assert filternum('r') == 2 assert filternum('i') == 3 assert filternum('z') == 4 # # Test default return value # fn = filternum() for k in range(5): assert fn[k] == k def test_sdss_calib(self): foo = sdss_calib(94, 6, 101) assert foo['NMGYPERCOUNT'] == 1.0 def test_sdss_name(self): # # Bad ftype # with raises(KeyError): p = sdss_name('fooBar', 137, 4, 42) for ftype in self.name_data: assert sdss_name(ftype, 137, 4, 42, '301', 'r', no_path=True) == self.name_data[ftype] def test_sdss_name_with_path(self): for ftype in self.name_data: assert (sdss_name(ftype, 137, 4, 42, '301', 'r') == os.path.join(self.path_data[ftype], self.name_data[ftype])) def test_sdss_name_reObj(self): assert sdss_name('reObj', 137, 4, 42, '301', 'r', no_path=True) == self.name_data['reObjGlobal'] resolve = os.environ['PHOTO_RESOLVE'] del os.environ['PHOTO_RESOLVE'] assert sdss_name('reObj', 137, 4, 42, '301', 'r', no_path=True) == self.name_data['reObjRun'] os.environ['PHOTO_RESOLVE'] = resolve def test_sdss_path(self): with raises(KeyError): p = sdss_path('fooBar', 137) for ftype in self.path_data: assert sdss_path(ftype, 137, 4, '301') == self.path_data[ftype] def test_sdssflux2ab(self): correction = np.array([-0.042, 0.036, 0.015, 0.013, -0.002]) mags = np.zeros((2, 5), dtype='d') mags[0, :] = 18.0 mags[1, :] = 19.0 ab = sdssflux2ab(mags, magnitude=True) assert (ab == (mags + correction)).all() flux = 10**((22.5 - mags)/2.5) # nanomaggies ab = sdssflux2ab(flux) assert np.allclose(ab, 10**((22.5 - (mags + correction))/2.5)) ivar = 1.0/flux ab = sdssflux2ab(ivar, ivar=True) assert np.allclose(ab, ivar/(10**(-2.0*correction/2.5))) pydl-0.6.0/pydl/photoop/tests/test_photoop.py0000664000111400017500000000211712775255301022200 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import raises from ..photoobj import sdss_calibv, unwrap_objid class TestPhotoobj(object): """Test the functions in pydl.photoop.photoobj. """ def setup(self): pass def teardown(self): pass def test_sdss_calibv(self): assert np.allclose(0.2650306748466258, sdss_calibv()) def test_unwrap_objid(self): objid = unwrap_objid(np.array([1237661382772195474])) assert objid.skyversion == 2 assert objid.rerun == 301 assert objid.run == 3704 assert objid.camcol == 3 assert objid.frame == 91 assert objid.id == 146 objid = unwrap_objid(np.array(['1237661382772195474'])) assert objid.skyversion == 2 assert objid.rerun == 301 assert objid.run == 3704 assert objid.camcol == 3 assert objid.frame == 91 assert objid.id == 146 with raises(ValueError): objid = unwrap_objid(np.array([3.14159])) pydl-0.6.0/pydl/photoop/tests/__init__.py0000664000111400017500000000021212775255301021202 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This is the pydl/photoop/tests directory. """ pydl-0.6.0/pydl/photoop/tests/test_window.py0000664000111400017500000000100112775255301022006 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import os import numpy as np from astropy.tests.helper import raises from ..window import sdss_score, window_read, window_score class TestWindow(object): """Test the functions in pydl.photoop.window. """ def setup(self): pass def teardown(self): pass def test_sdss_score(self): pass def test_window_read(self): pass def test_window_score(self): pass pydl-0.6.0/pydl/photoop/photoobj.py0000664000111400017500000000562512775255301020142 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the photoobj directory in photoop. """ def sdss_calibv(): """Return calibration for velocities from pix/frame to deg/day. Returns ------- :class:`float` The conversion from pixels per frame to degrees per day Notes ----- Assumes frame time difference of 71.72 seconds and pixel scale of 0.396 arcsec, both fixed. Also note that observations of the same part of sky from adjacent bands are separated by *two* frame numbers, so we multiply by a factor two. """ pixscale = 0.396 # arcsec ftime = 71.72 # seconds pixframe2degday = 2.0*pixscale/(3600.0) * (3600.0)*24.0/ftime return pixframe2degday def unwrap_objid(objid): """Unwrap CAS-style objID into run, camcol, field, id, rerun. See :func:`~pydl.pydlutils.sdss.sdss_objid` for details on how the bits within an objID are assigned. Parameters ---------- objid : :class:`numpy.ndarray` An array containing 64-bit integers or strings. If strings are passed, they will be converted to integers internally. Returns ------- :class:`numpy.recarray` A record array with the same length as objid, with the columns 'run', 'camcol', 'frame', 'id', 'rerun', 'skyversion'. Notes ----- For historical reasons, the inverse of this function, :func:`~pydl.pydlutils.sdss.sdss_objid` is not in the same namespace as this function. 'frame' is used instead of 'field' because record arrays have a method of the same name. Examples -------- >>> from numpy import array >>> from pydl.photoop.photoobj import unwrap_objid >>> unwrap_objid(array([1237661382772195474])) rec.array([(2, 301, 3704, 3, 91, 146)], dtype=[('skyversion', '> 59, 2**4 - 1) unwrap.rerun = np.bitwise_and(tempobjid >> 48, 2**11 - 1) unwrap.run = np.bitwise_and(tempobjid >> 32, 2**16 - 1) unwrap.camcol = np.bitwise_and(tempobjid >> 29, 2**3 - 1) # unwrap.firstfield = np.bitwise_and(tempobjid >> 28, 2**1 - 1) unwrap.frame = np.bitwise_and(tempobjid >> 16, 2**12 - 1) unwrap.id = np.bitwise_and(tempobjid, 2**16 - 1) return unwrap pydl-0.6.0/pydl/photoop/__init__.py0000664000111400017500000000071312775255301020046 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This subpackage implements functions from the photoop package. """ # # Define this early on so that submodules can use it # from .. import PydlException class PhotoopException(PydlException): """Exceptions raised by :mod:`pydl.photoop` that don't fit into a standard exception class like :exc:`ValueError`. """ pass __all__ = ['PhotoopException'] pydl-0.6.0/pydl/photoop/window.py0000664000111400017500000002614212775255301017622 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the window directory in photoop. """ def sdss_score(flist, silent=True): """Score a list of imaging fields from zero to one. Parameters ---------- flist : :class:`~astropy.io.fits.HDUList` Opened FITS file. silent : :class:`bool`, optional If ``False``, print extra information. Returns ------- :class:`numpy.ndarray` A vector of scores, one for each row of the FITS file. """ import time import numpy as np from astropy import log from astropy.io import fits from warnings import warn from .sdssio import sdss_name, sdss_calib from ..pydlutils.sdss import sdss_flagval lat = 32.780361 lng = 360.0 - 105.820417 tzone = 7 scores = 1 # # Read the PHOTO status bits # if not silent: log.info('Setting PHOTO status bits') t1 = time.time() nlist = flist[1].header.get('NAXIS2') fdata = flist[1].data for k in range(nlist): if not silent and ((k % 1000) == 0): log.info("Setting PHOTO status {0:d} {1:d}".format(k, nlist)) thisfile = sdss_name('fpFieldStat', fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k]) try: fpfield = fits.open(thisfile) except IOError: warn("Retrying fpFieldStat file for RUN = {0:d} CAMCOL = {1:d} FIELD = {2:d} RERUN = {3}".format(fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k])) try: fpfield = fits.open(thisfile) except IOError: warn("Bad fpFieldStat file for RUN = {0:d} CAMCOL = {1:d} FIELD = {2:d} RERUN = {3}".format(fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k])) fdata.field('PHOTO_STATUS')[k] = -1 if not silent: log.info('Trying tsField instead.') thisfile = sdss_name('tsField', fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k]) try: tsfield = fits.open(thisfile) except IOError: warn('Bad tsField file.') else: if not silent: log.info('tsField found, using frames_status.') fdata.field('PHOTO_STATUS')[k] = tsfield[1].data.field('frames_status')[0] else: fdata.field('PHOTO_STATUS')[k] = fpfield[1].data.field('status')[0] else: fdata.field('PHOTO_STATUS')[k] = fpfield[1].data.field('status')[0] if not silent: log.info("Time to set PHOTO status = {0:f} sec".format(time.time()-t1)) # # Read in the PSP status # if not silent: log.info('Setting PSP status bits') t2 = time.time() for k in range(nlist): if not silent and ((k % 1000) == 0): log.info("Setting PSP status {0:d} {1:d}".format(k, nlist)) thisfile = sdss_name('psField', fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k]) try: psfield = fits.open(thisfile) except IOError: warn("Retrying psField file for RUN = {0:d} CAMCOL = {1:d} FIELD = {2:d} RERUN = {3}".format(fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k])) try: psfield = fits.open(thisfile) except IOError: warn("Bad psField file for RUN = {0:d} CAMCOL = {1:d} FIELD = {2:d} RERUN = {3}".format(fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k])) fdata.field('PSP_STATUS')[k] = -1 fdata.field('PSF_FWHM')[k] = -1 fdata.field('SKYFLUX')[k] = -1 pixscale = 0.396 * np.sqrt(fdata.field('XBIN')[k]**2 + fdata.field('YBIN')[k]**2)/np.sqrt(2.0) calibinfo = sdss_calib(fdata.field('RUN')[k], fdata.field('CAMCOL')[k], fdata.field('FIELD')[k], fdata.field('RERUN')[k], **kwargs) fdata.field('PSP_STATUS')[k] = psfield[6].data.field('status')[0] fdata.field('PSF_FWHM')[k] = psfield[6].data.field('psf_width')[0] fdata.field('SKYFLUX')[k] = (psfield[6].data.field('sky')[0] * calibinfo['NMGYPERCOUNT']/pixscale**2) if not silent: log.info("Time to set PSP status = {0:f} sec".format(time.time()-t2)) # # Decide if each field exists in all 5 bands. # bad_bits = sdss_flagval('image_status', ['bad_rotator', 'bad_astrom', 'bad_focus', 'shutters']) if 'ignoreframesstatus' in kwargs: ignoreframesstatus = np.zeros(fdata.field('PHOTO_STATUS').shape) == 0 else: ignoreframesstatus = np.zeros(fdata.field('PHOTO_STATUS').shape) == 1 qexist = (fdata.field('PHOTO_STATUS') == 0) | ignoreframesstatus for k in range(5): qexist &= (fdata.field('IMAGE_STATUS')[:, k] & bad_bits) == 0 # # Decide if each field is phtometric in all 5 bands. # unphot_bits = sdss_flagval('image_status', ['cloudy', 'unknown', 'ff_petals', 'dead_ccd', 'noisy_ccd']) qphot = fdata.field('SUN_ANGLE') < -12 for k in range(5): qphot &= (fdata.field('IMAGE_STATUS')[:, k] & unphot_bits) == 0 for k in range(5): qphot &= (((fdata.field('PSP_STATUS')[:, k] & 31) <= 2) | (fdata.field('XBIN') > 1) | ignoreframesstatus) # # Now set the score for each field # sensitivity = (0.7 / (fdata.field('PSF_FWHM')[:, 2] * np.sqrt(fdata.field('SKYFLUX')[:, 2]))) < 0.4 fdata.field('SCORE')[:] = qexist * (0.1 + 0.5*qphot + sensitivity) ibinned = np.find(fdata.field('XBIN') > 1) if len(ibinned) > 0: fdata.field('SCORE')[ibinned] *= 0.1 # # Look for any NaN values, which could happen for example if there # is a corrupted psField file and PSF_FWHM or SKYFLUX was negative. # ibad = np.find(~np.isfinite(fdata.field('SCORE'))) if len(ibad) > 0: warn("Changing NaN scores for {0:d} fields to zero.".format(len(ibad))) fdata.field('SCORE')[ibad] = 0 return fdata.field('SCORE') def window_read(**kwargs): """Read window files in $PHOTO_RESOLVE. """ from os import getenv from os.path import exists, join from . import PhotoopException from ..pydlutils.mangle import set_use_caps from astropy.io import fits import numpy as np resolve_dir = getenv('PHOTO_RESOLVE') if resolve_dir is None: raise PhotoopException(('You have not set the environment variable ' + 'PHOTO_RESOLVE!')) if 'silent' not in kwargs: kwargs['silent'] = True r = dict() if 'flist' in kwargs: if 'rescore' in kwargs: flist_file = join(resolve_dir, 'window_flist_rescore.fits') if not exists(rescore_file): # # This will be called if window_flist_rescore.fits doesn't exist. # window_score() else: flist_file = join(resolve_dir, 'window_flist.fits') with fits.open(rescore_file) as fit: r['flist'] = fit[1].data if 'blist' in kwargs or 'balkans' in kwargs: blist_file = join(resolve_dir, 'window_blist.fits') with fits.open(balkan_file) as fit: r['blist'] = fit[1].data if 'bcaps' in kwargs or 'balkans' in kwargs: bcaps_file = join(resolve_dir, 'window_bcaps.fits') with fits.open(bcaps_file) as fit: r['bcaps'] = fit[1].data if 'findx' in kwargs: findx_file = join(resolve_dir, 'window_findx.fits') with fits.open(findx_file) as fit: r['findx'] = fit[1].data if 'bindx' in kwargs: bindx_file = join(resolve_dir, 'window_bindx.fits') with fits.open(bindx_file) as fit: r['bindx'] = fit[1].data if 'balkans' in kwargs: # # Copy blist data to balkans # r['balkans'] = r['blist'].copy() r['balkans']['caps'] = {'X': list(), 'CM': list()} r['balkans']['use_caps'] = np.zeros(r['balkans']['ICAP'].shape, dtype=np.uint64) if 'blist' not in kwargs: del r['blist'] # # Copy bcaps data into balkans # for k in range(len(r['balkans']['ICAP'])): r['balkans']['caps']['X'].append(r['bcaps']['X'][balkans['ICAP'][k]:balkans['ICAP'][k]+balkans['NCAPS'][k]]) r['balkans']['caps']['CM'].append(r['bcaps']['CM'][balkans['ICAP'][k]:balkans['ICAP'][k]+balkans['NCAPS'][k]]) r['balkans']['use_caps'][k] = set_use_caps( r['balkans']['caps']['X'][k], r['balkans']['caps']['CM'][k], r['balkans']['use_caps'][k], allow_doubles=True) if 'bcaps' not in kwargs: del r['bcaps'] return r def window_score(**kwargs): """For uber-resolve, score all the fields from zero to one. If 'rescore' is set, then write a new file 'window_flist_rescore.fits' rather than over-writing the file 'window_flist.fits' """ # import time from os import environ from astropy.io import fits as pyfits from . import PhotoopException # t0 = time.time() # # Be certain not to use global calibrations # try: calib_dir_save = environ['PHOTO_CALIB'] except KeyError: raise PhotoopException(('You have not set the environment variable ' + 'PHOTO_CALIB!')) del environ['PHOTO_CALIB'] # # Read the file # try: resolve_dir = environ['PHOTO_RESOLVE'] except KeyError: raise PhotoopException(('You have not set the environment variable ' + 'PHOTO_RESOLVE!')) filename = join(resolve_dir, 'window_flist.fits') if 'rescore' in kwargs: fitsmode = 'readonly' else: fitsmode = 'update' try: flist = pyfits.open(filename, mode=fitsmode) except IOError: raise PhotoopException('Unable to read FLIST file.') # # Construct the scores filling in the values to FLIST.SCORE # flist.field('SCORE')[:] = sdss_score(flist) if 'rescore' in kwargs: flist.writeto(join(resolve_dir, 'window_flist_rescore.fits')) flist.close() # # Restore the PHOTO_CALIB variable # environ['PHOTO_CALIB'] = calib_dir_save # print "Elapsed time = %f sec" % (time.time()-t0,) return pydl-0.6.0/pydl/photoop/sdssio.py0000664000111400017500000002133012775255301017611 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the sdssio directory of photoop. """ # # Filename formats used by sdss_name and sdss_path # _name_formats = { 'apObj': "{ftype}-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'calibMatch': "{ftype}-{run:06d}-{camcol:1d}.fits", 'calibPhotom': "{ftype}-{run:06d}-{camcol:1d}.fits", 'calibPhotomGlobal': "{ftype}-{run:06d}-{camcol:1d}.fits", 'fakeIdR': "idR-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'fpAtlas': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fit", 'fpBIN': "{ftype}-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'fpC': "{ftype}-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'fpFieldStat': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fit", 'fpM': "{ftype}-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'fpObjc': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fit", 'hoggObj': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fits", 'idFF': "{ftype}-{run:06d}-{filter}{camcol:1d}.fit", 'idR': "{ftype}-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'idRR': "{ftype}-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'psBB': "{ftype}-{run:06d}-{filter}{camcol:1d}-{field:04d}.fit", 'psFF': "{ftype}-{run:06d}-{filter}{camcol:1d}.fit", 'psField': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fit", 'reObjGlobal': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fits", 'reObjRun': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fits", 'reObjTmp': "{ftype}-{run:06d}-{camcol:1d}-{field:04d}.fits", 'tsField': "{ftype}-{run:06d}-{camcol:1d}-{rerun}-{field:04d}.fit", } _path_formats = { 'apObj': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'calibMatch': "{redux}/{rerun}/{run:d}/nfcalib", 'calibPhotom': "{redux}/{rerun}/{run:d}/nfcalib", 'calibPhotomGlobal': "{calib}/{rerun}/{run:d}/nfcalib", 'fakeIdR': "{data}/{run:d}/fake_fields/{camcol:1d}", 'fpAtlas': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'fpBIN': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'fpC': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'fpFieldStat': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'fpM': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'fpObjc': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'hoggObj': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'idFF': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'idR': "{data}/{run:d}/fields/{camcol:1d}", 'idRR': "{data}/{run:d}/fields/{camcol:1d}", 'psBB': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'psFF': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'psField': "{redux}/{rerun}/{run:d}/objcs/{camcol:1d}", 'reObjGlobal': "{resolve}/{rerun}/{run:d}/resolve/{camcol:1d}", 'reObjRun': "{redux}/{rerun}/{run:d}/resolve/{camcol:1d}", 'reObjTmp': "{resolve}/{rerun}/{run:d}/resolve/{camcol:1d}", 'tsField': "{redux}/{rerun}/{run:d}/calibChunks/{camcol:1d}", } def filtername(f): """Return the name of a filter given its number. Parameters ---------- f : :class:`int` The filter number. Returns ------- :class:`str` The corresponding filter name. Examples -------- >>> filtername(0) 'u' """ from astropy.extern.six import string_types if isinstance(f, string_types): return f fname = ('u', 'g', 'r', 'i', 'z') return fname[f] def filternum(filt='foo'): """Return index number for SDSS filters either from a number or name. Parameters ---------- filt : :class:`str` The filter name. Returns ------- :class:`int` The corresponding filter number Examples -------- >>> filternum('g') 1 """ if filt == 'foo': return list(range(5)) else: filters = {'u': 0, 'g': 1, 'r': 2, 'i': 3, 'z': 4} return filters[filt] def sdss_calib(run, camcol, field, rerun='', **kwargs): """Read photometric calibration solutions from calibPhotom or calibPhotomGlobal files. Parameters ---------- run : :class:`int` Photo run number camcol : :class:`int` Camcol number field : :class:`int` Field number rerun : :class:`str`, optional Photometric reduction number, as a string. Returns ------- :class:`dict` A dictionary containing the 'NMGYPERCOUNT' keyword. Notes ----- Currently, this is just a placeholder. """ return {'NMGYPERCOUNT': 1.0} def sdss_name(ftype, run, camcol, field, rerun='', thisfilter='r', no_path=False): """Return the name of an SDSS data file including path. Parameters ---------- ftype : :class:`str` The general type of the file, for example ``'reObj'`` run : :class:`int` The run number. camcol : :class:`int` The camcol number. field : :class:`int` The field number rerun : :class:`str`, optional If necessary, set the rerun name using this argument. thisfilter : :class:`int` or :class:`str`, optional If necessary, set the filter using this argument. no_path : :class:`bool`, optional Normally, sdss_name returns the full path. If `no_path` is ``True``, only the basename of the file is returned. Returns ------- :class:`str` The full file name, normally including the full path. Raises ------ KeyError If the file type is unknown. """ from os import getenv from os.path import join if ftype == 'reObj': if getenv('PHOTO_RESOLVE') is None: myftype = 'reObjRun' else: myftype = 'reObjGlobal' else: myftype = ftype thisfilter = filtername(thisfilter) indict = {'ftype': myftype, 'run': run, 'camcol': camcol, 'field': field, 'filter': thisfilter, 'rerun': rerun} try: fullname = _name_formats[myftype].format(**indict) except KeyError: raise KeyError("Unknown FTYPE = {0}".format(myftype)) if not no_path: datadir = sdss_path(myftype, run, camcol, rerun) fullname = join(datadir, fullname) return fullname def sdss_path(ftype, run, camcol=0, rerun=''): """Return the path name for SDSS data assuming SAS directory structure. Parameters ---------- ftype : :class:`str` The general type of the file, for example ``'reObj'`` run : :class:`int` The run number. camcol : :class:`int`, optional If necessary, set the camcol number using this argument. rerun : :class:`str`, optional If necessary, set the rerun name using this argument. Returns ------- :class:`str` The directory in which file `ftype` lives. Raises ------ KeyError If the file type is unknown. """ from os import getenv indict = { 'run': run, 'camcol': camcol, 'rerun': rerun, 'calib': getenv('PHOTO_CALIB'), 'data': getenv('PHOTO_DATA'), 'photoobj': getenv('BOSS_PHOTOOBJ'), 'redux': getenv('PHOTO_REDUX'), 'resolve': getenv('PHOTO_RESOLVE'), 'sky': getenv('PHOTO_SKY'), 'sweep': getenv('PHOTO_SWEEP'), } try: datadir = _path_formats[ftype].format(**indict) except KeyError: raise KeyError("Unknown FTYPE = {0}".format(ftype)) return datadir def sdssflux2ab(flux, magnitude=False, ivar=False): """Convert the SDSS calibrated fluxes (magnitudes) into AB fluxes (magnitudes). Parameters ---------- flux : :class:`numpy.ndarray` Array of calibrated fluxes or SDSS magnitudes with 5 columns, corresponding to the 5 filters *u*, *g*, *r*, *i*, *z*. magnitude : :class:`bool`, optional If set to ``True``, then assume `flux` are SDSS magnitudes instead of linear flux units. ivar : :class:`numpy.ndarray`, optional If set, the input fluxes are actually inverse variances. Returns ------- :class:`numpy.ndarray` Array of fluxes or magnitudes on the AB system. Notes ----- Uses the conversions posted by D.Hogg (sdss-calib/845):: u(AB,2.5m) = u(2.5m) - 0.042 g(AB,2.5m) = g(2.5m) + 0.036 r(AB,2.5m) = r(2.5m) + 0.015 i(AB,2.5m) = i(2.5m) + 0.013 z(AB,2.5m) = z(2.5m) - 0.002 """ import numpy as np # # Correction vector, adjust this as necessary # correction = np.array([-0.042, 0.036, 0.015, 0.013, -0.002]) rows, cols = flux.shape abflux = flux.copy() if magnitude: for i in range(rows): abflux[i, :] += correction else: factor = 10.0**(-correction/2.5) if ivar: factor = 1.0/factor**2 for i in range(rows): abflux[i, :] *= factor return abflux pydl-0.6.0/pydl/pydlspec2d/0000775000111400017500000000000013160273564016315 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlspec2d/tests/0000775000111400017500000000000013160273564017457 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlspec2d/tests/t/0000775000111400017500000000000013160273564017722 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlspec2d/tests/t/filter_thru_idl_data.txt0000664000111400017500000000260512775255301024636 0ustar weaverweaver00000000000000# Test data for use with test_filter_thru. 14.1224, 0.127368, 0.508305, 0.293359, 0.381092, 2.51200, -0.0306516, 0.298414, 0.185329, 1.11159, 0.166036, 1.08602, 0.162873, 0.529303, 0.290425, -0.0399243, 0.209576, 0.778746, 0.578854, 0.429100 18.3209, 0.0740831, 0.531655, 0.943787, 0.558309, 7.58337, 0.209387, 0.861768, 0.275096, 1.88644, 0.307924, 4.10726, 0.276175, 0.732941, 0.336644, 0.0456389, 0.725353, 2.25312, 0.413612, 1.08603 14.4588, -0.0113344, 1.15476, 3.76573, 1.35594, 17.3931, 0.647237, 2.96833, 1.00745, 1.46273, 0.815733, 11.2539, 0.957882, 2.57075, 0.821274, 0.162416, 2.08590, 5.34149, 1.32573, 3.67932 10.4319, -0.0250982, 1.83043, 5.57500, 1.64166, 19.9340, 1.18961, 3.89914, 1.83561, 0.884010, 1.22140, 13.7641, 1.66350, 3.35640, 1.41506, 0.105699, 3.04802, 6.47299, 2.31518, 5.10352 7.59458, 0.0395837, 2.31145, 6.42070, 2.04900, 20.3303, 1.56850, 4.25935, 2.38152, 0.984143, 1.51820, 14.4626, 2.01851, 3.74417, 1.86966, 0.133837, 3.53355, 6.86765, 2.98000, 5.82073 pydl-0.6.0/pydl/pydlspec2d/tests/t/spPlate-4055-55359-0020.fits0000664000111400017500000136730012775255301023675 0ustar weaverweaver00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -32 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 4637 NAXIS2 = 20 TELESCOP= 'SDSS 2.5-M' / Sloan Digital Sky Survey NGUIDE = 33.0000 / Number of guider frames during exposure SEEING20= 1.41527 / 20% seeing during exposure (arcsec) SEEING50= 1.52343 / 50% seeing during exposure (arcsec) SEEING80= 1.63672 / 80% seeing during exposure (arcsec) RMSOFF20= 0.0111816500000 / 20% RMS offset of guide fibers (arcsec) RMSOFF50= 0.0164329166667 / 50% RMS offset of guide fibers (arcsec) RMSOFF80= 0.0225299500000 / 80% RMS offset of guide fibers (arcsec) DAQVER = '1.2.14 ' CAMDAQ = '1.4.7:38' SUBFRAME= '' / the subframe readout command ERRCNT = 'NONE ' SYNCERR = 'NONE ' SLINES = 'NONE ' PIXERR = 'NONE ' PLINES = 'NONE ' PFERR = 'NONE ' DIDFLUSH= T / CCD was flushed before integration FLAVOR = 'science ' / exposure type, SDSS spectro style BOSSVER = '2 ' / ICC version MJD = 55359 / APO fMJD day at start of exposure MJDLIST = '55359 ' / TAI-BEG = 4783034232.00 / MJD(TAI) seconds at start of integration DATE-OBS= '2010-06-12T04:37:12' / TAI date at start of integration OBJSYS = 'ICRS ' / The TCC objSys RA = 236.433204 / RA of telescope boresight (deg) DEC = 1.836429 / Dec of telescope boresight (deg) EQUINOX = 2000.00 / RADECSYS= 'FK5 ' / RADEG = 236.4365 / RA of telescope pointing(deg) DECDEG = 1.8332 / Dec of telescope pointing (deg) ROTTYPE = 'Obj ' / Rotator request type ROTPOS = 0.0 / Rotator request position (deg) BOREOFFX= 0.0 / TCC Boresight offset, deg BOREOFFY= 0.0 / TCC Boresight offset, deg ARCOFFX = -0.003294 / TCC ObjArcOff, deg ARCOFFY = 0.003229 / TCC ObjArcOff, deg OBJOFFX = 0.0 / TCC ObjOff, deg OBJOFFY = 0.0 / TCC ObjOff, deg CALOFFX = 0.0 / TCC CalibOff, deg CALOFFY = 0.0 / TCC CalibOff, deg CALOFFR = 0.0 / TCC CalibOff, deg GUIDOFFX= 0.0 / TCC GuideOff, deg GUIDOFFY= 0.0 / TCC GuideOff, deg GUIDOFFR= 0.016592 / TCC GuideOff, deg AZ = 4.68733 / Azimuth axis pos. (approx, deg) ALT = 58.2660 / Altitude axis pos. (approx, deg) AIRMASS = 1.18604 / FOCUS = 198.16 / User-specified focus offset (um) M2PISTON= -1338.43 / TCC SecOrient M2XTILT = -4.0 / TCC SecOrient M2YTILT = -9.02 / TCC SecOrient M2XTRAN = 10.35 / TCC SecOrient M2YTRAN = 81.04000000000001 / TCC SecOrient M1PISTON= -3257.73 / TCC PrimOrient M1XTILT = -3.29 / TCC PrimOrient M1YTILT = 4.82 / TCC PrimOrient M1XTRAN = 278.26 / TCC PrimOrient M1YTRAN = 177.78 / TCC PrimOrient SCALE = 1.00033 / User-specified scale factor NAME = '4055-55359-01' / The name of the currently loaded plate PLATEID = 4055 / The currently loaded plate TILEID = 10375 /Cartridge used in this plugging CARTID = 14 /Cartridge used in this plugging MAPID = 1 / The mapping version of the loaded plate POINTING= 'A ' / The currently specified pointing GUIDER1 = 'proc-gimg-0222.fits' / The first guider image SLITID1 = 14 / Normalized slithead ID. sp1&2 should match. SLITID2 = 14 / Normalized slithead ID. sp1&2 should match. GUIDERN = 'proc-gimg-0278.fits' / The last guider image COLLA = 41802 / The position of the A collimator motor COLLB = 42513 / The position of the B collimator motor COLLC = 43176 / The position of the C collimator motor HARTMANN= 'Out ' / Hartmanns: Left,Right,Out MC1HUMHT= 12.2 / sp1 mech Hartmann humidity, % MC1HUMCO= 10.1 / sp1 mech Central optics humidity, % MC1TEMDN= 14.4 / sp1 mech Median temp, C MC1THT = 14.5 / sp1 mech Hartmann Top Temp, C MC1TRCB = 14.0 / sp1 mech Red Cam Bottom Temp, C MC1TRCT = 14.5 / sp1 mech Red Cam Top Temp, C MC1TBCB = 14.3 / sp1 mech Blue Cam Bottom Temp, C MC1TBCT = 14.4 / sp1 mech Blue Cam Top Temp, C NEXP = 24 / Number of exposures in this file BESTEXP = 116480 / EXPID01 = 'b1-00116475-00116481-00116482' / ID string for exposure 1 EXPID02 = 'b1-00116476-00116481-00116482' / ID string for exposure 2 EXPID03 = 'b1-00116477-00116481-00116482' / ID string for exposure 3 EXPID04 = 'b1-00116478-00116481-00116482' / ID string for exposure 4 EXPID05 = 'b1-00116479-00116481-00116482' / ID string for exposure 5 EXPID06 = 'b1-00116480-00116481-00116482' / ID string for exposure 6 EXPID07 = 'b2-00116475-00116481-00116482' / ID string for exposure 7 EXPID08 = 'b2-00116476-00116481-00116482' / ID string for exposure 8 EXPID09 = 'b2-00116477-00116481-00116482' / ID string for exposure 9 EXPID10 = 'b2-00116478-00116481-00116482' / ID string for exposure 10 EXPID11 = 'b2-00116479-00116481-00116482' / ID string for exposure 11 EXPID12 = 'b2-00116480-00116481-00116482' / ID string for exposure 12 EXPID13 = 'r1-00116475-00116481-00116482' / ID string for exposure 13 EXPID14 = 'r1-00116476-00116481-00116482' / ID string for exposure 14 EXPID15 = 'r1-00116477-00116481-00116482' / ID string for exposure 15 EXPID16 = 'r1-00116478-00116481-00116482' / ID string for exposure 16 EXPID17 = 'r1-00116479-00116481-00116482' / ID string for exposure 17 EXPID18 = 'r1-00116480-00116481-00116482' / ID string for exposure 18 EXPID19 = 'r2-00116475-00116481-00116482' / ID string for exposure 19 EXPID20 = 'r2-00116476-00116481-00116482' / ID string for exposure 20 EXPID21 = 'r2-00116477-00116481-00116482' / ID string for exposure 21 EXPID22 = 'r2-00116478-00116481-00116482' / ID string for exposure 22 EXPID23 = 'r2-00116479-00116481-00116482' / ID string for exposure 23 EXPID24 = 'r2-00116480-00116481-00116482' / ID string for exposure 24 NEXP_B1 = 6 / b1 camera number of exposures NEXP_B2 = 6 / b2 camera number of exposures NEXP_R1 = 6 / r1 camera number of exposures NEXP_R2 = 6 / r2 camera number of exposures EXPT_B1 = 5405.01 / b1 camera exposure time (seconds) EXPT_B2 = 5405.04 / b2 camera exposure time (seconds) EXPT_R1 = 5405.01 / r1 camera exposure time (seconds) EXPT_R2 = 5405.04 / r2 camera exposure time (seconds) EXPTIME = 5405.01 / Minimum of exposure times for all cameras SPCOADD = 'Sat Apr 12 18:49:38 2014' / SPCOADD finished SHOPETIM= 0.72 / open shutter transit time, s SHCLOTIM= 0.5600000000000001 / close shutter transit time, s AUTHOR = 'Scott Burles & David Schlegel' / VERSIDL = '7.0 ' / Version of IDL VERSUTIL= 'v5_5_14 ' / Version of idlutils VERSREAD= 'v5_7_0 ' / Version of idlspec2d for pre-processing raw datVERS2D = 'v5_7_0 ' / Version of idlspec2d for 2D reduction VERSCOMB= 'v5_7_0 ' / Version of idlspec2d for combining multiple speVERSLOG = 'trunk exported' / Version of SPECLOG product VERSFLAT= 'v1_21 ' / Version of SPECFLAT product TWOPHASE= F / RDNOISE0= 1.99069 /CCD read noise amp 0 [electrons] BADPIXEL= 'badpixels-55300-b1.fits.gz' / RUN2D = 'v5_7_0 ' / Spectro-2D reduction name TAI-END = 4783040074.84 / REDDEN01= 0.495500 / Median extinction in u-band REDDEN02= 0.364600 / Median extinction in g-band REDDEN03= 0.264500 / Median extinction in r-band REDDEN04= 0.200500 / Median extinction in i-band REDDEN05= 0.142200 / Median extinction in z-band XSIGMA = 1.07552 / XSIGMIN = 1.02262 / XSIGMAX = 1.18520 / WSIGMA = 1.08131 / WSIGMIN = 1.04033 / WSIGMAX = 1.15414 / PLUGFILE= 'plPlugMapM-4055-55359-01.par' / LAMPLIST= 'lamphgcdne.dat' / SKYLIST = 'skylines.dat' / HELIO_RV= 12.4068856707 / Heliocentric correction (added to velocities) VACUUM = T / Wavelengths are in vacuum SFLATTEN= T / Superflat has been applied PSFSKY = 3 / Order of PSF skysubtraction SKYCHI2 = 1.28961868553 / Mean chi^2 of sky-subtraction SCHI2MIN= 1.20423986980 / SCHI2MAX= 1.55158902875 / PREJECT = 0.200000 / Profile area rejection threshold SPEC1_G = 25.5228 / (S/N)^2 for spec 1 at mag 21.20 SN2EXT1G= 25.4134 / Extinction corrected (S/N)^2 SPEC1_R = 69.5485 / (S/N)^2 for spec 1 at mag 20.20 SN2EXT1R= 69.4692 / Extinction corrected (S/N)^2 SPEC1_I = 52.3166 / (S/N)^2 for spec 1 at mag 20.20 SN2EXT1I= 52.2564 / Extinction corrected (S/N)^2 SPEC2_G = 24.8694 / (S/N)^2 for spec 2 at mag 21.20 SN2EXT2G= 24.7601 / Extinction corrected (S/N)^2 SPEC2_R = 68.1589 / (S/N)^2 for spec 2 at mag 20.20 SN2EXT2R= 68.0796 / Extinction corrected (S/N)^2 SPEC2_I = 46.5307 / (S/N)^2 for spec 2 at mag 20.20 SN2EXT2I= 46.4706 / Extinction corrected (S/N)^2 NSTD = 20 / Number of (good) std stars GOFFSTD = -0.00487995 / Spectrophoto offset for std stars in G-band GRMSSTD = 0.0647212 / Spectrophoto RMS for std stars in G-band ROFFSTD = 0.0269613 / Spectrophoto offset for std stars in R-band RRMSSTD = 0.100117 / Spectrophoto RMS for std stars in R-band IOFFSTD = 0.0276089 / Spectrophoto offset for std stars in I-band IRMSSTD = 0.140843 / Spectrophoto RMS for std stars in I-band GROFFSTD= 0.0247478 / Spectrophoto offset for std stars in (GR) GRRMSSTD= 0.0879660 / Spectrophoto RMS for std stars in (GR) RIOFFSTD= -0.00171995 / Spectrophoto offset for std stars in (RI) RIRMSSTD= 0.0523789 / Spectrophoto RMS for std stars in (RI) LOWREJ = 5 / Extraction: low rejection HIGHREJ = 8 / Extraction: high rejection SCATPOLY= 0 / Extraction: Order of scattered light polynomialPROFTYPE= 1 / Extraction profile: 1=Gaussian NFITPOLY= 1 / Extraction: Number of parameters in each profilXCHI2 = 0.889458493246 / Extraction: Mean chi^2 XCHI2MIN= 0.698653000000 / XCHI2MAX= 1.11949002743 / NWORDER = 2 / Linear-log10 coefficients WFITTYPE= 'LOG-LINEAR' / Linear-log10 dispersion COEFF0 = 3.55290000000 / Central wavelength (log10) of first pixel COEFF1 = 0.000100000000000 / Log10 dispersion per pixel UNAME = 'n0011 ' / FBADPIX = 0.0469793 / Fraction of bad pixels FBADPIX1= 0.0294160 / Fraction of bad pixels on spectro-1 FBADPIX2= 0.0645426 / Fraction of bad pixels on spectro-2 WAT0_001= 'system=linear' / WAT1_001= 'wtype=linear label=Wavelength units=Angstroms' / CRVAL1 = 3.55290000000 / Central wavelength (log10) of first pixel CD1_1 = 0.000100000000000 / Log10 dispersion per pixel CRPIX1 = 1 / Starting pixel (1-indexed) CTYPE1 = 'LINEAR ' / DC-FLAG = 1 / Log-linear flag BUNIT = '1E-17 erg/cm^2/s/Ang' / END Au,A_GA=),A<ъAMAA.yAAjADADҐAKA? 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0.548297226429 EIGENOBJ 3745 55234 609 0.0677964612842 EIGENOBJ 3745 55234 728 0.612909734249 EIGENOBJ 3745 55234 764 0.388154536486 EIGENOBJ 3745 55234 800 0.0280731134117 pydl-0.6.0/pydl/pydlspec2d/tests/__init__.py0000664000111400017500000000021512775255301021566 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This is the pydl/pydlspec2d/tests directory. """ pydl-0.6.0/pydl/pydlspec2d/tests/test_spec2d.py0000664000111400017500000000670513040014667022252 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np import os from astropy.io import fits from astropy.tests.helper import raises from astropy.utils.data import get_pkg_data_filename from ..spec2d import aesthetics, combine1fiber, filter_thru from .. import Pydlspec2dException class TestSpec2d(object): """Test the functions in pydl.pydlspec2d.spec2d. """ def setup(self): self.env = {'BOSS_SPECTRO_REDUX': '/boss/spectro/redux', 'SPECTRO_REDUX': '/sdss/spectro/redux', 'RUN2D': 'v1_2_3', 'RUN1D': 'v1_2_3'} self.original_env = dict() for key in self.env: if key in os.environ: self.original_env[key] = os.environ[key] else: self.original_env[key] = None os.environ[key] = self.env[key] def teardown(self): for key in self.original_env: if self.original_env[key] is None: del os.environ[key] else: os.environ[key] = self.original_env[key] def test_aesthetics(self): np.random.seed(137) flux = np.random.rand(100) ivar = np.random.rand(100) # # No bad # f = aesthetics(flux, ivar) assert (f == flux).all() # # Bad points # ivar[ivar < 0.1] = 0.0 # # Bad method # with raises(Pydlspec2dException): f = aesthetics(flux, ivar, 'badmethod') # # Nothing # f = aesthetics(flux, ivar, 'nothing') assert (f == flux).all() def test_combine1fiber(self): pass def test_filter_thru(self): fname = get_pkg_data_filename('t/spPlate-4055-55359-0020.fits') with fits.open(fname) as hdulist: flux = hdulist[0].data npix = hdulist[0].header['NAXIS1'] ntrace = hdulist[0].header['NAXIS2'] crval1 = hdulist[0].header['COEFF0'] cd1_1 = hdulist[0].header['COEFF1'] assert flux.shape == (ntrace, npix) loglam0 = crval1 + cd1_1*np.arange(npix, dtype=flux.dtype) waveimg = 10**(np.tile(loglam0, 20).reshape(flux.shape)) assert waveimg.shape == flux.shape f = filter_thru(flux, waveimg=waveimg) idl_data_file = get_pkg_data_filename('t/filter_thru_idl_data.txt') idl_data = np.loadtxt(idl_data_file, dtype='f', delimiter=',').T assert f.shape == (20, 5) assert np.allclose(f, idl_data, atol=1.0e-6) # # Test bad input. # with raises(ValueError): f = filter_thru(flux) with raises(ValueError): f = filter_thru(flux, waveimg=waveimg, filter_prefix='sdss') return def prepare_data(): """Convert full spPlate file into a test-sized version. """ nTrace = 20 spPlate = os.path.join(os.getenv('HOME'), 'Downloads', 'spPlate-4055-55359.fits') spPlateOut = os.path.join(os.path.dirname(__file__), 't', 'spPlate-4055-55359-0020.fits') with fits.open(spPlate) as hdulist: newhdu = fits.PrimaryHDU(hdulist[0].data[0:20, :], header=hdulist[0].header) newhdulist = fits.HDUList([newhdu]) newhdulist.writeto(spPlateOut) return 0 if __name__ == '__main__': from sys import exit exit(prepare_data()) pydl-0.6.0/pydl/pydlspec2d/tests/test_spec1d.py0000664000111400017500000001056713040014667022252 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np import os from astropy.tests.helper import raises from astropy.utils.data import get_pkg_data_filename from .. import Pydlspec2dException from ..spec1d import (HMF, findspec, spec_append, spec_path, template_metadata, wavevector) class TestSpec1d(object): """Test the functions in pydl.pydlspec2d.spec1d. """ def setup(self): self.env = {'BOSS_SPECTRO_REDUX': '/boss/spectro/redux', 'SPECTRO_REDUX': '/sdss/spectro/redux', 'RUN2D': 'v1_2_3', 'RUN1D': 'v1_2_3'} self.original_env = dict() for key in self.env: if key in os.environ: self.original_env[key] = os.environ[key] else: self.original_env[key] = None os.environ[key] = self.env[key] def teardown(self): for key in self.original_env: if self.original_env[key] is None: del os.environ[key] else: os.environ[key] = self.original_env[key] def test_findspec(self): """This is just a placeholder for now. """ # slist = findspec(infile='file.in', sdss=True) assert True def test_hmf_init(self): """Test initialization of HMF object """ spec = np.random.random((20, 100)) invvar = np.random.random((20, 100)) hmf = HMF(spec, invvar) assert hmf.K == 4 assert hmf.log.level == 20 # INFO hmf = HMF(spec, invvar, K=6, verbose=True) assert hmf.K == 6 assert hmf.log.level == 10 # DEBUG def test_spec_append(self): spec1 = np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]) spec2 = np.array([[2, 2, 2, 2, 2], [2, 2, 2, 2, 2]]) s = spec_append(spec1, spec2) assert (s == np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [2, 2, 2, 2, 2]])).all() spec2 = np.array([[2, 2, 2, 2], [2, 2, 2, 2]]) s = spec_append(spec1, spec2) assert (s == np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [2, 2, 2, 2, 0], [2, 2, 2, 2, 0]])).all() s = spec_append(spec1, spec2, 1) assert (s == np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [0, 2, 2, 2, 2], [0, 2, 2, 2, 2]])).all() spec1 = np.array([[1, 1, 1], [1, 1, 1]]) spec2 = np.array([[2, 2, 2, 2, 2], [2, 2, 2, 2, 2]]) s = spec_append(spec1, spec2, -2) assert (s == np.array([[0, 0, 1, 1, 1], [0, 0, 1, 1, 1], [2, 2, 2, 2, 2], [2, 2, 2, 2, 2]])).all() def test_spec_path(self): bsr = self.env['BOSS_SPECTRO_REDUX'] run2d = self.env['RUN2D'] p = spec_path(123) assert p[0] == os.path.join(bsr, run2d, '0123') p = spec_path(1234) assert p[0] == os.path.join(bsr, run2d, '1234') p = spec_path(1234, topdir=bsr, run2d=run2d) assert p[0] == os.path.join(bsr, run2d, '1234') p = spec_path(np.array([1234, 5678]), topdir=bsr, run2d=run2d) assert p[0] == os.path.join(bsr, run2d, '1234') assert p[1] == os.path.join(bsr, run2d, '5678') p = spec_path(1234, path=bsr) assert p[0] == bsr def test_template_metadata(self): with raises(Pydlspec2dException): slist, metadata = template_metadata('/no/such/file.par') inputfile = get_pkg_data_filename('t/test_template_metadata.par') slist, metadata = template_metadata(inputfile) assert metadata['object'] == 'gal' assert not metadata['nonnegative'] def test_wavevector(self): l = wavevector(3, 4, binsz=0.1) ll = np.array([3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0]) assert np.allclose(l, ll) l = wavevector(3, 4, wavemin=3, binsz=0.1) ll = np.array([3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0]) assert np.allclose(l, ll) pydl-0.6.0/pydl/pydlspec2d/spec1d.py0000664000111400017500000022344013040014667020045 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the spec1d directory in idlspec2d. """ import numpy as np # # Used by findspec # findspec_cache = None class HMF(object): """Class used to manage data for Heteroscedastic Matrix Factorization (HMF). This is a replacement for :func:`~pydl.pydlspec2d.spec1d.pca_solve`. It can be called with:: hmf = HMF(spectra, invvar) output = hmf.solve() The input spectra should be pre-processed through :func:`~pydl.pydlspec2d.spec2d.combine1fiber`. Parameters ---------- spectra : array-like The input spectral flux, assumed to have a common wavelength and redshift system. invvar : array-like The inverse variance of the spectral flux. K : :class:`int`, optional The number of dimensions of the factorization (default 4). n_iter : :class:`int`, optional Number of iterations. seed : :class:`int`, optional. If set, pass this value to :func:`numpy.random.seed`. nonnegative : :class:`bool`, optional Set this to ``True`` to perform nonnegative HMF. epsilon : :class:`float`, optional Regularization parameter. Set to any non-negative float value to turn it on. verbose : :class:`bool`, optional If ``True``, print extra information. Notes ----- See [1]_ and [2]_ for the original derivation of this method. The HMF iteration is initialized using :func:`~scipy.cluster.vq.kmeans`, which itself uses random numbers to initialize its state. If you need to ensure reproducibility, call :func:`numpy.random.seed` before initializing HMF. The current algorithm cannot handle input data that contain *columns* of zeros. Columns of this type need to be *carefully* removed from the input data. This could also result in the output data having a different size compared to the input data. References ---------- .. [1] `Tsalmantza, P., Decarli, R., Dotti, M., Hogg, D. W., 2011 ApJ 738, 20 `_ .. [2] `Tsalmantza, P., Hogg, D. W., 2012 ApJ 753, 122 `_ """ def __init__(self, spectra, invvar, K=4, n_iter=None, seed=None, nonnegative=False, epsilon=None, verbose=False): from astropy import log self.log = log self.spectra = spectra self.invvar = invvar self.K = K if n_iter is None: if nonnegative: self.n_iter = 2048 else: self.n_iter = 20 else: self.n_iter = int(n_iter) self.seed = seed self.nonnegative = nonnegative self.epsilon = epsilon self.verbose = verbose self.a = None self.g = None if self.verbose: self.log.setLevel('DEBUG') return def solve(self): """Process the inputs. Returns ------- :class:`dict` The HMF solution. """ if len(self.spectra.shape) == 1: nobj = 1 npix = self.spectra.shape[0] else: nobj, npix = self.spectra.shape self.log.info("Building HMF from {0:d} object spectra.".format(nobj)) fluxdict = dict() # # If there is only one object spectrum, then all we can do is return it. # if nobj == 1: fluxdict['flux'] = self.spectra.astype('f') return fluxdict a, g = self.iterate() fluxdict['acoeff'] = a fluxdict['flux'] = g return fluxdict def model(self): """Compute the model. """ return np.dot(self.a, self.g) def resid(self): """Compute residuals. """ return self.spectra - self.model() def chi(self): """Compute :math:`\chi`, the scaled residual. """ return self.resid() * np.sqrt(self.invvar) def penalty(self): """Compute penalty for non-smoothness. """ if self.epsilon is None: return 0.0 return self.epsilon * np.sum(np.diff(self.g)**2) def badness(self): """Compute :math:`\chi^2`, including possible non-smoothness penalty. """ return np.sum(self.chi()**2) + self.penalty() def normbase(self): """Apply standard component normalization. """ return np.sqrt((self.g**2).mean(1)) def astep(self): """Update for coefficients at fixed component spectra. """ from numpy.linalg import solve N, M = self.spectra.shape K, M = self.g.shape a = np.zeros((N, K), dtype=self.g.dtype) for i in range(N): Gi = np.zeros((K, K), dtype=self.g.dtype) for k in range(K): for kp in range(k, K): Gi[k, kp] = np.sum(self.g[k, :] * self.g[kp, :] * self.invvar[i, :]) if kp > k: Gi[kp, k] = Gi[k, kp] Fi = np.dot(self.g, self.spectra[i, :]*self.invvar[i, :]) a[i, :] = solve(Gi, Fi) return a def gstep(self): """Update for component spectra at fixed coefficients. """ from numpy.linalg import solve N, M = self.spectra.shape N, K = self.a.shape g = np.zeros((K, M), dtype=self.a.dtype) e = np.zeros(self.g.shape, dtype=self.g.dtype) d = np.zeros((K, K, M), dtype=self.a.dtype) if self.epsilon is not None and self.epsilon > 0: foo = self.epsilon * np.eye(K, dtype=self.a.dtype) for l in range(M): d[:, :, l] = foo if l > 0 and l < M-1: d[:, :, l] *= 2 # d[:, :, 0] = foo # d[:, :, 1:M-1] = 2*foo # d[:, :, M-1] = foo e[:, 0] = self.epsilon*self.g[:, 1] e[:, 1:M-1] = self.epsilon*(self.g[:, 0:M-2] + self.g[:, 2:M]) e[:, M-1] = self.epsilon*self.g[:, M-2] for j in range(M): Aj = np.zeros((K, K), dtype=self.a.dtype) for k in range(K): for kp in range(k, K): Aj[k, kp] = np.sum(self.a[:, k] * self.a[:, kp] * self.invvar[:, j]) if kp > k: Aj[kp, k] = Aj[k, kp] Aj += d[:, :, j] Fj = (np.dot(self.a.T, self.spectra[:, j]*self.invvar[:, j]) + e[:, j]) g[:, j] = solve(Aj, Fj) return g def astepnn(self): """Non-negative update for coefficients at fixed component spectra. """ numerator = np.dot(self.spectra*self.invvar, self.g.T) denominator = np.dot(np.dot(self.a, self.g)*self.invvar, self.g.T) return self.a*(numerator/denominator) def gstepnn(self): """Non-negative update for component spectra at fixed coefficients. """ K, M = self.g.shape numerator = np.dot(self.a.T, (self.spectra*self.invvar)) if self.epsilon is not None and self.epsilon > 0: e = np.zeros(self.g.shape, dtype=self.g.dtype) e[:, 0] = self.epsilon*self.g[:, 1] e[:, 1:M-1] = self.epsilon*(self.g[:, 0:M-2] + self.g[:, 2:M]) e[:, M-1] = self.epsilon*self.g[:, M-2] numerator += e denominator = np.dot(self.a.T, np.dot(self.a, self.g)*self.invvar) if self.epsilon is not None and self.epsilon > 0: d = self.epsilon*self.g.copy() d[:, 1:M-1] *= 2 denominator += d return self.g*(numerator/denominator) def reorder(self): """Reorder and rotate basis analogous to PCA. """ from numpy.linalg import eigh l, U = eigh(np.dot(self.a.T, self.a)) return (np.dot(self.a, U), np.dot(U.T, self.g)) def iterate(self): """Handle the HMF iteration. Returns ------- :func:`tuple` of :class:`numpy.ndarray` The fitting coefficients and fitted functions, respectively. """ import time from scipy.cluster.vq import kmeans, whiten from ..pydlutils.math import find_contiguous N, M = self.spectra.shape # # Make spectra non-negative # if self.nonnegative: self.spectra[self.spectra < 0] = 0 self.invvar[self.spectra < 0] = 0 # # Detect and fix very bad columns. # si = self.spectra * self.invvar if (self.spectra.sum(0) == 0).any(): self.log.warn("Columns of zeros detected in spectra!") if (self.invvar.sum(0) == 0).any(): self.log.warn("Columns of zeros detected in invvar!") if (si.sum(0) == 0).any(): self.log.warn("Columns of zeros detected in spectra*invvar!") zerocol = ((self.spectra.sum(0) == 0) | (self.invvar.sum(0) == 0) | (si.sum(0) == 0)) n_zero = zerocol.sum() if n_zero > 0: self.log.warn("Found {0:d} bad columns in input data!".format(n_zero)) # # Find the largest set of contiguous pixels # goodcol = find_contiguous(~zerocol) self.spectra = self.spectra[:, goodcol] self.invvar = self.invvar[:, goodcol] # si = si[:, goodcol] # newloglam = fullloglam[goodcol] # # Initialize g matrix with kmeans # if self.seed is not None: np.random.seed(self.seed) whitespectra = whiten(self.spectra) self.log.debug(whitespectra[0:3, 0:3]) self.g, foo = kmeans(whitespectra, self.K) self.g /= np.repeat(self.normbase(), M).reshape(self.g.shape) self.log.debug(self.g[0:3, 0:3]) # # Initialize a matrix # self.a = np.outer(np.sqrt((self.spectra**2).mean(1)), np.repeat(1.0/self.K, self.K)) if self.nonnegative: for k in range(128): self.a = self.astepnn() # # Iterate! # t0 = time.time() for m in range(self.n_iter): self.log.info("Starting iteration #{0:4d}.".format(m+1)) if self.nonnegative: self.a = self.astepnn() self.g = self.gstepnn() else: self.a = self.astep() self.g = self.gstep() self.a, self.g = self.reorder() norm = self.normbase() self.g /= np.repeat(norm, M).reshape(self.g.shape) self.a = (self.a.T*np.repeat(norm, N).reshape(self.K, N)).T self.log.debug(self.a[0:3, 0:3]) self.log.debug(self.g[0:3, 0:3]) self.log.debug("Chi**2 after iteration #{0:4d} = {1:f}.".format(m+1, self.badness())) self.log.info("The elapsed time for iteration #{0:4d} is {1:6.2f} s.".format(m+1, time.time()-t0)) return (self.a, self.g) def findspec(*args, **kwargs): """Find SDSS/BOSS spectra that match a given RA, Dec. Parameters ---------- ra, dec : array-like, optional If set, the first two positional arguments will be interpreted as RA, Dec. best : :class:`bool`, optional If set, return only the best match for each input RA, Dec. infile : :class:`str`, optional If set, read RA, Dec data from this file. outfile : :class:`str`, optional If set, print match data to this file. print : :class:`bool`, optional If set, print the match data to the console. run1d : :class:`str`, optional Override the value of :envvar:`RUN1D`. run2d : :class:`str`, optional Override the value of :envvar:`RUN2D`. sdss : :class:`bool`, optional If set, search for SDSS-I/II spectra instead of BOSS spectra. searchrad : :class:`float`, optional Search for spectra in this radius around given RA, Dec. Default is 3 arcsec. topdir : :class:`str`, optional If set, override the value of :envvar:`SPECTRO_REDUX` or :envvar:`BOSS_SPECTRO_REDUX`. Returns ------- :class:`dict` A dictionary containing plate, MJD, fiber, etc. """ import os import os.path import glob from astropy.io import ascii, fits from warnings import warn from .. import uniq from ..pydlutils.misc import struct_print from ..pydlutils.spheregroup import spherematch from . import Pydlspec2dException, Pydlspec2dUserWarning global findspec_cache # # Set up default values # if 'sdss' in kwargs: if 'topdir' in kwargs: topdir = kwargs['topdir'] else: topdir = os.environ['SPECTRO_REDUX'] if 'run2d' in kwargs: run2d = str(kwargs['run2d']) else: run2d = '26' run1d = '' else: if 'topdir' in kwargs: topdir = kwargs['topdir'] else: topdir = os.environ['BOSS_SPECTRO_REDUX'] if 'run2d' in kwargs: run2d = str(kwargs['run2d']) else: run2d = os.environ['RUN2D'] if 'run1d' in kwargs: run1d = str(kwargs['run1d']) else: run1d = os.environ['RUN1D'] if findspec_cache is None: findspec_cache = {'lasttopdir': topdir, 'plist': None} if (findspec_cache['plist'] is None or topdir != findspec_cache['lasttopdir']): findspec_cache['lasttopdir'] = topdir platelist_file = os.path.join(topdir, "platelist.fits") plates_files = glob.glob(os.path.join(topdir, "plates-*.fits")) plist = None if os.path.exists(platelist_file): platelist = fits.open(platelist_file) plist = platelist[1].data platelist.close() if len(plates_files) > 0: plates = fits.open(plates_files[0]) plist = plates[1].data plates.close() if plist is None: raise Pydlspec2dException("Plate list (platelist.fits or plates-*.fits) not found in {0}.".format(topdir)) else: findspec_cache['plist'] = plist qdone = plist.field('STATUS1D') == 'Done' qdone2d = plist.field('RUN2D').strip() == run2d if run1d == '': qdone1d = np.ones(plist.size, dtype='bool') else: qdone1d = plist.field('RUN1D').strip() == run1d qfinal = qdone & qdone2d & qdone1d if not qfinal.any(): warn("No reduced plates!", Pydlspec2dUserWarning) return None idone = np.arange(plist.size)[qfinal] # # If there are positional arguments, interpret these as RA, Dec # if len(args) == 2: ra = args[0] dec = args[1] # # Read RA, Dec from infile if set # if 'infile' in kwargs: infile_data = ascii.read(kwargs['infile'], names=['ra', 'dec']) ra = infile_data["ra"].data dec = infile_data["dec"].data if 'searchrad' in kwargs: searchrad = float(kwargs['searchrad']) else: searchrad = 3.0/3600.0 # # Create output structure # slist_type = np.dtype([('PLATE', 'i4'), ('MJD', 'i4'), ('FIBERID', 'i4'), ('RA', 'f8'), ('DEC', 'f8'), ('MATCHRAD', 'f8')]) # # Match all plates with objects # imatch1, itmp, dist12 = spherematch(ra, dec, plist[qfinal].field('RACEN'), plist[qfinal].field('DECCEN'), searchrad+1.55, maxmatch=0) if imatch1.size == 0: warn("No matching plates found.", Pydlspec2dUserWarning) return None imatch2 = idone[itmp] # # Read all relevant plates # try: n_total = plist.field('N_TOTAL') except KeyError: n_total = np.zeros(plist.size, dtype='i4') + 640 iplate = imatch2[uniq(imatch2, imatch2.argsort())] i0 = 0 plugmap = np.zeros(n_total[iplate].sum(), dtype=[('PLATE', 'i4'), ('MJD', 'i4'), ('FIBERID', 'i4'), ('RA', 'd'), ('DEC', 'd')]) for i in range(iplate.size): spplate = readspec(plist[iplate[i]].field('PLATE'), mjd=plist[iplate[i]].field('MJD'), topdir=topdir, run2d=run2d, run1d=run1d) index_to = i0 + np.arange(n_total[iplate[i]], dtype='i4') plugmap['PLATE'][index_to] = plist[iplate[i]].field('PLATE') plugmap['MJD'][index_to] = plist[iplate[i]].field('MJD') plugmap['FIBERID'][index_to] = spplate['plugmap']['FIBERID'] plugmap['RA'][index_to] = spplate['plugmap']['RA'] plugmap['DEC'][index_to] = spplate['plugmap']['DEC'] i0 += n_total[iplate[i]] i1, i2, d12 = spherematch(ra, dec, plugmap['RA'], plugmap['DEC'], searchrad, maxmatch=0) if i1.size == 0: warn('No matching objects found.', Pydlspec2dUserWarning) return None if 'best' in kwargs: # # Return only best match per object # slist = np.zeros(ra.size, dtype=slist_type) spplate = readspec(plugmap[i2]['PLATE'], plugmap[i2]['FIBERID'], mjd=plugmap[i2]['MJD'], topdir=topdir, run2d=run2d, run1d=run1d) sn = spplate['zans']['SN_MEDIAN'] isort = (i1 + np.where(sn > 0, sn, 0)/(sn+1.0).max()).argsort() i1 = i1[isort] i2 = i2[isort] d12 = d12[isort] iuniq = uniq(i1) slist[i1[iuniq]]['PLATE'] = plugmap[i2[iuniq]]['PLATE'] slist[i1[iuniq]]['MJD'] = plugmap[i2[iuniq]]['MJD'] slist[i1[iuniq]]['FIBERID'] = plugmap[i2[iuniq]]['FIBERID'] slist[i1[iuniq]]['RA'] = plugmap[i2[iuniq]]['RA'] slist[i1[iuniq]]['DEC'] = plugmap[i2[iuniq]]['DEC'] slist[i1[iuniq]]['MATCHRAD'] = d12[iuniq] else: # # Return all matches # slist = np.zeros(i1.size, dtype=slist_type) slist['PLATE'] = plugmap[i2]['PLATE'] slist['MJD'] = plugmap[i2]['MJD'] slist['FIBERID'] = plugmap[i2]['FIBERID'] slist['RA'] = plugmap[i2]['RA'] slist['DEC'] = plugmap[i2]['DEC'] slist['MATCHRAD'] = d12 # # Print to terminal or output file # if 'print' in kwargs: foo = struct_print(slist) if 'outfile' in kwargs: foo = struct_print(slist, filename=outfile) return slist def latest_mjd(plate, **kwargs): """Find the most recent MJD associated with a plate. Parameters ---------- plate : :class:`int` or :class:`numpy.ndarray` The plate(s) to examine. Returns ------- :class:`numpy.ndarray` An array of MJD values for each plate. """ import glob import re from astropy.extern.six import integer_types if isinstance(plate, integer_types) or plate.shape == (): platevec = np.array([plate], dtype='i4') else: platevec = plate mjd = np.zeros(len(platevec), dtype='i4') mjdre = re.compile(r'spPlate-[0-9]{4}-([0-9]{5}).fits') unique_plates = np.unique(platevec) paths = spec_path(unique_plates, **kwargs) for p, q in zip(paths, unique_plates): plateglob = "{0}/spPlate-{1:04d}-*.fits".format(p, q) bigmjd = 0 for f in glob.glob(plateglob): thismjd = int(mjdre.search(f).groups()[0]) if thismjd > bigmjd: bigmjd = thismjd mjd[platevec == q] = bigmjd return mjd def number_of_fibers(plate, **kwargs): """Returns the total number of fibers per plate. Parameters ---------- plate : :class:`int` or :class:`numpy.ndarray` The plate(s) to examine. Returns ------- :class:`numpy.ndarray` The number of fibers on each plate. """ import os import os.path from astropy.io import fits as pyfits from astropy.extern.six import integer_types # # Get mjd values # if isinstance(plate, integer_types) or plate.shape == (): platevec = np.array([plate], dtype='i4') else: platevec = plate mjd = latest_mjd(plate, **kwargs) nfiber = np.zeros(mjd.size, dtype='i4') # # SDSS-I,II plates # nfiber[mjd < 55025] = 640 # # Short circuit if we're done. # if (nfiber == 640).all(): return nfiber # # Not all BOSS plates have 1000 fibers # if 'path' in kwargs: platelistpath = os.path.join(kwargs['path'], 'platelist.fits') else: platelistpath = os.path.join(os.environ['BOSS_SPECTRO_REDUX'], 'platelist.fits') platelist = pyfits.open(platelistpath) platentotal = platelist[1].data.field('N_TOTAL') plateplate = platelist[1].data.field('PLATE') platemjd = platelist[1].data.field('MJD') platerun2d = platelist[1].data.field('RUN2D') platerun1d = platelist[1].data.field('RUN1D') platelist.close() if 'run2d' in kwargs: run2d = kwargs['run2d'] else: run2d = os.environ['RUN2D'] if 'run1d' in kwargs: run1d = kwargs['run1d'] else: run1d = os.environ['RUN1D'] for k in range(mjd.size): nfiber[k] = platentotal[(plateplate == platevec[k]) & (platemjd == mjd[k]) & (platerun2d == run2d) & (platerun1d == run1d)] return nfiber def pca_solve(newflux, newivar, maxiter=0, niter=10, nkeep=3, nreturn=None, verbose=False): """Replacement for idlspec2d pca_solve.pro. Parameters ---------- newflux : array-like The input spectral flux, assumed to have a common wavelength and redshift system. newivar : array-like The inverse variance of the spectral flux. maxiter : :class:`int`, optional Stop PCA+reject iterations after this number. niter : :class:`int`, optional Stop PCA iterations after this number. nkeep : :class:`int`, optional Number of PCA components to keep. nreturn : :class:`int`, optional Number of PCA components to return, usually the same as `nkeep`. verbose : :class:`bool`, optional If ``True``, print extra information. Returns ------- :class:`dict` The PCA solution. """ import time from astropy import log from .. import pcomp from ..pydlutils.math import computechi2, djs_reject if verbose: log.setLevel('DEBUG') if nreturn is None: nreturn = nkeep if len(newflux.shape) == 1: nobj = 1 npix = newflux.shape[0] else: nobj, npix = newflux.shape log.info("Building PCA from {0:d} object spectra.".format(nobj)) nzi = newivar.nonzero() first_nonzero = (np.arange(nobj, dtype=nzi[0].dtype), np.array([nzi[1][nzi[0] == k].min() for k in range(nobj)])) # # Construct the synthetic weight vector, to be used when replacing the # low-S/N object pixels with the reconstructions. # synwvec = np.ones((npix,), dtype='d') for ipix in range(npix): indx = newivar[:, ipix] != 0 if indx.any(): synwvec[ipix] = newivar[indx, ipix].mean() fluxdict = dict() # # If there is only one object spectrum, then all we can do is return it. # if nobj == 1: fluxdict['flux'] = newflux.astype('f') return fluxdict # # Rejection iteration loop. # qdone = 0 iiter = 0 # # Begin with all points good. # outmask = None inmask = newivar != 0 ymodel = None # emevecs, emevals = pydlutils.empca(newflux, inmask) # fluxdict['emevecs'] = emevecs # fluxdict['emevals'] = emeveals while qdone == 0 and iiter <= maxiter: log.debug('starting djs_reject') outmask, qdone = djs_reject(newflux, ymodel, inmask=inmask, outmask=outmask, invvar=newivar) log.debug('finished with djs_reject') # # Iteratively do the PCA solution # filtflux = newflux.copy() acoeff = np.zeros((nobj, nkeep), dtype='d') t0 = time.time() for ipiter in range(niter): # # We want to get these values from the pcomp routine. # # eigenval = 1 # coeff = 1 flux0 = np.tile(filtflux[first_nonzero], npix).reshape(npix, nobj).transpose() # flux0 = np.tile(filtflux, npix).reshape(npix, nobj).transpose() totflux = np.absolute(filtflux - flux0).sum(1) goodobj = totflux > 0 if goodobj.all(): tmp = pcomp(filtflux.T) # , standardize=True) pres = tmp.derived eigenval = tmp.eigenvalues else: tmp = pcomp(filtflux[goodobj, :].T) # , standardize=True) pres = np.zeros((nobj, npix), dtype='d') pres[goodobj, :] = tmp.derived eigenval = np.zeros((nobj,), dtype='d') eigenval[goodobj] = tmp.eigenvalues maskivar = newivar * outmask sqivar = np.sqrt(maskivar) for iobj in range(nobj): out = computechi2(newflux[iobj, :], sqivar[iobj, :], pres[:, 0:nkeep]) filtflux[iobj, :] = (maskivar[iobj, :] * newflux[iobj, :] + synwvec*out.yfit) / (maskivar[iobj, :] + synwvec) acoeff[iobj, :] = out.acoeff log.info("The elapsed time for iteration #{0:2d} is {1:6.2f} s.".format(ipiter+1, time.time()-t0)) # # Now set ymodel for rejecting points. # ymodel = np.dot(acoeff, pres[:, 0:nkeep].T) iiter += 1 if nobj == 1: usemask = outmask else: usemask = outmask.sum(0) fluxdict['usemask'] = usemask fluxdict['outmask'] = outmask fluxdict['flux'] = pres[:, 0:nreturn].transpose().astype('f') fluxdict['eigenval'] = eigenval[0:nreturn] fluxdict['acoeff'] = acoeff return fluxdict def plot_eig(filename, title='Unknown'): """Plot spectra from an eigenspectra/template file. Parameters ---------- filename : :class:`str` Name of a FITS file containing eigenspectra/templates. title : :class:`str`, optional Title to put on the plot. Returns ------- None Raises ------ ValueError If an unknown template type was input in `filename`. """ from astropy.io import fits import matplotlib matplotlib.use('Agg') matplotlib.rcParams['figure.figsize'] = (16.0, 12.0) import matplotlib.pyplot as plt # from matplotlib.font_manager import fontManager, FontProperties # # Set title based on filename # if title == 'Unknown': if filename.find('Gal') > 0: title = 'Galaxies: Eigenspectra' elif filename.find('QSO') > 0: title = 'QSOs: Eigenspectra' elif filename.find('Star') > 0: title = 'Stars: Eigenspectra' elif filename.find('CVstar') > 0: title = 'CV Stars: Eigenspectra' else: raise ValueError('Unknown template type!') base, ext = filename.split('.') spectrum = fits.open(filename) newloglam0 = spectrum[0].header['COEFF0'] objdloglam = spectrum[0].header['COEFF1'] spectro_data = spectrum[0].data spectrum.close() (neig, ndata) = spectro_data.shape newloglam = np.arange(ndata) * objdloglam + newloglam0 lam = 10.0**newloglam fig = plt.figure(dpi=100) ax = fig.add_subplot(111) colorvec = ['k', 'r', 'g', 'b', 'm', 'c'] for l in range(neig): p = ax.plot(lam, spectro_data[l, :], colorvec[l % len(colorvec)]+'-', linewidth=1) ax.set_xlabel(r'Wavelength [$\AA$]') ax.set_ylabel('Flux [Arbitrary Units]') ax.set_title(title) # ax.set_xlim([3500.0,10000.0]) # ax.set_ylim([-400.0,500.0]) # fig.savefig(base+'.zoom.png') fig.savefig(base+'.png') plt.close(fig) return def readspec(platein, mjd=None, fiber=None, **kwargs): """Read SDSS/BOSS spec2d & spec1d files. Parameters ---------- platein : :class:`int` or :class:`numpy.ndarray` Plate number(s). mjd : :class:`int` or :class:`numpy.ndarray`, optional MJD numbers. If not provided, they will be calculated by :func:`latest_mjd`. fiber : array-like, optional Fibers to read. If not set, all fibers from all plates will be returned. topdir : :class:`str`, optional Override the value of :envvar:`BOSS_SPECTRO_REDUX`. run2d : :class:`str`, optional Override the value of :envvar:`RUN2D`. run1d : :class:`str`, optional Override the value of :envvar:`RUN1D`. path : :class:`str`, optional Override all path information with this directory name. align : :class:`bool`, optional If set, align all the spectra in wavelength. znum : :class:`int`, optional If set, return the znum-th best fit reshift fit, instead of the best. Returns ------- :class:`dict` A dictionary containing the data read. """ import os import os.path from astropy import log from astropy.io import fits as pyfits try: nplate = len(platein) plate = platein except TypeError: nplate = 1 plate = np.array([platein], dtype='i4') if 'run2d' in kwargs: run2d = kwargs['run2d'] else: run2d = os.environ['RUN2D'] if 'run1d' in kwargs: run1d = kwargs['run1d'] else: run1d = os.environ['RUN1D'] if fiber is None: # # Read all fibers # nfibers = number_of_fibers(plate, **kwargs) total_fibers = nfibers.sum() platevec = np.zeros(total_fibers, dtype='i4') fibervec = np.zeros(total_fibers, dtype='i4') k = 0 for p in np.unique(plate): n = np.unique(nfibers[plate == p])[0] platevec[k:k+n] = p fibervec[k:k+n] = np.arange(n) + 1 k += n else: try: nfiber = len(fiber) except TypeError: nfiber = 1 if nplate > 1 and nfiber > 1 and nplate != nfiber: raise TypeError("Plate & Fiber must have the same length!") if nplate > 1: platevec = np.array(plate, dtype='i4') else: platevec = np.zeros(nfiber, dtype='i4') + plate if nfiber > 1: fibervec = np.array(fiber, dtype='i4') else: fibervec = np.zeros(nplate, dtype='i4') + fiber if 'mjd' is None: mjdvec = latest_mjd(platevec, **kwargs) else: try: nmjd = len(mjd) except TypeError: nmjd = 1 if nmjd != nplate: raise TypeError("Plate & MJD must have the same length!") mjdvec = np.zeros(nplate, dtype='i4') + mjd # # Now select unique plate-mjd combinations & read them # pmjd = ((np.array(platevec, dtype='u8') << 16) + np.array(mjdvec, dtype='u8')) # log.debug(pmjd) upmjd = np.unique(pmjd) zupmjd = list(zip(upmjd >> 16, upmjd & ((1 << 16) - 1))) # log.debug(zupmjd) spplate_data = dict() hdunames = ('flux', 'invvar', 'andmask', 'ormask', 'disp', 'plugmap', 'sky', 'loglam',) for thisplate, thismjd in zupmjd: # thisplate = int(p>>16) # thismjd = int(np.bitwise_and(p, (1<<16)-1)) pmjdindex = ((platevec == thisplate) & (mjdvec == thismjd)).nonzero()[0] thisfiber = fibervec[pmjdindex] # log.debug(type(thisplate), type(thismjd)) # log.debug(repr(thisfiber)) # log.debug(type(thisfiber)) pmjdstr = "{0:04d}-{1:05d}".format(int(thisplate), int(thismjd)) if 'path' in kwargs: sppath = [kwargs['path']] else: sppath = spec_path(thisplate, run2d=run2d) spfile = os.path.join(sppath[0], "spPlate-{0}.fits".format(pmjdstr)) log.info(spfile) spplate = pyfits.open(spfile) # # Get wavelength coefficients from primary header # npix = spplate[0].header['NAXIS1'] c0 = spplate[0].header['COEFF0'] c1 = spplate[0].header['COEFF1'] coeff0 = np.zeros(thisfiber.size, dtype='d') + c0 coeff1 = np.zeros(thisfiber.size, dtype='d') + c1 loglam0 = c0 + c1*np.arange(npix, dtype='d') loglam = np.resize(loglam0, (thisfiber.size, npix)) # # Read the data images # for k in range(len(hdunames)): if hdunames[k] == 'loglam': tmp = loglam else: try: tmp = spplate[k].data[thisfiber-1, :] except IndexError: tmp = spplate[k].data[thisfiber-1] if hdunames[k] not in spplate_data: if k == 0: allpmjdindex = pmjdindex allcoeff0 = coeff0 allcoeff1 = coeff1 # # Put the data into the return structure # if hdunames[k] == 'plugmap': spplate_data['plugmap'] = dict() for c in spplate[k].columns.names: spplate_data['plugmap'][c] = tmp[c] else: spplate_data[hdunames[k]] = tmp else: # # Append data # if k == 0: allpmjdindex = np.concatenate((allpmjdindex, pmjdindex)) if 'align' in kwargs: mincoeff0 = min(allcoeff0) if mincoeff0 == 0 and coeff0[0] > 0: allcoeff0 = coeff0[0] allcoeff1 = coeff1[1] if mincoeff0 > 0 and coeff0[0] == 0: coeff0 = mincoeff0 coeff1 = allcoeff1[0] ps = np.floor((coeff0[0] - mincoeff0)/coeff1[0] + 0.5) if ps > 0: coeff0 = coeff0 - ps*coeff1 else: allcoeff0 = allcoeff0 + ps*allcoeff1 else: ps = 0 allcoeff0 = np.concatenate((allcoeff0, coeff0)) allcoeff1 = np.concatenate((allcoeff1, coeff1)) if hdunames[k] == 'plugmap': for c in spplate[5].columns.names: spplate_data['plugmap'][c] = np.concatenate( (spplate_data['plugmap'][c], tmp[c])) else: spplate_data[hdunames[k]] = spec_append(spplate_data[hdunames[k]], tmp, pixshift=ps) spplate.close() # # Read photoPlate information, if available # photofile = os.path.join(sppath[0], "photoPlate-{0}.fits".format(pmjdstr)) if not os.path.exists(photofile): # # Hmm, maybe this is an SDSS-I,II plate # photofile = os.path.join(os.environ['SPECTRO_MATCH'], run2d, os.path.basename(os.environ['PHOTO_RESOLVE']), "{0:04d}".format(int(thisplate)), "photoPlate-{0}.fits".format(pmjdstr)) if os.path.exists(photofile): photop = pyfits.open(photofile) tmp = photop[1].data[thisfiber-1] if 'tsobj' not in spplate_data: spplate_data['tsobj'] = dict() for c in photop[1].columns.names: spplate_data['tsobj'][c] = tmp[c] else: for c in photop[1].columns.names: spplate_data['tsobj'][c] = np.concatenate( (spplate_data['tsobj'][c], tmp[c])) photop.close() # # Read redshift information, if available. # if 'znum' in kwargs: zfile = os.path.join(sppath[0], run1d, "spZall-{0}.fits".format(pmjdstr)) else: zfile = os.path.join(sppath[0], run1d, "spZbest-{0}.fits".format(pmjdstr)) if os.path.exists(zfile): spz = pyfits.open(zfile) if 'znum' in kwargs: nper = spz[0].header['DIMS0'] zfiber = (thisfiber-1)*nper + kwargs['znum'] - 1 else: zfiber = thisfiber tmp = spz[1].data[zfiber-1] if 'zans' not in spplate_data: spplate_data['zans'] = dict() for c in spz[1].columns.names: spplate_data['zans'][c] = tmp[c] else: for c in spz[1].columns.names: spplate_data['zans'][c] = np.concatenate( (spplate_data['zans'][c], tmp[c])) spz.close() # # Reorder the data. At this point allpmjdindex is an index for which # fiber[allpmjdindex] == spplate['plugmap']['FIBERID'], so we have to # reverse this mapping. # j = allpmjdindex.argsort() for k in spplate_data: if isinstance(spplate_data[k], dict): for c in spplate_data[k]: if spplate_data[k][c].ndim == 2: spplate_data[k][c] = spplate_data[k][c][j, :] else: spplate_data[k][c] = spplate_data[k][c][j] else: spplate_data[k] = spplate_data[k][j, :] allcoeff0 = allcoeff0[j] allcoeff1 = allcoeff1[j] # # If necessary, recompute the wavelengths # nfibers, npixmax = spplate_data['flux'].shape if 'align' in kwargs: loglam0 = allcoeff0[0] + allcoeff1[1]*np.arange(npixmax, dtype='d') spplate_data['loglam'] = np.resize(loglam0, (nfibers, npixmax)) return spplate_data def skymask(invvar, andmask, ormask=None, ngrow=2): """Mask regions where sky-subtraction errors are expected to dominate. Parameters ---------- invvar : :class:`numpy.ndarray` Inverse variance. andmask : :class:`numpy.ndarray` An "and" mask. For historical reasons, this input is ignored. ormask : :class:`numpy.ndarray`, optional An "or" mask. Although technically this is optional, if it is not supplied, this function will have no effect. ngrow : :class:`int`, optional Expand bad areas by this number of pixels. Returns ------- :class:`numpy.ndarray` The `invvar` multiplied by the bad areas. """ from ..pydlutils.sdss import sdss_flagval from .. import smooth nrows, npix = invvar.shape badmask = np.zeros(invvar.shape, dtype='i4') badskychi = sdss_flagval('SPPIXMASK', 'BADSKYCHI') redmonster = sdss_flagval('SPPIXMASK', 'REDMONSTER') # brightsky = sdss_flagval('SPPIXMASK', 'BRIGHTSKY') if ormask is not None: badmask = badmask | ((ormask & badskychi) != 0) badmask = badmask | ((ormask & redmonster) != 0) # badmask = badmask | ((andmask & brightsky) != 0) if ngrow > 0: width = 2*ngrow + 1 for k in range(nrows): badmask[k, :] = smooth(badmask[k, :]*width, width, True) > 0 return invvar * (1 - badmask) def spec_append(spec1, spec2, pixshift=0): """Append the array spec2 to the array spec1 & return a new array. If the dimension of these arrays is the same, then append as [spec1,spec2]. If not, increase the size of the smaller array & fill with zeros. Parameters ---------- spec1, spec2 : :class:`numpy.ndarray` Append `spec2` to `spec1`. pixshift : :class:`int`, optional If `pixshift` is set to a positive integer, `spec2` will be padded with `pixshift` zeros on the left side. If `pixshift` is set to a negative integer, `spec1` will be padded with ``abs(pixshift)`` zeros on the left side. If not set, all zeros will be padded on the right side. Returns ------- :class:`numpy.ndarray` A new array containing both `spec1` and `spec2`. """ nrows1, npix1 = spec1.shape nrows2, npix2 = spec2.shape nrows = nrows1+nrows2 nadd1 = 0 nadd2 = 0 if pixshift != 0: if pixshift < 0: nadd1 = -pixshift else: nadd2 = pixshift maxpix = max(npix1 + nadd1, npix2 + nadd2) spec3 = np.zeros((nrows, maxpix), dtype=spec1.dtype) spec3[0:nrows1, nadd1:nadd1+npix1] = spec1 spec3[nrows1:nrows, nadd2:nadd2+npix2] = spec2 return spec3 def spec_path(plate, path=None, topdir=None, run2d=None): """Return the directory containing spPlate files. Parameters ---------- plate : :class:`int` or :class:`numpy.ndarray` The plate(s) to examine. path : :class:`str`, optional If set, `path` becomes the full path for every plate. In other words, it completely short-circuits this function. topdir : :class:`str`, optional Used to override the value of :envvar:`BOSS_SPECTRO_REDUX`. run2d : :class:`str`, optional Used to override the value of :envvar:`RUN2D`. Returns ------- :class:`list` A list of directories, one for each plate. Raises ------ KeyError If environment variables are not supplied. """ from os import environ from os.path import join from astropy.extern.six import integer_types if isinstance(plate, integer_types) or plate.shape == (): platevec = np.array([plate], dtype='i4') else: platevec = plate if path is None: if run2d is None: run2d = environ['RUN2D'] if topdir is None: env = "SPECTRO_REDUX" try: ir = int(run2d) except ValueError: env = 'BOSS_SPECTRO_REDUX' topdir = environ[env] paths = list() for p in platevec: if path is not None: paths.append(path) else: paths.append(join(topdir, run2d, '{0:04d}'.format(p))) return paths def preprocess_spectra(flux, ivar, loglam=None, zfit=None, aesthetics='mean', newloglam=None, wavemin=None, wavemax=None, verbose=False): """Handle the processing of input spectra through the :func:`~pydl.pydlspec2d.spec2d.combine1fiber` stage. Parameters ---------- flux : array-like The input spectral flux. ivar : array-like The inverse variance of the spectral flux. loglam : array-like, optional The input wavelength solution. zfit : array-like, optional The redshift of each input spectrum. aesthetics : :class:`str`, optional This parameter will be passed to :func:`~pydl.pydlspec2d.spec2d.combine1fiber`. newloglam : array-like, optional The output wavelength solution. wavemin : :class:`float`, optional Minimum wavelength if `newloglam` is not specified. wavemax : :class:`float`, optional Maximum wavelength if `newloglam` is not specified. verbose : :class:`bool`, optional If ``True``, print extra information. Returns ------- :func:`tuple` of :class:`numpy.ndarray` The resampled flux, inverse variance and wavelength solution, respectively. """ from astropy import log from .spec2d import combine1fiber if verbose: log.setLevel('DEBUG') if len(flux.shape) == 1: nobj = 1 npix = flux.shape[0] else: nobj, npix = flux.shape # # The redshift of each object in pixels would be logshift/objdloglam. # if zfit is None: logshift = np.zeros((nobj,), dtype=flux.dtype) else: logshift = np.log10(1.0 + zfit) # # Determine the new wavelength mapping. # if loglam is None: if newloglam is None: raise ValueError("newloglam must be set if loglam is not!") return (flux, ivar, newloglam) else: if newloglam is None: igood = loglam != 0 dloglam = loglam[1] - loglam[0] logmin = loglam[igood].min() - logshift.max() logmax = loglam[igood].max() - logshift.min() if wavemin is not None: logmin = max(logmin, np.log10(wavemin)) if wavemax is not None: logmax = min(logmax, np.log10(wavemax)) fullloglam = wavevector(logmin, logmax, binsz=dloglam) else: fullloglam = newloglam dloglam = fullloglam[1] - fullloglam[0] nnew = fullloglam.size fullflux = np.zeros((nobj, nnew), dtype='d') fullivar = np.zeros((nobj, nnew), dtype='d') # # Shift each spectrum to z = 0 and sample at the output wavelengths # if loglam.ndim == 1: indx = loglam > 0 rowloglam = loglam[indx] for iobj in range(nobj): log.info("OBJECT {0:5d}".format(iobj)) if loglam.ndim > 1: if loglam.shape[0] != nobj: raise ValueError('Wrong number of dimensions for loglam.') indx = loglam[iobj, :] > 0 rowloglam = loglam[iobj, indx] flux1, ivar1 = combine1fiber(rowloglam-logshift[iobj], flux[iobj, indx], fullloglam, objivar=ivar[iobj, indx], binsz=dloglam, aesthetics=aesthetics, verbose=verbose) fullflux[iobj, :] = flux1 fullivar[iobj, :] = ivar1 return (fullflux, fullivar, fullloglam) def template_metadata(inputfile, verbose=False): """Read template metadata from file. Parameters ---------- inputfile : :class:`str` Name of a Parameter file containing the input data and metadata. verbose : :class:`bool`, optional If ``True``, print lots of extra information. Returns ------- :func:`tuple` A tuple containing the list of input spectra and a dictionary containing other metadata. """ import os from astropy import log from . import Pydlspec2dException from ..pydlutils.yanny import yanny if verbose: log.setLevel('DEBUG') if not os.path.exists(inputfile): raise Pydlspec2dException("Could not find {0}!".format(inputfile)) log.debug("Reading input data from {0}.".format(inputfile)) par = yanny(inputfile) required_metadata = {'object': str, 'method': str, 'aesthetics': str, 'run2d': str, 'run1d': str, 'wavemin': float, 'wavemax': float, 'snmax': float, 'niter': int, 'nkeep': int, 'minuse': int} metadata = dict() for key in required_metadata: try: metadata[key] = required_metadata[key](par[key]) log.debug('{0} = {1}'.format(key, par[key])) except KeyError: raise KeyError('The {0} keyword was not found in {1}!'.format(key, inputfile)) except ValueError: raise ValueError('The {0} keyword has invalid value, {0}!'.format(key, par[key])) slist = par['EIGENOBJ'] for r in ('run2d', 'run1d'): try: metadata['orig_'+r] = os.environ[r.upper()] except KeyError: metadata['orig_'+r] = None os.environ[r.upper()] = metadata[r] if metadata['method'].lower() == 'hmf': required_hmf_metadata = {'nonnegative': lambda x: bool(int(x)), 'epsilon': float} for key in required_hmf_metadata: try: metadata[key] = required_hmf_metadata[key](par[key]) except KeyError: raise KeyError('The {0} keyword was not found in {1}!'.format(key, inputfile)) except ValueError: raise ValueError('The {0} keyword has invalid value, {0}!'.format(key, par[key])) return (slist, metadata) def template_input(inputfile, dumpfile, flux=False, verbose=False): """Collect spectra and pass them to PCA or HMF solvers to compute spectral templates. This function replaces the various ``PCA_GAL()``, ``PCA_STAR()``, etc., functions from idlspec2d. Parameters ---------- inputfile : :class:`str` Name of a Parameter file containing the input data and metadata. dumpfile : :class:`str` Name of a Pickle file used to store intermediate data. flux : :class:`bool`, optional If ``True``, plot the individual input spectra. verbose : :class:`bool`, optional If ``True``, print lots of extra information. Returns ------- None """ import os import pickle import matplotlib matplotlib.use('Agg') matplotlib.rcParams['figure.figsize'] = (16.0, 12.0) import matplotlib.pyplot as plt from matplotlib.font_manager import fontManager, FontProperties from astropy.io import fits from astropy.constants import c as cspeed from astropy import log from warnings import warn from . import Pydlspec2dException, Pydlspec2dUserWarning from .. import uniq from .. import __version__ as pydl_version from ..goddard.astro import get_juldate from ..pydlutils.image import djs_maskinterp from ..pydlutils.math import djs_median # # Logging # if verbose: log.setLevel('DEBUG') # # Read metadata. # slist, metadata = template_metadata(inputfile) # # Name the output files. # jd = get_juldate() outfile = "spEigen{0}-{1:d}".format(metadata['object'].title(), int(jd - 2400000.5)) # # Read the input spectra # if os.path.exists(dumpfile): log.info("Loading data from {0}.".format(dumpfile)) with open(dumpfile) as f: inputflux = pickle.load(f) newflux = inputflux['newflux'] newivar = inputflux['newivar'] newloglam = inputflux['newloglam'] else: if metadata['object'].lower() == 'star': spplate = readspec(slist.plate, mjd=slist.mjd, fiber=slist.fiberid, align=True) else: spplate = readspec(slist.plate, mjd=slist.mjd, fiber=slist.fiberid) # # Insist that all of the requested spectra exist. # missing = spplate['plugmap']['FIBERID'] == 0 if missing.any(): imissing = missing.nonzero()[0] for k in imissing: log.error("Missing plate={0:d} mjd={1:d} fiberid={2:d}".format( slist.plate[k], slist.mjd[k], slist.fiberid[k])) raise ValueError("{0:d} missing object(s).".format(missing.sum())) # # Do not fit where the spectrum may be dominated by sky-sub residuals. # objinvvar = skymask(spplate['invvar'], spplate['andmask'], spplate['ormask']) ifix = spplate['flux']**2 * objinvvar > metadata['snmax']**2 if ifix.any(): objinvvar[ifix.nonzero()] = (metadata['snmax']/spplate['flux'][ifix.nonzero()])**2 # # Set the new wavelength mapping here. If the binsz keyword is not set, # then bin size is determined from the first spectrum returned by readspec. # This is fine in the case where all spectra have the same bin size # (though their starting wavelengths may differ). However, this may not # be a safe assumption in the future. # try: objdloglam = float(par['binsz']) except: objdloglam = spplate['loglam'][0, 1] - spplate['loglam'][0, 0] if metadata['object'].lower() == 'star': newloglam = spplate['loglam'][0, :] else: newloglam = wavevector(np.log10(metadata['wavemin']), np.log10(metadata['wavemax']), binsz=objdloglam) try: zfit = slist.zfit except AttributeError: zfit = slist.cz/cspeed.to('km / s').value # # Shift to common wavelength grid. # newflux, newivar, newloglam = preprocess_spectra(spplate['flux'], objinvvar, loglam=spplate['loglam'], zfit=zfit, newloglam=newloglam, aesthetics=metadata['aesthetics'], verbose=verbose) # # Dump input fluxes to a file for debugging purposes. # if not os.path.exists(dumpfile): with open(dumpfile, 'w') as f: inputflux = {'newflux': newflux, 'newivar': newivar, 'newloglam': newloglam} pickle.dump(inputflux, f) # # Solve. # if metadata['object'].lower() == 'qso': pcaflux = template_qso(metadata, newflux, newivar, verbose) elif metadata['object'].lower() == 'star': pcaflux = template_star(metadata, newloglam, newflux, newivar, slist, outfile, verbose) else: if metadata['method'].lower() == 'pca': pcaflux = pca_solve(newflux, newivar, niter=metadata['niter'], nkeep=metadata['nkeep'], verbose=verbose) elif metadata['method'].lower() == 'hmf': hmf = HMF(newflux, newivar, K=metadata['nkeep'], n_iter=metadata['niter'], nonnegative=metadata['nonnegative'], epsilon=metadata['epsilon'], verbose=verbose) pcaflux = hmf.solve() else: raise ValueError("Unknown method: {0}!".format(metadata['method'])) pcaflux['newflux'] = newflux pcaflux['newivar'] = newivar pcaflux['newloglam'] = newloglam # # Fill in bad data with a running median of the good data. # The presence of boundary='nearest' means that this code snippet # was never meant to be called! In other words it should always # be the case that qgood.all() is True. # if 'usemask' in pcaflux: qgood = pcaflux['usemask'] >= metadata['minuse'] if not qgood.all(): warn("Would have triggered djs_median replacement!", Pydlspec2dUserWarning) if False: medflux = np.zeros(pcaflux['flux'].shape, dtype=pcaflux['flux'].dtype) for i in range(metadata['nkeep']): medflux[i, qgood] = djs_median(pcaflux['flux'][i, qgood], width=51, boundary='nearest') medflux[i, :] = djs_maskinterp(medflux[i, :], ~qgood, const=True) pcaflux['flux'][:, ~qgood] = medflux[:, ~qgood] # # Make plots # colorvec = ['k', 'r', 'g', 'b', 'm', 'c'] smallfont = FontProperties(size='xx-small') nspectra = pcaflux['newflux'].shape[0] # # Plot input spectra # if flux: nfluxes = 30 separation = 5.0 nplots = nspectra/nfluxes if nspectra % nfluxes > 0: nplots += 1 for k in range(nplots): istart = k*nfluxes iend = min(istart+nfluxes, nspectra) - 1 fig = plt.figure(dpi=100) ax = fig.add_subplot(111) for l in range(istart, iend+1): p = ax.plot(10.0**pcaflux['newloglam'], pcaflux['newflux'][l, :] + separation*(l % nfluxes), colorvec[l % len(colorvec)]+'-', linewidth=1) ax.set_xlabel(r'Wavelength [$\AA$]') ax.set_ylabel(r'Flux [$\mathsf{10^{-17} erg\, cm^{-2} s^{-1} \AA^{-1}}$] + Constant') ax.set_title('Input Spectra {0:04d}-{1:04d}'.format(istart+1, iend+1)) ax.set_ylim(pcaflux['newflux'][istart, :].min(), pcaflux['newflux'][iend-1, :].max()+separation*(nfluxes-1)) fig.savefig('{0}.flux.{1:04d}-{2:04d}.png'.format(outfile, istart+1, iend+1)) plt.close(fig) # # Missing data diagnostic. # fig = plt.figure(dpi=100) ax = fig.add_subplot(111) p = ax.plot(10.0**pcaflux['newloglam'], (pcaflux['newivar'] == 0).sum(0)/float(nspectra), 'k-') ax.set_xlabel(r'Wavelength [$\AA$]') ax.set_ylabel('Fraction of spectra with missing data') ax.set_title('Missing Data') ax.grid(True) fig.savefig(outfile+'.missing.png') plt.close(fig) # # usemask diagnostic # if 'usemask' in pcaflux: fig = plt.figure(dpi=100) ax = fig.add_subplot(111) p = ax.semilogy(10.0**pcaflux['newloglam'][pcaflux['usemask'] > 0], pcaflux['usemask'][pcaflux['usemask'] > 0], 'k-', 10.0**pcaflux['newloglam'], np.zeros(pcaflux['newloglam'].shape, dtype=pcaflux['newloglam'].dtype) + metadata['minuse'], 'k--') ax.set_xlabel(r'Wavelength [$\AA$]') ax.set_ylabel('Usemask') ax.set_title('UseMask') ax.grid(True) fig.savefig(outfile+'.usemask.png') plt.close(fig) # # This type of figure isn't really meaningful for stars. # if metadata['object'].lower() != 'star': aratio10 = pcaflux['acoeff'][:, 1]/pcaflux['acoeff'][:, 0] aratio20 = pcaflux['acoeff'][:, 2]/pcaflux['acoeff'][:, 0] aratio30 = pcaflux['acoeff'][:, 3]/pcaflux['acoeff'][:, 0] fig = plt.figure(dpi=100) ax = fig.add_subplot(111) p = ax.plot(aratio10, aratio20, marker='None', linestyle='None') for k in range(len(aratio10)): t = ax.text(aratio10[k], aratio20[k], '{0:04d}-{1:04d}'.format(slist.plate[k], slist.fiberid[k]), horizontalalignment='center', verticalalignment='center', color=colorvec[k % len(colorvec)], fontproperties=smallfont) # ax.set_xlim([aratio10.min(), aratio10.max]) # ax.set_xlim([aratio20.min(), aratio20.max]) ax.set_xlabel('Eigenvalue Ratio, $a_1/a_0$') ax.set_ylabel('Eigenvalue Ratio, $a_2/a_0$') ax.set_title('Eigenvalue Ratios') fig.savefig(outfile+'.a2_v_a1.png') plt.close(fig) fig = plt.figure(dpi=100) ax = fig.add_subplot(111) p = ax.plot(aratio20, aratio30, marker='None', linestyle='None') for k in range(len(aratio10)): t = ax.text(aratio20[k], aratio30[k], '{0:04d}-{1:04d}'.format(slist.plate[k], slist.fiberid[k]), horizontalalignment='center', verticalalignment='center', color=colorvec[k % len(colorvec)], fontproperties=smallfont) # ax.set_xlim([aratio10.min(), aratio10.max]) # ax.set_xlim([aratio20.min(), aratio20.max]) ax.set_xlabel('Eigenvalue Ratio, $a_2/a_0$') ax.set_ylabel('Eigenvalue Ratio, $a_3/a_0$') ax.set_title('Eigenvalue Ratios') fig.savefig(outfile+'.a3_v_a2.png') plt.close(fig) # # Save output to FITS file. # if os.path.exists(outfile+'.fits'): os.remove(outfile+'.fits') hdu0 = fits.PrimaryHDU(pcaflux['flux']) objtypes = {'gal': 'GALAXY', 'qso': 'QSO', 'star': 'STAR'} if not pydl_version: pydl_version = 'git' hdu0.header['OBJECT'] = (objtypes[metadata['object']], 'Type of template') hdu0.header['COEFF0'] = (pcaflux['newloglam'][0], 'Wavelength zeropoint') hdu0.header['COEFF1'] = (pcaflux['newloglam'][1]-pcaflux['newloglam'][0], 'Delta wavelength') hdu0.header['IDLUTILS'] = ('pydl-{0}'.format(pydl_version), 'Version of idlutils') hdu0.header['SPEC2D'] = ('pydl-{0}'.format(pydl_version), 'Version of idlspec2d') hdu0.header['RUN2D'] = (os.environ['RUN2D'], 'Version of 2d reduction') hdu0.header['RUN1D'] = (os.environ['RUN1D'], 'Version of 1d reduction') hdu0.header['FILENAME'] = (inputfile, 'Input file') hdu0.header['METHOD'] = (metadata['method'].upper(), 'Method used') if metadata['method'].lower() == 'hmf': hdu0.header['NONNEG'] = (metadata['nonnegative'], 'Was nonnegative HMF used?') hdu0.header['EPSILON'] = (metadata['epsilon'], 'Regularization parameter used.') # for i in range(len(namearr)): # hdu0.header["NAME{0:d}".format(i)] = namearr[i]+' ' c = [fits.Column(name='plate', format='J', array=slist.plate), fits.Column(name='mjd', format='J', array=slist.mjd), fits.Column(name='fiberid', format='J', array=slist.fiberid)] if metadata['object'].lower() == 'star': c.append(fits.Column(name='cz', format='D', unit='km/s', array=slist.cz)) for i, name in enumerate(pcaflux['namearr']): hdu0.header['NAME{0:d}'.format(i)] = (name, 'Name of class {0:d}.'.format(i)) else: c.append(fits.Column(name='zfit', format='D', array=slist.zfit)) hdu1 = fits.BinTableHDU.from_columns(fits.ColDefs(c)) hdulist = fits.HDUList([hdu0, hdu1]) hdulist.writeto(outfile+'.fits') if metadata['object'].lower() != 'star': plot_eig(outfile+'.fits') # # Clean up # for r in ('run2d', 'run1d'): if metadata['orig_'+r] is None: del os.environ[r.upper()] else: os.environ[r.upper()] = metadata['orig_'+r] return def template_qso(metadata, newflux, newivar, verbose=False): """Run PCA or HMF on QSO spectra. Historically, QSO templates were comptuted one at a time instead of all at once. Parameters ---------- metadata : :class:`dict` Dictionary containing metadata about the spectra. newflux : :class:`~numpy.ndarray` Flux shifted onto common wavelength. newivar : :class:`~numpy.ndarray` Inverse variances of the fluxes. verbose : :class:`bool`, optional If ``True``, print lots of extra information. Returns ------- :class:`dict` A dictonary containing flux, eigenvalues, etc. """ from . import Pydlspec2dException from ..pydlutils.math import computechi2 from astropy import log if metadata['object'].lower() != 'qso': raise Pydlspec2dException("You appear to be passing the wrong kind of object to template_qso()!") if len(newflux.shape) == 1: nobj = 1 npix = newflux.shape[0] else: nobj, npix = newflux.shape objflux = newflux.copy() for ikeep in range(metadata['nkeep']): log.info("Solving for eigencomponent #{0:d} of {1:d}".format(ikeep+1, nkeep)) if metadata['method'].lower() == 'pca': pcaflux1 = pca_solve(objflux, newivar, niter=metadata['niter'], nkeep=1, verbose=verbose) elif metadata['method'].lower() == 'hmf': hmf = HMF(objflux, newivar, K=metadata['nkeep'], n_iter=metadata['niter'], nonnegative=metadata['nonnegative'], epsilon=metadata['epsilon'], verbose=verbose) pcaflux1 = hmf.solve() else: raise ValueError("Unknown method: {0}!".format(metadata['method'])) if ikeep == 0: # # Create new pcaflux dict # pcaflux = dict() for k in pcaflux1: pcaflux[k] = pcaflux1[k].copy() else: # # Add to existing dict # # for k in pcaflux1: # pcaflux[k] = np.vstack((pcaflux[k],pcaflux1[k])) pcaflux['flux'] = np.vstack((pcaflux['flux'], pcaflux1['flux'])) pcaflux['eigenval'] = np.concatenate((pcaflux['eigenval'], pcaflux1['eigenval'])) # # Re-solve for the coefficients using all PCA components so far # acoeff = np.zeros((nobj, ikeep+1), dtype=pcaflux1['acoeff'].dtype) for iobj in range(nobj): out = computechi2(newflux[iobj, :], np.sqrt(pcaflux1['newivar'][iobj, :]), pcaflux['flux'].T) acoeff[iobj, :] = out['acoeff'] # # Prevent re-binning of spectra on subsequent calls to pca_solve() # # objloglam = None if ikeep == 0: objflux = newflux - np.outer(acoeff, pcaflux['flux']) else: objflux = newflux - np.dot(acoeff, pcaflux['flux']) # objflux = newflux - np.outer(acoeff,pcaflux['flux']) # objinvvar = pcaflux1['newivar'] pcaflux['acoeff'] = acoeff return pcaflux def template_star(metadata, newloglam, newflux, newivar, slist, outfile, verbose=False): """Run PCA or HMF on stellar spectra of various classes. Parameters ---------- metadata : :class:`dict` Dictionary containing metadata about the spectra. newloglam : :class:`~numpy.ndarray` The wavelength array, used only for plots. newflux : :class:`~numpy.ndarray` Flux shifted onto common wavelength. newivar : :class:`~numpy.ndarray` Inverse variances of the fluxes. slist : :class:`~numpy.recarray` The list of objects, containing stellar class information. outfile : :class:`str` The base name of output file, used for plots. verbose : :class:`bool`, optional If ``True``, print lots of extra information. Returns ------- :class:`dict` A dictonary containing flux, eigenvalues, etc. """ from . import Pydlspec2dException from .. import uniq from ..pydlutils.image import djs_maskinterp from astropy import log import matplotlib matplotlib.use('Agg') matplotlib.rcParams['figure.figsize'] = (16.0, 12.0) import matplotlib.pyplot as plt from matplotlib.font_manager import fontManager, FontProperties if metadata['object'].lower() != 'star': raise Pydlspec2dException("You appear to be passing the wrong kind of object to template_star()!") # # Find the list of unique star types # isort = np.argsort(slist['class']) classlist = slist['class'][isort[uniq(slist['class'][isort])]] # # Loop over each star type # npix, nstars = newflux.shape pcaflux = dict() pcaflux['namearr'] = list() for c in classlist: # # Find the subclasses for this stellar type # log.info("Finding eigenspectra for Stellar class {0}.".format(c)) indx = (slist['class'] == c).nonzero()[0] nindx = indx.size thesesubclass = slist['subclass'][indx] isort = np.argsort(thesesubclass) subclasslist = thesesubclass[isort[uniq(thesesubclass[isort])]] nsubclass = subclasslist.size # # Solve for 2 eigencomponents if we have specified subclasses for # this stellar type # if nsubclass == 1: nkeep = 1 else: nkeep = 2 if metadata['method'].lower() == 'pca': pcaflux1 = pca_solve(newflux[indx, :], newivar[indx, :], niter=metadata['niter'], nkeep=nkeep, verbose=verbose) elif metadata['method'].lower() == 'hmf': hmf = HMF(newflux[indx, :], newivar[indx, :], K=metadata['nkeep'], n_iter=metadata['niter'], nonnegative=metadata['nonnegative'], epsilon=metadata['epsilon'], verbose=verbose) pcaflux1 = hmf.solve() else: raise ValueError("Unknown method: {0}!".format(metadata['method'])) # # Some star templates are generated from only one spectrum, # and these will not have a usemask set. # if 'usemask' not in pcaflux1: pcaflux1['usemask'] = np.zeros((npix,), dtype='i4') + nindx # # Interpolate over bad flux values in the middle of a spectrum, # and set fluxes to zero at the blue+red ends of the spectrum # # minuse = 1 # ? minuse = np.floor((nindx+1) / 3.0) qbad = pcaflux1['usemask'] < minuse # # Interpolate over all bad pixels # for j in range(nkeep): pcaflux1['flux'][j, :] = djs_maskinterp(pcaflux1['flux'][j, :], qbad, const=True) # # Set bad pixels at the very start or end of the spectrum to zero # instead. # npix = qbad.size igood = (~qbad).nonzero()[0] if qbad[0]: pcaflux1['flux'][:, 0:igood[0]-1] = 0 if qbad[npix-1]: pcaflux1['flux'][:, igood[::-1][0]+1:npix] = 0 # # Re-normalize the first eigenspectrum to a mean of 1 # norm = pcaflux1['flux'][0, :].mean() pcaflux1['flux'] /= norm if 'acoeff' in pcaflux1: pcaflux1['acoeff'] *= norm # # Now loop through each stellar subclass and reconstruct # an eigenspectrum for that subclass # thesesubclassnum = np.zeros(thesesubclass.size, dtype='i4') colorvec = ['k', 'r', 'g', 'b', 'm', 'c'] smallfont = FontProperties(size='xx-small') fig = plt.figure(dpi=100) ax = fig.add_subplot(111) for isub in range(nsubclass): ii = (thesesubclass == subclasslist[isub]).nonzero()[0] thesesubclassnum[ii] = isub if nkeep == 1: thisflux = pcaflux1['flux'][0, :] else: aratio = pcaflux1['acoeff'][ii, 1]/pcaflux1['acoeff'][ii, 0] # # np.median(foo) is equivalent to MEDIAN(foo,/EVEN) # thisratio = np.median(aratio) thisflux = (pcaflux1['flux'][0, :] + thisratio.astype('f') * pcaflux1['flux'][1, :]) # # Plot spectra # plotflux = thisflux/thisflux.max() ax.plot(10.0**newloglam, plotflux, "{0}-".format(colorvec[isub % len(colorvec)]), linewidth=1) if isub == 0: ax.set_xlabel(r'Wavelength [$\AA$]') ax.set_ylabel('Flux [arbitrary units]') ax.set_title('STAR {0}: Eigenspectra Reconstructions'.format(c)) t = ax.text(10.0**newloglam[-1], plotflux[-1], subclasslist[isub], horizontalalignment='right', verticalalignment='center', color=colorvec[isub % len(colorvec)], fontproperties=smallfont) fig.savefig(outfile+'.{0}.png'.format(c)) plt.close(fig) if 'flux' in pcaflux: pcaflux['flux'] = np.vstack((pcaflux['flux'], thisflux)) # pcaflux['acoeff'] = np.vstack((pcaflux['acoeff'], pcaflux1['acoeff'])) # pcaflux['usemask'] = np.vstack((pcaflux['usemask'], pcaflux1['usemask'])) else: pcaflux['flux'] = thisflux # pcaflux['acoeff'] = pcaflux1['acoeff'] # pcaflux['usemask'] = pcaflux1['usemask'] pcaflux['namearr'].append(subclasslist[isub]) return pcaflux def template_input_main(): # pragma: no cover """Entry point for the compute_templates script. Returns ------- :class:`int` An integer suitable for passing to :func:`sys.exit`. """ # # Imports for main() # import os import sys from argparse import ArgumentParser # Get home directory in platform-independent way home_dir = os.path.expanduser('~') # # Get Options # parser = ArgumentParser(description="Compute spectral templates.", prog=os.path.basename(sys.argv[0])) parser.add_argument('-d', '--dump', action='store', dest='dump', metavar='FILE', default=os.path.join(home_dir, 'scratch', 'templates', 'compute_templates.dump'), help='Dump data to a pickle file (default: %(default)s).') parser.add_argument('-F', '--flux', action='store_true', dest='flux', help='Plot input spectra.') parser.add_argument('-f', '--file', action='store', dest='inputfile', metavar='FILE', default=os.path.join(home_dir, 'scratch', 'templates', 'compute_templates.par'), help='Read input spectra and redshifts from FILE (default: %(default)s).') parser.add_argument('-v', '--verbose', action='store_true', dest='verbose', help='Print lots of extra information.') options = parser.parse_args() template_input(options.inputfile, options.dump, options.flux, options.verbose) return 0 def wavevector(minfullwave, maxfullwave, zeropoint=3.5, binsz=1.0e-4, wavemin=None): """Return an array of wavelengths. Parameters ---------- minfullwave : :class:`float` Minimum wavelength. maxfullwave : :class:`float` Maximum wavelength. zeropoint : :class:`float`, optional Offset of the input wavelength values. binsz : :class:`float`, optional Separation between wavelength values. wavemin : :class:`float`, optional If this is set the values of `minfullwave` and `zeropoint` are ignored. Returns ------- :class:`numpy.ndarray` Depending on the values of `minfullwave`, `binsz`, etc., the resulting array could be interpreted as an array of wavelengths or an array of log(wavelength). """ if wavemin is not None: spotmin = 0 spotmax = int((maxfullwave - wavemin)/binsz) wavemax = spotmax * binsz + wavemin else: spotmin = int((minfullwave - zeropoint)/binsz) + 1 spotmax = int((maxfullwave - zeropoint)/binsz) wavemin = spotmin * binsz + zeropoint wavemax = spotmax * binsz + zeropoint nfinalpix = spotmax - spotmin + 1 finalwave = np.arange(nfinalpix, dtype='d') * binsz + wavemin return finalwave pydl-0.6.0/pydl/pydlspec2d/__init__.py0000664000111400017500000000123212775255301020424 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This subpackage implements functions from the idlspec2d package. """ # # Define this early on so that submodules can use it # from .. import PydlException import astropy.utils.exceptions as aue class Pydlspec2dException(PydlException): """Exceptions raised by :mod:`pydl.pydlspec2d` that don't fit into a standard exception class like :exc:`ValueError`. """ pass class Pydlspec2dUserWarning(aue.AstropyUserWarning): """Class for warnings issued by :mod:`pydl.pydlspec2d`. """ pass __all__ = ['Pydlspec2dException', 'Pydlspec2dUserWarning'] pydl-0.6.0/pydl/pydlspec2d/spec2d.py0000664000111400017500000004464013040014667020051 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the spec2d directory in idlspec2d. """ def aesthetics(flux, invvar, method='traditional'): """Add nice values to a spectrum where it is masked. Parameters ---------- flux : :class:`numpy.ndarray` The spectrum to clean up. invvar : :class:`numpy.ndarray` Inverse variance of the spectrum. method : { 'traditional', 'noconst', 'mean', 'damp', 'nothing' }, optional Apply this method to clean up the spectrum. Default is 'traditional'. Returns ------- :class:`numpy.ndarray` A cleaned-up spectrum. """ import numpy as np from scipy.special import erf from ..pydlutils.image import djs_maskinterp from . import Pydlspec2dException badpts = invvar == 0 if badpts.any(): if method == 'traditional': newflux = djs_maskinterp(flux, invvar == 0, const=True) elif method == 'noconst': newflux = djs_maskinterp(flux, invvar == 0) elif method == 'mean': newflux = flux.copy() goodpts = invvar > 0 newflux[~goodpts] = newflux[goodpts].mean() elif method == 'damp': l = 250 # damping length in pixels goodpts = invvar.nonzero()[0] nflux = flux.size mingood = goodpts.min() maxgood = goodpts.max() newflux = djs_maskinterp(flux, invvar == 0, const=True) pixels = np.arange(nflux, dtype='f') if mingood > 0: damp1 = float(min(mingood, l)) newflux *= 0.5*(1.0+erf((pixels-mingood)/damp1)) if maxgood < (nflux - 1): damp2 = float(min(maxgood, l)) newflux *= 0.5*(1.0+erf((maxgood-pixels)/damp2)) elif method == 'nothing': newflux = flux.copy() else: raise Pydlspec2dException("Unknown method: {0}".format(method)) return newflux else: return flux def combine1fiber(inloglam, objflux, newloglam, objivar=None, verbose=False, **kwargs): """Combine several spectra of the same object, or resample a single spectrum. Parameters ---------- inloglam : :class:`numpy.ndarray` Vector of log wavelength. objflux : :class:`numpy.ndarray` Input flux. newloglam : :class:`numpy.ndarray` Output wavelength pixels, vector of log wavelength. objivar : :class:`numpy.ndarray`, optional Inverse variance of the flux. verbose : :class:`bool`, optional If ``True``, set log level to DEBUG. Returns ------- :func:`tuple` of :class:`numpy.ndarray` The resulting flux and inverse variance. Raises ------ ValueError If input dimensions don't match. """ import numpy as np from astropy import log from warnings import warn from . import Pydlspec2dUserWarning from .. import smooth from ..pydlutils.bspline import iterfit from ..pydlutils.math import djs_median from ..pydlutils.sdss import sdss_flagval # # Log # # log.enable_warnings_logging() if verbose: log.setLevel('DEBUG') # # Check that dimensions of inputs are valid. # npix = inloglam.size nfinalpix = len(newloglam) if objflux.shape != inloglam.shape: raise ValueError('Dimensions of inloglam and objflux do not agree.') if objivar is not None: if objivar.shape != inloglam.shape: raise ValueError('Dimensions of inloglam and objivar do not agree.') if 'finalmask' in kwargs: if kwargs['finalmask'].shape != inloglam.shape: raise ValueError('Dimensions of inloglam and finalmask do not agree.') if 'indisp' in kwargs: if kwargs['indisp'].shape != inloglam.shape: raise ValueError('Dimensions of inloglam and indisp do not agree.') # # Set defaults # EPS = np.finfo(np.float32).eps if 'binsz' in kwargs: binsz = kwargs['binsz'] else: if inloglam.ndim == 2: binsz = inloglam[0, 1] - inloglam[0, 0] else: binsz = inloglam[1] - inloglam[0] if 'nord' in kwargs: nord = kwargs['nord'] else: nord = 3 if 'bkptbin' in kwargs: bkptbin = kwargs['bkptbin'] else: bkptbin = 1.2 * binsz if 'maxsep' in kwargs: maxsep = kwargs['maxsep'] else: maxsep = 2.0 * binsz if inloglam.ndim == 1: # # Set specnum = 0 for all elements # nspec = 1 specnum = np.zeros(inloglam.shape, dtype=inloglam.dtype) else: nspec, ncol = inloglam.shape specnum = np.tile(np.arange(nspec), ncol).reshape(ncol, nspec).transpose() # # Use fullcombmask for modifying the pixel masks in the original input files. # fullcombmask = np.zeros(npix) newflux = np.zeros(nfinalpix, dtype=inloglam.dtype) newmask = np.zeros(nfinalpix, dtype='i4') newivar = np.zeros(nfinalpix, dtype=inloglam.dtype) newdisp = np.zeros(nfinalpix, dtype=inloglam.dtype) newsky = np.zeros(nfinalpix, dtype=inloglam.dtype) newdispweight = np.zeros(nfinalpix, dtype=inloglam.dtype) if objivar is None: nonzero = np.arange(npix, dtype='i4') ngood = npix else: nonzero = (objivar.ravel() > 0).nonzero()[0] ngood = nonzero.size # # ormask is needed to create andmask # andmask = np.zeros(nfinalpix, dtype='i4') ormask = np.zeros(nfinalpix, dtype='i4') if ngood == 0: # # In this case of no good points, set the nodata bit everywhere. # Also if noplug is set in the first input bit-mask, assume it # should be set everywhere in the output bit masks. No other bits # are set. # warn('No good points!', Pydlspec2dUserWarning) bitval = sdss_flagval('SPPIXMASK', 'NODATA') if 'finalmask' in kwargs: bitval |= (sdss_flagval('SPPIXMASK', 'NOPLUG') * (finalmask[0] & sdss_flagval('SPPIXMASK', 'NODATA'))) andmask = andmask | bitval ormask = ormask | bitval return (newflux, newivar) else: # # Now let's break sorted wavelengths into groups where pixel # separations are larger than maxsep. # inloglam_r = inloglam.ravel() isort = nonzero[inloglam_r[nonzero].argsort()] wavesort = inloglam_r[isort] padwave = np.insert(wavesort, 0, wavesort.min() - 2.0*maxsep) padwave = np.append(padwave, wavesort.max() + 2.0*maxsep) ig1 = ((padwave[1:ngood+1]-padwave[0:ngood]) > maxsep).nonzero()[0] ig2 = ((padwave[2:ngood+2]-padwave[1:ngood+1]) > maxsep).nonzero()[0] if ig1.size != ig2.size: raise ValueError('Grouping tricks did not work!') # # Avoid flux-dependent bias when combining multiple spectra. # This call to djs_median contains a width that is both floating-point # and even, which is very strange. # if objivar is not None and objivar.ndim > 1: saved_objivar = objivar for spec in range(nspec): igood = (objivar[spec, :] > 0).nonzero()[0] if igood.size > 0: # objivar[spec, igood] = djs_median(saved_objivar[spec, igood], width=100.) objivar[spec, igood] = djs_median(saved_objivar[spec, igood], width=101) else: saved_objivar = None for igrp in range(ig1.size): ss = isort[ig1[igrp]:ig2[igrp]+1] if ss.size > 2: if objivar is None: # # Fit without variance # sset, bmask = iterfit(inloglam_r[ss], objflux.ravel()[ss], nord=nord, groupbadpix=True, requiren=1, bkspace=bkptbin, silent=True) else: # # Fit with variance # sset, bmask = iterfit(inloglam_r[ss], objflux.ravel()[ss], invvar=objivar.ravel()[ss], nord=nord, groupbadpix=True, requiren=1, bkspace=bkptbin, silent=True) if np.sum(np.absolute(sset.coeff)) == 0: sset = None bmask = np.zeros(len(ss)) warn('All B-spline coefficients have been set to zero!', Pydlspec2dUserWarning) else: bmask = np.zeros(len(ss)) sset = None warn('Not enough data for B-spline fit!', Pydlspec2dUserWarning) inside = ((newloglam >= (inloglam_r[ss]).min()-EPS) & (newloglam <= (inloglam_r[ss]).max()+EPS)).nonzero()[0] # # It is possible for numinside to be zero, if the input data points # span an extremely small wavelength range, within which there are # no output wavelengths. # if sset is not None and len(inside) > 0: newflux[inside], bvalumask = sset.value(newloglam[inside]) if bvalumask.any(): newmask[inside[bvalumask]] = 1 log.debug('Masked {0:d} of {1:d} pixels.'.format((1-bmask).sum(), bmask.size)) # # Determine which pixels should be masked based upon the spline # fit. Set the combinerej bit. # ireplace = ~bmask if ireplace.any(): # # The following would replace the original flux values of # masked pixels with b-spline evaluations. # # objflux[ss[ireplace]] = sset.value(inloglam[ss[ireplace]]) # # Set the inverse variance of these pixels to zero. # if objivar is not None: objivar.ravel()[ss[ireplace]] = 0.0 log.debug('Replaced {0:d} pixels in objivar.'.format(len(ss[ireplace]))) if 'finalmask' in kwargs: finalmask[ss[ireplace]] = (finalmask[ss[ireplace]] | sdss_flagval('SPPIXMASK', 'COMBINEREJ')) fullcombmask[ss] = bmask # # Restore objivar # if saved_objivar is not None: objivar = saved_objivar * (objivar > 0) # # Combine inverse variance and pixel masks. # # Start with all bits set in andmask # andmask[:] = -1 for j in range(int(specnum.max())+1): these = (specnum.ravel() == j).nonzero()[0] if these.any(): inbetween = ((newloglam >= inloglam_r[these].min()) & (newloglam <= inloglam_r[these].max())) if inbetween.any(): jnbetween = inbetween.nonzero()[0] # # Conserve inverse variance by doing a linear interpolation # on that quantity. # result = np.interp(newloglam[jnbetween], inloglam_r[these], (objivar.ravel()[these] * fullcombmask[these])) # # Grow the fullcombmask below to reject any new sampling # containing even a partial masked pixel. # smask = np.interp(newloglam[jnbetween], inloglam_r[these], fullcombmask[these].astype(inloglam.dtype)) result *= smask >= (1.0 - EPS) newivar[jnbetween] += result*newmask[jnbetween] lowside = np.floor((inloglam_r[these]-newloglam[0])/binsz).astype('i4') highside = lowside + 1 if 'finalmask' in kwargs: andmask[lowside] &= finalmask[these] andmask[highside] &= finalmask[these] ormask[lowside] |= finalmask[these] ormask[highside] |= finalmask[these] # # Combine the dispersions + skies in the dumbest way possible # [sic]. # if 'indisp' in kwargs: newdispweight[jnbetween] += result newdisp[jnbetween] += (result * np.interp(newloglam[jnbetween], inloglam_r[these], indisp.ravel()[these])) newsky[jnbetween] += (result * np.interp(newloglam[jnbetween], inloglam_r[these], skyflux.ravel()[these])) if 'indisp' in kwargs: newdisp /= newdispweight + (newdispweight == 0) newsky /= newdispweight + (newdispweight == 0) # # Grow regions where 3 or more pixels are rejected together ??? # foo = smooth(newivar, 3) badregion = np.absolute(foo) < EPS # badregion = foo == 0.0 if badregion.any(): warn('Growing bad pixel region, {0:d} pixels found.'.format(badregion.sum()), Pydlspec2dUserWarning) ibad = badregion.nonzero()[0] lowerregion = np.where(ibad-2 < 0, 0, ibad-2) upperregion = np.where(ibad+2 > nfinalpix-1, nfinalpix-1, ibad+2) newivar[lowerregion] = 0.0 newivar[upperregion] = 0.0 # # Replace NaNs in combined spectra; this should really never happen. # inff = ((~np.isfinite(newflux)) | (~np.isfinite(newivar))) if inff.any(): warn('{0:d} NaNs in combined spectra.'.format(inff.sum()), Pydlspec2dUserWarning) newflux[inff] = 0.0 newivar[inff] = 0.0 # # Interpolate over masked pixels, just for aesthetic purposoes. # goodpts = newivar > 0 if 'aesthetics' in kwargs: amethod = kwargs['aesthetics'] else: amethod = 'traditional' newflux = aesthetics(newflux, newivar, method=amethod) # if 'interpolate' in kwargs: # newflux = pydlutils.image.djs_maskinterp(newflux,~goodpts,const=True) # else: # newflux[~goodpts] = newflux[goodpts].mean() if goodpts.any(): minglam = newloglam[goodpts].min() maxglam = newloglam[goodpts].max() ibad = ((newloglam < minglam) | (newloglam > maxglam)) if ibad.any(): ormask[ibad] |= sdss_flagval('SPPIXMASK', 'NODATA') andmask[ibad] |= sdss_flagval('SPPIXMASK', 'NODATA') # # Replace values of -1 in the andmask with 0. # andmask *= (andmask != -1) return (newflux, newivar) def filter_thru(flux, waveimg=None, wset=None, mask=None, filter_prefix='sdss_jun2001', toair=False): """Compute throughput in SDSS filters. Parameters ---------- flux : array-like Spectral flux. waveimg : array-like, optional Full wavelength solution, with the same shape as `flux`. wset : :class:`~pydl.pydlutils.trace.TraceSet`, optional A trace set containing the wavelength solution. Must be specified if `waveimg` is not specified. mask : array-like, optional Interpolate over pixels where `mask` is non-zero. filter_prefix : :class:`str`, optional Specifies a set of filter curves. toair : :class:`bool`, optional If ``True``, convert the wavelengths to air from vacuum before computing. Returns ------- array-like Integrated flux in the filter bands. Raises ------ ValueError If neither `waveimg` nor `wset` are set. """ import numpy as np from astropy.io import ascii from ..goddard.astro import vactoair from ..pydlutils.image import djs_maskinterp from ..pydlutils.trace import traceset2xy, xy2traceset nTrace, nx = flux.shape if filter_prefix != 'sdss_jun2001': raise ValueError("Filters other than {0} are not available!".format('sdss_jun2001')) ffiles = [_get_pkg_filename_compat('data/filters/{0}_{1}_atm.dat'.format(filter_prefix, f), package='pydl.pydlutils') for f in 'ugriz'] if waveimg is None and wset is None: raise ValueError("Either waveimg or wset must be specified!") if waveimg is None: pixnorm, logwave = traceset2xy(wset) waveimg = 10**logwave if toair: newwaveimg = vactoair(waveimg) else: newwaveimg = waveimg logwave = np.log10(newwaveimg) diffx = np.outer(np.ones((nTrace,), dtype=flux.dtype), np.arange(nx-1, dtype=flux.dtype)) diffy = logwave[:, 1:] - logwave[:, 0:nx-1] diffset = xy2traceset(diffx, diffy, ncoeff=4, xmin=0, xmax=nx-1) pixnorm, logdiff = traceset2xy(diffset) logdiff = np.absolute(logdiff) if mask is not None: flux_interp = djs_maskinterp(flux, mask, iaxis=0) res = np.zeros((nTrace, len(ffiles)), dtype=flux.dtype) for i, f in enumerate(ffiles): filter_data = ascii.read(f, comment='#.*', names=('lam', 'respt', 'resbig', 'resnoa', 'xatm')) filtimg = logdiff * np.interp(newwaveimg.flatten(), filter_data['lam'].data, filter_data['respt'].data).reshape(logdiff.shape) if mask is not None: res[:, i] = (flux_interp * filtimg).sum(1) else: res[:, i] = (flux * filtimg).sum(1) sumfilt = filtimg.sum(1) res[:, i] = res[:, i] / (sumfilt + (sumfilt <= 0).astype(sumfilt.dtype)) return res def _get_pkg_filename_compat(filename, package): """Astropy 1.0.x/LTS does not accept the 'package' argument. """ from astropy.utils.data import get_pkg_data_filename try: f = get_pkg_data_filename(filename, package=package) except TypeError: from pkg_resources import resource_filename f = resource_filename(package, filename) return f pydl-0.6.0/pydl/_astropy_init.py0000664000111400017500000001234113064273637017506 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst __all__ = ['__version__', '__githash__', 'test'] # this indicates whether or not we are in the package's setup.py try: _ASTROPY_SETUP_ except NameError: from sys import version_info if version_info[0] >= 3: import builtins else: import __builtin__ as builtins builtins._ASTROPY_SETUP_ = False try: from .version import version as __version__ except ImportError: __version__ = '' try: from .version import githash as __githash__ except ImportError: __githash__ = '' # set up the test command def _get_test_runner(): import os from astropy.tests.helper import TestRunner return TestRunner(os.path.dirname(__file__)) def test(package=None, test_path=None, args=None, plugins=None, verbose=False, pastebin=None, remote_data=False, pep8=False, pdb=False, coverage=False, open_files=False, **kwargs): """ Run the tests using `py.test `__. A proper set of arguments is constructed and passed to `pytest.main`_. .. _py.test: http://pytest.org/latest/ .. _pytest.main: http://pytest.org/latest/builtin.html#pytest.main Parameters ---------- package : str, optional The name of a specific package to test, e.g. 'io.fits' or 'utils'. If nothing is specified all default tests are run. test_path : str, optional Specify location to test by path. May be a single file or directory. Must be specified absolutely or relative to the calling directory. args : str, optional Additional arguments to be passed to pytest.main_ in the ``args`` keyword argument. plugins : list, optional Plugins to be passed to pytest.main_ in the ``plugins`` keyword argument. verbose : bool, optional Convenience option to turn on verbose output from py.test_. Passing True is the same as specifying ``'-v'`` in ``args``. pastebin : {'failed','all',None}, optional Convenience option for turning on py.test_ pastebin output. Set to ``'failed'`` to upload info for failed tests, or ``'all'`` to upload info for all tests. remote_data : bool, optional Controls whether to run tests marked with @remote_data. These tests use online data and are not run by default. Set to True to run these tests. pep8 : bool, optional Turn on PEP8 checking via the `pytest-pep8 plugin `_ and disable normal tests. Same as specifying ``'--pep8 -k pep8'`` in ``args``. pdb : bool, optional Turn on PDB post-mortem analysis for failing tests. Same as specifying ``'--pdb'`` in ``args``. coverage : bool, optional Generate a test coverage report. The result will be placed in the directory htmlcov. open_files : bool, optional Fail when any tests leave files open. Off by default, because this adds extra run time to the test suite. Requires the `psutil `_ package. parallel : int, optional When provided, run the tests in parallel on the specified number of CPUs. If parallel is negative, it will use the all the cores on the machine. Requires the `pytest-xdist `_ plugin installed. Only available when using Astropy 0.3 or later. kwargs Any additional keywords passed into this function will be passed on to the astropy test runner. This allows use of test-related functionality implemented in later versions of astropy without explicitly updating the package template. """ test_runner = _get_test_runner() return test_runner.run_tests( package=package, test_path=test_path, args=args, plugins=plugins, verbose=verbose, pastebin=pastebin, remote_data=remote_data, pep8=pep8, pdb=pdb, coverage=coverage, open_files=open_files, **kwargs) if not _ASTROPY_SETUP_: # noqa import os from warnings import warn from astropy.config.configuration import ( update_default_config, ConfigurationDefaultMissingError, ConfigurationDefaultMissingWarning) # add these here so we only need to cleanup the namespace at the end config_dir = None if not os.environ.get('ASTROPY_SKIP_CONFIG_UPDATE', False): config_dir = os.path.dirname(__file__) config_template = os.path.join(config_dir, __package__ + ".cfg") if os.path.isfile(config_template): try: update_default_config( __package__, config_dir, version=__version__) except TypeError as orig_error: try: update_default_config(__package__, config_dir) except ConfigurationDefaultMissingError as e: wmsg = (e.args[0] + " Cannot install default profile. If you are " "importing from source, this is expected.") warn(ConfigurationDefaultMissingWarning(wmsg)) del e except Exception: raise orig_error pydl-0.6.0/pydl/uniq.py0000664000111400017500000000326513040014667015572 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- def uniq(x, index=None): """Replicates the IDL ``UNIQ()`` function. Returns the *subscripts* of the unique elements of an array. The elements must actually be *sorted* before being passed to this function. This can be done by sorting `x` explicitly or by passing the array subscripts that sort `x` as a second parameter. Parameters ---------- x : array-like Search this array for unique items. index : array-like, optional This array provides the array subscripts that sort `x`. Returns ------- array-like The subscripts of `x` that are the unique elements of `x`. Notes ----- Given a sorted array, and assuming that there is a set of adjacent identical items, ``uniq()`` will return the subscript of the *last* unique item. This charming feature is retained for reproducibility. References ---------- http://www.exelisvis.com/docs/UNIQ.html Examples -------- >>> import numpy as np >>> from pydl import uniq >>> data = np.array([ 1, 2, 3, 1, 5, 6, 1, 7, 3, 2, 5, 9, 11, 1 ]) >>> print(uniq(np.sort(data))) [ 3 5 7 9 10 11 12 13] """ from numpy import array, roll if index is None: indicies = (x != roll(x, -1)).nonzero()[0] if indicies.size > 0: return indicies else: return array([x.size - 1, ]) else: q = x[index] indicies = (q != roll(q, -1)).nonzero()[0] if indicies.size > 0: return index[indicies] else: return array([q.size - 1, ], dtype=index.dtype) pydl-0.6.0/pydl/tests/0000775000111400017500000000000013160273564015406 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/tests/t/0000775000111400017500000000000013160273564015651 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/tests/t/this-file-is-empty.txt0000664000111400017500000000000012775255301022031 0ustar weaverweaver00000000000000pydl-0.6.0/pydl/tests/t/this-file-contains-137-lines.txt0000664000111400017500000000067012775255301023535 0ustar weaverweaver000000000000001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 pydl-0.6.0/pydl/tests/t/this-file-contains-42-lines.txt0000664000111400017500000000016512775255301023447 0ustar weaverweaver000000000000001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 pydl-0.6.0/pydl/tests/t/pcomp_data.txt0000664000111400017500000000367712775255301020536 0ustar weaverweaver00000000000000# Data for testing the pcomp class. # input data 19.5, 43.1, 29.1, 11.9 24.7, 49.8, 28.2, 22.8 30.7, 51.9, 37.0, 18.7 29.8, 54.3, 31.1, 20.1 19.1, 42.2, 30.9, 12.9 25.6, 53.9, 23.7, 21.7 31.4, 58.5, 27.6, 27.1 27.9, 52.1, 30.6, 25.4 22.1, 49.9, 23.2, 21.3 25.5, 53.5, 24.8, 19.3 31.1, 56.6, 30.0, 25.4 30.4, 56.7, 28.3, 27.2 18.7, 46.5, 23.0, 11.7 19.7, 44.2, 28.6, 17.8 14.6, 42.7, 21.3, 12.8 29.5, 54.4, 30.1, 23.9 27.7, 55.3, 25.7, 22.6 30.2, 58.6, 24.6, 25.4 22.7, 48.2, 27.1, 14.8 25.2, 51.0, 27.5, 21.1 # derived values -107.377 , 13.3986 , -1.40521 , -0.0324538 3.20342, 0.699964, 5.95396 , -0.0154266 32.4969 , 38.6573 , -3.87102 , 0.00906474 40.8858 , 13.7870 , -4.98349 , -0.00512299 -107.236 , 19.3568 , 1.76589 , 0.0231152 18.4284 , -17.1468 , -1.46805 , -0.00318355 99.8885 , -6.22849 , 0.130080 , 0.0166338 45.3808 , 8.11078 , 6.53065 , -0.0114850 -21.3101 , -18.3147 , 3.75132 , -0.0133497 5.54411, -11.1707 , -4.51918 , 0.0224445 83.1352 , 4.96944 , 0.0912485, 0.0104372 87.1055 , -3.16057 , 2.81444 , 0.00460935 -101.319 , -11.7835 , -6.11614 , 0.00658332 -73.0708 , 6.23879 , 6.60536 , 0.0215680 -137.017 , -19.0988 , 1.32969 , 0.0120680 57.1149 , 6.95618 , 0.839940 , -0.00950313 42.1264 , -10.0721 , -2.14432 , 0.00948831 83.3046 , -16.6907 , -2.71600 , -0.0132752 -54.1269 , 2.55509 , -4.21364 , -0.0251960 2.84273, -1.06368 , 1.62462 , -0.00701279 # coefficients 4.87988 , 5.05684 , 1.02824 , 4.79357 1.01466 , -0.954475 , 3.48852 , -0.774333 -0.618291 , -0.955430 , 0.269045 , 1.57962 -0.0900205, 0.0751850, 0.0472409, 0.00219369 # eigenvalues 73.4205, 14.7100, 3.86270, 0.0159930 # variance 0.797969, 0.159875, 0.0419817, 0.000173819 # eigenvalues of standardized data 2.99966981, 1.05397516, 1.56189601e-01, 6.91743392e-04 # variance of standardized data 7.12421581e-01, 2.50319100e-01, 3.70950302e-02, 1.64289056e-04 pydl-0.6.0/pydl/tests/t/this-file-contains-1-lines.txt.gz0000664000111400017500000000006512775255301024000 0ustar weaverweaver000000000000007Qthis-file-contains-1-lines.txt3SQgpydl-0.6.0/pydl/tests/t/smooth_data.txt0000664000111400017500000000163012775255301020714 0ustar weaverweaver000000000000002.30610 -0.606922 -0.495611 0.319969 -0.852573 -0.718530 -0.822457 -0.914747 -2.06190 -0.367282 -1.07346 0.333589 -0.171405 1.29557 1.04241 0.745161 1.12171 -1.68132 1.33361 1.09108 1.16230 -0.663734 0.819509 -0.0675587 -1.30818 1.65162 1.54414 -0.801843 1.55333 -0.0529722 -0.535589 0.424731 -1.01041 1.78723 0.625269 -0.770034 0.837806 -1.29704 1.84804 0.331175 -0.0432724 0.430578 0.785313 -0.243586 -0.874319 -1.18494 0.204993 -0.620158 1.51457 1.12941 0.776386 2.25176 1.75678 0.327156 0.530501 0.648502 0.0279251 -0.395409 -0.341345 0.339561 0.414343 -0.513183 1.23809 -1.44090 -1.94348 -0.443420 0.483545 -0.408526 -0.0689184 -0.568676 1.89727 -1.54532 1.16730 0.898135 -1.21551 -0.525567 -0.0766497 1.47355 -0.735265 0.772411 0.143568 0.0425026 1.27208 0.499760 0.718399 0.577398 1.79356 -0.237896 -1.87300 1.95981 -0.0884865 -0.872654 0.133105 -1.24190 0.465836 -0.210268 0.675163 -1.54661 -0.993406 -0.0257759 pydl-0.6.0/pydl/tests/t/this-file-contains-42-lines.txt.gz0000664000111400017500000000016412775255301024065 0ustar weaverweaver000000000000007Qthis-file-contains-42-lines.txt 0MmB@„0\ 5:hE-.,l?_49d%,]hyupydl-0.6.0/pydl/tests/t/this-file-contains-137-lines.txt.gz0000664000111400017500000000041312775255301024147 0ustar weaverweaver00000000000000Q7Qthis-file-contains-137-lines.txtAY;+@$NfX7/F8DE>>=>_dBt)].He=B9q<].6+,Ppydl-0.6.0/pydl/tests/t/this-file-contains-1-lines.txt0000664000111400017500000000000212775255301023350 0ustar weaverweaver000000000000001 pydl-0.6.0/pydl/tests/t/README.rst0000664000111400017500000000011012775255301017330 0ustar weaverweaver00000000000000This directory contains files used by the tests in the directory above. pydl-0.6.0/pydl/tests/coveragerc0000664000111400017500000000127712775255301017460 0ustar weaverweaver00000000000000[run] source = pydl omit = pydl/_astropy_init* pydl/conftest* pydl/cython_version* pydl/setup_package* pydl/*/setup_package* pydl/*/*/setup_package* pydl/tests/* pydl/*/tests/* pydl/*/*/tests/* pydl/version* [report] exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain about packages we have installed except ImportError # Don't complain if tests don't hit assertions raise AssertionError raise NotImplementedError # Don't complain about script hooks def main\(.*\): # Ignore branches that don't pertain to this version of Python pragma: py{ignore_python_version} six.PY{ignore_python_version} pydl-0.6.0/pydl/tests/test_pydl.py0000664000111400017500000002001213040014667017754 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np import glob try: from astropy.tests.compat import assert_allclose except ImportError: from numpy.testing.utils import assert_allclose from astropy.tests.helper import raises from astropy.utils.data import get_pkg_data_filename from os.path import basename, dirname, join from ..file_lines import file_lines from ..median import median from ..pcomp import pcomp from ..rebin import rebin from ..smooth import smooth from ..uniq import uniq class TestPydl(object): """Test the top-level pydl functions. """ def setup(self): pass def teardown(self): pass def test_file_lines(self): # # Find the test files # line_numbers = (1, 42, 137) plainfiles = [get_pkg_data_filename('t/this-file-contains-{0:d}-lines.txt'.format(l)) for l in line_numbers] gzfiles = [get_pkg_data_filename('t/this-file-contains-{0:d}-lines.txt.gz'.format(l)) for l in line_numbers] for i, p in enumerate(plainfiles): n = file_lines(p) assert n == line_numbers[i] for i, p in enumerate(gzfiles): n = file_lines(p, compress=True) assert n == line_numbers[i] # # Test list passing # n = file_lines(plainfiles) assert tuple(n) == line_numbers n = file_lines(gzfiles, compress=True) assert tuple(n) == line_numbers # # Make sure empty files work # n = file_lines(get_pkg_data_filename('t/this-file-is-empty.txt')) assert n == 0 def test_median(self): odd_data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype=np.float32) even_data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=np.float32) assert median(odd_data) == 7 assert median(odd_data, even=True) == 7 assert median(even_data) == 7 assert median(even_data, even=True) == 6.5 assert (median(odd_data, 3) == odd_data).all() with raises(ValueError): foo = median(np.ones((9, 9, 9)), 3) odd_data2 = np.vstack((odd_data, odd_data, odd_data, odd_data, odd_data)) assert (median(odd_data2, 3) == odd_data2).all() assert (median(odd_data2, axis=0) == odd_data).all() assert (median(odd_data2, axis=1) == 7*np.ones((odd_data2.shape[0],), dtype=odd_data2.dtype)).all() def test_pcomp(self): test_data_file = get_pkg_data_filename('t/pcomp_data.txt') test_data = np.loadtxt(test_data_file, dtype='d', delimiter=',') with raises(ValueError): foo = pcomp(np.arange(10)) pcomp_data = test_data[0:20, :] m = 4 n = 20 means = np.tile(pcomp_data.mean(0), n).reshape(pcomp_data.shape) newarray = pcomp_data - means foo = pcomp(newarray, covariance=True) # # This array is obtained from the IDL version of PCOMP. # It is only accurate up to an overall sign on each column. # derived = test_data[20:40, :] for k in range(m): assert_allclose(abs(foo.derived[:, k]), abs(derived[:, k]), 1e-4) coefficients = test_data[40:44, :] coefficientsT = coefficients.T for k in range(m): assert_allclose(abs(foo.coefficients[:, k]), abs(coefficientsT[:, k]), 1e-4) eigenvalues = test_data[44, :] assert_allclose(foo.eigenvalues, eigenvalues, 1e-4) variance = test_data[45, :] assert_allclose(foo.variance, variance, 1e-4) # # Test the standardization. # foo = pcomp(pcomp_data, standardize=True, covariance=True) # for k in range(m): # assert_allclose(abs(foo.derived[:, k]), abs(derived[:, k]), 1e-4) # for k in range(m): # assert_allclose(abs(foo.coefficients[:, k]), # abs(coefficientsT[:, k]), # 1e-4) eigenvalues = test_data[46, :] assert_allclose(foo.eigenvalues, eigenvalues, 1e-4) variance = test_data[47, :] assert_allclose(foo.variance, variance, 1e-4) # assert_allclose(foo.derived[0, :], np.array([-1.64153312, # -9.12322038, # 1.41790708, # -8.29359322])) # # Make sure correlation is working at least. # foo = pcomp(pcomp_data, standardize=True) assert_allclose(foo.eigenvalues, np.array([2.84968632e+00, 1.00127640e+00, 1.48380121e-01, 6.57156222e-04])) assert_allclose(foo.variance, np.array([7.12421581e-01, 2.50319100e-01, 3.70950302e-02, 1.64289056e-04])) def test_rebin(self): x = np.arange(40) with raises(ValueError): r = rebin(x, d=(10, 10)) with raises(ValueError): r = rebin(x, d=(70,)) with raises(ValueError): r = rebin(x, d=(30,)) x = np.array([[1.0, 2.0], [2.0, 3.0]]) rexpect = np.array([[1.0, 2.0], [1.5, 2.5], [2.0, 3.0], [2.0, 3.0]]) r = rebin(x, d=(4, 2)) assert np.allclose(r, rexpect) rexpect = np.array([[1.0, 1.5, 2.0, 2.0], [2.0, 2.5, 3.0, 3.0]]) r = rebin(x, d=(2, 4)) assert np.allclose(r, rexpect) rexpect = np.array([[1.0, 2.0], [1.0, 2.0], [2.0, 3.0], [2.0, 3.0]]) r = rebin(x, d=(4, 2), sample=True) assert np.allclose(r, rexpect) rexpect = np.array([[1.0, 1.0, 2.0, 2.0], [2.0, 2.0, 3.0, 3.0]]) r = rebin(x, d=(2, 4), sample=True) assert np.allclose(r, rexpect) x = np.arange(10) rexpect = np.array([0.0, 2.0, 4.0, 6.0, 8.0]) r = rebin(x, d=(5,), sample=True) assert np.allclose(r, rexpect) x = np.array([[1.0, 2.0, 3.0, 4.0], [2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [4.0, 5.0, 6.0, 7.0]]) rexpect = np.array([[2.0, 4.0], [4.0, 6.0]]) r = rebin(x, d=(2, 2)) assert np.allclose(r, rexpect) rexpect = np.array([[1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 4.5], [3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 6.5]]) r = rebin(x, d=(2, 8)) assert np.allclose(r, rexpect) def test_smooth(self): test_data_file = get_pkg_data_filename('t/smooth_data.txt') noise = np.loadtxt(test_data_file, dtype='d') # # Test smooth function # x = 8.0*np.arange(100)/100.0 - 4.0 y = np.sin(x) + 0.1*noise s = smooth(y, 5) assert s.shape == (100,) s_edge = smooth(y, 5, True) assert s_edge.shape == (100,) s_w = smooth(y, 1) assert (s_w == y).all() def test_uniq(self): items = np.array([1, 2, 3, 1, 5, 6, 1, 7, 3, 2, 5, 9, 11, 1]) items_sorted = np.sort(items) items_argsorted = np.argsort(items) # # Test pre-sorted array. # u1 = uniq(items_sorted) assert (u1 == np.array([3, 5, 7, 9, 10, 11, 12, 13])).all() # # Test arg-sorted array. # u2 = uniq(items, items_argsorted) assert (u2 == np.array([13, 9, 8, 10, 5, 7, 11, 12])).all() assert (items_sorted[u1] == items[u2]).all() # # Test degenerate case of all identical items. # identical_items = np.ones((10,), dtype=items.dtype) u = uniq(identical_items) assert (u == np.array([9])).all() u = uniq(identical_items, np.arange(10, dtype=items.dtype)) assert (u == np.array([9])).all() pydl-0.6.0/pydl/tests/__init__.py0000664000111400017500000000020212775255301017511 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This is the pydl/tests directory. """ pydl-0.6.0/pydl/goddard/0000775000111400017500000000000013160273564015650 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/goddard/astro.py0000664000111400017500000001114612775255301017355 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the goddard/astro directory in idlutils. """ from __future__ import print_function def airtovac(air): """Convert air wavelengths to wavelengths in vacuum. Parameters ---------- air : array-like Values of wavelength in air in Angstroms. Returns ------- array-like Values of wavelength in vacuum in Angstroms. Notes ----- * Formula from `P. E. Ciddor, Applied Optics, 35, 1566 (1996) `_. * Values of wavelength below 2000 Å are not converted. """ from numpy import zeros try: vacuum = zeros(air.shape, dtype=air.dtype) + air g = vacuum < 2000.0 except AttributeError: # Most likely, vacuum is simply a float. vacuum = air g = None if air < 2000.0: return air for k in range(2): sigma2 = (1.0e4/vacuum)**2 fact = (1.0 + 5.792105e-2/(238.0185 - sigma2) + 1.67917e-3/(57.362 - sigma2)) vacuum = air * fact if g is not None: vacuum[g] = air[g] return vacuum def gcirc(ra1, dec1, ra2, dec2, units=2): """Computes rigorous great circle arc distances. Parameters ---------- ra1, dec1, ra2, dec2 : :class:`float` or array-like RA and Dec of two points. units : { 0, 1, 2 }, optional * units = 0: everything is already in radians * units = 1: RA in hours, dec in degrees, distance in arcsec. * units = 2: RA, dec in degrees, distance in arcsec (default) Returns ------- :class:`float` or array-like The angular distance. Units of the value returned depend on the input value of `units`. Notes ----- The formula below is the one best suited to handling small angular separations. See: http://en.wikipedia.org/wiki/Great-circle_distance """ from numpy import arcsin, cos, deg2rad, rad2deg, sin, sqrt if units == 0: rarad1 = ra1 dcrad1 = dec1 rarad2 = ra2 dcrad2 = dec2 elif units == 1: rarad1 = deg2rad(15.0*ra1) dcrad1 = deg2rad(dec1) rarad2 = deg2rad(15.0*ra2) dcrad2 = deg2rad(dec2) elif units == 2: rarad1 = deg2rad(ra1) dcrad1 = deg2rad(dec1) rarad2 = deg2rad(ra2) dcrad2 = deg2rad(dec2) else: raise ValueError('units must be 0, 1 or 2!') deldec2 = (dcrad2-dcrad1)/2.0 delra2 = (rarad2-rarad1)/2.0 sindis = sqrt(sin(deldec2)*sin(deldec2) + cos(dcrad1)*cos(dcrad2)*sin(delra2)*sin(delra2)) dis = 2.0*arcsin(sindis) if units == 0: return dis else: return rad2deg(dis)*3600.0 def get_juldate(seconds=None): """Returns the current Julian date. Uses the MJD trick & adds the offset to get JD. Parameters ---------- seconds : :class:`int` or :class:`float`, optional Time in seconds since the UNIX epoch. This should only be used for testing. Returns ------- :class:`float` The Julian Day number as a floating point number. Notes ----- Do not use this function if high precision is required, or if you are concerned about the distinction between UTC & TAI. """ from time import time if seconds is None: t = time() else: t = seconds mjd = t/86400.0 + 40587.0 return mjd + 2400000.5 def get_juldate_main(): # pragma: no cover """Entry point for the get_juldate command-line script. """ jd = get_juldate() print(jd) return 0 def vactoair(vacuum): """Convert vacuum wavelengths to wavelengths in air. Parameters ---------- vacuum : array-like Values of wavelength in vacuum in Angstroms. Returns ------- array-like Values of wavelength in air in Angstroms. Notes ----- * Formula from `P. E. Ciddor, Applied Optics, 35, 1566 (1996) `_. * Values of wavelength below 2000 Å are not converted. """ from numpy import zeros try: air = zeros(vacuum.shape, dtype=vacuum.dtype) g = vacuum < 2000.0 except AttributeError: # Most likely, vacuum is simply a float. air = vacuum g = None if vacuum < 2000.0: return air sigma2 = (1.0e4/vacuum)**2 fact = (1.0 + 5.792105e-2/(238.0185 - sigma2) + 1.67917e-3/(57.362 - sigma2)) air = vacuum/fact if g is not None: air[g] = vacuum[g] return air pydl-0.6.0/pydl/goddard/tests/0000775000111400017500000000000013160273564017012 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/goddard/tests/test_goddard.py0000664000111400017500000001144112775255301022030 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import raises from ..astro import airtovac, gcirc, get_juldate, vactoair from ..math import flegendre from ..misc import cirrange class TestGoddard(object): """Test the goddard package. """ def setup(self): pass def teardown(self): pass def test_airtovac(self): vacuum = airtovac(1900.0) assert vacuum == 1900.0 vacuum = airtovac(2000.0) assert np.allclose(vacuum, 2000.6475) air = np.array([1800.0, 1900.0, 2000.0, 2100.0, 2200.0, 2300.0]) vacuum = airtovac(air) assert np.allclose(vacuum, np.array([1800.0, 1900.0, 2000.6475, 2100.6664, 2200.6868, 2300.7083])) vacuum = airtovac(6056.125) assert np.allclose(vacuum, 6057.8019) # # Regression test for #8. # wave = air.reshape(2, 3) vacuum = airtovac(wave) assert np.allclose(vacuum, np.array([[1800.0, 1900.0, 2000.6475], [2100.6664, 2200.6868, 2300.7083]])) def test_cirrange(self): ra1 = np.linspace(-4.0*np.pi, 4.0*np.pi, 100) ra2 = cirrange(ra1, radians=True) assert (ra2 == (ra1 % (2.0 * np.pi))).all() ra1 = np.rad2deg(ra1) ra2 = cirrange(ra1) assert (ra2 == (ra1 % 360.0)).all() def test_flegendre(self): x = np.array([-1, -0.5, 0, 0.5, 1], dtype='d') # # Test order # with raises(ValueError): f = flegendre(x, 0) # # m = 1 # f = flegendre(x, 1) assert (f == np.ones((1, x.size), dtype='d')).all() # # m = 2 # f = flegendre(x, 2) foo = np.ones((2, x.size), dtype='d') foo[1, :] = x assert np.allclose(f, foo) # # m = 3 # f = flegendre(x, 3) foo = np.ones((3, x.size), dtype='d') foo[1, :] = x foo[2, :] = 0.5 * (3.0*x**2 - 1.0) assert np.allclose(f, foo) # # m = 4 # f = flegendre(x, 4) foo = np.ones((4, x.size), dtype='d') foo[1, :] = x foo[2, :] = 0.5*(3.0*x**2 - 1.0) foo[3, :] = 0.5*(5.0*x**3 - 3.0*x) assert np.allclose(f, foo) # # random float # f = flegendre(2.88, 3) assert np.allclose(f, np.array([[1.00], [2.88], [11.9416]])) def test_gcirc(self): np.random.seed(137) # # Start in radians # offset = 5.0e-6 # approx 1 arcsec ra1 = 2.0 * np.pi * np.random.rand(100) dec1 = np.pi/2.0 - np.arccos(2.0*np.random.rand(100) - 1.0) ra2 = ra1 + offset ra2 = np.where((ra2 > 2.0*np.pi), ra2 - 2.0*np.pi, ra2) dec2 = np.where((dec1 > 0), dec1 - offset, dec1 + offset) deldec2 = (dec2-dec1)/2.0 delra2 = (ra2-ra1)/2.0 sindis = np.sqrt(np.sin(deldec2) * np.sin(deldec2) + np.cos(dec1) * np.cos(dec2) * np.sin(delra2) * np.sin(delra2)) dis = 2.0*np.arcsin(sindis) # # units = 0 # d0 = gcirc(ra1, dec1, ra2, dec2, units=0) assert np.allclose(d0, dis) # # units = 2 # d0 = gcirc(np.rad2deg(ra1)/15.0, np.rad2deg(dec1), np.rad2deg(ra2)/15.0, np.rad2deg(dec2), units=1) assert np.allclose(d0, np.rad2deg(dis)*3600.0) # # units = 2 # d0 = gcirc(np.rad2deg(ra1), np.rad2deg(dec1), np.rad2deg(ra2), np.rad2deg(dec2), units=2) assert np.allclose(d0, np.rad2deg(dis)*3600.0) # # Units = whatever # with raises(ValueError): d0 = gcirc(ra1, dec1, ra2, dec2, units=5) def test_get_juldate(self): now = get_juldate() assert now > 2400000.5 assert get_juldate(0) == 40587.0 + 2400000.5 assert get_juldate(86400) == 40588.0 + 2400000.5 def test_vactoair(self): air = vactoair(1900.0) assert air == 1900.0 air = vactoair(2000.0) assert np.allclose(air, 1999.3526) vacuum = np.array([1800.0, 1900.0, 2000.0, 2100.0, 2200.0, 2300.0]) air = vactoair(vacuum) assert np.allclose(air, np.array([1800.0, 1900.0, 1999.3526, 2099.3337, 2199.3133, 2299.2918])) # # Regression test for #8. # wave = vacuum.reshape(2, 3) air = vactoair(wave) assert np.allclose(air, np.array([[1800.0, 1900.0, 1999.3526], [2099.3337, 2199.3133, 2299.2918]])) pydl-0.6.0/pydl/goddard/tests/__init__.py0000664000111400017500000000021212775255301021116 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This is the pydl/goddard/tests directory. """ pydl-0.6.0/pydl/goddard/__init__.py0000664000111400017500000000022012775255301017753 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This subpackage contains the Goddard utilities. """ pydl-0.6.0/pydl/goddard/misc.py0000664000111400017500000000201512775255301017153 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the goddard/misc directory in idlutils. """ from __future__ import absolute_import def cirrange(ang, radians=False): """Convert an angle larger than 360 degrees to one less than 360 degrees. Parameters ---------- ang : :class:`float` or array-like Angle to convert. If the angle is in radians, the `radians` argument should be set. radians : class:`bool`, optional If ``True``, the input angle is in radians, and the output will be between zero and 2π. Returns ------- :class:`float` or array-like Angle in the restricted range. Examples -------- >>> from pydl.goddard.misc import cirrange >>> cirrange(-270.0) 90.0 """ from math import pi if radians: cnst = pi * 2.0 else: cnst = 360.0 # # The modulo operator automatically deals with negative values # return ang % cnst pydl-0.6.0/pydl/goddard/math.py0000664000111400017500000000217012775255301017153 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the goddard/math directory in idlutils. """ from __future__ import absolute_import def flegendre(x, m): """Compute the first `m` Legendre polynomials. Parameters ---------- x : array-like Compute the Legendre polynomials at these abscissa values. m : :class:`int` The number of Legendre polynomials to compute. For example, if :math:`m = 3`, :math:`P_0 (x)`, :math:`P_1 (x)` and :math:`P_2 (x)` will be computed. Returns ------- :class:`numpy.ndarray` """ import numpy as np from scipy.special import legendre if isinstance(x, np.ndarray): n = x.size else: n = 1 if m < 1: raise ValueError('Number of Legendre polynomials must be at least 1.') try: dt = x.dtype except AttributeError: dt = np.float64 leg = np.ones((m, n), dtype=dt) if m >= 2: leg[1, :] = x if m >= 3: for k in range(2, m): leg[k, :] = np.polyval(legendre(k), x) return leg pydl-0.6.0/pydl/pydlutils/0000775000111400017500000000000013160273564016275 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlutils/image.py0000664000111400017500000001574212775255301017742 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the image directory in idlutils. """ def djs_maskinterp1(yval, mask, xval=None, const=False): """Interpolate over a masked, 1-d array. Parameters ---------- yval : :class:`numpy.ndarray` The input values. mask : :class:`numpy.ndarray` The mask. xval : :class:`numpy.ndarray`, optional If set, use these x values, otherwise use an array. const : :class:`bool`, optional If set to ``True``, bad values around the edges of the array will be set to a constant value. Because of the default behavior of :func:`numpy.interp`, this value actually makes no difference in the output. Returns ------- :class:`numpy.ndarray` The `yval` array with masked values replaced by interpolated values. """ import numpy as np good = mask == 0 if good.all(): return yval ngood = good.sum() igood = good.nonzero()[0] if ngood == 0: return yval if ngood == 1: return np.zeros(yval.shape, dtype=yval.dtype) + yval[igood[0]] ynew = yval.astype('d') ny = yval.size ibad = (mask != 0).nonzero()[0] if xval is None: ynew[ibad] = np.interp(ibad, igood, ynew[igood]) if const: if igood[0] != 0: ynew[0:igood[0]] = ynew[igood[0]] if igood[ngood-1] != ny-1: ynew[igood[ngood-1]+1:ny] = ynew[igood[ngood-1]] else: ii = xval.argsort() ibad = (mask[ii] != 0).nonzero()[0] igood = (mask[ii] == 0).nonzero()[0] ynew[ii[ibad]] = np.interp(xval[ii[ibad]], xval[ii[igood]], ynew[ii[igood]]) if const: if igood[0] != 0: ynew[ii[0:igood[0]]] = ynew[ii[igood[0]]] if igood[ngood-1] != ny-1: ynew[ii[igood[ngood-1]+1:ny]] = ynew[ii[igood[ngood-1]]] return ynew def djs_maskinterp(yval, mask, xval=None, axis=None, const=False): """Interpolate over masked pixels in a vector, image or 3-D array. Parameters ---------- yval : :class:`numpy.ndarray` The input values mask : :class:`numpy.ndarray` The mask xval : :class:`numpy.ndarray`, optional If set, use these x values, otherwise use an array axis : :class:`int`, optional Must be set if yval has more than one dimension. If set to zero, interpolate along the first axis of the array, if set to one, interpolate along the second axis of the array, and so on. const : :class:`bool`, optional This value is passed to a helper function, djs_maskinterp1. Returns ------- :class:`numpy.ndarray` The interpolated array. """ import numpy as np if mask.shape != yval.shape: raise ValueError('mask must have the same shape as yval.') if xval is not None: if xval.shape != yval.shape: raise ValueError('xval must have the same shape as yval.') ndim = yval.ndim if ndim == 1: ynew = djs_maskinterp1(yval, mask, xval=xval, const=const) else: if axis is None: raise ValueError('Must set axis if yval has more than one dimension.') if axis < 0 or axis > ndim-1 or axis - int(axis) != 0: raise ValueError('Invalid axis value.') ynew = np.zeros(yval.shape, dtype=yval.dtype) if ndim == 2: if xval is None: if axis == 0: for i in range(yval.shape[0]): ynew[i, :] = djs_maskinterp1(yval[i, :], mask[i, :], const=const) else: for i in range(yval.shape[1]): ynew[:, i] = djs_maskinterp1(yval[:, i], mask[:, i], const=const) else: if axis == 0: for i in range(yval.shape[0]): ynew[i, :] = djs_maskinterp1(yval[i, :], mask[i, :], xval=xval[i, :], const=const) else: for i in range(yval.shape[1]): ynew[:, i] = djs_maskinterp1(yval[:, i], mask[:, i], xval=xval[:, i], const=const) elif ndim == 3: if xval is None: if axis == 0: for i in range(yval.shape[0]): for j in range(yval.shape[1]): ynew[i, j, :] = djs_maskinterp1(yval[i, j, :], mask[i, j, :], const=const) elif axis == 1: for i in range(yval.shape[0]): for j in range(yval.shape[2]): ynew[i, :, j] = djs_maskinterp1(yval[i, :, j], mask[i, :, j], const=const) else: for i in range(yval.shape[1]): for j in range(yval.shape[2]): ynew[:, i, j] = djs_maskinterp1(yval[:, i, j], mask[:, i, j], const=const) else: if axis == 0: for i in range(yval.shape[0]): for j in range(yval.shape[1]): ynew[i, j, :] = djs_maskinterp1(yval[i, j, :], mask[i, j, :], xval=xval[i, j, :], const=const) elif axis == 1: for i in range(yval.shape[0]): for j in range(yval.shape[2]): ynew[i, :, j] = djs_maskinterp1(yval[i, :, j], mask[i, :, j], xval=xval[i, :, j], const=const) else: for i in range(yval.shape[1]): for j in range(yval.shape[2]): ynew[:, i, j] = djs_maskinterp1(yval[:, i, j], mask[:, i, j], xval=xval[:, i, j], const=const) else: raise ValueError('Unsupported number of dimensions.') return ynew pydl-0.6.0/pydl/pydlutils/coord.py0000664000111400017500000001521113154630165017752 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the coord directory in idlutils. """ import numpy as np import astropy.units as u import astropy.coordinates as ac class SDSSMuNu(ac.BaseCoordinateFrame): """SDSS Great Circle Coordinates Attributes ---------- stripe SDSS `Stripe Number`_ . node Node of the great circle with respect to the celestial equator. In SDSS, this is almost always RA = 95.0 degrees. incl Inclination of the great circle with respect to the celestial equator. phi Counter-clockwise position angle w.r.t. north for an arc in the +nu direction. .. _`Stripe Number`: http://www.sdss.org/dr14/help/glossary/#stripe Parameters ---------- mu : :class:`~astropy.coordinates.Angle` Angle corresponding to longitude measured along a stripe. nu : :class:`~astropy.coordinates.Angle` Angle corresponding to latitude measured perpendicular to a stripe. Notes ----- http://www.sdss.org/dr12/algorithms/surveycoords/ """ default_representation = ac.SphericalRepresentation frame_specific_representation_info = { 'spherical': [ ac.RepresentationMapping(reprname='lon', framename='mu', defaultunit=u.deg), ac.RepresentationMapping(reprname='lat', framename='nu', defaultunit=u.deg) ] } frame_specific_representation_info['unitspherical'] = ( frame_specific_representation_info['spherical']) stripe = ac.Attribute(default=0) node = ac.QuantityAttribute(default=ac.Angle(95.0, unit=u.deg), unit=u.deg) # phi = ac.QuantityFrameAttribute(default=None, unit=u.deg) @property def incl(self): return ac.Angle(stripe_to_incl(self.stripe), unit=u.deg) def current_mjd(): """Return the current MJD. """ from ..goddard.astro import get_juldate return get_juldate() - 2400000.5 @ac.frame_transform_graph.transform(ac.FunctionTransform, SDSSMuNu, ac.ICRS) def munu_to_radec(munu, icrs_frame): """Convert from SDSS great circle coordinates to equatorial coordinates. Parameters ---------- munu : :class:`~pydl.pydlutils.coord.SDSSMuNu` SDSS great circle coordinates (mu, nu). Returns ------- :class:`~astropy.coordinates.ICRS` Equatorial coordinates (RA, Dec). """ # from pydlutils.coord import stripe_to_eta # from pydlutils.goddard.misc import cirrange # if 'stripe' in kwargs: # node = 95.0 # incl = stripe_to_incl(kwargs['stripe']) # elif 'node' in kwargs and 'incl' in kwargs: # node = kwargs['node'] # incl = kwargs['incl'] # else: # raise ValueError('Must specify either STRIPE or NODE,INCL!') # if mu.size != nu.size: # raise ValueError('Number of elements in MU and NU must agree!') sinnu = np.sin(munu.nu.to(u.radian).value) cosnu = np.cos(munu.nu.to(u.radian).value) sini = np.sin(munu.incl.to(u.radian).value) cosi = np.cos(munu.incl.to(u.radian).value) sinmu = np.sin((munu.mu - munu.node).to(u.radian).value) cosmu = np.cos((munu.mu - munu.node).to(u.radian).value) xx = cosmu * cosnu yy = sinmu * cosnu * cosi - sinnu * sini zz = sinmu * cosnu * sini + sinnu * cosi ra = ac.Angle(np.arctan2(yy, xx), unit=u.radian) + munu.node dec = ac.Angle(np.arcsin(zz), unit=u.radian) # if 'phi' in kwargs: # phi = np.rad2deg(np.arctan2(cosmu * sini, # (-sinmu * sinnu * sini + cosnu * cosi)*cosnu)) # return (ra, dec, phi) # else: # return (ra, dec) return ac.ICRS(ra=ra, dec=dec).transform_to(icrs_frame) @ac.frame_transform_graph.transform(ac.FunctionTransform, ac.ICRS, SDSSMuNu) def radec_to_munu(icrs_frame, munu): """Convert from equatorial coordinates to SDSS great circle coordinates. Parameters ---------- icrs_frame : :class:`~astropy.coordinates.ICRS` Equatorial coordinates (RA, Dec). Returns ------- :class:`~pydl.pydlutils.coord.SDSSMuNu` SDSS great circle coordinates (mu, nu). """ # from pydlutils.coord import stripe_to_eta # from pydlutils.goddard.misc import cirrange # if 'stripe' in kwargs: # node = 95.0 # incl = stripe_to_incl(kwargs['stripe']) # elif 'node' in kwargs and 'incl' in kwargs: # node = kwargs['node'] # incl = kwargs['incl'] # else: # raise ValueError('Must specify either STRIPE or NODE,INCL!') # if ra.size != dec.size: # raise ValueError('Number of elements in RA and DEC must agree!') sinra = np.sin((icrs_frame.ra - munu.node).to(u.radian).value) cosra = np.cos((icrs_frame.ra - munu.node).to(u.radian).value) sindec = np.sin(icrs_frame.dec.to(u.radian).value) cosdec = np.cos(icrs_frame.dec.to(u.radian).value) sini = np.sin(munu.incl.to(u.radian).value) cosi = np.cos(munu.incl.to(u.radian).value) x1 = cosdec * cosra y1 = cosdec * sinra z1 = sindec x2 = x1 y2 = y1 * cosi + z1 * sini z2 = -y1 * sini + z1 * cosi mu = ac.Angle(np.arctan2(y2, x2), unit=u.radian) + munu.node nu = ac.Angle(np.arcsin(z2), unit=u.radian) # if 'phi' in kwargs: # sinnu = np.sin(np.deg2rad(nu)) # cosnu = np.cos(np.deg2rad(nu)) # sinmu = np.sin(np.deg2rad(mu-node)) # cosmu = np.cos(np.deg2rad(mu-node)) # phi = np.rad2deg(np.arctan2(cosmu * sini, # (-sinmu * sinnu * sini + cosnu * cosi)*cosnu)) # return (ra, dec, phi) # else: # return (ra, dec) return SDSSMuNu(mu=mu, nu=nu, stripe=munu.stripe) def stripe_to_eta(stripe): """Convert from SDSS great circle coordinates to equatorial coordinates. Parameters ---------- stripe : :class:`int` or :class:`numpy.ndarray` SDSS Stripe number. Returns ------- :class:`float` or :class:`numpy.ndarray` The eta value in the SDSS (lambda,eta) coordinate system. """ stripe_sep = 2.5 eta = stripe * stripe_sep - 57.5 if stripe > 46: eta -= 180.0 return eta def stripe_to_incl(stripe): """Convert from SDSS stripe number to an inclination relative to the equator. Parameters ---------- stripe : :class:`int` or :class:`numpy.ndarray` SDSS Stripe number. Returns ------- :class:`float` or :class:`numpy.ndarray` Inclination of the stripe relative to the equator (Dec = 0). """ dec_center = 32.5 eta_center = stripe_to_eta(stripe) incl = eta_center + dec_center return incl pydl-0.6.0/pydl/pydlutils/spheregroup.py0000664000111400017500000006314212775255301021220 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the spheregroup directory in idlutils. """ import numpy as np from astropy.extern.six import string_types from . import PydlutilsException, PydlutilsUserWarning class chunks(object): """chunks class Functions for creating and manipulating spherical chunks are implemented as methods on this class. """ def __init__(self, ra, dec, minSize): """Init creates an object whose attributes are similar those created by the setchunks() function in the spheregroup library. """ # # Save the value of minSize # self.minSize = minSize # # Find maximum and minimum dec (in degrees) # decMin = dec.min() decMax = dec.max() decRange = decMax - decMin # # Find the declination boundaries; make them an integer multiple of # minSize, with extra room (one cell) on the edges. # self.nDec = 3 + int(np.floor(decRange/minSize)) decRange = minSize*float(self.nDec) decMin = decMin - 0.5*(decRange - decMax + decMin) decMax = decMin + decRange if decMin < -90.0 + 3.0*minSize: decMin = -90.0 if decMax > 90.0 - 3.0*minSize: decMax = 90.0 self.decBounds = decMin + ((decMax - decMin) * np.arange(self.nDec + 1, dtype='d'))/float(self.nDec) # # Find ra offset which minimizes the range in ra (this should take care # of the case that ra crosses zero in some parts # if abs(self.decBounds[self.nDec]) > abs(self.decBounds[0]): cosDecMin = np.cos(np.deg2rad(self.decBounds[self.nDec])) else: cosDecMin = np.cos(np.deg2rad(self.decBounds[0])) if cosDecMin <= 0.0: raise PydlutilsException("cosDecMin={0:f} not positive in setchunks().".format(cosDecMin)) self.raRange, self.raOffset = self.rarange(ra, minSize/cosDecMin) self.raMin, self.raMax = self.getraminmax(ra, self.raOffset) # # Isn't this redundant? # self.raRange = self.raMax - self.raMin # # For each declination slice, find the number of ra divisions # necessary and set them # self.raBounds = list() self.nRa = list() for i in range(self.nDec): # # Get maximum declination and its cosine # if abs(self.decBounds[i]) > abs(self.decBounds[i+1]): cosDecMin = np.cos(np.deg2rad(self.decBounds[i])) else: cosDecMin = np.cos(np.deg2rad(self.decBounds[i+1])) if cosDecMin <= 0.0: raise PydlutilsException("cosDecMin={0:f} not positive in setchunks().".format(cosDecMin)) # # Get raBounds array for this declination array, leave an extra # cell on each end # self.nRa.append(3 + int(np.floor(cosDecMin*self.raRange/minSize))) raRangeTmp = minSize*float(self.nRa[i])/cosDecMin raMinTmp = self.raMin - 0.5*(raRangeTmp-self.raMax+self.raMin) raMaxTmp = raMinTmp + raRangeTmp # # If we cannot avoid the 0/360 point, embrace it # if (raRangeTmp >= 360.0 or raMinTmp <= minSize/cosDecMin or raMaxTmp >= 360.0 - minSize/cosDecMin or abs(self.decBounds[i]) == 90.0): raMinTmp = 0.0 raMaxTmp = 360.0 raRangeTmp = 360.0 if self.decBounds[i] == -90.0 or self.decBounds[i+1] == 90.0: self.nRa[i] = 1 self.raBounds.append(raMinTmp + (raMaxTmp - raMinTmp) * np.arange(self.nRa[i] + 1, dtype='d') / float(self.nRa[i])) # # Create an empty set of lists to hold the output of self.assign() # self.chunkList = [[list() for j in range(self.nRa[i])] for i in range(self.nDec)] # # nChunkMax will be the length of the largest list in chunkList # it is computed by chunks.assign() # self.nChunkMax = 0 return def rarange(self, ra, minSize): """Finds the offset which yields the smallest raRange & returns both. Notes ----- .. warning:: This is not (yet) well-defined for the case of only one point. """ NRA = 6 raRangeMin = 361.0 raOffset = 0.0 EPS = 1.0e-5 for j in range(NRA): raMin, raMax = self.getraminmax(ra, 360.0*float(j)/float(NRA)) raRange = raMax-raMin if (2.0*(raRange-raRangeMin)/(raRange+raRangeMin) < -EPS and raMin > minSize and raMax < 360.0 - minSize): raRangeMin = raRange raOffset = 360.0*float(j)/float(NRA) return (raRangeMin, raOffset) def getraminmax(self, ra, raOffset): """Utility function used by rarange. """ currRa = np.fmod(ra + raOffset, 360.0) return (currRa.min(), currRa.max()) def cosDecMin(self, i): """Frequently used utility function. """ if abs(self.decBounds[i]) > abs(self.decBounds[i+1]): return np.cos(np.deg2rad(self.decBounds[i])) else: return np.cos(np.deg2rad(self.decBounds[i+1])) def assign(self, ra, dec, marginSize): """Take the objects and the chunks (already defined in the constructor) and assign the objects to the appropriate chunks, with some leeway given by the parameter marginSize. Basically, at the end, each chunk should be associated with a list of the objects that belong to it. """ if marginSize >= self.minSize: raise PydlutilsException("marginSize>=minSize ({0:f}={1:f}) in chunks.assign().".format(marginSize, self.minSize)) chunkDone = [[False for j in range(self.nRa[i])] for i in range(self.nDec)] for i in range(ra.size): currRa = np.fmod(ra[i] + self.raOffset, 360.0) try: raChunkMin, raChunkMax, decChunkMin, decChunkMax = self.getbounds(currRa, dec[i], marginSize) except PydlutilsException: continue # # Reset chunkDone. This is silly, but is necessary to # reproduce the logic. # for decChunk in range(decChunkMin, decChunkMax+1): for raChunk in range(raChunkMin[decChunk-decChunkMin]-1, raChunkMax[decChunk-decChunkMin]+2): if raChunk < 0: currRaChunk = (raChunk+self.nRa[decChunk]) % self.nRa[decChunk] elif raChunk > self.nRa[decChunk]-1: currRaChunk = (raChunk-self.nRa[decChunk]) % self.nRa[decChunk] else: currRaChunk = raChunk if currRaChunk >= 0 and currRaChunk <= self.nRa[decChunk]-1: chunkDone[decChunk][currRaChunk] = False for decChunk in range(decChunkMin, decChunkMax+1): for raChunk in range(raChunkMin[decChunk-decChunkMin], raChunkMax[decChunk-decChunkMin]+1): if raChunk < 0: currRaChunk = (raChunk+self.nRa[decChunk]) % self.nRa[decChunk] elif raChunk > self.nRa[decChunk]-1: currRaChunk = (raChunk-self.nRa[decChunk]) % self.nRa[decChunk] else: currRaChunk = raChunk if currRaChunk >= 0 and currRaChunk <= self.nRa[decChunk]-1: if not chunkDone[decChunk][currRaChunk]: self.chunkList[decChunk][currRaChunk].append(i) # # Update nChunkMax # if len(self.chunkList[decChunk][currRaChunk]) > self.nChunkMax: self.nChunkMax = len(self.chunkList[decChunk][currRaChunk]) chunkDone[decChunk][currRaChunk] = True return def getbounds(self, ra, dec, marginSize): """Find the set of chunks a point (with margin) belongs to. """ # # Find the declination slice without regard to marginSize # decChunkMin = int(np.floor((dec - self.decBounds[0]) * float(self.nDec) / (self.decBounds[self.nDec]-self.decBounds[0]))) decChunkMax = decChunkMin if decChunkMin < 0 or decChunkMin > self.nDec - 1: raise PydlutilsException("decChunkMin out of range in chunks.getbounds().") # # Set minimum and maximum bounds of dec # while dec - self.decBounds[decChunkMin] < marginSize and decChunkMin > 0: decChunkMin -= 1 while self.decBounds[decChunkMax+1] - dec < marginSize and decChunkMax < self.nDec - 1: decChunkMax += 1 # # Find ra chunk bounds for each dec chunk # raChunkMin = np.zeros(decChunkMax-decChunkMin+1, dtype='i4') raChunkMax = np.zeros(decChunkMax-decChunkMin+1, dtype='i4') for i in range(decChunkMin, decChunkMax+1): cosDecMin = self.cosDecMin(i) raChunkMin[i-decChunkMin] = int(np.floor((ra - self.raBounds[i][0]) * float(self.nRa[i]) / (self.raBounds[i][self.nRa[i]] - self.raBounds[i][0]))) raChunkMax[i-decChunkMin] = raChunkMin[i-decChunkMin] if raChunkMin[i-decChunkMin] < 0 or raChunkMin[i-decChunkMin] > self.nRa[i]-1: raise PydlutilsException("raChunkMin out of range in chunks.getbounds().") # # Set minimum and maximum bounds of ra # raCheck = raChunkMin[i-decChunkMin] keepGoing = True while keepGoing and raCheck > -1: if raCheck >= 0 and raCheck < self.nRa[i]: keepGoing = (ra - self.raBounds[i][raCheck])*cosDecMin < marginSize else: keepGoing = False if keepGoing: raCheck -= 1 raChunkMin[i-decChunkMin] = raCheck raCheck = raChunkMax[i-decChunkMin] keepGoing = True while keepGoing and raCheck < self.nRa[i]: if raCheck >= 0 and raCheck < self.nRa[i]: keepGoing = (self.raBounds[i][raCheck+1]-ra)*cosDecMin < marginSize else: keepGoing = False if keepGoing: raCheck += 1 raChunkMax[i-decChunkMin] = raCheck return (raChunkMin, raChunkMax, decChunkMin, decChunkMax) def get(self, ra, dec): """Find the chunk to which a given point belongs. """ # # Find dec chunk # decChunk = int(np.floor((dec - self.decBounds[0]) * float(self.nDec) / (self.decBounds[self.nDec]-self.decBounds[0]))) # # Find ra chunk # if decChunk < self.nDec and decChunk >= 0: raChunk = int(np.floor((ra - self.raBounds[decChunk][0]) * float(self.nRa[decChunk]) / (self.raBounds[decChunk][self.nRa[decChunk]] - self.raBounds[decChunk][0]))) if raChunk < 0 or raChunk > self.nRa[decChunk]-1: raise PydlutilsException("raChunk out of range in chunks.get()") else: raChunk = -1 return (raChunk, decChunk) def friendsoffriends(self, ra, dec, linkSep): """Friends-of-friends using chunked data. """ nPoints = ra.size inGroup = np.zeros(nPoints, dtype='i4') - 1 # # mapGroups contains an equivalency mapping of groups. mapGroup[i]=j # means i and j are actually the same group. j<=i always, by design. # The largest number of groups you can get # (assuming linkSep < marginSize < minSize) is 9 times the number of # targets # mapGroups = np.zeros(9*nPoints, dtype='i4') - 1 nMapGroups = 0 for i in range(self.nDec): for j in range(self.nRa[i]): if len(self.chunkList[i][j]) > 0: chunkGroup = self.chunkfriendsoffriends(ra, dec, self.chunkList[i][j], linkSep) for k in range(chunkGroup.nGroups): minEarly = 9*nPoints l = chunkGroup.firstGroup[k] while l != -1: if inGroup[self.chunkList[i][j][l]] != -1: checkEarly = inGroup[self.chunkList[i][j][l]] while mapGroups[checkEarly] != checkEarly: checkEarly = mapGroups[checkEarly] minEarly = min(minEarly, checkEarly) else: inGroup[self.chunkList[i][j][l]] = nMapGroups l = chunkGroup.nextGroup[l] if minEarly == 9*nPoints: mapGroups[nMapGroups] = nMapGroups else: mapGroups[nMapGroups] = minEarly l = chunkGroup.firstGroup[k] while l != -1: checkEarly = inGroup[self.chunkList[i][j][l]] while mapGroups[checkEarly] != checkEarly: tmpEarly = mapGroups[checkEarly] mapGroups[checkEarly] = minEarly checkEarly = tmpEarly mapGroups[checkEarly] = minEarly l = chunkGroup.nextGroup[l] nMapGroups += 1 # # Now all groups which are mapped to themselves are the real groups # Make sure the mappings are set up to go all the way down. # nGroups = 0 for i in range(nMapGroups): if mapGroups[i] != -1: if mapGroups[i] == i: mapGroups[i] = nGroups nGroups += 1 else: mapGroups[i] = mapGroups[mapGroups[i]] else: raise PydlutilsException("MapGroups[{0:d}]={1:d} in chunks.friendsoffriends().".format(i, mapGroups[i])) for i in range(nPoints): inGroup[i] = mapGroups[inGroup[i]] firstGroup = np.zeros(nPoints, dtype='i4') - 1 nextGroup = np.zeros(nPoints, dtype='i4') - 1 multGroup = np.zeros(nPoints, dtype='i4') for i in range(nPoints-1, -1, -1): nextGroup[i] = firstGroup[inGroup[i]] firstGroup[inGroup[i]] = i for i in range(nGroups): j = firstGroup[i] while j != -1: multGroup[i] += 1 j = nextGroup[j] return (inGroup, multGroup, firstGroup, nextGroup, nGroups) def chunkfriendsoffriends(self, ra, dec, chunkList, linkSep): """Does friends-of-friends on the ra, dec that are defined by chunkList. """ # # Convert ra, dec into something that can be digested by the # groups object. # x = np.deg2rad(np.vstack((ra[chunkList], dec[chunkList]))) radLinkSep = np.deg2rad(linkSep) group = groups(x, radLinkSep, 'sphereradec') return group class groups(object): """Group a set of objects (a list of coordinates in some space) based on a friends-of-friends algorithm """ @staticmethod def euclid(x1, x2): """Pythagorean theorem in Euclidean space with arbitrary number of dimensions. """ return np.sqrt(((x1-x2)**2).sum()) @staticmethod def sphereradec(x1, x2): """Separation of two points on a 2D-sphere, assuming they are in longitude-latitude or right ascension-declination form. Assumes everything is already in radians. """ from ..goddard.astro import gcirc return gcirc(x1[0], x1[1], x2[0], x2[1], units=0) def __init__(self, coordinates, distance, separation='euclid'): """Init creates an object and performs the friends-of-friends algorithm. The coordinates can have arbitrary dimensions, with each column representing one of the dimensions. Each row defines an object. If separation is not defined it defaults to Euclidean space. """ # # Find a separation function # if callable(separation): self.separation = separation elif isinstance(separation, string_types): if separation == 'euclid': self.separation = self.euclid elif separation == 'sphereradec': self.separation = self.sphereradec else: raise PydlutilsException("Unknown separation function: {0}.".format(separation)) else: raise PydlutilsException("Improper type for separation!") # # Save information about the coordinates. # nGroups = 0 nTargets = coordinates.shape[1] multGroup = np.zeros(nTargets, dtype='i4') firstGroup = np.zeros(nTargets, dtype='i4') - 1 nextGroup = np.zeros(nTargets, dtype='i4') - 1 inGroup = np.arange(nTargets, dtype='i4') # # Find all the other targets associated with each target # for i in range(nTargets): nTmp = 0 minGroup = nGroups for j in range(nTargets): sep = self.separation(coordinates[:, i], coordinates[:, j]) if sep <= distance: multGroup[nTmp] = j minGroup = min(minGroup, inGroup[j]) nTmp += 1 # # Use this minimum for all # for j in range(nTmp): if inGroup[multGroup[j]] < nTargets: k = firstGroup[inGroup[multGroup[j]]] while k != -1: inGroup[k] = minGroup k = nextGroup[k] inGroup[multGroup[j]] = minGroup # # If it is a new group (no earlier groups), increment nGroups # if minGroup == nGroups: nGroups += 1 for j in range(i+1): firstGroup[j] = -1 for j in range(i, -1, -1): nextGroup[j] = firstGroup[inGroup[j]] firstGroup[inGroup[j]] = j # # Renumber to get rid of the numbers which were skipped # renumbered = np.zeros(nTargets, dtype='bool') nTmp = nGroups nGroups = 0 for i in range(nTargets): if not renumbered[i]: j = firstGroup[inGroup[i]] while j != -1: inGroup[j] = nGroups renumbered[j] = True j = nextGroup[j] nGroups += 1 # # Reset the values of firstGroup and inGroup # firstGroup[:] = -1 for i in range(nTargets-1, -1, -1): nextGroup[i] = firstGroup[inGroup[i]] firstGroup[inGroup[i]] = i # # Get the multiplicity # for i in range(nGroups): multGroup[i] = 0 j = firstGroup[i] while j != -1: multGroup[i] += 1 j = nextGroup[j] # # Set attributes # self.nGroups = nGroups self.nTargets = nTargets self.inGroup = inGroup self.multGroup = multGroup self.firstGroup = firstGroup self.nextGroup = nextGroup return def spheregroup(ra, dec, linklength, chunksize=None): """Perform friends-of-friends grouping given ra/dec coordinates. Parameters ---------- ra, dec : :class:`numpy.ndarray` Arrays of coordinates to group in decimal degrees. linklength : :class:`float` Linking length for the groups in decimal degrees. chunksize : :class:`float`, optional Break up the sphere into chunks of this size in decimal degrees. Returns ------- :func:`tuple` A tuple containing the group number of each object, the multiplicity of each group, the first member of each group, and the next member of the group for each object. Raises ------ PydlutilsException If the array of coordinates only contains one point. Notes ----- It is important that `chunksize` >= 4 * `linklength`. This is enforced. .. warning:: Behavior at the poles is not well tested. """ from warnings import warn npoints = ra.size if npoints == 1: raise PydlutilsException("Cannot group only one point!") # # Define the chunksize # if chunksize is not None: if chunksize < 4.0*linklength: chunksize = 4.0*linklength warn("chunksize changed to {0:.2f}.".format(chunksize), PydlutilsUserWarning) else: chunksize = max(4.0*linklength, 0.1) # # Initialize chunks # chunk = chunks(ra, dec, chunksize) chunk.assign(ra, dec, linklength) # # Run friends-of-friends # ingroup, multgroup, firstgroup, nextgroup, ngroups = chunk.friendsoffriends(ra, dec, linklength) # # Renumber the groups in order of appearance # renumbered = np.zeros(npoints, dtype='bool') iclump = 0 for i in range(npoints): if not renumbered[i]: j = firstgroup[ingroup[i]] while j != -1: ingroup[j] = iclump renumbered[j] = True j = nextgroup[j] iclump += 1 # # Reset the index lists # firstgroup[:] = -1 for i in range(npoints-1, -1, -1): nextgroup[i] = firstgroup[ingroup[i]] firstgroup[ingroup[i]] = i # # Reset the multiplicities # multgroup[:] = 0 for i in range(ngroups): j = firstgroup[i] while j != -1: multgroup[i] += 1 j = nextgroup[j] return (ingroup, multgroup, firstgroup, nextgroup) def spherematch(ra1, dec1, ra2, dec2, matchlength, chunksize=None, maxmatch=1): """Match points on a sphere. Parameters ---------- ra1, dec1, ra2, dec2 : :class:`numpy.ndarray` The sets of coordinates to match. Assumed to be in decimal degrees matchlength : :class:`float` Two points closer than this separation are matched. Assumed to be in decimal degrees. chunksize : :class:`float`, optional Value to pass to chunk assignment. maxmatch : :class:`int`, optional Allow up to `maxmatch` matches per coordinate. Default 1. If set to zero, All possible matches will be returned. Returns ------- :func:`tuple` A tuple containing the indices into the first set of points, the indices into the second set of points and the match distance in decimal degrees. Notes ----- If you have sets of coordinates that differ in size, call this function with the larger list first. This exploits the inherent asymmetry in the underlying code to reduce memory use. .. warning:: Behavior at the poles is not well tested. """ from ..goddard.astro import gcirc # # Set default values # if chunksize is None: chunksize = max(4.0*matchlength, 0.1) # # Check input size # if ra1.size == 1: raise PydlutilsException("Change the order of the sets of coordinates!") # # Initialize chunks # chunk = chunks(ra1, dec1, chunksize) chunk.assign(ra2, dec2, matchlength) # # Create return arrays # match1 = list() match2 = list() distance12 = list() for i in range(ra1.size): currra = np.fmod(ra1[i]+chunk.raOffset, 360.0) rachunk, decchunk = chunk.get(currra, dec1[i]) jmax = len(chunk.chunkList[decchunk][rachunk]) if jmax > 0: for j in range(jmax): k = chunk.chunkList[decchunk][rachunk][j] sep = gcirc(ra1[i], dec1[i], ra2[k], dec2[k], units=2)/3600.0 if sep < matchlength: match1.append(i) match2.append(k) distance12.append(sep) # # Sort distances # omatch1 = np.array(match1) omatch2 = np.array(match2) odistance12 = np.array(distance12) s = odistance12.argsort() # # Retain only desired matches # if maxmatch > 0: gotten1 = np.zeros(ra1.size, dtype='i4') gotten2 = np.zeros(ra2.size, dtype='i4') nmatch = 0 for i in range(omatch1.size): if (gotten1[omatch1[s[i]]] < maxmatch and gotten2[omatch2[s[i]]] < maxmatch): gotten1[omatch1[s[i]]] += 1 gotten2[omatch2[s[i]]] += 1 nmatch += 1 match1 = np.zeros(nmatch, dtype='i4') match2 = np.zeros(nmatch, dtype='i4') distance12 = np.zeros(nmatch, dtype='d') gotten1[:] = 0 gotten2[:] = 0 nmatch = 0 for i in range(omatch1.size): if (gotten1[omatch1[s[i]]] < maxmatch and gotten2[omatch2[s[i]]] < maxmatch): gotten1[omatch1[s[i]]] += 1 gotten2[omatch2[s[i]]] += 1 match1[nmatch] = omatch1[s[i]] match2[nmatch] = omatch2[s[i]] distance12[nmatch] = odistance12[s[i]] nmatch += 1 else: match1 = omatch1[s] match2 = omatch2[s] distance12 = odistance12[s] return (match1, match2, distance12) pydl-0.6.0/pydl/pydlutils/cooling.py0000664000111400017500000000373413040014667020302 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the cooling directory in idlutils. """ def read_ds_cooling(fname, logT=None): """Read in the `Sutherland & Dopita (1993) `_ cooling function. Parameters ---------- fname : { 'm-00.cie', 'm-05.cie', 'm+05.cie', 'm-10.cie', 'm-15.cie', 'm-20.cie', 'm-30.cie', 'mzero.cie' } Name of the data file to read. logT : :class:`numpy.ndarray`, optional If provided, values will be interpolated to the provided values. If not provided, the values in the data files will be returned. Returns ------- :func:`tuple` A tuple containing `logT` and `loglambda`, respectively. Raises ------ ValueError If the input file name is invalid. Notes ----- The data have been retrieved from http://www.mso.anu.edu.au/~ralph/data/cool/ and stored in the package. Examples -------- >>> from pydl.pydlutils.cooling import read_ds_cooling >>> logT, loglambda = read_ds_cooling('m-15.cie') >>> logT[0:5] array([ 4. , 4.05, 4.1 , 4.15, 4.2 ]) >>> loglambda[0:5] array([-26. , -24.66, -23.52, -22.62, -22.11]) """ from astropy.utils.data import get_pkg_data_contents from numpy import interp from astropy.io import ascii if fname not in ('m-00.cie', 'm-05.cie', 'm+05.cie', 'm-10.cie', 'm-15.cie', 'm-20.cie', 'm-30.cie', 'mzero.cie'): raise ValueError('Invalid value for data file: {0}'.format(fname)) coolingfile = get_pkg_data_contents('data/cooling/' + fname) data = ascii.read(coolingfile.split('\n')[2:], delimiter='\t') if logT is None: return (data['log(T)'].data, data['log(lambda net)'].data) else: loglambda = interp(logT, data['log(T)'].data, data['log(lambda net)'].data) return (logT, loglambda) pydl-0.6.0/pydl/pydlutils/sdss.py0000664000111400017500000006444313102426056017626 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the sdss directory in idlutils. """ import os import numpy as np from astropy.extern.six import string_types from astropy.io import fits from . import PydlutilsException # # Cache for the maskbits file. # maskbits = None # # Cache the sweep index # sweep_cache = {'star': None, 'gal': None, 'sky': None} # # Cache sdss_astrombad data # opbadfields = None def default_skyversion(): """Returns skyversion number to use for photoop imaging. Returns ------- :class:`int` The default skyversion number. Notes ----- The skyversion number is hard-coded to 2. Examples -------- >>> from pydl.pydlutils.sdss import default_skyversion >>> default_skyversion() 2 """ return 2 def sdss_astrombad(run, camcol, field, photolog_version='dr10'): """For a list of RUN, CAMCOL, FIELD, return whether each field has bad astrometry. Parameters ---------- run, camcol, field : :class:`int` or array of :class:`int` Run, camcol and field. If arrays are passed, all must have the same length. photolog_version : :class:`str`, optional Use this version of photolog to obtain the obBadfields.par file, if :envvar:`PHOTOLOG_DIR` is not set. Returns ------- :class:`numpy.ndarray` of :class:`bool` Array of bool. ``True`` indicates the field is bad. Raises ------ ValueError If the sizes of the arrays don't match or if the array values are out of bounds. Notes ----- Reads data from ``$PHOTOLOG_DIR/opfiles/opBadFields.par``. If there is a problem with one camcol, we assume a problem with all camcols. """ from astropy.utils.data import download_file from .yanny import yanny global opbadfields # # Check inputs # if isinstance(run, int): # # Assume all inputs are integers & promote to arrays. # run = np.array([run], dtype=np.int64) camcol = np.array([camcol], dtype=np.int64) field = np.array([field], dtype=np.int64) else: # # Check that all inputs have the same shape. # if run.shape != camcol.shape: raise ValueError("camcol.shape does not match run.shape!") if run.shape != field.shape: raise ValueError("field.shape does not match run.shape!") # # Check ranges of parameters # if ((run < 0) | (run >= 2**16)).any(): raise ValueError("run values are out-of-bounds!") if ((camcol < 1) | (camcol > 6)).any(): raise ValueError("camcol values are out-of-bounds!") if ((field < 0) | (field >= 2**12)).any(): raise ValueError("camcol values are out-of-bounds!") # # Read the file # if opbadfields is None: # pragma: no cover if os.getenv('PHOTOLOG_DIR') is None: if (photolog_version == 'trunk' or photolog_version.startswith('branches/')): iversion = photolog_version else: iversion = 'tags/'+photolog_version baseurl = ('https://svn.sdss.org/public/data/sdss/photolog/' + '{0}/opfiles/opBadfields.par').format(iversion) filename = download_file(baseurl, cache=True) else: filename = os.path.join(os.getenv('PHOTOLOG_DIR'), 'opfiles', 'opBadfields.par') astrombadfile = yanny(filename) w = ((astrombadfile['BADFIELDS']['problem'] == 'astrom'.encode()) | (astrombadfile['BADFIELDS']['problem'] == 'rotator'.encode())) opbadfields = astrombadfile['BADFIELDS'][w] # # opbadfields already has astrom problems selected at this point # bad = np.zeros(run.shape, dtype=bool) for row in opbadfields: w = ((run == row['run']) & (field >= row['firstfield']) & (field < row['lastfield'])) if w.any(): bad[w] = True return bad def sdss_flagexist(flagname, bitname, flagexist=False, whichexist=False): """Check for the existence of flags. Parameters ---------- flagname : :class:`str` The name of a bitmask group. Not case-sensitive. bitname : :class:`str` or :class:`list` The name(s) of the specific bitmask(s) within the `flagname` group. flagexist : :class:`bool`, optional If flagexist is True, return a tuple with the second component indicating whether the binary flag named `flagname` exists, even if `bitname` is wrong. whichexist : :class:`bool`, optional If whichexist is True, return a list containing existence test results for each individual flag. Returns ------- :class:`bool` or :func:`tuple` A boolean value or a tuple of bool. """ global maskbits if maskbits is None: # pragma: no cover maskbits = set_maskbits() # # Make sure label is a list # if isinstance(bitname, string_types): bitnames = [bitname.upper()] else: bitnames = [b.upper() for b in bitname] f = False l = False which = [False] * len(bitnames) if flagname.upper() in maskbits: f = True which = [n in maskbits[flagname.upper()] for n in bitnames] l = sum(which) == len(which) if flagexist and whichexist: return (l, f, which) elif flagexist: return (l, f) elif whichexist: return (l, which) else: return l def sdss_flagname(flagname, flagvalue, concat=False): """Return a list of flag names corresponding to the values. Parameters ---------- flagname : :class:`str` The name of a bitmask group. Not case-sensitive. flagvalue : :class:`long` The value to be converted into bitmask names. concat : :class:`bool`, optional If set to ``True``, the list of names is converted to a space-separated string. Returns ------- :class:`str` or :class:`list` The names of the bitmasks encoded in `flagvalue`. Raises ------ KeyError If `flagname` is invalid Examples -------- >>> from pydl.pydlutils.sdss import sdss_flagname >>> sdss_flagname('ANCILLARY_TARGET1',2310346608843161600) # doctest: +REMOTE_DATA ['BRIGHTGAL', 'BLAZGX', 'ELG'] """ global maskbits if maskbits is None: # pragma: no cover maskbits = set_maskbits() flagu = flagname.upper() flagvaluint = np.uint64(flagvalue) one = np.uint64(1) bits = [bit for bit in range(64) if (flagvaluint & (one << np.uint64(bit))) != 0] retval = list() for bit in bits: try: f = [x for x in maskbits[flagu].items() if x[1] == bit] except KeyError: raise KeyError("Unknown flag group {0}!".format(flagu)) if f: retval.append(f[0][0]) if concat: retval = ' '.join(retval) return retval def sdss_flagval(flagname, bitname): """Convert bitmask names into values. Converts human-readable bitmask names into numerical values. The inputs are not case-sensitive; all inputs are converted to upper case internally. Parameters ---------- flagname : :class:`str` The name of a bitmask group. bitname : :class:`str` or :class:`list` The name(s) of the specific bitmask(s) within the `flagname` group. Returns ------- :class:`numpy.uint64` The value of the bitmask name(s). Raises ------ KeyError If `flagname` or `bitname` are invalid names. Examples -------- >>> from pydl.pydlutils.sdss import sdss_flagval >>> sdss_flagval('ANCILLARY_TARGET1',['BLAZGX','ELG','BRIGHTGAL']) # doctest: +REMOTE_DATA 2310346608843161600 """ global maskbits if maskbits is None: # pragma: no cover maskbits = set_maskbits() # # Make sure inlabel is a list # if isinstance(bitname, string_types): bitnames = [bitname.upper()] else: bitnames = [b.upper() for b in bitname] flagu = flagname.upper() flagvalue = np.uint64(0) for bit in bitnames: if flagu in maskbits: if bit in maskbits[flagu]: flagvalue += np.uint64(2)**np.uint64(maskbits[flagu][bit]) else: raise KeyError("Unknown bit label {0} for flag group {1}!".format(bit, flagu)) else: raise KeyError("Unknown flag group {0}!".format(flagu)) return flagvalue def sdss_objid(run, camcol, field, objnum, rerun=301, skyversion=None): """Convert SDSS photometric identifiers into CAS-style ObjID. Bits are assigned in objid thus: ===== ========== =============================================== Bits Name Comment ===== ========== =============================================== 63 empty unassigned 59-62 skyVersion resolved sky version (0-15) 48-58 rerun number of pipeline rerun 32-47 run run number 29-31 camcol camera column (1-6) 28 firstField [is this the first field in segment?] 0 for now 16-27 field field number within run 0-15 object object number within field ===== ========== =============================================== Parameters ---------- run, camcol, field, objnum : :class:`int` or array of int Run, camcol, field and object number within field. If arrays are passed, all must have the same length. rerun, skyversion : :class:`int` or array of int, optional Rerun and skyversion usually don't change very much. If supplied, make sure the size matches all the other values. Returns ------- :class:`numpy.ndarray` of :class:`numpy.int64` The ObjIDs of the objects. Raises ------ ValueError If the sizes of the arrays don't match or if the array values are out of bounds. Notes ----- * ``firstField`` flag never set. * On 32-bit systems, makes sure to explicitly declare all inputs as 64-bit integers. Examples -------- >>> from pydl.pydlutils.sdss import sdss_objid >>> print(sdss_objid(3704,3,91,146)) [1237661382772195474] """ if skyversion is None: skyversion = default_skyversion() if isinstance(run, int): run = np.array([run], dtype=np.int64) if isinstance(camcol, int): camcol = np.array([camcol], dtype=np.int64) if isinstance(field, int): field = np.array([field], dtype=np.int64) if isinstance(objnum, int): objnum = np.array([objnum], dtype=np.int64) if isinstance(rerun, int): if rerun == 301: rerun = np.zeros(run.shape, dtype=np.int64) + 301 else: rerun = np.array([rerun], dtype=np.int64) if isinstance(skyversion, int): if skyversion == default_skyversion(): skyversion = np.zeros(run.shape, dtype=np.int64) + default_skyversion() else: skyversion = np.array([skyversion], dtype=np.int64) # # Check that all inputs have the same shape. # firstfield = np.zeros(run.shape, dtype=np.int64) if run.shape != camcol.shape: raise ValueError("camcol.shape does not match run.shape!") if run.shape != field.shape: raise ValueError("field.shape does not match run.shape!") if run.shape != objnum.shape: raise ValueError("objnum.shape does not match run.shape!") if run.shape != rerun.shape: raise ValueError("rerun.shape does not match run.shape!") if run.shape != skyversion.shape: raise ValueError("skyversion.shape does not match run.shape!") # # Check ranges of parameters # if ((skyversion < 0) | (skyversion >= 16)).any(): raise ValueError("skyversion values are out-of-bounds!") if ((rerun < 0) | (rerun >= 2**11)).any(): raise ValueError("rerun values are out-of-bounds!") if ((run < 0) | (run >= 2**16)).any(): raise ValueError("run values are out-of-bounds!") if ((camcol < 1) | (camcol > 6)).any(): raise ValueError("camcol values are out-of-bounds!") if ((field < 0) | (field >= 2**12)).any(): raise ValueError("camcol values are out-of-bounds!") if ((objnum < 0) | (objnum >= 2**16)).any(): raise ValueError("id values are out-of-bounds!") # # Compute the objid # objid = ((skyversion << 59) | (rerun << 48) | (run << 32) | (camcol << 29) | (firstfield << 28) | (field << 16) | (objnum)) return objid def sdss_specobjid(plate, fiber, mjd, run2d, line=None, index=None): """Convert SDSS spectrum identifiers into CAS-style specObjID. Bits are assigned in specObjID thus: ===== ========== ============================================================= Bits Name Comment ===== ========== ============================================================= 50-63 Plate ID 14 bits 38-49 Fiber ID 12 bits 24-37 MJD Date plate was observed minus 50000 (14 bits) 10-23 run2d Spectroscopic reduction version 0-9 line/index 0 for use in SpecObj files see below for other uses (10 bits) ===== ========== ============================================================= Parameters ---------- plate, fiber, mjd : :class:`int` or array of int Plate, fiber ID, and MJD for a spectrum. If arrays are passed, all must have the same length. The MJD value must be greater than 50000. run2d : :class:`int`, :class:`str` or array of int or str The run2d value must be an integer or a string of the form 'vN_M_P'. If an array is passed, it must have the same length as the other inputs listed above. If the string form is used, the values are restricted to :math:`5 \le N \le 6`, :math:`0 \le M \le 99`, :math:`0 \le P \le 99`. line : :class:`int`, optional A line index, only used for defining specObjID for SpecLine files. `line` and `index` cannot both be non-zero. index : :class:`int`, optional An index measure, only used for defining specObjID for SpecLineIndex files. `line` and `index` cannot both be non-zero. Returns ------- :class:`numpy.ndarray` of :class:`numpy.uint64` The specObjIDs of the objects. Raises ------ ValueError If the sizes of the arrays don't match or if the array values are out of bounds. Notes ----- * On 32-bit systems, makes sure to explicitly declare all inputs as 64-bit integers. * This function defines the SDSS-III/IV version of specObjID, used for SDSS DR8 and subsequent data releases. It is not compatible with SDSS DR7 or earlier. * If the string form of `run2d` is used, the bits are assigned by the formula :math:`(N - 5) \\times 10000 + M \\times 100 + P`. Examples -------- >>> from pydl.pydlutils.sdss import sdss_specobjid >>> print(sdss_specobjid(4055,408,55359,'v5_7_0')) [4565636362342690816] """ import re if line is not None and index is not None: raise ValueError("line and index inputs cannot both be non-zero!") if isinstance(plate, int): plate = np.array([plate], dtype=np.uint64) if isinstance(fiber, int): fiber = np.array([fiber], dtype=np.uint64) if isinstance(mjd, int): mjd = np.array([mjd], dtype=np.uint64) - 50000 if isinstance(run2d, str): try: run2d = np.array([int(run2d)], dtype=np.uint64) except ValueError: # Try a "vN_M_P" string. m = re.match(r'v(\d+)_(\d+)_(\d+)', run2d) if m is None: raise ValueError("Could not extract integer run2d value!") else: N, M, P = m.groups() run2d = np.array([(int(N) - 5)*10000 + int(M) * 100 + int(P)], dtype=np.uint64) elif isinstance(run2d, int): run2d = np.array([run2d], dtype=np.uint64) if line is None: line = np.zeros(plate.shape, dtype=plate.dtype) else: if isinstance(line, int): line = np.array([line], dtype=np.uint64) if index is None: index = np.zeros(plate.shape, dtype=plate.dtype) else: if isinstance(index, int): index = np.array([index], dtype=np.uint64) # # Check that all inputs have the same shape. # if plate.shape != fiber.shape: raise ValueError("fiber.shape does not match plate.shape!") if plate.shape != mjd.shape: raise ValueError("mjd.shape does not match plate.shape!") if plate.shape != run2d.shape: raise ValueError("run2d.shape does not match plate.shape!") if plate.shape != line.shape: raise ValueError("line.shape does not match plate.shape!") if plate.shape != index.shape: raise ValueError("index.shape does not match plate.shape!") # # Check ranges of parameters # if ((plate < 0) | (plate >= 2**14)).any(): raise ValueError("plate values are out-of-bounds!") if ((fiber < 0) | (fiber >= 2**12)).any(): raise ValueError("fiber values are out-of-bounds!") if ((mjd < 0) | (mjd >= 2**14)).any(): raise ValueError("MJD values are out-of-bounds!") if ((run2d < 0) | (run2d >= 2**14)).any(): raise ValueError("MJD values are out-of-bounds!") if ((line < 0) | (line >= 2**10)).any(): raise ValueError("line values are out-of-bounds!") if ((index < 0) | (index >= 2**10)).any(): raise ValueError("index values are out-of-bounds!") # # Compute the specObjID # specObjID = ((plate << 50) | (fiber << 38) | (mjd << 24) | (run2d << 10) | (line | index)) return specObjID def sdss_sweep_circle(ra, dec, radius, stype='star', allobj=False): """Read the SDSS datasweep files and return objects around a location. Parameters ---------- ra, dec : :class:`float` The sky location to search, J2000 degrees. radius : :class:`float` The radius around `ra`, `dec` to search. stype : :class:`str`, optional The type of object to search, 'star', 'gal' or 'sky'. The default is 'star'. allobj : :class:`bool`, optional If set to ``True``, return all objects found, not just SURVEY_PRIMARY. Returns ------- :class:`numpy.ndarray` The data extracted from the sweep files. Raises ------ PydlutilsException If :envvar:`PHOTO_SWEEP` is not set. Notes ----- Assumes that the sweep files exist in :envvar:`PHOTO_SWEEP` and that index files have been created. """ from .. import uniq from .spheregroup import spherematch global sweep_cache # # Check values # if stype not in ('star', 'gal', 'sky'): raise ValueError('Invalid type {0}!'.format(stype)) sweepdir = os.getenv('PHOTO_SWEEP') if sweepdir is None: raise PydlutilsException('PHOTO_SWEEP is not set!') # # Read the index # if sweep_cache[stype] is None: indexfile = os.path.join(sweepdir, 'datasweep-index-{0}.fits'.format(stype)) with fits.open(indexfile) as f: sweep_cache[stype] = f[1].data index = sweep_cache[stype] # # Match # ira = np.array([ra]) idec = np.array([dec]) m1, m2, d12 = spherematch(index['RA'], index['DEC'], ira, idec, radius+0.36, maxmatch=0) if len(m1) == 0: return None if not allobj: w = index['NPRIMARY'][m1] > 0 if w.any(): m1 = m1[w] else: return None # # Maximum number of objects # if allobj: n = index['IEND'][m1] - index['ISTART'][m1] + 1 ntot = (where(n > 0, n, np.zeros(n.shape, dtype=n.dtype))).sum() else: ntot = index['NPRIMARY'][m1].sum() # # Find unique run + camcol # rc = index['RUN'][m1]*6 + index['CAMCOL'][m1] - 1 isort = rc.argsort() iuniq = uniq(rc[isort]) istart = 0 objs = None nobjs = 0 for i in range(len(iuniq)): iend = iuniq[i] icurr = isort[istart:iend] # # Determine which file and range of rows # run = index['RUN'][m1[icurr[0]]] camcol = index['CAMCOL'][m1[icurr[0]]] rerun = index['RERUN'][m1[icurr[0]]] fields = index[m1[icurr]] ist = fields['ISTART'].min() ind = fields['IEND'].max() if ind >= ist: # # Read in the rows of that file # swfile = os.path.join(os.getenv('PHOTO_SWEEP'), rerun, 'calibObj-{0:06d}-{1:1d}-{2}.fits.gz'.format( int(run), int(camcol), stype)) with fits.open(swfile) as f: tmp_objs = f[1].data[ist:ind] if tmp_objs.size > 0: # # Keep only objects within the desired radius # tm1, tm2, d12 = spherematch(tmp_objs['RA'], tmp_objs['DEC'], ira, idec, radius, maxmatch=0) if len(tm1) > 0: tmp_objs = tmp_objs[tm1] # # Keep only SURVEY_PRIMARY objects by default # if not allobj: w = ((tmp_objs['RESOLVE_STATUS'] & sdss_flagval('RESOLVE_STATUS', 'SURVEY_PRIMARY')) > 0) if w.any(): tmp_objs = tmp_objs[w] else: tmp_objs = None if tmp_objs is not None: if objs is None: objs = np.zeros(ntot, dtype=tmp_objs.dtype) objs[nobjs:nobjs+tmp_objs.size] = tmp_objs nobjs += tmp_objs.size istart = iend+1 if nobjs > 0: return objs[0:nobjs] else: return None def set_maskbits(idlutils_version='v5_5_24', maskbits_file=None): """Populate the maskbits cache. Parameters ---------- idlutils_version : :class:`str`, optional Fetch the sdssMaskbits.par file corresponding to this idlutils version. maskbits_file : :class:`str`, optional Use an explicit file instead of downloading the official version. This should only be used for tests. Returns ------- :class:`dict` A dictionary of bitmasks suitable for caching. Raises ------ URLError If the data file could not be retrieved. """ from astropy.utils.data import download_file from .yanny import yanny if maskbits_file is None: # pragma: no cover if (idlutils_version == 'trunk' or idlutils_version.startswith('branches/')): iversion = idlutils_version else: iversion = 'tags/'+idlutils_version baseurl = ('https://svn.sdss.org/public/repo/sdss/idlutils/' + '{0}/data/sdss/sdssMaskbits.par').format(iversion) filename = download_file(baseurl, cache=True, show_progress=False) else: filename = maskbits_file maskfile = yanny(filename, raw=True) # # Parse the file & cache the results in maskbits # maskbits = dict() for k in range(maskfile.size('MASKBITS')): if maskfile['MASKBITS']['flag'][k] in maskbits: maskbits[maskfile['MASKBITS']['flag'][k]][maskfile['MASKBITS']['label'][k]] = maskfile['MASKBITS']['bit'][k] else: maskbits[maskfile['MASKBITS']['flag'][k]] = {maskfile['MASKBITS']['label'][k]: maskfile['MASKBITS']['bit'][k]} if 'MASKALIAS' in maskfile: for k in range(maskfile.size('MASKALIAS')): maskbits[maskfile['MASKALIAS']['alias'][k]] = maskbits[maskfile['MASKALIAS']['flag'][k]].copy() return maskbits def unwrap_specobjid(specObjID, run2d_integer=False, specLineIndex=False): """Unwrap CAS-style specObjID into plate, fiber, mjd, run2d. See :func:`~pydl.pydlutils.sdss.sdss_specobjid` for details on how the bits within a specObjID are assigned. Parameters ---------- specObjID : :class:`numpy.ndarray` An array containing 64-bit integers or strings. If strings are passed, they will be converted to integers internally. run2d_integer : :class:`bool`, optional If ``True``, do *not* attempt to convert the encoded run2d values to a string of the form 'vN_M_P'. specLineIndex : :class:`bool`, optional If ``True`` interpret any low-order bits as being an 'index' rather than a 'line'. Returns ------- :class:`numpy.recarray` A record array with the same length as `specObjID`, with the columns 'plate', 'fiber', 'mjd', 'run2d', 'line'. Examples -------- >>> from numpy import array, uint64 >>> from pydl.pydlutils.sdss import unwrap_specobjid >>> unwrap_specobjid(array([4565636362342690816], dtype=uint64)) rec.array([(4055, 408, 55359, 'v5_7_0', 0)], dtype=[('plate', '> 50, 2**14 - 1) unwrap.fiber = np.bitwise_and(tempobjid >> 38, 2**12 - 1) unwrap.mjd = np.bitwise_and(tempobjid >> 24, 2**14 - 1) + 50000 run2d = np.bitwise_and(tempobjid >> 10, 2**14 - 1) if run2d_integer: unwrap.run2d = run2d else: N = ((run2d // 10000) + 5).tolist() M = ((run2d % 10000) // 100).tolist() P = (run2d % 100).tolist() unwrap.run2d = ['v{0:d}_{1:d}_{2:d}'.format(n, m, p) for n, m, p in zip(N, M, P)] unwrap[line] = np.bitwise_and(tempobjid, 2**10 - 1) return unwrap pydl-0.6.0/pydl/pydlutils/tests/0000775000111400017500000000000013160273564017437 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlutils/tests/test_coord.py0000664000111400017500000000232512775255301022160 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy import units as u from astropy.coordinates import ICRS # from astropy.tests.helper import raises from ..coord import SDSSMuNu, current_mjd, stripe_to_eta, stripe_to_incl class TestCoord(object): """Test the functions in pydl.pydlutils.coord. """ def setup(self): pass def teardown(self): pass def test_current_mjd(self): assert current_mjd() > 50000.0 def test_munu_to_radec(self): munu = SDSSMuNu(mu=0.0*u.deg, nu=0.0*u.deg, stripe=10) radec = munu.transform_to(ICRS) assert radec.ra.value == 0.0 assert radec.dec.value == 0.0 def test_radec_to_munu(self): radec = ICRS(ra=0.0*u.deg, dec=0.0*u.deg) munu = radec.transform_to(SDSSMuNu(stripe=10)) assert munu.mu.value == 0.0 assert munu.nu.value == 0.0 assert munu.incl.value == 0.0 def test_stripe_to_eta(self): eta = stripe_to_eta(82) assert eta == -32.5 def test_stripe_to_incl(self): incl = stripe_to_incl(82) assert incl == 0.0 incl = stripe_to_incl(10) assert incl == 0.0 pydl-0.6.0/pydl/pydlutils/tests/test_math.py0000664000111400017500000000646013040014667022001 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import raises from astropy.utils.data import get_pkg_data_filename from ..math import computechi2, djs_median, find_contiguous class TestMath(object): """Test the functions in pydl.pydlutils.math. """ def setup(self): pass def teardown(self): pass def test_computechi2(self): x = np.arange(20) y = np.array([6.6198438, 1.3491303, 0.41035045, 9.4013375, 4.1103360, 4.3522868, 4.6338078, 4.7400367, 5.1860726, 5.1082748, 5.1084596, 5.2990997, 5.5987537, 5.7007504, 5.7855296, 4.1123709, 6.9437957, 4.9956179, 4.3724215, 3.6245063]) sqivar = np.array([1.32531, 0.886090, 1.08384, 1.04489, 1.46807, 1.30800, 0.507725, 1.12840, 0.955025, 1.35925, 1.10126, 1.45690, 0.575700, 0.949710, 1.23368, 0.536489, 0.772543, 0.957729, 0.883976, 1.11559]) templates = np.vstack((np.ones((20,), dtype='d'), x)).transpose() chi2 = computechi2(y, sqivar, templates) # # 20 data points, 2 parameters = 18 degrees of freedom. # assert chi2.dof == 18 # # The covariance matrix should be symmetric # assert (chi2.covar.T == chi2.covar).all() # # The variance vector should be the same as the diagonal of the # covariance matrix. # assert (chi2.var == np.diag(chi2.covar)).all() def test_djs_median(self): test_data_file = get_pkg_data_filename('t/median_data.txt') test_data = np.loadtxt(test_data_file, dtype='d', delimiter=',') np.random.seed(424242) data = 100.0*np.random.random(100) data2 = 100.0*np.random.random((10, 10)) data3 = 100.0*np.random.random((10, 10, 10)) data_width_5 = test_data[0, :] data_width_5_reflect = test_data[1, :] # # Degenerate cases that fall back on numpy.median(). # assert np.allclose(np.median(data), djs_median(data)) assert np.allclose(np.median(data2, axis=0), djs_median(data2, dimension=0)) assert np.allclose(np.median(data2, axis=1), djs_median(data2, dimension=1)) # # Test widths. # assert np.allclose(data, djs_median(data, width=1)) assert np.allclose(data_width_5, djs_median(data, width=5)) # assert np.allclose(data_width_5_reflect, # djs_median(data, width=5, boundary='reflect')) # # Exceptions # with raises(ValueError): foo = djs_median(data, width=5, boundary='nearest') with raises(ValueError): foo = djs_median(data, width=5, boundary='wrap') with raises(ValueError): foo = djs_median(data, width=5, boundary='foobar') with raises(ValueError): foo = djs_median(data2, width=5, dimension=1) with raises(ValueError): foo = djs_median(data3, width=5) with raises(ValueError): foo = djs_median(data3, width=5, boundary='reflect') def test_find_contiguous(self): assert (find_contiguous(np.array([0, 1, 1, 1, 0, 1, 1, 0, 1])) == [1, 2, 3]) pydl-0.6.0/pydl/pydlutils/tests/test_image.py0000664000111400017500000001052612775255301022136 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import raises from ..image import djs_maskinterp1, djs_maskinterp class TestImage(object): """Test the functions in pydl.pydlutils.image. """ def setup(self): pass def teardown(self): pass def test_djs_maskinterp1(self): y = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=np.float64) # Test all good yi = djs_maskinterp1(y, np.zeros(y.shape, dtype=np.int32)) assert (yi == y).all() # Test all bad yi = djs_maskinterp1(y, np.ones(y.shape, dtype=np.int32)) assert (yi == y).all() # Test one good value yt = np.zeros(y.shape, dtype=y.dtype) + y[0] yi = djs_maskinterp1(y, np.arange(len(y))) assert (yi == yt).all() # Test two bad values yi = djs_maskinterp1(y, np.array([0, 1, 0, 1, 0])) assert np.allclose(y, yi) # Because of the default behavior of np.interp(), the 'const' # keyword has no actual effect, because it performs the # same operation. yt = y.copy() yt[4] = 3.0 yi = djs_maskinterp1(y, np.array([0, 1, 0, 0, 1])) assert np.allclose(yt, yi) yi = djs_maskinterp1(y, np.array([0, 1, 0, 0, 1]), const=True) assert np.allclose(yt, yi) yt = y.copy() yt[0] = 1.0 yi = djs_maskinterp1(y, np.array([1, 0, 1, 0, 0]), const=True) assert np.allclose(yt, yi) # # Test other x values # x = np.array([0.0, 0.5, 1.0, 1.5, 2.0], dtype=y.dtype) yi = djs_maskinterp1(y, np.array([0, 1, 0, 1, 0]), xval=x) assert np.allclose(y, yi) yt = y.copy() yt[4] = 3.0 yi = djs_maskinterp1(y, np.array([0, 1, 0, 0, 1]), xval=x) assert np.allclose(yt, yi) yi = djs_maskinterp1(y, np.array([0, 1, 0, 0, 1]), xval=x, const=True) assert np.allclose(yt, yi) yt = y.copy() yt[0] = 1.0 yi = djs_maskinterp1(y, np.array([1, 0, 1, 0, 0]), xval=x, const=True) assert np.allclose(yt, yi) def test_djs_maskinterp(self): y = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=np.float64) mask = np.array([0, 1, 0]) with raises(ValueError): yi = djs_maskinterp(y, mask) mask = np.array([0, 1, 0, 0, 0]) x = np.array([0.0, 0.5, 1.0], dtype=y.dtype) with raises(ValueError): yi = djs_maskinterp(y, mask, xval=x) # 1-D case yi = djs_maskinterp(y, mask) assert np.allclose(y, yi) # 2-D case x = np.array([0.0, 0.5, 1.0, 1.5, 2.0], dtype=y.dtype) x = np.vstack((x, x, x)) y = np.vstack((y, y, y)) mask = np.vstack((mask, mask, mask)) with raises(ValueError): yi = djs_maskinterp(y, mask) with raises(ValueError): yi = djs_maskinterp(y, mask, axis=-1) with raises(ValueError): yi = djs_maskinterp(y, mask, axis=2) yi = djs_maskinterp(y, mask, axis=0) assert np.allclose(y, yi) yi = djs_maskinterp(y, mask, axis=0, xval=x) assert np.allclose(y, yi) mask[:, 1] = 0 mask[1, :] = 1 yi = djs_maskinterp(y, mask, axis=1) assert np.allclose(y, yi) yi = djs_maskinterp(y, mask, axis=1, xval=x) assert np.allclose(y, yi) # 3-D case x = np.dstack((x, x, x, x, x, x, x)) y = np.dstack((y, y, y, y, y, y, y)) mask = np.dstack((mask, mask, mask, mask, mask, mask, mask)) mask[:, :, :] = 0 mask[:, :, 5] = 1 yi = djs_maskinterp(y, mask, axis=0) assert np.allclose(y, yi) yi = djs_maskinterp(y, mask, axis=0, xval=x) assert np.allclose(y, yi) mask[:, :, :] = 0 mask[:, 3, :] = 1 yi = djs_maskinterp(y, mask, axis=1) assert np.allclose(y, yi) yi = djs_maskinterp(y, mask, axis=1, xval=x) assert np.allclose(y, yi) mask[:, :, :] = 0 mask[1, :, :] = 1 yi = djs_maskinterp(y, mask, axis=2) assert np.allclose(y, yi) yi = djs_maskinterp(y, mask, axis=2, xval=x) assert np.allclose(y, yi) # 4-D case y = np.random.random((2, 2, 2, 2)) with raises(ValueError): yi = djs_maskinterp(y, (y > 0.5), axis=0) pydl-0.6.0/pydl/pydlutils/tests/t/0000775000111400017500000000000013160273564017702 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlutils/tests/t/test.par0000664000111400017500000000475112775255301021374 0ustar weaverweaver00000000000000#%yanny # test.par # # FTCL/Yanny file for testing the perl & python yanny readers. # # This file is much more complicated than the usual yanny file, but should # be completely compliant with the specifications in # http://www.sdss.org/dr6/dm/flatFiles/yanny.html # # $Id: test.par 114735 2010-07-02 22:15:51Z weaver $ # mjd 54579 # a keyword/value pair alpha beta gamma delta # another pair semicolon This pair contains a semicolon; # semicolon test typedef enum { FALSE, TRUE } BOOLEAN; typedef enum { FAILURE, INCOMPLETE, SUCCESS } STATUS; typedef struct { float mag[5]; char b[5][]; char foo[25]; double c; int flags[2]; BOOLEAN new_flag; } MYSTRUCT; typedef struct { float foo<3>; # This is archaic array notation, strongly deprecated, char bar<10>; # but still technically supported. } OLD; typedef struct { STATUS state; char timestamp[]; #UTC timestamp in format 2008-06-21T00:27:33 } STATUS_UPDATE; mystruct {17.5 17.546 17.4 16.1 16.0} {the rain in "spain is" wet} \ mystruct 1.24345567 {123123 1231213} FALSE mystruct {17.5 17.446 17.4 16.1 16.0} {the snow in chile "is dry"} \ "My dog has no nose." 7.24345567 {123123 0} TRUE mystruct {17.5 17.446 17.4 16.1 16.0} {this string is empty ""} \ "" 7.24345567 {123123 0} TRUE mystruct {17.5 17.446 17.4 16.1 16.0} \ {"this array contains empty strings" "" "this array contains empty strings" "" empty} \ "" 7.24345567 {123123 0} TRUE mystruct {17.5 17.446 17.4 16.1 16.0} { this array doesn't contain quotes} \ "" 7.24345567 {123123 0} TRUE mystruct {19.3 18.2 17.1 16.0 15.9} {this array contains "#hash" characters} \ "#hashtag" 2.71828 {321321 54321} FALSE mystruct {19.3 18.2 17.1 16.0 15.9} {there is "a comment" "with a #hash" "at the end"} \ "#hashtag #hash" 2.71828 {321321 54321} FALSE # this last item should be "FALSE" status_update INCOMPLETE 2008-06-21T00:27:33 status_update SUCCESS 2008-06-21T02:27:33 status_update FAILURE 2008-06-21T03:27:33 STATUS_UPDATE SUCCESS 2008-06-22T00:27:33 STATUS_UPDATE FAILURE 2008-06-22T01:27:33 STATUS_UPDATE SUCCESS 2008-06-22T00:27:33 STATUS_UPDATE FAILURE 2008-06-22T01:27:33 # Appended by FTCL.pm at 2008-06-23 23:09:58 UTC. STATUS_UPDATE SUCCESS 2008-06-22T00:27:33 STATUS_UPDATE FAILURE 2008-06-22T01:27:33 # Appended by FTCL.pm at 2008-06-23 23:26:52 UTC. STATUS_UPDATE SUCCESS 2008-06-22T00:27:33 STATUS_UPDATE FAILURE 2008-06-22T01:27:33 # old {1.2 2.3 3.4} hello old {4.3 3.2 2.1} { { } } pydl-0.6.0/pydl/pydlutils/tests/t/spheregroup_data.txt0000664000111400017500000000170112775255301023776 0ustar weaverweaver00000000000000# Data for testing the spheregroup function. 227.48189,44.281521,223.18793,224.92843,41.775477,315.34848,44.242353,316.31759,313.46396,311.91193,43.149445,43.449271,317.18169,224.34194,223.57484,44.701994,228.38290,316.48056,222.82152,224.04855,223.21508,43.901901,49.183680,314.97164,228.35194,316.80579,44.441586,309.97655,313.81441,46.041358 -34.026420,33.976505,-36.634993,-32.535347,36.290420,0.54110965,35.642976,0.62491526,-0.66827180,-0.083615550,34.695414,34.645375,0.61938086,-35.870473,-35.199912,35.471107,-34.539174,1.5074932,-34.225069,-37.184979,-36.822858,37.650088,35.527187,-1.2731230,-37.131627,-3.1725828,35.996336,1.7463770,-0.95217681,35.159271 0,1,0,0,1,2,1,2,2,2,1,1,2,0,0,1,0,2,0,0,0,1,1,2,0,2,1,2,2,1 10,10,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,1,5,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 2,4,3,13,6,7,10,8,9,12,11,15,17,14,16,21,18,23,19,20,24,22,26,25,-1,27,29,28,-1,-1 pydl-0.6.0/pydl/pydlutils/tests/t/polygon.fits0000664000111400017500000003410012775255301022256 0ustar weaverweaver00000000000000SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T DATE = '2016-03-15' / Time of creation of polygon fits file IDLUTILS= 'pydl-0.5.0.dev550' / Version of idlutils used END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 320 / length of dimension 1 NAXIS2 = 20 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 8 / number of table fields TTYPE1 = 'XCAPS ' TFORM1 = '27D ' TDIM1 = '( 3, 9) ' TTYPE2 = 'CMCAPS ' TFORM2 = '9D ' TTYPE3 = 'IFIELD ' TFORM3 = 'J ' TTYPE4 = 'NCAPS ' TFORM4 = 'J ' TTYPE5 = 'WEIGHT ' TFORM5 = 'D ' TTYPE6 = 'PIXEL ' TFORM6 = 'J ' TTYPE7 = 'STR ' TFORM7 = 'D ' TTYPE8 = 'USE_CAPS' TFORM8 = 'J ' TSCAL8 = 1 TZERO8 = 2147483648 END ?,GU?9˨>zgt? 1M?%?1MH?ʧ?f)?7$;?1/ ?iY<U?Oָ€ӴX {g??;;?>^`Ŕ?,GU?9˨>zg?Oe ?榁r>zTͅ?1MH?ʧ?f)?7$;?1/ ?iY<U???>?Oe ?榁r>zTͅ?Zс?Pz'>y-t? 1M?%?1MH?ʧ?f)?7$;?1/ ?iY<U??lh>??>ǥ?Zс?Pz'>y-t? 1M?%?7$;?1/ ?iY<U??lh>ݿ?>"vL`?>#?P.:>{$Nt? 1M?%?1MH?ʧ?f)?7$;?1/ ?iY<U?Oָ€Ӵ\??;;?>a3?>#?P.:>{$N? Q?b->{J?1MH?ʧ?f)?7$;?1/ ?iY<U???>ve? Q?b->{J׾t? 1M?%?1MH?ʧ?f)?7$;?1/ ?iY<U??ﮎ??>#LnF? mF_9_˴@<?1MH?ʧ?f)?b@?K#?vy^?;?>Zۨ)?&M5 꿣7Pd?b@?K#?vy^?1MH?ʧ?f)? mF_9_˴@<;???> K€?4tOtG poW?b@?K#?vy^?1MH?ʧ?f)?&M5 꿣7Pd;???>/Rd?6DqaF(IjջB?b@?K#?vy^?1MH?ʧ?f)?4tOtG poW;???> ^Ӏ?+]_W/ ?1MH?ʧ?f)?7$;?1/ ?iY<U?6DqaF(IjջB?b@?K#?vy^??;?>ʮ_?6a̿*4L?1MH?ʧ?f)?7$;?1/ ?iY<U?+]_W/ ???>xUzG?r+ԂVMU7NP|?1MH?ʧ?f)?7$;?1/ ?iY<U?6a̿*4L???>tJ?싾)m-?+.Ke_ ?1MH?ʧ?f)?7$;?1/ ?iY<U?r+ԂVMU7NP|???>ҐMBѼ?싀t]f5 (Ro#?1MH?ʧ?f)?7$;?1/ ?iY<U?싾)m-?+.Ke_ ???>1&?5H[ 񌶥1m?1MH?ʧ?f)?7$;?1/ ?iY<U?싀t]f5 (Ro#???>Ǐtn?ݰC{Q]p2%?1MH?ʧ?f)?7$;?1/ ?iY<U?5H[ 񌶥1m???>{p ~?ySS>gyA?ꃐ/?PJ?v~{0?1MH?ʧ?f)?7$;?1/ ?iY<U?ݰC{Q]p2%?KeFq???>I,f?ySS>gyA?ꃐ/?PJ?v~{0?7$;?1/ ?iY<U?1Nſ3? ??KeFq?>.npydl-0.6.0/pydl/pydlutils/tests/t/polygon_one_cap.fits0000664000111400017500000002640012775255301023746 0ustar weaverweaver00000000000000SIMPLE = T /Primary Header created by MWRFITS v1.11 BITPIX = 16 / NAXIS = 0 / EXTEND = T /Extensions may be present DATE = 'Sat Jun 11 12:40:21 2016' /Time of creation of polygon fits 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polygons pixelization 6s snapped balkanized polygon 0 ( 1 caps, 0 weight, 6.283185307179586 str): 0.0436193873653360 0.9990482215818578 0.0000000000000000 -1 polygon 1 ( 2 caps, 0 weight, 6.108652381980153 str): 0.0436193873653360 0.9990482215818578 0.0000000000000000 1 -0.0436193873653360 0.9990482215818578 0.0000000000000000 1 polygon 2 ( 3 caps, 0 weight, 0.083459952963577 str): 0.0436193873653360 0.9990482215818578 0.0000000000000000 1 -0.0436193873653360 0.9990482215818578 0.0000000000000000 -1 0.0000000000000000 0.0000000000000000 1.0000000000000000 -1.043619387365336 polygon 3 ( 3 caps, 0 weight, 0.083459952963577 str): 0.0436193873653360 0.9990482215818578 0.0000000000000000 1 -0.0436193873653360 0.9990482215818578 0.0000000000000000 -1 0.0000000000000000 0.0000000000000000 1.0000000000000000 0.956380612634664 pydl-0.6.0/pydl/pydlutils/tests/t/yanny_data.json0000664000111400017500000000655012775255301022732 0ustar weaverweaver00000000000000{ "tempfile1.par": { "pairs": { "keyword1": "value1", "keyword2": "value2" }, "structures": { "MYSTRUCT0": { "dtype": [ ["ra", "f8"], ["dec", "f8"], ["mag", "f4", [5]], ["flags", "i4"], ["new_flag", "|S5"] ], "size": 4, "columns": { "ra": "double", "dec": "double", "mag": "float[5]", "flags": "int", "new_flag": "BOOLEAN" }, "data": { "ra": [10.0, 20.5, 30.75, 40.55], "dec": [-5.1234, -10.74832, 67.994523, 11.437281], "mag": [ [0.0, 1.0, 2.0, 3.0, 4.0], [5.1, 6.2, 7.3, 8.4, 9.5], [22.123, 23.95, 22.6657, 21.0286, 22.9876], [13.54126, 15.37456, 14.52647, 12.648640, 12.0218] ], "flags": [4, 16, 64, 264], "new_flag": ["FALSE", "TRUE", "TRUE", "FALSE"] } }, "MY_STATUS": { "dtype": [ ["timestamp", "i8"], ["state", "S10"] ], "size": 4, "columns": { "timestamp": "long", "state": "STATUS" }, "data": { "timestamp": [1382384327,1382384527,1382384727,1382384927], "state": ["SUCCESS","SUCCESS","FAILURE","INCOMPLETE"] } } }, "enums": { "new_flag": ["BOOLEAN", ["FALSE", "TRUE"]], "state": ["STATUS", ["FAILURE", "INCOMPLETE", "SUCCESS"]] } }, "test.par": { "pairs": { "mjd": "54579", "alpha": "beta gamma delta", "semicolon": "This pair contains a semicolon;" }, "structures": { "MYSTRUCT": { "dtype": [ ["mag", "23 in r-band)" maskbits TARGET 8 GALAXY_BRIGHT_CORE "Galaxy targets who fail all the surface brightness selection limits but have r-band fiber magnitudes brighter than 19" maskbits TARGET 9 ROSAT_A "ROSAT All-Sky Survey match, also a radio source" maskbits TARGET 10 ROSAT_B "ROSAT All-Sky Survey match, have SDSS colors of AGNs or quasars" maskbits TARGET 11 ROSAT_C "ROSAT All-Sky Survey match, fall in a broad intermediate category that includes stars that are bright, moderately blue, or both" maskbits TARGET 12 ROSAT_D "ROSAT All-Sky Survey match, are otherwise bright enough for SDSS spectroscopy" maskbits TARGET 13 STAR_BHB "blue horizontal-branch stars" maskbits TARGET 14 STAR_CARBON "dwarf and giant carbon stars" maskbits TARGET 15 STAR_BROWN_DWARF "brown dwarfs (note this sample is tiled)" maskbits TARGET 16 STAR_SUB_DWARF "low-luminosity subdwarfs" maskbits TARGET 17 STAR_CATY_VAR "cataclysmic variables" maskbits TARGET 18 STAR_RED_DWARF "red dwarfs" maskbits TARGET 19 STAR_WHITE_DWARF "hot white dwarfs" maskbits TARGET 20 SERENDIP_BLUE "lying outside the stellar locus in color space" maskbits TARGET 21 SERENDIP_FIRST "coincident with FIRST sources but fainter than the equivalent in quasar target selection (also includes non-PSF sources" maskbits TARGET 22 SERENDIP_RED "lying outside the stellar locus in color space" maskbits TARGET 23 SERENDIP_DISTANT "lying outside the stellar locus in color space" maskbits TARGET 24 SERENDIP_MANUAL "manual serendipity flag" maskbits TARGET 25 QSO_MAG_OUTLIER "Stellar outlier; too faint or too bright to be targeted" maskbits TARGET 26 GALAXY_RED_II "Luminous Red Galaxy target (Cut II criteria)" maskbits TARGET 27 ROSAT_E "ROSAT All-Sky Survey match, but too faint or too bright for SDSS spectroscopy" maskbits TARGET 28 STAR_PN "central stars of planetary nebulae" maskbits TARGET 29 QSO_REJECT "Object in explicitly excluded region of color space, therefore not targeted at QSO" maskbits TARGET 31 SOUTHERN_SURVEY "Set in primtarget if this is a special program target" maskalias TARGET PRIMTARGET "PRIMTARGET is a synonym for TARGET." maskalias TARGET LEGACY_TARGET1 "LEGACY_TARGET1 is a synonym for TARGET." # #------------------------------------------------------------------------------ masktype TTARGET 32 "Secondary target mask bits in SDSS-I, -II (for LEGACY_TARGET2, SPECIAL_TARGET2 or SECTARGET)." maskbits TTARGET 0 LIGHT_TRAP "hole drilled for bright star, to avoid scattered light" maskbits TTARGET 1 REDDEN_STD "reddening standard star" maskbits TTARGET 2 TEST_TARGET "a test target" maskbits TTARGET 3 QA "quality assurance target" maskbits TTARGET 4 SKY "sky target" maskbits TTARGET 5 SPECTROPHOTO_STD "spectrophotometry standard (typically an F-star)" maskbits TTARGET 6 GUIDE_STAR "guide star hole" maskbits TTARGET 7 BUNDLE_HOLE "fiber bundle hole" maskbits TTARGET 8 QUALITY_HOLE "hole drilled for plate shop quality measurements" maskbits TTARGET 9 HOT_STD "hot standard star" maskbits TTARGET 31 SOUTHERN_SURVEY "a segue or southern survey target" maskalias TTARGET SECTARGET "SECTARGET is identical to TTARGET." maskalias TTARGET LEGACY_TARGET2 "LEGACY_TARGET2 is identical to TTARGET." maskalias TTARGET SPECIAL_TARGET2 "SPECIAL_TARGET2 is identical to TTARGET." # #------------------------------------------------------------------------------ masktype ZWARNING 32 "Warnings for SDSS spectra." maskbits ZWARNING 0 SKY "sky fiber" maskbits ZWARNING 1 LITTLE_COVERAGE "too little wavelength coverage (WCOVERAGE < 0.18)" maskbits ZWARNING 2 SMALL_DELTA_CHI2 "chi-squared of best fit is too close to that of second best (<0.01 in reduced chi-sqaured)" maskbits ZWARNING 3 NEGATIVE_MODEL "synthetic spectrum is negative (only set for stars and QSOs)" maskbits ZWARNING 4 MANY_OUTLIERS "fraction of points more than 5 sigma away from best model is too large (>0.05)" maskbits ZWARNING 5 Z_FITLIMIT "chi-squared minimum at edge of the redshift fitting range (Z_ERR set to -1)" maskbits ZWARNING 6 NEGATIVE_EMISSION "a QSO line exhibits negative emission, triggered only in QSO spectra, if C_IV, C_III, Mg_II, H_beta, or H_alpha has LINEAREA + 3 * LINEAREA_ERR < 0" maskbits ZWARNING 7 UNPLUGGED "the fiber was unplugged, so no spectrum obtained" maskbits ZWARNING 8 BAD_TARGET "catastrophically bad targeting data (e.g. ASTROMBAD in CALIB_STATUS)" maskbits ZWARNING 9 NODATA "No data for this fiber, e.g. because spectrograph was broken during this exposure (ivar=0 for all pixels)" # #------------------------------------------------------------------------------ masktype FLUXMATCH_STATUS 16 "Flags from flux-based matching to SDSS photometry" maskbits FLUXMATCH_STATUS 0 ORIGINAL_FLUXMATCH "used the original positional match (which exists)" maskbits FLUXMATCH_STATUS 1 FIBER_FLUXMATCH "flagged due to fiberflux/aperflux issue" maskbits FLUXMATCH_STATUS 2 NONMATCH_FLUXMATCH "flagged due to non-match" maskbits FLUXMATCH_STATUS 3 NOPARENT_FLUXMATCH "no overlapping parent in primary field" maskbits FLUXMATCH_STATUS 4 PARENT_FLUXMATCH "overlapping parent has no children, so used it" maskbits FLUXMATCH_STATUS 5 BRIGHTEST_FLUXMATCH "picked the brightest child" # #------------------------------------------------------------------------------ #----------- boss target selection flags masktype BOSS_TARGET1 64 "BOSS survey primary target selection flags" # galaxies maskbits BOSS_TARGET1 0 GAL_LOZ "low-z lrgs" maskbits BOSS_TARGET1 1 GAL_CMASS "dperp > 0.55, color-mag cut " maskbits BOSS_TARGET1 2 GAL_CMASS_COMM "dperp > 0.55, commissioning color-mag cut" maskbits BOSS_TARGET1 3 GAL_CMASS_SPARSE "GAL_CMASS_COMM & (!GAL_CMASS) & (i < 19.9) sparsely sampled" maskbits BOSS_TARGET1 6 SDSS_KNOWN "Matches a known SDSS spectra" maskbits BOSS_TARGET1 7 GAL_CMASS_ALL "GAL_CMASS and the entire sparsely sampled region" maskbits BOSS_TARGET1 8 GAL_IFIBER2_FAINT "ifiber2 > 21.5, extinction corrected. Used after Nov 2010" # galaxies deprecated #maskbits BOSS_TARGET1 3 GAL_GRRED "red in g-r" #maskbits BOSS_TARGET1 4 GAL_TRIANGLE "GAL_HIZ and !GAL_CMASS" # part of galaxies deprecated, but some spectra actually have this bit set. maskbits BOSS_TARGET1 5 GAL_LODPERP_DEPRECATED "(DEPRECATED) Same as hiz but between dperp00 and dperp0" # qsos maskbits BOSS_TARGET1 10 QSO_CORE "restrictive qso selection: commissioning only" maskbits BOSS_TARGET1 11 QSO_BONUS "permissive qso selection: commissioning only" maskbits BOSS_TARGET1 12 QSO_KNOWN_MIDZ "known qso between [2.2,9.99]" maskbits BOSS_TARGET1 13 QSO_KNOWN_LOHIZ "known qso outside of miz range. never target" maskbits BOSS_TARGET1 14 QSO_NN "Neural Net that match to sweeps/pass cuts" maskbits BOSS_TARGET1 15 QSO_UKIDSS "UKIDSS stars that match sweeps/pass flag cuts" maskbits BOSS_TARGET1 16 QSO_KDE_COADD "kde targets from the stripe82 coadd " maskbits BOSS_TARGET1 17 QSO_LIKE "likelihood method" maskbits BOSS_TARGET1 18 QSO_FIRST_BOSS "FIRST radio match" maskbits BOSS_TARGET1 19 QSO_KDE "selected by kde+chi2" maskbits BOSS_TARGET1 40 QSO_CORE_MAIN "Main survey core sample" maskbits BOSS_TARGET1 41 QSO_BONUS_MAIN "Main survey bonus sample" maskbits BOSS_TARGET1 42 QSO_CORE_ED "Extreme Deconvolution in Core" maskbits BOSS_TARGET1 43 QSO_CORE_LIKE "Likelihood that make it into core" maskbits BOSS_TARGET1 44 QSO_KNOWN_SUPPZ "known qso between [1.8,2.15]" # standards maskbits BOSS_TARGET1 20 STD_FSTAR "standard f-stars" maskbits BOSS_TARGET1 21 STD_WD "white dwarfs" maskbits BOSS_TARGET1 22 STD_QSO "qso" # template stars maskbits BOSS_TARGET1 32 TEMPLATE_GAL_PHOTO "galaxy templates " maskbits BOSS_TARGET1 33 TEMPLATE_QSO_SDSS1 "QSO templates " maskbits BOSS_TARGET1 34 TEMPLATE_STAR_PHOTO "stellar templates " maskbits BOSS_TARGET1 35 TEMPLATE_STAR_SPECTRO "stellar templates (spectroscopically known)" # #------------------------------------------------------------------------------ #------------ ancillary target selection flags masktype ANCILLARY_TARGET1 64 "BOSS survey target flags for ancillary programs" maskbits ANCILLARY_TARGET1 0 AMC "defined in blake_boss_v2.descr" maskbits ANCILLARY_TARGET1 1 FLARE1 "defined in blake_boss_v2.descr" maskbits ANCILLARY_TARGET1 2 FLARE2 "defined in blake_boss_v2.descr" maskbits ANCILLARY_TARGET1 3 HPM "defined in blake_boss_v2.descr" maskbits ANCILLARY_TARGET1 4 LOW_MET "defined in blake_boss_v2.descr" maskbits ANCILLARY_TARGET1 5 VARS "defined in blake_boss_v2.descr" maskbits ANCILLARY_TARGET1 6 BLAZGVAR "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 7 BLAZR "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 8 BLAZXR "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 9 BLAZXRSAM "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 10 BLAZXRVAR "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 11 XMMBRIGHT "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 12 XMMGRIZ "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 13 XMMHR "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 14 XMMRED "defined in brandtxmm-andersonblazar-merged.descr " maskbits ANCILLARY_TARGET1 15 FBQSBAL "defined in master-BAL-targets.descr" maskbits ANCILLARY_TARGET1 16 LBQSBAL "defined in master-BAL-targets.descr" maskbits ANCILLARY_TARGET1 17 ODDBAL "defined in master-BAL-targets.descr" maskbits ANCILLARY_TARGET1 18 OTBAL "defined in master-BAL-targets.descr" maskbits ANCILLARY_TARGET1 19 PREVBAL "defined in master-BAL-targets.descr" maskbits ANCILLARY_TARGET1 20 VARBAL "defined in master-BAL-targets.descr" maskbits ANCILLARY_TARGET1 21 BRIGHTGAL "defined in bright_gal_v3.descr" maskbits ANCILLARY_TARGET1 22 QSO_AAL "defined in qsoals_v2.descr " maskbits ANCILLARY_TARGET1 23 QSO_AALS "defined in qsoals_v2.descr " maskbits ANCILLARY_TARGET1 24 QSO_IAL "defined in qsoals_v2.descr " maskbits ANCILLARY_TARGET1 25 QSO_RADIO "defined in qsoals_v2.descr " maskbits ANCILLARY_TARGET1 26 QSO_RADIO_AAL "defined in qsoals_v2.descr " maskbits ANCILLARY_TARGET1 27 QSO_RADIO_IAL "defined in qsoals_v2.descr " maskbits ANCILLARY_TARGET1 28 QSO_NOAALS "defined in qsoals_v2.descr " maskbits ANCILLARY_TARGET1 29 QSO_GRI "defined in sdss3_fan.descr " maskbits ANCILLARY_TARGET1 30 QSO_HIZ "defined in sdss3_fan.descr " maskbits ANCILLARY_TARGET1 31 QSO_RIZ "defined in sdss3_fan.descr " maskbits ANCILLARY_TARGET1 32 RQSS_SF "defined in rqss090630.descr" maskbits ANCILLARY_TARGET1 33 RQSS_SFC "defined in rqss090630.descr" maskbits ANCILLARY_TARGET1 34 RQSS_STM "defined in rqss090630.descr" maskbits ANCILLARY_TARGET1 35 RQSS_STMC "defined in rqss090630.descr" maskbits ANCILLARY_TARGET1 36 SN_GAL1 "defined in ancillary_supernova_hosts_v5.descr" maskbits ANCILLARY_TARGET1 37 SN_GAL2 "defined in ancillary_supernova_hosts_v5.descr" maskbits ANCILLARY_TARGET1 38 SN_GAL3 "defined in ancillary_supernova_hosts_v5.descr" maskbits ANCILLARY_TARGET1 39 SN_LOC "defined in ancillary_supernova_hosts_v5.descr" maskbits ANCILLARY_TARGET1 40 SPEC_SN "defined in ancillary_supernova_hosts_v5.descr" maskbits ANCILLARY_TARGET1 41 SPOKE "defined in BOSS_slowpokes_v2.descr" maskbits ANCILLARY_TARGET1 42 WHITEDWARF_NEW "defined in WDv5_eisenste_fixed.descr" maskbits ANCILLARY_TARGET1 43 WHITEDWARF_SDSS "defined in WDv5_eisenste_fixed.descr" maskbits ANCILLARY_TARGET1 44 BRIGHTERL "defined in sd3targets_final.descr" maskbits ANCILLARY_TARGET1 45 BRIGHTERM "defined in sd3targets_final.descr" maskbits ANCILLARY_TARGET1 46 FAINTERL "defined in sd3targets_final.descr" maskbits ANCILLARY_TARGET1 47 FAINTERM "defined in sd3targets_final.descr" maskbits ANCILLARY_TARGET1 48 RED_KG "defined in redkg.descr" maskbits ANCILLARY_TARGET1 49 RVTEST "defined in redkg.descr" maskbits ANCILLARY_TARGET1 50 BLAZGRFLAT "defined in anderson-blazar.par" maskbits ANCILLARY_TARGET1 51 BLAZGRQSO "defined in anderson-blazar.par " maskbits ANCILLARY_TARGET1 52 BLAZGX "defined in anderson-blazar.par" maskbits ANCILLARY_TARGET1 53 BLAZGXQSO "defined in anderson-blazar.par" maskbits ANCILLARY_TARGET1 54 BLAZGXR "defined in anderson-blazar.par" #maskbits ANCILLARY_TARGET1 55 BLAZXR "defined in anderson-blazar.par" maskbits ANCILLARY_TARGET1 56 BLUE_RADIO "defined in tremonti-blue-radio.fits.gz" maskbits ANCILLARY_TARGET1 57 CHANDRAV1 "defined in haggard-sf-accrete.fits" maskbits ANCILLARY_TARGET1 58 CXOBRIGHT "defined in brandt-xray.par" maskbits ANCILLARY_TARGET1 59 CXOGRIZ "defined in brandt-xray.par" maskbits ANCILLARY_TARGET1 60 CXORED "defined in brandt-xray.par" maskbits ANCILLARY_TARGET1 61 ELG "defined in kneib-cfht-elg.fits" maskbits ANCILLARY_TARGET1 62 GAL_NEAR_QSO "defined in weiner-qso-sightline.fits" maskbits ANCILLARY_TARGET1 63 MTEMP "defined in blake-transient-v3.fits" pydl-0.6.0/pydl/pydlutils/tests/t/polygon_no_id.fits0000664000111400017500000002640012775255301023432 0ustar weaverweaver00000000000000SIMPLE = T /Primary Header created by MWRFITS v1.11 BITPIX = 16 / NAXIS = 0 / EXTEND = T /Extensions may be present DATE = 'Fri Jun 10 21:24:31 2016' /Time of creation of polygon fits file IDLUTILS= 'v5_5_5 ' /Version of idlutils used END XTENSION= 'BINTABLE' /Binary table written by MWRFITS v1.11 BITPIX = 8 /Required value NAXIS = 2 /Required value NAXIS1 = 156 /Number of bytes per row NAXIS2 = 1 /Number of rows PCOUNT = 0 /Normally 0 (no varying arrays) GCOUNT = 1 /Required value TFIELDS = 7 /Number of columns in table COMMENT COMMENT *** End of mandatory fields *** COMMENT COMMENT COMMENT *** Column names *** COMMENT TTYPE1 = 'XCAPS ' / TTYPE2 = 'CMCAPS ' / TTYPE3 = 'NCAPS ' / TTYPE4 = 'WEIGHT ' / TTYPE5 = 'PIXEL ' / TTYPE6 = 'STR ' / TTYPE7 = 'USE_CAPS' / COMMENT COMMENT *** Column formats *** COMMENT TFORM1 = '12D ' / TFORM2 = '4D ' / TFORM3 = 'J ' 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""" def setup(self): pass def teardown(self): pass def test_spheregroup(self): test_data_file = get_pkg_data_filename('t/spheregroup_data.txt') test_data = np.loadtxt(test_data_file, dtype='d', delimiter=',') # np.random.seed(137) # Ngroup = 3 # N = 50 # spread = 0.05 linklength = 3.0 # degrees # x0 = np.concatenate( ( np.random.normal(loc=1,scale=spread,size=(N,)), # np.random.normal(loc=-1,scale=spread,size=(N,)), # np.random.normal(loc=1,scale=spread,size=(N,)), # )).reshape((N*Ngroup,)) # y0 = np.concatenate( ( np.random.normal(loc=1,scale=spread,size=(N,)), # np.random.normal(loc=-1,scale=spread,size=(N,)), # np.random.normal(loc=-1,scale=spread,size=(N,)), # )).reshape((N*Ngroup,)) # z0 = np.concatenate( ( np.random.normal(loc=1,scale=spread,size=(N,)), # np.random.normal(loc=-1,scale=spread,size=(N,)), # np.random.normal(loc=0,scale=spread,size=(N,)), # )).reshape((N*Ngroup,)) # foo = np.arange(N*Ngroup) # np.random.shuffle(foo) # x = x0[foo] # y = y0[foo] # z = z0[foo] # r = np.sqrt(x**2 + y**2 + z**2) # theta = np.degrees(np.arccos(z/r)) # phi = np.degrees(np.arctan2(y,x)) # ra = np.where(phi < 0, phi + 360.0,phi) # dec = 90.0 - theta # group = spheregroup(ra,dec,linklength) # # Reproduce IDL results # ra = test_data[0, :] dec = test_data[1, :] expected_ingroup = test_data[2, :].astype(np.int64) expected_multgroup = test_data[3, :].astype(np.int64) expected_firstgroup = test_data[4, :].astype(np.int64) expected_nextgroup = test_data[5, :].astype(np.int64) group = spheregroup(ra, dec, linklength) assert (group[0] == expected_ingroup).all() assert (group[1] == expected_multgroup).all() assert (group[2] == expected_firstgroup).all() assert (group[3] == expected_nextgroup).all() # # Exceptions # with raises(PydlutilsException): group = spheregroup(np.array([137.0]), np.array([55.0]), linklength) # # warnings # with catch_warnings(PydlutilsUserWarning) as w: group = spheregroup(ra, dec, linklength, chunksize=linklength) # w = recwarn.pop(PydlutilsUserWarning) assert "chunksize changed to" in str(w[0].message) def test_spherematch(self): i1_should_be = np.array([17, 0, 2, 16, 12, 13, 1, 5, 15, 7, 19, 8, 11, 10, 14, 18, 3, 9, 6, 4]) i2_should_be = np.array([2, 0, 17, 3, 16, 15, 14, 5, 6, 10, 8, 19, 18, 4, 9, 7, 11, 12, 1, 13]) np.random.seed(137) searchrad = 3.0/3600.0 n = 20 ra1 = 360.0*np.random.random((n,)) dec1 = 90.0 - np.rad2deg(np.arccos(2.0*np.random.random((n,)) - 1.0)) ra2 = ra1 + np.random.normal(0, 1.0/3600.0) dec2 = dec1 + np.random.normal(0, 1.0/3600.0) foo = np.arange(n) np.random.shuffle(foo) i1, i2, d12 = spherematch(ra1, dec1, ra2[foo], dec2[foo], searchrad, maxmatch=0) assert (i1 == i1_should_be).all() assert (i2 == i2_should_be).all() pydl-0.6.0/pydl/pydlutils/tests/test_misc.py0000664000111400017500000001715013040014667022001 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- from os import remove import numpy as np import tempfile from astropy.tests.helper import raises from .. import PydlutilsException from ..misc import djs_laxisgen, djs_laxisnum, hogg_iau_name, struct_print class TestMisc(object): """Test the functions in pydl.pydlutils.misc. """ def setup(self): pass def teardown(self): pass def test_djs_laxisgen(self): # # 1d # assert (np.arange(4, dtype='i4') == djs_laxisgen((4,))).all() # # 2d # l = np.array([[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], dtype='i4') assert (l == djs_laxisgen((4, 4))).all() assert (l.T == djs_laxisgen((4, 4), iaxis=1)).all() with raises(ValueError): foo = djs_laxisgen((4, 4), iaxis=2) # # 3d # l = np.zeros((4, 4, 4), dtype='i4') l[1, :, :] = 1 l[2, :, :] = 2 l[3, :, :] = 3 assert (l == djs_laxisgen((4, 4, 4))).all() assert (l.swapaxes(0, 1) == djs_laxisgen((4, 4, 4), iaxis=1)).all() assert (l.swapaxes(0, 2) == djs_laxisgen((4, 4, 4), iaxis=2)).all() with raises(ValueError): foo = djs_laxisgen((4, 4, 4), iaxis=3) # # More d # with raises(ValueError): foo = djs_laxisgen((4, 4, 4, 4)) def test_djs_laxisnum(self): # # 1d # assert (np.zeros((4,), dtype='i4') == djs_laxisnum((4,))).all() # # 2d # l = np.array([[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], dtype='i4') assert (l == djs_laxisnum((4, 4))).all() assert (l.T == djs_laxisnum((4, 4), iaxis=1)).all() with raises(ValueError): foo = djs_laxisnum((4, 4), iaxis=2) # # 3d # l = np.zeros((4, 4, 4), dtype='i4') l[1, :, :] = 1 l[2, :, :] = 2 l[3, :, :] = 3 assert (l == djs_laxisnum((4, 4, 4))).all() assert (l.swapaxes(0, 1) == djs_laxisnum((4, 4, 4), iaxis=1)).all() assert (l.swapaxes(0, 2) == djs_laxisnum((4, 4, 4), iaxis=2)).all() with raises(ValueError): foo = djs_laxisnum((4, 4, 4), iaxis=3) # # More d # with raises(ValueError): foo = djs_laxisnum((4, 4, 4, 4)) def test_hogg_iau_name(self): assert (hogg_iau_name(354.120375, -0.544777778) == 'SDSS J233628.89-003241.2') assert (hogg_iau_name(354.120375, -0.544777778, prefix='2MASS') == '2MASS J233628.89-003241.2') assert (hogg_iau_name(354.120375, -0.544777778, prefix='') == 'J233628.89-003241.2') assert (hogg_iau_name(354.120375, -0.544777778, precision=0) == 'SDSS J233628.8-003241') assert (hogg_iau_name(354.120375, -0.544777778, precision=2) == 'SDSS J233628.890-003241.20') ra = np.array([354.120375, 7.89439, 36.31915, 110.44730]) dec = np.array([-0.544777778, -0.35157, 0.47505, 39.35352]) names = hogg_iau_name(ra, dec) assert tuple(names) == ('SDSS J233628.89-003241.2', 'SDSS J003134.65-002105.6', 'SDSS J022516.59+002830.1', 'SDSS J072147.35+392112.6') def test_struct_print(self): slist = np.zeros((5,), dtype=[('a', 'c16'), ('b', np.bool)]) with raises(PydlutilsException): lines, css = struct_print(slist, silent=True) slist = np.array([(1, 2.34, 'five'), (2, 3.456, 'seven'), (3, -4.5678, 'nine')], dtype=[('a', 'i4'), ('bb', 'f4'), ('ccc', 'S5')]) lines, css = struct_print(slist, silent=True) assert lines[0] == 'a bb ccc ' assert lines[1] == '- ------------ -----' assert lines[2] == '1 2.34 five ' assert lines[3] == '2 3.456 seven' assert lines[4] == '3 -4.5678 nine ' assert len(css) == 0 lines, css = struct_print(slist, silent=True, alias={'ccc': 'c'}) assert lines[0] == 'a bb c ' assert lines[1] == '- ------------ -----' assert lines[2] == '1 2.34 five ' assert lines[3] == '2 3.456 seven' assert lines[4] == '3 -4.5678 nine ' assert len(css) == 0 lines, css = struct_print(slist, silent=True, formatcodes={'a': '{0:02d}'}) assert lines[0] == 'a bb ccc ' assert lines[1] == '-- ------------ -----' assert lines[2] == '01 2.34 five ' assert lines[3] == '02 3.456 seven' assert lines[4] == '03 -4.5678 nine ' assert len(css) == 0 lines, css = struct_print(slist, silent=True, fdigit=3) assert lines[0] == 'a bb ccc ' assert lines[1] == '- ---------- -----' assert lines[2] == '1 2.34 five ' assert lines[3] == '2 3.46 seven' assert lines[4] == '3 -4.57 nine ' assert len(css) == 0 lines, css = struct_print(slist, silent=True, html=True) assert lines[0] == '' assert lines[1] == '' assert lines[2] == '' assert lines[3] == '' assert lines[4] == '' assert lines[5] == '
abbccc
1 2.34five
2 3.456seven
3 -4.5678nine
' assert css[0] == '' slist = np.array([(1, 2.34, 'five'), (2, 3.456, 'seven'), (3, -4.5678, 'nine')], dtype=[('a', 'i4'), ('bb', 'f8'), ('ccc', 'S5')]) lines, css = struct_print(slist, silent=True, ddigit=3) assert lines[0] == 'a bb ccc ' assert lines[1] == '- ---------- -----' assert lines[2] == '1 2.34 five ' assert lines[3] == '2 3.46 seven' assert lines[4] == '3 -4.57 nine ' assert len(css) == 0 with tempfile.NamedTemporaryFile(delete=False) as spf1: spf1_name = spf1.name lines, css = struct_print(slist, silent=True, filename=spf1_name) with open(spf1_name, 'rb') as f: data = f.read().decode('utf-8') assert "\n".join(lines)+"\n" == data remove(spf1_name) with tempfile.TemporaryFile() as spf2: lines, css = struct_print(slist, silent=True, filename=spf2) spf2.seek(0) data = spf2.read().decode('utf-8') assert "\n".join(lines)+"\n" == data pydl-0.6.0/pydl/pydlutils/tests/test_trace.py0000664000111400017500000002147713040014667022153 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.io import fits from astropy.tests.helper import raises from astropy.utils.data import get_pkg_data_filename from ..trace import (fchebyshev, fchebyshev_split, fpoly, func_fit, TraceSet, traceset2xy, xy2traceset) from .. import PydlutilsException class TestTrace(object): """Test the functions in pydl.pydlutils.trace. """ def setup(self): # extracted from spFrame-b1-00057618.fits self.sdss = fits.open(get_pkg_data_filename('t/sdss_traceset.fits')) # extracted from spFrame-r1-00180406.fits self.boss = fits.open(get_pkg_data_filename('t/boss_traceset.fits')) return def teardown(self): self.sdss.close() self.boss.close() return def test_fchebyshev(self): x = np.array([-1, -0.5, 0, 0.5, 1], dtype='d') # # Test order # with raises(ValueError): f = fchebyshev(x, 0) # # m = 1 # f = fchebyshev(x, 1) assert (f == np.ones((1, x.size), dtype='d')).all() # # m = 2 # f = fchebyshev(x, 2) foo = np.ones((2, x.size), dtype='d') foo[1, :] = x assert np.allclose(f, foo) # # m = 3 # f = fchebyshev(x, 3) foo = np.ones((3, x.size), dtype='d') foo[1, :] = x foo[2, :] = (2.0*x**2 - 1.0) assert np.allclose(f, foo) # # m = 4 # f = fchebyshev(x, 4) foo = np.ones((4, x.size), dtype='d') foo[1, :] = x foo[2, :] = (2.0*x**2 - 1.0) foo[3, :] = (4.0*x**3 - 3.0*x) assert np.allclose(f, foo) # # random float # f = fchebyshev(2.88, 3) assert np.allclose(f, np.array([[1.00], [2.88], [15.5888]])) def test_fchebyshev_split(self): x = np.array([-1, -0.5, 0, 0.5, 1], dtype='d') # # Test order # with raises(ValueError): f = fchebyshev_split(x, 0) with raises(ValueError): f = fchebyshev_split(x, 1) # # m = 2 # f = fchebyshev_split(x, 2) foo = np.ones((2, x.size), dtype='d') foo[0, :] = (x >= 0).astype(x.dtype) assert np.allclose(f, foo) # # m = 3 # f = fchebyshev_split(x, 3) foo = np.ones((3, x.size), dtype='d') foo[0, :] = (x >= 0).astype(x.dtype) foo[2, :] = x assert np.allclose(f, foo) # # m = 4 # f = fchebyshev_split(x, 4) foo = np.ones((4, x.size), dtype='d') foo[0, :] = (x >= 0).astype(x.dtype) foo[2, :] = x foo[3, :] = (2.0*x**2 - 1.0) assert np.allclose(f, foo) # # m = 5 # f = fchebyshev_split(x, 5) foo = np.ones((5, x.size), dtype='d') foo[0, :] = (x >= 0).astype(x.dtype) foo[2, :] = x foo[3, :] = (2.0*x**2 - 1.0) foo[4, :] = (4.0*x**3 - 3.0*x) assert np.allclose(f, foo) # # random float # f = fchebyshev_split(2.88, 3) assert np.allclose(f, np.array([[1.00], [1.00], [2.88]])) def test_fpoly(self): x = np.array([-1, -0.5, 0, 0.5, 1], dtype='d') # # Test order # with raises(ValueError): f = fpoly(x, 0) # # m = 1 # f = fpoly(x, 1) assert (f == np.ones((1, x.size), dtype='d')).all() # # m = 2 # f = fpoly(x, 2) foo = np.ones((2, x.size), dtype='d') foo[1, :] = x assert np.allclose(f, foo) # # m = 3 # f = fpoly(x, 3) foo = np.ones((3, x.size), dtype='d') foo[1, :] = x foo[2, :] = x**2 assert np.allclose(f, foo) # # m = 4 # f = fpoly(x, 4) foo = np.ones((4, x.size), dtype='d') foo[1, :] = x foo[2, :] = x**2 foo[3, :] = x**3 assert np.allclose(f, foo) # # random float # f = fpoly(2.88, 3) assert np.allclose(f, np.array([[1.00], [2.88], [8.29440]])) def test_func_fit(self): np.random.seed(137) x = np.linspace(-5, 5, 50) y = x**2 + 2*x + 1 + 0.05*np.random.randn(50) # # Bad inputs # with raises(ValueError): foo = func_fit(x, np.array([1, 2, 3]), 3) with raises(ValueError): foo = func_fit(x, y, 3, invvar=np.array([0, 0, 0])) with raises(ValueError): foo = func_fit(x, y, 3, inputfunc=np.array([1.0, 1.0, 1.0])) with raises(KeyError): foo = func_fit(x, y, 3, function_name='npoly') # # No good points # invvar = np.zeros(x.shape, dtype=x.dtype) res, yfit = func_fit(x, y, 3, invvar=invvar) assert (res == np.zeros((3,), dtype=x.dtype)).all() assert (yfit == 0*x).all() # # One good point # invvar[2] = 1.0 res, yfit = func_fit(x, y, 3, invvar=invvar) assert (invvar > 0).nonzero()[0][0] == 2 assert res[0] == y[2] assert (yfit == y[2]).all() # # Various points # invvar = 1.0/(np.random.random(x.shape)**2) assert (invvar < 2).any() invvar[invvar < 2] = 0 res, yfit = func_fit(x, y, 3, invvar=invvar, function_name='poly') # assert np.allclose(res,np.array([0.99665423, 1.9945388, 1.00172303])) assert np.allclose(res, np.array([0.99996197, 1.99340315, 1.00148004])) # # Fixed points # res, yfit = func_fit(x, y, 3, invvar=invvar, function_name='poly', ia=np.array([False, True, True]), inputans=np.array([1.0, 0, 0])) # assert np.allclose(res,np.array([1., 1.99454782, 1.00149949])) assert np.allclose(res, np.array([1., 1.99340359, 1.00147743])) res, yfit = func_fit(x, y, 3, invvar=invvar, function_name='poly', ia=np.array([False, True, False]), inputans=np.array([1.0, 0, 1.0])) # assert np.allclose(res,np.array([1., 1.99403239, 1.])) assert np.allclose(res, np.array([1., 1.99735654, 1.])) # # inputfunc # res, yfit = func_fit(x, y, 3, invvar=invvar, function_name='poly', inputfunc=np.ones(x.shape, dtype=x.dtype)) # assert np.allclose(res,np.array([0.99665423, 1.9945388, 1.00172303])) assert np.allclose(res, np.array([0.99996197, 1.99340315, 1.00148004])) # # Generate inputans # y = x**2 + 2*x + 0.05*np.random.randn(50) res, yfit = func_fit(x, y, 3, invvar=invvar, function_name='poly', ia=np.array([False, True, True])) assert np.allclose(res, np.array([0., 1.99994188, 0.99915111])) def test_traceset_sdss(self): tset = TraceSet(self.sdss[1].data) assert tset.func == 'legendre' assert tset.coeff.shape == (320, 5) assert not tset.has_jump assert tset.xRange == tset.xmax - tset.xmin assert tset.nx == int(tset.xmax - tset.xmin + 1) assert tset.xmid == 0.5 * (tset.xmin + tset.xmax) x, y = traceset2xy(tset) tset2 = xy2traceset(x, y, ncoeff=tset.ncoeff) assert tset2.xmin == tset.xmin assert tset2.xmax == tset.xmax assert tset2.func == tset.func assert np.allclose(tset2.coeff, tset.coeff) def test_traceset_boss(self): tset = TraceSet(self.boss[1].data) assert tset.func == 'legendre' assert tset.coeff.shape == (500, 6) assert tset.has_jump assert tset.xRange == tset.xmax - tset.xmin assert tset.nx == int(tset.xmax - tset.xmin + 1) assert tset.xmid == 0.5 * (tset.xmin + tset.xmax) x, y = traceset2xy(tset) tset2 = xy2traceset(x, y, ncoeff=tset.ncoeff, xjumplo=tset.xjumplo, xjumphi=tset.xjumphi, xjumpval=tset.xjumpval) assert tset2.xmin == tset.xmin assert tset2.xmax == tset.xmax assert tset2.func == tset.func assert np.allclose(tset2.coeff, tset.coeff) def test_traceset_keywords(self): tset = TraceSet(self.boss[1].data) x, y = traceset2xy(tset) iv = 2.0 * np.ones(x.shape, dtype=x.dtype) im = np.ones(x.shape, dtype=np.bool) im[1] = False tset2 = TraceSet(x, y, func='chebyshev', invvar=iv, maxiter=20, inmask=im) assert tset2.func == 'chebyshev' def test_traceset_bad(self): with raises(PydlutilsException): tset = TraceSet(1, 2, 3) pydl-0.6.0/pydl/pydlutils/tests/test_mangle.py0000664000111400017500000002730613040014667022315 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import raises from astropy.utils.data import get_pkg_data_filename from .. import PydlutilsException from .. import mangle as mng class TestMangle(object): """Test the functions in pydl.pydlutils.mangle. """ def setup(self): self.poly_fits = get_pkg_data_filename('t/polygon.fits') self.no_id_fits = get_pkg_data_filename('t/polygon_no_id.fits') self.one_cap_fits = get_pkg_data_filename('t/polygon_one_cap.fits') self.poly_ply = get_pkg_data_filename('t/polygon.ply') self.bad_ply = get_pkg_data_filename('t/median_data.txt') def teardown(self): pass def test_ManglePolygon(self): # # Zero caps # poly = mng.ManglePolygon() assert np.allclose(poly.str, 4.0*np.pi) assert poly.cmminf() is None assert not poly.gzeroar() assert np.allclose(poly.garea(), 4.0*np.pi) # # One cap. # x = np.array([[0.0, 0.0, 1.0]]) cm = np.array([0.5]) poly = mng.ManglePolygon(x=x, cm=cm) assert np.allclose(poly.garea(), np.pi) # # Bad inputs # with raises(ValueError): poly = mng.ManglePolygon(weight=1.0) # # Multiple caps # x = np.array([[0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) cm = np.array([1.0, 1.0, 1.0]) poly = mng.ManglePolygon(x=x, cm=cm, str=np.pi/2.0) assert poly.ncaps == 3 assert poly.weight == 1.0 assert poly.use_caps == (1 << 3) - 1 poly = mng.ManglePolygon(x=x, cm=cm, weight=0.5) assert poly.weight == 0.5 poly = mng.ManglePolygon(x=x, cm=cm, pixel=20) assert poly.pixel == 20 poly = mng.ManglePolygon(x=x, cm=cm, use_caps=3, str=np.pi/2.0) assert poly.use_caps == 3 poly2 = poly.copy() assert poly2.use_caps == poly.use_caps assert (poly2.cm == poly.cm).all() assert np.allclose(poly2.str, np.pi/2.0) x = np.array([[0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]) cm = np.array([1.0, 1.0]) poly = mng.ManglePolygon(x=x, cm=cm) poly2 = poly.add_caps(np.array([[0.0, 1.0, 0.0], ]), np.array([1.0, ])) assert poly2.ncaps == 3 assert poly2.use_caps == poly.use_caps assert poly2.str == 1.0 # dummy value! poly3 = poly.polyn(poly2, 2) assert poly3.ncaps == 3 assert poly3.use_caps == poly.use_caps assert np.allclose(poly3.x[2, :], np.array([0.0, 1.0, 0.0])) poly3 = poly.polyn(poly2, 2, complement=True) assert poly3.ncaps == 3 assert poly3.use_caps == poly.use_caps assert np.allclose(poly3.cm[2], -1.0) def test_angles_to_x(self): x = mng.angles_to_x(np.array([[0.0, 0.0], [90.0, 90.0], [0.0, 90.0]])) assert np.allclose(x, np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])) a = mng.angles_to_x(np.array([[0.0, 90.0], [90.0, 0.0], [0.0, 0.0]]), latitude=True) assert np.allclose(x, np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])) def test_cap_distance(self): x = np.array([0.0, 0.0, 1.0]) cm = 1.0 with raises(ValueError): d = mng.cap_distance(x, cm, np.array([[1.0, 2.0, 3.0, 4.0], ])) d = mng.cap_distance(x, cm, np.array([[0.0, 45.0], ])) assert np.allclose(d, np.array([45.0])) y = mng.angles_to_x(np.array([[0.0, 45.0], ]), latitude=True) d = mng.cap_distance(x, cm, y) assert np.allclose(d, np.array([45.0])) d = mng.cap_distance(x, cm, np.array([[0.0, -45.0], ])) assert np.allclose(d, np.array([-45.0])) d = mng.cap_distance(x, -1.0, np.array([[0.0, -45.0], ])) assert np.allclose(d, np.array([45.0])) def test_circle_cap(self): with raises(ValueError): x, cm = mng.circle_cap(90.0, np.array([[1.0, 2.0, 3.0, 4.0], ])) xin = np.array([[0.0, 0.0, 1.0], ]) x, cm = mng.circle_cap(90.0, xin) assert np.allclose(x, xin) assert np.allclose(cm, 1.0) radec = np.array([[0.0, 90.0], ]) x, cm = mng.circle_cap(90.0, radec) assert np.allclose(x, xin) assert np.allclose(cm, 1.0) x, cm = mng.circle_cap(np.float32(90.0), radec) assert np.allclose(x, xin) assert np.allclose(cm, 1.0) x, cm = mng.circle_cap(np.array([90.0, ]), radec) assert np.allclose(x, xin) assert np.allclose(cm, np.array([1.0, ])) with raises(ValueError): x, cm = mng.circle_cap(np.array([90.0, 90.0]), radec) def test_is_cap_used(self): assert mng.is_cap_used(1 << 2, 2) assert not mng.is_cap_used(1 << 2, 1) def test_is_in_cap(self): x = np.array([0.0, 0.0, 1.0]) cm = 1.0 d = mng.is_in_cap(x, cm, np.array([[0.0, 45.0], [0.0, -45.0]])) assert (d == np.array([True, False])).all() def test_is_in_polygon(self): x = np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]]) cm = np.array([1.0, 1.0, 1.0]) p = mng.ManglePolygon(x=x, cm=cm) d = mng.is_in_polygon(p, np.array([[45.0, 45.0], [135.0, -45.0]])) assert (d == np.array([True, False])).all() d = mng.is_in_polygon(p, np.array([[45.0, 45.0], [-45.0, 45.0]]), ncaps=2) assert (d == np.array([True, False])).all() poly = mng.read_fits_polygons(self.no_id_fits) d = mng.is_in_polygon(poly[0], np.array([[0.0, 10.0], [4.0, 4.5], [90, 4.0], [135, 3.0], [180, 0.5], [270, 0.25]])) assert (d == np.array([False, True, False, False, False, False])).all() def test_is_in_window(self): np.random.seed(271828) RA = 7.0*np.random.random(1000) + 268.0 Dec = 90.0 - np.degrees(np.arccos(0.08*np.random.random(1000))) points = np.vstack((RA, Dec)).T poly = mng.read_fits_polygons(self.poly_fits) i = mng.is_in_window(poly, points) assert i[0].sum() == 3 def test_read_fits_polygons(self): poly = mng.read_fits_polygons(self.poly_fits) use_caps = np.array([31, 15, 31, 7, 31, 15, 15, 7, 15, 15, 15, 31, 15, 15, 15, 15, 15, 15, 31, 15], dtype=np.uint32) # # Attribute access doesn't work on unsigned columns. # assert (poly['USE_CAPS'] == use_caps).all() assert (poly['use_caps'] == use_caps).all() with raises(AttributeError): foo = poly.no_such_attribute cm0 = np.array([-1.0, -0.99369437, 1.0, -1.0, 0.00961538]) assert np.allclose(poly.cm[0][0:poly.ncaps[0]], cm0) assert poly[0]['NCAPS'] == 5 poly = mng.read_fits_polygons(self.poly_fits, convert=True) assert poly[0].use_caps == 31 assert np.allclose(poly[0].cm, cm0) assert poly[0].cmminf() == 4 # # A FITS file might not contain IFIELD. # poly = mng.read_fits_polygons(self.no_id_fits) assert len(poly) == 1 # # A FITS file might contain exactly one polygon with exactly one cap. # poly = mng.read_fits_polygons(self.one_cap_fits, convert=True) assert poly[0].ncaps == 1 def test_read_mangle_polygons(self): with raises(PydlutilsException): poly = mng.read_mangle_polygons(self.bad_ply) poly = mng.read_mangle_polygons(self.poly_ply) assert len(poly.header) == 3 assert poly.header[0] == 'pixelization 6s' assert len(poly) == 4 assert np.allclose(poly[0].x[0, :], np.array([0.0436193873653360, 0.9990482215818578, 0.0])) assert poly[3].ncaps == 3 def test_set_use_caps(self): poly = mng.read_fits_polygons(self.poly_fits, convert=True) old_use_caps = poly[0].use_caps index_list = list(range(poly[0].ncaps)) use_caps = mng.set_use_caps(poly[0], index_list, allow_doubles=True) assert use_caps == poly[0].use_caps use_caps = mng.set_use_caps(poly[0], index_list) assert use_caps == poly[0].use_caps x = np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [1.0-1.0e-8, 1.0e-8, 0.0]]) cm = np.array([1.0, 1.0, 1.0, 1.0-1.0e-8]) p = mng.ManglePolygon(x=x, cm=cm) index_list = list(range(p.ncaps)) assert p.use_caps == 2**4 - 1 use_caps = mng.set_use_caps(p, index_list, tol=1.0e-7) assert use_caps == 2**3 - 1 x = np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [1.0-1.0e-8, 1.0e-8, 0.0]]) cm = np.array([1.0, 1.0, 1.0, -1.0+1.0e-8]) p = mng.ManglePolygon(x=x, cm=cm) index_list = list(range(p.ncaps)) assert p.use_caps == 2**4 - 1 use_caps = mng.set_use_caps(p, index_list, tol=1.0e-7) assert use_caps == 2**3 - 1 use_caps = mng.set_use_caps(p, index_list, tol=1.0e-7, allow_neg_doubles=True) assert use_caps == 2**4 - 1 def test_x_to_angles(self): a = mng.x_to_angles(np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])) assert np.allclose(a, np.array([[0.0, 0.0], [90.0, 90.0], [0.0, 90.0]])) a = mng.x_to_angles(np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]]), latitude=True) assert np.allclose(a, np.array([[0.0, 90.0], [90.0, 0.0], [0.0, 0.0]])) def test_single_polygon(self): x = np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [1.0-1.0e-8, 1.0e-8, 0.0]]) cm = np.array([1.0, 1.0, 1.0, -1.0+1.0e-8]) p = mng.ManglePolygon(x=x, cm=cm) p2 = mng._single_polygon(p) assert id(p2) == id(p) pl = mng.PolygonList() pl.append(p) p2 = mng._single_polygon(pl) assert id(p2) == id(p) with raises(ValueError): pl.append(p2) p3 = mng._single_polygon(pl) p = mng.read_fits_polygons(self.no_id_fits) p2 = mng._single_polygon(p) assert p2.ncaps == 4 p2 = mng._single_polygon(p[0]) assert p2.use_caps == 15 def fits_polygon_file(): """Create a small test version of a FITS polygon file. """ from datetime import date from sys import argv from astropy.io import fits from pydl import __version__ as pydlversion with fits.open(argv[1], uint=True) as hdulist: header0 = hdulist[0].header data = hdulist[1].data if 'DATE' in header0: header0['DATE'] = date.today().strftime('%Y-%m-%d') if 'IDLUTILS' in header0: header0['IDLUTILS'] = 'pydl-'+pydlversion hdu0 = fits.PrimaryHDU(header=header0) hdu1 = fits.BinTableHDU(data[0:20]) hdulist2 = fits.HDUList([hdu0, hdu1]) hdulist2.writeto('polygon.fits') return 0 if __name__ == '__main__': from sys import exit exit(fits_polygon_file()) pydl-0.6.0/pydl/pydlutils/tests/test_rgbcolor.py0000664000111400017500000000513513040014667022657 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import catch_warnings, raises from .. import PydlutilsUserWarning from ..rgbcolor import (nw_arcsinh, nw_cut_to_box, nw_float_to_byte, nw_scale_rgb) class TestRGBColor(object): """Test the functions in pydl.pydlutils.rgbcolor. """ def setup(self): pass def teardown(self): pass def test_nw_arcsinh(self): colors = np.random.random((10, 10)) with raises(ValueError): fitted_colors = nw_arcsinh(colors) colors = np.random.random((10, 10, 5)) with raises(ValueError): fitted_colors = nw_arcsinh(colors) colors = np.random.random((10, 10, 3)) fitted_colors = nw_arcsinh(colors, nonlinearity=0) assert (fitted_colors == colors).all() colors = np.ones((2, 2, 3)) fac = np.arcsinh(9.0)/9.0 fitted_colors = nw_arcsinh(colors) assert np.allclose(fitted_colors, fac) def test_nw_cut_to_box(self): colors = np.random.random((10, 10)) with raises(ValueError): boxed_colors = nw_cut_to_box(colors) colors = np.random.random((10, 10, 5)) with raises(ValueError): boxed_colors = nw_cut_to_box(colors) colors = np.random.random((10, 10, 3)) with raises(ValueError): boxed_colors = nw_cut_to_box(colors, origin=(1.0, 1.0)) boxed_colors = nw_cut_to_box(colors) assert np.allclose(boxed_colors, colors) def test_nw_float_to_byte(self): colors = np.zeros((10, 10, 3), dtype=np.float) byte_colors = nw_float_to_byte(colors) assert (byte_colors == 0).all() colors = np.ones((10, 10, 3), dtype=np.float) byte_colors = nw_float_to_byte(colors) assert (byte_colors == 255).all() with catch_warnings(PydlutilsUserWarning) as w: byte_colors = nw_float_to_byte(colors, bits=16) assert len(w) > 0 def test_nw_scale_rgb(self): colors = np.random.random((10, 10)) with raises(ValueError): scaled_colors = nw_scale_rgb(colors) colors = np.random.random((10, 10, 5)) with raises(ValueError): scaled_colors = nw_scale_rgb(colors) colors = np.random.random((10, 10, 3)) with raises(ValueError): scaled_colors = nw_scale_rgb(colors, scales=(1.0, 1.0)) colors = np.ones((2, 2, 3)) scaled_colors = nw_scale_rgb(colors, scales=(2.0, 2.0, 2.0)) assert np.allclose(scaled_colors, 2.0) pydl-0.6.0/pydl/pydlutils/tests/__init__.py0000664000111400017500000000021412775255301021545 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This is the pydl/pydlutils/tests directory. """ pydl-0.6.0/pydl/pydlutils/tests/test_bspline.py0000664000111400017500000000400313040014667022473 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import raises from ..bspline import iterfit from ... import smooth class TestBspline(object): """Test the functions in pydl.pydlutils.bspline. """ def setup(self): pass def teardown(self): pass def test_iterfit(self): y0 = np.array([0.661984, 0.134913, 0.0410350, 0.940134, 0.411034, 0.484675, 0.169943, 0.325046, 0.269194, 0.552381, 0.797177, 0.971658, 0.251765, 0.531675, 0.854556, 0.411237, 0.694380, 0.499562, 0.437242, 0.362451, 0.343206, 0.524099, 0.158634, 0.728597, 0.198340, 0.571210, 0.477527, 0.962797, 0.973921, 0.413651, 0.736380, 0.516366, 0.104283, 0.675993, 0.467053, 0.230112, 0.866994, 0.469885, 0.964392, 0.541084, 0.332984, 0.581252, 0.422322, 0.872555, 0.803636, 0.520998, 0.918942, 0.241564, 0.169263, 0.686649, 0.708284, 0.707858, 0.00113957, 0.827920, 0.845985, 0.416961, 0.553842, 0.526549, 0.501051, 0.337514, 0.700873, 0.152816, 0.762935, 0.650039, 0.483321, 0.708600, 0.410033, 0.507671, 0.596956, 0.177692, 0.498112, 0.422037, 0.788333, 0.856578, 0.941245, 0.432411, 0.356469, 0.341916, 0.0331059, 0.641100, 0.690452, 0.168667, 0.915178, 0.158406, 0.701508, 0.841774, 0.434161, 0.153123, 0.420066, 0.0499331, 0.947241, 0.0768818, 0.410540, 0.843788, 0.0640255, 0.513463, 0.511104, 0.680434, 0.762480, 0.0563867]) assert y0.size == 100 y = smooth(y0, 10) assert y.size == 100 x = np.arange(y0.size, dtype='d') sset, outmask = iterfit(x, y, nord=3, maxiter=0, bkspace=10) assert sset.npoly == 1 assert sset.funcname == 'legendre' # print(sset) # yfit,mask = sset.value(x) # print(yfit) # pylab.plot(x, y, 'k-', x, yfit, 'r-') pydl-0.6.0/pydl/pydlutils/tests/test_sdss.py0000664000111400017500000002660613102426056022026 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np import pydl.pydlutils.sdss from astropy.tests.helper import remote_data, raises from astropy.utils.data import get_pkg_data_filename from ..sdss import (default_skyversion, sdss_flagexist, sdss_flagname, sdss_flagval, set_maskbits, sdss_astrombad, sdss_objid, sdss_specobjid, unwrap_specobjid) class TestSDSS(object): """Test the functions in pydl.pydlutils.sdss. """ def setup(self): pydl.pydlutils.sdss.maskbits = set_maskbits( maskbits_file=get_pkg_data_filename('t/testMaskbits.par')) self.opbadfields = np.array([ (77, 'astrom', 30, 73, 'Large astrometric offset at field 39... 72'), (85, 'astrom', 8, 28, 'Large astrometric offset at field 11... 27'), (85, 'rotator', 242, 253, 'Large rotator offset at field 251 252'), (209, 'astrom', 8, 116, 'Tel. offsets before r-band field 115 -DJS'), (209, 'astrom', 137, 175, 'Tel. offsets after r-band field 145 -DJS'), (250, 'astrom', 456, 468, 'Large astrometric offset -Manual'), (251, 'astrom', 147, 159, 'Large astrometric offset at field 156 158')], dtype=[('run', '; # This is archaic array notation, strongly deprecated, char bar<10>; # but still technically supported. } OLD;""", """typedef struct { STATUS state; char timestamp[]; #UTC timestamp in format 2008-06-21T00:27:33 } STATUS_UPDATE;""", ] enum = [ """typedef enum { FALSE, TRUE } BOOLEAN;""", """typedef enum { FAILURE, INCOMPLETE, SUCCESS } STATUS;""", ] with open(self.data('test.par')) as f: file_data = f.read() # # Open the object # par = yanny(self.data('test.par')) # # Test some static methods, etc. # assert par.get_token('abcd') == ('abcd', '') assert str(par) == file_data assert repr(par) == file_data par2 = yanny(self.data('test_table.par')) assert par != par2 # # Test types # assert par.type('MYSTRUCT', 'c') == 'double' assert par.type('FOOBAR', 'd') is None assert par.type('MYSTRUCT', 'foobar') is None assert not par2.isenum('TEST', 'a') assert par.array_length('MYSTRUCT', 'c') == 1 assert par.char_length('MYSTRUCT', 'c') is None # # Test the pairs # assert set(par.pairs()) == set(pair_data) # # Test the pair values # for p in par.pairs(): assert par[p] == pair_data[p] # # Test the structure of the object # assert (set(par) == set(list(pair_data) + list(struct_data))) assert (set(par._symbols) == set(list(struct_data) + ['struct', 'enum'])) assert set(par._symbols['struct']) == set(symbols) assert set(par._symbols['enum']) == set(enum) assert set(par.tables()) == set(struct_data) for t in par.tables(): assert (par.dtype(t) == self.json2dtype(struct_data[t]['dtype'])) assert par.size(t) == struct_data[t]['size'] assert (set(par.columns(t)) == set(struct_data[t]['columns'])) for c in par.columns(t): assert (par.type(t, c) == struct_data[t]['columns'][c]) # print(par[t][c]) assert par.isenum('MYSTRUCT', 'new_flag') assert par._enum_cache['BOOLEAN'] == ['FALSE', 'TRUE'] assert (par._enum_cache['STATUS'] == ['FAILURE', 'INCOMPLETE', 'SUCCESS']) # # Test values # assert np.allclose(par['MYSTRUCT'].mag[0], np.array([17.5, 17.546, 17.4, 16.1, 16.0])) assert np.allclose(par['MYSTRUCT'].mag[5], np.array([19.3, 18.2, 17.1, 16.0, 15.9])) assert par['MYSTRUCT'].foo[1] == six.b("My dog has no nose.") assert np.allclose(par['MYSTRUCT'].c[2], 7.24345567) assert (par['MYSTRUCT']['flags'][2] == np.array([123123, 0])).all() # # Test expected write failures. # # This should fail, since test.par already exists. with raises(PydlutilsException): par.write() with catch_warnings(PydlutilsUserWarning) as w: par.append({}) datatable = {'status_update': {'state': ['SUCCESS', 'SUCCESS'], 'timestamp': ['2008-06-22 01:27:33', '2008-06-22 01:27:36']}, 'new_keyword': 'new_value'} par.filename = self.temp('test_append.par') # This should also fail, because test_append.par does not exist. with raises(PydlutilsException): par.append(datatable) return pydl-0.6.0/pydl/pydlutils/tests/test_cooling.py0000664000111400017500000000170612775255301022506 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.tests.helper import raises from ..cooling import read_ds_cooling class TestCooling(object): """Test the functions in pydl.pydlutils.cooling. """ def setup(self): pass def teardown(self): pass def test_read_ds_cooling(self): with raises(ValueError): logT, loglambda = read_ds_cooling('m-99.cie') logT, logL = read_ds_cooling('m-15.cie') assert np.allclose(logT[0:5], np.array([4.0, 4.05, 4.1, 4.15, 4.2])) assert np.allclose(logL[0:5], np.array([-26.0, -24.66, -23.52, -22.62, -22.11])) logT = np.array([4.025, 4.125, 4.225, 4.325]) logT2, logL = read_ds_cooling('m-15.cie', logT) assert np.allclose(logT2, logT) assert np.allclose(logL, np.array([-25.33, -23.07, -22.04, -22.06])) pydl-0.6.0/pydl/pydlutils/yanny.py0000664000111400017500000013655112775255301020020 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the yanny directory in idlutils. This is a Python library for reading & writing yanny files. :class:`yanny` is an object-oriented interface to SDSS Parameter files (a.k.a. "FTCL" or "yanny" files) following these specifications_. Parameter files typically have and can be recognized by the extension ``.par``. These files may also be recognized if the first line of the file is:: #%yanny This is not part of the standard specification, but it suffices to identify, *e.g.*, files that have not yet been written to disk, but only exist as file objects. Because Parameter files can contain multiple tables, as well as metadata, there is no simple, one-to-one correspondence between these files and, say, an astropy :class:`~astropy.table.Table` object. Thus, :class:`yanny` objects are implemented as a subclass of :class:`~collections.OrderedDict` (to remember the order of keyword-value pairs), and the actual data are values accessed by keyword. Still, it is certainly possible to *write* a table-like object to a yanny file. Given the caveats above, we have introduced *experimental* support for reading and writing yanny files directly to/from :class:`~astropy.table.Table` objects. Because of the experimental nature of this support, it must be activated "by hand" by including this snippet in your code:: from astropy.table import Table from astropy.io.registry import (register_identifier, register_reader, register_writer) from pydl.pydlutils.yanny import (is_yanny, read_table_yanny, write_table_yanny) register_identifier('yanny', Table, is_yanny) register_reader('yanny', Table, read_table_yanny) register_writer('yanny', Table, write_table_yanny) Currently multidimensional arrays are only supported for type ``char``, and a close reading of the specifications indicates that multidimensional arrays were only ever intended to be supported for type ``char``. So no multidimensional arrays, sorry. .. _specifications: http://www.sdss.org/dr12/software/par/ """ import re import os import datetime import numpy import warnings from collections import OrderedDict from astropy.extern import six from astropy.table import Table # from astropy.io.registry import register_identifier, register_writer from . import PydlutilsException, PydlutilsUserWarning class yanny(OrderedDict): """An object interface to a yanny file. Create a yanny object using a yanny file, `filename`. If the file exists, it is read, & the dict structure of the object will be basically the same as that returned by ``read_yanny()`` in the efftickle package. If the file does not exist, or if no filename is given, a blank structure is returned. Other methods allow for subsequent writing to the file. Parameters ---------- filename : :class:`str` or file-like, optional The name of a yanny file or a file-like object representing a yanny file. raw : :class:`bool`, optional If ``True``, data in a yanny file will *not* be converted into astropy :class:`~astropy.table.Table` objects, but will instead be retained as raw Python lists. Attributes ---------- raw : :class:`bool` If ``True``, data in a yanny file will *not* be converted into astropy :class:`~astropy.table.Table` objects, but will instead be retained as raw Python lists. filename : :class:`str` The name of a yanny parameter file. If a file-like object was used to initialize the object, this will have the value 'in_memory.par'. _symbols : :class:`dict` A dictionary containing the metadata describing the tables. _contents : :class:`str` The complete contents of a yanny parameter file. _struct_type_caches : :class:`dict` A dictionary of dictionaries, one dictionary for every structure definition in a yanny parameter file. Contains the types of each column _struct_isarray_caches : :class:`dict` A dictionary of dictionaries, one dictionary for every structure definition in a yanny parameter file. Contains a boolean value for every column. _enum_cache : :class:`dict` Initially ``None``, this attribute is initialized the first time the :meth:`isenum` method is called. The keyword is the name of the enum type, the value is a list of the possible values of that type. """ @staticmethod def get_token(string): """Removes the first 'word' from string. If the 'word' is enclosed in double quotes, it returns the contents of the double quotes. If the 'word' is enclosed in braces, it returns the contents of the braces, but does not attempt to split the array. If the 'word' is the last word of the string, remainder is set equal to the empty string. This is basically a wrapper on some convenient regular expressions. Parameters ---------- string : :class:`str` A string containing words. Returns ------- :func:`tuple` A tuple containing the first word and the remainder of the string. Examples -------- >>> from pydl.pydlutils.yanny import yanny >>> yanny.get_token("The quick brown fox") ('The', 'quick brown fox') """ if string[0] == '"': (word, remainder) = re.search(r'^"([^"]*)"\s*(.*)', string).groups() elif string[0] == '{': (word, remainder) = re.search(r'^\{\s*([^}]*)\s*\}\s*(.*)', string).groups() else: try: (word, remainder) = re.split(r'\s+', string, 1) except ValueError: (word, remainder) = (string, '') # if remainder is None: # remainder = '' return (word, remainder) @staticmethod def protect(x): """Used to appropriately quote string that might contain whitespace. This method is mostly for internal use by the yanny object. Parameters ---------- x : :class:`str` The data to protect. Returns ------- :class:`str` The data with white space protected by quotes. Examples -------- >>> from pydl.pydlutils.yanny import yanny >>> yanny.protect('This string contains whitespace.') '"This string contains whitespace."' >>> yanny.protect('This string contains a #hashtag.') '"This string contains a #hashtag."' """ if isinstance(x, numpy.bytes_): s = x.decode() else: s = str(x) if len(s) == 0 or s.find('#') >= 0 or re.search(r'\s+', s) is not None: return '"' + s + '"' else: return s @staticmethod def trailing_comment(line): """Identify a trailing comment and strip it. This routine works on the theory that a properly quoted comment mark will be surrounted by an odd number of double quotes, & we can skip to searching for the last one in the line. Parameters ---------- line : :class:`str` A line from a yanny file potentially containing trailing comments. Returns ------- :class:`str` The line with any trailing comment and any residual white space trimmed off. Notes ----- This may fail in certain pathological cases, for example if a real trailing comment contains a single double-quote:: # a 'pathological" trailing comment or if someone is over-enthusiastically commenting:: # # # # # I like # characters. Examples -------- >>> from pydl.pydlutils.yanny import yanny >>> yanny.trailing_comment('mystruct 1234 "#hashtag" # a comment.') 'mystruct 1234 "#hashtag"' >>> yanny.trailing_comment('mystruct 1234 "#hashtag" # a "comment".') 'mystruct 1234 "#hashtag"' """ lastmark = line.rfind('#') if lastmark >= 0: # # Count the number of double quotes in the remainder of the line # if (len([c for c in line[lastmark:] if c == '"']) % 2) == 0: # # Even number of quotes # return line[0:lastmark].rstrip() return line @staticmethod def dtype_to_struct(dt, structname='mystruct', enums=None): """Convert a NumPy dtype object describing a record array to a typedef struct statement. The second argument is the name of the structure. If any of the columns are enum types, enums must be a dictionary with the keys the column names, and the values are a tuple containing the name of the enum type as the first item and a tuple or list of possible values as the second item. Parameters ---------- dt : :class:`numpy.dtype` The dtype of a NumPy record array. structname : :class:`str`, optional The name to give the structure in the yanny file. Defaults to 'MYSTRUCT'. enums : :class:`dict`, optional A dictionary containing enum information. See details above. Returns ------- :class:`dict` A dictionary suitable for setting the 'symbols' dictionary of a new yanny object. Examples -------- """ dtmap = {'i2': 'short', 'i4': 'int', 'i8': 'long', 'f4': 'float', 'f8': 'double'} returnenums = list() if enums is None: enums = dict() else: for e in enums: lines = list() lines.append('typedef enum {') for n in enums[e][1]: lines.append(" {0},".format(n)) lines[-1] = lines[-1].strip(',') lines.append('}} {0};'.format(enums[e][0].upper())) returnenums.append("\n".join(lines)) # lines.append('') lines = list() lines.append('typedef struct {') for c in dt.names: if dt[c].kind == 'V': t = dt[c].subdtype[0].str[1:] l = dt[c].subdtype[1][0] s = dt[c].subdtype[0].itemsize else: t = dt[c].str[1:] l = 0 s = dt[c].itemsize line = ' ' if t[0] in 'SU': if c in enums: line += enums[c][0].upper() else: line += 'char' else: line += dtmap[t] line += ' {0}'.format(c) if l > 0: line += "[{0:d}]".format(l) if t[0] in 'SU' and c not in enums: line += "[{0:d}]".format(s) line += ';' lines.append(line) lines.append('}} {0};'.format(structname.upper())) return {structname.upper(): list(dt.names), 'enum': returnenums, 'struct': ["\n".join(lines)]} def __init__(self, filename=None, raw=False): """Create a yanny object using a yanny file. """ super(yanny, self).__init__() # # The symbol hash is inherited from the old read_yanny # self._symbols = dict() # # Create special attributes that contain the internal status of the # object. This should prevent overlap with keywords in the data files. # self.filename = '' self._contents = '' # # Since the re is expensive, cache the structure types keyed by # the field. Create a dictionary for each structure found. # self._struct_type_caches = dict() self._struct_isarray_caches = dict() self._enum_cache = None # # Optionally convert numeric data into NumPy arrays # self.raw = raw # # If the file exists, read it # if filename is not None: # # Handle file-like objects # if isinstance(filename, six.string_types): if os.access(filename, os.R_OK): self.filename = filename with open(filename, 'r') as f: self._contents = f.read() else: # # Assume file-like # self.filename = 'in_memory.par' contents = filename.read() if 'b' in filename.mode: contents = contents.decode('ascii') self._contents = contents self._parse() return def __str__(self): """Implement the ``str()`` function for yanny objects. Simply prints the current contents of the yanny file. """ return self._contents __repr__ = __str__ def __eq__(self, other): """Test two yanny objects for equality. Two yanny objects are assumed to be equal if their contents are equal. """ if isinstance(other, yanny): return self._contents == other._contents return NotImplemented def __ne__(self, other): """Test two yanny objects for inequality. Two yanny objects are assumed to be unequal if their contents are unequal. """ if isinstance(other, yanny): return self._contents != other._contents return NotImplemented def __bool__(self): """Give a yanny object a definite truth value. A yanny object is considered ``True`` if its contents are non-zero. """ return len(self._contents) > 0 # `__nonzero__` is needed for Python 2. # Python 3 uses `__bool__`. # http://stackoverflow.com/a/2233850/498873 __nonzero__ = __bool__ def type(self, structure, variable): """Returns the type of a variable defined in a structure. Returns ``None`` if the structure or the variable is undefined. Parameters ---------- structure : :class:`str` The name of the structure that contains `variable`. variable : :class:`str` The name of the column whose type you want. Returns ------- :class:`str` The type of the variable. """ if structure not in self: return None if variable not in self.columns(structure): return None # # Added code to cache values to speed up parsing large files. # 2009.05.11 / Demitri Muna, NYU # Find (or create) the cache for this structure. # try: cache = self._struct_type_caches[structure] except KeyError: self._struct_type_caches[structure] = dict() # cache for one struct type cache = self._struct_type_caches[structure] # # Lookup (or create) the value for this variable # try: var_type = cache[variable] except KeyError: defl = [x for x in self._symbols['struct'] if x.find(structure.lower()) > 0] defu = [x for x in self._symbols['struct'] if x.find(structure.upper()) > 0] if len(defl) != 1 and len(defu) != 1: return None elif len(defl) == 1: definition = defl else: definition = defu typere = re.compile(r'(\S+)\s+{0}([[<].*[]>]|);'.format(variable)) (typ, array) = typere.search(definition[0]).groups() var_type = typ + array.replace('<', '[').replace('>', ']') cache[variable] = var_type return var_type def basetype(self, structure, variable): """Returns the bare type of a variable, stripping off any array information. Parameters ---------- structure : :class:`str` The name of the structure that contains `variable`. variable : :class:`str` The name of the column whose type you want. Returns ------- :class:`str` The type of the variable, stripped of array information. """ typ = self.type(structure, variable) try: return typ[0:typ.index('[')] except ValueError: return typ def isarray(self, structure, variable): """Returns ``True`` if the variable is an array type. For character types, this means a two-dimensional array, *e.g.*: ``char[5][20]``. Parameters ---------- structure : :class:`str` The name of the structure that contains `variable`. variable : :class:`str` The name of the column to check for array type. Returns ------- :class:`bool` ``True`` if the variable is an array. """ try: cache = self._struct_isarray_caches[structure] except KeyError: self._struct_isarray_caches[structure] = dict() cache = self._struct_isarray_caches[structure] try: result = cache[variable] except KeyError: typ = self.type(structure, variable) character_array = re.compile(r'char[[<]\d*[]>][[<]\d*[]>]') if ((character_array.search(typ) is not None) or (typ.find('char') < 0 and (typ.find('[') >= 0 or typ.find('<') >= 0))): cache[variable] = True else: cache[variable] = False result = cache[variable] return result def isenum(self, structure, variable): """Returns true if a variable is an enum type. Parameters ---------- structure : :class:`str` The name of the structure that contains `variable`. variable : :class:`str` The name of the column to check for enum type. Returns ------- :class:`bool` ``True`` if the variable is enum type. """ if self._enum_cache is None: self._enum_cache = dict() if 'enum' in self._symbols: for e in self._symbols['enum']: m = re.search(r'typedef\s+enum\s*\{([^}]+)\}\s*(\w+)\s*;', e).groups() self._enum_cache[m[1]] = re.split(r',\s*', m[0].strip()) else: return False return self.basetype(structure, variable) in self._enum_cache def array_length(self, structure, variable): """Returns the length of an array type or 1 if the variable is not an array. For character types, this is the length of a two-dimensional array, *e.g.*, ``char[5][20]`` has length 5. Parameters ---------- structure : :class:`str` The name of the structure that contains `variable`. variable : :class:`str` The name of the column to check for array length. Returns ------- :class:`int` The length of the array variable """ if self.isarray(structure, variable): typ = self.type(structure, variable) return int(typ[typ.index('[')+1:typ.index(']')]) else: return 1 def char_length(self, structure, variable): """Returns the length of a character field. *e.g.* ``char[5][20]`` is an array of 5 strings of length 20. Returns ``None`` if the variable is not a character type. If the length is not specified, *i.e.* ``char[]``, it returns the length of the largest string. Parameters ---------- structure : :class:`str` The name of the structure that contains `variable`. variable : :class:`str` The name of the column to check for char length. Returns ------- :class:`int` or None The length of the char variable. """ typ = self.type(structure, variable) if typ.find('char') < 0: return None try: return int(typ[typ.rfind('[')+1:typ.rfind(']')]) except ValueError: if self.isarray(structure, variable): return max([max([len(x) for x in r]) for r in self[structure][variable]]) else: return max([len(x) for x in self[structure][variable]]) def dtype(self, structure): """Returns a NumPy dtype object suitable for describing a table as a record array. Treats enums as string, which is what the IDL reader does. Parameters ---------- structure : :class:`str` The name of the structure. Returns ------- :class:`numpy.dtype` A dtype object suitable for describing the yanny structure as a record array. """ dt = list() dtmap = {'short': 'i2', 'int': 'i4', 'long': 'i8', 'float': 'f', 'double': 'd'} for c in self.columns(structure): typ = self.basetype(structure, c) if typ == 'char': d = "S{0:d}".format(self.char_length(structure, c)) elif self.isenum(structure, c): d = "S{0:d}".format(max([len(x) for x in self._enum_cache[typ]])) else: d = dtmap[typ] if self.isarray(structure, c): dt.append((str(c), str(d), (self.array_length(structure, c),))) else: dt.append((str(c), str(d))) dt = numpy.dtype(dt) return dt def convert(self, structure, variable, value): """Converts value into the appropriate (Python) type. * ``short`` & ``int`` are converted to Python :class:`int`. * ``long`` is converted to Python :class:`long`. * ``float`` & ``double`` are converted to Python :class:`float`. * Other types are not altered. There may be further conversions into NumPy types, but this is the first stage. Parameters ---------- structure : :class:`str` The name of the structure that contains `variable`. variable : :class:`str` The name of the column undergoing conversion. value : :class:`str` The value contained in a particular row of `variable`. Returns ------- :class:`int`, :class:`long`, :class:`float` or :class:`str` `value` converted to a Python numerical type. """ if six.PY3: intTypes = set(['short', 'int', 'long']) longTypes = set() mylong = int else: intTypes = set(['short', 'int']) longTypes = set(['long']) mylong = long floatTypes = set(['float', 'double']) typ = self.basetype(structure, variable) if typ in intTypes: if self.isarray(structure, variable): return [int(v) for v in value] else: return int(value) if typ in longTypes: if self.isarray(structure, variable): return [mylong(v) for v in value] else: return mylong(value) if typ in floatTypes: if self.isarray(structure, variable): return [float(v) for v in value] else: return float(value) return value def tables(self): """Returns a list of all the defined structures. This is just the list of keys of the object with the 'internal' keys removed. """ foo = list() for k in self._symbols.keys(): if k not in ('struct', 'enum'): foo.append(k) return foo def columns(self, table): """Returns an ordered list of column names associated with a particular table. The order is the same order as they are defined in the yanny file. Parameters ---------- table : :class:`str` The table whose columns are desired. Returns ------- :class:`list` The list of column names. """ foo = list() if table in self._symbols: return self._symbols[table] return foo def size(self, table): """Returns the number of rows in a table. Parameters ---------- table : :class:`str` The table whose size desired. Returns ------- :class:`int` The number of rows in `table`. """ foo = self.columns(table) return len(self[table][foo[0]]) def pairs(self): """Returns a list of keys to keyword/value pairs. Equivalent to doing ``self.keys()``, but with all the data tables & other control structures stripped out. """ p = list() foo = self.tables() for k in self.keys(): if k not in foo: p.append(k) return p def row(self, table, index): """Returns a list containing a single row from a specified table in column order. If index is out of range, it returns an empty list. If the yanny object instance is set up for NumPy record arrays, then a single row can be obtained with:: row0 = par['TABLE'][0] Parameters ---------- table : :class:`str` The table whose row is desired. index : :class:`int` The number of the row to return. Returns ------- :class:`list` A row from `table`. """ datarow = list() if table in self and index >= 0 and index < self.size(table): for c in self.columns(table): datarow.append(self[table][c][index]) return datarow def list_of_dicts(self, table): """Construct a list of dictionaries. Takes a table from the yanny object and constructs a list object containing one row per entry. Each item in the list is a dictionary keyed by the struct value names. If the yanny object instance is set up for NumPy record arrays, then the same functionality can be obtained with:: foo = par['TABLE'][0]['column'] Parameters ---------- table : :class:`str` The table to convert Returns ------- :class:`list` A list containing the rows of `table` converted to :class:`dict`. """ return_list = list() d = dict() # I'm assuming these are in order... struct_fields = self.columns(table) for i in range(self.size(table)): one_row = self.row(table, i) # one row as a list j = 0 for key in struct_fields: d[key] = one_row[j] j = j + 1 return_list.append(dict(d)) # append a new dict (copy of d) return return_list def new_dict_from_pairs(self): """Returns a new dictionary of keyword/value pairs. The new dictionary (*i.e.*, not a yanny object) contains the keys that :meth:`pairs` returns. There are two reasons this is convenient: * the key 'symbols' that is part of the yanny object will not be present * a simple yanny file can be read with no further processing Returns ------- :class:`~collections.OrderedDict` A dictionary of the keyword-value pairs that remembers the order in which they were defined in the file. Examples -------- Read a yanny file and return only the pairs:: >>> from os.path import dirname >>> from pydl.pydlutils.yanny import yanny >>> new_dict = yanny(dirname(__file__)+'/tests/t/test.par').new_dict_from_pairs() >>> new_dict['mjd'] '54579' >>> new_dict['alpha'] 'beta gamma delta' added: Demitri Muna, NYU 2009-04-28 """ new_dictionary = OrderedDict() for key in self.pairs(): new_dictionary[key] = self[key] return new_dictionary def write(self, newfile=None, comments=None): """Write a yanny object to a file. This assumes that the filename used to create the object was not that of a pre-existing file. If a file of the same name is detected, this method will *not* attempt to overwrite it, but will print a warning. This also assumes that the special 'symbols' key has been properly created. This will not necessarily make the file very human-readable, especially if the data lines are long. If the name of a new file is given, it will write to the new file (assuming it doesn't exist). If the writing is successful, the data in the object will be updated. Parameters ---------- newfile : :class:`str`, optional The name of the file to write. comments : :class:`str` or :class:`list` of :class:`str`, optional Comments that will be placed at the head of the file. If a single string is passed, it will be written out verbatim, although a '#' character will be added if it does not already have one. If a list of strings is passed, comment characters will be added and the strings will be joined together. """ if newfile is None: if len(self.filename) > 0: newfile = self.filename else: raise ValueError("No filename specified!") if os.access(newfile, os.F_OK): raise PydlutilsException( "{0} exists, aborting write!".format(newfile)) if comments is None: basefile = os.path.basename(newfile) timestamp = datetime.datetime.utcnow().strftime( '%Y-%m-%d %H:%M:%S UTC') comments = "#\n# {0}\n#\n# Created by pydl.pydlutils.yanny.yanny\n#\n# {1}\n#\n".format(basefile, timestamp) else: if isinstance(comments, six.string_types): if not comments.startswith('#'): comments = '# ' + comments if not comments.endswith('\n'): comments += '\n' else: comments = ("\n".join(["# {0}".format(c) for c in comments]) + "\n") contents = "#%yanny\n" + comments # # Print any key/value pairs # for key in self.pairs(): contents += "{0} {1}\n".format(key, self[key]) # # Print out enum definitions # if len(self._symbols['enum']) > 0: contents += "\n" + "\n\n".join(self._symbols['enum']) + "\n" # # Print out structure definitions # if len(self._symbols['struct']) > 0: contents += "\n" + "\n\n".join(self._symbols['struct']) + "\n" contents += "\n" # # Print out the data tables # for sym in self.tables(): columns = self.columns(sym) for k in range(self.size(sym)): line = list() line.append(sym) for col in columns: if self.isarray(sym, col): datum = ('{' + ' '.join([self.protect(x) for x in self[sym][col][k]]) + '}') else: datum = self.protect(self[sym][col][k]) line.append(datum) contents += "{0}\n".format(' '.join(line)) # # Actually write the data to file # with open(newfile, 'w') as f: f.write(contents) self._contents = contents self.filename = newfile self._parse() return def append(self, datatable): """Appends data to an existing FTCL/yanny file. Tries as much as possible to preserve the ordering & format of the original file. The datatable should adhere to the format of the yanny object, but it is not necessary to reproduce the 'symbols' dictionary. It will not try to append data to a file that does not exist. If the append is successful, the data in the object will be updated. Parameters ---------- datatable : :class:`dict` The data to append. """ if len(self.filename) == 0: raise ValueError("No filename is set for this object. " + "Use the filename attribute to set the filename!") if not isinstance(datatable, dict): raise ValueError("Data to append is not of the correct type. " + "Use a dict!") timestamp = datetime.datetime.utcnow().strftime( '%Y-%m-%d %H:%M:%S UTC') contents = '' # # Print any key/value pairs # for key in datatable.keys(): if key.upper() in self.tables() or key == 'symbols': continue contents += "{0} {1}\n".format(key, datatable[key]) # # Print out the data tables # for sym in self.tables(): if sym.lower() in datatable: datasym = sym.lower() else: datasym = sym if datasym in datatable: columns = self.columns(sym) for k in range(len(datatable[datasym][columns[0]])): line = list() line.append(sym) for col in columns: if self.isarray(sym, col): datum = ('{' + ' '.join([self.protect(x) for x in datatable[datasym][col][k]]) + '}') else: datum = self.protect(datatable[datasym][col][k]) line.append(datum) contents += "{0}\n".format(' '.join(line)) # # Actually write the data to file # if len(contents) > 0: contents = ("# Appended by yanny.py at {0}.\n".format(timestamp) + contents) if os.access(self.filename, os.W_OK): with open(self.filename, 'a') as f: f.write(contents) self._contents += contents self._parse() else: raise PydlutilsException(self.filename + " does not exist, aborting append!") else: warnings.warn("Nothing to be appended!", PydlutilsUserWarning) return def _parse(self): """Converts text into tables that users can use. This method is for use internally by the yanny object. It is not meant to be called by users. Parsing proceeds in this order: #. Lines that end with a backslash character ``\`` are reattached to following lines. #. Structure & enum definitions are identified, saved into the 'symbols' dictionary & stripped from the contents. #. Structure definitions are interpreted. #. At this point, the remaining lines of the original file can only contain these things: * 'blank' lines, including lines that only contain comments * keyword/value pairs * structure rows #. The remaining lines are scanned sequentially. #. 'Blank' lines are identified & ignored. #. Whitespace & comments are stripped from non-blank lines. #. Empty double braces ``{{}}`` are converted into empty double quotes ``""``. #. If the first word on a line matches the name of a structure, the line is broken up into tokens & each token or set of tokens (for arrays) is converted to the appropriate Python type. #. If the first word on a line does not match the name of a structure, it must be a keyword, so this line is interpreted as a keyword/value pair. No further processing is done to the value. #. At the conclusion of parsing, if ``self.raw`` is ``False``, the structures are converted into NumPy record arrays. """ # # there are five things we might find # 1. 'blank' lines including comments # 2. keyword/value pairs (which may have trailing comments) # 3. enumeration definitions # 4. structure definitions # 5. data # lines = self._contents # # Reattach lines ending with \ # lines = re.sub(r'\\\s*\n', ' ', lines) # # Find structure & enumeration definitions & strip them out # self._symbols['struct'] = re.findall( r'typedef\s+struct\s*\{[^}]+\}\s*\w+\s*;', lines) self._symbols['enum'] = re.findall( r'typedef\s+enum\s*\{[^}]+\}\s*\w+\s*;', lines) lines = re.sub(r'typedef\s+struct\s*\{[^}]+\}\s*\w+\s*;', '', lines) lines = re.sub(r'typedef\s+enum\s*\{[^}]+\}\s*\w+\s*;', '', lines) # # Interpret the structure definitions # typedefre = re.compile(r'typedef\s+struct\s*\{([^}]+)\}\s*(\w*)\s*;') for typedef in self._symbols['struct']: typedefm = typedefre.search(typedef) (definition, name) = typedefm.groups() self[name.upper()] = dict() self._symbols[name.upper()] = list() definitions = re.findall(r'\S+\s+\S+;', definition) for d in definitions: d = d.replace(';', '') (datatype, column) = re.split(r'\s+', d) column = re.sub(r'[[<].*[]>]$', '', column) self._symbols[name.upper()].append(column) self[name.upper()][column] = list() # Remove lines containing only comments comments = re.compile(r'^\s*#') # Remove lines containing only whitespace blanks = re.compile(r'^\s*$') # # Remove trailing comments, but not if they are enclosed in quotes. # # trailing_comments = re.compile(r'\s*\#.*$') # trailing_comments = re.compile(r'\s*\#[^"]+$') # # Double empty braces get replaced with empty quotes # double_braces = re.compile(r'\{\s*\{\s*\}\s*\}') if len(lines) > 0: for line in lines.split('\n'): if len(line) == 0: continue if comments.search(line) is not None: continue if blanks.search(line) is not None: continue # # Remove leading & trailing blanks & comments # line = line.strip() line = self.trailing_comment(line) # line = trailing_comments.sub('',line) line = double_braces.sub('""', line) # # Now if the first word on the line does not match a # structure definition it is a keyword/value pair # (key, value) = self.get_token(line) uckey = key.upper() if uckey in self._symbols: # # Structure data # for column in self._symbols[uckey]: if len(value) > 0 and blanks.search(value) is None: (data, value) = self.get_token(value) if self.isarray(uckey, column): # # An array value # if it's character data, it won't be # delimited by {} unless it is a multidimensional # string array. It may or may not be delimited # by double quotes # # Note, we're assuming here that the only # multidimensional arrays are string arrays # arraydata = list() while len(data) > 0: (token, data) = self.get_token(data) arraydata.append(token) self[uckey][column].append( self.convert(uckey, column, arraydata)) else: # # A single value # self[uckey][column].append( self.convert(uckey, column, data)) else: break else: # # Keyword/value pair # self[key] = value # # If self.raw is False, convert tables into NumPy record arrays # if not self.raw: for t in self.tables(): record = numpy.zeros((self.size(t),), dtype=self.dtype(t)) for c in self.columns(t): record[c] = self[t][c] self[t] = record.view(numpy.recarray) return def write_ndarray_to_yanny(filename, datatables, structnames=None, enums=None, hdr=None, comments=None): """Converts a NumPy record array into a new FTCL/yanny file. Returns a new yanny object corresponding to the file. Parameters ---------- filename : :class:`str` The name of a parameter file. datatables : :class:`numpy.ndarray`, :class:`numpy.recarray` or :class:`list` of these. A NumPy record array containing data that can be copied into a `yanny` object. structnames : :class:`str` or :class:`list` of :class:`str`, optional The name(s) to give the structure(s) in the yanny file. Defaults to 'MYSTRUCT0'. enums : :class:`dict`, optional A dictionary containing enum information. See the documentation for the :meth:`~pydl.pydlutils.yanny.yanny.dtype_to_struct` method of the yanny object. hdr : :class:`dict`, optional A dictionary containing keyword/value pairs for the 'header' of the yanny file. comments : :class:`str` or :class:`list` of :class:`str`, optional A string containing comments that will be added to the start of the new file. Returns ------- :class:`yanny` The `yanny` object resulting from writing the file. Raises ------ PydlutilsException If `filename` already exists, or if the metadata are incorrect. """ from numpy import ndarray, recarray from astropy.extern.six import string_types par = yanny(filename) if par: # # If the file already exists # raise PydlutilsException( "Apparently {0} already exists.".format(filename)) if isinstance(datatables, (ndarray, recarray, Table)): datatables = (datatables,) if structnames is None: structnames = ["MYSTRUCT{0:d}".format(k) for k in range(len(datatables))] if isinstance(structnames, string_types): structnames = (structnames,) if len(datatables) != len(structnames): raise PydlutilsException( "The data tables and their names do not match!") for k in range(len(datatables)): struct = par.dtype_to_struct(datatables[k].dtype, structname=structnames[k], enums=enums) par._symbols['struct'] += struct['struct'] par._symbols[structnames[k].upper()] = struct[structnames[k].upper()] if enums is not None and len(par._symbols['enum']) == 0: par._symbols['enum'] = struct['enum'] par[structnames[k].upper()] = datatables[k] if hdr is not None: for key in hdr: par[key] = hdr[key] par.write(filename, comments=comments) return par def is_yanny(origin, path, fileobj, *args, **kwargs): """Identifies Yanny files or objects. This function is for use with :func:`~astropy.io.registry.register_identifier`. Parameters ---------- origin : :class:`str` 'read' or 'write' path : :class:`str` Path to the file. fileobj : file object Open file object, if available. Returns ------- :class:`bool` ``True`` if the file or object is a Yanny file. """ if fileobj is not None: loc = fileobj.tell() fileobj.seek(0) try: signature = fileobj.read(7) finally: fileobj.seek(loc) return signature == b'#%yanny' elif path is not None: return path.endswith('.par') return isinstance(args[0], yanny) def read_table_yanny(filename, tablename=None): """Read a yanny file into a :class:`~astropy.table.Table`. Because yanny files can contain multiple tables, it is necessary to specify the table name to return. However, all "headers" (keyword-value pairs) will be included in the Table metadata. This function is for use with :func:`~astropy.io.registry.register_reader`. Parameters ---------- filename : :class:`str` Name of the file to read. tablename : :class:`str` The name of the table to read from the file. Returns ------- :class:`~astropy.table.Table` The table read from the file. Raises ------ PydlutilsException If `tablename` is not set. KeyError If `tablename` does not exist in the file. """ if tablename is None: raise PydlutilsException("The name of the table is required!") # # When opened by Table.read(), the filename will actually be a file-like # object opened in *binary* mode. # par = yanny(filename) try: t0 = par[tablename.upper()] except KeyError: raise KeyError("No table named {0} in {1}!".format(tablename, filename)) t = Table(t0) t.meta = par.new_dict_from_pairs() return t def write_table_yanny(table, filename, tablename=None, overwrite=False): """Write a :class:`~astropy.table.Table` to a yanny file. If `table` has any metadata, it will be written to the file as well. This function is for use with :func:`~astropy.io.registry.register_writer`. Parameters ---------- table : :class:`astropy.table.Table` The object to be written. filename : :class:`str` Name of the file to write to. tablename : :class:`str`, optional Name to give `table` within the file. overwrite : :class:`bool`, optional If ``True``, any existing file will be silently overwritten. """ if overwrite and os.path.exists(filename): os.remove(filename) if table.meta: hdr = table.meta else: hdr = None write_ndarray_to_yanny(filename, table, structnames=tablename, hdr=hdr, comments='Table') pydl-0.6.0/pydl/pydlutils/trace.py0000664000111400017500000004076512775255301017761 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the trace directory in idlutils. """ import numpy as np from . import PydlutilsException from ..goddard.math import flegendre def fchebyshev(x, m): """Compute the first `m` Chebyshev polynomials. Parameters ---------- x : array-like Compute the Chebyshev polynomials at these abscissa values. m : :class:`int` The number of Chebyshev polynomials to compute. For example, if :math:`m = 3`, :math:`T_0 (x)`, :math:`T_1 (x)` and :math:`T_2 (x)` will be computed. Returns ------- :class:`numpy.ndarray` """ from scipy.special import chebyt if isinstance(x, np.ndarray): n = x.size else: n = 1 if m < 1: raise ValueError('Order of Chebyshev polynomial must be at least 1.') try: dt = x.dtype except AttributeError: dt = np.float64 leg = np.ones((m, n), dtype=dt) if m >= 2: leg[1, :] = x if m >= 3: for k in range(2, m): leg[k, :] = np.polyval(chebyt(k), x) return leg def fchebyshev_split(x, m): """Compute the first `m` Chebyshev polynomials, but modified to allow a split in the baseline at :math:`x=0`. The intent is to allow a model fit where a constant term is different for positive and negative `x`. Parameters ---------- x : array-like Compute the Chebyshev polynomials at these abscissa values. m : :class:`int` The number of Chebyshev polynomials to compute. For example, if :math:`m = 3`, :math:`T_0 (x)`, :math:`T_1 (x)` and :math:`T_2 (x)` will be computed. Returns ------- :class:`numpy.ndarray` """ import numpy as np if isinstance(x, np.ndarray): n = x.size else: n = 1 if m < 2: raise ValueError('Order of polynomial must be at least 2.') try: dt = x.dtype except AttributeError: dt = np.float64 leg = np.ones((m, n), dtype=dt) try: leg[0, :] = (x >= 0).astype(x.dtype) except AttributeError: leg[0, :] = np.double(x >= 0) if m > 2: leg[2, :] = x if m > 3: for k in range(3, m): leg[k, :] = 2.0 * x * leg[k-1, :] - leg[k-2, :] return leg def fpoly(x, m): """Compute the first `m` simple polynomials. Parameters ---------- x : array-like Compute the simple polynomials at these abscissa values. m : :class:`int` The number of simple polynomials to compute. For example, if :math:`m = 3`, :math:`x^0`, :math:`x^1` and :math:`x^2` will be computed. Returns ------- :class:`numpy.ndarray` """ if isinstance(x, np.ndarray): n = x.size else: n = 1 if m < 1: raise ValueError('Order of polynomial must be at least 1.') try: dt = x.dtype except AttributeError: dt = np.float64 leg = np.ones((m, n), dtype=dt) if m >= 2: leg[1, :] = x if m >= 3: for k in range(2, m): leg[k, :] = leg[k-1, :] * x return leg def func_fit(x, y, ncoeff, invvar=None, function_name='legendre', ia=None, inputans=None, inputfunc=None): """Fit `x`, `y` positions to a functional form. Parameters ---------- x : array-like X values (independent variable). y : array-like Y values (dependent variable). ncoeff : :class:`int` Number of coefficients to fit. invvar : array-like, optional Weight values; inverse variance. function_name : :class:`str`, optional Function name, default 'legendre'. ia : array-like, optional An array of bool of length `ncoeff` specifying free (``True``) and fixed (``False``) parameters. inputans : array-like, optional An array of values of length `ncoeff` specifying the values of the fixed parameters. inputfunc : array-like, optional Multiply the function fit by these values. Returns ------- :func:`tuple` of array-like Fit coefficients, length `ncoeff`; fitted values. Raises ------ KeyError If an invalid function type is selected. ValueError If input dimensions do not agree. """ if x.shape != y.shape: raise ValueError('Dimensions of X and Y do not agree!') if invvar is None: invvar = np.ones(x.shape, dtype=x.dtype) else: if invvar.shape != x.shape: raise ValueError('Dimensions of X and invvar do not agree!') if ia is None: ia = np.ones((ncoeff,), dtype=np.bool) if not ia.all(): if inputans is None: inputans = np.zeros((ncoeff,), dtype=x.dtype) # # Select unmasked points # igood = (invvar > 0).nonzero()[0] ngood = len(igood) res = np.zeros((ncoeff,), dtype=x.dtype) yfit = np.zeros(x.shape, dtype=x.dtype) if ngood == 0: pass elif ngood == 1: res[0] = y[igood[0]] yfit += y[igood[0]] else: ncfit = min(ngood, ncoeff) function_map = { 'legendre': flegendre, 'flegendre': flegendre, 'chebyshev': fchebyshev, 'fchebyshev': fchebyshev, 'chebyshev_split': fchebyshev_split, 'fchebyshev_split': fchebyshev_split, 'poly': fpoly, 'fpoly': fpoly } try: legarr = function_map[function_name](x, ncfit) except KeyError: raise KeyError('Unknown function type: {0}'.format(function_name)) if inputfunc is not None: if inputfunc.shape != x.shape: raise ValueError('Dimensions of X and inputfunc do not agree!') legarr *= np.tile(inputfunc, ncfit).reshape(ncfit, x.shape[0]) yfix = np.zeros(x.shape, dtype=x.dtype) nonfix = ia[0:ncfit].nonzero()[0] nparams = len(nonfix) fixed = (~ia[0:ncfit]).nonzero()[0] if len(fixed) > 0: yfix = np.dot(legarr.T, inputans * (1 - ia)) ysub = y - yfix finalarr = legarr[nonfix, :] else: finalarr = legarr ysub = y # extra2 = finalarr * np.outer(np.ones((nparams,), dtype=x.dtype), # (invvar > 0)) extra2 = finalarr * np.outer(np.ones((nparams,), dtype=x.dtype), invvar) alpha = np.dot(finalarr, extra2.T) # assert alpha.dtype == x.dtype if nparams > 1: # beta = np.dot(ysub * (invvar > 0), finalarr.T) beta = np.dot(ysub * invvar, finalarr.T) assert beta.dtype == x.dtype # uu,ww,vv = np.linalg.svd(alpha, full_matrices=False) res[nonfix] = np.linalg.solve(alpha, beta) else: # res[nonfix] = (ysub * (invvar > 0) * finalarr).sum()/alpha res[nonfix] = (ysub * invvar * finalarr).sum()/alpha if len(fixed) > 0: res[fixed] = inputans[fixed] yfit = np.dot(legarr.T, res[0:ncfit]) return (res, yfit) class TraceSet(object): """Implements the idea of a trace set. Attributes ---------- func : :class:`str` Name of function type used to fit the trace set. xmin : float-like Minimum x value. xmax : float-like Maximum x value. coeff : array-like Coefficients of the trace set fit. nTrace : :class:`int` Number of traces in the object. ncoeff : :class:`int` Number of coefficients of the trace set fit. xjumplo : float-like Jump value, for BOSS readouts. xjumphi : float-like Jump value, for BOSS readouts. xjumpval : float-like Jump value, for BOSS readouts. outmask : array-like When initialized with x,y positions, this contains the rejected points. yfit : array-like When initialized with x,y positions, this contains the fitted y values. """ _func_map = {'poly': fpoly, 'legendre': flegendre, 'chebyshev': fchebyshev} def __init__(self, *args, **kwargs): """This class can be initialized either with a set of xy positions, or with a trace set HDU from a FITS file. """ from astropy.io.fits.fitsrec import FITS_rec from .math import djs_reject if len(args) == 1 and isinstance(args[0], FITS_rec): # # Initialize with FITS data # self.func = args[0]['FUNC'][0] self.xmin = args[0]['XMIN'][0] self.xmax = args[0]['XMAX'][0] self.coeff = args[0]['COEFF'][0] self.nTrace = self.coeff.shape[0] self.ncoeff = self.coeff.shape[1] if 'XJUMPLO' in args[0].dtype.names: self.xjumplo = args[0]['XJUMPLO'][0] self.xjumphi = args[0]['XJUMPHI'][0] self.xjumpval = args[0]['XJUMPVAL'][0] else: self.xjumplo = None self.xjumphi = None self.xjumpval = None self.outmask = None self.yfit = None elif len(args) == 2: # # Initialize with x, y positions. # xpos = args[0] ypos = args[1] self.nTrace = xpos.shape[0] if 'invvar' in kwargs: invvar = kwargs['invvar'] else: invvar = np.ones(xpos.shape, dtype=xpos.dtype) if 'func' in kwargs: self.func = kwargs['func'] else: self.func = 'legendre' if 'ncoeff' in kwargs: self.ncoeff = int(kwargs['ncoeff']) else: self.ncoeff = 3 if 'xmin' in kwargs: self.xmin = np.float64(kwargs['xmin']) else: self.xmin = xpos.min() if 'xmax' in kwargs: self.xmax = np.float64(kwargs['xmax']) else: self.xmax = xpos.max() if 'maxiter' in kwargs: maxiter = int(kwargs['maxiter']) else: maxiter = 10 if 'inmask' in kwargs: inmask = kwargs['inmask'] else: inmask = np.ones(xpos.shape, dtype=np.bool) do_jump = False if 'xjumplo' in kwargs: do_jump = True self.xjumplo = np.float64(kwargs['xjumplo']) else: self.xjumplo = None if 'xjumphi' in kwargs: self.xjumphi = np.float64(kwargs['xjumphi']) else: self.xjumphi = None if 'xjumpval' in kwargs: self.xjumpval = np.float64(kwargs['xjumpval']) else: self.xjumpval = None self.coeff = np.zeros((self.nTrace, self.ncoeff), dtype=xpos.dtype) self.outmask = np.zeros(xpos.shape, dtype=np.bool) self.yfit = np.zeros(xpos.shape, dtype=xpos.dtype) for iTrace in range(self.nTrace): xvec = self.xnorm(xpos[iTrace, :], do_jump) iIter = 0 qdone = False tempivar = (invvar[iTrace, :] * inmask[iTrace, :].astype(invvar.dtype)) thismask = tempivar > 0 while (not qdone) and (iIter <= maxiter): res, ycurfit = func_fit(xvec, ypos[iTrace, :], self.ncoeff, invvar=tempivar, function_name=self.func) thismask, qdone = djs_reject(ypos[iTrace, :], ycurfit, invvar=tempivar) iIter += 1 self.yfit[iTrace, :] = ycurfit self.coeff[iTrace, :] = res self.outmask[iTrace, :] = thismask else: raise PydlutilsException("Wrong number of arguments to TraceSet!") def xy(self, xpos=None, ignore_jump=False): """Convert from a trace set to an array of x,y positions. Parameters ---------- xpos : array-like, optional If provided, evaluate the trace set at these positions. Otherwise the positions will be constructed from the trace set object iself. ignore_jump : :class:`bool`, optional If ``True``, ignore any jump information in the `tset` object Returns ------- :func:`tuple` of array-like The x, y positions. """ from .misc import djs_laxisgen do_jump = self.has_jump and (not ignore_jump) if xpos is None: xpos = djs_laxisgen([self.nTrace, self.nx], iaxis=1) + self.xmin ypos = np.zeros(xpos.shape, dtype=xpos.dtype) for iTrace in range(self.nTrace): xvec = self.xnorm(xpos[iTrace, :], do_jump) legarr = self._func_map[self.func](xvec, self.ncoeff) ypos[iTrace, :] = np.dot(legarr.T, self.coeff[iTrace, :]) return (xpos, ypos) @property def has_jump(self): """``True`` if jump conditions are set. """ return self.xjumplo is not None @property def xRange(self): """Range of x values. """ return self.xmax - self.xmin @property def nx(self): """Number of x values. """ return int(self.xRange + 1) @property def xmid(self): """Midpoint of x values. """ return 0.5 * (self.xmin + self.xmax) def xnorm(self, xinput, jump): """Convert input x coordinates to normalized coordinates suitable for input to special polynomials. Parameters ---------- xinput : array-like Input coordinates. jump : :class:`bool` Set to ``True`` if there is a jump. Returns ------- array-like Normalized coordinates. """ if jump: # Vector specifying what fraction of the jump has passed: jfrac = np.minimum(np.maximum(((xinput - self.xjumplo) / (self.xjumphi - self.xjumplo)), 0.), 1.) # Conversion to "natural" x baseline: xnatural = xinput + jfrac * self.xjumpval else: xnatural = xinput return 2.0 * (xnatural - self.xmid)/self.xRange def traceset2xy(tset, xpos=None, ignore_jump=False): """Convert from a trace set to an array of x,y positions. Parameters ---------- tset : :class:`TraceSet` A :class:`TraceSet` object. xpos : array-like, optional If provided, evaluate the trace set at these positions. Otherwise the positions will be constructed from the trace set object iself. ignore_jump : bool, optional If ``True``, ignore any jump information in the `tset` object Returns ------- :func:`tuple` of array-like The x, y positions. """ return tset.xy(xpos, ignore_jump) def xy2traceset(xpos, ypos, **kwargs): """Convert from x,y positions to a trace set. Parameters ---------- xpos, ypos : array-like X,Y positions corresponding as [nx,Ntrace] arrays. invvar : array-like, optional Inverse variances for fitting. func : :class:`str`, optional Function type for fitting; defaults to 'legendre'. ncoeff : :class:`int`, optional Number of coefficients to fit. Defaults to 3. xmin, xmax : :class:`float`, optional Explicitly set minimum and maximum values, instead of computing them from `xpos`. maxiter : :class:`int`, optional Maximum number of rejection iterations; set to 0 for no rejection; default to 10. inmask : array-like, optional Mask set to 1 for good points and 0 for rejected points; same dimensions as `xpos`, `ypos`. Points rejected by `inmask` are always rejected from the fits (the rejection is "sticky"), and will also be marked as rejected in the outmask attribute. ia, inputans, inputfunc : array-like, optional These arguments will be passed to :func:`func_fit`. xjumplo : :class:`float`, optional x position locating start of an x discontinuity xjumphi : :class:`float`, optional x position locating end of that x discontinuity xjumpval : :class:`float`, optional magnitude of the discontinuity "jump" between those bounds (previous 3 keywords motivated by BOSS 2-phase readout) Returns ------- :class:`TraceSet` A :class:`TraceSet` object. """ return TraceSet(xpos, ypos, **kwargs) pydl-0.6.0/pydl/pydlutils/__init__.py0000664000111400017500000000122312775255301020404 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ This subpackage implements functions from the idlutils package. """ # # Define this early on so that submodules can use it # from .. import PydlException import astropy.utils.exceptions as aue class PydlutilsException(PydlException): """Exceptions raised by :mod:`pydl.pydlutils` that don't fit into a standard exception class like :exc:`ValueError`. """ pass class PydlutilsUserWarning(aue.AstropyUserWarning): """Class for warnings issued by :mod:`pydl.pydlutils`. """ pass __all__ = ['PydlutilsException', 'PydlutilsUserWarning'] pydl-0.6.0/pydl/pydlutils/misc.py0000664000111400017500000003072613154630165017607 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the misc directory in idlutils. """ from __future__ import print_function import numpy as np from . import PydlutilsException def decode_mixed(x): """Convert bytes in Numpy arrays into strings. Leave other stuff alone. Parameters ---------- x : object Input object. Returns ------- object If `x` has a ``decode()`` method, ``x.decode()`` will be returned. Otherwise `x` will be returned unchanged. """ try: return x.decode() except: return x def djs_laxisgen(dims, iaxis=0): """Returns an integer array where each element of the array is set equal to its index number along the specified axis. Parameters ---------- dims : :class:`list` Dimensions of the array to return. iaxis : :class:`int`, optional Index along this dimension. Returns ------- :class:`numpy.ndarray` An array of indexes with ``dtype=int32``. Raises ------ ValueError If `iaxis` is greater than or equal to the number of dimensions. Notes ----- For two or more dimensions, there is no difference between this routine and :func:`~pydl.pydlutils.misc.djs_laxisnum`. Examples -------- >>> from pydl.pydlutils.misc import djs_laxisgen >>> print(djs_laxisgen([4,4])) [[0 0 0 0] [1 1 1 1] [2 2 2 2] [3 3 3 3]] """ ndimen = len(dims) if ndimen == 1: return np.arange(dims[0], dtype='i4') return djs_laxisnum(dims, iaxis) def djs_laxisnum(dims, iaxis=0): """Returns an integer array where each element of the array is set equal to its index number in the specified axis. Parameters ---------- dims : :class:`list` Dimensions of the array to return. iaxis : :class:`int`, optional Index along this dimension. Returns ------- :class:`numpy.ndarray` An array of indexes with ``dtype=int32``. Raises ------ ValueError If `iaxis` is greater than or equal to the number of dimensions, or if number of dimensions is greater than three. Notes ----- For two or more dimensions, there is no difference between this routine and :func:`~pydl.pydlutils.misc.djs_laxisgen`. Examples -------- >>> from pydl.pydlutils.misc import djs_laxisnum >>> print(djs_laxisnum([4,4])) [[0 0 0 0] [1 1 1 1] [2 2 2 2] [3 3 3 3]] """ ndimen = len(dims) result = np.zeros(dims, dtype='i4') if ndimen == 1: pass elif ndimen == 2: if iaxis == 0: for k in range(dims[0]): result[k, :] = k elif iaxis == 1: for k in range(dims[1]): result[:, k] = k else: raise ValueError("Bad value for iaxis: {0:d}".format(iaxis)) elif ndimen == 3: if iaxis == 0: for k in range(dims[0]): result[k, :, :] = k elif iaxis == 1: for k in range(dims[1]): result[:, k, :] = k elif iaxis == 2: for k in range(dims[2]): result[:, :, k] = k else: raise ValueError("Bad value for iaxis: {0:d}".format(iaxis)) else: raise ValueError("{0:d} dimensions not supported.".format(ndimen)) return result def hogg_iau_name(ra, dec, prefix='SDSS', precision=1): """Properly format astronomical source names to the IAU convention. Parameters ---------- ra : :class:`float` or :class:`numpy.ndarray` Right ascencion in decimal degrees dec : :class:`float` or :class:`numpy.ndarray` Declination in decimal degrees. prefix : :class:`str`, optional Add this prefix to the string, defaults to 'SDSS'. precision : :class:`int`, optional Display this many digits of precision on seconds, default 1. Returns ------- :class:`str` or :class:`list` The IAU name for the coordinates. Examples -------- >>> from pydl.pydlutils.misc import hogg_iau_name >>> hogg_iau_name(354.120375,-0.544777778) 'SDSS J233628.89-003241.2' """ import numpy as np # # Promote scalar values to arrays. # if isinstance(ra, float): ra = np.array([ra]) if isinstance(dec, float): dec = np.array([dec]) h = ra/15.0 rah = np.floor(h) ram = np.floor(60.0*(h-rah)) ras = 60.0*(60.0*(h-rah) - ram) ras = np.floor(ras*10.0**(precision+1))/10.0**(precision+1) rasformat = "{{2:0{0:d}.{1:d}f}}".format(precision+4, precision+1) rah = rah.astype(np.int32) ram = ram.astype(np.int32) desgn = np.array(list('+'*len(dec))) desgn[dec < 0] = '-' adec = np.absolute(dec) ded = np.floor(adec) dem = np.floor(60.0*(adec-ded)) des = 60.0*(60.0*(adec-ded) - dem) des = np.floor(des*10.0**precision)/10.0**precision desformat = "{{6:0{0:d}.{1:d}f}}".format(precision+3, precision) if precision == 0: desformat = "{6:02d}" des = des.astype(np.int32) ded = ded.astype(np.int32) dem = dem.astype(np.int32) adformat = "{{0:02d}}{{1:02d}}{ras}{{3:s}}{{4:02d}}{{5:02d}}{des}".format( ras=rasformat, des=desformat) adstr = [adformat.format(*x) for x in zip( rah, ram, ras, desgn, ded, dem, des)] if prefix == '': jstr = 'J' else: jstr = ' J' name = ["{0}{1}{2}".format(prefix, jstr, x) for x in adstr] if len(ra) == 1: return name[0] else: return name def hogg_iau_name_main(): # pragma: no cover from argparse import ArgumentParser parser = ArgumentParser(description='Properly format astronomical ' + 'source names to the IAU convention.') parser.add_argument('-P', '--precision', dest='precision', action='store', metavar='N', default=1, type=int, help='Digits of precision to add to the declination.') parser.add_argument('-p', '--prefix', dest='prefix', action='store', metavar='STR', default='SDSS', help='Add this prefix to the name.') parser.add_argument('ra', metavar='RA', type=float, help='Right Ascension.') parser.add_argument('dec', metavar='Dec', type=float, help='Declination.') options = parser.parse_args() print(hogg_iau_name(options.ra, options.dec, prefix=options.prefix, precision=options.precision)) return 0 def struct_print(array, filename=None, formatcodes=None, alias=None, fdigit=5, ddigit=7, html=False, no_head=False, silent=False): """Print a NumPy record array (analogous to an IDL structure) in a nice way. Parameters ---------- array : :class:`numpy.ndarray` A record array to print. filename : :class:`str` or file-like, optional If supplied, write to this file. formatcodes : :class:`dict`, optional If supplied, use explicit format for certain columns. alias : :class:`dict`, optional If supplied, use this mapping of record array column names to printed column names. fdigit : :class:`int`, optional Width of 32-bit floating point columns, default 5. ddigit : :class:`int`, optional Width of 64-bit floating point columns, default 7. html : :class:`bool`, optional If ``True``, print an html table. no_head : :class:`bool`, optional If ``True``, *don't* print a header line. silent : :class:`bool`, optional If ``True``, do not print the table, just return it. Returns ------- :func:`tuple` A tuple containing a list of the lines in the table. If `html` is ``True``, also returns a list of lines of CSS for formatting the table. Examples -------- >>> import numpy as np >>> from pydl.pydlutils.misc import struct_print >>> struct_print(np.array([(1,2.34,'five'),(2,3.456,'seven'),(3,4.5678,'nine')],dtype=[('a','i4'),('bb','f4'),('ccc','S5')]),silent=True) (['a bb ccc ', '- ----------- -----', '1 2.34 five ', '2 3.456 seven', '3 4.5678 nine '], []) """ if html: headstart = '' headsep = '' headend = '' colstart = '' colsep = '' colend = '' css = [''] else: headstart = '' headsep = ' ' headend = '' colstart = '' colsep = ' ' colend = '' css = list() # # Alias should be a dictionary that maps structure names to column names # if alias is None: # # Create a dummy alias dictionary # alias = dict(list(zip(array.dtype.names, array.dtype.names))) else: # # Fill in any missing values of the alias dictionary # for tag in array.dtype.names: if tag not in alias: alias[tag] = tag # # Formatcodes allows an override for certain columns. # if formatcodes is None: formatcodes = dict() # # This dictionary will hold the number of characters in each column # nchar = dict() # # Construct format codes for each column # for k, tag in enumerate(array.dtype.names): if tag in formatcodes: thiscode = formatcodes[tag] thisn = len(thiscode.format(array[tag][0])) else: d = array.dtype.fields[tag][0] if d.kind == 'i' or d.kind == 'u': thisn = max(max(len(str(array[tag].min())), len(str(array[tag].max()))), len(tag)) thiscode = "{{{0:d}:{1:d}d}}".format(k, thisn) elif d.kind == 'f': if d.itemsize == 8: prec = ddigit else: prec = fdigit thisn = prec + 6 if array[tag].min() < 0: thisn += 1 thiscode = "{{{0:d}:{1:d}.{2:d}g}}".format(k, thisn, prec) elif d.kind == 'S' or d.kind == 'U': thisn = max(d.itemsize, len(tag)) thiscode = "{{{0:d}:{1:d}s}}".format(k, thisn) else: raise PydlutilsException( "Unsupported kind: {0}".format(d.kind)) formatcodes[tag] = thiscode nchar[tag] = thisn # # Start building an array of lines # lines = list() # # Construct header lines # if html: lines.append('') hdr1 = (headstart + headsep.join([alias[tag] for tag in array.dtype.names]) + headend) lines.append(hdr1) else: if not no_head: hdr1 = (headstart + headsep.join([("{{0:{0:d}s}}".format( nchar[tag])).format(alias[tag]) for tag in array.dtype.names]) + headend) hdr2 = (headstart + headsep.join(['-' * nchar[tag] for tag in array.dtype.names]) + headend) lines.append(hdr1) lines.append(hdr2) # # Create a format string for the data from the individual format codes # rowformat = (colstart + colsep.join([formatcodes[tag] for tag in array.dtype.names]) + colend) for k in range(array.size): lines.append(rowformat.format( *([decode_mixed(l) for l in array[k].tolist()]))) if html: lines.append('
') f = None # This variable will store a file handle close_file = False if filename is not None: if hasattr(filename, 'write'): f = filename else: f = open(filename, 'w+b') close_file = True if f is None: if not silent: # pragma: no cover print("\n".join(lines)+"\n") else: f.write(("\n".join(lines)+"\n").encode('utf-8')) if close_file: f.close() return (lines, css) pydl-0.6.0/pydl/pydlutils/data/0000775000111400017500000000000013160273564017206 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlutils/data/filters/0000775000111400017500000000000013160273564020656 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlutils/data/filters/sdss_jun2001_g_atm.dat0000664000111400017500000001535412775255301024662 0ustar weaverweaver00000000000000# The following file is from Jim Gunn, from June 2001. It should be # self-explanatory; for most purposes, you will want to use the second # column. Consider this file preliminary. # # These filter curves have been used to calculate the effective # wavelengths and the qtdl/l (see Chapter 8 of the Black Book) of the # filters; the values are: # # u 3551 0.0171 # g 4686 0.0893 # r 6166 0.0886 # i 7480 0.0591 # z 8932 0.0099 # # Table Caption For Response Functions # # The first column is the wavelength in \AAngstroms. The second column # (respt) is the quantum efficiency on the sky looking through 1.3 # airmasses at APO for a point source. The third column (resbig) is the # QE under these conditions for very large sources (size greater than # about 80 pixels) for which the infrared scattering is negligible. The # only filters for which the infrared scattering has any effect are r and # i; the scattering in the bluer chips is negligible, and the z chips are # not thinned and the phenomenon does not exist. The fourth column # (resnoa) is the response of the third column with {\it no} atmosphere, # and the fifth column is the assumed atmospheric transparency at {\it # one} airmass at APO. The tables were constructed using monochromator # illumination of the camera with a bandpass of about 100 \AA, sampled for # the u filter at 50 \AA intervals and for the others at 100 \AA # intervals. These measurements were compared with measured responses of # the component filters and detectors and three additional points were # interpolated using these data, two at the extreme toes and one # additional (in g, r, and i) at the point of the beginning of the sharp # cutoff of the shortpass interference filter. These points are necessary # in order to make spline interpolation of the response data well-behaved. # These spline-interpolated response data were then multiplied by measured # aluminum reflectivities and scaled atmospheric transmission to produce # the tables below. The overall normalization is somewhat uncertain, # but this uncertainty does not affect the shapes. Note, however, that # there has been no attempt to remove the finite resolution of the # monochromator measurements. These tables are the {\it averages} of the # responses for all six of the camera chips with a given filter. The # responses are in general very similar except in the z band, where the # nonuniformity of the infrared rolloff, presumably associated with # varying thickness of the epitaxial layer or perhaps the gate structures # in these thick devices, introduces variations in the effective wavelengths # of the filters of order 100 \AA. We are currently working on better # response functions and will present them when they become available, but # these will suffice for most applications. In all cases the first point # is a measured point, so the grid of wavelengths at which measurements # exist is a subset of the wavelength lists here. # # SDSS Camera g Response Function 89 Points # # lam respt resbig resnoa xatm 3630 0.0000 0.0000 0.0000 0.6476 3655 0.0003 0.0003 0.0005 0.6553 3680 0.0008 0.0008 0.0013 0.6631 3705 0.0013 0.0013 0.0022 0.6702 3730 0.0019 0.0019 0.0030 0.6763 3755 0.0024 0.0024 0.0039 0.6815 3780 0.0034 0.0034 0.0055 0.6863 3805 0.0055 0.0055 0.0087 0.6912 3830 0.0103 0.0103 0.0162 0.6965 3855 0.0194 0.0194 0.0301 0.7023 3880 0.0326 0.0326 0.0500 0.7088 3905 0.0492 0.0492 0.0745 0.7158 3930 0.0686 0.0686 0.1024 0.7235 3955 0.0900 0.0900 0.1324 0.7315 3980 0.1123 0.1123 0.1629 0.7393 4005 0.1342 0.1342 0.1924 0.7464 4030 0.1545 0.1545 0.2191 0.7526 4055 0.1722 0.1722 0.2419 0.7581 4080 0.1873 0.1873 0.2609 0.7631 4105 0.2003 0.2003 0.2767 0.7680 4130 0.2116 0.2116 0.2899 0.7727 4155 0.2214 0.2214 0.3010 0.7774 4180 0.2301 0.2301 0.3105 0.7820 4205 0.2378 0.2378 0.3186 0.7862 4230 0.2448 0.2448 0.3258 0.7902 4255 0.2513 0.2513 0.3324 0.7940 4280 0.2574 0.2574 0.3385 0.7976 4305 0.2633 0.2633 0.3442 0.8013 4330 0.2691 0.2691 0.3496 0.8049 4355 0.2747 0.2747 0.3548 0.8087 4380 0.2801 0.2801 0.3596 0.8124 4405 0.2852 0.2852 0.3640 0.8161 4430 0.2899 0.2899 0.3678 0.8199 4455 0.2940 0.2940 0.3709 0.8235 4480 0.2979 0.2979 0.3736 0.8271 4505 0.3016 0.3016 0.3763 0.8305 4530 0.3055 0.3055 0.3792 0.8337 4555 0.3097 0.3097 0.3827 0.8368 4580 0.3141 0.3141 0.3863 0.8397 4605 0.3184 0.3184 0.3899 0.8425 4630 0.3224 0.3224 0.3931 0.8453 4655 0.3257 0.3257 0.3955 0.8480 4680 0.3284 0.3284 0.3973 0.8505 4705 0.3307 0.3307 0.3986 0.8528 4730 0.3327 0.3327 0.3997 0.8549 4755 0.3346 0.3346 0.4008 0.8568 4780 0.3364 0.3364 0.4019 0.8587 4805 0.3383 0.3383 0.4030 0.8606 4830 0.3403 0.3403 0.4043 0.8625 4855 0.3425 0.3425 0.4057 0.8643 4880 0.3448 0.3448 0.4073 0.8661 4905 0.3472 0.3472 0.4091 0.8678 4930 0.3495 0.3495 0.4110 0.8693 4955 0.3519 0.3519 0.4129 0.8706 4980 0.3541 0.3541 0.4147 0.8719 5005 0.3562 0.3562 0.4165 0.8730 5030 0.3581 0.3581 0.4181 0.8740 5055 0.3597 0.3597 0.4194 0.8750 5080 0.3609 0.3609 0.4201 0.8759 5105 0.3613 0.3613 0.4201 0.8768 5130 0.3609 0.3609 0.4191 0.8777 5155 0.3595 0.3595 0.4169 0.8785 5180 0.3581 0.3581 0.4147 0.8794 5205 0.3558 0.3558 0.4115 0.8803 5230 0.3452 0.3452 0.3988 0.8813 5255 0.3194 0.3194 0.3684 0.8822 5280 0.2807 0.2807 0.3233 0.8832 5305 0.2339 0.2339 0.2690 0.8842 5330 0.1839 0.1839 0.2112 0.8852 5355 0.1352 0.1352 0.1550 0.8861 5380 0.0911 0.0911 0.1043 0.8869 5405 0.0548 0.0548 0.0627 0.8877 5430 0.0295 0.0295 0.0337 0.8885 5455 0.0166 0.0166 0.0190 0.8891 5480 0.0112 0.0112 0.0128 0.8897 5505 0.0077 0.0077 0.0087 0.8902 5530 0.0050 0.0050 0.0057 0.8907 5555 0.0032 0.0032 0.0037 0.8911 5580 0.0021 0.0021 0.0024 0.8914 5605 0.0015 0.0015 0.0017 0.8917 5630 0.0012 0.0012 0.0014 0.8920 5655 0.0010 0.0010 0.0012 0.8923 5680 0.0009 0.0009 0.0010 0.8926 5705 0.0008 0.0008 0.0009 0.8929 5730 0.0006 0.0006 0.0007 0.8933 5755 0.0005 0.0005 0.0005 0.8938 5780 0.0003 0.0003 0.0003 0.8945 5805 0.0001 0.0001 0.0001 0.8952 5830 0.0000 0.0000 0.0000 0.8962 pydl-0.6.0/pydl/pydlutils/data/filters/sdss_jun2001_z_atm.dat0000664000111400017500000002165112775255301024702 0ustar weaverweaver00000000000000# The following file is from Jim Gunn, from June 2001. It should be # self-explanatory; for most purposes, you will want to use the second # column. Consider this file preliminary. # # These filter curves have been used to calculate the effective # wavelengths and the qtdl/l (see Chapter 8 of the Black Book) of the # filters; the values are: # # u 3551 0.0171 # g 4686 0.0893 # r 6166 0.0886 # i 7480 0.0591 # z 8932 0.0099 # # Table Caption For Response Functions # # The first column is the wavelength in \AAngstroms. The second column # (respt) is the quantum efficiency on the sky looking through 1.3 # airmasses at APO for a point source. The third column (resbig) is the # QE under these conditions for very large sources (size greater than # about 80 pixels) for which the infrared scattering is negligible. The # only filters for which the infrared scattering has any effect are r and # i; the scattering in the bluer chips is negligible, and the z chips are # not thinned and the phenomenon does not exist. The fourth column # (resnoa) is the response of the third column with {\it no} atmosphere, # and the fifth column is the assumed atmospheric transparency at {\it # one} airmass at APO. The tables were constructed using monochromator # illumination of the camera with a bandpass of about 100 \AA, sampled for # the u filter at 50 \AA intervals and for the others at 100 \AA # intervals. These measurements were compared with measured responses of # the component filters and detectors and three additional points were # interpolated using these data, two at the extreme toes and one # additional (in g, r, and i) at the point of the beginning of the sharp # cutoff of the shortpass interference filter. These points are necessary # in order to make spline interpolation of the response data well-behaved. # These spline-interpolated response data were then multiplied by measured # aluminum reflectivities and scaled atmospheric transmission to produce # the tables below. The overall normalization is somewhat uncertain, # but this uncertainty does not affect the shapes. Note, however, that # there has been no attempt to remove the finite resolution of the # monochromator measurements. These tables are the {\it averages} of the # responses for all six of the camera chips with a given filter. The # responses are in general very similar except in the z band, where the # nonuniformity of the infrared rolloff, presumably associated with # varying thickness of the epitaxial layer or perhaps the gate structures # in these thick devices, introduces variations in the effective wavelengths # of the filters of order 100 \AA. We are currently working on better # response functions and will present them when they become available, but # these will suffice for most applications. In all cases the first point # is a measured point, so the grid of wavelengths at which measurements # exist is a subset of the wavelength lists here. # # SDSS Camera z Response Function 141 Points # # lam respt resbig resnoa xatm 7730 0.0000 0.0000 0.0000 0.9602 7755 0.0000 0.0000 0.0000 0.9615 7780 0.0001 0.0001 0.0001 0.9605 7805 0.0001 0.0001 0.0001 0.9583 7830 0.0001 0.0001 0.0001 0.9559 7855 0.0002 0.0002 0.0002 0.9541 7880 0.0002 0.0002 0.0002 0.9541 7905 0.0003 0.0003 0.0003 0.9567 7930 0.0005 0.0005 0.0005 0.9622 7955 0.0007 0.0007 0.0007 0.9692 7980 0.0011 0.0011 0.0011 0.9762 8005 0.0017 0.0017 0.0017 0.9814 8030 0.0027 0.0027 0.0027 0.9833 8055 0.0040 0.0040 0.0040 0.9801 8080 0.0057 0.0057 0.0058 0.9702 8105 0.0079 0.0079 0.0082 0.9524 8130 0.0106 0.0106 0.0114 0.9285 8155 0.0139 0.0139 0.0155 0.9075 8180 0.0178 0.0178 0.0202 0.8931 8205 0.0222 0.0222 0.0255 0.8853 8230 0.0271 0.0271 0.0311 0.8843 8255 0.0324 0.0324 0.0369 0.8902 8280 0.0382 0.0382 0.0428 0.9033 8305 0.0446 0.0446 0.0484 0.9242 8330 0.0511 0.0511 0.0536 0.9483 8355 0.0564 0.0564 0.0583 0.9591 8380 0.0603 0.0603 0.0625 0.9576 8405 0.0637 0.0637 0.0661 0.9567 8430 0.0667 0.0667 0.0693 0.9564 8455 0.0694 0.0694 0.0720 0.9565 8480 0.0717 0.0717 0.0744 0.9569 8505 0.0736 0.0736 0.0763 0.9576 8530 0.0752 0.0752 0.0779 0.9584 8555 0.0765 0.0765 0.0792 0.9592 8580 0.0775 0.0775 0.0801 0.9598 8605 0.0782 0.0782 0.0808 0.9602 8630 0.0786 0.0786 0.0812 0.9603 8655 0.0787 0.0787 0.0813 0.9599 8680 0.0785 0.0785 0.0812 0.9593 8705 0.0780 0.0780 0.0807 0.9586 8730 0.0772 0.0772 0.0801 0.9578 8755 0.0763 0.0763 0.0791 0.9571 8780 0.0751 0.0751 0.0779 0.9567 8805 0.0738 0.0738 0.0766 0.9566 8830 0.0723 0.0723 0.0750 0.9571 8855 0.0708 0.0708 0.0734 0.9582 8880 0.0693 0.0693 0.0716 0.9600 8905 0.0674 0.0674 0.0698 0.9591 8930 0.0632 0.0632 0.0679 0.9314 8955 0.0581 0.0581 0.0661 0.8923 8980 0.0543 0.0543 0.0642 0.8648 9005 0.0526 0.0526 0.0624 0.8633 9030 0.0523 0.0523 0.0607 0.8787 9055 0.0522 0.0522 0.0590 0.8961 9080 0.0512 0.0512 0.0574 0.9020 9105 0.0496 0.0496 0.0559 0.8980 9130 0.0481 0.0481 0.0546 0.8931 9155 0.0473 0.0473 0.0535 0.8962 9180 0.0476 0.0476 0.0524 0.9138 9205 0.0482 0.0482 0.0515 0.9352 9230 0.0476 0.0476 0.0505 0.9407 9255 0.0447 0.0447 0.0496 0.9103 9280 0.0391 0.0391 0.0485 0.8345 9305 0.0329 0.0329 0.0474 0.7441 9330 0.0283 0.0283 0.0462 0.6752 9355 0.0264 0.0264 0.0450 0.6524 9380 0.0271 0.0271 0.0438 0.6794 9405 0.0283 0.0283 0.0426 0.7178 9430 0.0275 0.0275 0.0415 0.7184 9455 0.0254 0.0254 0.0404 0.6897 9480 0.0252 0.0252 0.0393 0.7003 9505 0.0256 0.0256 0.0383 0.7214 9530 0.0246 0.0246 0.0373 0.7147 9555 0.0244 0.0244 0.0363 0.7251 9580 0.0252 0.0252 0.0353 0.7594 9605 0.0258 0.0258 0.0342 0.7923 9630 0.0265 0.0265 0.0331 0.8302 9655 0.0274 0.0274 0.0319 0.8766 9680 0.0279 0.0279 0.0307 0.9150 9705 0.0271 0.0271 0.0294 0.9253 9730 0.0252 0.0252 0.0280 0.9059 9755 0.0236 0.0236 0.0267 0.8947 9780 0.0227 0.0227 0.0253 0.9045 9805 0.0222 0.0222 0.0240 0.9262 9830 0.0216 0.0216 0.0227 0.9500 9855 0.0208 0.0208 0.0213 0.9652 9880 0.0196 0.0196 0.0201 0.9656 9905 0.0183 0.0183 0.0188 0.9642 9930 0.0171 0.0171 0.0176 0.9630 9955 0.0160 0.0160 0.0165 0.9618 9980 0.0149 0.0149 0.0153 0.9607 10005 0.0138 0.0138 0.0143 0.9597 10030 0.0128 0.0128 0.0132 0.9588 10055 0.0118 0.0118 0.0122 0.9579 10080 0.0108 0.0108 0.0112 0.9572 10105 0.0099 0.0099 0.0103 0.9565 10130 0.0091 0.0091 0.0094 0.9559 10155 0.0083 0.0083 0.0086 0.9553 10180 0.0075 0.0075 0.0078 0.9549 10205 0.0068 0.0068 0.0071 0.9545 10230 0.0061 0.0061 0.0064 0.9541 10255 0.0055 0.0055 0.0058 0.9539 10280 0.0050 0.0050 0.0052 0.9537 10305 0.0045 0.0045 0.0047 0.9535 10330 0.0041 0.0041 0.0042 0.9534 10355 0.0037 0.0037 0.0038 0.9534 10380 0.0033 0.0033 0.0035 0.9534 10405 0.0030 0.0030 0.0031 0.9535 10430 0.0027 0.0027 0.0028 0.9536 10455 0.0025 0.0025 0.0026 0.9537 10480 0.0023 0.0023 0.0024 0.9539 10505 0.0021 0.0021 0.0022 0.9541 10530 0.0019 0.0019 0.0020 0.9544 10555 0.0018 0.0018 0.0019 0.9547 10580 0.0017 0.0017 0.0018 0.9551 10605 0.0016 0.0016 0.0016 0.9554 10630 0.0015 0.0015 0.0015 0.9558 10655 0.0014 0.0014 0.0014 0.9563 10680 0.0013 0.0013 0.0013 0.9567 10705 0.0012 0.0012 0.0012 0.9572 10730 0.0011 0.0011 0.0011 0.9577 10755 0.0010 0.0010 0.0010 0.9582 10780 0.0009 0.0009 0.0009 0.9587 10805 0.0008 0.0008 0.0008 0.9593 10830 0.0008 0.0008 0.0008 0.9598 10855 0.0007 0.0007 0.0007 0.9604 10880 0.0006 0.0006 0.0007 0.9609 10905 0.0006 0.0006 0.0006 0.9615 10930 0.0006 0.0006 0.0006 0.9621 10955 0.0005 0.0005 0.0005 0.9626 10980 0.0005 0.0005 0.0005 0.9632 11005 0.0004 0.0004 0.0004 0.9638 11030 0.0004 0.0004 0.0004 0.9643 11055 0.0003 0.0003 0.0003 0.9648 11080 0.0003 0.0003 0.0003 0.9654 11105 0.0002 0.0002 0.0002 0.9659 11130 0.0002 0.0002 0.0002 0.9664 11155 0.0001 0.0001 0.0001 0.9669 11180 0.0001 0.0001 0.0001 0.9673 11205 0.0000 0.0000 0.0000 0.9677 11230 0.0000 0.0000 0.0000 0.9682 pydl-0.6.0/pydl/pydlutils/data/filters/sdss_jun2001_u_atm.dat0000664000111400017500000001173612775255301024700 0ustar weaverweaver00000000000000# The following file is from Jim Gunn, from June 2001. It should be # self-explanatory; for most purposes, you will want to use the second # column. Consider this file preliminary. # # These filter curves have been used to calculate the effective # wavelengths and the qtdl/l (see Chapter 8 of the Black Book) of the # filters; the values are: # # u 3551 0.0171 # g 4686 0.0893 # r 6166 0.0886 # i 7480 0.0591 # z 8932 0.0099 # # Table Caption For Response Functions # # The first column is the wavelength in \AAngstroms. The second column # (respt) is the quantum efficiency on the sky looking through 1.3 # airmasses at APO for a point source. The third column (resbig) is the # QE under these conditions for very large sources (size greater than # about 80 pixels) for which the infrared scattering is negligible. The # only filters for which the infrared scattering has any effect are r and # i; the scattering in the bluer chips is negligible, and the z chips are # not thinned and the phenomenon does not exist. The fourth column # (resnoa) is the response of the third column with {\it no} atmosphere, # and the fifth column is the assumed atmospheric transparency at {\it # one} airmass at APO. The tables were constructed using monochromator # illumination of the camera with a bandpass of about 100 \AA, sampled for # the u filter at 50 \AA intervals and for the others at 100 \AA # intervals. These measurements were compared with measured responses of # the component filters and detectors and three additional points were # interpolated using these data, two at the extreme toes and one # additional (in g, r, and i) at the point of the beginning of the sharp # cutoff of the shortpass interference filter. These points are necessary # in order to make spline interpolation of the response data well-behaved. # These spline-interpolated response data were then multiplied by measured # aluminum reflectivities and scaled atmospheric transmission to produce # the tables below. The overall normalization is somewhat uncertain, # but this uncertainty does not affect the shapes. Note, however, that # there has been no attempt to remove the finite resolution of the # monochromator measurements. These tables are the {\it averages} of the # responses for all six of the camera chips with a given filter. The # responses are in general very similar except in the z band, where the # nonuniformity of the infrared rolloff, presumably associated with # varying thickness of the epitaxial layer or perhaps the gate structures # in these thick devices, introduces variations in the effective wavelengths # of the filters of order 100 \AA. We are currently working on better # response functions and will present them when they become available, but # these will suffice for most applications. In all cases the first point # is a measured point, so the grid of wavelengths at which measurements # exist is a subset of the wavelength lists here. # # SDSS Camera u Response Function 47 Points # # lam respt resbig resnoa xatm 2980 0.0000 0.0000 0.0000 0.0727 3005 0.0001 0.0001 0.0014 0.0992 3030 0.0005 0.0005 0.0071 0.1308 3055 0.0013 0.0013 0.0127 0.1673 3080 0.0026 0.0026 0.0198 0.2075 3105 0.0052 0.0052 0.0314 0.2470 3130 0.0093 0.0093 0.0464 0.2862 3155 0.0161 0.0161 0.0629 0.3444 3180 0.0240 0.0240 0.0794 0.3920 3205 0.0323 0.0323 0.0949 0.4300 3230 0.0405 0.0405 0.1093 0.4585 3255 0.0485 0.0485 0.1229 0.4817 3280 0.0561 0.0561 0.1352 0.5007 3305 0.0634 0.0634 0.1458 0.5189 3330 0.0700 0.0700 0.1545 0.5351 3355 0.0756 0.0756 0.1617 0.5486 3380 0.0803 0.0803 0.1679 0.5581 3405 0.0848 0.0848 0.1737 0.5669 3430 0.0883 0.0883 0.1786 0.5727 3455 0.0917 0.0917 0.1819 0.5812 3480 0.0959 0.0959 0.1842 0.5959 3505 0.1001 0.1001 0.1860 0.6112 3530 0.1029 0.1029 0.1870 0.6221 3555 0.1044 0.1044 0.1868 0.6294 3580 0.1053 0.1053 0.1862 0.6350 3605 0.1063 0.1063 0.1858 0.6406 3630 0.1075 0.1075 0.1853 0.6476 3655 0.1085 0.1085 0.1841 0.6553 3680 0.1084 0.1084 0.1812 0.6631 3705 0.1064 0.1064 0.1754 0.6702 3730 0.1024 0.1024 0.1669 0.6763 3755 0.0966 0.0966 0.1558 0.6815 3780 0.0887 0.0887 0.1419 0.6863 3805 0.0787 0.0787 0.1247 0.6912 3830 0.0672 0.0672 0.1054 0.6965 3855 0.0549 0.0549 0.0851 0.7023 3880 0.0413 0.0413 0.0634 0.7088 3905 0.0268 0.0268 0.0405 0.7158 3930 0.0145 0.0145 0.0216 0.7235 3955 0.0075 0.0075 0.0110 0.7315 3980 0.0042 0.0042 0.0062 0.7393 4005 0.0022 0.0022 0.0032 0.7464 4030 0.0010 0.0010 0.0015 0.7526 4055 0.0006 0.0006 0.0008 0.7581 4080 0.0004 0.0004 0.0006 0.7631 4105 0.0002 0.0002 0.0003 0.7680 4130 0.0000 0.0000 0.0000 0.7727 pydl-0.6.0/pydl/pydlutils/data/filters/sdss_jun2001_r_atm.dat0000664000111400017500000001422312775255301024667 0ustar weaverweaver00000000000000# The following file is from Jim Gunn, from June 2001. It should be # self-explanatory; for most purposes, you will want to use the second # column. Consider this file preliminary. # # These filter curves have been used to calculate the effective # wavelengths and the qtdl/l (see Chapter 8 of the Black Book) of the # filters; the values are: # # u 3551 0.0171 # g 4686 0.0893 # r 6166 0.0886 # i 7480 0.0591 # z 8932 0.0099 # # Table Caption For Response Functions # # The first column is the wavelength in \AAngstroms. The second column # (respt) is the quantum efficiency on the sky looking through 1.3 # airmasses at APO for a point source. The third column (resbig) is the # QE under these conditions for very large sources (size greater than # about 80 pixels) for which the infrared scattering is negligible. The # only filters for which the infrared scattering has any effect are r and # i; the scattering in the bluer chips is negligible, and the z chips are # not thinned and the phenomenon does not exist. The fourth column # (resnoa) is the response of the third column with {\it no} atmosphere, # and the fifth column is the assumed atmospheric transparency at {\it # one} airmass at APO. The tables were constructed using monochromator # illumination of the camera with a bandpass of about 100 \AA, sampled for # the u filter at 50 \AA intervals and for the others at 100 \AA # intervals. These measurements were compared with measured responses of # the component filters and detectors and three additional points were # interpolated using these data, two at the extreme toes and one # additional (in g, r, and i) at the point of the beginning of the sharp # cutoff of the shortpass interference filter. These points are necessary # in order to make spline interpolation of the response data well-behaved. # These spline-interpolated response data were then multiplied by measured # aluminum reflectivities and scaled atmospheric transmission to produce # the tables below. The overall normalization is somewhat uncertain, # but this uncertainty does not affect the shapes. Note, however, that # there has been no attempt to remove the finite resolution of the # monochromator measurements. These tables are the {\it averages} of the # responses for all six of the camera chips with a given filter. The # responses are in general very similar except in the z band, where the # nonuniformity of the infrared rolloff, presumably associated with # varying thickness of the epitaxial layer or perhaps the gate structures # in these thick devices, introduces variations in the effective wavelengths # of the filters of order 100 \AA. We are currently working on better # response functions and will present them when they become available, but # these will suffice for most applications. In all cases the first point # is a measured point, so the grid of wavelengths at which measurements # exist is a subset of the wavelength lists here. # # SDSS Camera r Response Function 75 Points # # lam respt resbig resnoa xatm 5380 0.0000 0.0000 0.0000 0.8869 5405 0.0014 0.0014 0.0016 0.8877 5430 0.0099 0.0099 0.0113 0.8885 5455 0.0259 0.0260 0.0297 0.8891 5480 0.0497 0.0498 0.0568 0.8897 5505 0.0807 0.0809 0.0923 0.8902 5530 0.1186 0.1190 0.1356 0.8907 5555 0.1625 0.1630 0.1856 0.8911 5580 0.2093 0.2100 0.2390 0.8914 5605 0.2555 0.2564 0.2917 0.8917 5630 0.2975 0.2986 0.3395 0.8920 5655 0.3326 0.3339 0.3794 0.8923 5680 0.3609 0.3623 0.4116 0.8926 5705 0.3834 0.3849 0.4371 0.8929 5730 0.4010 0.4027 0.4570 0.8933 5755 0.4147 0.4165 0.4723 0.8938 5780 0.4253 0.4271 0.4839 0.8945 5805 0.4333 0.4353 0.4925 0.8952 5830 0.4395 0.4416 0.4990 0.8962 5855 0.4446 0.4467 0.5040 0.8973 5880 0.4489 0.4511 0.5080 0.8986 5905 0.4527 0.4550 0.5112 0.9001 5930 0.4563 0.4587 0.5141 0.9018 5955 0.4599 0.4624 0.5169 0.9037 5980 0.4634 0.4660 0.5194 0.9057 6005 0.4665 0.4692 0.5213 0.9079 6030 0.4689 0.4716 0.5222 0.9103 6055 0.4703 0.4731 0.5220 0.9128 6080 0.4711 0.4740 0.5212 0.9153 6105 0.4717 0.4747 0.5202 0.9177 6130 0.4727 0.4758 0.5197 0.9199 6155 0.4744 0.4776 0.5202 0.9220 6180 0.4767 0.4800 0.5215 0.9238 6205 0.4792 0.4827 0.5233 0.9253 6230 0.4819 0.4854 0.5254 0.9265 6255 0.4844 0.4881 0.5275 0.9275 6280 0.4867 0.4905 0.5294 0.9285 6305 0.4887 0.4926 0.5310 0.9294 6330 0.4902 0.4942 0.5319 0.9305 6355 0.4909 0.4951 0.5320 0.9316 6380 0.4912 0.4955 0.5316 0.9327 6405 0.4912 0.4956 0.5310 0.9337 6430 0.4912 0.4958 0.5305 0.9346 6455 0.4914 0.4961 0.5302 0.9354 6480 0.4915 0.4964 0.5299 0.9363 6505 0.4912 0.4962 0.5290 0.9373 6530 0.4901 0.4953 0.5271 0.9385 6555 0.4878 0.4931 0.5241 0.9395 6580 0.4852 0.4906 0.5211 0.9400 6605 0.4818 0.4873 0.5176 0.9398 6630 0.4697 0.4752 0.5057 0.9386 6655 0.4421 0.4474 0.4775 0.9366 6680 0.4009 0.4059 0.4341 0.9349 6705 0.3499 0.3544 0.3792 0.9345 6730 0.2924 0.2963 0.3162 0.9366 6755 0.2318 0.2350 0.2488 0.9421 6780 0.1715 0.1739 0.1824 0.9492 6805 0.1152 0.1168 0.1225 0.9494 6830 0.0687 0.0697 0.0747 0.9334 6855 0.0380 0.0386 0.0430 0.9057 6880 0.0212 0.0215 0.0247 0.8862 6905 0.0134 0.0136 0.0155 0.8893 6930 0.0099 0.0101 0.0112 0.9083 6955 0.0076 0.0077 0.0083 0.9311 6980 0.0055 0.0056 0.0059 0.9450 7005 0.0039 0.0039 0.0041 0.9464 7030 0.0027 0.0028 0.0029 0.9561 7055 0.0020 0.0020 0.0021 0.9709 7080 0.0015 0.0016 0.0016 0.9826 7105 0.0012 0.0013 0.0013 0.9827 7130 0.0010 0.0010 0.0010 0.9629 7155 0.0007 0.0007 0.0008 0.9192 7180 0.0004 0.0004 0.0005 0.8849 7205 0.0002 0.0002 0.0002 0.8974 7230 0.0000 0.0000 0.0000 0.9182 pydl-0.6.0/pydl/pydlutils/data/filters/sdss_jun2001_i_atm.dat0000664000111400017500000001535512775255301024665 0ustar weaverweaver00000000000000# The following file is from Jim Gunn, from June 2001. It should be # self-explanatory; for most purposes, you will want to use the second # column. Consider this file preliminary. # # These filter curves have been used to calculate the effective # wavelengths and the qtdl/l (see Chapter 8 of the Black Book) of the # filters; the values are: # # u 3551 0.0171 # g 4686 0.0893 # r 6166 0.0886 # i 7480 0.0591 # z 8932 0.0099 # # Table Caption For Response Functions # # The first column is the wavelength in \AAngstroms. The second column # (respt) is the quantum efficiency on the sky looking through 1.3 # airmasses at APO for a point source. The third column (resbig) is the # QE under these conditions for very large sources (size greater than # about 80 pixels) for which the infrared scattering is negligible. The # only filters for which the infrared scattering has any effect are r and # i; the scattering in the bluer chips is negligible, and the z chips are # not thinned and the phenomenon does not exist. The fourth column # (resnoa) is the response of the third column with {\it no} atmosphere, # and the fifth column is the assumed atmospheric transparency at {\it # one} airmass at APO. The tables were constructed using monochromator # illumination of the camera with a bandpass of about 100 \AA, sampled for # the u filter at 50 \AA intervals and for the others at 100 \AA # intervals. These measurements were compared with measured responses of # the component filters and detectors and three additional points were # interpolated using these data, two at the extreme toes and one # additional (in g, r, and i) at the point of the beginning of the sharp # cutoff of the shortpass interference filter. These points are necessary # in order to make spline interpolation of the response data well-behaved. # These spline-interpolated response data were then multiplied by measured # aluminum reflectivities and scaled atmospheric transmission to produce # the tables below. The overall normalization is somewhat uncertain, # but this uncertainty does not affect the shapes. Note, however, that # there has been no attempt to remove the finite resolution of the # monochromator measurements. These tables are the {\it averages} of the # responses for all six of the camera chips with a given filter. The # responses are in general very similar except in the z band, where the # nonuniformity of the infrared rolloff, presumably associated with # varying thickness of the epitaxial layer or perhaps the gate structures # in these thick devices, introduces variations in the effective wavelengths # of the filters of order 100 \AA. We are currently working on better # response functions and will present them when they become available, but # these will suffice for most applications. In all cases the first point # is a measured point, so the grid of wavelengths at which measurements # exist is a subset of the wavelength lists here. # # SDSS Camera i Response Function 89 Points # # lam respt resbig resnoa xatm 6430 0.0000 0.0000 0.0000 0.9346 6455 0.0001 0.0001 0.0001 0.9354 6480 0.0003 0.0003 0.0003 0.9363 6505 0.0004 0.0004 0.0004 0.9373 6530 0.0004 0.0004 0.0005 0.9385 6555 0.0003 0.0004 0.0004 0.9395 6580 0.0003 0.0003 0.0003 0.9400 6605 0.0004 0.0004 0.0005 0.9398 6630 0.0009 0.0009 0.0010 0.9386 6655 0.0019 0.0019 0.0021 0.9366 6680 0.0034 0.0034 0.0036 0.9349 6705 0.0056 0.0056 0.0060 0.9345 6730 0.0103 0.0104 0.0111 0.9366 6755 0.0194 0.0197 0.0208 0.9421 6780 0.0344 0.0349 0.0366 0.9492 6805 0.0561 0.0569 0.0597 0.9494 6830 0.0839 0.0851 0.0913 0.9334 6855 0.1164 0.1181 0.1317 0.9057 6880 0.1528 0.1552 0.1779 0.8862 6905 0.1948 0.1980 0.2260 0.8893 6930 0.2408 0.2448 0.2719 0.9083 6955 0.2857 0.2906 0.3125 0.9311 6980 0.3233 0.3290 0.3470 0.9450 7005 0.3503 0.3566 0.3755 0.9464 7030 0.3759 0.3829 0.3978 0.9561 7055 0.3990 0.4067 0.4142 0.9709 7080 0.4162 0.4245 0.4256 0.9826 7105 0.4233 0.4320 0.4331 0.9827 7130 0.4165 0.4252 0.4377 0.9629 7155 0.3943 0.4028 0.4405 0.9192 7180 0.3760 0.3844 0.4416 0.8849 7205 0.3823 0.3911 0.4411 0.8974 7230 0.3918 0.4011 0.4392 0.9182 7255 0.3892 0.3988 0.4358 0.9195 7280 0.3828 0.3924 0.4315 0.9153 7305 0.3820 0.3919 0.4265 0.9225 7330 0.3884 0.3988 0.4214 0.9436 7355 0.3872 0.3979 0.4165 0.9505 7380 0.3821 0.3930 0.4119 0.9496 7405 0.3787 0.3898 0.4077 0.9512 7430 0.3759 0.3872 0.4039 0.9531 7455 0.3727 0.3842 0.4006 0.9535 7480 0.3681 0.3799 0.3975 0.9508 7505 0.3618 0.3737 0.3943 0.9449 7530 0.3565 0.3685 0.3906 0.9416 7555 0.3554 0.3678 0.3862 0.9483 7580 0.3478 0.3603 0.3812 0.9429 7605 0.1473 0.1527 0.3757 0.4926 7630 0.2096 0.2176 0.3700 0.6546 7655 0.2648 0.2752 0.3641 0.7939 7680 0.3300 0.3434 0.3583 0.9530 7705 0.3256 0.3392 0.3526 0.9558 7730 0.3223 0.3361 0.3473 0.9602 7755 0.3179 0.3319 0.3424 0.9615 7780 0.3129 0.3272 0.3379 0.9605 7805 0.3077 0.3221 0.3337 0.9583 7830 0.3026 0.3173 0.3297 0.9559 7855 0.2980 0.3129 0.3259 0.9541 7880 0.2944 0.3095 0.3224 0.9541 7905 0.2921 0.3077 0.3194 0.9567 7930 0.2916 0.3075 0.3169 0.9622 7955 0.2921 0.3086 0.3150 0.9692 7980 0.2927 0.3098 0.3132 0.9762 8005 0.2923 0.3098 0.3111 0.9814 8030 0.2896 0.3076 0.3081 0.9833 8055 0.2840 0.3021 0.3039 0.9801 8080 0.2758 0.2939 0.2996 0.9702 8105 0.2642 0.2821 0.2945 0.9524 8130 0.2427 0.2597 0.2803 0.9285 8155 0.2091 0.2242 0.2493 0.9075 8180 0.1689 0.1815 0.2060 0.8931 8205 0.1276 0.1374 0.1578 0.8853 8230 0.0901 0.0973 0.1118 0.8843 8255 0.0603 0.0652 0.0743 0.8902 8280 0.0378 0.0410 0.0458 0.9033 8305 0.0218 0.0237 0.0257 0.9242 8330 0.0117 0.0128 0.0134 0.9483 8355 0.0068 0.0074 0.0077 0.9591 8380 0.0048 0.0053 0.0055 0.9576 8405 0.0033 0.0036 0.0037 0.9567 8430 0.0020 0.0022 0.0023 0.9564 8455 0.0013 0.0014 0.0015 0.9565 8480 0.0010 0.0011 0.0011 0.9569 8505 0.0009 0.0010 0.0011 0.9576 8530 0.0009 0.0010 0.0011 0.9584 8555 0.0008 0.0009 0.0009 0.9592 8580 0.0005 0.0006 0.0006 0.9598 8605 0.0002 0.0003 0.0003 0.9602 8630 0.0000 0.0000 0.0000 0.9603 pydl-0.6.0/pydl/pydlutils/data/cooling/0000775000111400017500000000000013160273564020640 5ustar weaverweaver00000000000000pydl-0.6.0/pydl/pydlutils/data/cooling/m+05.cie0000664000111400017500000001525412775255301022005 0ustar weaverweaver00000000000000Cooling Curve : CIE:[Fe/H] = +0.5 log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.10 0.0711 1.000 1.103 -22.98 -21.88 -11.51 10.29 2.055 2.486 9.092 2.117 4.15 0.2547 1.000 1.103 -22.26 -21.71 -11.40 10.17 2.666 2.486 10.36 1.831 4.20 0.5700 1.000 1.103 -21.80 -21.60 -11.26 10.11 3.686 2.486 12.18 1.486 4.25 0.8202 1.000 1.103 -21.64 -21.60 -11.15 10.16 4.755 2.486 13.83 1.293 4.30 0.9356 1.000 1.103 -21.61 -21.62 -11.07 10.24 5.655 2.487 15.08 1.220 4.35 0.9812 1.000 1.103 -21.57 -21.60 -11.01 10.27 6.487 2.487 16.15 1.193 4.40 1.008 1.000 1.103 -21.48 -21.53 -10.96 10.24 7.371 2.487 17.22 1.178 4.45 1.039 1.000 1.103 -21.36 -21.42 -10.90 10.19 8.393 2.487 18.37 1.161 4.50 1.071 1.000 1.103 -21.22 -21.30 -10.85 10.11 9.559 2.487 19.61 1.144 4.55 1.090 1.000 1.103 -21.09 -21.17 -10.79 10.04 10.82 2.487 20.86 1.134 4.60 1.098 1.000 1.103 -20.97 -21.05 -10.74 9.969 12.19 2.487 22.14 1.130 4.65 1.102 1.000 1.103 -20.86 -20.94 -10.69 9.911 13.69 2.487 23.47 1.128 4.70 1.104 1.000 1.103 -20.75 -20.83 -10.64 9.852 15.38 2.487 24.87 1.127 4.75 1.107 1.000 1.103 -20.65 -20.74 -10.59 9.803 17.28 2.487 26.36 1.125 4.80 1.116 1.000 1.103 -20.57 -20.66 -10.54 9.776 19.46 2.487 27.98 1.121 4.85 1.134 1.000 1.103 -20.50 -20.59 -10.48 9.763 22.02 2.487 29.76 1.112 4.90 1.160 1.000 1.103 -20.43 -20.54 -10.43 9.753 25.00 2.487 31.71 1.099 4.95 1.183 1.000 1.103 -20.39 -20.50 -10.37 9.772 28.33 2.487 33.75 1.088 5.00 1.197 1.000 1.103 -20.39 -20.51 -10.32 9.827 31.97 2.487 35.86 1.082 5.05 1.204 1.000 1.103 -20.42 -20.54 -10.27 9.909 35.99 2.487 38.04 1.078 5.10 1.208 1.000 1.103 -20.42 -20.55 -10.22 9.961 40.44 2.487 40.33 1.076 5.15 1.210 1.000 1.103 -20.40 -20.52 -10.17 9.992 45.42 2.487 42.74 1.075 5.20 1.211 1.000 1.103 -20.37 -20.49 -10.12 10.01 50.99 2.487 45.28 1.075 5.25 1.212 1.000 1.103 -20.35 -20.48 -10.07 10.04 57.24 2.487 47.98 1.074 5.30 1.213 1.000 1.103 -20.34 -20.47 -10.02 10.08 64.26 2.487 50.83 1.074 5.35 1.215 1.000 1.103 -20.34 -20.46 -9.969 10.13 72.14 2.487 53.86 1.073 5.40 1.216 1.000 1.103 -20.37 -20.50 -9.919 10.21 81.00 2.487 57.07 1.072 5.45 1.219 1.000 1.103 -20.51 -20.63 -9.868 10.40 90.97 2.487 60.48 1.071 5.50 1.221 1.000 1.103 -20.73 -20.86 -9.818 10.68 102.20 2.487 64.09 1.070 5.55 1.221 1.000 1.103 -20.93 -21.06 -9.768 10.93 114.70 2.487 67.91 1.070 5.60 1.222 1.000 1.103 -21.05 -21.18 -9.717 11.10 128.70 2.487 71.94 1.070 5.65 1.222 1.000 1.103 -21.09 -21.22 -9.667 11.19 144.4 2.487 76.21 1.069 5.70 1.223 1.000 1.103 -21.09 -21.22 -9.617 11.24 162.1 2.487 80.73 1.069 5.75 1.223 1.000 1.103 -21.09 -21.22 -9.567 11.29 181.9 2.487 85.52 1.069 5.80 1.224 1.000 1.103 -21.14 -21.27 -9.517 11.39 204.1 2.487 90.60 1.069 5.85 1.224 1.000 1.103 -21.24 -21.38 -9.467 11.54 229.1 2.487 95.98 1.069 5.90 1.225 1.000 1.103 -21.32 -21.45 -9.417 11.67 257.1 2.487 101.70 1.068 5.95 1.226 1.000 1.103 -21.34 -21.47 -9.367 11.74 288.5 2.487 107.7 1.068 6.00 1.226 1.000 1.103 -21.35 -21.48 -9.317 11.80 323.8 2.487 114.1 1.068 6.05 1.227 1.000 1.103 -21.35 -21.48 -9.266 11.85 363.4 2.487 120.9 1.067 6.10 1.227 1.000 1.103 -21.34 -21.47 -9.216 11.89 407.9 2.487 128.1 1.067 6.15 1.228 1.000 1.103 -21.33 -21.46 -9.166 11.93 457.9 2.487 135.7 1.067 6.20 1.229 1.000 1.103 -21.32 -21.46 -9.116 11.97 513.9 2.487 143.8 1.066 6.25 1.230 1.000 1.103 -21.35 -21.49 -9.066 12.05 576.8 2.487 152.3 1.066 6.30 1.231 1.000 1.103 -21.46 -21.59 -9.016 12.21 647.5 2.487 161.4 1.066 6.35 1.232 1.000 1.103 -21.59 -21.73 -8.966 12.39 726.8 2.487 171.0 1.065 6.40 1.233 1.000 1.103 -21.71 -21.85 -8.915 12.56 815.8 2.487 181.1 1.065 6.45 1.234 1.000 1.103 -21.80 -21.94 -8.865 12.70 915.7 2.487 191.9 1.064 6.50 1.234 1.000 1.103 -21.86 -22.00 -8.815 12.81 1027.7 2.487 203.3 1.064 6.55 1.235 1.000 1.103 -21.90 -22.03 -8.765 12.90 1153.3 2.487 215.4 1.064 6.60 1.235 1.000 1.103 -21.93 -22.07 -8.715 12.98 1294.3 2.487 228.1 1.064 6.65 1.236 1.000 1.103 -21.96 -22.10 -8.665 13.06 1452.5 2.487 241.7 1.063 6.70 1.236 1.000 1.103 -22.00 -22.14 -8.615 13.15 1629.9 2.487 256.0 1.063 6.75 1.236 1.000 1.103 -22.04 -22.17 -8.565 13.24 1829.0 2.487 271.2 1.063 6.80 1.237 1.000 1.103 -22.06 -22.20 -8.515 13.31 2052.3 2.487 287.3 1.063 6.85 1.237 1.000 1.103 -22.06 -22.20 -8.465 13.36 2303.0 2.487 304.3 1.063 6.90 1.237 1.000 1.103 -22.05 -22.18 -8.415 13.40 2584.2 2.487 322.4 1.063 6.95 1.237 1.000 1.103 -22.04 -22.17 -8.365 13.44 2899.7 2.487 341.5 1.063 7.00 1.237 1.000 1.103 -22.04 -22.17 -8.315 13.49 3253.8 2.487 361.7 1.063 7.05 1.237 1.000 1.103 -22.06 -22.20 -8.265 13.56 3651.2 2.487 383.2 1.063 7.10 1.238 1.000 1.103 -22.11 -22.24 -8.214 13.66 4097.0 2.487 405.9 1.063 7.15 1.238 1.000 1.103 -22.16 -22.30 -8.164 13.76 4597.2 2.487 430.0 1.062 7.20 1.238 1.000 1.103 -22.21 -22.35 -8.114 13.86 5158.4 2.487 455.4 1.062 7.25 1.238 1.000 1.103 -22.26 -22.40 -8.064 13.96 5788.0 2.487 482.4 1.062 7.30 1.238 1.000 1.103 -22.30 -22.43 -8.014 14.05 6494.4 2.487 511.0 1.062 7.35 1.238 1.000 1.103 -22.32 -22.46 -7.964 14.12 7287.0 2.487 541.3 1.062 7.40 1.238 1.000 1.103 -22.35 -22.48 -7.914 14.20 8176.3 2.487 573.4 1.062 7.45 1.238 1.000 1.103 -22.36 -22.49 -7.864 14.26 9174.0 2.487 607.4 1.062 7.50 1.238 1.000 1.103 -22.37 -22.50 -7.814 14.32 10293.6 2.487 643.4 1.062 7.55 1.238 1.000 1.103 -22.37 -22.51 -7.764 14.37 11549.7 2.487 681.5 1.062 7.60 1.238 1.000 1.103 -22.37 -22.51 -7.714 14.42 12959.1 2.487 721.9 1.062 7.65 1.238 1.000 1.103 -22.37 -22.51 -7.664 14.47 14540.5 2.487 764.7 1.062 7.70 1.238 1.000 1.103 -22.37 -22.50 -7.614 14.52 16314.8 2.487 810.0 1.062 7.75 1.238 1.000 1.103 -22.36 -22.49 -7.564 14.56 18305.7 2.487 858.0 1.062 7.80 1.238 1.000 1.103 -22.35 -22.48 -7.514 14.60 20539.6 2.487 908.8 1.062 7.85 1.238 1.000 1.103 -22.34 -22.47 -7.464 14.64 23046.0 2.487 962.7 1.062 7.90 1.238 1.000 1.103 -22.33 -22.46 -7.414 14.68 25858.3 2.487 1019.7 1.062 7.95 1.238 1.000 1.103 -22.31 -22.45 -7.364 14.71 29013.8 2.487 1080.1 1.062 8.00 1.238 1.000 1.103 -22.30 -22.44 -7.314 14.75 32554.3 2.487 1144.2 1.062 8.05 1.238 1.000 1.103 -22.29 -22.42 -7.264 14.79 36526.9 2.487 1211.9 1.062 8.10 1.238 1.000 1.103 -22.27 -22.41 -7.214 14.82 40984.2 2.487 1283.8 1.062 8.15 1.238 1.000 1.103 -22.26 -22.40 -7.164 14.86 45985.4 2.487 1359.8 1.062 8.20 1.238 1.000 1.103 -22.25 -22.38 -7.114 14.90 51596.8 2.487 1440.4 1.062 8.25 1.238 1.000 1.103 -22.23 -22.37 -7.064 14.93 57892.9 2.487 1525.8 1.062 8.30 1.238 1.000 1.103 -22.21 -22.35 -7.014 14.96 64957.3 2.487 1616.2 1.062 8.35 1.239 1.000 1.103 -22.20 -22.33 -6.964 15.00 72883.6 2.487 1712.0 1.062 8.40 1.239 1.000 1.103 -22.18 -22.32 -6.914 15.03 81777.0 2.487 1813.4 1.062 8.45 1.239 1.000 1.103 -22.16 -22.30 -6.864 15.06 91755.6 2.487 1920.9 1.062 8.50 1.239 1.000 1.103 -22.14 -22.28 -6.814 15.09 102952 2.487 2034.7 1.062 pydl-0.6.0/pydl/pydlutils/data/cooling/cooling.sha1sum0000664000111400017500000000063112775255301023575 0ustar weaverweaver0000000000000034b041f8241bebada7bbc5aa3791d7b80f346ca1 m-00.cie 2d74d9093fbef1786ebc29d10f8c301002a2ca91 m-05.cie d202615848505dd58f7e1786c00c5f30224e8252 m+05.cie 9d370f6d2cec327fb6b5ce79c5f3943f41466582 m-10.cie 7835424157683c2b090c2a41552acb4c45c80798 m-15.cie 482a27f65a24e1431cb8a2f8a89e32e8ea5bd4e8 m-20.cie a735ba627510bb315bfe7158e0e55a19671eb6f6 m-30.cie f35c2dc6c376aa7486f5be2436b1791087431062 mzero.cie pydl-0.6.0/pydl/pydlutils/data/cooling/m-10.cie0000664000111400017500000001544712775255301022007 0ustar weaverweaver00000000000000Cooling Curve : CIE:[Fe/H] = -1.0 log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.00 0.0022 1.000 1.064 -25.94 -23.31 -11.66 11.63 1.483 2.125 8.354 1.992 4.05 0.0141 1.000 1.064 -24.61 -22.78 -11.60 11.15 1.683 2.125 8.898 1.971 4.10 0.0702 1.000 1.064 -23.49 -22.36 -11.53 10.78 1.986 2.125 9.667 1.873 4.15 0.2529 1.000 1.064 -22.60 -22.03 -11.41 10.50 2.587 2.125 11.03 1.613 4.20 0.5667 1.000 1.064 -22.10 -21.88 -11.27 10.40 3.595 2.126 13.00 1.303 4.25 0.8163 1.000 1.064 -21.95 -21.89 -11.16 10.45 4.650 2.126 14.79 1.130 4.30 0.9312 1.000 1.064 -21.99 -21.98 -11.08 10.60 5.537 2.126 16.14 1.065 4.35 0.9758 1.000 1.064 -22.07 -22.08 -11.02 10.75 6.351 2.126 17.28 1.042 4.40 0.9984 1.000 1.064 -22.13 -22.16 -10.97 10.87 7.205 2.126 18.41 1.031 4.45 1.021 1.000 1.064 -22.15 -22.19 -10.91 10.95 8.172 2.126 19.61 1.020 4.50 1.043 1.000 1.064 -22.15 -22.19 -10.86 11.01 9.265 2.126 20.88 1.009 4.55 1.055 1.000 1.064 -22.11 -22.16 -10.81 11.03 10.46 2.126 22.18 1.003 4.60 1.061 1.000 1.064 -22.06 -22.11 -10.76 11.03 11.76 2.126 23.52 1.000 4.65 1.063 1.000 1.064 -21.98 -22.04 -10.71 11.00 13.21 2.126 24.93 0.999 4.70 1.064 1.000 1.064 -21.90 -21.95 -10.66 10.97 14.83 2.126 26.41 0.999 4.75 1.066 1.000 1.064 -21.81 -21.86 -10.61 10.93 16.66 2.126 27.99 0.998 4.80 1.071 1.000 1.064 -21.72 -21.77 -10.55 10.89 18.73 2.126 29.68 0.996 4.85 1.082 1.000 1.064 -21.63 -21.69 -10.50 10.86 21.13 2.126 31.53 0.990 4.90 1.099 1.000 1.064 -21.57 -21.64 -10.45 10.85 23.90 2.126 33.53 0.983 4.95 1.114 1.000 1.064 -21.54 -21.61 -10.40 10.88 26.99 2.126 35.63 0.976 5.00 1.122 1.000 1.064 -21.53 -21.60 -10.34 10.92 30.40 2.126 37.81 0.973 5.05 1.126 1.000 1.064 -21.52 -21.60 -10.29 10.96 34.17 2.126 40.09 0.971 5.10 1.127 1.000 1.064 -21.50 -21.58 -10.24 11.00 38.37 2.126 42.48 0.970 5.15 1.128 1.000 1.064 -21.47 -21.55 -10.19 11.02 43.07 2.126 45.01 0.970 5.20 1.129 1.000 1.064 -21.44 -21.52 -10.14 11.04 48.33 2.126 47.68 0.969 5.25 1.129 1.000 1.064 -21.41 -21.49 -10.09 11.06 54.23 2.126 50.51 0.969 5.30 1.129 1.000 1.064 -21.38 -21.46 -10.04 11.08 60.86 2.126 53.50 0.969 5.35 1.129 1.000 1.064 -21.36 -21.44 -9.993 11.11 68.29 2.126 56.67 0.969 5.40 1.129 1.000 1.064 -21.40 -21.48 -9.943 11.20 76.63 2.126 60.03 0.969 5.45 1.130 1.000 1.064 -21.53 -21.61 -9.893 11.38 85.99 2.126 63.59 0.969 5.50 1.130 1.000 1.064 -21.75 -21.84 -9.843 11.65 96.49 2.126 67.37 0.969 5.55 1.130 1.000 1.064 -21.95 -22.03 -9.793 11.90 108.3 2.126 71.36 0.969 5.60 1.130 1.000 1.064 -22.07 -22.15 -9.743 12.07 121.5 2.126 75.59 0.969 5.65 1.130 1.000 1.064 -22.11 -22.19 -9.693 12.16 136.3 2.126 80.07 0.969 5.70 1.130 1.000 1.064 -22.12 -22.20 -9.643 12.22 152.9 2.126 84.81 0.969 5.75 1.130 1.000 1.064 -22.14 -22.22 -9.593 12.29 171.6 2.126 89.84 0.969 5.80 1.130 1.000 1.064 -22.20 -22.28 -9.543 12.40 192.5 2.126 95.16 0.969 5.85 1.130 1.000 1.064 -22.33 -22.41 -9.493 12.58 216.0 2.126 100.8 0.969 5.90 1.130 1.000 1.064 -22.45 -22.53 -9.443 12.75 242.4 2.126 106.8 0.969 5.95 1.130 1.000 1.064 -22.51 -22.59 -9.393 12.86 272.0 2.126 113.1 0.969 6.00 1.130 1.000 1.064 -22.55 -22.63 -9.343 12.95 305.2 2.126 119.8 0.969 6.05 1.130 1.000 1.064 -22.58 -22.66 -9.293 13.03 342.4 2.126 126.9 0.969 6.10 1.131 1.000 1.064 -22.60 -22.68 -9.242 13.10 384.2 2.126 134.4 0.969 6.15 1.131 1.000 1.064 -22.59 -22.67 -9.192 13.14 431.1 2.126 142.4 0.969 6.20 1.131 1.000 1.064 -22.59 -22.67 -9.142 13.19 483.7 2.126 150.8 0.969 6.25 1.131 1.000 1.064 -22.61 -22.69 -9.092 13.26 542.8 2.126 159.8 0.969 6.30 1.131 1.000 1.064 -22.67 -22.75 -9.042 13.37 609.0 2.126 169.2 0.969 6.35 1.131 1.000 1.064 -22.74 -22.82 -8.992 13.49 683.3 2.126 179.3 0.969 6.40 1.131 1.000 1.064 -22.80 -22.88 -8.942 13.60 766.7 2.126 189.9 0.969 6.45 1.131 1.000 1.064 -22.84 -22.92 -8.892 13.69 860.3 2.126 201.2 0.969 6.50 1.131 1.000 1.064 -22.87 -22.95 -8.842 13.77 965.3 2.126 213.1 0.968 6.55 1.131 1.000 1.064 -22.89 -22.98 -8.792 13.84 1083.1 2.126 225.7 0.968 6.60 1.131 1.000 1.064 -22.91 -22.99 -8.742 13.91 1215.3 2.126 239.1 0.968 6.65 1.131 1.000 1.064 -22.92 -23.00 -8.692 13.97 1363.6 2.126 253.2 0.968 6.70 1.131 1.000 1.064 -22.93 -23.01 -8.642 14.03 1530.0 2.126 268.3 0.968 6.75 1.131 1.000 1.064 -22.94 -23.02 -8.592 14.09 1716.7 2.126 284.2 0.968 6.80 1.131 1.000 1.064 -22.94 -23.02 -8.542 14.14 1926.2 2.126 301.0 0.968 6.85 1.131 1.000 1.064 -22.94 -23.02 -8.492 14.19 2161.3 2.126 318.8 0.968 6.90 1.131 1.000 1.064 -22.93 -23.01 -8.442 14.23 2425.0 2.126 337.7 0.968 6.95 1.131 1.000 1.064 -22.92 -23.00 -8.392 14.27 2720.9 2.126 357.7 0.968 7.00 1.131 1.000 1.064 -22.91 -22.99 -8.342 14.31 3052.9 2.126 378.9 0.968 7.05 1.131 1.000 1.064 -22.90 -22.98 -8.292 14.35 3425.5 2.126 401.4 0.968 7.10 1.131 1.000 1.064 -22.90 -22.98 -8.242 14.40 3843.4 2.126 425.2 0.968 7.15 1.131 1.000 1.064 -22.90 -22.98 -8.192 14.45 4312.4 2.126 450.4 0.968 7.20 1.131 1.000 1.064 -22.89 -22.97 -8.142 14.49 4838.6 2.126 477.0 0.968 7.25 1.131 1.000 1.064 -22.88 -22.96 -8.092 14.53 5429.1 2.126 505.3 0.968 7.30 1.131 1.000 1.064 -22.87 -22.95 -8.042 14.57 6091.5 2.126 535.3 0.968 7.35 1.131 1.000 1.064 -22.85 -22.94 -7.992 14.60 6834.8 2.126 567.0 0.968 7.40 1.131 1.000 1.064 -22.84 -22.92 -7.942 14.64 7668.8 2.126 600.6 0.968 7.45 1.131 1.000 1.064 -22.82 -22.90 -7.892 14.67 8604.5 2.126 636.2 0.968 7.50 1.131 1.000 1.064 -22.81 -22.89 -7.842 14.71 9654.4 2.126 673.9 0.968 7.55 1.131 1.000 1.064 -22.79 -22.87 -7.792 14.74 10832.4 2.126 713.8 0.968 7.60 1.131 1.000 1.064 -22.77 -22.85 -7.742 14.77 12154.2 2.126 756.1 0.968 7.65 1.131 1.000 1.064 -22.75 -22.83 -7.692 14.80 13637.2 2.126 800.9 0.968 7.70 1.131 1.000 1.064 -22.73 -22.81 -7.642 14.83 15301.2 2.126 848.3 0.968 7.75 1.131 1.000 1.064 -22.71 -22.79 -7.592 14.86 17168.3 2.126 898.6 0.968 7.80 1.131 1.000 1.064 -22.69 -22.77 -7.542 14.89 19263.1 2.126 951.8 0.968 7.85 1.131 1.000 1.064 -22.67 -22.75 -7.492 14.92 21613.6 2.126 1008.2 0.968 7.90 1.131 1.000 1.064 -22.65 -22.73 -7.442 14.95 24250.9 2.126 1068.0 0.968 7.95 1.131 1.000 1.064 -22.62 -22.70 -7.392 14.97 27209.9 2.126 1131.3 0.968 8.00 1.131 1.000 1.064 -22.60 -22.68 -7.342 15.00 30530.1 2.126 1198.3 0.968 8.05 1.131 1.000 1.064 -22.58 -22.66 -7.292 15.03 34255.3 2.126 1269.3 0.968 8.10 1.131 1.000 1.064 -22.56 -22.64 -7.242 15.06 38435.1 2.126 1344.5 0.968 8.15 1.131 1.000 1.064 -22.54 -22.62 -7.192 15.09 43124.9 2.126 1424.2 0.968 8.20 1.131 1.000 1.064 -22.51 -22.60 -7.142 15.11 48387.0 2.126 1508.6 0.968 8.25 1.131 1.000 1.064 -22.49 -22.57 -7.092 15.14 54291.1 2.126 1598.0 0.968 8.30 1.131 1.000 1.064 -22.47 -22.55 -7.042 15.17 60915.6 2.126 1692.7 0.968 8.35 1.131 1.000 1.064 -22.45 -22.53 -6.992 15.20 68348.4 2.126 1792.9 0.968 8.40 1.131 1.000 1.064 -22.42 -22.51 -6.942 15.22 76688.2 2.126 1899.2 0.968 8.45 1.131 1.000 1.064 -22.40 -22.48 -6.892 15.25 86045.6 2.126 2011.7 0.968 8.50 1.131 1.000 1.064 -22.38 -22.46 -6.842 15.28 96544.8 2.126 2130.9 0.968pydl-0.6.0/pydl/pydlutils/data/cooling/m-05.cie0000664000111400017500000001544512775255301022011 0ustar weaverweaver00000000000000Cooling Curve : CIE:[Fe/H] = -0.5 log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.00 0.0023 1.000 1.080 -25.83 -23.22 -11.65 11.54 1.505 2.239 8.198 2.069 4.05 0.0141 1.000 1.080 -24.49 -22.68 -11.59 11.04 1.707 2.239 8.732 2.046 4.10 0.0703 1.000 1.080 -23.42 -22.30 -11.52 10.72 2.014 2.239 9.483 1.947 4.15 0.2530 1.000 1.080 -22.56 -22.00 -11.41 10.46 2.619 2.240 10.81 1.680 4.20 0.5673 1.000 1.080 -22.07 -21.85 -11.27 10.37 3.631 2.240 12.73 1.360 4.25 0.8162 1.000 1.080 -21.92 -21.86 -11.16 10.43 4.689 2.240 14.47 1.181 4.30 0.9317 1.000 1.080 -21.94 -21.94 -11.08 10.56 5.582 2.240 15.78 1.113 4.35 0.9767 1.000 1.080 -22.00 -22.02 -11.02 10.69 6.403 2.240 16.91 1.089 4.40 1.001 1.000 1.080 -22.02 -22.06 -10.97 10.77 7.269 2.240 18.01 1.076 4.45 1.027 1.000 1.080 -22.00 -22.05 -10.91 10.81 8.259 2.240 19.20 1.063 4.50 1.054 1.000 1.080 -21.94 -22.00 -10.85 10.81 9.383 2.240 20.47 1.050 4.55 1.069 1.000 1.080 -21.87 -21.93 -10.80 10.80 10.61 2.240 21.76 1.042 4.60 1.076 1.000 1.080 -21.78 -21.85 -10.75 10.76 11.94 2.240 23.08 1.039 4.65 1.079 1.000 1.080 -21.69 -21.75 -10.70 10.72 13.41 2.240 24.46 1.038 4.70 1.080 1.000 1.080 -21.59 -21.66 -10.65 10.67 15.05 2.240 25.92 1.037 4.75 1.082 1.000 1.080 -21.50 -21.57 -10.60 10.63 16.91 2.240 27.47 1.036 4.80 1.088 1.000 1.080 -21.41 -21.48 -10.55 10.60 19.03 2.240 29.14 1.033 4.85 1.103 1.000 1.080 -21.33 -21.41 -10.49 10.57 21.49 2.240 30.97 1.026 4.90 1.124 1.000 1.080 -21.27 -21.35 -10.44 10.57 24.34 2.240 32.96 1.016 4.95 1.142 1.000 1.080 -21.23 -21.32 -10.39 10.59 27.53 2.240 35.06 1.008 5.00 1.152 1.000 1.080 -21.22 -21.32 -10.34 10.63 31.04 2.240 37.22 1.004 5.05 1.157 1.000 1.080 -21.23 -21.33 -10.28 10.69 34.90 2.240 39.47 1.002 5.10 1.159 1.000 1.080 -21.22 -21.32 -10.23 10.73 39.20 2.240 41.83 1.001 5.15 1.160 1.000 1.080 -21.19 -21.29 -10.18 10.75 44.00 2.240 44.32 1.000 5.20 1.161 1.000 1.080 -21.16 -21.26 -10.13 10.77 49.38 2.240 46.95 1.000 5.25 1.161 1.000 1.080 -21.14 -21.23 -10.08 10.80 55.42 2.240 49.74 1.000 5.30 1.161 1.000 1.080 -21.12 -21.21 -10.03 10.83 62.19 2.240 52.69 0.999 5.35 1.162 1.000 1.080 -21.10 -21.20 -9.983 10.86 69.79 2.240 55.81 0.999 5.40 1.162 1.000 1.080 -21.14 -21.23 -9.933 10.95 78.31 2.240 59.12 0.999 5.45 1.162 1.000 1.080 -21.27 -21.37 -9.883 11.13 87.88 2.240 62.63 0.999 5.50 1.163 1.000 1.080 -21.50 -21.60 -9.833 11.42 98.62 2.240 66.35 0.999 5.55 1.163 1.000 1.080 -21.70 -21.79 -9.783 11.67 110.7 2.240 70.28 0.999 5.60 1.163 1.000 1.080 -21.81 -21.91 -9.733 11.83 124.2 2.240 74.45 0.999 5.65 1.163 1.000 1.080 -21.85 -21.95 -9.683 11.92 139.3 2.240 78.86 0.999 5.70 1.163 1.000 1.080 -21.86 -21.96 -9.633 11.98 156.3 2.240 83.53 0.999 5.75 1.163 1.000 1.080 -21.87 -21.97 -9.583 12.04 175.4 2.240 88.48 0.999 5.80 1.163 1.000 1.080 -21.93 -22.03 -9.533 12.15 196.8 2.240 93.73 0.999 5.85 1.163 1.000 1.080 -22.05 -22.15 -9.483 12.32 220.9 2.240 99.29 0.999 5.90 1.163 1.000 1.080 -22.15 -22.24 -9.433 12.47 247.8 2.240 105.2 0.999 5.95 1.164 1.000 1.080 -22.19 -22.29 -9.383 12.56 278.1 2.240 111.4 0.999 6.00 1.164 1.000 1.080 -22.22 -22.32 -9.333 12.64 312.0 2.240 118.0 0.999 6.05 1.164 1.000 1.080 -22.24 -22.34 -9.283 12.71 350.1 2.240 125.0 0.998 6.10 1.164 1.000 1.080 -22.24 -22.34 -9.233 12.76 392.8 2.240 132.4 0.998 6.15 1.164 1.000 1.080 -22.24 -22.34 -9.183 12.81 440.8 2.240 140.3 0.998 6.20 1.164 1.000 1.080 -22.23 -22.33 -9.133 12.85 494.6 2.240 148.6 0.998 6.25 1.164 1.000 1.080 -22.26 -22.36 -9.083 12.93 554.9 2.240 157.4 0.998 6.30 1.164 1.000 1.080 -22.34 -22.44 -9.033 13.06 622.7 2.240 166.7 0.998 6.35 1.164 1.000 1.080 -22.44 -22.54 -8.983 13.21 698.7 2.240 176.6 0.998 6.40 1.164 1.000 1.080 -22.53 -22.63 -8.933 13.35 784.0 2.240 187.1 0.998 6.45 1.165 1.000 1.080 -22.59 -22.69 -8.883 13.46 879.7 2.240 198.2 0.998 6.50 1.165 1.000 1.080 -22.64 -22.74 -8.833 13.56 987.1 2.240 209.9 0.998 6.55 1.165 1.000 1.080 -22.66 -22.76 -8.783 13.63 1107.6 2.240 222.3 0.998 6.60 1.165 1.000 1.080 -22.69 -22.79 -8.733 13.71 1242.8 2.240 235.5 0.998 6.65 1.165 1.000 1.080 -22.71 -22.81 -8.683 13.78 1394.5 2.240 249.5 0.998 6.70 1.165 1.000 1.080 -22.73 -22.83 -8.633 13.85 1564.7 2.240 264.3 0.998 6.75 1.165 1.000 1.080 -22.75 -22.85 -8.583 13.92 1755.6 2.240 279.9 0.998 6.80 1.165 1.000 1.080 -22.76 -22.86 -8.533 13.98 1969.9 2.240 296.5 0.998 6.85 1.165 1.000 1.080 -22.75 -22.85 -8.483 14.02 2210.3 2.240 314.1 0.998 6.90 1.165 1.000 1.080 -22.74 -22.84 -8.433 14.06 2480.0 2.240 332.7 0.998 6.95 1.165 1.000 1.080 -22.74 -22.84 -8.383 14.11 2782.6 2.240 352.4 0.998 7.00 1.165 1.000 1.080 -22.73 -22.83 -8.333 14.15 3122.2 2.240 373.3 0.998 7.05 1.165 1.000 1.080 -22.73 -22.83 -8.283 14.20 3503.2 2.240 395.4 0.998 7.10 1.165 1.000 1.080 -22.75 -22.85 -8.233 14.27 3930.7 2.240 418.9 0.998 7.15 1.165 1.000 1.080 -22.76 -22.86 -8.183 14.33 4410.4 2.240 443.7 0.998 7.20 1.165 1.000 1.080 -22.77 -22.87 -8.133 14.39 4948.5 2.240 470.0 0.998 7.25 1.165 1.000 1.080 -22.77 -22.87 -8.083 14.44 5552.4 2.240 497.8 0.998 7.30 1.165 1.000 1.080 -22.77 -22.87 -8.033 14.49 6229.9 2.240 527.3 0.998 7.35 1.165 1.000 1.080 -22.76 -22.86 -7.983 14.53 6990.0 2.240 558.6 0.998 7.40 1.165 1.000 1.080 -22.75 -22.85 -7.933 14.57 7843.0 2.240 591.7 0.998 7.45 1.165 1.000 1.080 -22.74 -22.84 -7.883 14.61 8800.0 2.240 626.7 0.998 7.50 1.165 1.000 1.080 -22.73 -22.83 -7.833 14.65 9873.8 2.240 663.9 0.998 7.55 1.165 1.000 1.080 -22.72 -22.82 -7.783 14.69 11078.6 2.240 703.2 0.998 7.60 1.165 1.000 1.080 -22.70 -22.80 -7.733 14.72 12430.4 2.240 744.9 0.998 7.65 1.165 1.000 1.080 -22.68 -22.78 -7.683 14.75 13947.1 2.240 789.0 0.998 7.70 1.165 1.000 1.080 -22.66 -22.76 -7.633 14.78 15648.9 2.240 835.8 0.998 7.75 1.165 1.000 1.080 -22.65 -22.75 -7.583 14.82 17558.4 2.240 885.3 0.998 7.80 1.165 1.000 1.080 -22.63 -22.73 -7.533 14.85 19700.9 2.240 937.7 0.998 7.85 1.165 1.000 1.080 -22.61 -22.71 -7.483 14.88 22104.8 2.240 993.3 0.998 7.90 1.165 1.000 1.080 -22.59 -22.69 -7.433 14.91 24802.0 2.240 1052.2 0.998 7.95 1.165 1.000 1.080 -22.57 -22.67 -7.383 14.94 27828.3 2.240 1114.5 0.998 8.00 1.165 1.000 1.080 -22.55 -22.65 -7.333 14.97 31223.9 2.240 1180.5 0.998 8.05 1.165 1.000 1.080 -22.53 -22.63 -7.283 15.00 35033.9 2.240 1250.5 0.998 8.10 1.165 1.000 1.080 -22.50 -22.60 -7.233 15.02 39308.7 2.240 1324.6 0.998 8.15 1.165 1.000 1.080 -22.48 -22.58 -7.183 15.05 44105.1 2.240 1403.1 0.998 8.20 1.165 1.000 1.080 -22.46 -22.56 -7.133 15.08 49486.8 2.240 1486.2 0.998 8.25 1.165 1.000 1.080 -22.44 -22.54 -7.083 15.11 55525.1 2.240 1574.3 0.998 8.30 1.165 1.000 1.080 -22.42 -22.52 -7.033 15.14 62300.2 2.240 1667.6 0.998 8.35 1.165 1.000 1.080 -22.40 -22.50 -6.983 15.17 69902.0 2.240 1766.4 0.998 8.40 1.165 1.000 1.080 -22.38 -22.48 -6.933 15.20 78431.4 2.240 1871.0 0.998 8.45 1.165 1.000 1.080 -22.35 -22.45 -6.883 15.22 88001.5 2.240 1981.9 0.998 8.50 1.165 1.000 1.080 -22.33 -22.43 -6.833 15.25 98739.4 2.240 2099.3 0.998pydl-0.6.0/pydl/pydlutils/data/cooling/mzero.cie0000664000111400017500000001544512775255301022467 0ustar weaverweaver00000000000000Cooling Curve : CIE:Zero Metals log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.00 0.0022 1.000 1.064 -26.04 -23.41 -11.66 11.73 1.483 2.114 8.374 1.983 4.05 0.0141 1.000 1.064 -24.69 -22.87 -11.60 11.23 1.682 2.114 8.919 1.961 4.10 0.0701 1.000 1.064 -23.53 -22.40 -11.53 10.82 1.985 2.114 9.690 1.864 4.15 0.2528 1.000 1.064 -22.63 -22.06 -11.41 10.53 2.586 2.114 11.06 1.606 4.20 0.5669 1.000 1.064 -22.12 -21.90 -11.27 10.42 3.594 2.115 13.04 1.297 4.25 0.8163 1.000 1.064 -21.98 -21.92 -11.16 10.48 4.649 2.115 14.83 1.125 4.30 0.9310 1.000 1.064 -22.03 -22.02 -11.08 10.64 5.535 2.115 16.18 1.060 4.35 0.9755 1.000 1.064 -22.13 -22.15 -11.02 10.81 6.349 2.115 17.33 1.037 4.40 0.9980 1.000 1.064 -22.24 -22.26 -10.97 10.98 7.202 2.115 18.45 1.026 4.45 1.020 1.000 1.064 -22.33 -22.36 -10.91 11.13 8.169 2.115 19.65 1.015 4.50 1.042 1.000 1.064 -22.41 -22.45 -10.86 11.27 9.261 2.115 20.93 1.004 4.55 1.055 1.000 1.064 -22.49 -22.54 -10.81 11.41 10.45 2.115 22.23 0.998 4.60 1.060 1.000 1.064 -22.58 -22.63 -10.76 11.55 11.76 2.115 23.58 0.996 4.65 1.062 1.000 1.064 -22.64 -22.70 -10.71 11.66 13.21 2.115 24.99 0.995 4.70 1.064 1.000 1.064 -22.65 -22.71 -10.66 11.72 14.83 2.115 26.48 0.994 4.75 1.065 1.000 1.064 -22.57 -22.63 -10.61 11.69 16.65 2.115 28.06 0.993 4.80 1.070 1.000 1.064 -22.41 -22.47 -10.55 11.58 18.73 2.115 29.75 0.991 4.85 1.082 1.000 1.064 -22.24 -22.30 -10.50 11.47 21.12 2.115 31.60 0.986 4.90 1.099 1.000 1.064 -22.16 -22.23 -10.45 11.44 23.89 2.115 33.61 0.978 4.95 1.113 1.000 1.064 -22.17 -22.25 -10.40 11.51 26.98 2.115 35.71 0.972 5.00 1.121 1.000 1.064 -22.25 -22.33 -10.34 11.64 30.38 2.115 37.90 0.968 5.05 1.125 1.000 1.064 -22.36 -22.44 -10.29 11.80 34.15 2.115 40.18 0.966 5.10 1.126 1.000 1.064 -22.46 -22.54 -10.24 11.95 38.34 2.115 42.58 0.966 5.15 1.127 1.000 1.064 -22.56 -22.64 -10.19 12.11 43.04 2.115 45.11 0.965 5.20 1.128 1.000 1.064 -22.66 -22.74 -10.14 12.26 48.30 2.115 47.78 0.965 5.25 1.128 1.000 1.064 -22.74 -22.82 -10.09 12.39 54.19 2.115 50.62 0.965 5.30 1.128 1.000 1.064 -22.82 -22.90 -10.04 12.52 60.81 2.115 53.62 0.965 5.35 1.128 1.000 1.064 -22.89 -22.97 -9.993 12.64 68.23 2.115 56.80 0.965 5.40 1.128 1.000 1.064 -22.95 -23.03 -9.943 12.75 76.56 2.115 60.16 0.965 5.45 1.128 1.000 1.064 -23.01 -23.08 -9.893 12.86 85.90 2.115 63.73 0.965 5.50 1.128 1.000 1.064 -23.05 -23.13 -9.843 12.95 96.38 2.115 67.50 0.965 5.55 1.128 1.000 1.064 -23.09 -23.17 -9.793 13.04 108.1 2.115 71.50 0.965 5.60 1.128 1.000 1.064 -23.13 -23.21 -9.743 13.13 121.3 2.115 75.74 0.965 5.65 1.128 1.000 1.064 -23.16 -23.24 -9.693 13.21 136.1 2.115 80.23 0.965 5.70 1.128 1.000 1.064 -23.18 -23.26 -9.643 13.28 152.8 2.115 84.98 0.965 5.75 1.128 1.000 1.064 -23.20 -23.28 -9.593 13.35 171.4 2.115 90.02 0.965 5.80 1.128 1.000 1.064 -23.22 -23.30 -9.543 13.42 192.3 2.115 95.35 0.965 5.85 1.128 1.000 1.064 -23.23 -23.31 -9.493 13.48 215.8 2.115 101.0 0.965 5.90 1.128 1.000 1.064 -23.24 -23.32 -9.443 13.54 242.1 2.115 107.0 0.965 5.95 1.128 1.000 1.064 -23.25 -23.33 -9.393 13.60 271.6 2.115 113.3 0.965 6.00 1.128 1.000 1.064 -23.25 -23.33 -9.343 13.65 304.8 2.115 120.0 0.965 6.05 1.128 1.000 1.064 -23.25 -23.33 -9.293 13.70 342.0 2.115 127.2 0.965 6.10 1.128 1.000 1.064 -23.25 -23.33 -9.243 13.75 383.7 2.115 134.7 0.965 6.15 1.128 1.000 1.064 -23.25 -23.33 -9.193 13.80 430.5 2.115 142.7 0.965 6.20 1.128 1.000 1.064 -23.25 -23.32 -9.143 13.85 483.1 2.115 151.1 0.965 6.25 1.128 1.000 1.064 -23.24 -23.32 -9.093 13.89 542.0 2.115 160.1 0.965 6.30 1.128 1.000 1.064 -23.23 -23.31 -9.043 13.93 608.1 2.115 169.6 0.965 6.35 1.128 1.000 1.064 -23.23 -23.31 -8.993 13.98 682.3 2.115 179.6 0.965 6.40 1.128 1.000 1.064 -23.23 -23.30 -8.943 14.03 765.6 2.115 190.3 0.965 6.45 1.128 1.000 1.064 -23.21 -23.29 -8.893 14.06 859.0 2.115 201.5 0.965 6.50 1.128 1.000 1.064 -23.20 -23.28 -8.843 14.10 963.8 2.115 213.5 0.965 6.55 1.128 1.000 1.064 -23.19 -23.27 -8.793 14.14 1081.4 2.115 226.1 0.965 6.60 1.128 1.000 1.064 -23.18 -23.25 -8.743 14.18 1213.4 2.115 239.5 0.965 6.65 1.128 1.000 1.064 -23.16 -23.24 -8.693 14.21 1361.5 2.115 253.7 0.965 6.70 1.128 1.000 1.064 -23.15 -23.22 -8.643 14.25 1527.6 2.115 268.7 0.965 6.75 1.128 1.000 1.064 -23.13 -23.21 -8.593 14.28 1714.0 2.115 284.7 0.965 6.80 1.128 1.000 1.064 -23.11 -23.19 -8.543 14.31 1923.1 2.115 301.5 0.965 6.85 1.128 1.000 1.064 -23.10 -23.17 -8.493 14.35 2157.8 2.115 319.4 0.965 6.90 1.128 1.000 1.064 -23.08 -23.16 -8.443 14.38 2421.1 2.115 338.3 0.965 6.95 1.128 1.000 1.064 -23.06 -23.14 -8.393 14.41 2716.5 2.115 358.4 0.965 7.00 1.128 1.000 1.064 -23.05 -23.12 -8.343 14.45 3047.9 2.115 379.6 0.965 7.05 1.128 1.000 1.064 -23.03 -23.11 -8.293 14.48 3419.8 2.115 402.1 0.965 7.10 1.128 1.000 1.064 -23.01 -23.09 -8.243 14.51 3837.1 2.115 425.9 0.965 7.15 1.128 1.000 1.064 -22.99 -23.07 -8.193 14.54 4305.3 2.115 451.2 0.965 7.20 1.128 1.000 1.064 -22.97 -23.05 -8.143 14.57 4830.7 2.115 477.9 0.965 7.25 1.128 1.000 1.064 -22.95 -23.03 -8.093 14.60 5420.1 2.115 506.2 0.965 7.30 1.128 1.000 1.064 -22.93 -23.01 -8.043 14.63 6081.4 2.115 536.2 0.965 7.35 1.128 1.000 1.064 -22.91 -22.99 -7.993 14.66 6823.5 2.115 568.0 0.965 7.40 1.128 1.000 1.064 -22.89 -22.96 -7.943 14.69 7656.1 2.115 601.6 0.965 7.45 1.128 1.000 1.064 -22.86 -22.94 -7.893 14.71 8590.3 2.115 637.3 0.965 7.50 1.128 1.000 1.064 -22.84 -22.92 -7.843 14.74 9638.4 2.115 675.0 0.965 7.55 1.128 1.000 1.064 -22.82 -22.90 -7.793 14.77 10814.5 2.115 715.0 0.965 7.60 1.128 1.000 1.064 -22.80 -22.88 -7.743 14.80 12134.1 2.115 757.4 0.965 7.65 1.128 1.000 1.064 -22.78 -22.86 -7.693 14.83 13614.6 2.115 802.3 0.965 7.70 1.128 1.000 1.064 -22.76 -22.84 -7.643 14.86 15275.9 2.115 849.8 0.965 7.75 1.128 1.000 1.064 -22.74 -22.81 -7.593 14.89 17139.8 2.115 900.2 0.965 7.80 1.128 1.000 1.064 -22.71 -22.79 -7.543 14.91 19231.2 2.115 953.5 0.965 7.85 1.128 1.000 1.064 -22.69 -22.77 -7.493 14.94 21577.8 2.115 1010.0 0.965 7.90 1.128 1.000 1.064 -22.67 -22.75 -7.443 14.97 24210.6 2.115 1069.9 0.965 7.95 1.128 1.000 1.064 -22.65 -22.73 -7.393 15.00 27164.8 2.115 1133.3 0.965 8.00 1.128 1.000 1.064 -22.62 -22.70 -7.343 15.02 30479.4 2.115 1200.4 0.965 8.05 1.128 1.000 1.064 -22.60 -22.68 -7.293 15.05 34198.4 2.115 1271.5 0.965 8.10 1.128 1.000 1.064 -22.58 -22.66 -7.243 15.08 38371.3 2.115 1346.9 0.965 8.15 1.128 1.000 1.064 -22.56 -22.63 -7.193 15.11 43053.3 2.115 1426.7 0.965 8.20 1.128 1.000 1.064 -22.53 -22.61 -7.143 15.13 48306.6 2.115 1511.2 0.965 8.25 1.128 1.000 1.064 -22.51 -22.59 -7.093 15.16 54200.9 2.115 1600.8 0.965 8.30 1.128 1.000 1.064 -22.49 -22.57 -7.043 15.19 60814.4 2.115 1695.6 0.965 8.35 1.128 1.000 1.064 -22.46 -22.54 -6.993 15.21 68234.9 2.115 1796.1 0.965 8.40 1.128 1.000 1.064 -22.44 -22.52 -6.943 15.24 76560.8 2.115 1902.5 0.965 8.45 1.128 1.000 1.064 -22.42 -22.50 -6.893 15.27 85902.6 2.115 2015.3 0.965 8.50 1.128 1.000 1.064 -22.39 -22.47 -6.843 15.29 96384.3 2.115 2134.7 0.965pydl-0.6.0/pydl/pydlutils/data/cooling/m-15.cie0000664000111400017500000001544712775255301022014 0ustar weaverweaver00000000000000Cooling Curve : CIE:[Fe/H] = -1.5 log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.00 0.0022 1.000 1.064 -26.00 -23.38 -11.66 11.69 1.483 2.118 8.368 1.986 4.05 0.0141 1.000 1.064 -24.66 -22.84 -11.60 11.20 1.682 2.118 8.913 1.964 4.10 0.0702 1.000 1.064 -23.52 -22.39 -11.53 10.81 1.986 2.118 9.683 1.867 4.15 0.2525 1.000 1.064 -22.62 -22.05 -11.41 10.52 2.586 2.118 11.05 1.609 4.20 0.5666 1.000 1.064 -22.11 -21.89 -11.27 10.41 3.594 2.118 13.03 1.299 4.25 0.8163 1.000 1.064 -21.97 -21.91 -11.16 10.47 4.650 2.118 14.82 1.127 4.30 0.9311 1.000 1.064 -22.01 -22.01 -11.08 10.62 5.535 2.118 16.16 1.062 4.35 0.9756 1.000 1.064 -22.11 -22.12 -11.02 10.79 6.349 2.118 17.31 1.039 4.40 0.9981 1.000 1.064 -22.20 -22.23 -10.97 10.94 7.203 2.119 18.44 1.027 4.45 1.021 1.000 1.064 -22.27 -22.30 -10.91 11.07 8.170 2.119 19.64 1.016 4.50 1.042 1.000 1.064 -22.31 -22.35 -10.86 11.17 9.263 2.119 20.91 1.006 4.55 1.055 1.000 1.064 -22.33 -22.38 -10.81 11.25 10.46 2.119 22.22 1.000 4.60 1.060 1.000 1.064 -22.34 -22.39 -10.76 11.31 11.76 2.119 23.56 0.997 4.65 1.063 1.000 1.064 -22.32 -22.37 -10.71 11.34 13.21 2.119 24.97 0.996 4.70 1.064 1.000 1.064 -22.26 -22.31 -10.66 11.33 14.83 2.119 26.46 0.996 4.75 1.066 1.000 1.064 -22.17 -22.23 -10.61 11.29 16.65 2.119 28.04 0.995 4.80 1.070 1.000 1.064 -22.06 -22.12 -10.55 11.23 18.73 2.119 29.73 0.992 4.85 1.082 1.000 1.064 -21.95 -22.01 -10.50 11.18 21.13 2.119 31.58 0.987 4.90 1.099 1.000 1.064 -21.88 -21.95 -10.45 11.16 23.89 2.119 33.58 0.979 4.95 1.113 1.000 1.064 -21.86 -21.94 -10.40 11.20 26.98 2.119 35.69 0.973 5.00 1.121 1.000 1.064 -21.88 -21.96 -10.34 11.27 30.39 2.119 37.87 0.970 5.05 1.125 1.000 1.064 -21.90 -21.98 -10.29 11.34 34.15 2.119 40.15 0.968 5.10 1.127 1.000 1.064 -21.91 -21.99 -10.24 11.41 38.35 2.119 42.55 0.967 5.15 1.128 1.000 1.064 -21.90 -21.98 -10.19 11.45 43.05 2.119 45.08 0.967 5.20 1.128 1.000 1.064 -21.88 -21.96 -10.14 11.48 48.31 2.119 47.75 0.967 5.25 1.128 1.000 1.064 -21.86 -21.94 -10.09 11.51 54.21 2.119 50.58 0.966 5.30 1.128 1.000 1.064 -21.85 -21.93 -10.04 11.55 60.83 2.119 53.58 0.966 5.35 1.128 1.000 1.064 -21.84 -21.92 -9.993 11.59 68.25 2.119 56.76 0.966 5.40 1.128 1.000 1.064 -21.87 -21.95 -9.943 11.67 76.58 2.119 60.12 0.966 5.45 1.128 1.000 1.064 -22.00 -22.08 -9.893 11.85 85.93 2.119 63.69 0.966 5.50 1.129 1.000 1.064 -22.21 -22.29 -9.843 12.11 96.42 2.119 67.46 0.966 5.55 1.129 1.000 1.064 -22.39 -22.47 -9.793 12.34 108.2 2.119 71.46 0.966 5.60 1.129 1.000 1.064 -22.49 -22.57 -9.743 12.49 121.4 2.119 75.69 0.966 5.65 1.129 1.000 1.064 -22.54 -22.62 -9.693 12.59 136.2 2.119 80.18 0.966 5.70 1.129 1.000 1.064 -22.55 -22.63 -9.643 12.65 152.8 2.119 84.93 0.966 5.75 1.129 1.000 1.064 -22.56 -22.64 -9.593 12.71 171.5 2.119 89.96 0.966 5.80 1.129 1.000 1.064 -22.62 -22.70 -9.543 12.82 192.4 2.119 95.29 0.966 5.85 1.129 1.000 1.064 -22.73 -22.81 -9.493 12.98 215.9 2.119 100.9 0.966 5.90 1.129 1.000 1.064 -22.82 -22.90 -9.443 13.12 242.2 2.119 106.9 0.966 5.95 1.129 1.000 1.064 -22.86 -22.94 -9.393 13.21 271.8 2.119 113.3 0.966 6.00 1.129 1.000 1.064 -22.89 -22.97 -9.343 13.29 304.9 2.119 120.0 0.966 6.05 1.129 1.000 1.064 -22.92 -23.00 -9.293 13.37 342.1 2.119 127.1 0.966 6.10 1.129 1.000 1.064 -22.93 -23.01 -9.243 13.43 383.9 2.119 134.6 0.966 6.15 1.129 1.000 1.064 -22.93 -23.00 -9.193 13.48 430.7 2.119 142.6 0.966 6.20 1.129 1.000 1.064 -22.92 -23.00 -9.143 13.52 483.3 2.119 151.0 0.966 6.25 1.129 1.000 1.064 -22.93 -23.01 -9.093 13.58 542.2 2.119 160.0 0.966 6.30 1.129 1.000 1.064 -22.97 -23.04 -9.043 13.67 608.4 2.119 169.5 0.966 6.35 1.129 1.000 1.064 -23.01 -23.09 -8.993 13.76 682.7 2.119 179.5 0.966 6.40 1.129 1.000 1.064 -23.04 -23.12 -8.943 13.84 766.0 2.119 190.1 0.966 6.45 1.129 1.000 1.064 -23.06 -23.14 -8.893 13.91 859.4 2.119 201.4 0.966 6.50 1.129 1.000 1.064 -23.07 -23.15 -8.843 13.97 964.3 2.119 213.3 0.966 6.55 1.129 1.000 1.064 -23.07 -23.15 -8.793 14.02 1082.0 2.119 226.0 0.966 6.60 1.129 1.000 1.064 -23.07 -23.15 -8.743 14.07 1214.0 2.119 239.4 0.966 6.65 1.129 1.000 1.064 -23.07 -23.15 -8.693 14.12 1362.2 2.119 253.6 0.966 6.70 1.129 1.000 1.064 -23.07 -23.15 -8.643 14.17 1528.4 2.119 268.6 0.966 6.75 1.129 1.000 1.064 -23.06 -23.14 -8.593 14.21 1714.8 2.119 284.5 0.966 6.80 1.129 1.000 1.064 -23.05 -23.13 -8.543 14.25 1924.1 2.119 301.4 0.966 6.85 1.129 1.000 1.064 -23.04 -23.12 -8.493 14.29 2158.9 2.119 319.2 0.966 6.90 1.129 1.000 1.064 -23.02 -23.10 -8.443 14.32 2422.3 2.119 338.1 0.966 6.95 1.129 1.000 1.064 -23.01 -23.09 -8.393 14.36 2717.9 2.119 358.2 0.966 7.00 1.129 1.000 1.064 -23.00 -23.08 -8.343 14.40 3049.5 2.119 379.4 0.966 7.05 1.129 1.000 1.064 -22.98 -23.06 -8.293 14.43 3421.6 2.119 401.9 0.966 7.10 1.129 1.000 1.064 -22.97 -23.05 -8.243 14.47 3839.1 2.119 425.7 0.966 7.15 1.129 1.000 1.064 -22.96 -23.04 -8.193 14.51 4307.6 2.119 450.9 0.966 7.20 1.129 1.000 1.064 -22.94 -23.02 -8.143 14.54 4833.2 2.119 477.6 0.966 7.25 1.129 1.000 1.064 -22.93 -23.00 -8.093 14.58 5422.9 2.119 505.9 0.966 7.30 1.129 1.000 1.064 -22.91 -22.99 -8.043 14.61 6084.6 2.119 535.9 0.966 7.35 1.129 1.000 1.064 -22.89 -22.97 -7.993 14.64 6827.1 2.119 567.7 0.966 7.40 1.129 1.000 1.064 -22.87 -22.95 -7.943 14.67 7660.1 2.119 601.3 0.966 7.45 1.129 1.000 1.064 -22.85 -22.93 -7.893 14.70 8594.8 2.119 636.9 0.966 7.50 1.129 1.000 1.064 -22.83 -22.91 -7.843 14.73 9643.5 2.119 674.7 0.966 7.55 1.129 1.000 1.064 -22.81 -22.89 -7.793 14.76 10820.2 2.119 714.6 0.966 7.60 1.129 1.000 1.064 -22.79 -22.87 -7.743 14.79 12140.4 2.119 757.0 0.966 7.65 1.129 1.000 1.064 -22.77 -22.85 -7.693 14.82 13621.8 2.119 801.8 0.966 7.70 1.129 1.000 1.064 -22.75 -22.83 -7.643 14.85 15283.9 2.119 849.4 0.966 7.75 1.129 1.000 1.064 -22.73 -22.81 -7.593 14.88 17148.8 2.119 899.7 0.966 7.80 1.129 1.000 1.064 -22.70 -22.78 -7.543 14.90 19241.3 2.119 953.0 0.966 7.85 1.129 1.000 1.064 -22.68 -22.76 -7.493 14.93 21589.1 2.119 1009.5 0.966 7.90 1.129 1.000 1.064 -22.66 -22.74 -7.443 14.96 24223.4 2.119 1069.3 0.966 7.95 1.129 1.000 1.064 -22.64 -22.72 -7.393 14.99 27179.1 2.119 1132.6 0.966 8.00 1.129 1.000 1.064 -22.62 -22.70 -7.343 15.02 30495.4 2.119 1199.7 0.966 8.05 1.129 1.000 1.064 -22.59 -22.67 -7.293 15.04 34216.4 2.119 1270.8 0.966 8.10 1.129 1.000 1.064 -22.57 -22.65 -7.243 15.07 38391.5 2.119 1346.1 0.966 8.15 1.129 1.000 1.064 -22.55 -22.63 -7.193 15.10 43075.9 2.119 1425.9 0.966 8.20 1.129 1.000 1.064 -22.53 -22.61 -7.143 15.13 48332.0 2.119 1510.4 0.966 8.25 1.129 1.000 1.064 -22.50 -22.58 -7.093 15.15 54229.4 2.119 1599.9 0.966 8.30 1.129 1.000 1.064 -22.48 -22.56 -7.043 15.18 60846.4 2.119 1694.7 0.966 8.35 1.129 1.000 1.064 -22.46 -22.54 -6.993 15.21 68270.8 2.119 1795.1 0.966 8.40 1.129 1.000 1.064 -22.44 -22.52 -6.943 15.24 76601.1 2.119 1901.5 0.966 8.45 1.129 1.000 1.064 -22.41 -22.49 -6.893 15.26 85947.8 2.119 2014.1 0.966 8.50 1.129 1.000 1.064 -22.39 -22.47 -6.843 15.29 96435.1 2.119 2133.5 0.966pydl-0.6.0/pydl/pydlutils/data/cooling/m-30.cie0000664000111400017500000001544712775255301022011 0ustar weaverweaver00000000000000Cooling Curve : CIE:[Fe/H] = -3.0 log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.00 0.0022 1.000 1.064 -26.04 -23.41 -11.66 11.73 1.479 2.114 8.365 1.987 4.05 0.0141 1.000 1.064 -24.69 -22.87 -11.60 11.23 1.682 2.114 8.919 1.961 4.10 0.0701 1.000 1.064 -23.53 -22.40 -11.53 10.82 1.985 2.114 9.690 1.864 4.15 0.2528 1.000 1.064 -22.63 -22.06 -11.41 10.53 2.586 2.114 11.06 1.606 4.20 0.5669 1.000 1.064 -22.12 -21.90 -11.27 10.42 3.594 2.115 13.04 1.297 4.25 0.8163 1.000 1.064 -21.98 -21.92 -11.16 10.48 4.649 2.115 14.83 1.125 4.30 0.9310 1.000 1.064 -22.03 -22.02 -11.08 10.64 5.535 2.115 16.18 1.060 4.35 0.9755 1.000 1.064 -22.13 -22.15 -11.02 10.81 6.349 2.115 17.32 1.037 4.40 0.9980 1.000 1.064 -22.24 -22.26 -10.97 10.98 7.202 2.115 18.45 1.026 4.45 1.020 1.000 1.064 -22.33 -22.36 -10.91 11.13 8.169 2.115 19.65 1.015 4.50 1.042 1.000 1.064 -22.40 -22.45 -10.86 11.26 9.261 2.115 20.92 1.004 4.55 1.055 1.000 1.064 -22.49 -22.54 -10.81 11.41 10.45 2.115 22.23 0.998 4.60 1.060 1.000 1.064 -22.57 -22.62 -10.76 11.54 11.76 2.115 23.58 0.996 4.65 1.062 1.000 1.064 -22.63 -22.68 -10.71 11.65 13.21 2.115 24.99 0.995 4.70 1.064 1.000 1.064 -22.63 -22.69 -10.66 11.70 14.83 2.115 26.48 0.994 4.75 1.065 1.000 1.064 -22.55 -22.61 -10.61 11.67 16.65 2.115 28.06 0.993 4.80 1.070 1.000 1.064 -22.39 -22.45 -10.55 11.56 18.73 2.115 29.75 0.991 4.85 1.082 1.000 1.064 -22.23 -22.29 -10.50 11.46 21.12 2.115 31.60 0.986 4.90 1.099 1.000 1.064 -22.15 -22.21 -10.45 11.43 23.89 2.115 33.60 0.978 4.95 1.113 1.000 1.064 -22.16 -22.23 -10.40 11.50 26.98 2.115 35.71 0.972 5.00 1.121 1.000 1.064 -22.24 -22.31 -10.34 11.63 30.38 2.115 37.90 0.968 5.05 1.125 1.000 1.064 -22.33 -22.41 -10.29 11.77 34.15 2.115 40.18 0.966 5.10 1.126 1.000 1.064 -22.43 -22.51 -10.24 11.92 38.34 2.115 42.58 0.966 5.15 1.127 1.000 1.064 -22.52 -22.60 -10.19 12.07 43.04 2.115 45.11 0.965 5.20 1.128 1.000 1.064 -22.60 -22.67 -10.14 12.20 48.30 2.115 47.78 0.965 5.25 1.128 1.000 1.064 -22.66 -22.74 -10.09 12.31 54.20 2.115 50.62 0.965 5.30 1.128 1.000 1.064 -22.72 -22.80 -10.04 12.42 60.81 2.115 53.62 0.965 5.35 1.128 1.000 1.064 -22.77 -22.85 -9.993 12.52 68.23 2.115 56.80 0.965 5.40 1.128 1.000 1.064 -22.82 -22.90 -9.943 12.62 76.56 2.115 60.16 0.965 5.45 1.128 1.000 1.064 -22.90 -22.98 -9.893 12.75 85.90 2.115 63.73 0.965 5.50 1.128 1.000 1.064 -22.98 -23.06 -9.843 12.88 96.38 2.115 67.50 0.965 5.55 1.128 1.000 1.064 -23.04 -23.12 -9.793 12.99 108.1 2.115 71.50 0.965 5.60 1.128 1.000 1.064 -23.08 -23.16 -9.743 13.08 121.3 2.115 75.74 0.965 5.65 1.128 1.000 1.064 -23.12 -23.20 -9.693 13.17 136.1 2.115 80.23 0.965 5.70 1.128 1.000 1.064 -23.14 -23.22 -9.643 13.24 152.8 2.115 84.98 0.965 5.75 1.128 1.000 1.064 -23.16 -23.24 -9.593 13.31 171.4 2.115 90.02 0.965 5.80 1.128 1.000 1.064 -23.18 -23.26 -9.543 13.38 192.3 2.115 95.35 0.965 5.85 1.128 1.000 1.064 -23.20 -23.28 -9.493 13.45 215.8 2.115 101.0 0.965 5.90 1.128 1.000 1.064 -23.22 -23.30 -9.443 13.52 242.1 2.115 107.0 0.965 5.95 1.128 1.000 1.064 -23.23 -23.31 -9.393 13.58 271.7 2.115 113.3 0.965 6.00 1.128 1.000 1.064 -23.23 -23.31 -9.343 13.63 304.8 2.115 120.0 0.965 6.05 1.128 1.000 1.064 -23.24 -23.32 -9.293 13.69 342.0 2.115 127.2 0.965 6.10 1.128 1.000 1.064 -23.24 -23.32 -9.243 13.74 383.7 2.115 134.7 0.965 6.15 1.128 1.000 1.064 -23.23 -23.31 -9.193 13.78 430.5 2.115 142.7 0.965 6.20 1.128 1.000 1.064 -23.23 -23.31 -9.143 13.83 483.1 2.115 151.1 0.965 6.25 1.128 1.000 1.064 -23.23 -23.31 -9.093 13.88 542.0 2.115 160.1 0.965 6.30 1.128 1.000 1.064 -23.22 -23.30 -9.043 13.92 608.2 2.115 169.6 0.965 6.35 1.128 1.000 1.064 -23.23 -23.30 -8.993 13.98 682.4 2.115 179.6 0.965 6.40 1.128 1.000 1.064 -23.22 -23.30 -8.943 14.02 765.6 2.115 190.2 0.965 6.45 1.128 1.000 1.064 -23.21 -23.29 -8.893 14.06 859.0 2.115 201.5 0.965 6.50 1.128 1.000 1.064 -23.20 -23.28 -8.843 14.10 963.9 2.115 213.5 0.965 6.55 1.128 1.000 1.064 -23.19 -23.26 -8.793 14.14 1081.5 2.115 226.1 0.965 6.60 1.128 1.000 1.064 -23.17 -23.25 -8.743 14.17 1213.4 2.115 239.5 0.965 6.65 1.128 1.000 1.064 -23.16 -23.24 -8.693 14.21 1361.5 2.115 253.7 0.965 6.70 1.128 1.000 1.064 -23.14 -23.22 -8.643 14.24 1527.6 2.115 268.7 0.965 6.75 1.128 1.000 1.064 -23.13 -23.21 -8.593 14.28 1714.0 2.115 284.7 0.965 6.80 1.128 1.000 1.064 -23.11 -23.19 -8.543 14.31 1923.2 2.115 301.5 0.965 6.85 1.128 1.000 1.064 -23.09 -23.17 -8.493 14.34 2157.8 2.115 319.4 0.965 6.90 1.128 1.000 1.064 -23.08 -23.16 -8.443 14.38 2421.1 2.115 338.3 0.965 6.95 1.128 1.000 1.064 -23.06 -23.14 -8.393 14.41 2716.5 2.115 358.4 0.965 7.00 1.128 1.000 1.064 -23.04 -23.12 -8.343 14.44 3048.0 2.115 379.6 0.965 7.05 1.128 1.000 1.064 -23.03 -23.10 -8.293 14.48 3419.9 2.115 402.1 0.965 7.10 1.128 1.000 1.064 -23.01 -23.09 -8.243 14.51 3837.2 2.115 425.9 0.965 7.15 1.128 1.000 1.064 -22.99 -23.07 -8.193 14.54 4305.4 2.115 451.2 0.965 7.20 1.128 1.000 1.064 -22.97 -23.05 -8.143 14.57 4830.7 2.115 477.9 0.965 7.25 1.128 1.000 1.064 -22.95 -23.03 -8.093 14.60 5420.2 2.115 506.2 0.965 7.30 1.128 1.000 1.064 -22.93 -23.01 -8.043 14.63 6081.5 2.115 536.2 0.965 7.35 1.128 1.000 1.064 -22.91 -22.99 -7.993 14.66 6823.6 2.115 568.0 0.965 7.40 1.128 1.000 1.064 -22.89 -22.96 -7.943 14.69 7656.2 2.115 601.6 0.965 7.45 1.128 1.000 1.064 -22.86 -22.94 -7.893 14.71 8590.4 2.115 637.3 0.965 7.50 1.128 1.000 1.064 -22.84 -22.92 -7.843 14.74 9638.6 2.115 675.0 0.965 7.55 1.128 1.000 1.064 -22.82 -22.90 -7.793 14.77 10814.7 2.115 715.0 0.965 7.60 1.128 1.000 1.064 -22.80 -22.88 -7.743 14.80 12134.3 2.115 757.4 0.965 7.65 1.128 1.000 1.064 -22.78 -22.86 -7.693 14.83 13614.9 2.115 802.3 0.965 7.70 1.128 1.000 1.064 -22.76 -22.84 -7.643 14.86 15276.1 2.115 849.8 0.965 7.75 1.128 1.000 1.064 -22.73 -22.81 -7.593 14.88 17140.1 2.115 900.2 0.965 7.80 1.128 1.000 1.064 -22.71 -22.79 -7.543 14.91 19231.5 2.115 953.5 0.965 7.85 1.128 1.000 1.064 -22.69 -22.77 -7.493 14.94 21578.1 2.115 1010.0 0.965 7.90 1.128 1.000 1.064 -22.67 -22.75 -7.443 14.97 24211.0 2.115 1069.8 0.965 7.95 1.128 1.000 1.064 -22.65 -22.73 -7.393 15.00 27165.2 2.115 1133.2 0.965 8.00 1.128 1.000 1.064 -22.62 -22.70 -7.343 15.02 30479.9 2.115 1200.4 0.965 8.05 1.128 1.000 1.064 -22.60 -22.68 -7.293 15.05 34199.0 2.115 1271.5 0.965 8.10 1.128 1.000 1.064 -22.58 -22.66 -7.243 15.08 38371.9 2.115 1346.9 0.965 8.15 1.128 1.000 1.064 -22.56 -22.63 -7.193 15.11 43054.0 2.115 1426.7 0.965 8.20 1.128 1.000 1.064 -22.53 -22.61 -7.143 15.13 48307.4 2.115 1511.2 0.965 8.25 1.128 1.000 1.064 -22.51 -22.59 -7.093 15.16 54201.8 2.115 1600.8 0.965 8.30 1.128 1.000 1.064 -22.49 -22.57 -7.043 15.19 60815.4 2.115 1695.6 0.965 8.35 1.128 1.000 1.064 -22.46 -22.54 -6.993 15.21 68236.0 2.115 1796.1 0.965 8.40 1.128 1.000 1.064 -22.44 -22.52 -6.943 15.24 76562.1 2.115 1902.5 0.965 8.45 1.128 1.000 1.064 -22.42 -22.50 -6.893 15.27 85904.0 2.115 2015.2 0.965 8.50 1.128 1.000 1.064 -22.39 -22.47 -6.843 15.29 96385.9 2.115 2134.6 0.965pydl-0.6.0/pydl/pydlutils/data/cooling/README.rst0000664000111400017500000000510512775255301022330 0ustar weaverweaver00000000000000=========================== ABOUT THE COOLING FUNCTIONS =========================== Introduction ------------ This file has been adapted from http://www.mso.anu.edu.au/~ralph/data/cool/ABOUT.txt. The associated files contain the cooling functions calculated for the paper by `Sutherland & Dopita (1993) `_. See paper or preprint for details. FILE LIST --------- Collisonal Ionisation Equilibrium cooling functions as a function of metallicity are given in the files: mzero.cie cie cooling for primordial hydrogen/helium mix log(T) = 4-8.5 m-00.cie cie cooling for solar abundances mix log(T) = 4-8.5 m-05.cie [Fe/H] = -0.5, solar/primordial average ratios m-10.cie [Fe/H] = -1.0, primordial ratios (ie enhanced oxygen) m-15.cie [Fe/H] = -1.5, primordial ratios m-20.cie [Fe/H] = -2.0, primordial ratios m-30.cie [Fe/H] = -3.0, primordial ratios m+05.cie [Fe/H] = +0.5, solar ratios log(T) = 4.1-8.5 (due to charge exchange problems at log(T) = 4.0) FILE FORMAT ----------- The data files consist of tab separated ASCII column data described as follows: log(T) log temperature in K ne, nH, nt number densities, electrons, hydrogen and total ion in cm^-3. log(lambda net), log(lambda norm) log on the net cooling function and the normalised cooling function. lambda norm = lambda net / (ne nt). lambda net in ergs cm^-3 s^-1, lambda net in ergs cm^3 s^-1. While the densities are kept less than about p/k 10^8 both isobaric and isochoric curves can be constructed from the normalised function using an appropriate density function. The non-equilibrium net cooling function is from the isobaric model used to calculate the curves. In the CIE curves the net function is for the isochoric cie model. log(U) U = 3/2 N kT, N = ne + nt the total internal energy. ergs cm^-3 log(taucool) The normalized cooling timescale Nr*( U/(lambda net)) Nr = (ne nt)/(ne+nt). s cm^-3 P12 Gas pressure NkT. ergs ergs cm^-3 times 10^12 rho24 Density g cm^-3 times 10^24 Ci The isothermal sound speed kms s^-1 mubar mean molecular weight grams times 10^24 NOTES ----- Tests showed that density quenching begins to affect the curves in the low temperature parts of the cooling functions when log(p/k) = 8 or more. Above log(T) = 7.5-8.5 it should be pretty safe to use a powerlaw fit to the free-free losses Good luck, and let me know if you have any problems. These files will be available via anonymous ftp from Yours sincerely Ralph S. Sutherland 1992-1993. pydl-0.6.0/pydl/pydlutils/data/cooling/m-20.cie0000664000111400017500000001544712775255301022010 0ustar weaverweaver00000000000000Cooling Curve : CIE:[Fe/H] = -2.0 log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.00 0.0022 1.000 1.064 -26.03 -23.40 -11.66 11.72 1.483 2.115 8.372 1.984 4.05 0.0141 1.000 1.064 -24.68 -22.86 -11.60 11.22 1.682 2.115 8.917 1.962 4.10 0.0700 1.000 1.064 -23.53 -22.40 -11.53 10.82 1.985 2.115 9.687 1.865 4.15 0.2528 1.000 1.064 -22.63 -22.05 -11.41 10.53 2.586 2.115 11.06 1.607 4.20 0.5668 1.000 1.064 -22.12 -21.90 -11.27 10.42 3.594 2.116 13.03 1.297 4.25 0.8163 1.000 1.064 -21.98 -21.92 -11.16 10.48 4.649 2.116 14.82 1.125 4.30 0.9310 1.000 1.064 -22.02 -22.02 -11.08 10.63 5.535 2.116 16.17 1.061 4.35 0.9755 1.000 1.064 -22.12 -22.14 -11.02 10.80 6.349 2.116 17.32 1.038 4.40 0.9980 1.000 1.064 -22.23 -22.25 -10.97 10.97 7.202 2.116 18.45 1.026 4.45 1.020 1.000 1.064 -22.31 -22.34 -10.91 11.11 8.169 2.116 19.65 1.015 4.50 1.042 1.000 1.064 -22.37 -22.42 -10.86 11.23 9.262 2.116 20.92 1.005 4.55 1.055 1.000 1.064 -22.44 -22.49 -10.81 11.36 10.45 2.116 22.23 0.999 4.60 1.060 1.000 1.064 -22.49 -22.54 -10.76 11.46 11.76 2.116 23.57 0.996 4.65 1.062 1.000 1.064 -22.51 -22.56 -10.71 11.53 13.21 2.116 24.98 0.995 4.70 1.064 1.000 1.064 -22.49 -22.54 -10.66 11.56 14.83 2.116 26.47 0.995 4.75 1.065 1.000 1.064 -22.40 -22.46 -10.61 11.52 16.65 2.116 28.05 0.994 4.80 1.070 1.000 1.064 -22.27 -22.32 -10.55 11.44 18.73 2.116 29.75 0.991 4.85 1.082 1.000 1.064 -22.13 -22.19 -10.50 11.36 21.12 2.116 31.59 0.986 4.90 1.099 1.000 1.064 -22.05 -22.12 -10.45 11.33 23.89 2.116 33.60 0.978 4.95 1.113 1.000 1.064 -22.05 -22.12 -10.40 11.39 26.98 2.116 35.70 0.972 5.00 1.121 1.000 1.064 -22.10 -22.17 -10.34 11.49 30.38 2.116 37.89 0.969 5.05 1.125 1.000 1.064 -22.16 -22.24 -10.29 11.60 34.15 2.116 40.17 0.967 5.10 1.127 1.000 1.064 -22.21 -22.28 -10.24 11.71 38.35 2.116 42.57 0.966 5.15 1.127 1.000 1.064 -22.23 -22.31 -10.19 11.78 43.04 2.116 45.10 0.966 5.20 1.128 1.000 1.064 -22.25 -22.33 -10.14 11.85 48.30 2.116 47.77 0.966 5.25 1.128 1.000 1.064 -22.26 -22.33 -10.09 11.91 54.20 2.116 50.61 0.965 5.30 1.128 1.000 1.064 -22.26 -22.34 -10.04 11.96 60.81 2.116 53.61 0.965 5.35 1.128 1.000 1.064 -22.26 -22.34 -9.993 12.01 68.24 2.116 56.78 0.965 5.40 1.128 1.000 1.064 -22.30 -22.38 -9.943 12.10 76.57 2.116 60.15 0.965 5.45 1.128 1.000 1.064 -22.42 -22.50 -9.893 12.27 85.91 2.116 63.71 0.965 5.50 1.128 1.000 1.064 -22.59 -22.67 -9.843 12.49 96.39 2.116 67.49 0.965 5.55 1.128 1.000 1.064 -22.74 -22.81 -9.793 12.69 108.2 2.116 71.49 0.965 5.60 1.128 1.000 1.064 -22.82 -22.90 -9.743 12.82 121.4 2.116 75.73 0.965 5.65 1.128 1.000 1.064 -22.86 -22.94 -9.693 12.91 136.2 2.116 80.21 0.965 5.70 1.128 1.000 1.064 -22.87 -22.95 -9.643 12.97 152.8 2.116 84.97 0.965 5.75 1.128 1.000 1.064 -22.89 -22.97 -9.593 13.04 171.4 2.116 90.00 0.965 5.80 1.128 1.000 1.064 -22.93 -23.01 -9.543 13.13 192.3 2.116 95.33 0.965 5.85 1.128 1.000 1.064 -23.00 -23.08 -9.493 13.25 215.8 2.116 101.0 0.965 5.90 1.128 1.000 1.064 -23.06 -23.14 -9.443 13.36 242.1 2.116 107.0 0.965 5.95 1.128 1.000 1.064 -23.09 -23.17 -9.393 13.44 271.7 2.116 113.3 0.965 6.00 1.128 1.000 1.064 -23.10 -23.18 -9.343 13.50 304.8 2.116 120.0 0.965 6.05 1.128 1.000 1.064 -23.12 -23.20 -9.293 13.57 342.0 2.116 127.1 0.965 6.10 1.128 1.000 1.064 -23.12 -23.20 -9.243 13.62 383.8 2.116 134.7 0.965 6.15 1.128 1.000 1.064 -23.12 -23.20 -9.193 13.67 430.6 2.116 142.6 0.965 6.20 1.128 1.000 1.064 -23.12 -23.20 -9.143 13.72 483.1 2.116 151.1 0.965 6.25 1.128 1.000 1.064 -23.12 -23.20 -9.093 13.77 542.1 2.116 160.0 0.965 6.30 1.128 1.000 1.064 -23.13 -23.21 -9.043 13.83 608.2 2.116 169.5 0.965 6.35 1.128 1.000 1.064 -23.15 -23.23 -8.993 13.90 682.4 2.116 179.6 0.965 6.40 1.128 1.000 1.064 -23.16 -23.24 -8.943 13.96 765.7 2.116 190.2 0.965 6.45 1.128 1.000 1.064 -23.16 -23.24 -8.893 14.01 859.2 2.116 201.5 0.965 6.50 1.128 1.000 1.064 -23.16 -23.24 -8.843 14.06 964.0 2.116 213.4 0.965 6.55 1.128 1.000 1.064 -23.15 -23.23 -8.793 14.10 1081.6 2.116 226.1 0.965 6.60 1.128 1.000 1.064 -23.14 -23.22 -8.743 14.14 1213.6 2.116 239.5 0.965 6.65 1.128 1.000 1.064 -23.13 -23.21 -8.693 14.18 1361.7 2.116 253.7 0.965 6.70 1.128 1.000 1.064 -23.12 -23.20 -8.643 14.22 1527.8 2.116 268.7 0.965 6.75 1.128 1.000 1.064 -23.11 -23.19 -8.593 14.26 1714.3 2.116 284.6 0.965 6.80 1.128 1.000 1.064 -23.09 -23.17 -8.543 14.29 1923.4 2.116 301.5 0.965 6.85 1.128 1.000 1.064 -23.08 -23.16 -8.493 14.33 2158.1 2.116 319.3 0.965 6.90 1.128 1.000 1.064 -23.06 -23.14 -8.443 14.36 2421.5 2.116 338.3 0.965 6.95 1.128 1.000 1.064 -23.05 -23.13 -8.393 14.40 2716.9 2.116 358.3 0.965 7.00 1.128 1.000 1.064 -23.03 -23.11 -8.343 14.43 3048.4 2.116 379.5 0.965 7.05 1.128 1.000 1.064 -23.01 -23.09 -8.293 14.46 3420.4 2.116 402.0 0.965 7.10 1.128 1.000 1.064 -23.00 -23.07 -8.243 14.50 3837.8 2.116 425.8 0.965 7.15 1.128 1.000 1.064 -22.98 -23.06 -8.193 14.53 4306.0 2.116 451.1 0.965 7.20 1.128 1.000 1.064 -22.96 -23.04 -8.143 14.56 4831.5 2.116 477.8 0.965 7.25 1.128 1.000 1.064 -22.94 -23.02 -8.093 14.59 5421.0 2.116 506.1 0.965 7.30 1.128 1.000 1.064 -22.92 -23.00 -8.043 14.62 6082.4 2.116 536.1 0.965 7.35 1.128 1.000 1.064 -22.90 -22.98 -7.993 14.65 6824.6 2.116 567.9 0.965 7.40 1.128 1.000 1.064 -22.88 -22.96 -7.943 14.68 7657.4 2.116 601.5 0.965 7.45 1.128 1.000 1.064 -22.86 -22.94 -7.893 14.71 8591.7 2.116 637.2 0.965 7.50 1.128 1.000 1.064 -22.84 -22.92 -7.843 14.74 9640.0 2.116 674.9 0.965 7.55 1.128 1.000 1.064 -22.82 -22.90 -7.793 14.77 10816.3 2.116 714.9 0.965 7.60 1.128 1.000 1.064 -22.80 -22.88 -7.743 14.80 12136.1 2.116 757.3 0.965 7.65 1.128 1.000 1.064 -22.78 -22.86 -7.693 14.83 13616.9 2.116 802.1 0.965 7.70 1.128 1.000 1.064 -22.75 -22.83 -7.643 14.85 15278.4 2.116 849.7 0.965 7.75 1.128 1.000 1.064 -22.73 -22.81 -7.593 14.88 17142.7 2.116 900.0 0.965 7.80 1.128 1.000 1.064 -22.71 -22.79 -7.543 14.91 19234.4 2.116 953.4 0.965 7.85 1.128 1.000 1.064 -22.69 -22.77 -7.493 14.94 21581.3 2.116 1009.8 0.965 7.90 1.128 1.000 1.064 -22.67 -22.75 -7.443 14.97 24214.7 2.116 1069.7 0.965 7.95 1.128 1.000 1.064 -22.64 -22.72 -7.393 14.99 27169.3 2.116 1133.1 0.965 8.00 1.128 1.000 1.064 -22.62 -22.70 -7.343 15.02 30484.5 2.116 1200.2 0.965 8.05 1.128 1.000 1.064 -22.60 -22.68 -7.293 15.05 34204.1 2.116 1271.3 0.965 8.10 1.128 1.000 1.064 -22.58 -22.66 -7.243 15.08 38377.7 2.116 1346.7 0.965 8.15 1.128 1.000 1.064 -22.55 -22.63 -7.193 15.10 43060.5 2.116 1426.4 0.965 8.20 1.128 1.000 1.064 -22.53 -22.61 -7.143 15.13 48314.6 2.116 1511.0 0.965 8.25 1.128 1.000 1.064 -22.51 -22.59 -7.093 15.16 54209.9 2.116 1600.5 0.965 8.30 1.128 1.000 1.064 -22.49 -22.56 -7.043 15.19 60824.5 2.116 1695.3 0.965 8.35 1.128 1.000 1.064 -22.46 -22.54 -6.993 15.21 68246.2 2.116 1795.8 0.965 8.40 1.128 1.000 1.064 -22.44 -22.52 -6.943 15.24 76573.5 2.116 1902.2 0.965 8.45 1.128 1.000 1.064 -22.42 -22.50 -6.893 15.27 85916.9 2.116 2014.9 0.965 8.50 1.128 1.000 1.064 -22.39 -22.47 -6.843 15.29 96400.4 2.116 2134.3 0.965pydl-0.6.0/pydl/pydlutils/data/cooling/m-00.cie0000664000111400017500000001544512775255301022004 0ustar weaverweaver00000000000000Cooling Curve : CIE:Solar log(T) ne nH nt log(lambda net) log(lambda norm) log(U) log(taucool) P12 rho24 Ci mubar 4.00 0.0023 1.000 1.099 -25.65 -23.06 -11.64 11.37 1.532 2.386 8.012 2.166 4.05 0.0142 1.000 1.099 -24.27 -22.46 -11.59 10.83 1.737 2.386 8.533 2.143 4.10 0.0704 1.000 1.099 -23.28 -22.17 -11.52 10.58 2.048 2.386 9.264 2.040 4.15 0.2534 1.000 1.099 -22.47 -21.92 -11.40 10.38 2.657 2.386 10.55 1.764 4.20 0.5678 1.000 1.099 -21.99 -21.79 -11.26 10.30 3.674 2.387 12.41 1.431 4.25 0.8177 1.000 1.099 -21.84 -21.80 -11.15 10.36 4.740 2.387 14.09 1.245 4.30 0.9324 1.000 1.099 -21.85 -21.86 -11.08 10.48 5.637 2.387 15.37 1.175 4.35 0.9780 1.000 1.099 -21.87 -21.90 -11.02 10.57 6.467 2.387 16.46 1.149 4.40 1.004 1.000 1.099 -21.84 -21.88 -10.96 10.60 7.347 2.387 17.54 1.135 4.45 1.035 1.000 1.099 -21.77 -21.82 -10.90 10.59 8.366 2.387 18.72 1.118 4.50 1.067 1.000 1.099 -21.66 -21.73 -10.85 10.55 9.528 2.387 19.98 1.102 4.55 1.086 1.000 1.099 -21.56 -21.63 -10.79 10.50 10.78 2.387 21.25 1.092 4.60 1.094 1.000 1.099 -21.45 -21.53 -10.74 10.45 12.14 2.387 22.56 1.088 4.65 1.098 1.000 1.099 -21.34 -21.42 -10.69 10.39 13.65 2.387 23.91 1.087 4.70 1.100 1.000 1.099 -21.24 -21.32 -10.64 10.34 15.32 2.387 25.34 1.086 4.75 1.102 1.000 1.099 -21.14 -21.22 -10.59 10.29 17.22 2.387 26.86 1.084 4.80 1.110 1.000 1.099 -21.05 -21.14 -10.54 10.25 19.38 2.387 28.50 1.080 4.85 1.128 1.000 1.099 -20.97 -21.07 -10.49 10.23 21.92 2.387 30.31 1.072 4.90 1.154 1.000 1.099 -20.91 -21.01 -10.43 10.23 24.89 2.387 32.29 1.059 4.95 1.176 1.000 1.099 -20.87 -20.98 -10.38 10.25 28.19 2.387 34.37 1.049 5.00 1.188 1.000 1.099 -20.87 -20.99 -10.32 10.30 31.81 2.387 36.50 1.043 5.05 1.195 1.000 1.099 -20.90 -21.02 -10.27 10.38 35.79 2.387 38.72 1.041 5.10 1.198 1.000 1.099 -20.91 -21.03 -10.22 10.45 40.21 2.387 41.04 1.039 5.15 1.199 1.000 1.099 -20.89 -21.01 -10.17 10.48 45.14 2.387 43.49 1.039 5.20 1.200 1.000 1.099 -20.86 -20.98 -10.12 10.50 50.67 2.387 46.07 1.038 5.25 1.201 1.000 1.099 -20.85 -20.97 -10.07 10.54 56.87 2.387 48.81 1.038 5.30 1.201 1.000 1.099 -20.84 -20.96 -10.02 10.58 63.82 2.387 51.71 1.038 5.35 1.201 1.000 1.099 -20.84 -20.96 -9.972 10.63 71.62 2.387 54.78 1.038 5.40 1.202 1.000 1.099 -20.87 -20.99 -9.922 10.71 80.38 2.387 58.03 1.037 5.45 1.203 1.000 1.099 -21.01 -21.13 -9.872 10.90 90.22 2.387 61.48 1.037 5.50 1.203 1.000 1.099 -21.23 -21.35 -9.822 11.17 101.2 2.387 65.13 1.037 5.55 1.204 1.000 1.099 -21.43 -21.55 -9.772 11.42 113.6 2.387 68.99 1.037 5.60 1.204 1.000 1.099 -21.54 -21.66 -9.722 11.58 127.5 2.387 73.08 1.036 5.65 1.204 1.000 1.099 -21.58 -21.71 -9.672 11.67 143.1 2.387 77.41 1.036 5.70 1.204 1.000 1.099 -21.59 -21.71 -9.622 11.73 160.5 2.387 82.00 1.036 5.75 1.204 1.000 1.099 -21.59 -21.71 -9.572 11.78 180.1 2.387 86.87 1.036 5.80 1.204 1.000 1.099 -21.64 -21.76 -9.522 11.88 202.1 2.387 92.02 1.036 5.85 1.205 1.000 1.099 -21.74 -21.86 -9.471 12.03 226.8 2.387 97.47 1.036 5.90 1.205 1.000 1.099 -21.81 -21.93 -9.421 12.15 254.5 2.387 103.3 1.036 5.95 1.205 1.000 1.099 -21.83 -21.95 -9.371 12.22 285.6 2.387 109.4 1.036 6.00 1.205 1.000 1.099 -21.84 -21.96 -9.321 12.28 320.4 2.387 115.9 1.036 6.05 1.205 1.000 1.099 -21.84 -21.96 -9.271 12.33 359.6 2.387 122.7 1.036 6.10 1.206 1.000 1.099 -21.83 -21.96 -9.221 12.37 403.5 2.387 130.0 1.036 6.15 1.206 1.000 1.099 -21.82 -21.95 -9.171 12.41 452.8 2.387 137.7 1.036 6.20 1.206 1.000 1.099 -21.82 -21.94 -9.121 12.46 508.1 2.387 145.9 1.035 6.25 1.206 1.000 1.099 -21.85 -21.97 -9.071 12.54 570.1 2.387 154.5 1.035 6.30 1.207 1.000 1.099 -21.95 -22.07 -9.021 12.69 639.8 2.387 163.7 1.035 6.35 1.207 1.000 1.099 -22.08 -22.20 -8.971 12.87 717.9 2.387 173.4 1.035 6.40 1.207 1.000 1.099 -22.19 -22.31 -8.921 13.03 805.6 2.387 183.7 1.035 6.45 1.208 1.000 1.099 -22.27 -22.39 -8.871 13.16 904.0 2.387 194.6 1.035 6.50 1.208 1.000 1.099 -22.32 -22.44 -8.821 13.26 1014.5 2.387 206.1 1.035 6.55 1.208 1.000 1.099 -22.35 -22.48 -8.771 13.34 1138.3 2.387 218.4 1.035 6.60 1.208 1.000 1.099 -22.38 -22.50 -8.721 13.42 1277.3 2.387 231.3 1.035 6.65 1.208 1.000 1.099 -22.41 -22.53 -8.671 13.50 1433.2 2.387 245.0 1.034 6.70 1.208 1.000 1.099 -22.44 -22.56 -8.621 13.58 1608.2 2.387 259.6 1.034 6.75 1.208 1.000 1.099 -22.46 -22.59 -8.571 13.65 1804.4 2.387 274.9 1.034 6.80 1.208 1.000 1.099 -22.48 -22.60 -8.521 13.72 2024.7 2.387 291.2 1.034 6.85 1.209 1.000 1.099 -22.48 -22.60 -8.471 13.77 2271.8 2.387 308.5 1.034 6.90 1.209 1.000 1.099 -22.46 -22.59 -8.421 13.80 2549.1 2.387 326.8 1.034 6.95 1.209 1.000 1.099 -22.45 -22.57 -8.371 13.84 2860.2 2.387 346.1 1.034 7.00 1.209 1.000 1.099 -22.45 -22.57 -8.321 13.89 3209.2 2.387 366.7 1.034 7.05 1.209 1.000 1.099 -22.46 -22.59 -8.271 13.95 3600.9 2.387 388.4 1.034 7.10 1.209 1.000 1.099 -22.49 -22.62 -8.221 14.03 4040.4 2.387 411.4 1.034 7.15 1.209 1.000 1.099 -22.53 -22.65 -8.171 14.12 4533.5 2.387 435.8 1.034 7.20 1.209 1.000 1.099 -22.56 -22.68 -8.121 14.20 5086.8 2.387 461.6 1.034 7.25 1.209 1.000 1.099 -22.58 -22.70 -8.071 14.27 5707.5 2.387 489.0 1.034 7.30 1.209 1.000 1.099 -22.60 -22.72 -8.021 14.34 6404.0 2.387 518.0 1.034 7.35 1.209 1.000 1.099 -22.61 -22.73 -7.971 14.40 7185.4 2.387 548.6 1.034 7.40 1.209 1.000 1.099 -22.61 -22.73 -7.921 14.45 8062.2 2.387 581.2 1.034 7.45 1.209 1.000 1.099 -22.61 -22.73 -7.871 14.50 9046.0 2.387 615.6 1.034 7.50 1.209 1.000 1.099 -22.60 -22.73 -7.821 14.54 10149.8 2.387 652.1 1.034 7.55 1.209 1.000 1.099 -22.60 -22.72 -7.771 14.59 11388.3 2.387 690.7 1.034 7.60 1.209 1.000 1.099 -22.59 -22.71 -7.721 14.63 12777.9 2.387 731.6 1.034 7.65 1.209 1.000 1.099 -22.57 -22.70 -7.671 14.66 14337.1 2.387 775.0 1.034 7.70 1.209 1.000 1.099 -22.56 -22.68 -7.621 14.70 16086.6 2.387 820.9 1.034 7.75 1.209 1.000 1.099 -22.54 -22.67 -7.571 14.73 18049.5 2.387 869.6 1.034 7.80 1.209 1.000 1.099 -22.53 -22.65 -7.521 14.77 20251.9 2.387 921.1 1.034 7.85 1.209 1.000 1.099 -22.51 -22.64 -7.471 14.80 22723.1 2.387 975.7 1.034 7.90 1.209 1.000 1.099 -22.49 -22.62 -7.421 14.83 25495.8 2.387 1033.5 1.034 7.95 1.209 1.000 1.099 -22.48 -22.60 -7.371 14.87 28606.9 2.387 1094.7 1.034 8.00 1.209 1.000 1.099 -22.46 -22.58 -7.321 14.90 32097.6 2.387 1159.6 1.034 8.05 1.209 1.000 1.099 -22.44 -22.56 -7.271 14.93 36014.2 2.387 1228.3 1.034 8.10 1.209 1.000 1.099 -22.42 -22.54 -7.221 14.96 40408.7 2.387 1301.1 1.034 8.15 1.209 1.000 1.099 -22.40 -22.53 -7.171 14.99 45339.4 2.387 1378.2 1.034 8.20 1.209 1.000 1.099 -22.38 -22.51 -7.121 15.02 50871.7 2.387 1459.8 1.034 8.25 1.209 1.000 1.099 -22.36 -22.49 -7.071 15.05 57079.1 2.387 1546.3 1.034 8.30 1.209 1.000 1.099 -22.34 -22.47 -7.021 15.08 64044.0 2.387 1638.0 1.034 8.35 1.209 1.000 1.099 -22.32 -22.45 -6.971 15.11 71858.6 2.387 1735.0 1.034 8.40 1.209 1.000 1.099 -22.30 -22.43 -6.921 15.14 80626.8 2.387 1837.8 1.034 8.45 1.209 1.000 1.099 -22.28 -22.40 -6.871 15.17 90464.8 2.387 1946.7 1.034 8.50 1.209 1.000 1.099 -22.26 -22.38 -6.821 15.20 101503 2.387 2062.1 1.034 pydl-0.6.0/pydl/pydlutils/math.py0000664000111400017500000004654712775255301017620 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the math directory in idlutils. """ import numpy as np import astropy.utils as au class computechi2(object): """Solve the linear set of equations :math:`A x = b` using SVD. The attributes of this class are all read-only properties, implemented with :class:`~astropy.utils.decorators.lazyproperty`. Parameters ---------- bvec : :class:`numpy.ndarray` The :math:`b` vector in :math:`A x = b`. This vector has length :math:`N`. sqivar : :class:`numpy.ndarray` The reciprocal of the errors in `bvec`. The name comes from the square root of the inverse variance, which is what this is. amatrix : :class:`numpy.ndarray` The matrix :math:`A` in :math:`A x = b`. The shape of this matrix is (:math:`N`, :math:`M`). """ def __init__(self, bvec, sqivar, amatrix): """Initialize the object and perform initial computations. """ from numpy.linalg import svd # # Save the inputs # # self.bvec = bvec self.sqivar = sqivar self.amatrix = amatrix if len(amatrix.shape) > 1: self.nstar = amatrix.shape[1] else: self.nstar = 1 self.bvec = bvec * sqivar self.mmatrix = self.amatrix * np.tile(sqivar, self.nstar).reshape( self.nstar, bvec.size).transpose() mm = np.dot(self.mmatrix.T, self.mmatrix) self.uu, self.ww, self.vv = svd(mm, full_matrices=False) self.mmi = np.dot((self.vv.T / np.tile(self.ww, self.nstar).reshape( self.nstar, self.nstar)), self.uu.T) return @au.lazyproperty def acoeff(self): """(:class:`~numpy.ndarray`) The fit parameters, :math:`x`, in :math:`A x = b`. This vector has length :math:`M`. """ return np.dot(self.mmi, np.dot(self.mmatrix.T, self.bvec)) @au.lazyproperty def chi2(self): """(:class:`float `) The :math:`\chi^2` value of the fit. """ return np.sum((np.dot(self.mmatrix, self.acoeff) - self.bvec)**2) @au.lazyproperty def yfit(self): """(:class:`~numpy.ndarray`) The evaluated best-fit at each point. This vector has length :math:`N`. """ return np.dot(self.amatrix, self.acoeff) @au.lazyproperty def dof(self): """(:class:`int `) The degrees of freedom of the fit. This is the number of values of `bvec` that have `sqivar` > 0 minus the number of fit paramaters, which is equal to :math:`M`. """ return (self.sqivar > 0).sum() - self.nstar @au.lazyproperty def covar(self): """(:class:`~numpy.ndarray`) The covariance matrix. The shape of this matrix is (:math:`M`, :math:`M`). """ wwt = self.ww.copy() wwt[self.ww > 0] = 1.0/self.ww[self.ww > 0] covar = np.zeros((self.nstar, self.nstar), dtype=self.ww.dtype) for i in range(self.nstar): for j in range(i + 1): covar[i, j] = np.sum(wwt * self.vv[:, i] * self.vv[:, j]) covar[j, i] = covar[i, j] return covar @au.lazyproperty def var(self): """(:class:`~numpy.ndarray`) The variances of the fit. This is identical to the diagonal of the covariance matrix. This vector has length :math:`M`. """ return np.diag(self.covar) def djs_median(array, dimension=None, width=None, boundary='none'): """Compute the median of an array. Use a filtering box or collapse the image along one dimension. Parameters ---------- array : :class:`numpy.ndarray` input array dimension : :class:`int`, optional Compute the median over this dimension. It is an error to specify both `dimension` and `width`. width : :class:`int`, optional Width of the median window. In general, this should be an odd integer. It is an error to specify both `dimension` and `width`. boundary : { 'none', 'reflect', 'nearest', 'wrap' }, optional Boundary condition to impose. 'none' means no filtering is done within `width`/2 of the boundary. 'reflect' means reflect pixel values around the boundary. 'nearest' means use the values of the nearest boundary pixel. 'wrap' means wrap pixel values around the boundary. 'nearest' and 'wrap' are not implemented. Returns ------- :class:`numpy.ndarray` The output. If neither `dimension` nor `width` are set, this is a scalar value, just the output of ``numpy.median()``. If `dimension` is set, then the result simply ``numpy.median(array,dimension)``. If `width` is set, the result has the same shape as the input array. """ from ..median import median if dimension is None and width is None: return np.median(array) elif width is None: return np.median(array, axis=dimension) elif dimension is None: if width == 1: return array if boundary == 'none': if array.ndim == 1: return median(array, width) elif array.ndim == 2: return median(array, width) else: raise ValueError('Unsupported number of dimensions with ' + 'this boundary condition.') elif boundary == 'reflect': padsize = int(np.ceil(width/2.0)) if array.ndim == 1: bigarr = np.zeros(array.shape[0]+2*padsize, dtype=array.dtype) bigarr[padsize:padsize+array.shape[0]] = array bigarr[0:padsize] = array[0:padsize][::-1] bigarr[padsize+array.shape[0]:padsize*2+array.shape[0]] = ( array[array.shape[0]-padsize:array.shape[0]][::-1]) f = median(bigarr, width) medarray = f[padsize:padsize+array.shape[0]] return medarray elif array.ndim == 2: bigarr = np.zeros((array.shape[0]+2*padsize, array.shape[1]+2*padsize), dtype=array.dtype) bigarr[padsize:padsize+array.shape[0], padsize:padsize+array.shape[1]] = array # Copy into top + bottom bigarr[0:padsize, padsize:array.shape[1]+padsize] = array[0:padsize, :][::-1, :] bigarr[array.shape[0]+padsize:bigarr.shape[0], padsize:array.shape[1]+padsize] = array[array.shape[0]-padsize:array.shape[0], :][::-1, :] # Copy into left + right bigarr[padsize:array.shape[0]+padsize, 0:padsize] = array[:, 0:padsize][:, ::-1] bigarr[padsize:array.shape[0]+padsize, array.shape[1]+padsize:bigarr.shape[1]] = array[:, array.shape[1]-padsize:array.shape[1]][:, ::-1] # Copy into top left bigarr[0:padsize, 0:padsize] = array[0:padsize, 0:padsize][::-1, ::-1] # Copy into top right bigarr[0:padsize, bigarr.shape[1]-padsize:bigarr.shape[1]] = array[0:padsize, array.shape[1]-padsize:array.shape[1]][::-1, ::-1] # Copy into bottom left bigarr[bigarr.shape[0]-padsize:bigarr.shape[0], 0:padsize] = array[array.shape[0]-padsize:array.shape[0], 0:padsize][::-1, ::-1] # Copy into bottom right bigarr[bigarr.shape[0]-padsize:bigarr.shape[0], bigarr.shape[1]-padsize:bigarr.shape[1]] = array[array.shape[0]-padsize:array.shape[0], array.shape[1]-padsize:array.shape[1]][::-1, ::-1] f = median(bigarr, min(width, array.size)) medarray = f[padsize:array.shape[0]+padsize, padsize:array.shape[1]+padsize] return medarray else: raise ValueError('Unsupported number of dimensions with ' + 'this boundary condition.') elif boundary == 'nearest': raise ValueError('This boundary condition not implemented') elif boundary == 'wrap': raise ValueError('This boundary condition not implemented') else: raise ValueError('Unknown boundary condition.') else: raise ValueError('Invalid to specify both dimension & width.') def djs_reject(data, model, outmask=None, inmask=None, sigma=None, invvar=None, lower=None, upper=None, maxdev=None, maxrej=None, groupdim=None, groupsize=None, groupbadpix=False, grow=0, sticky=False): """Routine to reject points when doing an iterative fit to data. Parameters ---------- data : :class:`numpy.ndarray` The data model : :class:`numpy.ndarray` The model, must have the same number of dimensions as `data`. outmask : :class:`numpy.ndarray`, optional Output mask, generated by a previous call to `djs_reject`. If not supplied, this mask will be initialized to a mask that masks nothing. Although this parameter is technically optional, it will almost always be set. inmask : :class:`numpy.ndarray`, optional Input mask. Bad points are marked with a value that evaluates to ``False``. Must have the same number of dimensions as `data`. sigma : :class:`numpy.ndarray`, optional Standard deviation of the data, used to reject points based on the values of `upper` and `lower`. invvar : :class:`numpy.ndarray`, optional Inverse variance of the data, used to reject points based on the values of `upper` and `lower`. If both `sigma` and `invvar` are set, `invvar` will be ignored. lower : :class:`int` or :class:`float`, optional If set, reject points with data < model - lower * sigma. upper : :class:`int` or :class:`float`, optional If set, reject points with data > model + upper * sigma. maxdev : :class:`int` or :class:`float`, optional If set, reject points with abs(data-model) > maxdev. It is permitted to set all three of `lower`, `upper` and `maxdev`. maxrej : :class:`int` or :class:`numpy.ndarray`, optional Maximum number of points to reject in this iteration. If `groupsize` or `groupdim` are set to arrays, this should be an array as well. groupdim To be documented. groupsize To be documented. groupbadpix : :class:`bool`, optional If set to ``True``, consecutive sets of bad pixels are considered groups, overriding the values of `groupsize`. grow : :class:`int`, optional If set to a non-zero integer, N, the N nearest neighbors of rejected pixels will also be rejected. sticky : :class:`bool`, optional If set to ``True``, pixels rejected in one iteration remain rejected in subsequent iterations, even if the model changes. Returns ------- :func:`tuple` A tuple containing a mask where rejected data values are ``False`` and a boolean value set to ``True`` if `djs_reject` believes there is no further rejection to be done. Raises ------ ValueError If dimensions of various inputs do not match. """ from .misc import djs_laxisnum # # Create outmask setting = 1 for good data. # if outmask is None: outmask = np.ones(data.shape, dtype='bool') else: if data.shape != outmask.shape: raise ValueError('Dimensions of data and outmask do not agree.') # # Check other inputs. # if model is None: if inmask is not None: outmask = inmask return (outmask, False) else: if data.shape != model.shape: raise ValueError('Dimensions of data and model do not agree.') if inmask is not None: if data.shape != inmask.shape: raise ValueError('Dimensions of data and inmask do not agree.') if maxrej is not None: if groupdim is not None: if len(maxrej) != len(groupdim): raise ValueError('maxrej and groupdim must have the same number of elements.') else: groupdim = [] if groupsize is not None: if len(maxrej) != len(groupsize): raise ValueError('maxrej and groupsize must have the same number of elements.') else: groupsize = len(data) if sigma is None and invvar is None: if inmask is not None: igood = (inmask & outmask).nonzero()[0] else: igood = outmask.nonzero()[0] if len(igood > 1): sigma = np.std(data[igood] - model[igood]) else: sigma = 0 diff = data - model # # The working array is badness, which is set to zero for good points # (or points already rejected), and positive values for bad points. # The values determine just how bad a point is, either corresponding # to the number of sigma above or below the fit, or to the number # of multiples of maxdev away from the fit. # badness = np.zeros(outmask.shape, dtype=data.dtype) # # Decide how bad a point is according to lower. # if lower is not None: if sigma is not None: qbad = diff < (-lower * sigma) badness += ((-diff/(sigma + (sigma == 0))) > 0) * qbad else: qbad = (diff * np.sqrt(invvar)) < -lower badness += ((-diff * np.sqrt(invvar)) > 0) * qbad # # Decide how bad a point is according to upper. # if upper is not None: if sigma is not None: qbad = diff > (upper * sigma) badness += ((diff/(sigma + (sigma == 0))) > 0) * qbad else: qbad = (diff * np.sqrt(invvar)) > upper badness += ((diff * np.sqrt(invvar)) > 0) * qbad # # Decide how bad a point is according to maxdev. # if maxdev is not None: qbad = np.absolute(diff) > maxdev badness += np.absolute(diff) / maxdev * qbad # # Do not consider rejecting points that are already rejected by inmask. # Do not consider rejecting points that are already rejected by outmask, # if sticky is set. # if inmask is not None: badness *= inmask if sticky: badness *= outmask # # Reject a maximum of maxrej (additional) points in all the data, or # in each group as specified by groupsize, and optionally along each # dimension specified by groupdim. # if maxrej is not None: # # Loop over each dimension of groupdim or loop once if not set. # for iloop in range(max(len(groupdim), 1)): # # Assign an index number in this dimension to each data point. # if len(groupdim) > 0: yndim = len(ydata.shape) if groupdim[iloop] > yndim: raise ValueError('groupdim is larger than the number of dimensions for ydata.') dimnum = djs_laxisnum(ydata.shape, iaxis=groupdim[iloop]-1) else: dimnum = 0 # # Loop over each vector specified by groupdim. For example, if # this is a 2-D array with groupdim=1, then loop over each # column of the data. If groupdim=2, then loop over each row. # If groupdim is not set, then use the whole image. # for ivec in range(max(dimnum)): # # At this point it is not possible that dimnum is not set. # indx = (dimnum == ivec).nonzero()[0] # # Within this group of points, break it down into groups # of points specified by groupsize, if set. # nin = len(indx) if groupbadpix: goodtemp = badness == 0 groups_lower = (-1*np.diff(np.insert(goodtemp, 0, 1)) == 1).nonzero()[0] groups_upper = (np.diff(np.append(goodtemp, 1)) == 1).nonzero()[0] ngroups = len(groups_lower) else: # # The IDL version of this test makes no sense because # groupsize will always be set. # if 'groupsize' in kwargs: ngroups = nin/groupsize + 1 groups_lower = np.arange(ngroups, dtype='i4')*groupsize foo = (np.arange(ngroups, dtype='i4')+1)*groupsize groups_upper = np.where(foo < nin, foo, nin) - 1 else: ngroups = 1 groups_lower = [0, ] groups_upper = [nin - 1, ] for igroup in range(ngroups): i1 = groups_lower[igroup] i2 = groups_upper[igroup] nii = i2 - i1 + 1 # # Need the test that i1 != -1 below to prevent a crash # condition, but why is it that we ever get groups # without any points? Because this is badly-written, # that's why. # if nii > 0 and i1 != -1: jj = indx[i1:i2+1] # # Test if too many points rejected in this group. # if np.sum(badness[jj] != 0) > maxrej[iloop]: isort = badness[jj].argsort() # # Make the following points good again. # badness[jj[isort[0:nii-maxrej[iloop]]]] = 0 i1 += groupsize[iloop] # # Now modify outmask, rejecting points specified by inmask=0, outmask=0 # if sticky is set, or badness > 0. # # print(badness) newmask = badness == 0 # print(newmask) if grow > 0: rejects = newmask == 0 if rejects.any(): irejects = rejects.nonzero()[0] for k in range(1, grow): newmask[(irejects - k) > 0] = 0 newmask[(irejects + k) < (data.shape[0]-1)] = 0 if inmask is not None: newmask = newmask & inmask if sticky: newmask = newmask & outmask # # Set qdone if the input outmask is identical to the output outmask. # qdone = np.all(newmask == outmask) outmask = newmask return (outmask, qdone) def find_contiguous(x): """Find the longest sequence of contiguous non-zero array elements. Parameters ---------- x : :class:`numpy.ndarray` A 1d array. A dtype of bool is preferred although any dtype where the operation ``if x[k]:`` is well-defined should work. Returns ------- :class:`list` A list of indices of the longest contiguous non-zero sequence. Examples -------- >>> import numpy as np >>> from pydl.pydlutils.math import find_contiguous >>> find_contiguous(np.array([0,1,1,1,0,1,1,0,1])) [1, 2, 3] """ contig = list() for k in range(x.size): if x[k]: if len(contig) == 0: contig.append([k]) else: if k == contig[-1][-1]+1: contig[-1].append(k) else: contig.append([k]) lengths = [len(c) for c in contig] longest = contig[lengths.index(max(lengths))] return longest pydl-0.6.0/pydl/pydlutils/rgbcolor.py0000664000111400017500000001132412775255301020461 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the rgbcolor directory in idlutils. This code reproduces the algorithms of Lupton *et al.* (2004) [2]_. The purpose is to produce nice color (RGB, JPEG, PNG, etc.) from FITS images in astronomical filter bands. References ---------- .. [2] `Lupton, Robert, et al., 2004 PASP 116, 113 `_. """ import numpy as np def nw_arcsinh(colors, nonlinearity=3.0): """Scales `colors` by a degree of nonlinearity specified by user. The input image must have zero background (*i.e.*, it must already be background-subtracted). Parameters ---------- colors : :class:`~numpy.ndarray` 3D Array containing RGB image. The dimensions should be (X, Y, 3). nonlinearity : :class:`float` Amount of nonlinear scaling. If set to zero, no scaling will be performed (this is equivalent to linear scaling). Returns ------- :class:`~numpy.ndarray` The scaled image. Raises ------ ValueError If `colors` has the wrong shape. """ if nonlinearity == 0: return colors if colors.ndim != 3: raise ValueError("A 3D image is required!") nx, ny, nc = colors.shape if nc != 3: raise ValueError("The 3D image must have 3 image planes.") radius = colors.sum(2) w = radius == 0 radius[w] = radius[w] + 1.0 fac = np.arcsinh(radius*nonlinearity)/nonlinearity/radius fitted_colors = np.zeros(colors.shape, dtype=colors.dtype) for k in range(3): fitted_colors[:, :, k] = colors[:, :, k]*fac return fitted_colors def nw_cut_to_box(colors, origin=(0.0, 0.0, 0.0)): """Limits the pixel values of the image to a 'box', so that the colors do not saturate to white but to a specific color. Parameters ---------- colors : :class:`~numpy.ndarray` 3D Array containing RGB image. The dimensions should be (X, Y, 3). origin : :func:`tuple` or :class:`~numpy.ndarray` An array with 3 elements. The "distance" from this origin is considered saturated. Returns ------- :class:`~numpy.ndarray` The "boxed" image. Raises ------ ValueError If `colors` or `origin` has the wrong shape. """ if len(origin) != 3: raise ValueError("The origin array must contain 3 elements!") if colors.ndim != 3: raise ValueError("A 3D image is required!") nx, ny, nc = colors.shape if nc != 3: raise ValueError("The 3D image must have 3 image planes.") pos_dist = 1.0 - np.array(origin, dtype=colors.dtype) factors = np.zeros(colors.shape, dtype=colors.dtype) for k in range(3): factors[:, :, k] = colors[:, :, k]/pos_dist[k] factor = factors.max(2) factor = np.where(factor > 1.0, factor, 1.0) boxed_colors = np.zeros(colors.shape, dtype=colors.dtype) for k in range(3): boxed_colors[:, :, k] = origin[k] + colors[:, :, k]/factor return boxed_colors def nw_float_to_byte(image, bits=8): """Converts an array of floats in [0.0, 1.0] to bytes in [0, 255]. Parameters ---------- image : :class:`~numpy.ndarray` Image to convert. bits : :class:`int`, optional Number of bits in final image. Returns ------- :class:`~numpy.ndarray` Converted image. """ from warnings import warn from . import PydlutilsUserWarning if bits > 8: warn("bits > 8 not supported; setting bits = 8.", PydlutilsUserWarning) bits = 8 fmax = 1 << bits bmax = fmax - 1 f1 = np.floor(image * fmax) f2 = np.where(f1 > 0, f1, 0) f3 = np.where(f2 < bmax, f2, bmax) return f3.astype(np.uint8) def nw_scale_rgb(colors, scales=(1.0, 1.0, 1.0)): """Multiply RGB image by color-dependent scale factor. Parameters ---------- colors : :class:`~numpy.ndarray` 3D Array containing RGB image. The dimensions should be (X, Y, 3). scales : :func:`tuple` or :class:`~numpy.ndarray`, optional An array with 3 elements. Returns ------- :class:`~numpy.ndarray` The scaled image. Raises ------ ValueError If `colors` or `scales` has the wrong shape. """ if len(scales) != 3: raise ValueError("The scale factor must contain 3 elements!") if colors.ndim != 3: raise ValueError("A 3D image is required!") nx, ny, nc = colors.shape if nc != 3: raise ValueError("The 3D image must have 3 image planes.") scaled_colors = np.zeros(colors.shape, dtype=colors.dtype) for k in range(3): scaled_colors[:, :, k] = colors[:, :, k]*scales[k] return scaled_colors pydl-0.6.0/pydl/pydlutils/mangle.py0000664000111400017500000006600012775255301020114 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the mangle directory in idlutils. Mangle_ [1]_ is a software suite that supports the concept of polygons on a sphere, and is used to, for example, describe the `window function`_ of the Sloan Digital Sky Survey. This implementation is intended to support the portions of Mangle that are included in idlutils. To simplify the coding somewhat, unlike idlutils, the caps information is accessed through ``polygon.x`` and ``polygon.cm``, not ``polygon.caps.x`` or ``polygon.caps.cm``. Window function operations are already supported by :func:`~is_in_polygon` and :func:`~is_in_window`. However, calculation of polygon area (solid angle) from first principles is not yet implemented. Note that in traditional geometry "spherical polygon" means a figure bounded by *great circles*. Mangle allows polygons to be bounded by *any* circle, great or not. Use care when comparing formulas in this module to formulas in the mathematical literature. .. _Mangle: http://space.mit.edu/~molly/mangle/ .. _`window function`: http://www.sdss.org/dr12/algorithms/resolve/ References ---------- .. [1] `Swanson, M. E. C.; Tegmark, Max; Hamilton, Andrew J. S.; Hill, J. Colin, 2008 MNRAS 387, 1391 `_. """ import numpy as np from astropy.io import fits from astropy.extern import six # import astropy.utils as au import warnings from . import PydlutilsException, PydlutilsUserWarning class PolygonList(list): """A :class:`list` that contains :class:`ManglePolygon` objects and possibly some metadata. Parameters ---------- header : :class:`list`, optional Set the `header` attribute. Attributes ---------- header : :class:`list` A list of strings containing metadata. """ def __init__(self, *args, **kwargs): super(PolygonList, self).__init__(*args) if 'header' in kwargs: self.header = kwargs['header'] else: self.header = list() return class ManglePolygon(object): """Simple object to represent a polygon. A polygon may be instantiated with a row (:class:`~astropy.io.fits.fitsrec.FITS_record`) from a :class:`FITS_polygon` object, another :class:`ManglePolygon` object (copy constructor), keyword arguments, or with no arguments at all, in which case it represents the whole sky. Attributes ---------- cm : :class:`~numpy.ndarray` The size of each cap in the polygon. id : :class:`int` An arbitrary ID number. pixel : :class:`int` Pixel this polygon is in. use_caps : :class:`int` Bitmask indicating which caps to use. weight : :class:`float` Weight factor assigned to the polygon. """ def __init__(self, *args, **kwargs): try: a0 = args[0] except IndexError: a0 = None if isinstance(a0, fits.fitsrec.FITS_record): self._ncaps = int(a0['NCAPS']) self.weight = float(a0['WEIGHT']) self.pixel = int(a0['PIXEL']) try: self.id = int(a0['IFIELD']) except KeyError: self.id = -1 self._str = float(a0['STR']) self.use_caps = int(a0['USE_CAPS']) xcaps = a0['XCAPS'] if xcaps.shape == (3, ): self._x = np.zeros((1, 3), dtype=xcaps.dtype) + xcaps else: self._x = xcaps[0:self._ncaps, :].copy() # assert self._x.shape == (self._ncaps, 3) cmcaps = a0['CMCAPS'] if cmcaps.shape == (): self.cm = np.zeros((1,), dtype=cmcaps.dtype) + cmcaps else: self.cm = cmcaps[0:self._ncaps].copy() # assert self.cm.shape == (self._ncaps, ) elif isinstance(a0, ManglePolygon): self._ncaps = a0._ncaps self.weight = a0.weight self.pixel = a0.pixel self.id = a0.id self._str = a0._str self.use_caps = a0.use_caps self._x = a0._x.copy() self.cm = a0.cm.copy() elif kwargs: if 'x' in kwargs and 'cm' in kwargs: xs = kwargs['x'].shape cm = kwargs['cm'].shape assert xs[0] == cm[0] else: raise ValueError('Input values are missing!') self._ncaps = xs[0] if 'weight' in kwargs: self.weight = float(kwargs['weight']) else: self.weight = 1.0 if 'pixel' in kwargs: self.pixel = int(kwargs['pixel']) else: self.pixel = -1 if 'id' in kwargs: self.id = int(kwargs['id']) else: self.id = -1 if 'use_caps' in kwargs: self.use_caps = int(kwargs['use_caps']) else: # Use all caps by default self.use_caps = (1 << self.ncaps) - 1 self._x = kwargs['x'].copy() self.cm = kwargs['cm'].copy() if 'str' in kwargs: self._str = float(kwargs['str']) else: self._str = None else: # # An "empty" polygon represents the whole sky. # self._ncaps = 0 self.weight = 1.0 self.pixel = -1 self.id = -1 self.use_caps = 0 self._x = None self.cm = None self._str = 4.0*np.pi return @property def ncaps(self): """Number of caps in the polygon. """ return self._ncaps @property def x(self): """The orientation of each cap in the polygon. The direction is specified by a unit 3-vector. """ return self._x @property def str(self): """Solid angle of this polygon (steradians). """ if self._str is None: self._str = self.garea() return self._str def cmminf(self): """The index of the smallest cap in the polygon, accounting for negative caps and ``use_caps``. Returns ------- :class:`int` Integer index of the smallest cap. """ if self.ncaps == 0: return None cmmin = 2.0 kmin = -1 for k in range(self.ncaps): if is_cap_used(self.use_caps, k): if self.cm[k] >= 0: cmk = self.cm[k] else: cmk = 2.0 + self.cm[k] if cmk < cmmin: cmmin = cmk kmin = k return kmin def garea(self): """Compute the area of a polygon. See [1]_ for the detailed area formula, which is summarized here: * An empty polygon with no caps is defined to be the whole sky. * A polygon with one cap has area ``2*pi*self.cm``. * A polygon with at least one cap with an area less than :math:`2\pi` has an area less than :math:`2\pi`. * If every cap has an area greater than :math:`2\pi`, split the polygon into two smaller polygons and sum the two areas. Returns ------- :class:`float` The area of the polygon. References ---------- .. [1] `Hamilton, A. J. S.; Tegmark, Max, 2004 MNRAS 349, 115 `_. """ smallest_cap = self.cmminf() if smallest_cap is None: return 4.0 * np.pi cmmin = self.cm[smallest_cap] used_caps = list() for k in range(self.ncaps): if is_cap_used(self.use_caps, k): used_caps.append(k) nused_caps = len(used_caps) if nused_caps == 0: return 0.0 if nused_caps >= 2 and cmmin > 1.0: npl = nused_caps + 1 else: npl = nused_caps if npl == nused_caps: if nused_caps == 1: # # Short-circuit this case. # if cmmin < 0: return 2.0*np.pi*(2.0+cmmin) else: return 2.0*np.pi*cmmin else: # # Two or more caps, at least one has area < 2.0*pi. # return self._garea_helper() else: # # More than two caps, and all have area > 2.0*pi. # This section still needs to account for use_caps. # dpoly = self.polyn(self, smallest_cap) dpoly.cm[nused_caps] = cmmin / 2.0 area1 = dpoly._garea_helper() dpoly.cm[nused_caps] = -1.0 * dpoly.cm[nused_caps] area2 = dpoly._garea_helper() return area1 + area2 def _garea_helper(self): """Reproduces the Fortran routine garea in Mangle. *Placeholder for now.* Returns ------- :class:`float` Area of polygon in steradians. """ if self.gzeroar(): return 0.0 warnings.warn(("The ManglePolygon.garea() method is incomplete" + "and is returning a dummy value."), PydlutilsUserWarning) return 1.0 def gzeroar(self): """If at least one cap has zero area, then the whole polygon has zero area. Returns ------- :class:`bool` ``True`` if the area is zero. """ if self.ncaps == 0: # # Catch the case of an "allsky" polygon. # return False return (self.cm == 0.0).any() or (self.cm <= -2.0).any() def copy(self): """Return an exact copy of the polygon. Returns ------- :class:`ManglePolygon` A new polygon object. """ return ManglePolygon(self) def add_caps(self, x, cm): """Create a new polygon that contains additional caps. The caps are appended to the existing polygon's caps. Parameters ---------- x : :class:`~numpy.ndarray` or :class:`~numpy.recarray` ``X`` values of the new caps. cm : :class:`~numpy.ndarray` or :class:`~numpy.recarray` ``CM`` values of the new caps. Returns ------- :class:`ManglePolygon` A new polygon object. """ ncaps = self.ncaps + cm.size newdata = dict() newdata['weight'] = self.weight newdata['pixel'] = self.pixel newdata['id'] = self.id newdata['use_caps'] = self.use_caps newdata['x'] = np.zeros((ncaps, 3), dtype=self.x.dtype) newdata['cm'] = np.zeros((ncaps,), dtype=self.cm.dtype) newdata['x'][0:self.ncaps, :] = self.x.copy() newdata['cm'][0:self.ncaps] = self.cm.copy() newdata['x'][self.ncaps:, :] = x.copy() newdata['cm'][self.ncaps:] = cm.copy() return ManglePolygon(**newdata) def polyn(self, other, n, complement=False): """Intersection of a polygon with the `n` th cap of another polygon. Parameters ---------- other : :class:`~pydl.pydlutils.mangle.ManglePolygon` Polygon containing a cap to intersect the first polygon with. n : :class:`int` Index of the cap in `other`. complement : :class:`bool`, optional If ``True``, set the sign of the cm value of `other` to be the complement of the original value. Returns ------- :class:`~pydl.pydlutils.mangle.ManglePolygon` A polygon containing the intersected caps. """ x = other.x[n, :] sign = 1.0 if complement: sign = -1.0 cm = sign * other.cm[n] return self.add_caps(x, cm) class FITS_polygon(fits.FITS_rec): """Handle polygons read in from a FITS file. This class provides aliases for the columns in a typical FITS polygons file. """ _pkey = {'X': 'XCAPS', 'x': 'XCAPS', 'CM': 'CMCAPS', 'cm': 'CMCAPS', 'ID': 'IFIELD', 'id': 'IFIELD', 'ncaps': 'NCAPS', 'weight': 'WEIGHT', 'pixel': 'PIXEL', 'str': 'STR', 'use_caps': 'USE_CAPS'} # # Right now, this class is only instantiated by calling .view() on # a FITS_rec object, so only __array_finalize__ is needed. # # def __new__(*args): # self = super(FITS_polygon, self).__new__(*args) # self._caps = None # return self def __array_finalize__(self, obj): super(FITS_polygon, self).__array_finalize__(obj) def __getitem__(self, key): if isinstance(key, six.string_types): if key in self._pkey: return super(FITS_polygon, self).__getitem__(self._pkey[key]) return super(FITS_polygon, self).__getitem__(key) def __getattr__(self, key): if key in self._pkey: return super(FITS_polygon, self).__getattribute__(self._pkey[key]) raise AttributeError("FITS_polygon has no attribute {0}.".format(key)) def angles_to_x(points, latitude=False): """Convert spherical angles to unit Cartesian vectors. Parameters ---------- points : :class:`~numpy.ndarray` A set of angles in the form phi, theta (in degrees). latitude : :class:`bool`, optional If ``True``, assume that the angles actually represent longitude, latitude, or equivalently, RA, Dec. Returns ------- :class:`~numpy.ndarray` The corresponding Cartesian vectors. """ npoints, ncol = points.shape x = np.zeros((npoints, 3), dtype=points.dtype) phi = np.radians(points[:, 0]) if latitude: theta = np.radians(90.0 - points[:, 1]) else: theta = np.radians(points[:, 1]) st = np.sin(theta) x[:, 0] = np.cos(phi) * st x[:, 1] = np.sin(phi) * st x[:, 2] = np.cos(theta) return x def cap_distance(x, cm, points): """Compute the distance from a point to a cap, and also determine whether the point is inside or outside the cap. Parameters ---------- x : :class:`~numpy.ndarray` or :class:`~numpy.recarray` ``X`` value of the cap (3-vector). cm : :class:`~numpy.ndarray` or :class:`~numpy.recarray` ``CM`` value of the cap. points : :class:`~numpy.ndarray` or :class:`~numpy.recarray` If `points` is a 3-vector, or set of 3-vectors, then assume the point is a Cartesian unit vector. If `point` is a 2-vector or set of 2-vectors, assume the point is RA, Dec. Returns ------- :class:`~numpy.ndarray` The distance(s) to the point(s) in degrees. If the distance is negative, the point is outside the cap. """ npoints, ncol = points.shape if ncol == 2: xyz = angles_to_x(points, latitude=True) elif ncol == 3: xyz = points else: raise ValueError("Inappropriate shape for point!") dotprod = np.dot(xyz, x) cdist = np.degrees(np.arccos(1.0 - np.abs(cm)) - np.arccos(dotprod)) if cm < 0: cdist *= -1.0 return cdist def circle_cap(radius, points): """Construct caps based on input points (directions on the unit sphere). Parameters ---------- radius : :class:`float` or :class:`~numpy.ndarray` Radii of the caps in degrees. This will become the ``CM`` value. points : :class:`~numpy.ndarray` or :class:`~numpy.recarray` If `points` is a 3-vector, or set of 3-vectors, then assume the point is a Cartesian unit vector. If `point` is a 2-vector or set of 2-vectors, assume the point is RA, Dec. Returns ------- :func:`tuple` A tuple containing ``X`` and ``CM`` values for the cap. """ npoints, ncol = points.shape if ncol == 2: x = angles_to_x(points, latitude=True) elif ncol == 3: x = points.copy() else: raise ValueError("Inappropriate shape for point!") if isinstance(radius, float): radius = np.zeros((npoints,), dtype=points.dtype) + radius elif radius.shape == (): radius = np.zeros((npoints,), dtype=points.dtype) + radius elif radius.size == npoints: pass else: raise ValueError("radius does not appear to be the correct shape.") cm = 1.0 - np.cos(np.radians(radius)) return (x, cm) def is_cap_used(use_caps, i): """Returns ``True`` if a cap is used. Parameters ---------- use_caps : :class:`int` Bit mask indicating which cap is used. i : :class:`int` Number indicating which cap we are interested in. Returns ------- :class:`bool` ``True`` if a cap is used. """ return (use_caps & 1 << i) != 0 def is_in_cap(x, cm, points): """Are the points in a (single) given cap? Parameters ---------- x : :class:`~numpy.ndarray` or :class:`~numpy.recarray` ``X`` value of the cap (3-vector). cm : :class:`~numpy.ndarray` or :class:`~numpy.recarray` ``CM`` value of the cap. points : :class:`~numpy.ndarray` or :class:`~numpy.recarray` If `points` is a 3-vector, or set of 3-vectors, then assume the point is a Cartesian unit vector. If `point` is a 2-vector or set of 2-vectors, assume the point is RA, Dec. Returns ------- :class:`~numpy.ndarray` A boolean vector giving the result for each point. """ return cap_distance(x, cm, points) >= 0.0 def is_in_polygon(polygon, points, ncaps=0): """Are the points in a given (single) polygon? Parameters ---------- polygon : :class:`~pydl.pydlutils.mangle.ManglePolygon` A polygon object. points : :class:`~numpy.ndarray` or :class:`~numpy.recarray` If `points` is a 3-vector, or set of 3-vectors, then assume the point is a Cartesian unit vector. If `point` is a 2-vector or set of 2-vectors, assume the point is RA, Dec. ncaps : :class:`int`, optional If set, use only the first `ncaps` caps in `polygon`. Returns ------- :class:`~numpy.ndarray` A boolean vector giving the result for each point. """ npoints, ncol = points.shape p = dict() pmap = {'ncaps': 'NCAPS', 'use_caps': 'USE_CAPS', 'x': 'XCAPS', 'cm': 'CMCAPS'} for key in pmap: try: p[key] = getattr(polygon, key) except AttributeError: p[key] = polygon[pmap[key]] usencaps = p['ncaps'] if ncaps > 0: usencaps = min(ncaps, p['ncaps']) in_polygon = np.ones((npoints,), dtype=np.bool) for icap in range(usencaps): if is_cap_used(p['use_caps'], icap): in_polygon &= is_in_cap(p['x'][icap, :], p['cm'][icap], points) return in_polygon def is_in_window(polygons, points, ncaps=0): """Check to see if `points` lie within a set of `polygons`. Parameters ---------- polygons : :class:`PolygonList` or :class:`FITS_polygon` A set of polygons. points : :class:`~numpy.ndarray` or :class:`~numpy.recarray` If `points` is a 3-vector, or set of 3-vectors, then assume the point is a Cartesian unit vector. If `point` is a 2-vector or set of 2-vectors, assume the point is RA, Dec. ncaps : :class:`int`, optional If set, use only the first `ncaps` caps in `polygon`. This only exists to be passed to :func:`is_in_polygon`. Returns ------- :func:`tuple` A tuple containing two :class:`~numpy.ndarray`. First, a boolean vector giving the result for each point. Second, an integer vector giving the index of the polygon that contains the point. """ npoints, ncol = points.shape npoly = len(polygons) in_polygon = np.zeros((npoints, ), dtype=np.int32) - 1 curr_polygon = 0 while curr_polygon < npoly: indx_not_in = (in_polygon == -1).nonzero()[0] if len(indx_not_in) > 0: indx_in_curr_polygon = is_in_polygon(polygons[curr_polygon], points[indx_not_in], ncaps=ncaps) if indx_in_curr_polygon.any(): in_polygon[indx_not_in[indx_in_curr_polygon]] = curr_polygon curr_polygon += 1 return (in_polygon >= 0, in_polygon) def read_fits_polygons(filename, convert=False): """Read a "polygon" format FITS file. This function returns a subclass of :class:`~astropy.io.fits.FITS_rec` that provides some convenient aliases for the columns of a typical FITS polygon file (which might be all upper-case). Parameters ---------- filename : :class:`str` Name of FITS file to read. convert : :class:`bool`, optional If ``True``, convert the data to a list of :class:`ManglePolygon` objects. *Caution: This could result in some data being discarded!* Returns ------- :class:`~pydl.pydlutils.mangle.FITS_polygon` or :class:`list` The data contained in HDU 1 of the FITS file. """ with fits.open(filename, uint=True) as hdulist: data = hdulist[1].data if convert: poly = PolygonList() for k in range(data.size): poly.append(ManglePolygon(data[k])) else: poly = data.view(FITS_polygon) return poly def read_mangle_polygons(filename): """Read a "polygon" format ASCII file in Mangle's own format. These files typically have extension ``.ply`` or ``.pol``. Parameters ---------- filename : :class:`str` Name of FITS file to read. Returns ------- :class:`~pydl.pydlutils.mangle.PolygonList` A list-like object containing :class:`~pydl.pydlutils.mangle.ManglePolygon` objects and any metadata. """ import re with open(filename, 'rU') as ply: lines = ply.read().split(ply.newlines) try: npoly = int(lines[0].split()[0]) except ValueError: raise PydlutilsException(("Invalid first line of {0}! " + "Are you sure this is a Mangle " + "polygon file?").format(filename)) p_lines = [i for i, l in enumerate(lines) if l.startswith('polygon')] header = lines[1:p_lines[0]] poly = PolygonList(header=header) r1 = re.compile(r'polygon\s+(\d+)\s+\(([^)]+)\):') mtypes = {'str': float, 'weight': float, 'pixel': int, 'caps': int} for p in p_lines: m = r1.match(lines[p]) g = m.groups() pid = int(g[0]) meta = g[1].strip().split(',') m1 = [m.strip().split()[1] for m in meta] m0 = [mtypes[m1[i]](m.strip().split()[0]) for i, m in enumerate(meta)] metad = dict(zip(m1, m0)) metad['id'] = pid metad['x'] = list() metad['cm'] = list() for cap in lines[p+1:p+1+metad['caps']]: data = [float(d) for d in re.split(r'\s+', cap.strip())] metad['x'].append(data[0:3]) metad['cm'].append(data[-1]) metad['x'] = np.array(metad['x']) assert metad['x'].shape == (metad['caps'], 3) metad['cm'] = np.array(metad['cm']) assert metad['cm'].shape == (metad['caps'],) poly.append(ManglePolygon(**metad)) return poly def set_use_caps(polygon, index_list, add=False, tol=1.0e-10, allow_doubles=False, allow_neg_doubles=False): """Set the bits in use_caps for a polygon. Parameters ---------- polygon : :class:`~pydl.pydlutils.mangle.ManglePolygon` A polygon object. index_list : array-like A list of indices of caps to set in the polygon. Should be no longer, nor contain indices greater than the number of caps (``polygon.ncaps``). add : :class:`bool`, optional If ``True``, don't initialize the use_caps value to zero, use the existing value associated with `polygon` instead. tol : :class:`float`, optional Tolerance used to determine whether two caps are identical. allow_doubles : :class:`bool`, optional Normally, this routine automatically sets use_caps such that no two caps with use_caps set are identical. allow_neg_doubles : :class:`bool`, optional Normally, two caps that are identical except for the sign of `cm` would be set unused. This inhibits that behaviour. Returns ------- :class:`int` Value of use_caps. """ if not add: polygon.use_caps = 0 t2 = tol**2 for i in index_list: polygon.use_caps |= (1 << index_list[i]) if not allow_doubles: # # Check for doubles # for i in range(polygon.ncaps): if is_cap_used(polygon.use_caps, i): for j in range(i+1, polygon.ncaps): if is_cap_used(polygon.use_caps, j): if np.sum((polygon.x[i, :] - polygon.x[j, :])**2) < t2: if ((np.absolute(polygon.cm[i] - polygon.cm[j]) < tol) or ((polygon.cm[i] + polygon.cm[j]) < tol and not allow_neg_doubles)): # # Don't use # polygon.use_caps -= 1 << j return polygon.use_caps def x_to_angles(points, latitude=False): """Convert unit Cartesian vectors to spherical angles. Parameters ---------- points : :class:`~numpy.ndarray` A set of x, y, z coordinates. latitude : :class:`bool`, optional If ``True``, convert the angles to longitude, latitude, or equivalently, RA, Dec. Returns ------- :class:`~numpy.ndarray` The corresponding spherical angles. """ npoints, ncol = points.shape phi = np.degrees(np.arctan2(points[:, 1], points[:, 0])) r = (points**2).sum(1) theta = np.degrees(np.arccos(points[:, 2]/r)) if latitude: theta = 90.0 - theta x = np.zeros((npoints, 2), dtype=points.dtype) x[:, 0] = phi x[:, 1] = theta return x def _single_polygon(poly): """Given certain classes of `poly`, return a :class:`ManglePolygon` object. This function is used to allow various types of input in functions that take a *single* polygon as an argument. Parameters ---------- poly : object An object containing data that could potentially be converted into a :class:`ManglePolygon`. Returns ------- ManglePolygon The resulting *single* polygon object. Raises ------ ValueError If the conversion is not possible. """ if isinstance(poly, ManglePolygon): return poly if isinstance(poly, PolygonList) and len(poly) == 1: return poly[0] if isinstance(poly, FITS_polygon) and len(poly) == 1: return ManglePolygon(poly[0]) if isinstance(poly, fits.fitsrec.FITS_record): return ManglePolygon(poly) raise ValueError("Can't convert input into a single polygon!") pydl-0.6.0/pydl/pydlutils/bspline.py0000664000111400017500000005601112775255301020306 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """This module corresponds to the bspline directory in idlutils. """ import numpy as np class bspline(object): """Bspline class. Functions in the bspline library are implemented as methods on this class. Parameters ---------- x : :class:`numpy.ndarray` The data. nord : :class:`int`, optional To be documented. npoly : :class:`int`, optional To be documented. bkpt : :class:`numpy.ndarray`, optional To be documented. bkspread : :class:`float`, optional To be documented. verbose : :class:`bool`, optional. If ``True`` print extra information. Attributes ---------- breakpoints To be documented. nord To be documented. npoly To be documented. mask To be documented. coeff To be documented. icoeff To be documented. xmin To be documented. xmax To be documented. funcname To be documented. """ def __init__(self, x, nord=4, npoly=1, bkpt=None, bkspread=1.0, verbose=False, **kwargs): """Init creates an object whose attributes are similar to the structure returned by the create_bspline function. """ # # Set the breakpoints. # if bkpt is None: startx = x.min() rangex = x.max() - startx if 'placed' in kwargs: w = ((kwargs['placed'] >= startx) & (kwargs['placed'] <= startx+rangex)) if w.sum() < 2: bkpt = np.arange(2, dtype='f') * rangex + startx else: bkpt = kwargs['placed'][w] elif 'bkspace' in kwargs: nbkpts = int(rangex/kwargs['bkspace']) + 1 if nbkpts < 2: nbkpts = 2 tempbkspace = rangex/float(nbkpts-1) bkpt = np.arange(nbkpts, dtype='f')*tempbkspace + startx elif 'nbkpts' in kwargs: nbkpts = kwargs['nbkpts'] if nbkpts < 2: nbkpts = 2 tempbkspace = rangex/float(nbkpts-1) bkpt = np.arange(nbkpts, dtype='f') * tempbkspace + startx elif 'everyn' in kwargs: npkpts = max(nx/kwargs['everyn'], 1) if nbkpts == 1: xspot = [0] else: xspot = int(nx/(nbkpts-1)) * np.arange(nbkpts, dtype='i4') bkpt = x[xspot].astype('f') else: raise ValueError('No information for bkpts.') imin = bkpt.argmin() imax = bkpt.argmax() if x.min() < bkpt[imin]: if verbose: print('Lowest breakpoint does not cover lowest x value: changing.') bkpt[imin] = x.min() if x.max() > bkpt[imax]: if verbose: print('Highest breakpoint does not cover highest x value: changing.') bkpt[imax] = x.max() nshortbkpt = bkpt.size fullbkpt = bkpt.copy() if nshortbkpt == 1: bkspace = np.float32(bkspread) else: bkspace = (bkpt[1] - bkpt[0]) * np.float32(bkspread) for i in np.arange(1, nord, dtype=np.float32): fullbkpt = np.insert(fullbkpt, 0, bkpt[0]-bkspace*i) fullbkpt = np.insert(fullbkpt, fullbkpt.shape[0], bkpt[nshortbkpt-1] + bkspace*i) # # Set the attributes # nc = fullbkpt.size - nord self.breakpoints = fullbkpt self.nord = nord self.npoly = npoly self.mask = np.ones((fullbkpt.size,), dtype='bool') if npoly > 1: self.coeff = np.zeros((npoly, nc), dtype='d') self.icoeff = np.zeros((npoly, nc), dtype='d') else: self.coeff = np.zeros((nc,), dtype='d') self.icoeff = np.zeros((nc,), dtype='d') self.xmin = 0.0 self.xmax = 1.0 self.funcname = 'legendre' return def fit(self, xdata, ydata, invvar, x2=None): """Calculate a B-spline in the least-squares sense. Fit is based on two variables: x which is sorted and spans a large range where bkpts are required y which can be described with a low order polynomial. Parameters ---------- xdata : :class:`numpy.ndarray` Independent variable. ydata : :class:`numpy.ndarray` Dependent variable. invvar : :class:`numpy.ndarray` Inverse variance of `ydata`. x2 : :class:`numpy.ndarray`, optional Orthogonal dependent variable for 2d fits. Returns ------- :func:`tuple` A tuple containing an integer error code, and the evaluation of the b-spline at the input values. An error code of -2 is a failure, -1 indicates dropped breakpoints, 0 is success, and positive integers indicate ill-conditioned breakpoints. """ goodbk = self.mask[self.nord:] nn = goodbk.sum() if nn < self.nord: yfit = np.zeros(ydata.shape, dtype='f') return (-2, yfit) nfull = nn * self.npoly bw = self.npoly * self.nord a1, lower, upper = self.action(xdata, x2=x2) foo = np.tile(invvar, bw).reshape(bw, invvar.size).transpose() a2 = a1 * foo alpha = np.zeros((bw, nfull+bw), dtype='d') beta = np.zeros((nfull+bw,), dtype='d') bi = np.arange(bw, dtype='i4') bo = np.arange(bw, dtype='i4') for k in range(1, bw): bi = np.append(bi, np.arange(bw-k, dtype='i4')+(bw+1)*k) bo = np.append(bo, np.arange(bw-k, dtype='i4')+bw*k) for k in range(nn-self.nord+1): itop = k*self.npoly ibottom = min(itop, nfull) + bw - 1 ict = upper[k] - lower[k] + 1 if ict > 0: work = np.dot(a1[lower[k]:upper[k]+1, :].T, a2[lower[k]:upper[k]+1, :]) wb = np.dot(ydata[lower[k]:upper[k]+1], a2[lower[k]:upper[k]+1, :]) alpha.T.flat[bo+itop*bw] += work.flat[bi] beta[itop:ibottom+1] += wb min_influence = 1.0e-10 * invvar.sum() / nfull errb = cholesky_band(alpha, mininf=min_influence) # ,verbose=True) if isinstance(errb[0], int) and errb[0] == -1: a = errb[1] else: yfit, foo = self.value(xdata, x2=x2, action=a1, upper=upper, lower=lower) return (self.maskpoints(errb[0]), yfit) errs = cholesky_solve(a, beta) if isinstance(errs[0], int) and errs[0] == -1: sol = errs[1] else: # # It is not possible for this to get called, because cholesky_solve # has only one return statement, & that statement guarantees that # errs[0] == -1 # yfit, foo = self.value(xdata, x2=x2, action=a1, upper=upper, lower=lower) return (self.maskpoints(errs[0]), yfit) if self.npoly > 1: self.icoeff[:, goodbk] = np.array(a[0, 0:nfull].reshape(self.npoly, nn), dtype=a.dtype) self.coeff[:, goodbk] = np.array(sol[0:nfull].reshape(self.npoly, nn), dtype=sol.dtype) else: self.icoeff[goodbk] = np.array(a[0, 0:nfull], dtype=a.dtype) self.coeff[goodbk] = np.array(sol[0:nfull], dtype=sol.dtype) yfit, foo = self.value(xdata, x2=x2, action=a1, upper=upper, lower=lower) return (0, yfit) def action(self, x, x2=None): """Construct banded bspline matrix, with dimensions [ndata, bandwidth]. Parameters ---------- x : :class:`numpy.ndarray` Independent variable. x2 : :class:`numpy.ndarray`, optional Orthogonal dependent variable for 2d fits. Returns ------- :func:`tuple` A tuple containing the b-spline action matrix; the 'lower' parameter, a list of pixel positions, each corresponding to the first occurence of position greater than breakpoint indx; and 'upper', Same as lower, but denotes the upper pixel positions. """ from .. import uniq from ..goddard.math import flegendre from .trace import fchebyshev nx = x.size nbkpt = self.mask.sum() if nbkpt < 2*self.nord: return (-2, 0, 0) n = nbkpt - self.nord gb = self.breakpoints[self.mask] bw = self.npoly*self.nord lower = np.zeros((n - self.nord + 1,), dtype='i4') upper = np.zeros((n - self.nord + 1,), dtype='i4') - 1 indx = self.intrv(x) bf1 = self.bsplvn(x, indx) action = bf1 aa = uniq(indx, np.arange(indx.size, dtype='i4')) upper[indx[aa]-self.nord+1] = aa rindx = indx[::-1] bb = uniq(rindx, np.arange(rindx.size, dtype='i4')) lower[rindx[bb]-self.nord+1] = nx - bb - 1 if x2 is not None: if x2.size != nx: raise ValueError('Dimensions of x and x2 do not match.') x2norm = 2.0 * (x2 - self.xmin) / (self.xmax - self.xmin) - 1.0 if self.funcname == 'poly': temppoly = np.ones((nx, self.npoly), dtype='f') for i in range(1, self.npoly): temppoly[:, i] = temppoly[:, i-1] * x2norm elif self.funcname == 'poly1': temppoly = np.tile(x2norm, self.npoly).reshape(nx, self.npoly) for i in range(1, self.npoly): temppoly[:, i] = temppoly[:, i-1] * x2norm elif self.funcname == 'chebyshev': temppoly = fchebyshev(x2norm, self.npoly) elif self.funcname == 'legendre': temppoly = flegendre(x2norm, self.npoly) else: raise ValueError('Unknown value of funcname.') action = np.zeros((nx, bw), dtype='d') counter = -1 for ii in range(self.nord): for jj in range(self.npoly): counter += 1 action[:, counter] = bf1[:, ii]*temppoly[:, jj] return (action, lower, upper) def intrv(self, x): """Find the segment between breakpoints which contain each value in the array x. The minimum breakpoint is nbkptord -1, and the maximum is nbkpt - nbkptord - 1. Parameters ---------- x : :class:`numpy.ndarray` Data values, assumed to be monotonically increasing. Returns ------- :class:`numpy.ndarray` Position of array elements with respect to breakpoints. """ gb = self.breakpoints[self.mask] n = gb.size - self.nord indx = np.zeros((x.size,), dtype='i4') ileft = self.nord - 1 for i in range(x.size): while x[i] > gb[ileft+1] and ileft < n - 1: ileft += 1 indx[i] = ileft return indx def bsplvn(self, x, ileft): """To be documented. Parameters ---------- x : :class:`numpy.ndarray` To be documented. ileft : :class:`int` To be documented Returns ------- :class:`numpy.ndarray` To be documented. """ bkpt = self.breakpoints[self.mask] vnikx = np.zeros((x.size, self.nord), dtype=x.dtype) deltap = vnikx.copy() deltam = vnikx.copy() j = 0 vnikx[:, 0] = 1.0 while j < self.nord - 1: ipj = ileft+j+1 deltap[:, j] = bkpt[ipj] - x imj = ileft-j deltam[:, j] = x - bkpt[imj] vmprev = 0.0 for l in range(j+1): vm = vnikx[:, l]/(deltap[:, l] + deltam[:, j-l]) vnikx[:, l] = vm*deltap[:, l] + vmprev vmprev = vm*deltam[:, j-l] j += 1 vnikx[:, j] = vmprev return vnikx def value(self, x, x2=None, action=None, lower=None, upper=None): """Evaluate a bspline at specified values. Parameters ---------- x : :class:`numpy.ndarray` Independent variable. x2 : :class:`numpy.ndarray`, optional Orthogonal dependent variable for 2d fits. action : :class:`numpy.ndarray`, optional Action matrix to use. If not supplied it is calculated. lower : :class:`numpy.ndarray`, optional If the action parameter is supplied, this parameter must also be supplied. upper : :class:`numpy.ndarray`, optional If the action parameter is supplied, this parameter must also be supplied. Returns ------- :func:`tuple` A tuple containing the results of the bspline evaluation and a mask indicating where the evaluation was good. """ xsort = x.argsort() xwork = x[xsort] if x2 is not None: x2work = x2[xsort] else: x2work = None if action is not None: if lower is None or upper is None: raise ValueError('Must specify lower and upper if action is set.') else: action, lower, upper = self.action(xwork, x2=x2work) yfit = np.zeros(x.shape, dtype=x.dtype) bw = self.npoly * self.nord spot = np.arange(bw, dtype='i4') goodbk = self.mask.nonzero()[0] coeffbk = self.mask[self.nord:].nonzero()[0] n = self.mask.sum() - self.nord if self.npoly > 1: goodcoeff = self.coeff[:, coeffbk] else: goodcoeff = self.coeff[coeffbk] # maskthis = np.zeros(xwork.shape,dtype=xwork.dtype) for i in range(n-self.nord+1): ict = upper[i] - lower[i] + 1 if ict > 0: yfit[lower[i]:upper[i]+1] = np.dot( action[lower[i]:upper[i]+1, :], goodcoeff[i*self.npoly+spot]) yy = yfit.copy() yy[xsort] = yfit mask = np.ones(x.shape, dtype='bool') gb = self.breakpoints[goodbk] outside = ((x < gb[self.nord-1]) | (x > gb[n])) if outside.any(): mask[outside] = False hmm = ((np.diff(goodbk) > 2).nonzero())[0] for jj in range(hmm.size): inside = ((x >= self.breakpoints[goodbk[hmm[jj]]]) & (x <= self.breakpoints[goodbk[hmm[jj]+1]-1])) if inside.any(): mask[inside] = False return (yy, mask) def maskpoints(self, err): """Perform simple logic of which breakpoints to mask. Parameters ---------- err : :class:`numpy.ndarray` The list of indexes returned by the cholesky routines. Returns ------- :class:`int` An integer indicating the results of the masking. -1 indicates that the error points were successfully masked. -2 indicates failure; the calculation should be aborted. Notes ----- The mask attribute is modified, assuming it is possible to create the mask. """ nbkpt = self.mask.sum() if nbkpt <= 2*self.nord: return -2 hmm = err[np.unique(err/self.npoly)]/self.npoly n = nbkpt - self.nord if np.any(hmm >= n): return -2 test = np.zeros(nbkpt, dtype='bool') for jj in range(-np.ceil(nord/2.0), nord/2.0): foo = np.where((hmm+jj) > 0, hmm+jj, np.zeros(hmm.shape, dtype=hmm.dtype)) inside = np.where((foo+nord) < n-1, foo+nord, np.zeros(hmm.shape, dtype=hmm.dtype)+n-1) test[inside] = True if test.any(): reality = self.mask[test] if self.mask[reality].any(): self.mask[reality] = False return -1 else: return -2 else: return -2 def cholesky_band(l, mininf=0.0, verbose=False): """Compute Cholesky decomposition of banded matrix. Parameters ---------- l : :class:`numpy.ndarray` A matrix on which to perform the Cholesky decomposition. mininf : :class:`float`, optional Entries in the `l` matrix are considered negative if they are less than this value (default 0.0). verbose : :class:`bool`, optional If set to ``True``, print some debugging information. Returns ------- :func:`tuple` If problems were detected, the first item will be the index or indexes where the problem was detected, and the second item will simply be the input matrix. If no problems were detected, the first item will be -1, and the second item will be the Cholesky decomposition. """ from warnings import warn from . import PydlutilsUserWarning lower = l.copy() bw, nn = lower.shape n = nn - bw negative = lower[0, 0:n] <= mininf if negative.any() or not np.all(np.isfinite(lower)): warn('Bad entries: ' + str(negative.nonzero()[0]), PydlutilsUserWarning) return (negative.nonzero()[0], l) kn = bw - 1 spot = np.arange(kn, dtype='i4') + 1 bi = np.arange(kn, dtype='i4') for i in range(1, kn): bi = np.append(bi, np.arange(kn-i, dtype='i4') + (kn+1)*i) for j in range(n): lower[0, j] = np.sqrt(lower[0, j]) lower[spot, j] /= lower[0, j] x = lower[spot, j] if not np.all(np.isfinite(x)): warn('NaN found in cholesky_band.', PydlutilsUserWarning) return (j, l) hmm = np.outer(x, x) here = bi+(j+1)*bw lower.T.flat[here] -= hmm.flat[bi] return (-1, lower) def cholesky_solve(a, bb): """Solve the equation Ax=b where A is a Cholesky-banded matrix. Parameters ---------- a : :class:`numpy.ndarray` :math:`A` in :math:`A x = b`. bb : :class:`numpy.ndarray` :math:`b` in :math:`A x = b`. Returns ------- :func:`tuple` A tuple containing the status and the result of the solution. The status is always -1. """ b = bb.copy() bw = a.shape[0] n = b.shape[0] - bw kn = bw - 1 spot = np.arange(kn, dtype='i4') + 1 for j in range(n): b[j] /= a[0, j] b[j+spot] -= b[j]*a[spot, j] spot = kn - np.arange(kn, dtype='i4') for j in range(n-1, -1, -1): b[j] = (b[j] - np.sum(a[spot, j] * b[j+spot]))/a[0, j] return (-1, b) def iterfit(xdata, ydata, invvar=None, upper=5, lower=5, x2=None, maxiter=10, **kwargs): """Iteratively fit a b-spline set to data, with rejection. Parameters ---------- xdata : :class:`numpy.ndarray` Independent variable. ydata : :class:`numpy.ndarray` Dependent variable. invvar : :class:`numpy.ndarray` Inverse variance of `ydata`. If not set, it will be calculated based on the standard deviation. upper : :class:`int` or :class:`float` Upper rejection threshold in units of sigma, defaults to 5 sigma. lower : :class:`int` or :class:`float` Lower rejection threshold in units of sigma, defaults to 5 sigma. x2 : :class:`numpy.ndarray`, optional Orthogonal dependent variable for 2d fits. maxiter : :class:`int`, optional Maximum number of rejection iterations, default 10. Set this to zero to disable rejection. Returns ------- :func:`tuple` A tuple containing the fitted bspline object and an output mask. """ from .math import djs_reject nx = xdata.size if ydata.size != nx: raise ValueError('Dimensions of xdata and ydata do not agree.') if invvar is not None: if invvar.size != nx: raise ValueError('Dimensions of xdata and invvar do not agree.') else: # # This correction to the variance makes it the same # as IDL's variance() # var = ydata.var()*(float(nx)/float(nx-1)) if var == 0: var = 1.0 invvar = np.ones(ydata.shape, dtype=ydata.dtype)/var if x2 is not None: if x2.size != nx: raise ValueError('Dimensions of xdata and x2 do not agree.') yfit = np.zeros(ydata.shape) if invvar.size == 1: outmask = True else: outmask = np.ones(invvar.shape, dtype='bool') xsort = xdata.argsort() maskwork = (outmask & (invvar > 0))[xsort] if 'oldset' in kwargs: sset = kwargs['oldset'] sset.mask = True sset.coeff = 0 else: if not maskwork.any(): raise ValueError('No valid data points.') # return (None,None) if 'fullbkpt' in kwargs: fullbkpt = kwargs['fullbkpt'] else: sset = bspline(xdata[xsort[maskwork]], **kwargs) if maskwork.sum() < sset.nord: print('Number of good data points fewer than nord.') return (sset, outmask) if x2 is not None: if 'xmin' in kwargs: xmin = kwargs['xmin'] else: xmin = x2.min() if 'xmax' in kwargs: xmax = kwargs['xmax'] else: xmax = x2.max() if xmin == xmax: xmax = xmin + 1 sset.xmin = xmin sset.xmax = xmax if 'funcname' in kwargs: sset.funcname = kwargs['funcname'] xwork = xdata[xsort] ywork = ydata[xsort] invwork = invvar[xsort] if x2 is not None: x2work = x2[xsort] else: x2work = None iiter = 0 error = 0 qdone = -1 while (error != 0 or qdone == -1) and iiter <= maxiter: goodbk = sset.mask.nonzero()[0] if maskwork.sum() <= 1 or not sset.mask.any(): sset.coeff = 0 iiter = maxiter + 1 else: if 'requiren' in kwargs: i = 0 while xwork[i] < sset.breakpoints[goodbk[sset.nord]] and i < nx-1: i += 1 ct = 0 for ileft in range(sset.nord, sset.mask.sum()-sset.nord+1): while (xwork[i] >= sset.breakpoints[goodbk[ileft]] and xwork[i] < sset.breakpoints[goodbk[ileft+1]] and i < nx-1): ct += invwork[i]*maskwork[i] > 0 i += 1 if ct >= kwargs['requiren']: ct = 0 else: sset.mask[goodbk[ileft]] = False error, yfit = sset.fit(xwork, ywork, invwork*maskwork, x2=x2work) iiter += 1 inmask = maskwork if error == -2: return (sset, outmask) elif error == 0: maskwork, qdone = djs_reject(ywork, yfit, invvar=invwork, inmask=inmask, outmask=maskwork, upper=upper, lower=lower) else: pass outmask[xsort] = maskwork temp = yfit yfit[xsort] = temp return (sset, outmask) pydl-0.6.0/pydl/version.py0000664000111400017500000001544713160273564016316 0ustar weaverweaver00000000000000# Autogenerated by Astropy-affiliated package pydl's setup.py on 2017-09-19 13:00:52.875210 from __future__ import unicode_literals import datetime import locale import os import subprocess import warnings def _decode_stdio(stream): try: stdio_encoding = locale.getdefaultlocale()[1] or 'utf-8' except ValueError: stdio_encoding = 'utf-8' try: text = stream.decode(stdio_encoding) except UnicodeDecodeError: # Final fallback text = stream.decode('latin1') return text def update_git_devstr(version, path=None): """ Updates the git revision string if and only if the path is being imported directly from a git working copy. This ensures that the revision number in the version string is accurate. """ try: # Quick way to determine if we're in git or not - returns '' if not devstr = get_git_devstr(sha=True, show_warning=False, path=path) except OSError: return version if not devstr: # Probably not in git so just pass silently return version if 'dev' in version: # update to the current git revision version_base = version.split('.dev', 1)[0] devstr = get_git_devstr(sha=False, show_warning=False, path=path) return version_base + '.dev' + devstr else: # otherwise it's already the true/release version return version def get_git_devstr(sha=False, show_warning=True, path=None): """ Determines the number of revisions in this repository. Parameters ---------- sha : bool If True, the full SHA1 hash will be returned. Otherwise, the total count of commits in the repository will be used as a "revision number". show_warning : bool If True, issue a warning if git returns an error code, otherwise errors pass silently. path : str or None If a string, specifies the directory to look in to find the git repository. If `None`, the current working directory is used, and must be the root of the git repository. If given a filename it uses the directory containing that file. Returns ------- devversion : str Either a string with the revision number (if `sha` is False), the SHA1 hash of the current commit (if `sha` is True), or an empty string if git version info could not be identified. """ if path is None: path = os.getcwd() if not _get_repo_path(path, levels=0): return '' if not os.path.isdir(path): path = os.path.abspath(os.path.dirname(path)) if sha: # Faster for getting just the hash of HEAD cmd = ['rev-parse', 'HEAD'] else: cmd = ['rev-list', '--count', 'HEAD'] def run_git(cmd): try: p = subprocess.Popen(['git'] + cmd, cwd=path, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) stdout, stderr = p.communicate() except OSError as e: if show_warning: warnings.warn('Error running git: ' + str(e)) return (None, b'', b'') if p.returncode == 128: if show_warning: warnings.warn('No git repository present at {0!r}! Using ' 'default dev version.'.format(path)) return (p.returncode, b'', b'') if p.returncode == 129: if show_warning: warnings.warn('Your git looks old (does it support {0}?); ' 'consider upgrading to v1.7.2 or ' 'later.'.format(cmd[0])) return (p.returncode, stdout, stderr) elif p.returncode != 0: if show_warning: warnings.warn('Git failed while determining revision ' 'count: {0}'.format(_decode_stdio(stderr))) return (p.returncode, stdout, stderr) return p.returncode, stdout, stderr returncode, stdout, stderr = run_git(cmd) if not sha and returncode == 129: # git returns 129 if a command option failed to parse; in # particular this could happen in git versions older than 1.7.2 # where the --count option is not supported # Also use --abbrev-commit and --abbrev=0 to display the minimum # number of characters needed per-commit (rather than the full hash) cmd = ['rev-list', '--abbrev-commit', '--abbrev=0', 'HEAD'] returncode, stdout, stderr = run_git(cmd) # Fall back on the old method of getting all revisions and counting # the lines if returncode == 0: return str(stdout.count(b'\n')) else: return '' elif sha: return _decode_stdio(stdout)[:40] else: return _decode_stdio(stdout).strip() def _get_repo_path(pathname, levels=None): """ Given a file or directory name, determine the root of the git repository this path is under. If given, this won't look any higher than ``levels`` (that is, if ``levels=0`` then the given path must be the root of the git repository and is returned if so. Returns `None` if the given path could not be determined to belong to a git repo. """ if os.path.isfile(pathname): current_dir = os.path.abspath(os.path.dirname(pathname)) elif os.path.isdir(pathname): current_dir = os.path.abspath(pathname) else: return None current_level = 0 while levels is None or current_level <= levels: if os.path.exists(os.path.join(current_dir, '.git')): return current_dir current_level += 1 if current_dir == os.path.dirname(current_dir): break current_dir = os.path.dirname(current_dir) return None _packagename = "pydl" _last_generated_version = "0.6.0" _last_githash = "f905b6cb9a174550a61f42ec38b326fc8511565b" # Determine where the source code for this module # lives. If __file__ is not a filesystem path then # it is assumed not to live in a git repo at all. if _get_repo_path(__file__, levels=len(_packagename.split('.'))): version = update_git_devstr(_last_generated_version, path=__file__) githash = get_git_devstr(sha=True, show_warning=False, path=__file__) or _last_githash else: # The file does not appear to live in a git repo so don't bother # invoking git version = _last_generated_version githash = _last_githash major = 0 minor = 6 bugfix = 0 release = True timestamp = datetime.datetime(2017, 9, 19, 13, 0, 52, 875210) debug = False try: from ._compiler import compiler except ImportError: compiler = "unknown" try: from .cython_version import cython_version except ImportError: cython_version = "unknown" pydl-0.6.0/pydl/__init__.py0000664000111400017500000000233413064273637016363 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- """ ==== pydl ==== Python replacements for functions that are part of the IDL_ built-in library, or part of astronomical IDL libraries. The emphasis is on reproducing results of the astronomical library functions. Only the bare minimum of IDL_ built-in functions are implemented to support this. .. _IDL: http://www.exelisvis.com/language/en-us/productsservices/idl.aspx """ # Affiliated packages may add whatever they like to this file, but # should keep this content at the top. # ---------------------------------------------------------------------------- from ._astropy_init import * # ---------------------------------------------------------------------------- # For egg_info test builds to pass, put package imports here. if not _ASTROPY_SETUP_: from .file_lines import file_lines from .median import median from .pcomp import pcomp from .rebin import rebin from .smooth import smooth from .uniq import uniq class PydlException(Exception): """Base class for exceptions raised in PyDL functions. """ pass __all__ = ['file_lines', 'median', 'pcomp', 'rebin', 'smooth', 'uniq', 'PydlException'] pydl-0.6.0/pydl/setup_package.py0000664000111400017500000000075612775255301017441 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license # -*- coding: utf-8 -*- def get_package_data(): # Installs the testing data files. Unable to get package_data # to deal with a directory hierarchy of files, so just explicitly list. return { 'pydl.tests': ['coveragerc', 't/*'], 'pydl.pydlspec2d.tests': ['t/*'], 'pydl.pydlutils': ['data/cooling/*', 'data/filters/*'], 'pydl.pydlutils.tests': ['t/*'], } # def requires_2to3(): # return False pydl-0.6.0/pydl/pcomp.py0000664000111400017500000000610612775255301015737 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import numpy as np from astropy.utils import lazyproperty class pcomp(object): """Replicates the IDL ``PCOMP()`` function. The attributes of this class are all read-only properties, implemented with :class:`~astropy.utils.decorators.lazyproperty`. Parameters ---------- x : array-like A 2-D array with :math:`N` rows and :math:`M` columns. standardize : :class:`bool`, optional If set to ``True``, the input data will have its mean subtracted off and will be scaled to unit variance. covariance : :class:`bool`, optional. If set to ``True``, the covariance matrix of the data will be used for the computation. Otherwise the correlation matrix will be used. Notes ----- References ---------- http://www.exelisvis.com/docs/PCOMP.html Examples -------- """ def __init__(self, x, standardize=False, covariance=False): from scipy.linalg import eigh if x.ndim != 2: raise ValueError('Input array must be two-dimensional') no, nv = x.shape self._nv = nv if standardize: xstd = x - np.tile(x.mean(0), no).reshape(x.shape) s = np.tile(xstd.std(0), no).reshape(x.shape) self._array = xstd/s self._xstd = xstd else: self._array = x self._xstd = None self._standardize = standardize if covariance: self._c = np.cov(self._array, rowvar=0) else: self._c = np.corrcoef(self._array, rowvar=0) self._covariance = covariance # # eigh is used for symmetric matrices # evals, evecs = eigh(self._c) # # Sort eigenvalues in descending order # ie = evals.argsort()[::-1] self._evals = evals[ie] self._evecs = evecs[:, ie] # # If necessary, add code to fix the signs of the eigenvectors. # http://www3.interscience.wiley.com/journal/117912150/abstract # return @lazyproperty def coefficients(self): """(:class:`~numpy.ndarray`) The principal components. These are the coefficients of `derived`. Basically, they are a re-scaling of the eigenvectors. """ return self._evecs * np.tile(np.sqrt(self._evals), self._nv).reshape( self._nv, self._nv) @lazyproperty def derived(self): """(:class:`~numpy.ndarray`) The derived variables. """ derived_data = np.dot(self._array, self.coefficients) if self._standardize: derived_data += self._xstd return derived_data @lazyproperty def variance(self): """(:class:`~numpy.ndarray`) The variances of each derived variable. """ return self._evals/self._c.trace() @lazyproperty def eigenvalues(self): """(:class:`~numpy.ndarray`) The eigenvalues. There is one eigenvalue for each principal component. """ return self._evals pydl-0.6.0/pydl/conftest.py0000664000111400017500000000253513064273637016454 0ustar weaverweaver00000000000000# this contains imports plugins that configure py.test for astropy tests. # by importing them here in conftest.py they are discoverable by py.test # no matter how it is invoked within the source tree. from astropy.tests.pytest_plugins import * # Uncomment the following line to treat all DeprecationWarnings as # exceptions # enable_deprecations_as_exceptions() # Uncomment and customize the following lines to add/remove entries # from the list of packages for which version numbers are displayed # when running the tests try: PYTEST_HEADER_MODULES['Astropy'] = 'astropy' PYTEST_HEADER_MODULES['PyDL'] = 'pydl' try: del PYTEST_HEADER_MODULES['h5py'] except KeyError: pass try: del PYTEST_HEADER_MODULES['Pandas'] except KeyError: pass except NameError: # needed to support Astropy < 1.0 pass # Uncomment the following lines to display the version number of the # package rather than the version number of Astropy in the top line when # running the tests. import os # This is to figure out the affiliated package version, rather than # using Astropy's try: from .version import version except ImportError: version = 'dev' try: packagename = os.path.basename(os.path.dirname(__file__)) TESTED_VERSIONS[packagename] = version except NameError: # Needed to support Astropy <= 1.0.0 pass pydl-0.6.0/pydl/median.py0000664000111400017500000000552113040014667016050 0ustar weaverweaver00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- def median(array, width=None, axis=None, even=False): """Replicate the IDL ``MEDIAN()`` function. Parameters ---------- array : array-like Compute the median of this array. width : :class:`int`, optional Size of the neighborhood in which to compute the median (*i.e.*, perform median filtering). If omitted, the median of the whole array is returned. axis : :class:`int`, optional Compute the median over this axis for a multi-dimensional array. If ommitted, the median over the entire array will be returned. If set, this function will behave as though `even` is ``True``. even : :class:`bool`, optional If set to ``True``, the median of arrays with an even number of elements will be the average of the middle two values. Returns ------- array-like The median of the array. Raises ------ ValueError If `width` is set, and the input `array` is not 1 or 2 dimensional. Notes ----- * For arrays with an even number of elements, the :func:`numpy.median` function behaves like ``MEDIAN(array, /EVEN)``, so the absence of the `even` keyword has to turn *off* that behavior. * For median filtering, this uses :func:`scipy.signal.medfilt` and :func:`scipy.signal.medfilt2d` under the hood, but patches up the values on the array boundaries to match the return values of the IDL ``MEDIAN()`` function. """ import numpy as np from scipy.signal import medfilt, medfilt2d if width is None: if axis is None: f = array.flatten() if f.size % 2 == 1 or even: return np.median(array) else: i = f.argsort() return f[i[f.size//2]] else: return np.median(array, axis=axis) else: if array.ndim == 1: medarray = medfilt(array, min(width, array.size)) istart = int((width - 1)/2) iend = array.size - int((width + 1)/2) i = np.arange(array.size) w = (i < istart) | (i > iend) medarray[w] = array[w] return medarray elif array.ndim == 2: medarray = medfilt2d(array, min(width, array.size)) istart = int((width-1)/2) iend = (array.shape[0] - int((width+1)/2), array.shape[1] - int((width+1)/2)) i = np.arange(array.shape[0]) j = np.arange(array.shape[1]) w = ((i < istart) | (i > iend[0]), (j < istart) | (j > iend[1])) medarray[w[0], :] = array[w[0], :] medarray[:, w[1]] = array[:, w[1]] return medarray else: raise ValueError("Invalid number of dimensions for input array!") pydl-0.6.0/PKG-INFO0000664000111400017500000000223213160273564014370 0ustar weaverweaver00000000000000Metadata-Version: 1.1 Name: pydl Version: 0.6.0 Summary: Astropy affiliated package Home-page: http://github.com/weaverba137/pydl Author: Benjamin Alan Weaver Author-email: baweaver@lbl.gov License: BSD Description-Content-Type: UNKNOWN Description: ==== pydl ==== Python replacements for functions that are part of the IDL_ built-in library, or part of astronomical IDL libraries. The emphasis is on reproducing results of the astronomical library functions. Only the bare minimum of IDL_ built-in functions are implemented to support this. .. _IDL: http://www.exelisvis.com/language/en-us/productsservices/idl.aspx Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Environment :: Console Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: BSD License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Topic :: Scientific/Engineering :: Physics Classifier: Topic :: Scientific/Engineering :: Astronomy pydl-0.6.0/cextern/0000775000111400017500000000000013160273564014744 5ustar weaverweaver00000000000000pydl-0.6.0/cextern/README.rst0000664000111400017500000000054212775255301016434 0ustar weaverweaver00000000000000External Packages/Libraries =========================== This directory contains C extensions included with the package. Note that only C extensions should be included in this directory - pure Cython code should be placed in the package source tree, and wrapper Cython code for C libraries included here should be in the packagename/wrappers directory. pydl-0.6.0/ez_setup.py0000664000111400017500000003037113154630165015505 0ustar weaverweaver00000000000000#!/usr/bin/env python """ Setuptools bootstrapping installer. Maintained at https://github.com/pypa/setuptools/tree/bootstrap. Run this script to install or upgrade setuptools. This method is DEPRECATED. Check https://github.com/pypa/setuptools/issues/581 for more details. """ import os import shutil import sys import tempfile import zipfile import optparse import subprocess import platform import textwrap import contextlib from distutils import log try: from urllib.request import urlopen except ImportError: from urllib2 import urlopen try: from site import USER_SITE except ImportError: USER_SITE = None # 33.1.1 is the last version that supports setuptools self upgrade/installation. DEFAULT_VERSION = "33.1.1" DEFAULT_URL = "https://pypi.io/packages/source/s/setuptools/" DEFAULT_SAVE_DIR = os.curdir DEFAULT_DEPRECATION_MESSAGE = "ez_setup.py is deprecated and when using it setuptools will be pinned to {0} since it's the last version that supports setuptools self upgrade/installation, check https://github.com/pypa/setuptools/issues/581 for more info; use pip to install setuptools" MEANINGFUL_INVALID_ZIP_ERR_MSG = 'Maybe {0} is corrupted, delete it and try again.' log.warn(DEFAULT_DEPRECATION_MESSAGE.format(DEFAULT_VERSION)) def _python_cmd(*args): """ Execute a command. Return True if the command succeeded. """ args = (sys.executable,) + args return subprocess.call(args) == 0 def _install(archive_filename, install_args=()): """Install Setuptools.""" with archive_context(archive_filename): # installing log.warn('Installing Setuptools') if not _python_cmd('setup.py', 'install', *install_args): log.warn('Something went wrong during the installation.') log.warn('See the error message above.') # exitcode will be 2 return 2 def _build_egg(egg, archive_filename, to_dir): """Build Setuptools egg.""" with archive_context(archive_filename): # building an egg log.warn('Building a Setuptools egg in %s', to_dir) _python_cmd('setup.py', '-q', 'bdist_egg', '--dist-dir', to_dir) # returning the result log.warn(egg) if not os.path.exists(egg): raise IOError('Could not build the egg.') class ContextualZipFile(zipfile.ZipFile): """Supplement ZipFile class to support context manager for Python 2.6.""" def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def __new__(cls, *args, **kwargs): """Construct a ZipFile or ContextualZipFile as appropriate.""" if hasattr(zipfile.ZipFile, '__exit__'): return zipfile.ZipFile(*args, **kwargs) return super(ContextualZipFile, cls).__new__(cls) @contextlib.contextmanager def archive_context(filename): """ Unzip filename to a temporary directory, set to the cwd. The unzipped target is cleaned up after. """ tmpdir = tempfile.mkdtemp() log.warn('Extracting in %s', tmpdir) old_wd = os.getcwd() try: os.chdir(tmpdir) try: with ContextualZipFile(filename) as archive: archive.extractall() except zipfile.BadZipfile as err: if not err.args: err.args = ('', ) err.args = err.args + ( MEANINGFUL_INVALID_ZIP_ERR_MSG.format(filename), ) raise # going in the directory subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0]) os.chdir(subdir) log.warn('Now working in %s', subdir) yield finally: os.chdir(old_wd) shutil.rmtree(tmpdir) def _do_download(version, download_base, to_dir, download_delay): """Download Setuptools.""" py_desig = 'py{sys.version_info[0]}.{sys.version_info[1]}'.format(sys=sys) tp = 'setuptools-{version}-{py_desig}.egg' egg = os.path.join(to_dir, tp.format(**locals())) if not os.path.exists(egg): archive = download_setuptools(version, download_base, to_dir, download_delay) _build_egg(egg, archive, to_dir) sys.path.insert(0, egg) # Remove previously-imported pkg_resources if present (see # https://bitbucket.org/pypa/setuptools/pull-request/7/ for details). if 'pkg_resources' in sys.modules: _unload_pkg_resources() import setuptools setuptools.bootstrap_install_from = egg def use_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, download_delay=15): """ Ensure that a setuptools version is installed. Return None. Raise SystemExit if the requested version or later cannot be installed. """ to_dir = os.path.abspath(to_dir) # prior to importing, capture the module state for # representative modules. rep_modules = 'pkg_resources', 'setuptools' imported = set(sys.modules).intersection(rep_modules) try: import pkg_resources pkg_resources.require("setuptools>=" + version) # a suitable version is already installed return except ImportError: # pkg_resources not available; setuptools is not installed; download pass except pkg_resources.DistributionNotFound: # no version of setuptools was found; allow download pass except pkg_resources.VersionConflict as VC_err: if imported: _conflict_bail(VC_err, version) # otherwise, unload pkg_resources to allow the downloaded version to # take precedence. del pkg_resources _unload_pkg_resources() return _do_download(version, download_base, to_dir, download_delay) def _conflict_bail(VC_err, version): """ Setuptools was imported prior to invocation, so it is unsafe to unload it. Bail out. """ conflict_tmpl = textwrap.dedent(""" The required version of setuptools (>={version}) is not available, and can't be installed while this script is running. Please install a more recent version first, using 'easy_install -U setuptools'. (Currently using {VC_err.args[0]!r}) """) msg = conflict_tmpl.format(**locals()) sys.stderr.write(msg) sys.exit(2) def _unload_pkg_resources(): sys.meta_path = [ importer for importer in sys.meta_path if importer.__class__.__module__ != 'pkg_resources.extern' ] del_modules = [ name for name in sys.modules if name.startswith('pkg_resources') ] for mod_name in del_modules: del sys.modules[mod_name] def _clean_check(cmd, target): """ Run the command to download target. If the command fails, clean up before re-raising the error. """ try: subprocess.check_call(cmd) except subprocess.CalledProcessError: if os.access(target, os.F_OK): os.unlink(target) raise def download_file_powershell(url, target): """ Download the file at url to target using Powershell. Powershell will validate trust. Raise an exception if the command cannot complete. """ target = os.path.abspath(target) ps_cmd = ( "[System.Net.WebRequest]::DefaultWebProxy.Credentials = " "[System.Net.CredentialCache]::DefaultCredentials; " '(new-object System.Net.WebClient).DownloadFile("%(url)s", "%(target)s")' % locals() ) cmd = [ 'powershell', '-Command', ps_cmd, ] _clean_check(cmd, target) def has_powershell(): """Determine if Powershell is available.""" if platform.system() != 'Windows': return False cmd = ['powershell', '-Command', 'echo test'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_powershell.viable = has_powershell def download_file_curl(url, target): cmd = ['curl', url, '--location', '--silent', '--output', target] _clean_check(cmd, target) def has_curl(): cmd = ['curl', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_curl.viable = has_curl def download_file_wget(url, target): cmd = ['wget', url, '--quiet', '--output-document', target] _clean_check(cmd, target) def has_wget(): cmd = ['wget', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_wget.viable = has_wget def download_file_insecure(url, target): """Use Python to download the file, without connection authentication.""" src = urlopen(url) try: # Read all the data in one block. data = src.read() finally: src.close() # Write all the data in one block to avoid creating a partial file. with open(target, "wb") as dst: dst.write(data) download_file_insecure.viable = lambda: True def get_best_downloader(): downloaders = ( download_file_powershell, download_file_curl, download_file_wget, download_file_insecure, ) viable_downloaders = (dl for dl in downloaders if dl.viable()) return next(viable_downloaders, None) def download_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, delay=15, downloader_factory=get_best_downloader): """ Download setuptools from a specified location and return its filename. `version` should be a valid setuptools version number that is available as an sdist for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where the egg will be downloaded. `delay` is the number of seconds to pause before an actual download attempt. ``downloader_factory`` should be a function taking no arguments and returning a function for downloading a URL to a target. """ # making sure we use the absolute path to_dir = os.path.abspath(to_dir) zip_name = "setuptools-%s.zip" % version url = download_base + zip_name saveto = os.path.join(to_dir, zip_name) if not os.path.exists(saveto): # Avoid repeated downloads log.warn("Downloading %s", url) downloader = downloader_factory() downloader(url, saveto) return os.path.realpath(saveto) def _build_install_args(options): """ Build the arguments to 'python setup.py install' on the setuptools package. Returns list of command line arguments. """ return ['--user'] if options.user_install else [] def _parse_args(): """Parse the command line for options.""" parser = optparse.OptionParser() parser.add_option( '--user', dest='user_install', action='store_true', default=False, help='install in user site package') parser.add_option( '--download-base', dest='download_base', metavar="URL", default=DEFAULT_URL, help='alternative URL from where to download the setuptools package') parser.add_option( '--insecure', dest='downloader_factory', action='store_const', const=lambda: download_file_insecure, default=get_best_downloader, help='Use internal, non-validating downloader' ) parser.add_option( '--version', help="Specify which version to download", default=DEFAULT_VERSION, ) parser.add_option( '--to-dir', help="Directory to save (and re-use) package", default=DEFAULT_SAVE_DIR, ) options, args = parser.parse_args() # positional arguments are ignored return options def _download_args(options): """Return args for download_setuptools function from cmdline args.""" return dict( version=options.version, download_base=options.download_base, downloader_factory=options.downloader_factory, to_dir=options.to_dir, ) def main(): """Install or upgrade setuptools and EasyInstall.""" options = _parse_args() archive = download_setuptools(**_download_args(options)) return _install(archive, _build_install_args(options)) if __name__ == '__main__': sys.exit(main()) pydl-0.6.0/ah_bootstrap.py0000664000111400017500000010434413154630165016336 0ustar weaverweaver00000000000000""" This bootstrap module contains code for ensuring that the astropy_helpers package will be importable by the time the setup.py script runs. It also includes some workarounds to ensure that a recent-enough version of setuptools is being used for the installation. This module should be the first thing imported in the setup.py of distributions that make use of the utilities in astropy_helpers. If the distribution ships with its own copy of astropy_helpers, this module will first attempt to import from the shipped copy. However, it will also check PyPI to see if there are any bug-fix releases on top of the current version that may be useful to get past platform-specific bugs that have been fixed. When running setup.py, use the ``--offline`` command-line option to disable the auto-upgrade checks. When this module is imported or otherwise executed it automatically calls a main function that attempts to read the project's setup.cfg file, which it checks for a configuration section called ``[ah_bootstrap]`` the presences of that section, and options therein, determine the next step taken: If it contains an option called ``auto_use`` with a value of ``True``, it will automatically call the main function of this module called `use_astropy_helpers` (see that function's docstring for full details). Otherwise no further action is taken (however, ``ah_bootstrap.use_astropy_helpers`` may be called manually from within the setup.py script). Additional options in the ``[ah_boostrap]`` section of setup.cfg have the same names as the arguments to `use_astropy_helpers`, and can be used to configure the bootstrap script when ``auto_use = True``. See https://github.com/astropy/astropy-helpers for more details, and for the latest version of this module. """ import contextlib import errno import imp import io import locale import os import re import subprocess as sp import sys try: from ConfigParser import ConfigParser, RawConfigParser except ImportError: from configparser import ConfigParser, RawConfigParser if sys.version_info[0] < 3: _str_types = (str, unicode) _text_type = unicode PY3 = False else: _str_types = (str, bytes) _text_type = str PY3 = True # What follows are several import statements meant to deal with install-time # issues with either missing or misbehaving pacakges (including making sure # setuptools itself is installed): # Some pre-setuptools checks to ensure that either distribute or setuptools >= # 0.7 is used (over pre-distribute setuptools) if it is available on the path; # otherwise the latest setuptools will be downloaded and bootstrapped with # ``ez_setup.py``. This used to be included in a separate file called # setuptools_bootstrap.py; but it was combined into ah_bootstrap.py try: import pkg_resources _setuptools_req = pkg_resources.Requirement.parse('setuptools>=0.7') # This may raise a DistributionNotFound in which case no version of # setuptools or distribute is properly installed _setuptools = pkg_resources.get_distribution('setuptools') if _setuptools not in _setuptools_req: # Older version of setuptools; check if we have distribute; again if # this results in DistributionNotFound we want to give up _distribute = pkg_resources.get_distribution('distribute') if _setuptools != _distribute: # It's possible on some pathological systems to have an old version # of setuptools and distribute on sys.path simultaneously; make # sure distribute is the one that's used sys.path.insert(1, _distribute.location) _distribute.activate() imp.reload(pkg_resources) except: # There are several types of exceptions that can occur here; if all else # fails bootstrap and use the bootstrapped version from ez_setup import use_setuptools use_setuptools() # typing as a dependency for 1.6.1+ Sphinx causes issues when imported after # initializing submodule with ah_boostrap.py # See discussion and references in # https://github.com/astropy/astropy-helpers/issues/302 try: import typing # noqa except ImportError: pass # Note: The following import is required as a workaround to # https://github.com/astropy/astropy-helpers/issues/89; if we don't import this # module now, it will get cleaned up after `run_setup` is called, but that will # later cause the TemporaryDirectory class defined in it to stop working when # used later on by setuptools try: import setuptools.py31compat # noqa except ImportError: pass # matplotlib can cause problems if it is imported from within a call of # run_setup(), because in some circumstances it will try to write to the user's # home directory, resulting in a SandboxViolation. See # https://github.com/matplotlib/matplotlib/pull/4165 # Making sure matplotlib, if it is available, is imported early in the setup # process can mitigate this (note importing matplotlib.pyplot has the same # issue) try: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot except: # Ignore if this fails for *any* reason* pass # End compatibility imports... # In case it didn't successfully import before the ez_setup checks import pkg_resources from setuptools import Distribution from setuptools.package_index import PackageIndex from setuptools.sandbox import run_setup from distutils import log from distutils.debug import DEBUG # TODO: Maybe enable checking for a specific version of astropy_helpers? DIST_NAME = 'astropy-helpers' PACKAGE_NAME = 'astropy_helpers' # Defaults for other options DOWNLOAD_IF_NEEDED = True INDEX_URL = 'https://pypi.python.org/simple' USE_GIT = True OFFLINE = False AUTO_UPGRADE = True # A list of all the configuration options and their required types CFG_OPTIONS = [ ('auto_use', bool), ('path', str), ('download_if_needed', bool), ('index_url', str), ('use_git', bool), ('offline', bool), ('auto_upgrade', bool) ] class _Bootstrapper(object): """ Bootstrapper implementation. See ``use_astropy_helpers`` for parameter documentation. """ def __init__(self, path=None, index_url=None, use_git=None, offline=None, download_if_needed=None, auto_upgrade=None): if path is None: path = PACKAGE_NAME if not (isinstance(path, _str_types) or path is False): raise TypeError('path must be a string or False') if PY3 and not isinstance(path, _text_type): fs_encoding = sys.getfilesystemencoding() path = path.decode(fs_encoding) # path to unicode self.path = path # Set other option attributes, using defaults where necessary self.index_url = index_url if index_url is not None else INDEX_URL self.offline = offline if offline is not None else OFFLINE # If offline=True, override download and auto-upgrade if self.offline: download_if_needed = False auto_upgrade = False self.download = (download_if_needed if download_if_needed is not None else DOWNLOAD_IF_NEEDED) self.auto_upgrade = (auto_upgrade if auto_upgrade is not None else AUTO_UPGRADE) # If this is a release then the .git directory will not exist so we # should not use git. git_dir_exists = os.path.exists(os.path.join(os.path.dirname(__file__), '.git')) if use_git is None and not git_dir_exists: use_git = False self.use_git = use_git if use_git is not None else USE_GIT # Declared as False by default--later we check if astropy-helpers can be # upgraded from PyPI, but only if not using a source distribution (as in # the case of import from a git submodule) self.is_submodule = False @classmethod def main(cls, argv=None): if argv is None: argv = sys.argv config = cls.parse_config() config.update(cls.parse_command_line(argv)) auto_use = config.pop('auto_use', False) bootstrapper = cls(**config) if auto_use: # Run the bootstrapper, otherwise the setup.py is using the old # use_astropy_helpers() interface, in which case it will run the # bootstrapper manually after reconfiguring it. bootstrapper.run() return bootstrapper @classmethod def parse_config(cls): if not os.path.exists('setup.cfg'): return {} cfg = ConfigParser() try: cfg.read('setup.cfg') except Exception as e: if DEBUG: raise log.error( "Error reading setup.cfg: {0!r}\n{1} will not be " "automatically bootstrapped and package installation may fail." "\n{2}".format(e, PACKAGE_NAME, _err_help_msg)) return {} if not cfg.has_section('ah_bootstrap'): return {} config = {} for option, type_ in CFG_OPTIONS: if not cfg.has_option('ah_bootstrap', option): continue if type_ is bool: value = cfg.getboolean('ah_bootstrap', option) else: value = cfg.get('ah_bootstrap', option) config[option] = value return config @classmethod def parse_command_line(cls, argv=None): if argv is None: argv = sys.argv config = {} # For now we just pop recognized ah_bootstrap options out of the # arg list. This is imperfect; in the unlikely case that a setup.py # custom command or even custom Distribution class defines an argument # of the same name then we will break that. However there's a catch22 # here that we can't just do full argument parsing right here, because # we don't yet know *how* to parse all possible command-line arguments. if '--no-git' in argv: config['use_git'] = False argv.remove('--no-git') if '--offline' in argv: config['offline'] = True argv.remove('--offline') return config def run(self): strategies = ['local_directory', 'local_file', 'index'] dist = None # First, remove any previously imported versions of astropy_helpers; # this is necessary for nested installs where one package's installer # is installing another package via setuptools.sandbox.run_setup, as in # the case of setup_requires for key in list(sys.modules): try: if key == PACKAGE_NAME or key.startswith(PACKAGE_NAME + '.'): del sys.modules[key] except AttributeError: # Sometimes mysterious non-string things can turn up in # sys.modules continue # Check to see if the path is a submodule self.is_submodule = self._check_submodule() for strategy in strategies: method = getattr(self, 'get_{0}_dist'.format(strategy)) dist = method() if dist is not None: break else: raise _AHBootstrapSystemExit( "No source found for the {0!r} package; {0} must be " "available and importable as a prerequisite to building " "or installing this package.".format(PACKAGE_NAME)) # This is a bit hacky, but if astropy_helpers was loaded from a # directory/submodule its Distribution object gets a "precedence" of # "DEVELOP_DIST". However, in other cases it gets a precedence of # "EGG_DIST". However, when activing the distribution it will only be # placed early on sys.path if it is treated as an EGG_DIST, so always # do that dist = dist.clone(precedence=pkg_resources.EGG_DIST) # Otherwise we found a version of astropy-helpers, so we're done # Just active the found distribution on sys.path--if we did a # download this usually happens automatically but it doesn't hurt to # do it again # Note: Adding the dist to the global working set also activates it # (makes it importable on sys.path) by default. try: pkg_resources.working_set.add(dist, replace=True) except TypeError: # Some (much) older versions of setuptools do not have the # replace=True option here. These versions are old enough that all # bets may be off anyways, but it's easy enough to work around just # in case... if dist.key in pkg_resources.working_set.by_key: del pkg_resources.working_set.by_key[dist.key] pkg_resources.working_set.add(dist) @property def config(self): """ A `dict` containing the options this `_Bootstrapper` was configured with. """ return dict((optname, getattr(self, optname)) for optname, _ in CFG_OPTIONS if hasattr(self, optname)) def get_local_directory_dist(self): """ Handle importing a vendored package from a subdirectory of the source distribution. """ if not os.path.isdir(self.path): return log.info('Attempting to import astropy_helpers from {0} {1!r}'.format( 'submodule' if self.is_submodule else 'directory', self.path)) dist = self._directory_import() if dist is None: log.warn( 'The requested path {0!r} for importing {1} does not ' 'exist, or does not contain a copy of the {1} ' 'package.'.format(self.path, PACKAGE_NAME)) elif self.auto_upgrade and not self.is_submodule: # A version of astropy-helpers was found on the available path, but # check to see if a bugfix release is available on PyPI upgrade = self._do_upgrade(dist) if upgrade is not None: dist = upgrade return dist def get_local_file_dist(self): """ Handle importing from a source archive; this also uses setup_requires but points easy_install directly to the source archive. """ if not os.path.isfile(self.path): return log.info('Attempting to unpack and import astropy_helpers from ' '{0!r}'.format(self.path)) try: dist = self._do_download(find_links=[self.path]) except Exception as e: if DEBUG: raise log.warn( 'Failed to import {0} from the specified archive {1!r}: ' '{2}'.format(PACKAGE_NAME, self.path, str(e))) dist = None if dist is not None and self.auto_upgrade: # A version of astropy-helpers was found on the available path, but # check to see if a bugfix release is available on PyPI upgrade = self._do_upgrade(dist) if upgrade is not None: dist = upgrade return dist def get_index_dist(self): if not self.download: log.warn('Downloading {0!r} disabled.'.format(DIST_NAME)) return None log.warn( "Downloading {0!r}; run setup.py with the --offline option to " "force offline installation.".format(DIST_NAME)) try: dist = self._do_download() except Exception as e: if DEBUG: raise log.warn( 'Failed to download and/or install {0!r} from {1!r}:\n' '{2}'.format(DIST_NAME, self.index_url, str(e))) dist = None # No need to run auto-upgrade here since we've already presumably # gotten the most up-to-date version from the package index return dist def _directory_import(self): """ Import astropy_helpers from the given path, which will be added to sys.path. Must return True if the import succeeded, and False otherwise. """ # Return True on success, False on failure but download is allowed, and # otherwise raise SystemExit path = os.path.abspath(self.path) # Use an empty WorkingSet rather than the man # pkg_resources.working_set, since on older versions of setuptools this # will invoke a VersionConflict when trying to install an upgrade ws = pkg_resources.WorkingSet([]) ws.add_entry(path) dist = ws.by_key.get(DIST_NAME) if dist is None: # We didn't find an egg-info/dist-info in the given path, but if a # setup.py exists we can generate it setup_py = os.path.join(path, 'setup.py') if os.path.isfile(setup_py): with _silence(): run_setup(os.path.join(path, 'setup.py'), ['egg_info']) for dist in pkg_resources.find_distributions(path, True): # There should be only one... return dist return dist def _do_download(self, version='', find_links=None): if find_links: allow_hosts = '' index_url = None else: allow_hosts = None index_url = self.index_url # Annoyingly, setuptools will not handle other arguments to # Distribution (such as options) before handling setup_requires, so it # is not straightforward to programmatically augment the arguments which # are passed to easy_install class _Distribution(Distribution): def get_option_dict(self, command_name): opts = Distribution.get_option_dict(self, command_name) if command_name == 'easy_install': if find_links is not None: opts['find_links'] = ('setup script', find_links) if index_url is not None: opts['index_url'] = ('setup script', index_url) if allow_hosts is not None: opts['allow_hosts'] = ('setup script', allow_hosts) return opts if version: req = '{0}=={1}'.format(DIST_NAME, version) else: req = DIST_NAME attrs = {'setup_requires': [req]} try: if DEBUG: _Distribution(attrs=attrs) else: with _silence(): _Distribution(attrs=attrs) # If the setup_requires succeeded it will have added the new dist to # the main working_set return pkg_resources.working_set.by_key.get(DIST_NAME) except Exception as e: if DEBUG: raise msg = 'Error retrieving {0} from {1}:\n{2}' if find_links: source = find_links[0] elif index_url != INDEX_URL: source = index_url else: source = 'PyPI' raise Exception(msg.format(DIST_NAME, source, repr(e))) def _do_upgrade(self, dist): # Build up a requirement for a higher bugfix release but a lower minor # release (so API compatibility is guaranteed) next_version = _next_version(dist.parsed_version) req = pkg_resources.Requirement.parse( '{0}>{1},<{2}'.format(DIST_NAME, dist.version, next_version)) package_index = PackageIndex(index_url=self.index_url) upgrade = package_index.obtain(req) if upgrade is not None: return self._do_download(version=upgrade.version) def _check_submodule(self): """ Check if the given path is a git submodule. See the docstrings for ``_check_submodule_using_git`` and ``_check_submodule_no_git`` for further details. """ if (self.path is None or (os.path.exists(self.path) and not os.path.isdir(self.path))): return False if self.use_git: return self._check_submodule_using_git() else: return self._check_submodule_no_git() def _check_submodule_using_git(self): """ Check if the given path is a git submodule. If so, attempt to initialize and/or update the submodule if needed. This function makes calls to the ``git`` command in subprocesses. The ``_check_submodule_no_git`` option uses pure Python to check if the given path looks like a git submodule, but it cannot perform updates. """ cmd = ['git', 'submodule', 'status', '--', self.path] try: log.info('Running `{0}`; use the --no-git option to disable git ' 'commands'.format(' '.join(cmd))) returncode, stdout, stderr = run_cmd(cmd) except _CommandNotFound: # The git command simply wasn't found; this is most likely the # case on user systems that don't have git and are simply # trying to install the package from PyPI or a source # distribution. Silently ignore this case and simply don't try # to use submodules return False stderr = stderr.strip() if returncode != 0 and stderr: # Unfortunately the return code alone cannot be relied on, as # earlier versions of git returned 0 even if the requested submodule # does not exist # This is a warning that occurs in perl (from running git submodule) # which only occurs with a malformatted locale setting which can # happen sometimes on OSX. See again # https://github.com/astropy/astropy/issues/2749 perl_warning = ('perl: warning: Falling back to the standard locale ' '("C").') if not stderr.strip().endswith(perl_warning): # Some other unknown error condition occurred log.warn('git submodule command failed ' 'unexpectedly:\n{0}'.format(stderr)) return False # Output of `git submodule status` is as follows: # # 1: Status indicator: '-' for submodule is uninitialized, '+' if # submodule is initialized but is not at the commit currently indicated # in .gitmodules (and thus needs to be updated), or 'U' if the # submodule is in an unstable state (i.e. has merge conflicts) # # 2. SHA-1 hash of the current commit of the submodule (we don't really # need this information but it's useful for checking that the output is # correct) # # 3. The output of `git describe` for the submodule's current commit # hash (this includes for example what branches the commit is on) but # only if the submodule is initialized. We ignore this information for # now _git_submodule_status_re = re.compile( '^(?P[+-U ])(?P[0-9a-f]{40}) ' '(?P\S+)( .*)?$') # The stdout should only contain one line--the status of the # requested submodule m = _git_submodule_status_re.match(stdout) if m: # Yes, the path *is* a git submodule self._update_submodule(m.group('submodule'), m.group('status')) return True else: log.warn( 'Unexpected output from `git submodule status`:\n{0}\n' 'Will attempt import from {1!r} regardless.'.format( stdout, self.path)) return False def _check_submodule_no_git(self): """ Like ``_check_submodule_using_git``, but simply parses the .gitmodules file to determine if the supplied path is a git submodule, and does not exec any subprocesses. This can only determine if a path is a submodule--it does not perform updates, etc. This function may need to be updated if the format of the .gitmodules file is changed between git versions. """ gitmodules_path = os.path.abspath('.gitmodules') if not os.path.isfile(gitmodules_path): return False # This is a minimal reader for gitconfig-style files. It handles a few of # the quirks that make gitconfig files incompatible with ConfigParser-style # files, but does not support the full gitconfig syntax (just enough # needed to read a .gitmodules file). gitmodules_fileobj = io.StringIO() # Must use io.open for cross-Python-compatible behavior wrt unicode with io.open(gitmodules_path) as f: for line in f: # gitconfig files are more flexible with leading whitespace; just # go ahead and remove it line = line.lstrip() # comments can start with either # or ; if line and line[0] in (':', ';'): continue gitmodules_fileobj.write(line) gitmodules_fileobj.seek(0) cfg = RawConfigParser() try: cfg.readfp(gitmodules_fileobj) except Exception as exc: log.warn('Malformatted .gitmodules file: {0}\n' '{1} cannot be assumed to be a git submodule.'.format( exc, self.path)) return False for section in cfg.sections(): if not cfg.has_option(section, 'path'): continue submodule_path = cfg.get(section, 'path').rstrip(os.sep) if submodule_path == self.path.rstrip(os.sep): return True return False def _update_submodule(self, submodule, status): if status == ' ': # The submodule is up to date; no action necessary return elif status == '-': if self.offline: raise _AHBootstrapSystemExit( "Cannot initialize the {0} submodule in --offline mode; " "this requires being able to clone the submodule from an " "online repository.".format(submodule)) cmd = ['update', '--init'] action = 'Initializing' elif status == '+': cmd = ['update'] action = 'Updating' if self.offline: cmd.append('--no-fetch') elif status == 'U': raise _AHBootstrapSystemExit( 'Error: Submodule {0} contains unresolved merge conflicts. ' 'Please complete or abandon any changes in the submodule so that ' 'it is in a usable state, then try again.'.format(submodule)) else: log.warn('Unknown status {0!r} for git submodule {1!r}. Will ' 'attempt to use the submodule as-is, but try to ensure ' 'that the submodule is in a clean state and contains no ' 'conflicts or errors.\n{2}'.format(status, submodule, _err_help_msg)) return err_msg = None cmd = ['git', 'submodule'] + cmd + ['--', submodule] log.warn('{0} {1} submodule with: `{2}`'.format( action, submodule, ' '.join(cmd))) try: log.info('Running `{0}`; use the --no-git option to disable git ' 'commands'.format(' '.join(cmd))) returncode, stdout, stderr = run_cmd(cmd) except OSError as e: err_msg = str(e) else: if returncode != 0: err_msg = stderr if err_msg is not None: log.warn('An unexpected error occurred updating the git submodule ' '{0!r}:\n{1}\n{2}'.format(submodule, err_msg, _err_help_msg)) class _CommandNotFound(OSError): """ An exception raised when a command run with run_cmd is not found on the system. """ def run_cmd(cmd): """ Run a command in a subprocess, given as a list of command-line arguments. Returns a ``(returncode, stdout, stderr)`` tuple. """ try: p = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE) # XXX: May block if either stdout or stderr fill their buffers; # however for the commands this is currently used for that is # unlikely (they should have very brief output) stdout, stderr = p.communicate() except OSError as e: if DEBUG: raise if e.errno == errno.ENOENT: msg = 'Command not found: `{0}`'.format(' '.join(cmd)) raise _CommandNotFound(msg, cmd) else: raise _AHBootstrapSystemExit( 'An unexpected error occurred when running the ' '`{0}` command:\n{1}'.format(' '.join(cmd), str(e))) # Can fail of the default locale is not configured properly. See # https://github.com/astropy/astropy/issues/2749. For the purposes under # consideration 'latin1' is an acceptable fallback. try: stdio_encoding = locale.getdefaultlocale()[1] or 'latin1' except ValueError: # Due to an OSX oddity locale.getdefaultlocale() can also crash # depending on the user's locale/language settings. See: # http://bugs.python.org/issue18378 stdio_encoding = 'latin1' # Unlikely to fail at this point but even then let's be flexible if not isinstance(stdout, _text_type): stdout = stdout.decode(stdio_encoding, 'replace') if not isinstance(stderr, _text_type): stderr = stderr.decode(stdio_encoding, 'replace') return (p.returncode, stdout, stderr) def _next_version(version): """ Given a parsed version from pkg_resources.parse_version, returns a new version string with the next minor version. Examples ======== >>> _next_version(pkg_resources.parse_version('1.2.3')) '1.3.0' """ if hasattr(version, 'base_version'): # New version parsing from setuptools >= 8.0 if version.base_version: parts = version.base_version.split('.') else: parts = [] else: parts = [] for part in version: if part.startswith('*'): break parts.append(part) parts = [int(p) for p in parts] if len(parts) < 3: parts += [0] * (3 - len(parts)) major, minor, micro = parts[:3] return '{0}.{1}.{2}'.format(major, minor + 1, 0) class _DummyFile(object): """A noop writeable object.""" errors = '' # Required for Python 3.x encoding = 'utf-8' def write(self, s): pass def flush(self): pass @contextlib.contextmanager def _silence(): """A context manager that silences sys.stdout and sys.stderr.""" old_stdout = sys.stdout old_stderr = sys.stderr sys.stdout = _DummyFile() sys.stderr = _DummyFile() exception_occurred = False try: yield except: exception_occurred = True # Go ahead and clean up so that exception handling can work normally sys.stdout = old_stdout sys.stderr = old_stderr raise if not exception_occurred: sys.stdout = old_stdout sys.stderr = old_stderr _err_help_msg = """ If the problem persists consider installing astropy_helpers manually using pip (`pip install astropy_helpers`) or by manually downloading the source archive, extracting it, and installing by running `python setup.py install` from the root of the extracted source code. """ class _AHBootstrapSystemExit(SystemExit): def __init__(self, *args): if not args: msg = 'An unknown problem occurred bootstrapping astropy_helpers.' else: msg = args[0] msg += '\n' + _err_help_msg super(_AHBootstrapSystemExit, self).__init__(msg, *args[1:]) BOOTSTRAPPER = _Bootstrapper.main() def use_astropy_helpers(**kwargs): """ Ensure that the `astropy_helpers` module is available and is importable. This supports automatic submodule initialization if astropy_helpers is included in a project as a git submodule, or will download it from PyPI if necessary. Parameters ---------- path : str or None, optional A filesystem path relative to the root of the project's source code that should be added to `sys.path` so that `astropy_helpers` can be imported from that path. If the path is a git submodule it will automatically be initialized and/or updated. The path may also be to a ``.tar.gz`` archive of the astropy_helpers source distribution. In this case the archive is automatically unpacked and made temporarily available on `sys.path` as a ``.egg`` archive. If `None` skip straight to downloading. download_if_needed : bool, optional If the provided filesystem path is not found an attempt will be made to download astropy_helpers from PyPI. It will then be made temporarily available on `sys.path` as a ``.egg`` archive (using the ``setup_requires`` feature of setuptools. If the ``--offline`` option is given at the command line the value of this argument is overridden to `False`. index_url : str, optional If provided, use a different URL for the Python package index than the main PyPI server. use_git : bool, optional If `False` no git commands will be used--this effectively disables support for git submodules. If the ``--no-git`` option is given at the command line the value of this argument is overridden to `False`. auto_upgrade : bool, optional By default, when installing a package from a non-development source distribution ah_boostrap will try to automatically check for patch releases to astropy-helpers on PyPI and use the patched version over any bundled versions. Setting this to `False` will disable that functionality. If the ``--offline`` option is given at the command line the value of this argument is overridden to `False`. offline : bool, optional If `False` disable all actions that require an internet connection, including downloading packages from the package index and fetching updates to any git submodule. Defaults to `True`. """ global BOOTSTRAPPER config = BOOTSTRAPPER.config config.update(**kwargs) # Create a new bootstrapper with the updated configuration and run it BOOTSTRAPPER = _Bootstrapper(**config) BOOTSTRAPPER.run() pydl-0.6.0/astropy_helpers/0000775000111400017500000000000013160273564016517 5ustar weaverweaver00000000000000pydl-0.6.0/astropy_helpers/ez_setup.py0000664000111400017500000003037113154626456020740 0ustar weaverweaver00000000000000#!/usr/bin/env python """ Setuptools bootstrapping installer. Maintained at https://github.com/pypa/setuptools/tree/bootstrap. Run this script to install or upgrade setuptools. This method is DEPRECATED. Check https://github.com/pypa/setuptools/issues/581 for more details. """ import os import shutil import sys import tempfile import zipfile import optparse import subprocess import platform import textwrap import contextlib from distutils import log try: from urllib.request import urlopen except ImportError: from urllib2 import urlopen try: from site import USER_SITE except ImportError: USER_SITE = None # 33.1.1 is the last version that supports setuptools self upgrade/installation. DEFAULT_VERSION = "33.1.1" DEFAULT_URL = "https://pypi.io/packages/source/s/setuptools/" DEFAULT_SAVE_DIR = os.curdir DEFAULT_DEPRECATION_MESSAGE = "ez_setup.py is deprecated and when using it setuptools will be pinned to {0} since it's the last version that supports setuptools self upgrade/installation, check https://github.com/pypa/setuptools/issues/581 for more info; use pip to install setuptools" MEANINGFUL_INVALID_ZIP_ERR_MSG = 'Maybe {0} is corrupted, delete it and try again.' log.warn(DEFAULT_DEPRECATION_MESSAGE.format(DEFAULT_VERSION)) def _python_cmd(*args): """ Execute a command. Return True if the command succeeded. """ args = (sys.executable,) + args return subprocess.call(args) == 0 def _install(archive_filename, install_args=()): """Install Setuptools.""" with archive_context(archive_filename): # installing log.warn('Installing Setuptools') if not _python_cmd('setup.py', 'install', *install_args): log.warn('Something went wrong during the installation.') log.warn('See the error message above.') # exitcode will be 2 return 2 def _build_egg(egg, archive_filename, to_dir): """Build Setuptools egg.""" with archive_context(archive_filename): # building an egg log.warn('Building a Setuptools egg in %s', to_dir) _python_cmd('setup.py', '-q', 'bdist_egg', '--dist-dir', to_dir) # returning the result log.warn(egg) if not os.path.exists(egg): raise IOError('Could not build the egg.') class ContextualZipFile(zipfile.ZipFile): """Supplement ZipFile class to support context manager for Python 2.6.""" def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def __new__(cls, *args, **kwargs): """Construct a ZipFile or ContextualZipFile as appropriate.""" if hasattr(zipfile.ZipFile, '__exit__'): return zipfile.ZipFile(*args, **kwargs) return super(ContextualZipFile, cls).__new__(cls) @contextlib.contextmanager def archive_context(filename): """ Unzip filename to a temporary directory, set to the cwd. The unzipped target is cleaned up after. """ tmpdir = tempfile.mkdtemp() log.warn('Extracting in %s', tmpdir) old_wd = os.getcwd() try: os.chdir(tmpdir) try: with ContextualZipFile(filename) as archive: archive.extractall() except zipfile.BadZipfile as err: if not err.args: err.args = ('', ) err.args = err.args + ( MEANINGFUL_INVALID_ZIP_ERR_MSG.format(filename), ) raise # going in the directory subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0]) os.chdir(subdir) log.warn('Now working in %s', subdir) yield finally: os.chdir(old_wd) shutil.rmtree(tmpdir) def _do_download(version, download_base, to_dir, download_delay): """Download Setuptools.""" py_desig = 'py{sys.version_info[0]}.{sys.version_info[1]}'.format(sys=sys) tp = 'setuptools-{version}-{py_desig}.egg' egg = os.path.join(to_dir, tp.format(**locals())) if not os.path.exists(egg): archive = download_setuptools(version, download_base, to_dir, download_delay) _build_egg(egg, archive, to_dir) sys.path.insert(0, egg) # Remove previously-imported pkg_resources if present (see # https://bitbucket.org/pypa/setuptools/pull-request/7/ for details). if 'pkg_resources' in sys.modules: _unload_pkg_resources() import setuptools setuptools.bootstrap_install_from = egg def use_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, download_delay=15): """ Ensure that a setuptools version is installed. Return None. Raise SystemExit if the requested version or later cannot be installed. """ to_dir = os.path.abspath(to_dir) # prior to importing, capture the module state for # representative modules. rep_modules = 'pkg_resources', 'setuptools' imported = set(sys.modules).intersection(rep_modules) try: import pkg_resources pkg_resources.require("setuptools>=" + version) # a suitable version is already installed return except ImportError: # pkg_resources not available; setuptools is not installed; download pass except pkg_resources.DistributionNotFound: # no version of setuptools was found; allow download pass except pkg_resources.VersionConflict as VC_err: if imported: _conflict_bail(VC_err, version) # otherwise, unload pkg_resources to allow the downloaded version to # take precedence. del pkg_resources _unload_pkg_resources() return _do_download(version, download_base, to_dir, download_delay) def _conflict_bail(VC_err, version): """ Setuptools was imported prior to invocation, so it is unsafe to unload it. Bail out. """ conflict_tmpl = textwrap.dedent(""" The required version of setuptools (>={version}) is not available, and can't be installed while this script is running. Please install a more recent version first, using 'easy_install -U setuptools'. (Currently using {VC_err.args[0]!r}) """) msg = conflict_tmpl.format(**locals()) sys.stderr.write(msg) sys.exit(2) def _unload_pkg_resources(): sys.meta_path = [ importer for importer in sys.meta_path if importer.__class__.__module__ != 'pkg_resources.extern' ] del_modules = [ name for name in sys.modules if name.startswith('pkg_resources') ] for mod_name in del_modules: del sys.modules[mod_name] def _clean_check(cmd, target): """ Run the command to download target. If the command fails, clean up before re-raising the error. """ try: subprocess.check_call(cmd) except subprocess.CalledProcessError: if os.access(target, os.F_OK): os.unlink(target) raise def download_file_powershell(url, target): """ Download the file at url to target using Powershell. Powershell will validate trust. Raise an exception if the command cannot complete. """ target = os.path.abspath(target) ps_cmd = ( "[System.Net.WebRequest]::DefaultWebProxy.Credentials = " "[System.Net.CredentialCache]::DefaultCredentials; " '(new-object System.Net.WebClient).DownloadFile("%(url)s", "%(target)s")' % locals() ) cmd = [ 'powershell', '-Command', ps_cmd, ] _clean_check(cmd, target) def has_powershell(): """Determine if Powershell is available.""" if platform.system() != 'Windows': return False cmd = ['powershell', '-Command', 'echo test'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_powershell.viable = has_powershell def download_file_curl(url, target): cmd = ['curl', url, '--location', '--silent', '--output', target] _clean_check(cmd, target) def has_curl(): cmd = ['curl', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_curl.viable = has_curl def download_file_wget(url, target): cmd = ['wget', url, '--quiet', '--output-document', target] _clean_check(cmd, target) def has_wget(): cmd = ['wget', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_wget.viable = has_wget def download_file_insecure(url, target): """Use Python to download the file, without connection authentication.""" src = urlopen(url) try: # Read all the data in one block. data = src.read() finally: src.close() # Write all the data in one block to avoid creating a partial file. with open(target, "wb") as dst: dst.write(data) download_file_insecure.viable = lambda: True def get_best_downloader(): downloaders = ( download_file_powershell, download_file_curl, download_file_wget, download_file_insecure, ) viable_downloaders = (dl for dl in downloaders if dl.viable()) return next(viable_downloaders, None) def download_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, delay=15, downloader_factory=get_best_downloader): """ Download setuptools from a specified location and return its filename. `version` should be a valid setuptools version number that is available as an sdist for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where the egg will be downloaded. `delay` is the number of seconds to pause before an actual download attempt. ``downloader_factory`` should be a function taking no arguments and returning a function for downloading a URL to a target. """ # making sure we use the absolute path to_dir = os.path.abspath(to_dir) zip_name = "setuptools-%s.zip" % version url = download_base + zip_name saveto = os.path.join(to_dir, zip_name) if not os.path.exists(saveto): # Avoid repeated downloads log.warn("Downloading %s", url) downloader = downloader_factory() downloader(url, saveto) return os.path.realpath(saveto) def _build_install_args(options): """ Build the arguments to 'python setup.py install' on the setuptools package. Returns list of command line arguments. """ return ['--user'] if options.user_install else [] def _parse_args(): """Parse the command line for options.""" parser = optparse.OptionParser() parser.add_option( '--user', dest='user_install', action='store_true', default=False, help='install in user site package') parser.add_option( '--download-base', dest='download_base', metavar="URL", default=DEFAULT_URL, help='alternative URL from where to download the setuptools package') parser.add_option( '--insecure', dest='downloader_factory', action='store_const', const=lambda: download_file_insecure, default=get_best_downloader, help='Use internal, non-validating downloader' ) parser.add_option( '--version', help="Specify which version to download", default=DEFAULT_VERSION, ) parser.add_option( '--to-dir', help="Directory to save (and re-use) package", default=DEFAULT_SAVE_DIR, ) options, args = parser.parse_args() # positional arguments are ignored return options def _download_args(options): """Return args for download_setuptools function from cmdline args.""" return dict( version=options.version, download_base=options.download_base, downloader_factory=options.downloader_factory, to_dir=options.to_dir, ) def main(): """Install or upgrade setuptools and EasyInstall.""" options = _parse_args() archive = download_setuptools(**_download_args(options)) return _install(archive, _build_install_args(options)) if __name__ == '__main__': sys.exit(main()) pydl-0.6.0/astropy_helpers/CHANGES.rst0000664000111400017500000003535113154626533020331 0ustar weaverweaver00000000000000astropy-helpers Changelog ************************* 2.0.1 (2017-07-28) ------------------ - Fix compatibility with Sphinx <1.5. [#326] 2.0 (2017-07-06) ---------------- - Add support for package that lies in a subdirectory. [#249] - Removing ``compat.subprocess``. [#298] - Python 3.3 is no longer supported. [#300] - The 'automodapi' Sphinx extension (and associated dependencies) has now been moved to a standalone package which can be found at https://github.com/astropy/sphinx-automodapi - this is now bundled in astropy-helpers under astropy_helpers.extern.automodapi for convenience. Version shipped with astropy-helpers is v0.6. [#278, #303, #309, #323] - The ``numpydoc`` Sphinx extension has now been moved to ``astropy_helpers.extern``. [#278] - Fix ``build_docs`` error catching, so it doesn't hide Sphinx errors. [#292] - Fix compatibility with Sphinx 1.6. [#318] - Updating ez_setup.py to the last version before it's removal. [#321] 1.3.1 (2017-03-18) ------------------ - Fixed the missing button to hide output in documentation code blocks. [#287] - Fixed bug when ``build_docs`` when running with the clean (-l) option. [#289] - Add alternative location for various intersphinx inventories to fall back to. [#293] 1.3 (2016-12-16) ---------------- - ``build_sphinx`` has been deprecated in favor of the ``build_docs`` command. [#246] - Force the use of Cython's old ``build_ext`` command. A new ``build_ext`` command was added in Cython 0.25, but it does not work with astropy-helpers currently. [#261] 1.2 (2016-06-18) ---------------- - Added sphinx configuration value ``automodsumm_inherited_members``. If ``True`` this will include members that are inherited from a base class in the generated API docs. Defaults to ``False`` which matches the previous behavior. [#215] - Fixed ``build_sphinx`` to recognize builds that succeeded but have output *after* the "build succeeded." statement. This only applies when ``--warnings-returncode`` is given (which is primarily relevant for Travis documentation builds). [#223] - Fixed ``build_sphinx`` the sphinx extensions to not output a spurious warning for sphinx versions > 1.4. [#229] - Add Python version dependent local sphinx inventories that contain otherwise missing references. [#216] - ``astropy_helpers`` now require Sphinx 1.3 or later. [#226] 1.1.2 (2016-03-9) ----------------- - The CSS for the sphinx documentation was altered to prevent some text overflow problems. [#217] 1.1.1 (2015-12-23) ------------------ - Fixed crash in build with ``AttributeError: cython_create_listing`` with older versions of setuptools. [#209, #210] 1.1 (2015-12-10) ---------------- - The original ``AstropyTest`` class in ``astropy_helpers``, which implements the ``setup.py test`` command, is deprecated in favor of moving the implementation of that command closer to the actual Astropy test runner in ``astropy.tests``. Now a dummy ``test`` command is provided solely for informing users that they need ``astropy`` installed to run the tests (however, the previous, now deprecated implementation is still provided and continues to work with older versions of Astropy). See the related issue for more details. [#184] - Added a useful new utility function to ``astropy_helpers.utils`` called ``find_data_files``. This is similar to the ``find_packages`` function in setuptools in that it can be used to search a package for data files (matching a pattern) that can be passed to the ``package_data`` argument for ``setup()``. See the docstring to ``astropy_helpers.utils.find_data_files`` for more details. [#42] - The ``astropy_helpers`` module now sets the global ``_ASTROPY_SETUP_`` flag upon import (from within a ``setup.py``) script, so it's not necessary to have this in the ``setup.py`` script explicitly. If in doubt though, there's no harm in setting it twice. Putting it in ``astropy_helpers`` just ensures that any other imports that occur during build will have this flag set. [#191] - It is now possible to use Cython as a ``setup_requires`` build requirement, and still build Cython extensions even if Cython wasn't available at the beginning of the build processes (that is, is automatically downloaded via setuptools' processing of ``setup_requires``). [#185] - Moves the ``adjust_compiler`` check into the ``build_ext`` command itself, so it's only used when actually building extension modules. This also deprecates the stand-alone ``adjust_compiler`` function. [#76] - When running the ``build_sphinx`` / ``build_docs`` command with the ``-w`` option, the output from Sphinx is streamed as it runs instead of silently buffering until the doc build is complete. [#197] 1.0.7 (unreleased) ------------------ - Fix missing import in ``astropy_helpers/utils.py``. [#196] 1.0.6 (2015-12-04) ------------------ - Fixed bug where running ``./setup.py build_sphinx`` could return successfully even when the build was not successful (and should have returned a non-zero error code). [#199] 1.0.5 (2015-10-02) ------------------ - Fixed a regression in the ``./setup.py test`` command that was introduced in v1.0.4. 1.0.4 (2015-10-02) ------------------ - Fixed issue with the sphinx documentation css where the line numbers for code blocks were not aligned with the code. [#179, #180] - Fixed crash that could occur when trying to build Cython extension modules when Cython isn't installed. Normally this still results in a failed build, but was supposed to provide a useful error message rather than crash outright (this was a regression introduced in v1.0.3). [#181] - Fixed a crash that could occur on Python 3 when a working C compiler isn't found. [#182] - Quieted warnings about deprecated Numpy API in Cython extensions, when building Cython extensions against Numpy >= 1.7. [#183, #186] - Improved support for py.test >= 2.7--running the ``./setup.py test`` command now copies all doc pages into the temporary test directory as well, so that all test files have a "common root directory". [#189, #190] 1.0.3 (2015-07-22) ------------------ - Added workaround for sphinx-doc/sphinx#1843, a but in Sphinx which prevented descriptor classes with a custom metaclass from being documented correctly. [#158] - Added an alias for the ``./setup.py build_sphinx`` command as ``./setup.py build_docs`` which, to a new contributor, should hopefully be less cryptic. [#161] - The fonts in graphviz diagrams now match the font of the HTML content. [#169] - When the documentation is built on readthedocs.org, MathJax will be used for math rendering. When built elsewhere, the "pngmath" extension is still used for math rendering. [#170] - Fix crash when importing astropy_helpers when running with ``python -OO`` [#171] - The ``build`` and ``build_ext`` stages now correctly recognize the presence of C++ files in Cython extensions (previously only vanilla C worked). [#173] 1.0.2 (2015-04-02) ------------------ - Various fixes enabling the astropy-helpers Sphinx build command and Sphinx extensions to work with Sphinx 1.3. [#148] - More improvement to the ability to handle multiple versions of astropy-helpers being imported in the same Python interpreter session in the (somewhat rare) case of nested installs. [#147] - To better support high resolution displays, use SVG for the astropy logo and linkout image, falling back to PNGs for browsers that support it. [#150, #151] - Improve ``setup_helpers.get_compiler_version`` to work with more compilers, and to return more info. This will help fix builds of Astropy on less common compilers, like Sun C. [#153] 1.0.1 (2015-03-04) ------------------ - Released in concert with v0.4.8 to address the same issues. 0.4.8 (2015-03-04) ------------------ - Improved the ``ah_bootstrap`` script's ability to override existing installations of astropy-helpers with new versions in the context of installing multiple packages simultaneously within the same Python interpreter (e.g. when one package has in its ``setup_requires`` another package that uses a different version of astropy-helpers. [#144] - Added a workaround to an issue in matplotlib that can, in rare cases, lead to a crash when installing packages that import matplotlib at build time. [#144] 1.0 (2015-02-17) ---------------- - Added new pre-/post-command hook points for ``setup.py`` commands. Now any package can define code to run before and/or after any ``setup.py`` command without having to manually subclass that command by adding ``pre__hook`` and ``post__hook`` callables to the package's ``setup_package.py`` module. See the PR for more details. [#112] - The following objects in the ``astropy_helpers.setup_helpers`` module have been relocated: - ``get_dummy_distribution``, ``get_distutils_*``, ``get_compiler_option``, ``add_command_option``, ``is_distutils_display_option`` -> ``astropy_helpers.distutils_helpers`` - ``should_build_with_cython``, ``generate_build_ext_command`` -> ``astropy_helpers.commands.build_ext`` - ``AstropyBuildPy`` -> ``astropy_helpers.commands.build_py`` - ``AstropyBuildSphinx`` -> ``astropy_helpers.commands.build_sphinx`` - ``AstropyInstall`` -> ``astropy_helpers.commands.install`` - ``AstropyInstallLib`` -> ``astropy_helpers.commands.install_lib`` - ``AstropyRegister`` -> ``astropy_helpers.commands.register`` - ``get_pkg_version_module`` -> ``astropy_helpers.version_helpers`` - ``write_if_different``, ``import_file``, ``get_numpy_include_path`` -> ``astropy_helpers.utils`` All of these are "soft" deprecations in the sense that they are still importable from ``astropy_helpers.setup_helpers`` for now, and there is no (easy) way to produce deprecation warnings when importing these objects from ``setup_helpers`` rather than directly from the modules they are defined in. But please consider updating any imports to these objects. [#110] - Use of the ``astropy.sphinx.ext.astropyautosummary`` extension is deprecated for use with Sphinx < 1.2. Instead it should suffice to remove this extension for the ``extensions`` list in your ``conf.py`` and add the stock ``sphinx.ext.autosummary`` instead. [#131] 0.4.7 (2015-02-17) ------------------ - Fixed incorrect/missing git hash being added to the generated ``version.py`` when creating a release. [#141] 0.4.6 (2015-02-16) ------------------ - Fixed problems related to the automatically generated _compiler module not being created properly. [#139] 0.4.5 (2015-02-11) ------------------ - Fixed an issue where ah_bootstrap.py could blow up when astropy_helper's version number is 1.0. - Added a workaround for documentation of properties in the rare case where the class's metaclass has a property of the same name. [#130] - Fixed an issue on Python 3 where importing a package using astropy-helper's generated version.py module would crash when the current working directory is an empty git repository. [#114, #137] - Fixed an issue where the "revision count" appended to .dev versions by the generated version.py did not accurately reflect the revision count for the package it belongs to, and could be invalid if the current working directory is an unrelated git repository. [#107, #137] - Likewise, fixed a confusing warning message that could occur in the same circumstances as the above issue. [#121, #137] 0.4.4 (2014-12-31) ------------------ - More improvements for building the documentation using Python 3.x. [#100] - Additional minor fixes to Python 3 support. [#115] - Updates to support new test features in Astropy [#92, #106] 0.4.3 (2014-10-22) ------------------ - The generated ``version.py`` file now preserves the git hash of installed copies of the package as well as when building a source distribution. That is, the git hash of the changeset that was installed/released is preserved. [#87] - In smart resolver add resolution for class links when they exist in the intersphinx inventory, but not the mapping of the current package (e.g. when an affiliated package uses an astropy core class of which "actual" and "documented" location differs) [#88] - Fixed a bug that could occur when running ``setup.py`` for the first time in a repository that uses astropy-helpers as a submodule: ``AttributeError: 'NoneType' object has no attribute 'mkdtemp'`` [#89] - Fixed a bug where optional arguments to the ``doctest-skip`` Sphinx directive were sometimes being left in the generated documentation output. [#90] - Improved support for building the documentation using Python 3.x. [#96] - Avoid error message if .git directory is not present. [#91] 0.4.2 (2014-08-09) ------------------ - Fixed some CSS issues in generated API docs. [#69] - Fixed the warning message that could be displayed when generating a version number with some older versions of git. [#77] - Fixed automodsumm to work with new versions of Sphinx (>= 1.2.2). [#80] 0.4.1 (2014-08-08) ------------------ - Fixed git revision count on systems with git versions older than v1.7.2. [#70] - Fixed display of warning text when running a git command fails (previously the output of stderr was not being decoded properly). [#70] - The ``--offline`` flag to ``setup.py`` understood by ``ah_bootstrap.py`` now also prevents git from going online to fetch submodule updates. [#67] - The Sphinx extension for converting issue numbers to links in the changelog now supports working on arbitrary pages via a new ``conf.py`` setting: ``changelog_links_docpattern``. By default it affects the ``changelog`` and ``whatsnew`` pages in one's Sphinx docs. [#61] - Fixed crash that could result from users with missing/misconfigured locale settings. [#58] - The font used for code examples in the docs is now the system-defined ``monospace`` font, rather than ``Minaco``, which is not available on all platforms. [#50] 0.4 (2014-07-15) ---------------- - Initial release of astropy-helpers. See `APE4 `_ for details of the motivation and design of this package. - The ``astropy_helpers`` package replaces the following modules in the ``astropy`` package: - ``astropy.setup_helpers`` -> ``astropy_helpers.setup_helpers`` - ``astropy.version_helpers`` -> ``astropy_helpers.version_helpers`` - ``astropy.sphinx`` - > ``astropy_helpers.sphinx`` These modules should be considered deprecated in ``astropy``, and any new, non-critical changes to those modules will be made in ``astropy_helpers`` instead. Affiliated packages wishing to make use those modules (as in the Astropy package-template) should use the versions from ``astropy_helpers`` instead, and include the ``ah_bootstrap.py`` script in their project, for bootstrapping the ``astropy_helpers`` package in their setup.py script. pydl-0.6.0/astropy_helpers/ah_bootstrap.py0000664000111400017500000010434413154626456021571 0ustar weaverweaver00000000000000""" This bootstrap module contains code for ensuring that the astropy_helpers package will be importable by the time the setup.py script runs. It also includes some workarounds to ensure that a recent-enough version of setuptools is being used for the installation. This module should be the first thing imported in the setup.py of distributions that make use of the utilities in astropy_helpers. If the distribution ships with its own copy of astropy_helpers, this module will first attempt to import from the shipped copy. However, it will also check PyPI to see if there are any bug-fix releases on top of the current version that may be useful to get past platform-specific bugs that have been fixed. When running setup.py, use the ``--offline`` command-line option to disable the auto-upgrade checks. When this module is imported or otherwise executed it automatically calls a main function that attempts to read the project's setup.cfg file, which it checks for a configuration section called ``[ah_bootstrap]`` the presences of that section, and options therein, determine the next step taken: If it contains an option called ``auto_use`` with a value of ``True``, it will automatically call the main function of this module called `use_astropy_helpers` (see that function's docstring for full details). Otherwise no further action is taken (however, ``ah_bootstrap.use_astropy_helpers`` may be called manually from within the setup.py script). Additional options in the ``[ah_boostrap]`` section of setup.cfg have the same names as the arguments to `use_astropy_helpers`, and can be used to configure the bootstrap script when ``auto_use = True``. See https://github.com/astropy/astropy-helpers for more details, and for the latest version of this module. """ import contextlib import errno import imp import io import locale import os import re import subprocess as sp import sys try: from ConfigParser import ConfigParser, RawConfigParser except ImportError: from configparser import ConfigParser, RawConfigParser if sys.version_info[0] < 3: _str_types = (str, unicode) _text_type = unicode PY3 = False else: _str_types = (str, bytes) _text_type = str PY3 = True # What follows are several import statements meant to deal with install-time # issues with either missing or misbehaving pacakges (including making sure # setuptools itself is installed): # Some pre-setuptools checks to ensure that either distribute or setuptools >= # 0.7 is used (over pre-distribute setuptools) if it is available on the path; # otherwise the latest setuptools will be downloaded and bootstrapped with # ``ez_setup.py``. This used to be included in a separate file called # setuptools_bootstrap.py; but it was combined into ah_bootstrap.py try: import pkg_resources _setuptools_req = pkg_resources.Requirement.parse('setuptools>=0.7') # This may raise a DistributionNotFound in which case no version of # setuptools or distribute is properly installed _setuptools = pkg_resources.get_distribution('setuptools') if _setuptools not in _setuptools_req: # Older version of setuptools; check if we have distribute; again if # this results in DistributionNotFound we want to give up _distribute = pkg_resources.get_distribution('distribute') if _setuptools != _distribute: # It's possible on some pathological systems to have an old version # of setuptools and distribute on sys.path simultaneously; make # sure distribute is the one that's used sys.path.insert(1, _distribute.location) _distribute.activate() imp.reload(pkg_resources) except: # There are several types of exceptions that can occur here; if all else # fails bootstrap and use the bootstrapped version from ez_setup import use_setuptools use_setuptools() # typing as a dependency for 1.6.1+ Sphinx causes issues when imported after # initializing submodule with ah_boostrap.py # See discussion and references in # https://github.com/astropy/astropy-helpers/issues/302 try: import typing # noqa except ImportError: pass # Note: The following import is required as a workaround to # https://github.com/astropy/astropy-helpers/issues/89; if we don't import this # module now, it will get cleaned up after `run_setup` is called, but that will # later cause the TemporaryDirectory class defined in it to stop working when # used later on by setuptools try: import setuptools.py31compat # noqa except ImportError: pass # matplotlib can cause problems if it is imported from within a call of # run_setup(), because in some circumstances it will try to write to the user's # home directory, resulting in a SandboxViolation. See # https://github.com/matplotlib/matplotlib/pull/4165 # Making sure matplotlib, if it is available, is imported early in the setup # process can mitigate this (note importing matplotlib.pyplot has the same # issue) try: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot except: # Ignore if this fails for *any* reason* pass # End compatibility imports... # In case it didn't successfully import before the ez_setup checks import pkg_resources from setuptools import Distribution from setuptools.package_index import PackageIndex from setuptools.sandbox import run_setup from distutils import log from distutils.debug import DEBUG # TODO: Maybe enable checking for a specific version of astropy_helpers? DIST_NAME = 'astropy-helpers' PACKAGE_NAME = 'astropy_helpers' # Defaults for other options DOWNLOAD_IF_NEEDED = True INDEX_URL = 'https://pypi.python.org/simple' USE_GIT = True OFFLINE = False AUTO_UPGRADE = True # A list of all the configuration options and their required types CFG_OPTIONS = [ ('auto_use', bool), ('path', str), ('download_if_needed', bool), ('index_url', str), ('use_git', bool), ('offline', bool), ('auto_upgrade', bool) ] class _Bootstrapper(object): """ Bootstrapper implementation. See ``use_astropy_helpers`` for parameter documentation. """ def __init__(self, path=None, index_url=None, use_git=None, offline=None, download_if_needed=None, auto_upgrade=None): if path is None: path = PACKAGE_NAME if not (isinstance(path, _str_types) or path is False): raise TypeError('path must be a string or False') if PY3 and not isinstance(path, _text_type): fs_encoding = sys.getfilesystemencoding() path = path.decode(fs_encoding) # path to unicode self.path = path # Set other option attributes, using defaults where necessary self.index_url = index_url if index_url is not None else INDEX_URL self.offline = offline if offline is not None else OFFLINE # If offline=True, override download and auto-upgrade if self.offline: download_if_needed = False auto_upgrade = False self.download = (download_if_needed if download_if_needed is not None else DOWNLOAD_IF_NEEDED) self.auto_upgrade = (auto_upgrade if auto_upgrade is not None else AUTO_UPGRADE) # If this is a release then the .git directory will not exist so we # should not use git. git_dir_exists = os.path.exists(os.path.join(os.path.dirname(__file__), '.git')) if use_git is None and not git_dir_exists: use_git = False self.use_git = use_git if use_git is not None else USE_GIT # Declared as False by default--later we check if astropy-helpers can be # upgraded from PyPI, but only if not using a source distribution (as in # the case of import from a git submodule) self.is_submodule = False @classmethod def main(cls, argv=None): if argv is None: argv = sys.argv config = cls.parse_config() config.update(cls.parse_command_line(argv)) auto_use = config.pop('auto_use', False) bootstrapper = cls(**config) if auto_use: # Run the bootstrapper, otherwise the setup.py is using the old # use_astropy_helpers() interface, in which case it will run the # bootstrapper manually after reconfiguring it. bootstrapper.run() return bootstrapper @classmethod def parse_config(cls): if not os.path.exists('setup.cfg'): return {} cfg = ConfigParser() try: cfg.read('setup.cfg') except Exception as e: if DEBUG: raise log.error( "Error reading setup.cfg: {0!r}\n{1} will not be " "automatically bootstrapped and package installation may fail." "\n{2}".format(e, PACKAGE_NAME, _err_help_msg)) return {} if not cfg.has_section('ah_bootstrap'): return {} config = {} for option, type_ in CFG_OPTIONS: if not cfg.has_option('ah_bootstrap', option): continue if type_ is bool: value = cfg.getboolean('ah_bootstrap', option) else: value = cfg.get('ah_bootstrap', option) config[option] = value return config @classmethod def parse_command_line(cls, argv=None): if argv is None: argv = sys.argv config = {} # For now we just pop recognized ah_bootstrap options out of the # arg list. This is imperfect; in the unlikely case that a setup.py # custom command or even custom Distribution class defines an argument # of the same name then we will break that. However there's a catch22 # here that we can't just do full argument parsing right here, because # we don't yet know *how* to parse all possible command-line arguments. if '--no-git' in argv: config['use_git'] = False argv.remove('--no-git') if '--offline' in argv: config['offline'] = True argv.remove('--offline') return config def run(self): strategies = ['local_directory', 'local_file', 'index'] dist = None # First, remove any previously imported versions of astropy_helpers; # this is necessary for nested installs where one package's installer # is installing another package via setuptools.sandbox.run_setup, as in # the case of setup_requires for key in list(sys.modules): try: if key == PACKAGE_NAME or key.startswith(PACKAGE_NAME + '.'): del sys.modules[key] except AttributeError: # Sometimes mysterious non-string things can turn up in # sys.modules continue # Check to see if the path is a submodule self.is_submodule = self._check_submodule() for strategy in strategies: method = getattr(self, 'get_{0}_dist'.format(strategy)) dist = method() if dist is not None: break else: raise _AHBootstrapSystemExit( "No source found for the {0!r} package; {0} must be " "available and importable as a prerequisite to building " "or installing this package.".format(PACKAGE_NAME)) # This is a bit hacky, but if astropy_helpers was loaded from a # directory/submodule its Distribution object gets a "precedence" of # "DEVELOP_DIST". However, in other cases it gets a precedence of # "EGG_DIST". However, when activing the distribution it will only be # placed early on sys.path if it is treated as an EGG_DIST, so always # do that dist = dist.clone(precedence=pkg_resources.EGG_DIST) # Otherwise we found a version of astropy-helpers, so we're done # Just active the found distribution on sys.path--if we did a # download this usually happens automatically but it doesn't hurt to # do it again # Note: Adding the dist to the global working set also activates it # (makes it importable on sys.path) by default. try: pkg_resources.working_set.add(dist, replace=True) except TypeError: # Some (much) older versions of setuptools do not have the # replace=True option here. These versions are old enough that all # bets may be off anyways, but it's easy enough to work around just # in case... if dist.key in pkg_resources.working_set.by_key: del pkg_resources.working_set.by_key[dist.key] pkg_resources.working_set.add(dist) @property def config(self): """ A `dict` containing the options this `_Bootstrapper` was configured with. """ return dict((optname, getattr(self, optname)) for optname, _ in CFG_OPTIONS if hasattr(self, optname)) def get_local_directory_dist(self): """ Handle importing a vendored package from a subdirectory of the source distribution. """ if not os.path.isdir(self.path): return log.info('Attempting to import astropy_helpers from {0} {1!r}'.format( 'submodule' if self.is_submodule else 'directory', self.path)) dist = self._directory_import() if dist is None: log.warn( 'The requested path {0!r} for importing {1} does not ' 'exist, or does not contain a copy of the {1} ' 'package.'.format(self.path, PACKAGE_NAME)) elif self.auto_upgrade and not self.is_submodule: # A version of astropy-helpers was found on the available path, but # check to see if a bugfix release is available on PyPI upgrade = self._do_upgrade(dist) if upgrade is not None: dist = upgrade return dist def get_local_file_dist(self): """ Handle importing from a source archive; this also uses setup_requires but points easy_install directly to the source archive. """ if not os.path.isfile(self.path): return log.info('Attempting to unpack and import astropy_helpers from ' '{0!r}'.format(self.path)) try: dist = self._do_download(find_links=[self.path]) except Exception as e: if DEBUG: raise log.warn( 'Failed to import {0} from the specified archive {1!r}: ' '{2}'.format(PACKAGE_NAME, self.path, str(e))) dist = None if dist is not None and self.auto_upgrade: # A version of astropy-helpers was found on the available path, but # check to see if a bugfix release is available on PyPI upgrade = self._do_upgrade(dist) if upgrade is not None: dist = upgrade return dist def get_index_dist(self): if not self.download: log.warn('Downloading {0!r} disabled.'.format(DIST_NAME)) return None log.warn( "Downloading {0!r}; run setup.py with the --offline option to " "force offline installation.".format(DIST_NAME)) try: dist = self._do_download() except Exception as e: if DEBUG: raise log.warn( 'Failed to download and/or install {0!r} from {1!r}:\n' '{2}'.format(DIST_NAME, self.index_url, str(e))) dist = None # No need to run auto-upgrade here since we've already presumably # gotten the most up-to-date version from the package index return dist def _directory_import(self): """ Import astropy_helpers from the given path, which will be added to sys.path. Must return True if the import succeeded, and False otherwise. """ # Return True on success, False on failure but download is allowed, and # otherwise raise SystemExit path = os.path.abspath(self.path) # Use an empty WorkingSet rather than the man # pkg_resources.working_set, since on older versions of setuptools this # will invoke a VersionConflict when trying to install an upgrade ws = pkg_resources.WorkingSet([]) ws.add_entry(path) dist = ws.by_key.get(DIST_NAME) if dist is None: # We didn't find an egg-info/dist-info in the given path, but if a # setup.py exists we can generate it setup_py = os.path.join(path, 'setup.py') if os.path.isfile(setup_py): with _silence(): run_setup(os.path.join(path, 'setup.py'), ['egg_info']) for dist in pkg_resources.find_distributions(path, True): # There should be only one... return dist return dist def _do_download(self, version='', find_links=None): if find_links: allow_hosts = '' index_url = None else: allow_hosts = None index_url = self.index_url # Annoyingly, setuptools will not handle other arguments to # Distribution (such as options) before handling setup_requires, so it # is not straightforward to programmatically augment the arguments which # are passed to easy_install class _Distribution(Distribution): def get_option_dict(self, command_name): opts = Distribution.get_option_dict(self, command_name) if command_name == 'easy_install': if find_links is not None: opts['find_links'] = ('setup script', find_links) if index_url is not None: opts['index_url'] = ('setup script', index_url) if allow_hosts is not None: opts['allow_hosts'] = ('setup script', allow_hosts) return opts if version: req = '{0}=={1}'.format(DIST_NAME, version) else: req = DIST_NAME attrs = {'setup_requires': [req]} try: if DEBUG: _Distribution(attrs=attrs) else: with _silence(): _Distribution(attrs=attrs) # If the setup_requires succeeded it will have added the new dist to # the main working_set return pkg_resources.working_set.by_key.get(DIST_NAME) except Exception as e: if DEBUG: raise msg = 'Error retrieving {0} from {1}:\n{2}' if find_links: source = find_links[0] elif index_url != INDEX_URL: source = index_url else: source = 'PyPI' raise Exception(msg.format(DIST_NAME, source, repr(e))) def _do_upgrade(self, dist): # Build up a requirement for a higher bugfix release but a lower minor # release (so API compatibility is guaranteed) next_version = _next_version(dist.parsed_version) req = pkg_resources.Requirement.parse( '{0}>{1},<{2}'.format(DIST_NAME, dist.version, next_version)) package_index = PackageIndex(index_url=self.index_url) upgrade = package_index.obtain(req) if upgrade is not None: return self._do_download(version=upgrade.version) def _check_submodule(self): """ Check if the given path is a git submodule. See the docstrings for ``_check_submodule_using_git`` and ``_check_submodule_no_git`` for further details. """ if (self.path is None or (os.path.exists(self.path) and not os.path.isdir(self.path))): return False if self.use_git: return self._check_submodule_using_git() else: return self._check_submodule_no_git() def _check_submodule_using_git(self): """ Check if the given path is a git submodule. If so, attempt to initialize and/or update the submodule if needed. This function makes calls to the ``git`` command in subprocesses. The ``_check_submodule_no_git`` option uses pure Python to check if the given path looks like a git submodule, but it cannot perform updates. """ cmd = ['git', 'submodule', 'status', '--', self.path] try: log.info('Running `{0}`; use the --no-git option to disable git ' 'commands'.format(' '.join(cmd))) returncode, stdout, stderr = run_cmd(cmd) except _CommandNotFound: # The git command simply wasn't found; this is most likely the # case on user systems that don't have git and are simply # trying to install the package from PyPI or a source # distribution. Silently ignore this case and simply don't try # to use submodules return False stderr = stderr.strip() if returncode != 0 and stderr: # Unfortunately the return code alone cannot be relied on, as # earlier versions of git returned 0 even if the requested submodule # does not exist # This is a warning that occurs in perl (from running git submodule) # which only occurs with a malformatted locale setting which can # happen sometimes on OSX. See again # https://github.com/astropy/astropy/issues/2749 perl_warning = ('perl: warning: Falling back to the standard locale ' '("C").') if not stderr.strip().endswith(perl_warning): # Some other unknown error condition occurred log.warn('git submodule command failed ' 'unexpectedly:\n{0}'.format(stderr)) return False # Output of `git submodule status` is as follows: # # 1: Status indicator: '-' for submodule is uninitialized, '+' if # submodule is initialized but is not at the commit currently indicated # in .gitmodules (and thus needs to be updated), or 'U' if the # submodule is in an unstable state (i.e. has merge conflicts) # # 2. SHA-1 hash of the current commit of the submodule (we don't really # need this information but it's useful for checking that the output is # correct) # # 3. The output of `git describe` for the submodule's current commit # hash (this includes for example what branches the commit is on) but # only if the submodule is initialized. We ignore this information for # now _git_submodule_status_re = re.compile( '^(?P[+-U ])(?P[0-9a-f]{40}) ' '(?P\S+)( .*)?$') # The stdout should only contain one line--the status of the # requested submodule m = _git_submodule_status_re.match(stdout) if m: # Yes, the path *is* a git submodule self._update_submodule(m.group('submodule'), m.group('status')) return True else: log.warn( 'Unexpected output from `git submodule status`:\n{0}\n' 'Will attempt import from {1!r} regardless.'.format( stdout, self.path)) return False def _check_submodule_no_git(self): """ Like ``_check_submodule_using_git``, but simply parses the .gitmodules file to determine if the supplied path is a git submodule, and does not exec any subprocesses. This can only determine if a path is a submodule--it does not perform updates, etc. This function may need to be updated if the format of the .gitmodules file is changed between git versions. """ gitmodules_path = os.path.abspath('.gitmodules') if not os.path.isfile(gitmodules_path): return False # This is a minimal reader for gitconfig-style files. It handles a few of # the quirks that make gitconfig files incompatible with ConfigParser-style # files, but does not support the full gitconfig syntax (just enough # needed to read a .gitmodules file). gitmodules_fileobj = io.StringIO() # Must use io.open for cross-Python-compatible behavior wrt unicode with io.open(gitmodules_path) as f: for line in f: # gitconfig files are more flexible with leading whitespace; just # go ahead and remove it line = line.lstrip() # comments can start with either # or ; if line and line[0] in (':', ';'): continue gitmodules_fileobj.write(line) gitmodules_fileobj.seek(0) cfg = RawConfigParser() try: cfg.readfp(gitmodules_fileobj) except Exception as exc: log.warn('Malformatted .gitmodules file: {0}\n' '{1} cannot be assumed to be a git submodule.'.format( exc, self.path)) return False for section in cfg.sections(): if not cfg.has_option(section, 'path'): continue submodule_path = cfg.get(section, 'path').rstrip(os.sep) if submodule_path == self.path.rstrip(os.sep): return True return False def _update_submodule(self, submodule, status): if status == ' ': # The submodule is up to date; no action necessary return elif status == '-': if self.offline: raise _AHBootstrapSystemExit( "Cannot initialize the {0} submodule in --offline mode; " "this requires being able to clone the submodule from an " "online repository.".format(submodule)) cmd = ['update', '--init'] action = 'Initializing' elif status == '+': cmd = ['update'] action = 'Updating' if self.offline: cmd.append('--no-fetch') elif status == 'U': raise _AHBootstrapSystemExit( 'Error: Submodule {0} contains unresolved merge conflicts. ' 'Please complete or abandon any changes in the submodule so that ' 'it is in a usable state, then try again.'.format(submodule)) else: log.warn('Unknown status {0!r} for git submodule {1!r}. Will ' 'attempt to use the submodule as-is, but try to ensure ' 'that the submodule is in a clean state and contains no ' 'conflicts or errors.\n{2}'.format(status, submodule, _err_help_msg)) return err_msg = None cmd = ['git', 'submodule'] + cmd + ['--', submodule] log.warn('{0} {1} submodule with: `{2}`'.format( action, submodule, ' '.join(cmd))) try: log.info('Running `{0}`; use the --no-git option to disable git ' 'commands'.format(' '.join(cmd))) returncode, stdout, stderr = run_cmd(cmd) except OSError as e: err_msg = str(e) else: if returncode != 0: err_msg = stderr if err_msg is not None: log.warn('An unexpected error occurred updating the git submodule ' '{0!r}:\n{1}\n{2}'.format(submodule, err_msg, _err_help_msg)) class _CommandNotFound(OSError): """ An exception raised when a command run with run_cmd is not found on the system. """ def run_cmd(cmd): """ Run a command in a subprocess, given as a list of command-line arguments. Returns a ``(returncode, stdout, stderr)`` tuple. """ try: p = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE) # XXX: May block if either stdout or stderr fill their buffers; # however for the commands this is currently used for that is # unlikely (they should have very brief output) stdout, stderr = p.communicate() except OSError as e: if DEBUG: raise if e.errno == errno.ENOENT: msg = 'Command not found: `{0}`'.format(' '.join(cmd)) raise _CommandNotFound(msg, cmd) else: raise _AHBootstrapSystemExit( 'An unexpected error occurred when running the ' '`{0}` command:\n{1}'.format(' '.join(cmd), str(e))) # Can fail of the default locale is not configured properly. See # https://github.com/astropy/astropy/issues/2749. For the purposes under # consideration 'latin1' is an acceptable fallback. try: stdio_encoding = locale.getdefaultlocale()[1] or 'latin1' except ValueError: # Due to an OSX oddity locale.getdefaultlocale() can also crash # depending on the user's locale/language settings. See: # http://bugs.python.org/issue18378 stdio_encoding = 'latin1' # Unlikely to fail at this point but even then let's be flexible if not isinstance(stdout, _text_type): stdout = stdout.decode(stdio_encoding, 'replace') if not isinstance(stderr, _text_type): stderr = stderr.decode(stdio_encoding, 'replace') return (p.returncode, stdout, stderr) def _next_version(version): """ Given a parsed version from pkg_resources.parse_version, returns a new version string with the next minor version. Examples ======== >>> _next_version(pkg_resources.parse_version('1.2.3')) '1.3.0' """ if hasattr(version, 'base_version'): # New version parsing from setuptools >= 8.0 if version.base_version: parts = version.base_version.split('.') else: parts = [] else: parts = [] for part in version: if part.startswith('*'): break parts.append(part) parts = [int(p) for p in parts] if len(parts) < 3: parts += [0] * (3 - len(parts)) major, minor, micro = parts[:3] return '{0}.{1}.{2}'.format(major, minor + 1, 0) class _DummyFile(object): """A noop writeable object.""" errors = '' # Required for Python 3.x encoding = 'utf-8' def write(self, s): pass def flush(self): pass @contextlib.contextmanager def _silence(): """A context manager that silences sys.stdout and sys.stderr.""" old_stdout = sys.stdout old_stderr = sys.stderr sys.stdout = _DummyFile() sys.stderr = _DummyFile() exception_occurred = False try: yield except: exception_occurred = True # Go ahead and clean up so that exception handling can work normally sys.stdout = old_stdout sys.stderr = old_stderr raise if not exception_occurred: sys.stdout = old_stdout sys.stderr = old_stderr _err_help_msg = """ If the problem persists consider installing astropy_helpers manually using pip (`pip install astropy_helpers`) or by manually downloading the source archive, extracting it, and installing by running `python setup.py install` from the root of the extracted source code. """ class _AHBootstrapSystemExit(SystemExit): def __init__(self, *args): if not args: msg = 'An unknown problem occurred bootstrapping astropy_helpers.' else: msg = args[0] msg += '\n' + _err_help_msg super(_AHBootstrapSystemExit, self).__init__(msg, *args[1:]) BOOTSTRAPPER = _Bootstrapper.main() def use_astropy_helpers(**kwargs): """ Ensure that the `astropy_helpers` module is available and is importable. This supports automatic submodule initialization if astropy_helpers is included in a project as a git submodule, or will download it from PyPI if necessary. Parameters ---------- path : str or None, optional A filesystem path relative to the root of the project's source code that should be added to `sys.path` so that `astropy_helpers` can be imported from that path. If the path is a git submodule it will automatically be initialized and/or updated. The path may also be to a ``.tar.gz`` archive of the astropy_helpers source distribution. In this case the archive is automatically unpacked and made temporarily available on `sys.path` as a ``.egg`` archive. If `None` skip straight to downloading. download_if_needed : bool, optional If the provided filesystem path is not found an attempt will be made to download astropy_helpers from PyPI. It will then be made temporarily available on `sys.path` as a ``.egg`` archive (using the ``setup_requires`` feature of setuptools. If the ``--offline`` option is given at the command line the value of this argument is overridden to `False`. index_url : str, optional If provided, use a different URL for the Python package index than the main PyPI server. use_git : bool, optional If `False` no git commands will be used--this effectively disables support for git submodules. If the ``--no-git`` option is given at the command line the value of this argument is overridden to `False`. auto_upgrade : bool, optional By default, when installing a package from a non-development source distribution ah_boostrap will try to automatically check for patch releases to astropy-helpers on PyPI and use the patched version over any bundled versions. Setting this to `False` will disable that functionality. If the ``--offline`` option is given at the command line the value of this argument is overridden to `False`. offline : bool, optional If `False` disable all actions that require an internet connection, including downloading packages from the package index and fetching updates to any git submodule. Defaults to `True`. """ global BOOTSTRAPPER config = BOOTSTRAPPER.config config.update(**kwargs) # Create a new bootstrapper with the updated configuration and run it BOOTSTRAPPER = _Bootstrapper(**config) BOOTSTRAPPER.run() pydl-0.6.0/astropy_helpers/astropy_helpers/0000775000111400017500000000000013160273564021742 5ustar weaverweaver00000000000000pydl-0.6.0/astropy_helpers/astropy_helpers/compat/0000775000111400017500000000000013160273564023225 5ustar weaverweaver00000000000000pydl-0.6.0/astropy_helpers/astropy_helpers/compat/__init__.py0000664000111400017500000000056013006206216025324 0ustar weaverweaver00000000000000def _fix_user_options(options): """ This is for Python 2.x and 3.x compatibility. distutils expects Command options to all be byte strings on Python 2 and Unicode strings on Python 3. """ def to_str_or_none(x): if x is None: return None return str(x) return [tuple(to_str_or_none(x) for x in y) for y in options] pydl-0.6.0/astropy_helpers/astropy_helpers/sphinx/0000775000111400017500000000000013160273564023253 5ustar weaverweaver00000000000000pydl-0.6.0/astropy_helpers/astropy_helpers/sphinx/conf.py0000664000111400017500000002770413154626456024571 0ustar weaverweaver00000000000000# -*- coding: utf-8 -*- # Licensed under a 3-clause BSD style license - see LICENSE.rst # # Astropy shared Sphinx settings. These settings are shared between # astropy itself and affiliated packages. # # Note that not all possible configuration values are present in this file. # # All configuration values have a default; values that are commented out # serve to show the default. import os import sys import warnings from os import path import sphinx from distutils.version import LooseVersion # -- General configuration ---------------------------------------------------- # The version check in Sphinx itself can only compare the major and # minor parts of the version number, not the micro. To do a more # specific version check, call check_sphinx_version("x.y.z.") from # your project's conf.py needs_sphinx = '1.3' on_rtd = os.environ.get('READTHEDOCS', None) == 'True' def check_sphinx_version(expected_version): sphinx_version = LooseVersion(sphinx.__version__) expected_version = LooseVersion(expected_version) if sphinx_version < expected_version: raise RuntimeError( "At least Sphinx version {0} is required to build this " "documentation. Found {1}.".format( expected_version, sphinx_version)) # Configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { 'python': ('https://docs.python.org/3/', (None, 'http://data.astropy.org/intersphinx/python3.inv')), 'pythonloc': ('http://docs.python.org/', path.abspath(path.join(path.dirname(__file__), 'local/python3_local_links.inv'))), 'numpy': ('https://docs.scipy.org/doc/numpy/', (None, 'http://data.astropy.org/intersphinx/numpy.inv')), 'scipy': ('https://docs.scipy.org/doc/scipy/reference/', (None, 'http://data.astropy.org/intersphinx/scipy.inv')), 'matplotlib': ('http://matplotlib.org/', (None, 'http://data.astropy.org/intersphinx/matplotlib.inv')), 'astropy': ('http://docs.astropy.org/en/stable/', None), 'h5py': ('http://docs.h5py.org/en/latest/', None)} if sys.version_info[0] == 2: intersphinx_mapping['python'] = ( 'https://docs.python.org/2/', (None, 'http://data.astropy.org/intersphinx/python2.inv')) intersphinx_mapping['pythonloc'] = ( 'http://docs.python.org/', path.abspath(path.join(path.dirname(__file__), 'local/python2_local_links.inv'))) # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # Add any paths that contain templates here, relative to this directory. # templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # The reST default role (used for this markup: `text`) to use for all # documents. Set to the "smart" one. default_role = 'obj' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # This is added to the end of RST files - a good place to put substitutions to # be used globally. rst_epilog = """ .. _Astropy: http://astropy.org """ # A list of warning types to suppress arbitrary warning messages. We mean to # override directives in astropy_helpers.sphinx.ext.autodoc_enhancements, # thus need to ignore those warning. This can be removed once the patch gets # released in upstream Sphinx (https://github.com/sphinx-doc/sphinx/pull/1843). # Suppress the warnings requires Sphinx v1.4.2 suppress_warnings = ['app.add_directive', ] # -- Project information ------------------------------------------------------ # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. #pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Settings for extensions and extension options ---------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.inheritance_diagram', 'sphinx.ext.viewcode', 'astropy_helpers.extern.numpydoc', 'astropy_helpers.extern.automodapi.automodapi', 'astropy_helpers.extern.automodapi.smart_resolver', 'astropy_helpers.sphinx.ext.tocdepthfix', 'astropy_helpers.sphinx.ext.doctest', 'astropy_helpers.sphinx.ext.changelog_links'] if on_rtd: extensions.append('sphinx.ext.mathjax') elif LooseVersion(sphinx.__version__) < LooseVersion('1.4'): extensions.append('sphinx.ext.pngmath') else: extensions.append('sphinx.ext.imgmath') # Above, we use a patched version of viewcode rather than 'sphinx.ext.viewcode' # This can be changed to the sphinx version once the following issue is fixed # in sphinx: # https://bitbucket.org/birkenfeld/sphinx/issue/623/ # extension-viewcode-fails-with-function try: import matplotlib.sphinxext.plot_directive extensions += [matplotlib.sphinxext.plot_directive.__name__] # AttributeError is checked here in case matplotlib is installed but # Sphinx isn't. Note that this module is imported by the config file # generator, even if we're not building the docs. except (ImportError, AttributeError): warnings.warn( "matplotlib's plot_directive could not be imported. " + "Inline plots will not be included in the output") # Don't show summaries of the members in each class along with the # class' docstring numpydoc_show_class_members = False autosummary_generate = True automodapi_toctreedirnm = 'api' # Class documentation should contain *both* the class docstring and # the __init__ docstring autoclass_content = "both" # Render inheritance diagrams in SVG graphviz_output_format = "svg" graphviz_dot_args = [ '-Nfontsize=10', '-Nfontname=Helvetica Neue, Helvetica, Arial, sans-serif', '-Efontsize=10', '-Efontname=Helvetica Neue, Helvetica, Arial, sans-serif', '-Gfontsize=10', '-Gfontname=Helvetica Neue, Helvetica, Arial, sans-serif' ] # -- Options for HTML output ------------------------------------------------- # Add any paths that contain custom themes here, relative to this directory. html_theme_path = [path.abspath(path.join(path.dirname(__file__), 'themes'))] # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'bootstrap-astropy' # Custom sidebar templates, maps document names to template names. html_sidebars = { '**': ['localtoc.html'], 'search': [], 'genindex': [], 'py-modindex': [], } # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # included in the bootstrap-astropy theme html_favicon = path.join(html_theme_path[0], html_theme, 'static', 'astropy_logo.ico') # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%d %b %Y' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # -- Options for LaTeX output ------------------------------------------------ # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. latex_toplevel_sectioning = 'part' # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False latex_elements = {} # Additional stuff for the LaTeX preamble. latex_elements['preamble'] = r""" % Use a more modern-looking monospace font \usepackage{inconsolata} % The enumitem package provides unlimited nesting of lists and enums. % Sphinx may use this in the future, in which case this can be removed. % See https://bitbucket.org/birkenfeld/sphinx/issue/777/latex-output-too-deeply-nested \usepackage{enumitem} \setlistdepth{15} % In the parameters section, place a newline after the Parameters % header. (This is stolen directly from Numpy's conf.py, since it % affects Numpy-style docstrings). \usepackage{expdlist} \let\latexdescription=\description \def\description{\latexdescription{}{} \breaklabel} % Support the superscript Unicode numbers used by the "unicode" units % formatter \DeclareUnicodeCharacter{2070}{\ensuremath{^0}} \DeclareUnicodeCharacter{00B9}{\ensuremath{^1}} \DeclareUnicodeCharacter{00B2}{\ensuremath{^2}} \DeclareUnicodeCharacter{00B3}{\ensuremath{^3}} \DeclareUnicodeCharacter{2074}{\ensuremath{^4}} \DeclareUnicodeCharacter{2075}{\ensuremath{^5}} \DeclareUnicodeCharacter{2076}{\ensuremath{^6}} \DeclareUnicodeCharacter{2077}{\ensuremath{^7}} \DeclareUnicodeCharacter{2078}{\ensuremath{^8}} \DeclareUnicodeCharacter{2079}{\ensuremath{^9}} \DeclareUnicodeCharacter{207B}{\ensuremath{^-}} \DeclareUnicodeCharacter{00B0}{\ensuremath{^{\circ}}} \DeclareUnicodeCharacter{2032}{\ensuremath{^{\prime}}} \DeclareUnicodeCharacter{2033}{\ensuremath{^{\prime\prime}}} % Make the "warning" and "notes" sections use a sans-serif font to % make them stand out more. \renewenvironment{notice}[2]{ \def\py@noticetype{#1} \csname py@noticestart@#1\endcsname \textsf{\textbf{#2}} }{\csname py@noticeend@\py@noticetype\endcsname} """ # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # -- Options for the linkcheck builder ---------------------------------------- # A timeout value, in seconds, for the linkcheck builder linkcheck_timeout = 60 pydl-0.6.0/astropy_helpers/astropy_helpers/sphinx/local/0000775000111400017500000000000013160273564024345 5ustar weaverweaver00000000000000pydl-0.6.0/astropy_helpers/astropy_helpers/sphinx/local/python3_local_links.inv0000664000111400017500000000122213154626456031043 0ustar weaverweaver00000000000000# Sphinx inventory version 2 # Project: Python # Version: 3.5 # The remainder of this file should be compressed using zlib. x0{b$.!YTUa*!Qq{h\;ٯgɁlv VA#jolGN dk~#k40Zv]'`Z*H? %Z_H{\aj% Gব,:j'/xU2(j%PR\7(j֥5J?,Cf/բO4FZsz ouЏO l;4`6yDMA-}Jwq!dj!#T" h2oS߈~` t8RwjnKcRxr?%+\Ob 3s˻`Vһv@>2b;!I,=Wh_'l!Q%^B#Ô }inuD#e³\:{tu;/wxy. !nX{0BzoH /LxA&UXS{⮸5ߣ\RBiJF?pydl-0.6.0/astropy_helpers/astropy_helpers/sphinx/local/python2_local_links.inv0000664000111400017500000000106213154626456031044 0ustar weaverweaver00000000000000# Sphinx inventory version 2 # Project: Python # Version: 2.7 and 3.5 # The remainder of this file should be compressed using zlib. x=O0@w Z!nU bw+1rpKïIiQeI˽w8g"Wf ʬxK%lS(ϭ1 k&Qrp)ɐ.Bi۠3H]a)_ZI>dH, _M_"撠bvIzЀ8 {% if not embedded %}{% endif %} {% endblock %} {% block header %} {% endblock %} {% block relbar1 %}

{{ _('Navigation') }}

    {%- if next %}
  • next {{ "»"|safe }}
    • {%- endif %} {%- if prev %}
  • {{ "«"|safe }} previous {% if next %}{{ reldelim2 }}{% endif %}
    • {%- endif %} {%- block rootrellink %}
  • {{ shorttitle|e }} {{ reldelim1 }}
    • {%- endblock %} {%- for parent in parents %}
  • {{ parent.title }}{{ reldelim1 }}
    • {%- endfor %} {# Don't put the title in the relbar for the first (index) page. #} {% if prev %}
  • {{ title }}
    • {% endif %} {%- block relbaritems %} {% endblock %}
{% endblock %} {# Silence the bottom relbar. #} {% block relbar2 %}{% endblock %} {%- block footer %}

{%- if edit_on_github %} {{ edit_on_github_page_message }}   {%- endif %} {%- if show_source and has_source and sourcename %} {{ _('Page Source') }} {%- endif %}   Back to Top

{%- if show_copyright %} {%- if hasdoc('copyright') %} {% trans path=pathto('copyright'), copyright=copyright|e %}© Copyright {{ copyright }}.{% endtrans %}
{%- else %} {% trans copyright=copyright|e %}© Copyright {{ copyright }}.{% endtrans %}
{%- endif %} {%- endif %} {%- if show_sphinx %} {% trans sphinx_version=sphinx_version|e %}Created using Sphinx {{ sphinx_version }}.{% endtrans %}   {%- endif %} {%- if last_updated %} {% trans last_updated=last_updated|e %}Last built {{ last_updated }}.{% endtrans %}
{%- endif %}

{%- endblock %} pydl-0.6.0/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/theme.conf0000664000111400017500000000030013006206216032203 0ustar weaverweaver00000000000000# AstroPy theme based on Twitter Bootstrap CSS [theme] inherit = basic stylesheet = bootstrap-astropy.css pygments_style = sphinx [options] logotext1 = astro logotext2 = py logotext3 = :docs pydl-0.6.0/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/globaltoc.html0000664000111400017500000000011113006206216033066 0ustar weaverweaver00000000000000

Table of Contents

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{{ theme_logotext1 }}{{ theme_logotext2 }}{{ theme_logotext3 }}
  • Index
  • Modules
  • {% block sidebarsearch %} {% include "searchbox.html" %} {% endblock %}