spectral-cube-0.4.3/0000755000077000000240000000000013261442571014275 5ustar adamstaff00000000000000spectral-cube-0.4.3/ah_bootstrap.py0000644000077000000240000010776213245574450017355 0ustar adamstaff00000000000000""" 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 and by default the system-installed version of astropy-helpers will be used (however, ``ah_bootstrap.use_astropy_helpers`` may be called manually from within the setup.py script). This behavior can also be controlled using the ``--auto-use`` and ``--no-auto-use`` command-line flags. For clarity, an alias for ``--no-auto-use`` is ``--use-system-astropy-helpers``, and we recommend using the latter if needed. 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 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' if PY3: UPPER_VERSION_EXCLUSIVE = None else: UPPER_VERSION_EXCLUSIVE = '3' # 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') if '--auto-use' in argv: config['auto_use'] = True argv.remove('--auto-use') if '--no-auto-use' in argv: config['auto_use'] = False argv.remove('--no-auto-use') if '--use-system-astropy-helpers' in argv: config['auto_use'] = False argv.remove('--use-system-astropy-helpers') 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): # We use subprocess instead of run_setup from setuptools to # avoid segmentation faults - see the following for more details: # https://github.com/cython/cython/issues/2104 sp.check_output([sys.executable, 'setup.py', 'egg_info'], cwd=path) 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: if UPPER_VERSION_EXCLUSIVE is None: req = DIST_NAME else: req = '{0}<{1}'.format(DIST_NAME, UPPER_VERSION_EXCLUSIVE) attrs = {'setup_requires': [req]} # NOTE: we need to parse the config file (e.g. setup.cfg) to make sure # it honours the options set in the [easy_install] section, and we need # to explicitly fetch the requirement eggs as setup_requires does not # get honored in recent versions of setuptools: # https://github.com/pypa/setuptools/issues/1273 try: context = _verbose if DEBUG else _silence with context(): dist = _Distribution(attrs=attrs) try: dist.parse_config_files(ignore_option_errors=True) dist.fetch_build_eggs(req) except TypeError: # On older versions of setuptools, ignore_option_errors # doesn't exist, and the above two lines are not needed # so we can just continue pass # 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 _verbose(): yield @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() spectral-cube-0.4.3/astropy_helpers/0000755000077000000240000000000013261442571017520 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/.travis.yml0000644000077000000240000000556213245574455021652 0ustar adamstaff00000000000000# We set the language to c because python isn't supported on the MacOS X nodes # on Travis. However, the language ends up being irrelevant anyway, since we # install Python ourselves using conda. language: c os: - osx - linux # Setting sudo to false opts in to Travis-CI container-based builds. sudo: false env: matrix: - PYTHON_VERSION=2.7 EVENT_TYPE='push pull_request cron' - PYTHON_VERSION=3.4 SETUPTOOLS_VERSION=20 - PYTHON_VERSION=3.5 - PYTHON_VERSION=3.6 SETUPTOOLS_VERSION=dev DEBUG=True CONDA_DEPENDENCIES='sphinx cython numpy six pytest-cov' EVENT_TYPE='push pull_request cron' global: - CONDA_DEPENDENCIES="setuptools sphinx cython numpy" - PIP_DEPENDENCIES="coveralls pytest-cov" - EVENT_TYPE='push pull_request' matrix: include: - os: linux env: PYTHON_VERSION=3.6 SPHINX_VERSION='>1.6' - os: linux env: PYTHON_VERSION=3.6 PIP_DEPENDENCIES='git+https://github.com/sphinx-doc/sphinx.git#egg=sphinx coveralls pytest-cov' CONDA_DEPENDENCIES="setuptools cython numpy" - os: linux env: PYTHON_VERSION=3.5 SPHINX_VERSION='<1.4' - os: linux env: PYTHON_VERSION=3.5 SPHINX_VERSION='<1.5' SETUPTOOLS_VERSION=27 - os: linux env: PYTHON_VERSION=3.6 SPHINX_VERSION='<1.6' SETUPTOOLS_VERSION=27 # Uncomment the following if there are issues in setuptools that we # can't work around quickly - otherwise leave uncommented so that # we notice when things go wrong. # # allow_failures: # - env: PYTHON_VERSION=3.6 SETUPTOOLS_VERSION=dev DEBUG=True # CONDA_DEPENDENCIES='sphinx cython numpy six pytest-cov' # EVENT_TYPE='push pull_request cron' install: - git clone git://github.com/astropy/ci-helpers.git - source ci-helpers/travis/setup_conda.sh # We cannot install the developer version of setuptools using pip because # pip tries to remove the previous version of setuptools before the # installation is complete, which causes issues. Instead, we just install # setuptools manually. - if [[ $SETUPTOOLS_VERSION == dev ]]; then git clone http://github.com/pypa/setuptools.git; cd setuptools; python bootstrap.py; python setup.py install; cd ..; fi before_script: # Some of the tests use git commands that require a user to be configured - git config --global user.name "A U Thor" - git config --global user.email "author@example.com" script: # Use full path for coveragerc; see issue #193 - py.test --cov astropy_helpers --cov-config $(pwd)/astropy_helpers/tests/coveragerc astropy_helpers # In conftest.py we produce a .coverage.subprocess that contains coverage # statistics for sub-processes, so we combine it with the main one here. - mv .coverage .coverage.main - coverage combine .coverage.main .coverage.subprocess - coverage report after_success: - coveralls --rcfile=astropy_helpers/tests/coveragerc spectral-cube-0.4.3/astropy_helpers/ah_bootstrap.py0000644000077000000240000010776213245574455022605 0ustar adamstaff00000000000000""" 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 and by default the system-installed version of astropy-helpers will be used (however, ``ah_bootstrap.use_astropy_helpers`` may be called manually from within the setup.py script). This behavior can also be controlled using the ``--auto-use`` and ``--no-auto-use`` command-line flags. For clarity, an alias for ``--no-auto-use`` is ``--use-system-astropy-helpers``, and we recommend using the latter if needed. 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 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' if PY3: UPPER_VERSION_EXCLUSIVE = None else: UPPER_VERSION_EXCLUSIVE = '3' # 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') if '--auto-use' in argv: config['auto_use'] = True argv.remove('--auto-use') if '--no-auto-use' in argv: config['auto_use'] = False argv.remove('--no-auto-use') if '--use-system-astropy-helpers' in argv: config['auto_use'] = False argv.remove('--use-system-astropy-helpers') 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): # We use subprocess instead of run_setup from setuptools to # avoid segmentation faults - see the following for more details: # https://github.com/cython/cython/issues/2104 sp.check_output([sys.executable, 'setup.py', 'egg_info'], cwd=path) 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: if UPPER_VERSION_EXCLUSIVE is None: req = DIST_NAME else: req = '{0}<{1}'.format(DIST_NAME, UPPER_VERSION_EXCLUSIVE) attrs = {'setup_requires': [req]} # NOTE: we need to parse the config file (e.g. setup.cfg) to make sure # it honours the options set in the [easy_install] section, and we need # to explicitly fetch the requirement eggs as setup_requires does not # get honored in recent versions of setuptools: # https://github.com/pypa/setuptools/issues/1273 try: context = _verbose if DEBUG else _silence with context(): dist = _Distribution(attrs=attrs) try: dist.parse_config_files(ignore_option_errors=True) dist.fetch_build_eggs(req) except TypeError: # On older versions of setuptools, ignore_option_errors # doesn't exist, and the above two lines are not needed # so we can just continue pass # 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 _verbose(): yield @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() spectral-cube-0.4.3/astropy_helpers/appveyor.yml0000644000077000000240000000250213245574455022120 0ustar adamstaff00000000000000# AppVeyor.com is a Continuous Integration service to build and run tests under # Windows environment: global: PYTHON: "C:\\conda" MINICONDA_VERSION: "latest" CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\ci-helpers\\appveyor\\windows_sdk.cmd" PYTHON_ARCH: "64" # needs to be set for CMD_IN_ENV to succeed. If a mix # of 32 bit and 64 bit builds are needed, move this # to the matrix section. # babel 2.0 is known to break on Windows: # https://github.com/python-babel/babel/issues/174 CONDA_DEPENDENCIES: "numpy Cython sphinx pytest babel!=2.0 setuptools" matrix: - PYTHON_VERSION: "2.7" - PYTHON_VERSION: "3.4" - PYTHON_VERSION: "3.5" - PYTHON_VERSION: "3.6" platform: -x64 install: # Set up ci-helpers - "git clone git://github.com/astropy/ci-helpers.git" - "powershell ci-helpers/appveyor/install-miniconda.ps1" - "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%" - "activate test" # Some of the tests use git commands that require a user to be configured - git config --global user.name "A U Thor" - git config --global user.email "author@example.com" # Not a .NET project, we build the package in the install step instead build: false test_script: - "%CMD_IN_ENV% py.test astropy_helpers" spectral-cube-0.4.3/astropy_helpers/astropy_helpers/0000755000077000000240000000000013261442571022743 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/__init__.py0000644000077000000240000000345412657374605025074 0ustar adamstaff00000000000000try: from .version import version as __version__ from .version import githash as __githash__ except ImportError: __version__ = '' __githash__ = '' # If we've made it as far as importing astropy_helpers, we don't need # ah_bootstrap in sys.modules anymore. Getting rid of it is actually necessary # if the package we're installing has a setup_requires of another package that # uses astropy_helpers (and possibly a different version at that) # See https://github.com/astropy/astropy/issues/3541 import sys if 'ah_bootstrap' in sys.modules: del sys.modules['ah_bootstrap'] # Note, this is repeated from ah_bootstrap.py, but is here too in case this # astropy-helpers was upgraded to from an older version that did not have this # check in its ah_bootstrap. # 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 import os # Ensure that all module-level code in astropy or other packages know that # we're in setup mode: if ('__main__' in sys.modules and hasattr(sys.modules['__main__'], '__file__')): filename = os.path.basename(sys.modules['__main__'].__file__) if filename.rstrip('co') == 'setup.py': if sys.version_info[0] >= 3: import builtins else: import __builtin__ as builtins builtins._ASTROPY_SETUP_ = True del filename spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/0000755000077000000240000000000013261442571024544 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/__init__.py0000644000077000000240000000000012533471373026646 0ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/_dummy.py0000644000077000000240000000557412657374605026435 0ustar adamstaff00000000000000""" Provides a base class for a 'dummy' setup.py command that has no functionality (probably due to a missing requirement). This dummy command can raise an exception when it is run, explaining to the user what dependencies must be met to use this command. The reason this is at all tricky is that we want the command to be able to provide this message even when the user passes arguments to the command. If we don't know ahead of time what arguments the command can take, this is difficult, because distutils does not allow unknown arguments to be passed to a setup.py command. This hacks around that restriction to provide a useful error message even when a user passes arguments to the dummy implementation of a command. Use this like: try: from some_dependency import SetupCommand except ImportError: from ._dummy import _DummyCommand class SetupCommand(_DummyCommand): description = \ 'Implementation of SetupCommand from some_dependency; ' 'some_dependency must be installed to run this command' # This is the message that will be raised when a user tries to # run this command--define it as a class attribute. error_msg = \ "The 'setup_command' command requires the some_dependency " "package to be installed and importable." """ import sys from setuptools import Command from distutils.errors import DistutilsArgError from textwrap import dedent class _DummyCommandMeta(type): """ Causes an exception to be raised on accessing attributes of a command class so that if ``./setup.py command_name`` is run with additional command-line options we can provide a useful error message instead of the default that tells users the options are unrecognized. """ def __init__(cls, name, bases, members): if bases == (Command, object): # This is the _DummyCommand base class, presumably return if not hasattr(cls, 'description'): raise TypeError( "_DummyCommand subclass must have a 'description' " "attribute.") if not hasattr(cls, 'error_msg'): raise TypeError( "_DummyCommand subclass must have an 'error_msg' " "attribute.") def __getattribute__(cls, attr): if attr in ('description', 'error_msg'): # Allow cls.description to work so that `./setup.py # --help-commands` still works return super(_DummyCommandMeta, cls).__getattribute__(attr) raise DistutilsArgError(cls.error_msg) if sys.version_info[0] < 3: exec(dedent(""" class _DummyCommand(Command, object): __metaclass__ = _DummyCommandMeta """)) else: exec(dedent(""" class _DummyCommand(Command, object, metaclass=_DummyCommandMeta): pass """)) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/_test_compat.py0000644000077000000240000002671213126505434027605 0ustar adamstaff00000000000000""" Old implementation of ``./setup.py test`` command. This has been moved to astropy.tests as of Astropy v1.1.0, but a copy of the implementation is kept here for backwards compatibility. """ from __future__ import absolute_import, unicode_literals import inspect import os import shutil import subprocess import sys import tempfile from setuptools import Command from ..compat import _fix_user_options PY3 = sys.version_info[0] == 3 class AstropyTest(Command, object): description = 'Run the tests for this package' user_options = [ ('package=', 'P', "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=', 't', 'Specify a test location by path. If a relative path to a .py file, ' 'it is relative to the built package, so e.g., a leading "astropy/" ' 'is necessary. If a relative path to a .rst file, it is relative to ' 'the directory *below* the --docs-path directory, so a leading ' '"docs/" is usually necessary. May also be an absolute path.'), ('verbose-results', 'V', 'Turn on verbose output from pytest.'), ('plugins=', 'p', 'Plugins to enable when running pytest.'), ('pastebin=', 'b', "Enable pytest pastebin output. Either 'all' or 'failed'."), ('args=', 'a', 'Additional arguments to be passed to pytest.'), ('remote-data', 'R', 'Run tests that download remote data.'), ('pep8', '8', 'Enable PEP8 checking and disable regular tests. ' 'Requires the pytest-pep8 plugin.'), ('pdb', 'd', 'Start the interactive Python debugger on errors.'), ('coverage', 'c', 'Create a coverage report. Requires the coverage package.'), ('open-files', 'o', 'Fail if any tests leave files open. Requires the ' 'psutil package.'), ('parallel=', 'j', 'Run the tests in parallel on the specified number of ' 'CPUs. If negative, all the cores on the machine will be ' 'used. Requires the pytest-xdist plugin.'), ('docs-path=', None, 'The path to the documentation .rst files. If not provided, and ' 'the current directory contains a directory called "docs", that ' 'will be used.'), ('skip-docs', None, "Don't test the documentation .rst files."), ('repeat=', None, 'How many times to repeat each test (can be used to check for ' 'sporadic failures).'), ('temp-root=', None, 'The root directory in which to create the temporary testing files. ' 'If unspecified the system default is used (e.g. /tmp) as explained ' 'in the documentation for tempfile.mkstemp.') ] user_options = _fix_user_options(user_options) package_name = '' def initialize_options(self): self.package = None self.test_path = None self.verbose_results = False self.plugins = None self.pastebin = None self.args = None self.remote_data = False self.pep8 = False self.pdb = False self.coverage = False self.open_files = False self.parallel = 0 self.docs_path = None self.skip_docs = False self.repeat = None self.temp_root = None def finalize_options(self): # Normally we would validate the options here, but that's handled in # run_tests pass # Most of the test runner arguments have the same name as attributes on # this command class, with one exception (for now) _test_runner_arg_attr_map = { 'verbose': 'verbose_results' } def generate_testing_command(self): """ Build a Python script to run the tests. """ cmd_pre = '' # Commands to run before the test function cmd_post = '' # Commands to run after the test function if self.coverage: pre, post = self._generate_coverage_commands() cmd_pre += pre cmd_post += post def get_attr(arg): attr = self._test_runner_arg_attr_map.get(arg, arg) return getattr(self, attr) test_args = filter(lambda arg: hasattr(self, arg), self._get_test_runner_args()) test_args = ', '.join('{0}={1!r}'.format(arg, get_attr(arg)) for arg in test_args) if PY3: set_flag = "import builtins; builtins._ASTROPY_TEST_ = True" else: set_flag = "import __builtin__; __builtin__._ASTROPY_TEST_ = True" cmd = ('{cmd_pre}{0}; import {1.package_name}, sys; result = ' '{1.package_name}.test({test_args}); {cmd_post}' 'sys.exit(result)') return cmd.format(set_flag, self, cmd_pre=cmd_pre, cmd_post=cmd_post, test_args=test_args) def _validate_required_deps(self): """ This method checks that any required modules are installed before running the tests. """ try: import astropy # noqa except ImportError: raise ImportError( "The 'test' command requires the astropy package to be " "installed and importable.") def run(self): """ Run the tests! """ # Ensure there is a doc path if self.docs_path is None: if os.path.exists('docs'): self.docs_path = os.path.abspath('docs') # Build a testing install of the package self._build_temp_install() # Ensure all required packages are installed self._validate_required_deps() # Run everything in a try: finally: so that the tmp dir gets deleted. try: # Construct this modules testing command cmd = self.generate_testing_command() # Run the tests in a subprocess--this is necessary since # new extension modules may have appeared, and this is the # easiest way to set up a new environment # On Python 3.x prior to 3.3, the creation of .pyc files # is not atomic. py.test jumps through some hoops to make # this work by parsing import statements and carefully # importing files atomically. However, it can't detect # when __import__ is used, so its carefulness still fails. # The solution here (admittedly a bit of a hack), is to # turn off the generation of .pyc files altogether by # passing the `-B` switch to `python`. This does mean # that each core will have to compile .py file to bytecode # itself, rather than getting lucky and borrowing the work # already done by another core. Compilation is an # insignificant fraction of total testing time, though, so # it's probably not worth worrying about. retcode = subprocess.call([sys.executable, '-B', '-c', cmd], cwd=self.testing_path, close_fds=False) finally: # Remove temporary directory shutil.rmtree(self.tmp_dir) raise SystemExit(retcode) def _build_temp_install(self): """ Build the package and copy the build to a temporary directory for the purposes of testing this avoids creating pyc and __pycache__ directories inside the build directory """ self.reinitialize_command('build', inplace=True) self.run_command('build') build_cmd = self.get_finalized_command('build') new_path = os.path.abspath(build_cmd.build_lib) # On OSX the default path for temp files is under /var, but in most # cases on OSX /var is actually a symlink to /private/var; ensure we # dereference that link, because py.test is very sensitive to relative # paths... tmp_dir = tempfile.mkdtemp(prefix=self.package_name + '-test-', dir=self.temp_root) self.tmp_dir = os.path.realpath(tmp_dir) self.testing_path = os.path.join(self.tmp_dir, os.path.basename(new_path)) shutil.copytree(new_path, self.testing_path) new_docs_path = os.path.join(self.tmp_dir, os.path.basename(self.docs_path)) shutil.copytree(self.docs_path, new_docs_path) self.docs_path = new_docs_path shutil.copy('setup.cfg', self.tmp_dir) def _generate_coverage_commands(self): """ This method creates the post and pre commands if coverage is to be generated """ if self.parallel != 0: raise ValueError( "--coverage can not be used with --parallel") try: import coverage # noqa except ImportError: raise ImportError( "--coverage requires that the coverage package is " "installed.") # Don't use get_pkg_data_filename here, because it # requires importing astropy.config and thus screwing # up coverage results for those packages. coveragerc = os.path.join( self.testing_path, self.package_name, 'tests', 'coveragerc') # We create a coveragerc that is specific to the version # of Python we're running, so that we can mark branches # as being specifically for Python 2 or Python 3 with open(coveragerc, 'r') as fd: coveragerc_content = fd.read() if PY3: ignore_python_version = '2' else: ignore_python_version = '3' coveragerc_content = coveragerc_content.replace( "{ignore_python_version}", ignore_python_version).replace( "{packagename}", self.package_name) tmp_coveragerc = os.path.join(self.tmp_dir, 'coveragerc') with open(tmp_coveragerc, 'wb') as tmp: tmp.write(coveragerc_content.encode('utf-8')) cmd_pre = ( 'import coverage; ' 'cov = coverage.coverage(data_file="{0}", config_file="{1}"); ' 'cov.start();'.format( os.path.abspath(".coverage"), tmp_coveragerc)) cmd_post = ( 'cov.stop(); ' 'from astropy.tests.helper import _save_coverage; ' '_save_coverage(cov, result, "{0}", "{1}");'.format( os.path.abspath('.'), self.testing_path)) return cmd_pre, cmd_post def _get_test_runner_args(self): """ A hack to determine what arguments are supported by the package's test() function. In the future there should be a more straightforward API to determine this (really it should be determined by the ``TestRunner`` class for whatever version of Astropy is in use). """ if PY3: import builtins builtins._ASTROPY_TEST_ = True else: import __builtin__ __builtin__._ASTROPY_TEST_ = True try: pkg = __import__(self.package_name) if not hasattr(pkg, 'test'): raise ImportError( 'package {0} does not have a {0}.test() function as ' 'required by the Astropy test runner'.format(self.package_name)) argspec = inspect.getargspec(pkg.test) return argspec.args finally: if PY3: del builtins._ASTROPY_TEST_ else: del __builtin__._ASTROPY_TEST_ spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/build_ext.py0000644000077000000240000004636413126505434027110 0ustar adamstaff00000000000000import errno import os import re import shlex import shutil import subprocess import sys import textwrap from distutils import log, ccompiler, sysconfig from distutils.core import Extension from distutils.ccompiler import get_default_compiler from setuptools.command.build_ext import build_ext as SetuptoolsBuildExt from ..utils import get_numpy_include_path, invalidate_caches, classproperty from ..version_helpers import get_pkg_version_module def should_build_with_cython(package, release=None): """Returns the previously used Cython version (or 'unknown' if not previously built) if Cython should be used to build extension modules from pyx files. If the ``release`` parameter is not specified an attempt is made to determine the release flag from `astropy.version`. """ try: version_module = __import__(package + '.cython_version', fromlist=['release', 'cython_version']) except ImportError: version_module = None if release is None and version_module is not None: try: release = version_module.release except AttributeError: pass try: cython_version = version_module.cython_version except AttributeError: cython_version = 'unknown' # Only build with Cython if, of course, Cython is installed, we're in a # development version (i.e. not release) or the Cython-generated source # files haven't been created yet (cython_version == 'unknown'). The latter # case can happen even when release is True if checking out a release tag # from the repository have_cython = False try: import Cython # noqa have_cython = True except ImportError: pass if have_cython and (not release or cython_version == 'unknown'): return cython_version else: return False _compiler_versions = {} def get_compiler_version(compiler): if compiler in _compiler_versions: return _compiler_versions[compiler] # Different flags to try to get the compiler version # TODO: It might be worth making this configurable to support # arbitrary odd compilers; though all bets may be off in such # cases anyway flags = ['--version', '--Version', '-version', '-Version', '-v', '-V'] def try_get_version(flag): process = subprocess.Popen( shlex.split(compiler, posix=('win' not in sys.platform)) + [flag], stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = process.communicate() if process.returncode != 0: return 'unknown' output = stdout.strip().decode('latin-1') # Safest bet if not output: # Some compilers return their version info on stderr output = stderr.strip().decode('latin-1') if not output: output = 'unknown' return output for flag in flags: version = try_get_version(flag) if version != 'unknown': break # Cache results to speed up future calls _compiler_versions[compiler] = version return version # TODO: I think this can be reworked without having to create the class # programmatically. def generate_build_ext_command(packagename, release): """ Creates a custom 'build_ext' command that allows for manipulating some of the C extension options at build time. We use a function to build the class since the base class for build_ext may be different depending on certain build-time parameters (for example, we may use Cython's build_ext instead of the default version in distutils). Uses the default distutils.command.build_ext by default. """ class build_ext(SetuptoolsBuildExt, object): package_name = packagename is_release = release _user_options = SetuptoolsBuildExt.user_options[:] _boolean_options = SetuptoolsBuildExt.boolean_options[:] _help_options = SetuptoolsBuildExt.help_options[:] force_rebuild = False _broken_compiler_mapping = [ ('i686-apple-darwin[0-9]*-llvm-gcc-4.2', 'clang') ] # Warning: Spaghetti code ahead. # During setup.py, the setup_helpers module needs the ability to add # items to a command's user_options list. At this stage we don't know # whether or not we can build with Cython, and so don't know for sure # what base class will be used for build_ext; nevertheless we want to # be able to provide a list to add options into. # # Later, once setup() has been called we should have all build # dependencies included via setup_requires available. distutils needs # to be able to access the user_options as a *class* attribute before # the class has been initialized, but we do need to be able to # enumerate the options for the correct base class at that point @classproperty def user_options(cls): from distutils import core if core._setup_distribution is None: # We haven't gotten into setup() yet, and the Distribution has # not yet been initialized return cls._user_options return cls._final_class.user_options @classproperty def boolean_options(cls): # Similar to user_options above from distutils import core if core._setup_distribution is None: # We haven't gotten into setup() yet, and the Distribution has # not yet been initialized return cls._boolean_options return cls._final_class.boolean_options @classproperty def help_options(cls): # Similar to user_options above from distutils import core if core._setup_distribution is None: # We haven't gotten into setup() yet, and the Distribution has # not yet been initialized return cls._help_options return cls._final_class.help_options @classproperty(lazy=True) def _final_class(cls): """ Late determination of what the build_ext base class should be, depending on whether or not Cython is available. """ uses_cython = should_build_with_cython(cls.package_name, cls.is_release) if uses_cython: # We need to decide late on whether or not to use Cython's # build_ext (since Cython may not be available earlier in the # setup.py if it was brought in via setup_requires) try: from Cython.Distutils.old_build_ext import old_build_ext as base_cls except ImportError: from Cython.Distutils import build_ext as base_cls else: base_cls = SetuptoolsBuildExt # Create and return an instance of a new class based on this class # using one of the above possible base classes def merge_options(attr): base = getattr(base_cls, attr) ours = getattr(cls, '_' + attr) all_base = set(opt[0] for opt in base) return base + [opt for opt in ours if opt[0] not in all_base] boolean_options = (base_cls.boolean_options + [opt for opt in cls._boolean_options if opt not in base_cls.boolean_options]) members = dict(cls.__dict__) members.update({ 'user_options': merge_options('user_options'), 'help_options': merge_options('help_options'), 'boolean_options': boolean_options, 'uses_cython': uses_cython, }) # Update the base class for the original build_ext command build_ext.__bases__ = (base_cls, object) # Create a new class for the existing class, but now with the # appropriate base class depending on whether or not to use Cython. # Ensure that object is one of the bases to make a new-style class. return type(cls.__name__, (build_ext,), members) def __new__(cls, *args, **kwargs): # By the time the command is actually instantialized, the # Distribution instance for the build has been instantiated, which # means setup_requires has been processed--now we can determine # what base class we can use for the actual build, and return an # instance of a build_ext command that uses that base class (right # now the options being Cython.Distutils.build_ext, or the stock # setuptools build_ext) new_cls = super(build_ext, cls._final_class).__new__( cls._final_class) # Since the new cls is not a subclass of the original cls, we must # manually call its __init__ new_cls.__init__(*args, **kwargs) return new_cls def finalize_options(self): # Add a copy of the _compiler.so module as well, but only if there # are in fact C modules to compile (otherwise there's no reason to # include a record of the compiler used) # Note, self.extensions may not be set yet, but # self.distribution.ext_modules is where any extension modules # passed to setup() can be found self._adjust_compiler() extensions = self.distribution.ext_modules if extensions: build_py = self.get_finalized_command('build_py') package_dir = build_py.get_package_dir(packagename) src_path = os.path.relpath( os.path.join(os.path.dirname(__file__), 'src')) shutil.copy(os.path.join(src_path, 'compiler.c'), os.path.join(package_dir, '_compiler.c')) ext = Extension(self.package_name + '._compiler', [os.path.join(package_dir, '_compiler.c')]) extensions.insert(0, ext) super(build_ext, self).finalize_options() # Generate if self.uses_cython: try: from Cython import __version__ as cython_version except ImportError: # This shouldn't happen if we made it this far cython_version = None if (cython_version is not None and cython_version != self.uses_cython): self.force_rebuild = True # Update the used cython version self.uses_cython = cython_version # Regardless of the value of the '--force' option, force a rebuild # if the debug flag changed from the last build if self.force_rebuild: self.force = True def run(self): # For extensions that require 'numpy' in their include dirs, # replace 'numpy' with the actual paths np_include = get_numpy_include_path() for extension in self.extensions: if 'numpy' in extension.include_dirs: idx = extension.include_dirs.index('numpy') extension.include_dirs.insert(idx, np_include) extension.include_dirs.remove('numpy') self._check_cython_sources(extension) super(build_ext, self).run() # Update cython_version.py if building with Cython try: cython_version = get_pkg_version_module( packagename, fromlist=['cython_version'])[0] except (AttributeError, ImportError): cython_version = 'unknown' if self.uses_cython and self.uses_cython != cython_version: build_py = self.get_finalized_command('build_py') package_dir = build_py.get_package_dir(packagename) cython_py = os.path.join(package_dir, 'cython_version.py') with open(cython_py, 'w') as f: f.write('# Generated file; do not modify\n') f.write('cython_version = {0!r}\n'.format(self.uses_cython)) if os.path.isdir(self.build_lib): # The build/lib directory may not exist if the build_py # command was not previously run, which may sometimes be # the case self.copy_file(cython_py, os.path.join(self.build_lib, cython_py), preserve_mode=False) invalidate_caches() def _adjust_compiler(self): """ This function detects broken compilers and switches to another. If the environment variable CC is explicitly set, or a compiler is specified on the commandline, no override is performed -- the purpose here is to only override a default compiler. The specific compilers with problems are: * The default compiler in XCode-4.2, llvm-gcc-4.2, segfaults when compiling wcslib. The set of broken compilers can be updated by changing the compiler_mapping variable. It is a list of 2-tuples where the first in the pair is a regular expression matching the version of the broken compiler, and the second is the compiler to change to. """ if 'CC' in os.environ: # Check that CC is not set to llvm-gcc-4.2 c_compiler = os.environ['CC'] try: version = get_compiler_version(c_compiler) except OSError: msg = textwrap.dedent( """ The C compiler set by the CC environment variable: {compiler:s} cannot be found or executed. """.format(compiler=c_compiler)) log.warn(msg) sys.exit(1) for broken, fixed in self._broken_compiler_mapping: if re.match(broken, version): msg = textwrap.dedent( """Compiler specified by CC environment variable ({compiler:s}:{version:s}) will fail to compile {pkg:s}. Please set CC={fixed:s} and try again. You can do this, for example, by running: CC={fixed:s} python setup.py where is the command you ran. """.format(compiler=c_compiler, version=version, pkg=self.package_name, fixed=fixed)) log.warn(msg) sys.exit(1) # If C compiler is set via CC, and isn't broken, we are good to go. We # should definitely not try accessing the compiler specified by # ``sysconfig.get_config_var('CC')`` lower down, because this may fail # if the compiler used to compile Python is missing (and maybe this is # why the user is setting CC). For example, the official Python 2.7.3 # MacOS X binary was compiled with gcc-4.2, which is no longer available # in XCode 4. return if self.compiler is not None: # At this point, self.compiler will be set only if a compiler # was specified in the command-line or via setup.cfg, in which # case we don't do anything return compiler_type = ccompiler.get_default_compiler() if compiler_type == 'unix': # We have to get the compiler this way, as this is the one that is # used if os.environ['CC'] is not set. It is actually read in from # the Python Makefile. Note that this is not necessarily the same # compiler as returned by ccompiler.new_compiler() c_compiler = sysconfig.get_config_var('CC') try: version = get_compiler_version(c_compiler) except OSError: msg = textwrap.dedent( """ The C compiler used to compile Python {compiler:s}, and which is normally used to compile C extensions, is not available. You can explicitly specify which compiler to use by setting the CC environment variable, for example: CC=gcc python setup.py or if you are using MacOS X, you can try: CC=clang python setup.py """.format(compiler=c_compiler)) log.warn(msg) sys.exit(1) for broken, fixed in self._broken_compiler_mapping: if re.match(broken, version): os.environ['CC'] = fixed break def _check_cython_sources(self, extension): """ Where relevant, make sure that the .c files associated with .pyx modules are present (if building without Cython installed). """ # Determine the compiler we'll be using if self.compiler is None: compiler = get_default_compiler() else: compiler = self.compiler # Replace .pyx with C-equivalents, unless c files are missing for jdx, src in enumerate(extension.sources): base, ext = os.path.splitext(src) pyxfn = base + '.pyx' cfn = base + '.c' cppfn = base + '.cpp' if not os.path.isfile(pyxfn): continue if self.uses_cython: extension.sources[jdx] = pyxfn else: if os.path.isfile(cfn): extension.sources[jdx] = cfn elif os.path.isfile(cppfn): extension.sources[jdx] = cppfn else: msg = ( 'Could not find C/C++ file {0}.(c/cpp) for Cython ' 'file {1} when building extension {2}. Cython ' 'must be installed to build from a git ' 'checkout.'.format(base, pyxfn, extension.name)) raise IOError(errno.ENOENT, msg, cfn) # Current versions of Cython use deprecated Numpy API features # the use of which produces a few warnings when compiling. # These additional flags should squelch those warnings. # TODO: Feel free to remove this if/when a Cython update # removes use of the deprecated Numpy API if compiler == 'unix': extension.extra_compile_args.extend([ '-Wp,-w', '-Wno-unused-function']) return build_ext spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/build_py.py0000644000077000000240000000265612533471373026741 0ustar adamstaff00000000000000from setuptools.command.build_py import build_py as SetuptoolsBuildPy from ..utils import _get_platlib_dir class AstropyBuildPy(SetuptoolsBuildPy): user_options = SetuptoolsBuildPy.user_options[:] boolean_options = SetuptoolsBuildPy.boolean_options[:] def finalize_options(self): # Update build_lib settings from the build command to always put # build files in platform-specific subdirectories of build/, even # for projects with only pure-Python source (this is desirable # specifically for support of multiple Python version). build_cmd = self.get_finalized_command('build') platlib_dir = _get_platlib_dir(build_cmd) build_cmd.build_purelib = platlib_dir build_cmd.build_lib = platlib_dir self.build_lib = platlib_dir SetuptoolsBuildPy.finalize_options(self) def run_2to3(self, files, doctests=False): # Filter the files to exclude things that shouldn't be 2to3'd skip_2to3 = self.distribution.skip_2to3 filtered_files = [] for filename in files: for package in skip_2to3: if filename[len(self.build_lib) + 1:].startswith(package): break else: filtered_files.append(filename) SetuptoolsBuildPy.run_2to3(self, filtered_files, doctests) def run(self): # first run the normal build_py SetuptoolsBuildPy.run(self) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/build_sphinx.py0000644000077000000240000002441313245574455027623 0ustar adamstaff00000000000000from __future__ import print_function import inspect import os import pkgutil import re import shutil import subprocess import sys import textwrap import warnings from distutils import log from distutils.cmd import DistutilsOptionError import sphinx from sphinx.setup_command import BuildDoc as SphinxBuildDoc from ..utils import minversion, AstropyDeprecationWarning PY3 = sys.version_info[0] >= 3 class AstropyBuildDocs(SphinxBuildDoc): """ A version of the ``build_docs`` command that uses the version of Astropy that is built by the setup ``build`` command, rather than whatever is installed on the system. To build docs against the installed version, run ``make html`` in the ``astropy/docs`` directory. This also automatically creates the docs/_static directories--this is needed because GitHub won't create the _static dir because it has no tracked files. """ description = 'Build Sphinx documentation for Astropy environment' user_options = SphinxBuildDoc.user_options[:] user_options.append( ('warnings-returncode', 'w', 'Parses the sphinx output and sets the return code to 1 if there ' 'are any warnings. Note that this will cause the sphinx log to ' 'only update when it completes, rather than continuously as is ' 'normally the case.')) user_options.append( ('clean-docs', 'l', 'Completely clean previous builds, including ' 'automodapi-generated files before building new ones')) user_options.append( ('no-intersphinx', 'n', 'Skip intersphinx, even if conf.py says to use it')) user_options.append( ('open-docs-in-browser', 'o', 'Open the docs in a browser (using the webbrowser module) if the ' 'build finishes successfully.')) boolean_options = SphinxBuildDoc.boolean_options[:] boolean_options.append('warnings-returncode') boolean_options.append('clean-docs') boolean_options.append('no-intersphinx') boolean_options.append('open-docs-in-browser') _self_iden_rex = re.compile(r"self\.([^\d\W][\w]+)", re.UNICODE) def initialize_options(self): SphinxBuildDoc.initialize_options(self) self.clean_docs = False self.no_intersphinx = False self.open_docs_in_browser = False self.warnings_returncode = False def finalize_options(self): SphinxBuildDoc.finalize_options(self) # Clear out previous sphinx builds, if requested if self.clean_docs: dirstorm = [os.path.join(self.source_dir, 'api'), os.path.join(self.source_dir, 'generated')] if self.build_dir is None: dirstorm.append('docs/_build') else: dirstorm.append(self.build_dir) for d in dirstorm: if os.path.isdir(d): log.info('Cleaning directory ' + d) shutil.rmtree(d) else: log.info('Not cleaning directory ' + d + ' because ' 'not present or not a directory') def run(self): # TODO: Break this method up into a few more subroutines and # document them better import webbrowser if PY3: from urllib.request import pathname2url else: from urllib import pathname2url # This is used at the very end of `run` to decide if sys.exit should # be called. If it's None, it won't be. retcode = None # If possible, create the _static dir if self.build_dir is not None: # the _static dir should be in the same place as the _build dir # for Astropy basedir, subdir = os.path.split(self.build_dir) if subdir == '': # the path has a trailing /... basedir, subdir = os.path.split(basedir) staticdir = os.path.join(basedir, '_static') if os.path.isfile(staticdir): raise DistutilsOptionError( 'Attempted to build_docs in a location where' + staticdir + 'is a file. Must be a directory.') self.mkpath(staticdir) # Now make sure Astropy is built and determine where it was built build_cmd = self.reinitialize_command('build') build_cmd.inplace = 0 self.run_command('build') build_cmd = self.get_finalized_command('build') build_cmd_path = os.path.abspath(build_cmd.build_lib) ah_importer = pkgutil.get_importer('astropy_helpers') if ah_importer is None: ah_path = '.' else: ah_path = os.path.abspath(ah_importer.path) # Now generate the source for and spawn a new process that runs the # command. This is needed to get the correct imports for the built # version runlines, runlineno = inspect.getsourcelines(SphinxBuildDoc.run) subproccode = textwrap.dedent(""" from sphinx.setup_command import * os.chdir({srcdir!r}) sys.path.insert(0, {build_cmd_path!r}) sys.path.insert(0, {ah_path!r}) """).format(build_cmd_path=build_cmd_path, ah_path=ah_path, srcdir=self.source_dir) # runlines[1:] removes 'def run(self)' on the first line subproccode += textwrap.dedent(''.join(runlines[1:])) # All "self.foo" in the subprocess code needs to be replaced by the # values taken from the current self in *this* process subproccode = self._self_iden_rex.split(subproccode) for i in range(1, len(subproccode), 2): iden = subproccode[i] val = getattr(self, iden) if iden.endswith('_dir'): # Directories should be absolute, because the `chdir` call # in the new process moves to a different directory subproccode[i] = repr(os.path.abspath(val)) else: subproccode[i] = repr(val) subproccode = ''.join(subproccode) optcode = textwrap.dedent(""" class Namespace(object): pass self = Namespace() self.pdb = {pdb!r} self.verbosity = {verbosity!r} self.traceback = {traceback!r} """).format(pdb=getattr(self, 'pdb', False), verbosity=getattr(self, 'verbosity', 0), traceback=getattr(self, 'traceback', False)) subproccode = optcode + subproccode # This is a quick gross hack, but it ensures that the code grabbed from # SphinxBuildDoc.run will work in Python 2 if it uses the print # function if minversion(sphinx, '1.3'): subproccode = 'from __future__ import print_function' + subproccode if self.no_intersphinx: # the confoverrides variable in sphinx.setup_command.BuildDoc can # be used to override the conf.py ... but this could well break # if future versions of sphinx change the internals of BuildDoc, # so remain vigilant! subproccode = subproccode.replace( 'confoverrides = {}', 'confoverrides = {\'intersphinx_mapping\':{}}') log.debug('Starting subprocess of {0} with python code:\n{1}\n' '[CODE END])'.format(sys.executable, subproccode)) # To return the number of warnings, we need to capture stdout. This # prevents a continuous updating at the terminal, but there's no # apparent way around this. if self.warnings_returncode: proc = subprocess.Popen([sys.executable, '-c', subproccode], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) retcode = 1 with proc.stdout: for line in iter(proc.stdout.readline, b''): line = line.strip(b'\r\n') print(line.decode('utf-8')) if 'build succeeded.' == line.decode('utf-8'): retcode = 0 # Poll to set proc.retcode proc.wait() if retcode != 0: if os.environ.get('TRAVIS', None) == 'true': # this means we are in the travis build, so customize # the message appropriately. msg = ('The build_docs travis build FAILED ' 'because sphinx issued documentation ' 'warnings (scroll up to see the warnings).') else: # standard failure message msg = ('build_docs returning a non-zero exit ' 'code because sphinx issued documentation ' 'warnings.') log.warn(msg) else: proc = subprocess.Popen([sys.executable], stdin=subprocess.PIPE) proc.communicate(subproccode.encode('utf-8')) if proc.returncode == 0: if self.open_docs_in_browser: if self.builder == 'html': absdir = os.path.abspath(self.builder_target_dir) index_path = os.path.join(absdir, 'index.html') fileurl = 'file://' + pathname2url(index_path) webbrowser.open(fileurl) else: log.warn('open-docs-in-browser option was given, but ' 'the builder is not html! Ignoring.') else: log.warn('Sphinx Documentation subprocess failed with return ' 'code ' + str(proc.returncode)) retcode = proc.returncode if retcode is not None: # this is potentially dangerous in that there might be something # after the call to `setup` in `setup.py`, and exiting here will # prevent that from running. But there's no other apparent way # to signal what the return code should be. sys.exit(retcode) class AstropyBuildSphinx(AstropyBuildDocs): # pragma: no cover description = 'deprecated alias to the build_docs command' def run(self): warnings.warn( 'The "build_sphinx" command is now deprecated. Use' '"build_docs" instead.', AstropyDeprecationWarning) AstropyBuildDocs.run(self) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/install.py0000644000077000000240000000074612533471373026576 0ustar adamstaff00000000000000from setuptools.command.install import install as SetuptoolsInstall from ..utils import _get_platlib_dir class AstropyInstall(SetuptoolsInstall): user_options = SetuptoolsInstall.user_options[:] boolean_options = SetuptoolsInstall.boolean_options[:] def finalize_options(self): build_cmd = self.get_finalized_command('build') platlib_dir = _get_platlib_dir(build_cmd) self.build_lib = platlib_dir SetuptoolsInstall.finalize_options(self) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/install_lib.py0000644000077000000240000000100012533471373027404 0ustar adamstaff00000000000000from setuptools.command.install_lib import install_lib as SetuptoolsInstallLib from ..utils import _get_platlib_dir class AstropyInstallLib(SetuptoolsInstallLib): user_options = SetuptoolsInstallLib.user_options[:] boolean_options = SetuptoolsInstallLib.boolean_options[:] def finalize_options(self): build_cmd = self.get_finalized_command('build') platlib_dir = _get_platlib_dir(build_cmd) self.build_dir = platlib_dir SetuptoolsInstallLib.finalize_options(self) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/register.py0000644000077000000240000000454712533471373026757 0ustar adamstaff00000000000000from setuptools.command.register import register as SetuptoolsRegister class AstropyRegister(SetuptoolsRegister): """Extends the built in 'register' command to support a ``--hidden`` option to make the registered version hidden on PyPI by default. The result of this is that when a version is registered as "hidden" it can still be downloaded from PyPI, but it does not show up in the list of actively supported versions under http://pypi.python.org/pypi/astropy, and is not set as the most recent version. Although this can always be set through the web interface it may be more convenient to be able to specify via the 'register' command. Hidden may also be considered a safer default when running the 'register' command, though this command uses distutils' normal behavior if the ``--hidden`` option is omitted. """ user_options = SetuptoolsRegister.user_options + [ ('hidden', None, 'mark this release as hidden on PyPI by default') ] boolean_options = SetuptoolsRegister.boolean_options + ['hidden'] def initialize_options(self): SetuptoolsRegister.initialize_options(self) self.hidden = False def build_post_data(self, action): data = SetuptoolsRegister.build_post_data(self, action) if action == 'submit' and self.hidden: data['_pypi_hidden'] = '1' return data def _set_config(self): # The original register command is buggy--if you use .pypirc with a # server-login section *at all* the repository you specify with the -r # option will be overwritten with either the repository in .pypirc or # with the default, # If you do not have a .pypirc using the -r option will just crash. # Way to go distutils # If we don't set self.repository back to a default value _set_config # can crash if there was a user-supplied value for this option; don't # worry, we'll get the real value back afterwards self.repository = 'pypi' SetuptoolsRegister._set_config(self) options = self.distribution.get_option_dict('register') if 'repository' in options: source, value = options['repository'] # Really anything that came from setup.cfg or the command line # should override whatever was in .pypirc self.repository = value spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/setup_package.py0000644000077000000240000000017013100750165027720 0ustar adamstaff00000000000000from os.path import join def get_package_data(): return {'astropy_helpers.commands': [join('src', 'compiler.c')]} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/src/0000755000077000000240000000000013261442571025333 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/src/compiler.c0000644000077000000240000000573112533471373027322 0ustar adamstaff00000000000000#include /*************************************************************************** * Macros for determining the compiler version. * * These are borrowed from boost, and majorly abridged to include only * the compilers we care about. ***************************************************************************/ #ifndef PY3K #if PY_MAJOR_VERSION >= 3 #define PY3K 1 #else #define PY3K 0 #endif #endif #define STRINGIZE(X) DO_STRINGIZE(X) #define DO_STRINGIZE(X) #X #if defined __clang__ /* Clang C++ emulates GCC, so it has to appear early. */ # define COMPILER "Clang version " __clang_version__ #elif defined(__INTEL_COMPILER) || defined(__ICL) || defined(__ICC) || defined(__ECC) /* Intel */ # if defined(__INTEL_COMPILER) # define INTEL_VERSION __INTEL_COMPILER # elif defined(__ICL) # define INTEL_VERSION __ICL # elif defined(__ICC) # define INTEL_VERSION __ICC # elif defined(__ECC) # define INTEL_VERSION __ECC # endif # define COMPILER "Intel C compiler version " STRINGIZE(INTEL_VERSION) #elif defined(__GNUC__) /* gcc */ # define COMPILER "GCC version " __VERSION__ #elif defined(__SUNPRO_CC) /* Sun Workshop Compiler */ # define COMPILER "Sun compiler version " STRINGIZE(__SUNPRO_CC) #elif defined(_MSC_VER) /* Microsoft Visual C/C++ Must be last since other compilers define _MSC_VER for compatibility as well */ # if _MSC_VER < 1200 # define COMPILER_VERSION 5.0 # elif _MSC_VER < 1300 # define COMPILER_VERSION 6.0 # elif _MSC_VER == 1300 # define COMPILER_VERSION 7.0 # elif _MSC_VER == 1310 # define COMPILER_VERSION 7.1 # elif _MSC_VER == 1400 # define COMPILER_VERSION 8.0 # elif _MSC_VER == 1500 # define COMPILER_VERSION 9.0 # elif _MSC_VER == 1600 # define COMPILER_VERSION 10.0 # else # define COMPILER_VERSION _MSC_VER # endif # define COMPILER "Microsoft Visual C++ version " STRINGIZE(COMPILER_VERSION) #else /* Fallback */ # define COMPILER "Unknown compiler" #endif /*************************************************************************** * Module-level ***************************************************************************/ struct module_state { /* The Sun compiler can't handle empty structs */ #if defined(__SUNPRO_C) || defined(_MSC_VER) int _dummy; #endif }; #if PY3K static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, "_compiler", NULL, sizeof(struct module_state), NULL, NULL, NULL, NULL, NULL }; #define INITERROR return NULL PyMODINIT_FUNC PyInit__compiler(void) #else #define INITERROR return PyMODINIT_FUNC init_compiler(void) #endif { PyObject* m; #if PY3K m = PyModule_Create(&moduledef); #else m = Py_InitModule3("_compiler", NULL, NULL); #endif if (m == NULL) INITERROR; PyModule_AddStringConstant(m, "compiler", COMPILER); #if PY3K return m; #endif } spectral-cube-0.4.3/astropy_helpers/astropy_helpers/commands/test.py0000644000077000000240000000252413126505434026076 0ustar adamstaff00000000000000""" Different implementations of the ``./setup.py test`` command depending on what's locally available. If Astropy v1.1.0.dev or later is available it should be possible to import AstropyTest from ``astropy.tests.command``. If ``astropy`` can be imported but not ``astropy.tests.command`` (i.e. an older version of Astropy), we can use the backwards-compat implementation of the command. If Astropy can't be imported at all then there is a skeleton implementation that allows users to at least discover the ``./setup.py test`` command and learn that they need Astropy to run it. """ # Previously these except statements caught only ImportErrors, but there are # some other obscure exceptional conditions that can occur when importing # astropy.tests (at least on older versions) that can cause these imports to # fail try: import astropy # noqa try: from astropy.tests.command import AstropyTest except Exception: from ._test_compat import AstropyTest except Exception: # No astropy at all--provide the dummy implementation from ._dummy import _DummyCommand class AstropyTest(_DummyCommand): command_name = 'test' description = 'Run the tests for this package' error_msg = ( "The 'test' command requires the astropy package to be " "installed and importable.") spectral-cube-0.4.3/astropy_helpers/astropy_helpers/compat/0000755000077000000240000000000013261442571024226 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/compat/__init__.py0000644000077000000240000000056012340434262026333 0ustar adamstaff00000000000000def _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] spectral-cube-0.4.3/astropy_helpers/astropy_helpers/conftest.py0000644000077000000240000000315713245574455025161 0ustar adamstaff00000000000000# This file contains settings for pytest that are specific to astropy-helpers. # Since we run many of the tests in sub-processes, we need to collect coverage # data inside each subprocess and then combine it into a single .coverage file. # To do this we set up a list which run_setup appends coverage objects to. # This is not intended to be used by packages other than astropy-helpers. import os from collections import defaultdict try: from coverage import CoverageData except ImportError: HAS_COVERAGE = False else: HAS_COVERAGE = True if HAS_COVERAGE: SUBPROCESS_COVERAGE = [] def pytest_configure(config): if HAS_COVERAGE: SUBPROCESS_COVERAGE[:] = [] def pytest_unconfigure(config): if HAS_COVERAGE: # We create an empty coverage data object combined_cdata = CoverageData() lines = defaultdict(list) for cdata in SUBPROCESS_COVERAGE: # For each CoverageData object, we go through all the files and # change the filename from one which might be a temporary path # to the local filename. We then only keep files that actually # exist. for filename in cdata.measured_files(): try: pos = filename.rindex('astropy_helpers') except ValueError: continue short_filename = filename[pos:] if os.path.exists(short_filename): lines[os.path.abspath(short_filename)].extend(cdata.lines(filename)) combined_cdata.add_lines(lines) combined_cdata.write_file('.coverage.subprocess') spectral-cube-0.4.3/astropy_helpers/astropy_helpers/distutils_helpers.py0000644000077000000240000001736213126505434027072 0ustar adamstaff00000000000000""" This module contains various utilities for introspecting the distutils module and the setup process. Some of these utilities require the `astropy_helpers.setup_helpers.register_commands` function to be called first, as it will affect introspection of setuptools command-line arguments. Other utilities in this module do not have that restriction. """ import os import sys from distutils import ccompiler, log from distutils.dist import Distribution from distutils.errors import DistutilsError from .utils import silence # This function, and any functions that call it, require the setup in # `astropy_helpers.setup_helpers.register_commands` to be run first. def get_dummy_distribution(): """ Returns a distutils Distribution object used to instrument the setup environment before calling the actual setup() function. """ from .setup_helpers import _module_state if _module_state['registered_commands'] is None: raise RuntimeError( 'astropy_helpers.setup_helpers.register_commands() must be ' 'called before using ' 'astropy_helpers.setup_helpers.get_dummy_distribution()') # Pre-parse the Distutils command-line options and config files to if # the option is set. dist = Distribution({'script_name': os.path.basename(sys.argv[0]), 'script_args': sys.argv[1:]}) dist.cmdclass.update(_module_state['registered_commands']) with silence(): try: dist.parse_config_files() dist.parse_command_line() except (DistutilsError, AttributeError, SystemExit): # Let distutils handle DistutilsErrors itself AttributeErrors can # get raise for ./setup.py --help SystemExit can be raised if a # display option was used, for example pass return dist def get_distutils_option(option, commands): """ Returns the value of the given distutils option. Parameters ---------- option : str The name of the option commands : list of str The list of commands on which this option is available Returns ------- val : str or None the value of the given distutils option. If the option is not set, returns None. """ dist = get_dummy_distribution() for cmd in commands: cmd_opts = dist.command_options.get(cmd) if cmd_opts is not None and option in cmd_opts: return cmd_opts[option][1] else: return None def get_distutils_build_option(option): """ Returns the value of the given distutils build option. Parameters ---------- option : str The name of the option Returns ------- val : str or None The value of the given distutils build option. If the option is not set, returns None. """ return get_distutils_option(option, ['build', 'build_ext', 'build_clib']) def get_distutils_install_option(option): """ Returns the value of the given distutils install option. Parameters ---------- option : str The name of the option Returns ------- val : str or None The value of the given distutils build option. If the option is not set, returns None. """ return get_distutils_option(option, ['install']) def get_distutils_build_or_install_option(option): """ Returns the value of the given distutils build or install option. Parameters ---------- option : str The name of the option Returns ------- val : str or None The value of the given distutils build or install option. If the option is not set, returns None. """ return get_distutils_option(option, ['build', 'build_ext', 'build_clib', 'install']) def get_compiler_option(): """ Determines the compiler that will be used to build extension modules. Returns ------- compiler : str The compiler option specified for the build, build_ext, or build_clib command; or the default compiler for the platform if none was specified. """ compiler = get_distutils_build_option('compiler') if compiler is None: return ccompiler.get_default_compiler() return compiler def add_command_option(command, name, doc, is_bool=False): """ Add a custom option to a setup command. Issues a warning if the option already exists on that command. Parameters ---------- command : str The name of the command as given on the command line name : str The name of the build option doc : str A short description of the option, for the `--help` message is_bool : bool, optional When `True`, the option is a boolean option and doesn't require an associated value. """ dist = get_dummy_distribution() cmdcls = dist.get_command_class(command) if (hasattr(cmdcls, '_astropy_helpers_options') and name in cmdcls._astropy_helpers_options): return attr = name.replace('-', '_') if hasattr(cmdcls, attr): raise RuntimeError( '{0!r} already has a {1!r} class attribute, barring {2!r} from ' 'being usable as a custom option name.'.format(cmdcls, attr, name)) for idx, cmd in enumerate(cmdcls.user_options): if cmd[0] == name: log.warn('Overriding existing {0!r} option ' '{1!r}'.format(command, name)) del cmdcls.user_options[idx] if name in cmdcls.boolean_options: cmdcls.boolean_options.remove(name) break cmdcls.user_options.append((name, None, doc)) if is_bool: cmdcls.boolean_options.append(name) # Distutils' command parsing requires that a command object have an # attribute with the same name as the option (with '-' replaced with '_') # in order for that option to be recognized as valid setattr(cmdcls, attr, None) # This caches the options added through add_command_option so that if it is # run multiple times in the same interpreter repeated adds are ignored # (this way we can still raise a RuntimeError if a custom option overrides # a built-in option) if not hasattr(cmdcls, '_astropy_helpers_options'): cmdcls._astropy_helpers_options = set([name]) else: cmdcls._astropy_helpers_options.add(name) def get_distutils_display_options(): """ Returns a set of all the distutils display options in their long and short forms. These are the setup.py arguments such as --name or --version which print the project's metadata and then exit. Returns ------- opts : set The long and short form display option arguments, including the - or -- """ short_display_opts = set('-' + o[1] for o in Distribution.display_options if o[1]) long_display_opts = set('--' + o[0] for o in Distribution.display_options) # Include -h and --help which are not explicitly listed in # Distribution.display_options (as they are handled by optparse) short_display_opts.add('-h') long_display_opts.add('--help') # This isn't the greatest approach to hardcode these commands. # However, there doesn't seem to be a good way to determine # whether build *will be* run as part of the command at this # phase. display_commands = set([ 'clean', 'register', 'setopt', 'saveopts', 'egg_info', 'alias']) return short_display_opts.union(long_display_opts.union(display_commands)) def is_distutils_display_option(): """ Returns True if sys.argv contains any of the distutils display options such as --version or --name. """ display_options = get_distutils_display_options() return bool(set(sys.argv[1:]).intersection(display_options)) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/0000755000077000000240000000000013261442571024250 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/__init__.py0000644000077000000240000000111513126505434026355 0ustar adamstaff00000000000000# The ``astropy_helpers.extern`` sub-module includes modules developed elsewhere # that are bundled here for convenience. At the moment, this consists of the # following two sphinx extensions: # # * `numpydoc `_, a Sphinx extension # developed as part of the Numpy project. This is used to parse docstrings # in Numpy format # # * `sphinx-automodapi `_, a Sphinx # developed as part of the Astropy project. This used to be developed directly # in ``astropy-helpers`` but is now a standalone package. spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/0000755000077000000240000000000013261442571026412 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/__init__.py0000644000077000000240000000002413242700737030517 0ustar adamstaff00000000000000__version__ = '0.7' spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/autodoc_enhancements.py0000644000077000000240000001230413126505434033150 0ustar adamstaff00000000000000""" Miscellaneous enhancements to help autodoc along. """ import inspect import sys import types import sphinx from distutils.version import LooseVersion from sphinx.ext.autodoc import AttributeDocumenter, ModuleDocumenter from sphinx.util.inspect import isdescriptor if sys.version_info[0] == 3: class_types = (type,) else: class_types = (type, types.ClassType) SPHINX_LT_15 = (LooseVersion(sphinx.__version__) < LooseVersion('1.5')) MethodDescriptorType = type(type.__subclasses__) # See # https://github.com/astropy/astropy-helpers/issues/116#issuecomment-71254836 # for further background on this. def type_object_attrgetter(obj, attr, *defargs): """ This implements an improved attrgetter for type objects (i.e. classes) that can handle class attributes that are implemented as properties on a metaclass. Normally `getattr` on a class with a `property` (say, "foo"), would return the `property` object itself. However, if the class has a metaclass which *also* defines a `property` named "foo", ``getattr(cls, 'foo')`` will find the "foo" property on the metaclass and resolve it. For the purposes of autodoc we just want to document the "foo" property defined on the class, not on the metaclass. For example:: >>> class Meta(type): ... @property ... def foo(cls): ... return 'foo' ... >>> class MyClass(metaclass=Meta): ... @property ... def foo(self): ... \"\"\"Docstring for MyClass.foo property.\"\"\" ... return 'myfoo' ... >>> getattr(MyClass, 'foo') 'foo' >>> type_object_attrgetter(MyClass, 'foo') >>> type_object_attrgetter(MyClass, 'foo').__doc__ 'Docstring for MyClass.foo property.' The last line of the example shows the desired behavior for the purposes of autodoc. """ for base in obj.__mro__: if attr in base.__dict__: if isinstance(base.__dict__[attr], property): # Note, this should only be used for properties--for any other # type of descriptor (classmethod, for example) this can mess # up existing expectations of what getattr(cls, ...) returns return base.__dict__[attr] break return getattr(obj, attr, *defargs) if SPHINX_LT_15: # Provided to work around a bug in Sphinx # See https://github.com/sphinx-doc/sphinx/pull/1843 class AttributeDocumenter(AttributeDocumenter): @classmethod def can_document_member(cls, member, membername, isattr, parent): non_attr_types = cls.method_types + class_types + \ (MethodDescriptorType,) isdatadesc = isdescriptor(member) and not \ isinstance(member, non_attr_types) and not \ type(member).__name__ == "instancemethod" # That last condition addresses an obscure case of C-defined # methods using a deprecated type in Python 3, that is not # otherwise exported anywhere by Python return isdatadesc or (not isinstance(parent, ModuleDocumenter) and not inspect.isroutine(member) and not isinstance(member, class_types)) def setup(app): # Must have the autodoc extension set up first so we can override it app.setup_extension('sphinx.ext.autodoc') # Need to import this too since it re-registers all the documenter types # =_= import sphinx.ext.autosummary.generate app.add_autodoc_attrgetter(type, type_object_attrgetter) if sphinx.version_info < (1, 4, 2): # this is a really ugly hack to supress a warning that sphinx 1.4 # generates when overriding an existing directive (which is *desired* # behavior here). As of sphinx v1.4.2, this has been fixed: # https://github.com/sphinx-doc/sphinx/issues/2451 # But we leave it in for 1.4.0/1.4.1 . But if the "needs_sphinx" is # eventually updated to >= 1.4.2, this should be removed entirely (in # favor of the line in the "else" clause) _oldwarn = app._warning _oldwarncount = app._warncount try: try: # *this* is in a try/finally because we don't want to force six as # a real dependency. In sphinx 1.4, six is a prerequisite, so # there's no issue. But in older sphinxes this may not be true... # but the inderlying warning is absent anyway so we let it slide. from six import StringIO app._warning = StringIO() except ImportError: pass app.add_autodocumenter(AttributeDocumenter) finally: app._warning = _oldwarn app._warncount = _oldwarncount else: suppress_warnigns_orig = app.config.suppress_warnings[:] if 'app.add_directive' not in app.config.suppress_warnings: app.config.suppress_warnings.append('app.add_directive') try: app.add_autodocumenter(AttributeDocumenter) finally: app.config.suppress_warnings = suppress_warnigns_orig spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/automodapi.py0000644000077000000240000003665113126505434031137 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This directive takes a single argument that must be a module or package. It will produce a block of documentation that includes the docstring for the package, an :ref:`automodsumm` directive, and an :ref:`automod-diagram` if there are any classes in the module. If only the main docstring of the module/package is desired in the documentation, use `automodule`_ instead of `automodapi`_. It accepts the following options: * ``:include-all-objects:`` If present, include not just functions and classes, but all objects. This includes variables, for which a possible docstring after the variable definition will be shown. * ``:no-inheritance-diagram:`` If present, the inheritance diagram will not be shown even if the module/package has classes. * ``:skip: str`` This option results in the specified object being skipped, that is the object will *not* be included in the generated documentation. This option may appear any number of times to skip multiple objects. * ``:no-main-docstr:`` If present, the docstring for the module/package will not be generated. The function and class tables will still be used, however. * ``:headings: str`` Specifies the characters (in one string) used as the heading levels used for the generated section. This must have at least 2 characters (any after 2 will be ignored). This also *must* match the rest of the documentation on this page for sphinx to be happy. Defaults to "-^", which matches the convention used for Python's documentation, assuming the automodapi call is inside a top-level section (which usually uses '='). * ``:no-heading:`` If specified do not create a top level heading for the section. That is, do not create a title heading with text like "packagename Package". The actual docstring for the package/module will still be shown, though, unless ``:no-main-docstr:`` is given. * ``:allowed-package-names: str`` Specifies the packages that functions/classes documented here are allowed to be from, as comma-separated list of package names. If not given, only objects that are actually in a subpackage of the package currently being documented are included. * ``:inherited-members:`` / ``:no-inherited-members:`` The global sphinx configuration option ``automodsumm_inherited_members`` decides if members that a class inherits from a base class are included in the generated documentation. The option ``:inherited-members:`` or ``:no-inherited-members:`` allows the user to overrride the global setting. This extension also adds three sphinx configuration options: * ``automodapi_toctreedirnm`` This must be a string that specifies the name of the directory the automodsumm generated documentation ends up in. This directory path should be relative to the documentation root (e.g., same place as ``index.rst``). Defaults to ``'api'``. * ``automodapi_writereprocessed`` Should be a bool, and if `True`, will cause `automodapi`_ to write files with any `automodapi`_ sections replaced with the content Sphinx processes after `automodapi`_ has run. The output files are not actually used by sphinx, so this option is only for figuring out the cause of sphinx warnings or other debugging. Defaults to `False`. * ``automodsumm_inherited_members`` Should be a bool and if ``True`` members that a class inherits from a base class are included in the generated documentation. Defaults to ``False``. .. _automodule: http://sphinx-doc.org/latest/ext/autodoc.html?highlight=automodule#directive-automodule """ # Implementation note: # The 'automodapi' directive is not actually implemented as a docutils # directive. Instead, this extension searches for the 'automodapi' text in # all sphinx documents, and replaces it where necessary from a template built # into this extension. This is necessary because automodsumm (and autosummary) # use the "builder-inited" event, which comes before the directives are # actually built. import inspect import io import os import re import sys from .utils import find_mod_objs if sys.version_info[0] == 3: text_type = str else: text_type = unicode automod_templ_modheader = """ {modname} {pkgormod} {modhds}{pkgormodhds} {automoduleline} """ automod_templ_classes = """ Classes {clshds} .. automodsumm:: {modname} :classes-only: {clsfuncoptions} """ automod_templ_funcs = """ Functions {funchds} .. automodsumm:: {modname} :functions-only: {clsfuncoptions} """ automod_templ_vars = """ Variables {otherhds} .. automodsumm:: {modname} :variables-only: {clsfuncoptions} """ automod_templ_inh = """ Class Inheritance Diagram {clsinhsechds} .. automod-diagram:: {modname} :private-bases: :parts: 1 {allowedpkgnms} {skip} """ _automodapirex = re.compile(r'^(?:\.\.\s+automodapi::\s*)([A-Za-z0-9_.]+)' r'\s*$((?:\n\s+:[a-zA-Z_\-]+:.*$)*)', flags=re.MULTILINE) # the last group of the above regex is intended to go into finall with the below _automodapiargsrex = re.compile(r':([a-zA-Z_\-]+):(.*)$', flags=re.MULTILINE) def automodapi_replace(sourcestr, app, dotoctree=True, docname=None, warnings=True): """ Replaces `sourcestr`'s entries of ".. automdapi::" with the automodapi template form based on provided options. This is used with the sphinx event 'source-read' to replace `automodapi`_ entries before sphinx actually processes them, as automodsumm needs the code to be present to generate stub documentation. Parameters ---------- sourcestr : str The string with sphinx source to be checked for automodapi replacement. app : `sphinx.application.Application` The sphinx application. dotoctree : bool If `True`, a ":toctree:" option will be added in the ".. automodsumm::" sections of the template, pointing to the appropriate "generated" directory based on the Astropy convention (e.g. in ``docs/api``) docname : str The name of the file for this `sourcestr` (if known - if not, it can be `None`). If not provided and `dotoctree` is `True`, the generated files may end up in the wrong place. warnings : bool If `False`, all warnings that would normally be issued are silenced. Returns ------- newstr :str The string with automodapi entries replaced with the correct sphinx markup. """ spl = _automodapirex.split(sourcestr) if len(spl) > 1: # automodsumm is in this document # Use app.srcdir because api folder should be inside source folder not # at folder where sphinx is run. if dotoctree: toctreestr = ':toctree: ' api_dir = os.path.join(app.srcdir, app.config.automodapi_toctreedirnm) if docname is None: doc_path = '.' else: doc_path = os.path.join(app.srcdir, docname) toctreestr += os.path.relpath(api_dir, os.path.dirname(doc_path)) else: toctreestr = '' newstrs = [spl[0]] for grp in range(len(spl) // 3): modnm = spl[grp * 3 + 1] # find where this is in the document for warnings if docname is None: location = None else: location = (docname, spl[0].count('\n')) # initialize default options toskip = [] inhdiag = maindocstr = top_head = True hds = '-^' allowedpkgnms = [] allowothers = False # look for actual options unknownops = [] inherited_members = None for opname, args in _automodapiargsrex.findall(spl[grp * 3 + 2]): if opname == 'skip': toskip.append(args.strip()) elif opname == 'no-inheritance-diagram': inhdiag = False elif opname == 'no-main-docstr': maindocstr = False elif opname == 'headings': hds = args elif opname == 'no-heading': top_head = False elif opname == 'allowed-package-names': allowedpkgnms.append(args.strip()) elif opname == 'inherited-members': inherited_members = True elif opname == 'no-inherited-members': inherited_members = False elif opname == 'include-all-objects': allowothers = True else: unknownops.append(opname) # join all the allowedpkgnms if len(allowedpkgnms) == 0: allowedpkgnms = '' onlylocals = True else: allowedpkgnms = ':allowed-package-names: ' + ','.join(allowedpkgnms) onlylocals = allowedpkgnms # get the two heading chars if len(hds) < 2: msg = 'Not enough headings (got {0}, need 2), using default -^' if warnings: app.warn(msg.format(len(hds)), location) hds = '-^' h1, h2 = hds.lstrip()[:2] # tell sphinx that the remaining args are invalid. if len(unknownops) > 0 and app is not None: opsstrs = ','.join(unknownops) msg = 'Found additional options ' + opsstrs + ' in automodapi.' if warnings: app.warn(msg, location) ispkg, hascls, hasfuncs, hasother = _mod_info( modnm, toskip, onlylocals=onlylocals) # add automodule directive only if no-main-docstr isn't present if maindocstr: automodline = '.. automodule:: {modname}'.format(modname=modnm) else: automodline = '' if top_head: newstrs.append(automod_templ_modheader.format( modname=modnm, modhds=h1 * len(modnm), pkgormod='Package' if ispkg else 'Module', pkgormodhds=h1 * (8 if ispkg else 7), automoduleline=automodline)) # noqa else: newstrs.append(automod_templ_modheader.format( modname='', modhds='', pkgormod='', pkgormodhds='', automoduleline=automodline)) # construct the options for the class/function sections # start out indented at 4 spaces, but need to keep the indentation. clsfuncoptions = [] if toctreestr: clsfuncoptions.append(toctreestr) if toskip: clsfuncoptions.append(':skip: ' + ','.join(toskip)) if allowedpkgnms: clsfuncoptions.append(allowedpkgnms) if hascls: # This makes no sense unless there are classes. if inherited_members is True: clsfuncoptions.append(':inherited-members:') if inherited_members is False: clsfuncoptions.append(':no-inherited-members:') clsfuncoptionstr = '\n '.join(clsfuncoptions) if hasfuncs: newstrs.append(automod_templ_funcs.format( modname=modnm, funchds=h2 * 9, clsfuncoptions=clsfuncoptionstr)) if hascls: newstrs.append(automod_templ_classes.format( modname=modnm, clshds=h2 * 7, clsfuncoptions=clsfuncoptionstr)) if allowothers and hasother: newstrs.append(automod_templ_vars.format( modname=modnm, otherhds=h2 * 9, clsfuncoptions=clsfuncoptionstr)) if inhdiag and hascls: # add inheritance diagram if any classes are in the module if toskip: clsskip = ':skip: ' + ','.join(toskip) else: clsskip = '' diagram_entry = automod_templ_inh.format( modname=modnm, clsinhsechds=h2 * 25, allowedpkgnms=allowedpkgnms, skip=clsskip) diagram_entry = diagram_entry.replace(' \n', '') newstrs.append(diagram_entry) newstrs.append(spl[grp * 3 + 3]) newsourcestr = ''.join(newstrs) if app.config.automodapi_writereprocessed: # sometimes they are unicode, sometimes not, depending on how # sphinx has processed things if isinstance(newsourcestr, text_type): ustr = newsourcestr else: ustr = newsourcestr.decode(app.config.source_encoding) if docname is None: with io.open(os.path.join(app.srcdir, 'unknown.automodapi'), 'a', encoding='utf8') as f: f.write(u'\n**NEW DOC**\n\n') f.write(ustr) else: env = app.builder.env # Determine the filename associated with this doc (specifically # the extension) filename = docname + os.path.splitext(env.doc2path(docname))[1] filename += '.automodapi' with io.open(os.path.join(app.srcdir, filename), 'w', encoding='utf8') as f: f.write(ustr) return newsourcestr else: return sourcestr def _mod_info(modname, toskip=[], onlylocals=True): """ Determines if a module is a module or a package and whether or not it has classes or functions. """ hascls = hasfunc = hasother = False for localnm, fqnm, obj in zip(*find_mod_objs(modname, onlylocals=onlylocals)): if localnm not in toskip: hascls = hascls or inspect.isclass(obj) hasfunc = hasfunc or inspect.isroutine(obj) hasother = hasother or (not inspect.isclass(obj) and not inspect.isroutine(obj)) if hascls and hasfunc and hasother: break # find_mod_objs has already imported modname # TODO: There is probably a cleaner way to do this, though this is pretty # reliable for all Python versions for most cases that we care about. pkg = sys.modules[modname] ispkg = (hasattr(pkg, '__file__') and isinstance(pkg.__file__, str) and os.path.split(pkg.__file__)[1].startswith('__init__.py')) return ispkg, hascls, hasfunc, hasother def process_automodapi(app, docname, source): source[0] = automodapi_replace(source[0], app, True, docname) def setup(app): app.setup_extension('sphinx.ext.autosummary') # Note: we use __name__ here instead of just writing the module name in # case this extension is bundled into another package from . import automodsumm app.setup_extension(automodsumm.__name__) app.connect('source-read', process_automodapi) app.add_config_value('automodapi_toctreedirnm', 'api', True) app.add_config_value('automodapi_writereprocessed', False, True) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/automodsumm.py0000644000077000000240000006363013242700737031346 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This directive will produce an "autosummary"-style table for public attributes of a specified module. See the `sphinx.ext.autosummary`_ extension for details on this process. The main difference from the `autosummary`_ directive is that `autosummary`_ requires manually inputting all attributes that appear in the table, while this captures the entries automatically. This directive requires a single argument that must be a module or package. It also accepts any options supported by the `autosummary`_ directive- see `sphinx.ext.autosummary`_ for details. It also accepts some additional options: * ``:classes-only:`` If present, the autosummary table will only contain entries for classes. This cannot be used at the same time with ``:functions-only:`` or ``:variables-only:``. * ``:functions-only:`` If present, the autosummary table will only contain entries for functions. This cannot be used at the same time with ``:classes-only:`` or ``:variables-only:``. * ``:variables-only:`` If present, the autosummary table will only contain entries for variables (everything except functions and classes). This cannot be used at the same time with ``:classes-only:`` or ``:functions-only:``. * ``:skip: obj1, [obj2, obj3, ...]`` If present, specifies that the listed objects should be skipped and not have their documentation generated, nor be included in the summary table. * ``:allowed-package-names: pkgormod1, [pkgormod2, pkgormod3, ...]`` Specifies the packages that functions/classes documented here are allowed to be from, as comma-separated list of package names. If not given, only objects that are actually in a subpackage of the package currently being documented are included. * ``:inherited-members:`` or ``:no-inherited-members:`` The global sphinx configuration option ``automodsumm_inherited_members`` decides if members that a class inherits from a base class are included in the generated documentation. The flags ``:inherited-members:`` or ``:no-inherited-members:`` allows overrriding this global setting. This extension also adds two sphinx configuration options: * ``automodsumm_writereprocessed`` Should be a bool, and if ``True``, will cause `automodsumm`_ to write files with any ``automodsumm`` sections replaced with the content Sphinx processes after ``automodsumm`` has run. The output files are not actually used by sphinx, so this option is only for figuring out the cause of sphinx warnings or other debugging. Defaults to ``False``. * ``automodsumm_inherited_members`` Should be a bool and if ``True``, will cause `automodsumm`_ to document class members that are inherited from a base class. This value can be overriden for any particular automodsumm directive by including the ``:inherited-members:`` or ``:no-inherited-members:`` options. Defaults to ``False``. .. _sphinx.ext.autosummary: http://sphinx-doc.org/latest/ext/autosummary.html .. _autosummary: http://sphinx-doc.org/latest/ext/autosummary.html#directive-autosummary .. _automod-diagram: automod-diagram directive ========================= This directive will produce an inheritance diagram like that of the `sphinx.ext.inheritance_diagram`_ extension. This directive requires a single argument that must be a module or package. It accepts no options. .. note:: Like 'inheritance-diagram', 'automod-diagram' requires `graphviz `_ to generate the inheritance diagram. .. _sphinx.ext.inheritance_diagram: http://sphinx-doc.org/latest/ext/inheritance.html """ import inspect import os import re import io from distutils.version import LooseVersion from sphinx import __version__ from sphinx.ext.autosummary import Autosummary from sphinx.ext.inheritance_diagram import InheritanceDiagram from docutils.parsers.rst.directives import flag from .utils import find_mod_objs, cleanup_whitespace SPHINX_LT_17 = LooseVersion(__version__) < LooseVersion('1.7') def _str_list_converter(argument): """ A directive option conversion function that converts the option into a list of strings. Used for 'skip' option. """ if argument is None: return [] else: return [s.strip() for s in argument.split(',')] class Automodsumm(Autosummary): required_arguments = 1 optional_arguments = 0 final_argument_whitespace = False has_content = False option_spec = dict(Autosummary.option_spec) option_spec['functions-only'] = flag option_spec['classes-only'] = flag option_spec['variables-only'] = flag option_spec['skip'] = _str_list_converter option_spec['allowed-package-names'] = _str_list_converter option_spec['inherited-members'] = flag option_spec['no-inherited-members'] = flag def run(self): env = self.state.document.settings.env modname = self.arguments[0] self.warnings = [] nodelist = [] try: localnames, fqns, objs = find_mod_objs(modname) except ImportError: self.warnings = [] self.warn("Couldn't import module " + modname) return self.warnings try: # set self.content to trick the autosummary internals. # Be sure to respect functions-only and classes-only. funconly = 'functions-only' in self.options clsonly = 'classes-only' in self.options varonly = 'variables-only' in self.options if [clsonly, funconly, varonly].count(True) > 1: self.warning('more than one of functions-only, classes-only, ' 'or variables-only defined. Ignoring.') clsonly = funconly = varonly = False skipnames = [] if 'skip' in self.options: option_skipnames = set(self.options['skip']) for lnm in localnames: if lnm in option_skipnames: option_skipnames.remove(lnm) skipnames.append(lnm) if len(option_skipnames) > 0: self.warn('Tried to skip objects {objs} in module {mod}, ' 'but they were not present. Ignoring.' .format(objs=option_skipnames, mod=modname)) if funconly: cont = [] for nm, obj in zip(localnames, objs): if nm not in skipnames and inspect.isroutine(obj): cont.append(nm) elif clsonly: cont = [] for nm, obj in zip(localnames, objs): if nm not in skipnames and inspect.isclass(obj): cont.append(nm) elif varonly: cont = [] for nm, obj in zip(localnames, objs): if nm not in skipnames and not (inspect.isclass(obj) or inspect.isroutine(obj)): cont.append(nm) else: cont = [nm for nm in localnames if nm not in skipnames] self.content = cont # for some reason, even though ``currentmodule`` is substituted in, # sphinx doesn't necessarily recognize this fact. So we just force # it internally, and that seems to fix things env.temp_data['py:module'] = modname env.ref_context['py:module'] = modname # can't use super because Sphinx/docutils has trouble return # super(Autosummary,self).run() nodelist.extend(Autosummary.run(self)) return self.warnings + nodelist finally: # has_content = False for the Automodsumm self.content = [] def get_items(self, names): self.genopt['imported-members'] = True return Autosummary.get_items(self, names) # <-------------------automod-diagram stuff-----------------------------------> class Automoddiagram(InheritanceDiagram): option_spec = dict(InheritanceDiagram.option_spec) option_spec['allowed-package-names'] = _str_list_converter option_spec['skip'] = _str_list_converter def run(self): try: ols = self.options.get('allowed-package-names', []) ols = True if len(ols) == 0 else ols # if none are given, assume only local nms, objs = find_mod_objs(self.arguments[0], onlylocals=ols)[1:] except ImportError: self.warnings = [] self.warn("Couldn't import module " + self.arguments[0]) return self.warnings # Check if some classes should be skipped skip = self.options.get('skip', []) clsnms = [] for n, o in zip(nms, objs): if n.split('.')[-1] in skip: continue if inspect.isclass(o): clsnms.append(n) oldargs = self.arguments try: if len(clsnms) > 0: self.arguments = [' '.join(clsnms)] return InheritanceDiagram.run(self) finally: self.arguments = oldargs # <---------------------automodsumm generation stuff--------------------------> def process_automodsumm_generation(app): env = app.builder.env filestosearch = [] for docname in env.found_docs: filename = env.doc2path(docname) if os.path.isfile(filename): filestosearch.append(docname + os.path.splitext(filename)[1]) liness = [] for sfn in filestosearch: lines = automodsumm_to_autosummary_lines(sfn, app) liness.append(lines) if app.config.automodsumm_writereprocessed: if lines: # empty list means no automodsumm entry is in the file outfn = os.path.join(app.srcdir, sfn) + '.automodsumm' with open(outfn, 'w') as f: for l in lines: f.write(l) f.write('\n') for sfn, lines in zip(filestosearch, liness): suffix = os.path.splitext(sfn)[1] if len(lines) > 0: generate_automodsumm_docs( lines, sfn, app=app, builder=app.builder, warn=app.warn, info=app.info, suffix=suffix, base_path=app.srcdir, inherited_members=app.config.automodsumm_inherited_members) # _automodsummrex = re.compile(r'^(\s*)\.\. automodsumm::\s*([A-Za-z0-9_.]+)\s*' # r'\n\1(\s*)(\S|$)', re.MULTILINE) _lineendrex = r'(?:\n|$)' _hdrex = r'^\n?(\s*)\.\. automodsumm::\s*(\S+)\s*' + _lineendrex _oprex1 = r'(?:\1(\s+)\S.*' + _lineendrex + ')' _oprex2 = r'(?:\1\4\S.*' + _lineendrex + ')' _automodsummrex = re.compile(_hdrex + '(' + _oprex1 + '?' + _oprex2 + '*)', re.MULTILINE) def automodsumm_to_autosummary_lines(fn, app): """ Generates lines from a file with an "automodsumm" entry suitable for feeding into "autosummary". Searches the provided file for `automodsumm` directives and returns a list of lines specifying the `autosummary` commands for the modules requested. This does *not* return the whole file contents - just an autosummary section in place of any :automodsumm: entries. Note that any options given for `automodsumm` are also included in the generated `autosummary` section. Parameters ---------- fn : str The name of the file to search for `automodsumm` entries. app : sphinx.application.Application The sphinx Application object Returns ------- lines : list of str Lines for all `automodsumm` entries with the entries replaced by `autosummary` and the module's members added. """ fullfn = os.path.join(app.builder.env.srcdir, fn) with io.open(fullfn, encoding='utf8') as fr: # Note: we use __name__ here instead of just writing the module name in # case this extension is bundled into another package from . import automodapi try: extensions = app.extensions except AttributeError: # Sphinx <1.6 extensions = app._extensions if automodapi.__name__ in extensions: # Must do the automodapi on the source to get the automodsumm # that might be in there docname = os.path.splitext(fn)[0] filestr = automodapi.automodapi_replace(fr.read(), app, True, docname, False) else: filestr = fr.read() spl = _automodsummrex.split(filestr) # 0th entry is the stuff before the first automodsumm line indent1s = spl[1::5] mods = spl[2::5] opssecs = spl[3::5] indent2s = spl[4::5] remainders = spl[5::5] # only grab automodsumm sections and convert them to autosummary with the # entries for all the public objects newlines = [] # loop over all automodsumms in this document for i, (i1, i2, modnm, ops, rem) in enumerate(zip(indent1s, indent2s, mods, opssecs, remainders)): allindent = i1 + (' ' if i2 is None else i2) # filter out functions-only, classes-only, and ariables-only # options if present. oplines = ops.split('\n') toskip = [] allowedpkgnms = [] funcsonly = clssonly = varsonly = False for i, ln in reversed(list(enumerate(oplines))): if ':functions-only:' in ln: funcsonly = True del oplines[i] if ':classes-only:' in ln: clssonly = True del oplines[i] if ':variables-only:' in ln: varsonly = True del oplines[i] if ':skip:' in ln: toskip.extend(_str_list_converter(ln.replace(':skip:', ''))) del oplines[i] if ':allowed-package-names:' in ln: allowedpkgnms.extend(_str_list_converter(ln.replace(':allowed-package-names:', ''))) del oplines[i] if [funcsonly, clssonly, varsonly].count(True) > 1: msg = ('Defined more than one of functions-only, classes-only, ' 'and variables-only. Skipping this directive.') lnnum = sum([spl[j].count('\n') for j in range(i * 5 + 1)]) app.warn('[automodsumm]' + msg, (fn, lnnum)) continue # Use the currentmodule directive so we can just put the local names # in the autosummary table. Note that this doesn't always seem to # actually "take" in Sphinx's eyes, so in `Automodsumm.run`, we have to # force it internally, as well. newlines.extend([i1 + '.. currentmodule:: ' + modnm, '', '.. autosummary::']) newlines.extend(oplines) ols = True if len(allowedpkgnms) == 0 else allowedpkgnms for nm, fqn, obj in zip(*find_mod_objs(modnm, onlylocals=ols)): if nm in toskip: continue if funcsonly and not inspect.isroutine(obj): continue if clssonly and not inspect.isclass(obj): continue if varsonly and (inspect.isclass(obj) or inspect.isroutine(obj)): continue newlines.append(allindent + nm) # add one newline at the end of the autosummary block newlines.append('') return newlines def generate_automodsumm_docs(lines, srcfn, app=None, suffix='.rst', warn=None, info=None, base_path=None, builder=None, template_dir=None, inherited_members=False): """ This function is adapted from `sphinx.ext.autosummary.generate.generate_autosummmary_docs` to generate source for the automodsumm directives that should be autosummarized. Unlike generate_autosummary_docs, this function is called one file at a time. """ from sphinx.jinja2glue import BuiltinTemplateLoader from sphinx.ext.autosummary import import_by_name, get_documenter from sphinx.ext.autosummary.generate import (_simple_info, _simple_warn) from sphinx.util.osutil import ensuredir from sphinx.util.inspect import safe_getattr from jinja2 import FileSystemLoader, TemplateNotFound from jinja2.sandbox import SandboxedEnvironment from .utils import find_autosummary_in_lines_for_automodsumm as find_autosummary_in_lines if info is None: info = _simple_info if warn is None: warn = _simple_warn # info('[automodsumm] generating automodsumm for: ' + srcfn) # Create our own templating environment - here we use Astropy's # templates rather than the default autosummary templates, in order to # allow docstrings to be shown for methods. template_dirs = [os.path.join(os.path.dirname(__file__), 'templates'), os.path.join(base_path, '_templates')] if builder is not None: # allow the user to override the templates template_loader = BuiltinTemplateLoader() template_loader.init(builder, dirs=template_dirs) else: if template_dir: template_dirs.insert(0, template_dir) template_loader = FileSystemLoader(template_dirs) template_env = SandboxedEnvironment(loader=template_loader) # read # items = find_autosummary_in_files(sources) items = find_autosummary_in_lines(lines, filename=srcfn) if len(items) > 0: msg = '[automodsumm] {1}: found {0} automodsumm entries to generate' info(msg.format(len(items), srcfn)) # gennms = [item[0] for item in items] # if len(gennms) > 20: # gennms = gennms[:10] + ['...'] + gennms[-10:] # info('[automodsumm] generating autosummary for: ' + ', '.join(gennms)) # remove possible duplicates items = list(set(items)) # keep track of new files new_files = [] # write for name, path, template_name, inherited_mem in sorted(items): if path is None: # The corresponding autosummary:: directive did not have # a :toctree: option continue path = os.path.abspath(os.path.join(base_path, path)) ensuredir(path) try: import_by_name_values = import_by_name(name) except ImportError as e: warn('[automodsumm] failed to import %r: %s' % (name, e)) continue # if block to accommodate Sphinx's v1.2.2 and v1.2.3 respectively if len(import_by_name_values) == 3: name, obj, parent = import_by_name_values elif len(import_by_name_values) == 4: name, obj, parent, module_name = import_by_name_values fn = os.path.join(path, name + suffix) # skip it if it exists if os.path.isfile(fn): continue new_files.append(fn) f = open(fn, 'w') try: if SPHINX_LT_17: doc = get_documenter(obj, parent) else: doc = get_documenter(app, obj, parent) if template_name is not None: template = template_env.get_template(template_name) else: tmplstr = 'autosummary_core/%s.rst' try: template = template_env.get_template(tmplstr % doc.objtype) except TemplateNotFound: template = template_env.get_template(tmplstr % 'base') def get_members_mod(obj, typ, include_public=[]): """ typ = None -> all """ items = [] for name in dir(obj): try: if SPHINX_LT_17: documenter = get_documenter(safe_getattr(obj, name), obj) else: documenter = get_documenter(app, safe_getattr(obj, name), obj) except AttributeError: continue if typ is None or documenter.objtype == typ: items.append(name) public = [x for x in items if x in include_public or not x.startswith('_')] return public, items def get_members_class(obj, typ, include_public=[], include_base=False): """ typ = None -> all include_base -> include attrs that are from a base class """ items = [] # using dir gets all of the attributes, including the elements # from the base class, otherwise use __slots__ or __dict__ if include_base: names = dir(obj) else: if hasattr(obj, '__slots__'): names = tuple(getattr(obj, '__slots__')) else: names = getattr(obj, '__dict__').keys() for name in names: try: if SPHINX_LT_17: documenter = get_documenter(safe_getattr(obj, name), obj) else: documenter = get_documenter(app, safe_getattr(obj, name), obj) except AttributeError: continue if typ is None or documenter.objtype == typ: items.append(name) public = [x for x in items if x in include_public or not x.startswith('_')] return public, items ns = {} if doc.objtype == 'module': ns['members'] = get_members_mod(obj, None) ns['functions'], ns['all_functions'] = \ get_members_mod(obj, 'function') ns['classes'], ns['all_classes'] = \ get_members_mod(obj, 'class') ns['exceptions'], ns['all_exceptions'] = \ get_members_mod(obj, 'exception') elif doc.objtype == 'class': if inherited_mem is not None: # option set in this specifc directive include_base = inherited_mem else: # use default value include_base = inherited_members api_class_methods = ['__init__', '__call__'] ns['members'] = get_members_class(obj, None, include_base=include_base) ns['methods'], ns['all_methods'] = \ get_members_class(obj, 'method', api_class_methods, include_base=include_base) ns['attributes'], ns['all_attributes'] = \ get_members_class(obj, 'attribute', include_base=include_base) ns['methods'].sort() ns['attributes'].sort() parts = name.split('.') if doc.objtype in ('method', 'attribute'): mod_name = '.'.join(parts[:-2]) cls_name = parts[-2] obj_name = '.'.join(parts[-2:]) ns['class'] = cls_name else: mod_name, obj_name = '.'.join(parts[:-1]), parts[-1] ns['fullname'] = name ns['module'] = mod_name ns['objname'] = obj_name ns['name'] = parts[-1] ns['objtype'] = doc.objtype ns['underline'] = len(obj_name) * '=' # We now check whether a file for reference footnotes exists for # the module being documented. We first check if the # current module is a file or a directory, as this will give a # different path for the reference file. For example, if # documenting astropy.wcs then the reference file is at # ../wcs/references.txt, while if we are documenting # astropy.config.logging_helper (which is at # astropy/config/logging_helper.py) then the reference file is set # to ../config/references.txt if '.' in mod_name: mod_name_dir = mod_name.replace('.', '/').split('/', 1)[1] else: mod_name_dir = mod_name if not os.path.isdir(os.path.join(base_path, mod_name_dir)) \ and os.path.isdir(os.path.join(base_path, mod_name_dir.rsplit('/', 1)[0])): mod_name_dir = mod_name_dir.rsplit('/', 1)[0] # We then have to check whether it exists, and if so, we pass it # to the template. if os.path.exists(os.path.join(base_path, mod_name_dir, 'references.txt')): # An important subtlety here is that the path we pass in has # to be relative to the file being generated, so we have to # figure out the right number of '..'s ndirsback = path.replace(base_path, '').count('/') ref_file_rel_segments = ['..'] * ndirsback ref_file_rel_segments.append(mod_name_dir) ref_file_rel_segments.append('references.txt') ns['referencefile'] = os.path.join(*ref_file_rel_segments) rendered = template.render(**ns) f.write(cleanup_whitespace(rendered)) finally: f.close() def setup(app): # need autodoc fixes # Note: we use __name__ here instead of just writing the module name in # case this extension is bundled into another package from . import autodoc_enhancements app.setup_extension(autodoc_enhancements.__name__) # need inheritance-diagram for automod-diagram app.setup_extension('sphinx.ext.inheritance_diagram') app.add_directive('automod-diagram', Automoddiagram) app.add_directive('automodsumm', Automodsumm) app.connect('builder-inited', process_automodsumm_generation) app.add_config_value('automodsumm_writereprocessed', False, True) app.add_config_value('automodsumm_inherited_members', False, 'env') spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/smart_resolver.py0000644000077000000240000000717713126505434032045 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ The classes in the astropy docs are documented by their API location, which is not necessarily where they are defined in the source. This causes a problem when certain automated features of the doc build, such as the inheritance diagrams or the `Bases` list of a class reference a class by its canonical location rather than its "user" location. In the `autodoc-process-docstring` event, a mapping from the actual name to the API name is maintained. Later, in the `missing-reference` event, unresolved references are looked up in this dictionary and corrected if possible. """ from docutils.nodes import literal, reference def process_docstring(app, what, name, obj, options, lines): if isinstance(obj, type): env = app.env if not hasattr(env, 'class_name_mapping'): env.class_name_mapping = {} mapping = env.class_name_mapping mapping[obj.__module__ + '.' + obj.__name__] = name def missing_reference_handler(app, env, node, contnode): if not hasattr(env, 'class_name_mapping'): env.class_name_mapping = {} mapping = env.class_name_mapping reftype = node['reftype'] reftarget = node['reftarget'] if reftype in ('obj', 'class', 'exc', 'meth'): reftarget = node['reftarget'] suffix = '' if reftarget not in mapping: if '.' in reftarget: front, suffix = reftarget.rsplit('.', 1) else: suffix = reftarget if suffix.startswith('_') and not suffix.startswith('__'): # If this is a reference to a hidden class or method, # we can't link to it, but we don't want to have a # nitpick warning. return node[0].deepcopy() if reftype in ('obj', 'meth') and '.' in reftarget: if front in mapping: reftarget = front suffix = '.' + suffix if (reftype in ('class', ) and '.' in reftarget and reftarget not in mapping): if '.' in front: reftarget, _ = front.rsplit('.', 1) suffix = '.' + suffix reftarget = reftarget + suffix prefix = reftarget.rsplit('.')[0] inventory = env.intersphinx_named_inventory if (reftarget not in mapping and prefix in inventory): if reftarget in inventory[prefix]['py:class']: newtarget = inventory[prefix]['py:class'][reftarget][2] if not node['refexplicit'] and \ '~' not in node.rawsource: contnode = literal(text=reftarget) newnode = reference('', '', internal=True) newnode['reftitle'] = reftarget newnode['refuri'] = newtarget newnode.append(contnode) return newnode if reftarget in mapping: newtarget = mapping[reftarget] + suffix if not node['refexplicit'] and '~' not in node.rawsource: contnode = literal(text=newtarget) newnode = env.domains['py'].resolve_xref( env, node['refdoc'], app.builder, 'class', newtarget, node, contnode) if newnode is not None: newnode['reftitle'] = reftarget return newnode def setup(app): app.connect('autodoc-process-docstring', process_docstring) app.connect('missing-reference', missing_reference_handler) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/0000755000077000000240000000000013261442571030410 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/autosummary_core/0000755000077000000240000000000013261442571034006 5ustar adamstaff00000000000000././@LongLink0000000000000000000000000000015200000000000011213 Lustar 00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/autosummary_core/base.rstspectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/autosummary_core/bas0000644000077000000240000000025213126505434034473 0ustar adamstaff00000000000000{% if referencefile %} .. include:: {{ referencefile }} {% endif %} {{ objname }} {{ underline }} .. currentmodule:: {{ module }} .. auto{{ objtype }}:: {{ objname }} ././@LongLink0000000000000000000000000000015300000000000011214 Lustar 00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/autosummary_core/class.rstspectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/autosummary_core/cla0000644000077000000240000000221113126505434034462 0ustar adamstaff00000000000000{% if referencefile %} .. include:: {{ referencefile }} {% endif %} {{ objname }} {{ underline }} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} :show-inheritance: {% if '__init__' in methods %} {% set caught_result = methods.remove('__init__') %} {% endif %} {% block attributes_summary %} {% if attributes %} .. rubric:: Attributes Summary .. autosummary:: {% for item in attributes %} ~{{ name }}.{{ item }} {%- endfor %} {% endif %} {% endblock %} {% block methods_summary %} {% if methods %} .. rubric:: Methods Summary .. autosummary:: {% for item in methods %} ~{{ name }}.{{ item }} {%- endfor %} {% endif %} {% endblock %} {% block attributes_documentation %} {% if attributes %} .. rubric:: Attributes Documentation {% for item in attributes %} .. autoattribute:: {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block methods_documentation %} {% if methods %} .. rubric:: Methods Documentation {% for item in methods %} .. automethod:: {{ item }} {%- endfor %} {% endif %} {% endblock %} ././@LongLink0000000000000000000000000000015400000000000011215 Lustar 00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/autosummary_core/module.rstspectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/templates/autosummary_core/mod0000644000077000000240000000127713126505434034515 0ustar adamstaff00000000000000{% if referencefile %} .. include:: {{ referencefile }} {% endif %} {{ objname }} {{ underline }} .. automodule:: {{ fullname }} {% block functions %} {% if functions %} .. rubric:: Functions .. autosummary:: {% for item in functions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block classes %} {% if classes %} .. rubric:: Classes .. autosummary:: {% for item in classes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block exceptions %} {% if exceptions %} .. rubric:: Exceptions .. autosummary:: {% for item in exceptions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/automodapi/utils.py0000644000077000000240000001574413126505434030135 0ustar adamstaff00000000000000import inspect import sys import re import os from warnings import warn from sphinx.ext.autosummary.generate import find_autosummary_in_docstring if sys.version_info[0] >= 3: def iteritems(dictionary): return dictionary.items() else: def iteritems(dictionary): return dictionary.iteritems() # We use \n instead of os.linesep because even on Windows, the generated files # use \n as the newline character. SPACE_NEWLINE = ' \n' SINGLE_NEWLINE = '\n' DOUBLE_NEWLINE = '\n\n' TRIPLE_NEWLINE = '\n\n\n' def cleanup_whitespace(text): """ Make sure there are never more than two consecutive newlines, and that there are no trailing whitespaces. """ # Get rid of overall leading/trailing whitespace text = text.strip() + '\n' # Get rid of trailing whitespace on each line while SPACE_NEWLINE in text: text = text.replace(SPACE_NEWLINE, SINGLE_NEWLINE) # Avoid too many consecutive newlines while TRIPLE_NEWLINE in text: text = text.replace(TRIPLE_NEWLINE, DOUBLE_NEWLINE) return text def find_mod_objs(modname, onlylocals=False): """ Returns all the public attributes of a module referenced by name. .. note:: The returned list *not* include subpackages or modules of `modname`,nor does it include private attributes (those that beginwith '_' or are not in `__all__`). Parameters ---------- modname : str The name of the module to search. onlylocals : bool If True, only attributes that are either members of `modname` OR one of its modules or subpackages will be included. Returns ------- localnames : list of str A list of the names of the attributes as they are named in the module `modname` . fqnames : list of str A list of the full qualified names of the attributes (e.g., ``astropy.utils.misc.find_mod_objs``). For attributes that are simple variables, this is based on the local name, but for functions or classes it can be different if they are actually defined elsewhere and just referenced in `modname`. objs : list of objects A list of the actual attributes themselves (in the same order as the other arguments) """ __import__(modname) mod = sys.modules[modname] if hasattr(mod, '__all__'): pkgitems = [(k, mod.__dict__[k]) for k in mod.__all__] else: pkgitems = [(k, mod.__dict__[k]) for k in dir(mod) if k[0] != '_'] # filter out modules and pull the names and objs out ismodule = inspect.ismodule localnames = [k for k, v in pkgitems if not ismodule(v)] objs = [v for k, v in pkgitems if not ismodule(v)] # fully qualified names can be determined from the object's module fqnames = [] for obj, lnm in zip(objs, localnames): if hasattr(obj, '__module__') and hasattr(obj, '__name__'): fqnames.append(obj.__module__ + '.' + obj.__name__) else: fqnames.append(modname + '.' + lnm) if onlylocals: valids = [fqn.startswith(modname) for fqn in fqnames] localnames = [e for i, e in enumerate(localnames) if valids[i]] fqnames = [e for i, e in enumerate(fqnames) if valids[i]] objs = [e for i, e in enumerate(objs) if valids[i]] return localnames, fqnames, objs def find_autosummary_in_lines_for_automodsumm(lines, module=None, filename=None): """Find out what items appear in autosummary:: directives in the given lines. Returns a list of (name, toctree, template, inherited_members) where *name* is a name of an object and *toctree* the :toctree: path of the corresponding autosummary directive (relative to the root of the file name), *template* the value of the :template: option, and *inherited_members* is the value of the :inherited-members: option. *toctree*, *template*, and *inherited_members* are ``None`` if the directive does not have the corresponding options set. .. note:: This is a slightly modified version of ``sphinx.ext.autosummary.generate.find_autosummary_in_lines`` which recognizes the ``inherited-members`` option. """ autosummary_re = re.compile(r'^(\s*)\.\.\s+autosummary::\s*') automodule_re = re.compile( r'^\s*\.\.\s+automodule::\s*([A-Za-z0-9_.]+)\s*$') module_re = re.compile( r'^\s*\.\.\s+(current)?module::\s*([a-zA-Z0-9_.]+)\s*$') autosummary_item_re = re.compile(r'^\s+(~?[_a-zA-Z][a-zA-Z0-9_.]*)\s*.*?') toctree_arg_re = re.compile(r'^\s+:toctree:\s*(.*?)\s*$') template_arg_re = re.compile(r'^\s+:template:\s*(.*?)\s*$') inherited_members_arg_re = re.compile(r'^\s+:inherited-members:\s*$') no_inherited_members_arg_re = re.compile(r'^\s+:no-inherited-members:\s*$') documented = [] toctree = None template = None inherited_members = None current_module = module in_autosummary = False base_indent = "" for line in lines: if in_autosummary: m = toctree_arg_re.match(line) if m: toctree = m.group(1) if filename: toctree = os.path.join(os.path.dirname(filename), toctree) continue m = template_arg_re.match(line) if m: template = m.group(1).strip() continue m = inherited_members_arg_re.match(line) if m: inherited_members = True continue m = no_inherited_members_arg_re.match(line) if m: inherited_members = False continue if line.strip().startswith(':'): warn(line) continue # skip options m = autosummary_item_re.match(line) if m: name = m.group(1).strip() if name.startswith('~'): name = name[1:] if current_module and \ not name.startswith(current_module + '.'): name = "%s.%s" % (current_module, name) documented.append((name, toctree, template, inherited_members)) continue if not line.strip() or line.startswith(base_indent + " "): continue in_autosummary = False m = autosummary_re.match(line) if m: in_autosummary = True base_indent = m.group(1) toctree = None template = None inherited_members = None continue m = automodule_re.search(line) if m: current_module = m.group(1).strip() # recurse into the automodule docstring documented.extend(find_autosummary_in_docstring( current_module, filename=filename)) continue m = module_re.match(line) if m: current_module = m.group(2) continue return documented spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/numpydoc/0000755000077000000240000000000013261442571026106 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/numpydoc/__init__.py0000644000077000000240000000016513242700737030221 0ustar adamstaff00000000000000from __future__ import division, absolute_import, print_function __version__ = '0.7.0' from .numpydoc import setup spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/numpydoc/docscrape.py0000644000077000000240000004452113242700737030431 0ustar adamstaff00000000000000"""Extract reference documentation from the NumPy source tree. """ from __future__ import division, absolute_import, print_function import inspect import textwrap import re import pydoc from warnings import warn import collections import copy import sys class Reader(object): """A line-based string reader. """ def __init__(self, data): """ Parameters ---------- data : str String with lines separated by '\n'. """ if isinstance(data, list): self._str = data else: self._str = data.split('\n') # store string as list of lines self.reset() def __getitem__(self, n): return self._str[n] def reset(self): self._l = 0 # current line nr def read(self): if not self.eof(): out = self[self._l] self._l += 1 return out else: return '' def seek_next_non_empty_line(self): for l in self[self._l:]: if l.strip(): break else: self._l += 1 def eof(self): return self._l >= len(self._str) def read_to_condition(self, condition_func): start = self._l for line in self[start:]: if condition_func(line): return self[start:self._l] self._l += 1 if self.eof(): return self[start:self._l+1] return [] def read_to_next_empty_line(self): self.seek_next_non_empty_line() def is_empty(line): return not line.strip() return self.read_to_condition(is_empty) def read_to_next_unindented_line(self): def is_unindented(line): return (line.strip() and (len(line.lstrip()) == len(line))) return self.read_to_condition(is_unindented) def peek(self, n=0): if self._l + n < len(self._str): return self[self._l + n] else: return '' def is_empty(self): return not ''.join(self._str).strip() class ParseError(Exception): def __str__(self): message = self.args[0] if hasattr(self, 'docstring'): message = "%s in %r" % (message, self.docstring) return message class NumpyDocString(collections.Mapping): sections = { 'Signature': '', 'Summary': [''], 'Extended Summary': [], 'Parameters': [], 'Returns': [], 'Yields': [], 'Raises': [], 'Warns': [], 'Other Parameters': [], 'Attributes': [], 'Methods': [], 'See Also': [], 'Notes': [], 'Warnings': [], 'References': '', 'Examples': '', 'index': {} } def __init__(self, docstring, config={}): orig_docstring = docstring docstring = textwrap.dedent(docstring).split('\n') self._doc = Reader(docstring) self._parsed_data = copy.deepcopy(self.sections) try: self._parse() except ParseError as e: e.docstring = orig_docstring raise def __getitem__(self, key): return self._parsed_data[key] def __setitem__(self, key, val): if key not in self._parsed_data: warn("Unknown section %s" % key) else: self._parsed_data[key] = val def __iter__(self): return iter(self._parsed_data) def __len__(self): return len(self._parsed_data) def _is_at_section(self): self._doc.seek_next_non_empty_line() if self._doc.eof(): return False l1 = self._doc.peek().strip() # e.g. Parameters if l1.startswith('.. index::'): return True l2 = self._doc.peek(1).strip() # ---------- or ========== return l2.startswith('-'*len(l1)) or l2.startswith('='*len(l1)) def _strip(self, doc): i = 0 j = 0 for i, line in enumerate(doc): if line.strip(): break for j, line in enumerate(doc[::-1]): if line.strip(): break return doc[i:len(doc)-j] def _read_to_next_section(self): section = self._doc.read_to_next_empty_line() while not self._is_at_section() and not self._doc.eof(): if not self._doc.peek(-1).strip(): # previous line was empty section += [''] section += self._doc.read_to_next_empty_line() return section def _read_sections(self): while not self._doc.eof(): data = self._read_to_next_section() name = data[0].strip() if name.startswith('..'): # index section yield name, data[1:] elif len(data) < 2: yield StopIteration else: yield name, self._strip(data[2:]) def _parse_param_list(self, content): r = Reader(content) params = [] while not r.eof(): header = r.read().strip() if ' : ' in header: arg_name, arg_type = header.split(' : ')[:2] else: arg_name, arg_type = header, '' desc = r.read_to_next_unindented_line() desc = dedent_lines(desc) params.append((arg_name, arg_type, desc)) return params _name_rgx = re.compile(r"^\s*(:(?P\w+):`(?P[a-zA-Z0-9_.-]+)`|" r" (?P[a-zA-Z0-9_.-]+))\s*", re.X) def _parse_see_also(self, content): """ func_name : Descriptive text continued text another_func_name : Descriptive text func_name1, func_name2, :meth:`func_name`, func_name3 """ items = [] def parse_item_name(text): """Match ':role:`name`' or 'name'""" m = self._name_rgx.match(text) if m: g = m.groups() if g[1] is None: return g[3], None else: return g[2], g[1] raise ParseError("%s is not a item name" % text) def push_item(name, rest): if not name: return name, role = parse_item_name(name) items.append((name, list(rest), role)) del rest[:] current_func = None rest = [] for line in content: if not line.strip(): continue m = self._name_rgx.match(line) if m and line[m.end():].strip().startswith(':'): push_item(current_func, rest) current_func, line = line[:m.end()], line[m.end():] rest = [line.split(':', 1)[1].strip()] if not rest[0]: rest = [] elif not line.startswith(' '): push_item(current_func, rest) current_func = None if ',' in line: for func in line.split(','): if func.strip(): push_item(func, []) elif line.strip(): current_func = line elif current_func is not None: rest.append(line.strip()) push_item(current_func, rest) return items def _parse_index(self, section, content): """ .. index: default :refguide: something, else, and more """ def strip_each_in(lst): return [s.strip() for s in lst] out = {} section = section.split('::') if len(section) > 1: out['default'] = strip_each_in(section[1].split(','))[0] for line in content: line = line.split(':') if len(line) > 2: out[line[1]] = strip_each_in(line[2].split(',')) return out def _parse_summary(self): """Grab signature (if given) and summary""" if self._is_at_section(): return # If several signatures present, take the last one while True: summary = self._doc.read_to_next_empty_line() summary_str = " ".join([s.strip() for s in summary]).strip() if re.compile('^([\w., ]+=)?\s*[\w\.]+\(.*\)$').match(summary_str): self['Signature'] = summary_str if not self._is_at_section(): continue break if summary is not None: self['Summary'] = summary if not self._is_at_section(): self['Extended Summary'] = self._read_to_next_section() def _parse(self): self._doc.reset() self._parse_summary() sections = list(self._read_sections()) section_names = set([section for section, content in sections]) has_returns = 'Returns' in section_names has_yields = 'Yields' in section_names # We could do more tests, but we are not. Arbitrarily. if has_returns and has_yields: msg = 'Docstring contains both a Returns and Yields section.' raise ValueError(msg) for (section, content) in sections: if not section.startswith('..'): section = (s.capitalize() for s in section.split(' ')) section = ' '.join(section) if self.get(section): if hasattr(self, '_obj'): # we know where the docs came from: try: filename = inspect.getsourcefile(self._obj) except TypeError: filename = None msg = ("The section %s appears twice in " "the docstring of %s in %s." % (section, self._obj, filename)) raise ValueError(msg) else: msg = ("The section %s appears twice" % section) raise ValueError(msg) if section in ('Parameters', 'Returns', 'Yields', 'Raises', 'Warns', 'Other Parameters', 'Attributes', 'Methods'): self[section] = self._parse_param_list(content) elif section.startswith('.. index::'): self['index'] = self._parse_index(section, content) elif section == 'See Also': self['See Also'] = self._parse_see_also(content) else: self[section] = content # string conversion routines def _str_header(self, name, symbol='-'): return [name, len(name)*symbol] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' '*indent + line] return out def _str_signature(self): if self['Signature']: return [self['Signature'].replace('*', '\*')] + [''] else: return [''] def _str_summary(self): if self['Summary']: return self['Summary'] + [''] else: return [] def _str_extended_summary(self): if self['Extended Summary']: return self['Extended Summary'] + [''] else: return [] def _str_param_list(self, name): out = [] if self[name]: out += self._str_header(name) for param, param_type, desc in self[name]: if param_type: out += ['%s : %s' % (param, param_type)] else: out += [param] out += self._str_indent(desc) out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += self[name] out += [''] return out def _str_see_also(self, func_role): if not self['See Also']: return [] out = [] out += self._str_header("See Also") last_had_desc = True for func, desc, role in self['See Also']: if role: link = ':%s:`%s`' % (role, func) elif func_role: link = ':%s:`%s`' % (func_role, func) else: link = "`%s`_" % func if desc or last_had_desc: out += [''] out += [link] else: out[-1] += ", %s" % link if desc: out += self._str_indent([' '.join(desc)]) last_had_desc = True else: last_had_desc = False out += [''] return out def _str_index(self): idx = self['index'] out = [] out += ['.. index:: %s' % idx.get('default', '')] for section, references in idx.items(): if section == 'default': continue out += [' :%s: %s' % (section, ', '.join(references))] return out def __str__(self, func_role=''): out = [] out += self._str_signature() out += self._str_summary() out += self._str_extended_summary() for param_list in ('Parameters', 'Returns', 'Yields', 'Other Parameters', 'Raises', 'Warns'): out += self._str_param_list(param_list) out += self._str_section('Warnings') out += self._str_see_also(func_role) for s in ('Notes', 'References', 'Examples'): out += self._str_section(s) for param_list in ('Attributes', 'Methods'): out += self._str_param_list(param_list) out += self._str_index() return '\n'.join(out) def indent(str, indent=4): indent_str = ' '*indent if str is None: return indent_str lines = str.split('\n') return '\n'.join(indent_str + l for l in lines) def dedent_lines(lines): """Deindent a list of lines maximally""" return textwrap.dedent("\n".join(lines)).split("\n") def header(text, style='-'): return text + '\n' + style*len(text) + '\n' class FunctionDoc(NumpyDocString): def __init__(self, func, role='func', doc=None, config={}): self._f = func self._role = role # e.g. "func" or "meth" if doc is None: if func is None: raise ValueError("No function or docstring given") doc = inspect.getdoc(func) or '' NumpyDocString.__init__(self, doc) if not self['Signature'] and func is not None: func, func_name = self.get_func() try: try: signature = str(inspect.signature(func)) except (AttributeError, ValueError): # try to read signature, backward compat for older Python if sys.version_info[0] >= 3: argspec = inspect.getfullargspec(func) else: argspec = inspect.getargspec(func) signature = inspect.formatargspec(*argspec) signature = '%s%s' % (func_name, signature.replace('*', '\*')) except TypeError: signature = '%s()' % func_name self['Signature'] = signature def get_func(self): func_name = getattr(self._f, '__name__', self.__class__.__name__) if inspect.isclass(self._f): func = getattr(self._f, '__call__', self._f.__init__) else: func = self._f return func, func_name def __str__(self): out = '' func, func_name = self.get_func() signature = self['Signature'].replace('*', '\*') roles = {'func': 'function', 'meth': 'method'} if self._role: if self._role not in roles: print("Warning: invalid role %s" % self._role) out += '.. %s:: %s\n \n\n' % (roles.get(self._role, ''), func_name) out += super(FunctionDoc, self).__str__(func_role=self._role) return out class ClassDoc(NumpyDocString): extra_public_methods = ['__call__'] def __init__(self, cls, doc=None, modulename='', func_doc=FunctionDoc, config={}): if not inspect.isclass(cls) and cls is not None: raise ValueError("Expected a class or None, but got %r" % cls) self._cls = cls self.show_inherited_members = config.get( 'show_inherited_class_members', True) if modulename and not modulename.endswith('.'): modulename += '.' self._mod = modulename if doc is None: if cls is None: raise ValueError("No class or documentation string given") doc = pydoc.getdoc(cls) NumpyDocString.__init__(self, doc) if config.get('show_class_members', True): def splitlines_x(s): if not s: return [] else: return s.splitlines() for field, items in [('Methods', self.methods), ('Attributes', self.properties)]: if not self[field]: doc_list = [] for name in sorted(items): try: doc_item = pydoc.getdoc(getattr(self._cls, name)) doc_list.append((name, '', splitlines_x(doc_item))) except AttributeError: pass # method doesn't exist self[field] = doc_list @property def methods(self): if self._cls is None: return [] return [name for name, func in inspect.getmembers(self._cls) if ((not name.startswith('_') or name in self.extra_public_methods) and isinstance(func, collections.Callable) and self._is_show_member(name))] @property def properties(self): if self._cls is None: return [] return [name for name, func in inspect.getmembers(self._cls) if (not name.startswith('_') and (func is None or isinstance(func, property) or inspect.isgetsetdescriptor(func)) and self._is_show_member(name))] def _is_show_member(self, name): if self.show_inherited_members: return True # show all class members if name not in self._cls.__dict__: return False # class member is inherited, we do not show it return True spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/numpydoc/docscrape_sphinx.py0000644000077000000240000002510613242700737032020 0ustar adamstaff00000000000000from __future__ import division, absolute_import, print_function import sys import re import inspect import textwrap import pydoc import collections import os from jinja2 import FileSystemLoader from jinja2.sandbox import SandboxedEnvironment import sphinx from sphinx.jinja2glue import BuiltinTemplateLoader from .docscrape import NumpyDocString, FunctionDoc, ClassDoc if sys.version_info[0] >= 3: sixu = lambda s: s else: sixu = lambda s: unicode(s, 'unicode_escape') class SphinxDocString(NumpyDocString): def __init__(self, docstring, config={}): NumpyDocString.__init__(self, docstring, config=config) self.load_config(config) def load_config(self, config): self.use_plots = config.get('use_plots', False) self.class_members_toctree = config.get('class_members_toctree', True) self.template = config.get('template', None) if self.template is None: template_dirs = [os.path.join(os.path.dirname(__file__), 'templates')] template_loader = FileSystemLoader(template_dirs) template_env = SandboxedEnvironment(loader=template_loader) self.template = template_env.get_template('numpydoc_docstring.rst') # string conversion routines def _str_header(self, name, symbol='`'): return ['.. rubric:: ' + name, ''] def _str_field_list(self, name): return [':' + name + ':'] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' '*indent + line] return out def _str_signature(self): return [''] if self['Signature']: return ['``%s``' % self['Signature']] + [''] else: return [''] def _str_summary(self): return self['Summary'] + [''] def _str_extended_summary(self): return self['Extended Summary'] + [''] def _str_returns(self, name='Returns'): out = [] if self[name]: out += self._str_field_list(name) out += [''] for param, param_type, desc in self[name]: if param_type: out += self._str_indent(['**%s** : %s' % (param.strip(), param_type)]) else: out += self._str_indent([param.strip()]) if desc: out += [''] out += self._str_indent(desc, 8) out += [''] return out def _str_param_list(self, name): out = [] if self[name]: out += self._str_field_list(name) out += [''] for param, param_type, desc in self[name]: if param_type: out += self._str_indent(['**%s** : %s' % (param.strip(), param_type)]) else: out += self._str_indent(['**%s**' % param.strip()]) if desc: out += [''] out += self._str_indent(desc, 8) out += [''] return out @property def _obj(self): if hasattr(self, '_cls'): return self._cls elif hasattr(self, '_f'): return self._f return None def _str_member_list(self, name): """ Generate a member listing, autosummary:: table where possible, and a table where not. """ out = [] if self[name]: out += ['.. rubric:: %s' % name, ''] prefix = getattr(self, '_name', '') if prefix: prefix = '~%s.' % prefix autosum = [] others = [] for param, param_type, desc in self[name]: param = param.strip() # Check if the referenced member can have a docstring or not param_obj = getattr(self._obj, param, None) if not (callable(param_obj) or isinstance(param_obj, property) or inspect.isgetsetdescriptor(param_obj)): param_obj = None if param_obj and (pydoc.getdoc(param_obj) or not desc): # Referenced object has a docstring autosum += [" %s%s" % (prefix, param)] else: others.append((param, param_type, desc)) if autosum: out += ['.. autosummary::'] if self.class_members_toctree: out += [' :toctree:'] out += [''] + autosum if others: maxlen_0 = max(3, max([len(x[0]) + 4 for x in others])) hdr = sixu("=") * maxlen_0 + sixu(" ") + sixu("=") * 10 fmt = sixu('%%%ds %%s ') % (maxlen_0,) out += ['', '', hdr] for param, param_type, desc in others: desc = sixu(" ").join(x.strip() for x in desc).strip() if param_type: desc = "(%s) %s" % (param_type, desc) out += [fmt % ("**" + param.strip() + "**", desc)] out += [hdr] out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += [''] content = textwrap.dedent("\n".join(self[name])).split("\n") out += content out += [''] return out def _str_see_also(self, func_role): out = [] if self['See Also']: see_also = super(SphinxDocString, self)._str_see_also(func_role) out = ['.. seealso::', ''] out += self._str_indent(see_also[2:]) return out def _str_warnings(self): out = [] if self['Warnings']: out = ['.. warning::', ''] out += self._str_indent(self['Warnings']) return out def _str_index(self): idx = self['index'] out = [] if len(idx) == 0: return out out += ['.. index:: %s' % idx.get('default', '')] for section, references in idx.items(): if section == 'default': continue elif section == 'refguide': out += [' single: %s' % (', '.join(references))] else: out += [' %s: %s' % (section, ','.join(references))] return out def _str_references(self): out = [] if self['References']: out += self._str_header('References') if isinstance(self['References'], str): self['References'] = [self['References']] out.extend(self['References']) out += [''] # Latex collects all references to a separate bibliography, # so we need to insert links to it if sphinx.__version__ >= "0.6": out += ['.. only:: latex', ''] else: out += ['.. latexonly::', ''] items = [] for line in self['References']: m = re.match(r'.. \[([a-z0-9._-]+)\]', line, re.I) if m: items.append(m.group(1)) out += [' ' + ", ".join(["[%s]_" % item for item in items]), ''] return out def _str_examples(self): examples_str = "\n".join(self['Examples']) if (self.use_plots and 'import matplotlib' in examples_str and 'plot::' not in examples_str): out = [] out += self._str_header('Examples') out += ['.. plot::', ''] out += self._str_indent(self['Examples']) out += [''] return out else: return self._str_section('Examples') def __str__(self, indent=0, func_role="obj"): ns = { 'signature': self._str_signature(), 'index': self._str_index(), 'summary': self._str_summary(), 'extended_summary': self._str_extended_summary(), 'parameters': self._str_param_list('Parameters'), 'returns': self._str_returns('Returns'), 'yields': self._str_returns('Yields'), 'other_parameters': self._str_param_list('Other Parameters'), 'raises': self._str_param_list('Raises'), 'warns': self._str_param_list('Warns'), 'warnings': self._str_warnings(), 'see_also': self._str_see_also(func_role), 'notes': self._str_section('Notes'), 'references': self._str_references(), 'examples': self._str_examples(), 'attributes': self._str_member_list('Attributes'), 'methods': self._str_member_list('Methods'), } ns = dict((k, '\n'.join(v)) for k, v in ns.items()) rendered = self.template.render(**ns) return '\n'.join(self._str_indent(rendered.split('\n'), indent)) class SphinxFunctionDoc(SphinxDocString, FunctionDoc): def __init__(self, obj, doc=None, config={}): self.load_config(config) FunctionDoc.__init__(self, obj, doc=doc, config=config) class SphinxClassDoc(SphinxDocString, ClassDoc): def __init__(self, obj, doc=None, func_doc=None, config={}): self.load_config(config) ClassDoc.__init__(self, obj, doc=doc, func_doc=None, config=config) class SphinxObjDoc(SphinxDocString): def __init__(self, obj, doc=None, config={}): self._f = obj self.load_config(config) SphinxDocString.__init__(self, doc, config=config) def get_doc_object(obj, what=None, doc=None, config={}, builder=None): if what is None: if inspect.isclass(obj): what = 'class' elif inspect.ismodule(obj): what = 'module' elif isinstance(obj, collections.Callable): what = 'function' else: what = 'object' template_dirs = [os.path.join(os.path.dirname(__file__), 'templates')] if builder is not None: template_loader = BuiltinTemplateLoader() template_loader.init(builder, dirs=template_dirs) else: template_loader = FileSystemLoader(template_dirs) template_env = SandboxedEnvironment(loader=template_loader) config['template'] = template_env.get_template('numpydoc_docstring.rst') if what == 'class': return SphinxClassDoc(obj, func_doc=SphinxFunctionDoc, doc=doc, config=config) elif what in ('function', 'method'): return SphinxFunctionDoc(obj, doc=doc, config=config) else: if doc is None: doc = pydoc.getdoc(obj) return SphinxObjDoc(obj, doc, config=config) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/numpydoc/numpydoc.py0000644000077000000240000002253313242700737030323 0ustar adamstaff00000000000000""" ======== numpydoc ======== Sphinx extension that handles docstrings in the Numpy standard format. [1] It will: - Convert Parameters etc. sections to field lists. - Convert See Also section to a See also entry. - Renumber references. - Extract the signature from the docstring, if it can't be determined otherwise. .. [1] https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt """ from __future__ import division, absolute_import, print_function import sys import re import pydoc import sphinx import inspect import collections if sphinx.__version__ < '1.0.1': raise RuntimeError("Sphinx 1.0.1 or newer is required") from .docscrape_sphinx import get_doc_object, SphinxDocString if sys.version_info[0] >= 3: sixu = lambda s: s else: sixu = lambda s: unicode(s, 'unicode_escape') def rename_references(app, what, name, obj, options, lines, reference_offset=[0]): # replace reference numbers so that there are no duplicates references = [] for line in lines: line = line.strip() m = re.match(sixu('^.. \\[(%s)\\]') % app.config.numpydoc_citation_re, line, re.I) if m: references.append(m.group(1)) if references: for i, line in enumerate(lines): for r in references: if re.match(sixu('^\\d+$'), r): new_r = sixu("R%d") % (reference_offset[0] + int(r)) else: new_r = sixu("%s%d") % (r, reference_offset[0]) lines[i] = lines[i].replace(sixu('[%s]_') % r, sixu('[%s]_') % new_r) lines[i] = lines[i].replace(sixu('.. [%s]') % r, sixu('.. [%s]') % new_r) reference_offset[0] += len(references) def mangle_docstrings(app, what, name, obj, options, lines): cfg = {'use_plots': app.config.numpydoc_use_plots, 'show_class_members': app.config.numpydoc_show_class_members, 'show_inherited_class_members': app.config.numpydoc_show_inherited_class_members, 'class_members_toctree': app.config.numpydoc_class_members_toctree} u_NL = sixu('\n') if what == 'module': # Strip top title pattern = '^\\s*[#*=]{4,}\\n[a-z0-9 -]+\\n[#*=]{4,}\\s*' title_re = re.compile(sixu(pattern), re.I | re.S) lines[:] = title_re.sub(sixu(''), u_NL.join(lines)).split(u_NL) else: doc = get_doc_object(obj, what, u_NL.join(lines), config=cfg, builder=app.builder) if sys.version_info[0] >= 3: doc = str(doc) else: doc = unicode(doc) lines[:] = doc.split(u_NL) if (app.config.numpydoc_edit_link and hasattr(obj, '__name__') and obj.__name__): if hasattr(obj, '__module__'): v = dict(full_name=sixu("%s.%s") % (obj.__module__, obj.__name__)) else: v = dict(full_name=obj.__name__) lines += [sixu(''), sixu('.. htmlonly::'), sixu('')] lines += [sixu(' %s') % x for x in (app.config.numpydoc_edit_link % v).split("\n")] # call function to replace reference numbers so that there are no # duplicates rename_references(app, what, name, obj, options, lines) def mangle_signature(app, what, name, obj, options, sig, retann): # Do not try to inspect classes that don't define `__init__` if (inspect.isclass(obj) and (not hasattr(obj, '__init__') or 'initializes x; see ' in pydoc.getdoc(obj.__init__))): return '', '' if not (isinstance(obj, collections.Callable) or hasattr(obj, '__argspec_is_invalid_')): return if not hasattr(obj, '__doc__'): return doc = SphinxDocString(pydoc.getdoc(obj)) sig = doc['Signature'] or getattr(obj, '__text_signature__', None) if sig: sig = re.sub(sixu("^[^(]*"), sixu(""), sig) return sig, sixu('') def setup(app, get_doc_object_=get_doc_object): if not hasattr(app, 'add_config_value'): return # probably called by nose, better bail out global get_doc_object get_doc_object = get_doc_object_ app.connect('autodoc-process-docstring', mangle_docstrings) app.connect('autodoc-process-signature', mangle_signature) app.add_config_value('numpydoc_edit_link', None, False) app.add_config_value('numpydoc_use_plots', None, False) app.add_config_value('numpydoc_show_class_members', True, True) app.add_config_value('numpydoc_show_inherited_class_members', True, True) app.add_config_value('numpydoc_class_members_toctree', True, True) app.add_config_value('numpydoc_citation_re', '[a-z0-9_.-]+', True) # Extra mangling domains app.add_domain(NumpyPythonDomain) app.add_domain(NumpyCDomain) metadata = {'parallel_read_safe': True} return metadata # ------------------------------------------------------------------------------ # Docstring-mangling domains # ------------------------------------------------------------------------------ from docutils.statemachine import ViewList from sphinx.domains.c import CDomain from sphinx.domains.python import PythonDomain class ManglingDomainBase(object): directive_mangling_map = {} def __init__(self, *a, **kw): super(ManglingDomainBase, self).__init__(*a, **kw) self.wrap_mangling_directives() def wrap_mangling_directives(self): for name, objtype in list(self.directive_mangling_map.items()): self.directives[name] = wrap_mangling_directive( self.directives[name], objtype) class NumpyPythonDomain(ManglingDomainBase, PythonDomain): name = 'np' directive_mangling_map = { 'function': 'function', 'class': 'class', 'exception': 'class', 'method': 'function', 'classmethod': 'function', 'staticmethod': 'function', 'attribute': 'attribute', } indices = [] class NumpyCDomain(ManglingDomainBase, CDomain): name = 'np-c' directive_mangling_map = { 'function': 'function', 'member': 'attribute', 'macro': 'function', 'type': 'class', 'var': 'object', } def match_items(lines, content_old): """Create items for mangled lines. This function tries to match the lines in ``lines`` with the items (source file references and line numbers) in ``content_old``. The ``mangle_docstrings`` function changes the actual docstrings, but doesn't keep track of where each line came from. The manging does many operations on the original lines, which are hard to track afterwards. Many of the line changes come from deleting or inserting blank lines. This function tries to match lines by ignoring blank lines. All other changes (such as inserting figures or changes in the references) are completely ignored, so the generated line numbers will be off if ``mangle_docstrings`` does anything non-trivial. This is a best-effort function and the real fix would be to make ``mangle_docstrings`` actually keep track of the ``items`` together with the ``lines``. Examples -------- >>> lines = ['', 'A', '', 'B', ' ', '', 'C', 'D'] >>> lines_old = ['a', '', '', 'b', '', 'c'] >>> items_old = [('file1.py', 0), ('file1.py', 1), ('file1.py', 2), ... ('file2.py', 0), ('file2.py', 1), ('file2.py', 2)] >>> content_old = ViewList(lines_old, items=items_old) >>> match_items(lines, content_old) # doctest: +NORMALIZE_WHITESPACE [('file1.py', 0), ('file1.py', 0), ('file2.py', 0), ('file2.py', 0), ('file2.py', 2), ('file2.py', 2), ('file2.py', 2), ('file2.py', 2)] >>> # first 2 ``lines`` are matched to 'a', second 2 to 'b', rest to 'c' >>> # actual content is completely ignored. Notes ----- The algorithm tries to match any line in ``lines`` with one in ``lines_old``. It skips over all empty lines in ``lines_old`` and assigns this line number to all lines in ``lines``, unless a non-empty line is found in ``lines`` in which case it goes to the next line in ``lines_old``. """ items_new = [] lines_old = content_old.data items_old = content_old.items j = 0 for i, line in enumerate(lines): # go to next non-empty line in old: # line.strip() checks whether the string is all whitespace while j < len(lines_old) - 1 and not lines_old[j].strip(): j += 1 items_new.append(items_old[j]) if line.strip() and j < len(lines_old) - 1: j += 1 assert(len(items_new) == len(lines)) return items_new def wrap_mangling_directive(base_directive, objtype): class directive(base_directive): def run(self): env = self.state.document.settings.env name = None if self.arguments: m = re.match(r'^(.*\s+)?(.*?)(\(.*)?', self.arguments[0]) name = m.group(2).strip() if not name: name = self.arguments[0] lines = list(self.content) mangle_docstrings(env.app, objtype, name, None, None, lines) if self.content: items = match_items(lines, self.content) self.content = ViewList(lines, items=items, parent=self.content.parent) return base_directive.run(self) return directive spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/numpydoc/templates/0000755000077000000240000000000013261442571030104 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/numpydoc/templates/numpydoc_docstring.rst0000644000077000000240000000032613242700737034551 0ustar adamstaff00000000000000{{index}} {{summary}} {{extended_summary}} {{parameters}} {{returns}} {{yields}} {{other_parameters}} {{raises}} {{warns}} {{warnings}} {{see_also}} {{notes}} {{references}} {{examples}} {{attributes}} {{methods}} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/extern/setup_package.py0000644000077000000240000000027513242700737027441 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst def get_package_data(): return {'astropy_helpers.extern': ['automodapi/templates/*/*.rst', 'numpydoc/templates/*.rst']} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/git_helpers.py0000644000077000000240000001450113242700737025623 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ Utilities for retrieving revision information from a project's git repository. """ # Do not remove the following comment; it is used by # astropy_helpers.version_helpers to determine the beginning of the code in # this module # BEGIN 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 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 == 128: # git returns 128 if the command is not run from within a git # repository tree. In this case, a warning is produced above but we # return the default dev version of '0'. return '0' elif 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() # This function is tested but it is only ever executed within a subprocess when # creating a fake package, so it doesn't get picked up by coverage metrics. def _get_repo_path(pathname, levels=None): # pragma: no cover """ 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 spectral-cube-0.4.3/astropy_helpers/astropy_helpers/openmp_helpers.py0000644000077000000240000000610513245574455026350 0ustar adamstaff00000000000000# This module defines functions that can be used to check whether OpenMP is # available and if so what flags to use. To use this, import the # add_openmp_flags_if_available function in a setup_package.py file where you # are defining your extensions: # # from astropy_helpers.openmp_helpers import add_openmp_flags_if_available # # then call it with a single extension as the only argument: # # add_openmp_flags_if_available(extension) # # this will add the OpenMP flags if available. from __future__ import absolute_import, print_function import os import sys import glob import tempfile import subprocess from distutils import log from distutils.ccompiler import new_compiler from distutils.sysconfig import customize_compiler from distutils.errors import CompileError, LinkError from .setup_helpers import get_compiler_option __all__ = ['add_openmp_flags_if_available'] CCODE = """ #include #include int main(void) { #pragma omp parallel printf("nthreads=%d\\n", omp_get_num_threads()); return 0; } """ def add_openmp_flags_if_available(extension): """ Add OpenMP compilation flags, if available (if not a warning will be printed to the console and no flags will be added) Returns `True` if the flags were added, `False` otherwise. """ ccompiler = new_compiler() customize_compiler(ccompiler) tmp_dir = tempfile.mkdtemp() start_dir = os.path.abspath('.') if get_compiler_option() == 'msvc': compile_flag = '-openmp' link_flag = '' else: compile_flag = '-fopenmp' link_flag = '-fopenmp' try: os.chdir(tmp_dir) with open('test_openmp.c', 'w') as f: f.write(CCODE) os.mkdir('objects') # Compile, link, and run test program ccompiler.compile(['test_openmp.c'], output_dir='objects', extra_postargs=[compile_flag]) ccompiler.link_executable(glob.glob(os.path.join('objects', '*' + ccompiler.obj_extension)), 'test_openmp', extra_postargs=[link_flag]) output = subprocess.check_output('./test_openmp').decode(sys.stdout.encoding or 'utf-8').splitlines() if 'nthreads=' in output[0]: nthreads = int(output[0].strip().split('=')[1]) if len(output) == nthreads: using_openmp = True else: log.warn("Unexpected number of lines from output of test OpenMP " "program (output was {0})".format(output)) using_openmp = False else: log.warn("Unexpected output from test OpenMP " "program (output was {0})".format(output)) using_openmp = False except (CompileError, LinkError): using_openmp = False finally: os.chdir(start_dir) if using_openmp: log.info("Compiling Cython extension with OpenMP support") extension.extra_compile_args.append(compile_flag) extension.extra_link_args.append(link_flag) else: log.warn("Cannot compile Cython extension with OpenMP, reverting to non-parallel code") return using_openmp spectral-cube-0.4.3/astropy_helpers/astropy_helpers/setup_helpers.py0000644000077000000240000006636013245574455026223 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This module contains a number of utilities for use during setup/build/packaging that are useful to astropy as a whole. """ from __future__ import absolute_import, print_function import collections import os import re import subprocess import sys import traceback import warnings from distutils import log from distutils.dist import Distribution from distutils.errors import DistutilsOptionError, DistutilsModuleError from distutils.core import Extension from distutils.core import Command from distutils.command.sdist import sdist as DistutilsSdist from setuptools import find_packages as _find_packages from .distutils_helpers import (add_command_option, get_compiler_option, get_dummy_distribution, get_distutils_build_option, get_distutils_build_or_install_option) from .version_helpers import get_pkg_version_module from .utils import (walk_skip_hidden, import_file, extends_doc, resolve_name, AstropyDeprecationWarning) from .commands.build_ext import generate_build_ext_command from .commands.build_py import AstropyBuildPy from .commands.install import AstropyInstall from .commands.install_lib import AstropyInstallLib from .commands.register import AstropyRegister from .commands.test import AstropyTest # These imports are not used in this module, but are included for backwards # compat with older versions of this module from .utils import get_numpy_include_path, write_if_different # noqa from .commands.build_ext import should_build_with_cython, get_compiler_version # noqa _module_state = {'registered_commands': None, 'have_sphinx': False, 'package_cache': None, 'exclude_packages': set(), 'excludes_too_late': False} try: import sphinx # noqa _module_state['have_sphinx'] = True except ValueError as e: # This can occur deep in the bowels of Sphinx's imports by way of docutils # and an occurrence of this bug: http://bugs.python.org/issue18378 # In this case sphinx is effectively unusable if 'unknown locale' in e.args[0]: log.warn( "Possible misconfiguration of one of the environment variables " "LC_ALL, LC_CTYPES, LANG, or LANGUAGE. For an example of how to " "configure your system's language environment on OSX see " "http://blog.remibergsma.com/2012/07/10/" "setting-locales-correctly-on-mac-osx-terminal-application/") except ImportError: pass except SyntaxError: # occurs if markupsafe is recent version, which doesn't support Python 3.2 pass PY3 = sys.version_info[0] >= 3 # This adds a new keyword to the setup() function Distribution.skip_2to3 = [] def adjust_compiler(package): """ This function detects broken compilers and switches to another. If the environment variable CC is explicitly set, or a compiler is specified on the commandline, no override is performed -- the purpose here is to only override a default compiler. The specific compilers with problems are: * The default compiler in XCode-4.2, llvm-gcc-4.2, segfaults when compiling wcslib. The set of broken compilers can be updated by changing the compiler_mapping variable. It is a list of 2-tuples where the first in the pair is a regular expression matching the version of the broken compiler, and the second is the compiler to change to. """ warnings.warn( 'Direct use of the adjust_compiler function in setup.py is ' 'deprecated and can be removed from your setup.py. This ' 'functionality is now incorporated directly into the build_ext ' 'command.', AstropyDeprecationWarning) def get_debug_option(packagename): """ Determines if the build is in debug mode. Returns ------- debug : bool True if the current build was started with the debug option, False otherwise. """ try: current_debug = get_pkg_version_module(packagename, fromlist=['debug'])[0] except (ImportError, AttributeError): current_debug = None # Only modify the debug flag if one of the build commands was explicitly # run (i.e. not as a sub-command of something else) dist = get_dummy_distribution() if any(cmd in dist.commands for cmd in ['build', 'build_ext']): debug = bool(get_distutils_build_option('debug')) else: debug = bool(current_debug) if current_debug is not None and current_debug != debug: build_ext_cmd = dist.get_command_class('build_ext') build_ext_cmd.force_rebuild = True return debug def add_exclude_packages(excludes): if _module_state['excludes_too_late']: raise RuntimeError( "add_package_excludes must be called before all other setup helper " "functions in order to properly handle excluded packages") _module_state['exclude_packages'].update(set(excludes)) def register_commands(package, version, release, srcdir='.'): if _module_state['registered_commands'] is not None: return _module_state['registered_commands'] if _module_state['have_sphinx']: try: from .commands.build_sphinx import (AstropyBuildSphinx, AstropyBuildDocs) except ImportError: AstropyBuildSphinx = AstropyBuildDocs = FakeBuildSphinx else: AstropyBuildSphinx = AstropyBuildDocs = FakeBuildSphinx _module_state['registered_commands'] = registered_commands = { 'test': generate_test_command(package), # Use distutils' sdist because it respects package_data. # setuptools/distributes sdist requires duplication of information in # MANIFEST.in 'sdist': DistutilsSdist, # The exact form of the build_ext command depends on whether or not # we're building a release version 'build_ext': generate_build_ext_command(package, release), # We have a custom build_py to generate the default configuration file 'build_py': AstropyBuildPy, # Since install can (in some circumstances) be run without # first building, we also need to override install and # install_lib. See #2223 'install': AstropyInstall, 'install_lib': AstropyInstallLib, 'register': AstropyRegister, 'build_sphinx': AstropyBuildSphinx, 'build_docs': AstropyBuildDocs } # Need to override the __name__ here so that the commandline options are # presented as being related to the "build" command, for example; normally # this wouldn't be necessary since commands also have a command_name # attribute, but there is a bug in distutils' help display code that it # uses __name__ instead of command_name. Yay distutils! for name, cls in registered_commands.items(): cls.__name__ = name # Add a few custom options; more of these can be added by specific packages # later for option in [ ('use-system-libraries', "Use system libraries whenever possible", True)]: add_command_option('build', *option) add_command_option('install', *option) add_command_hooks(registered_commands, srcdir=srcdir) return registered_commands def add_command_hooks(commands, srcdir='.'): """ Look through setup_package.py modules for functions with names like ``pre__hook`` and ``post__hook`` where ```` is the name of a ``setup.py`` command (e.g. build_ext). If either hook is present this adds a wrapped version of that command to the passed in ``commands`` `dict`. ``commands`` may be pre-populated with other custom distutils command classes that should be wrapped if there are hooks for them (e.g. `AstropyBuildPy`). """ hook_re = re.compile(r'^(pre|post)_(.+)_hook$') # Distutils commands have a method of the same name, but it is not a # *classmethod* (which probably didn't exist when distutils was first # written) def get_command_name(cmdcls): if hasattr(cmdcls, 'command_name'): return cmdcls.command_name else: return cmdcls.__name__ packages = filter_packages(find_packages(srcdir)) dist = get_dummy_distribution() hooks = collections.defaultdict(dict) for setuppkg in iter_setup_packages(srcdir, packages): for name, obj in vars(setuppkg).items(): match = hook_re.match(name) if not match: continue hook_type = match.group(1) cmd_name = match.group(2) if hook_type not in hooks[cmd_name]: hooks[cmd_name][hook_type] = [] hooks[cmd_name][hook_type].append((setuppkg.__name__, obj)) for cmd_name, cmd_hooks in hooks.items(): commands[cmd_name] = generate_hooked_command( cmd_name, dist.get_command_class(cmd_name), cmd_hooks) def generate_hooked_command(cmd_name, cmd_cls, hooks): """ Returns a generated subclass of ``cmd_cls`` that runs the pre- and post-command hooks for that command before and after the ``cmd_cls.run`` method. """ def run(self, orig_run=cmd_cls.run): self.run_command_hooks('pre_hooks') orig_run(self) self.run_command_hooks('post_hooks') return type(cmd_name, (cmd_cls, object), {'run': run, 'run_command_hooks': run_command_hooks, 'pre_hooks': hooks.get('pre', []), 'post_hooks': hooks.get('post', [])}) def run_command_hooks(cmd_obj, hook_kind): """Run hooks registered for that command and phase. *cmd_obj* is a finalized command object; *hook_kind* is either 'pre_hook' or 'post_hook'. """ hooks = getattr(cmd_obj, hook_kind, None) if not hooks: return for modname, hook in hooks: if isinstance(hook, str): try: hook_obj = resolve_name(hook) except ImportError as exc: raise DistutilsModuleError( 'cannot find hook {0}: {1}'.format(hook, exc)) else: hook_obj = hook if not callable(hook_obj): raise DistutilsOptionError('hook {0!r} is not callable' % hook) log.info('running {0} from {1} for {2} command'.format( hook_kind.rstrip('s'), modname, cmd_obj.get_command_name())) try: hook_obj(cmd_obj) except Exception: log.error('{0} command hook {1} raised an exception: %s\n'.format( hook_obj.__name__, cmd_obj.get_command_name())) log.error(traceback.format_exc()) sys.exit(1) def generate_test_command(package_name): """ Creates a custom 'test' command for the given package which sets the command's ``package_name`` class attribute to the name of the package being tested. """ return type(package_name.title() + 'Test', (AstropyTest,), {'package_name': package_name}) def update_package_files(srcdir, extensions, package_data, packagenames, package_dirs): """ This function is deprecated and maintained for backward compatibility with affiliated packages. Affiliated packages should update their setup.py to use `get_package_info` instead. """ info = get_package_info(srcdir) extensions.extend(info['ext_modules']) package_data.update(info['package_data']) packagenames = list(set(packagenames + info['packages'])) package_dirs.update(info['package_dir']) def get_package_info(srcdir='.', exclude=()): """ Collates all of the information for building all subpackages and returns a dictionary of keyword arguments that can be passed directly to `distutils.setup`. The purpose of this function is to allow subpackages to update the arguments to the package's ``setup()`` function in its setup.py script, rather than having to specify all extensions/package data directly in the ``setup.py``. See Astropy's own ``setup.py`` for example usage and the Astropy development docs for more details. This function obtains that information by iterating through all packages in ``srcdir`` and locating a ``setup_package.py`` module. This module can contain the following functions: ``get_extensions()``, ``get_package_data()``, ``get_build_options()``, ``get_external_libraries()``, and ``requires_2to3()``. Each of those functions take no arguments. - ``get_extensions`` returns a list of `distutils.extension.Extension` objects. - ``get_package_data()`` returns a dict formatted as required by the ``package_data`` argument to ``setup()``. - ``get_build_options()`` returns a list of tuples describing the extra build options to add. - ``get_external_libraries()`` returns a list of libraries that can optionally be built using external dependencies. - ``get_entry_points()`` returns a dict formatted as required by the ``entry_points`` argument to ``setup()``. - ``requires_2to3()`` should return `True` when the source code requires `2to3` processing to run on Python 3.x. If ``requires_2to3()`` is missing, it is assumed to return `True`. """ ext_modules = [] packages = [] package_data = {} package_dir = {} skip_2to3 = [] if exclude: warnings.warn( "Use of the exclude parameter is no longer supported since it does " "not work as expected. Use add_exclude_packages instead. Note that " "it must be called prior to any other calls from setup helpers.", AstropyDeprecationWarning) # Use the find_packages tool to locate all packages and modules packages = filter_packages(find_packages(srcdir, exclude=exclude)) # Update package_dir if the package lies in a subdirectory if srcdir != '.': package_dir[''] = srcdir # For each of the setup_package.py modules, extract any # information that is needed to install them. The build options # are extracted first, so that their values will be available in # subsequent calls to `get_extensions`, etc. for setuppkg in iter_setup_packages(srcdir, packages): if hasattr(setuppkg, 'get_build_options'): options = setuppkg.get_build_options() for option in options: add_command_option('build', *option) if hasattr(setuppkg, 'get_external_libraries'): libraries = setuppkg.get_external_libraries() for library in libraries: add_external_library(library) if hasattr(setuppkg, 'requires_2to3'): requires_2to3 = setuppkg.requires_2to3() else: requires_2to3 = True if not requires_2to3: skip_2to3.append( os.path.dirname(setuppkg.__file__)) for setuppkg in iter_setup_packages(srcdir, packages): # get_extensions must include any Cython extensions by their .pyx # filename. if hasattr(setuppkg, 'get_extensions'): ext_modules.extend(setuppkg.get_extensions()) if hasattr(setuppkg, 'get_package_data'): package_data.update(setuppkg.get_package_data()) # Locate any .pyx files not already specified, and add their extensions in. # The default include dirs include numpy to facilitate numerical work. ext_modules.extend(get_cython_extensions(srcdir, packages, ext_modules, ['numpy'])) # Now remove extensions that have the special name 'skip_cython', as they # exist Only to indicate that the cython extensions shouldn't be built for i, ext in reversed(list(enumerate(ext_modules))): if ext.name == 'skip_cython': del ext_modules[i] # On Microsoft compilers, we need to pass the '/MANIFEST' # commandline argument. This was the default on MSVC 9.0, but is # now required on MSVC 10.0, but it doesn't seem to hurt to add # it unconditionally. if get_compiler_option() == 'msvc': for ext in ext_modules: ext.extra_link_args.append('/MANIFEST') return { 'ext_modules': ext_modules, 'packages': packages, 'package_dir': package_dir, 'package_data': package_data, 'skip_2to3': skip_2to3 } def iter_setup_packages(srcdir, packages): """ A generator that finds and imports all of the ``setup_package.py`` modules in the source packages. Returns ------- modgen : generator A generator that yields (modname, mod), where `mod` is the module and `modname` is the module name for the ``setup_package.py`` modules. """ for packagename in packages: package_parts = packagename.split('.') package_path = os.path.join(srcdir, *package_parts) setup_package = os.path.relpath( os.path.join(package_path, 'setup_package.py')) if os.path.isfile(setup_package): module = import_file(setup_package, name=packagename + '.setup_package') yield module def iter_pyx_files(package_dir, package_name): """ A generator that yields Cython source files (ending in '.pyx') in the source packages. Returns ------- pyxgen : generator A generator that yields (extmod, fullfn) where `extmod` is the full name of the module that the .pyx file would live in based on the source directory structure, and `fullfn` is the path to the .pyx file. """ for dirpath, dirnames, filenames in walk_skip_hidden(package_dir): for fn in filenames: if fn.endswith('.pyx'): fullfn = os.path.relpath(os.path.join(dirpath, fn)) # Package must match file name extmod = '.'.join([package_name, fn[:-4]]) yield (extmod, fullfn) break # Don't recurse into subdirectories def get_cython_extensions(srcdir, packages, prevextensions=tuple(), extincludedirs=None): """ Looks for Cython files and generates Extensions if needed. Parameters ---------- srcdir : str Path to the root of the source directory to search. prevextensions : list of `~distutils.core.Extension` objects The extensions that are already defined. Any .pyx files already here will be ignored. extincludedirs : list of str or None Directories to include as the `include_dirs` argument to the generated `~distutils.core.Extension` objects. Returns ------- exts : list of `~distutils.core.Extension` objects The new extensions that are needed to compile all .pyx files (does not include any already in `prevextensions`). """ # Vanilla setuptools and old versions of distribute include Cython files # as .c files in the sources, not .pyx, so we cannot simply look for # existing .pyx sources in the previous sources, but we should also check # for .c files with the same remaining filename. So we look for .pyx and # .c files, and we strip the extension. prevsourcepaths = [] ext_modules = [] for ext in prevextensions: for s in ext.sources: if s.endswith(('.pyx', '.c', '.cpp')): sourcepath = os.path.realpath(os.path.splitext(s)[0]) prevsourcepaths.append(sourcepath) for package_name in packages: package_parts = package_name.split('.') package_path = os.path.join(srcdir, *package_parts) for extmod, pyxfn in iter_pyx_files(package_path, package_name): sourcepath = os.path.realpath(os.path.splitext(pyxfn)[0]) if sourcepath not in prevsourcepaths: ext_modules.append(Extension(extmod, [pyxfn], include_dirs=extincludedirs)) return ext_modules class DistutilsExtensionArgs(collections.defaultdict): """ A special dictionary whose default values are the empty list. This is useful for building up a set of arguments for `distutils.Extension` without worrying whether the entry is already present. """ def __init__(self, *args, **kwargs): def default_factory(): return [] super(DistutilsExtensionArgs, self).__init__( default_factory, *args, **kwargs) def update(self, other): for key, val in other.items(): self[key].extend(val) def pkg_config(packages, default_libraries, executable='pkg-config'): """ Uses pkg-config to update a set of distutils Extension arguments to include the flags necessary to link against the given packages. If the pkg-config lookup fails, default_libraries is applied to libraries. Parameters ---------- packages : list of str A list of pkg-config packages to look up. default_libraries : list of str A list of library names to use if the pkg-config lookup fails. Returns ------- config : dict A dictionary containing keyword arguments to `distutils.Extension`. These entries include: - ``include_dirs``: A list of include directories - ``library_dirs``: A list of library directories - ``libraries``: A list of libraries - ``define_macros``: A list of macro defines - ``undef_macros``: A list of macros to undefine - ``extra_compile_args``: A list of extra arguments to pass to the compiler """ flag_map = {'-I': 'include_dirs', '-L': 'library_dirs', '-l': 'libraries', '-D': 'define_macros', '-U': 'undef_macros'} command = "{0} --libs --cflags {1}".format(executable, ' '.join(packages)), result = DistutilsExtensionArgs() try: pipe = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE) output = pipe.communicate()[0].strip() except subprocess.CalledProcessError as e: lines = [ ("{0} failed. This may cause the build to fail below." .format(executable)), " command: {0}".format(e.cmd), " returncode: {0}".format(e.returncode), " output: {0}".format(e.output) ] log.warn('\n'.join(lines)) result['libraries'].extend(default_libraries) else: if pipe.returncode != 0: lines = [ "pkg-config could not lookup up package(s) {0}.".format( ", ".join(packages)), "This may cause the build to fail below." ] log.warn('\n'.join(lines)) result['libraries'].extend(default_libraries) else: for token in output.split(): # It's not clear what encoding the output of # pkg-config will come to us in. It will probably be # some combination of pure ASCII (for the compiler # flags) and the filesystem encoding (for any argument # that includes directories or filenames), but this is # just conjecture, as the pkg-config documentation # doesn't seem to address it. arg = token[:2].decode('ascii') value = token[2:].decode(sys.getfilesystemencoding()) if arg in flag_map: if arg == '-D': value = tuple(value.split('=', 1)) result[flag_map[arg]].append(value) else: result['extra_compile_args'].append(value) return result def add_external_library(library): """ Add a build option for selecting the internal or system copy of a library. Parameters ---------- library : str The name of the library. If the library is `foo`, the build option will be called `--use-system-foo`. """ for command in ['build', 'build_ext', 'install']: add_command_option(command, str('use-system-' + library), 'Use the system {0} library'.format(library), is_bool=True) def use_system_library(library): """ Returns `True` if the build configuration indicates that the given library should use the system copy of the library rather than the internal one. For the given library `foo`, this will be `True` if `--use-system-foo` or `--use-system-libraries` was provided at the commandline or in `setup.cfg`. Parameters ---------- library : str The name of the library Returns ------- use_system : bool `True` if the build should use the system copy of the library. """ return ( get_distutils_build_or_install_option('use_system_{0}'.format(library)) or get_distutils_build_or_install_option('use_system_libraries')) @extends_doc(_find_packages) def find_packages(where='.', exclude=(), invalidate_cache=False): """ This version of ``find_packages`` caches previous results to speed up subsequent calls. Use ``invalide_cache=True`` to ignore cached results from previous ``find_packages`` calls, and repeat the package search. """ if exclude: warnings.warn( "Use of the exclude parameter is no longer supported since it does " "not work as expected. Use add_exclude_packages instead. Note that " "it must be called prior to any other calls from setup helpers.", AstropyDeprecationWarning) # Calling add_exclude_packages after this point will have no effect _module_state['excludes_too_late'] = True if not invalidate_cache and _module_state['package_cache'] is not None: return _module_state['package_cache'] packages = _find_packages( where=where, exclude=list(_module_state['exclude_packages'])) _module_state['package_cache'] = packages return packages def filter_packages(packagenames): """ Removes some packages from the package list that shouldn't be installed on the current version of Python. """ if PY3: exclude = '_py2' else: exclude = '_py3' return [x for x in packagenames if not x.endswith(exclude)] class FakeBuildSphinx(Command): """ A dummy build_sphinx command that is called if Sphinx is not installed and displays a relevant error message """ # user options inherited from sphinx.setup_command.BuildDoc user_options = [ ('fresh-env', 'E', ''), ('all-files', 'a', ''), ('source-dir=', 's', ''), ('build-dir=', None, ''), ('config-dir=', 'c', ''), ('builder=', 'b', ''), ('project=', None, ''), ('version=', None, ''), ('release=', None, ''), ('today=', None, ''), ('link-index', 'i', '')] # user options appended in astropy.setup_helpers.AstropyBuildSphinx user_options.append(('warnings-returncode', 'w', '')) user_options.append(('clean-docs', 'l', '')) user_options.append(('no-intersphinx', 'n', '')) user_options.append(('open-docs-in-browser', 'o', '')) def initialize_options(self): try: raise RuntimeError("Sphinx and its dependencies must be installed " "for build_docs.") except: log.error('error: Sphinx and its dependencies must be installed ' 'for build_docs.') sys.exit(1) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/0000755000077000000240000000000013261442571024254 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/__init__.py0000644000077000000240000000066513126505434026372 0ustar adamstaff00000000000000""" This package contains utilities and extensions for the Astropy sphinx documentation. In particular, the `astropy.sphinx.conf` should be imported by the sphinx ``conf.py`` file for affiliated packages that wish to make use of the Astropy documentation format. Note that some sphinx extensions which are bundled as-is (numpydoc and sphinx-automodapi) are included in astropy_helpers.extern rather than astropy_helpers.sphinx.ext. """ spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/conf.py0000644000077000000240000002721713242700737025564 0ustar adamstaff00000000000000# -*- 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 not on_rtd and LooseVersion(sphinx.__version__) < LooseVersion('1.4'): extensions.append('sphinx.ext.pngmath') else: extensions.append('sphinx.ext.mathjax') 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 spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/0000755000077000000240000000000013261442571025054 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/__init__.py0000644000077000000240000000010213126505434027154 0ustar adamstaff00000000000000from __future__ import division, absolute_import, print_function spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/changelog_links.py0000644000077000000240000000554313242700737030564 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This sphinx extension makes the issue numbers in the changelog into links to GitHub issues. """ from __future__ import print_function import re from docutils.nodes import Text, reference BLOCK_PATTERN = re.compile('\[#.+\]', flags=re.DOTALL) ISSUE_PATTERN = re.compile('#[0-9]+') def process_changelog_links(app, doctree, docname): for rex in app.changelog_links_rexes: if rex.match(docname): break else: # if the doc doesn't match any of the changelog regexes, don't process return app.info('[changelog_links] Adding changelog links to "{0}"'.format(docname)) for item in doctree.traverse(): if not isinstance(item, Text): continue # We build a new list of items to replace the current item. If # a link is found, we need to use a 'reference' item. children = [] # First cycle through blocks of issues (delimited by []) then # iterate inside each one to find the individual issues. prev_block_end = 0 for block in BLOCK_PATTERN.finditer(item): block_start, block_end = block.start(), block.end() children.append(Text(item[prev_block_end:block_start])) block = item[block_start:block_end] prev_end = 0 for m in ISSUE_PATTERN.finditer(block): start, end = m.start(), m.end() children.append(Text(block[prev_end:start])) issue_number = block[start:end] refuri = app.config.github_issues_url + issue_number[1:] children.append(reference(text=issue_number, name=issue_number, refuri=refuri)) prev_end = end prev_block_end = block_end # If no issues were found, this adds the whole item, # otherwise it adds the remaining text. children.append(Text(block[prev_end:block_end])) # If no blocks were found, this adds the whole item, otherwise # it adds the remaining text. children.append(Text(item[prev_block_end:])) # Replace item by the new list of items we have generated, # which may contain links. item.parent.replace(item, children) def setup_patterns_rexes(app): app.changelog_links_rexes = [re.compile(pat) for pat in app.config.changelog_links_docpattern] def setup(app): app.connect('doctree-resolved', process_changelog_links) app.connect('builder-inited', setup_patterns_rexes) app.add_config_value('github_issues_url', None, True) app.add_config_value('changelog_links_docpattern', ['.*changelog.*', 'whatsnew/.*'], True) return {'parallel_read_safe': True, 'parallel_write_safe': True} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/doctest.py0000644000077000000240000000364113242700737027077 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This is a set of three directives that allow us to insert metadata about doctests into the .rst files so the testing framework knows which tests to skip. This is quite different from the doctest extension in Sphinx itself, which actually does something. For astropy, all of the testing is centrally managed from py.test and Sphinx is not used for running tests. """ import re from docutils.nodes import literal_block from docutils.parsers.rst import Directive class DoctestSkipDirective(Directive): has_content = True def run(self): # Check if there is any valid argument, and skip it. Currently only # 'win32' is supported in astropy.tests.pytest_plugins. if re.match('win32', self.content[0]): self.content = self.content[2:] code = '\n'.join(self.content) return [literal_block(code, code)] class DoctestOmitDirective(Directive): has_content = True def run(self): # Simply do not add any content when this directive is encountered return [] class DoctestRequiresDirective(DoctestSkipDirective): # This is silly, but we really support an unbounded number of # optional arguments optional_arguments = 64 def setup(app): app.add_directive('doctest-requires', DoctestRequiresDirective) app.add_directive('doctest-skip', DoctestSkipDirective) app.add_directive('doctest-skip-all', DoctestSkipDirective) app.add_directive('doctest', DoctestSkipDirective) # Code blocks that use this directive will not appear in the generated # documentation. This is intended to hide boilerplate code that is only # useful for testing documentation using doctest, but does not actually # belong in the documentation itself. app.add_directive('testsetup', DoctestOmitDirective) return {'parallel_read_safe': True, 'parallel_write_safe': True} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/edit_on_github.py0000644000077000000240000001346413242700737030421 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This extension makes it easy to edit documentation on github. It adds links associated with each docstring that go to the corresponding view source page on Github. From there, the user can push the "Edit" button, edit the docstring, and submit a pull request. It has the following configuration options (to be set in the project's ``conf.py``): * ``edit_on_github_project`` The name of the github project, in the form "username/projectname". * ``edit_on_github_branch`` The name of the branch to edit. If this is a released version, this should be a git tag referring to that version. For a dev version, it often makes sense for it to be "master". It may also be a git hash. * ``edit_on_github_source_root`` The location within the source tree of the root of the Python package. Defaults to "lib". * ``edit_on_github_doc_root`` The location within the source tree of the root of the documentation source. Defaults to "doc", but it may make sense to set it to "doc/source" if the project uses a separate source directory. * ``edit_on_github_docstring_message`` The phrase displayed in the links to edit a docstring. Defaults to "[edit on github]". * ``edit_on_github_page_message`` The phrase displayed in the links to edit a RST page. Defaults to "[edit this page on github]". * ``edit_on_github_help_message`` The phrase displayed as a tooltip on the edit links. Defaults to "Push the Edit button on the next page" * ``edit_on_github_skip_regex`` When the path to the .rst file matches this regular expression, no "edit this page on github" link will be added. Defaults to ``"_.*"``. """ import inspect import os import re import sys from docutils import nodes from sphinx import addnodes def import_object(modname, name): """ Import the object given by *modname* and *name* and return it. If not found, or the import fails, returns None. """ try: __import__(modname) mod = sys.modules[modname] obj = mod for part in name.split('.'): obj = getattr(obj, part) return obj except: return None def get_url_base(app): return 'http://github.com/%s/tree/%s/' % ( app.config.edit_on_github_project, app.config.edit_on_github_branch) def doctree_read(app, doctree): # Get the configuration parameters if app.config.edit_on_github_project == 'REQUIRED': raise ValueError( "The edit_on_github_project configuration variable must be " "provided in the conf.py") source_root = app.config.edit_on_github_source_root url = get_url_base(app) docstring_message = app.config.edit_on_github_docstring_message # Handle the docstring-editing links for objnode in doctree.traverse(addnodes.desc): if objnode.get('domain') != 'py': continue names = set() for signode in objnode: if not isinstance(signode, addnodes.desc_signature): continue modname = signode.get('module') if not modname: continue fullname = signode.get('fullname') if fullname in names: # only one link per name, please continue names.add(fullname) obj = import_object(modname, fullname) anchor = None if obj is not None: try: lines, lineno = inspect.getsourcelines(obj) except: pass else: anchor = '#L%d' % lineno if anchor: real_modname = inspect.getmodule(obj).__name__ path = '%s%s%s.py%s' % ( url, source_root, real_modname.replace('.', '/'), anchor) onlynode = addnodes.only(expr='html') onlynode += nodes.reference( reftitle=app.config.edit_on_github_help_message, refuri=path) onlynode[0] += nodes.inline( '', '', nodes.raw('', ' ', format='html'), nodes.Text(docstring_message), classes=['edit-on-github', 'viewcode-link']) signode += onlynode def html_page_context(app, pagename, templatename, context, doctree): if (templatename == 'page.html' and not re.match(app.config.edit_on_github_skip_regex, pagename)): doc_root = app.config.edit_on_github_doc_root if doc_root != '' and not doc_root.endswith('/'): doc_root += '/' doc_path = os.path.relpath(doctree.get('source'), app.builder.srcdir) url = get_url_base(app) page_message = app.config.edit_on_github_page_message context['edit_on_github'] = url + doc_root + doc_path context['edit_on_github_page_message'] = page_message def setup(app): app.add_config_value('edit_on_github_project', 'REQUIRED', True) app.add_config_value('edit_on_github_branch', 'master', True) app.add_config_value('edit_on_github_source_root', 'lib', True) app.add_config_value('edit_on_github_doc_root', 'doc', True) app.add_config_value('edit_on_github_docstring_message', '[edit on github]', True) app.add_config_value('edit_on_github_page_message', 'Edit This Page on Github', True) app.add_config_value('edit_on_github_help_message', 'Push the Edit button on the next page', True) app.add_config_value('edit_on_github_skip_regex', '_.*', True) app.connect('doctree-read', doctree_read) app.connect('html-page-context', html_page_context) return {'parallel_read_safe': True, 'parallel_write_safe': True} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/tests/0000755000077000000240000000000013261442571026216 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/tests/__init__.py0000644000077000000240000000000013126505434030313 0ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/ext/tocdepthfix.py0000644000077000000240000000137013242700737027750 0ustar adamstaff00000000000000from sphinx import addnodes def fix_toc_entries(app, doctree): # Get the docname; I don't know why this isn't just passed in to the # callback # This seems a bit unreliable as it's undocumented, but it's not "private" # either: docname = app.builder.env.temp_data['docname'] if app.builder.env.metadata[docname].get('tocdepth', 0) != 0: # We need to reprocess any TOC nodes in the doctree and make sure all # the files listed in any TOCs are noted for treenode in doctree.traverse(addnodes.toctree): app.builder.env.note_toctree(docname, treenode) def setup(app): app.connect('doctree-read', fix_toc_entries) return {'parallel_read_safe': True, 'parallel_write_safe': True} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/local/0000755000077000000240000000000013261442571025346 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/local/python2_local_links.inv0000644000077000000240000000106213100750165032031 0ustar adamstaff00000000000000# Sphinx inventory version 2 # Project: Python # Version: 2.7 and 3.5 # The remainder of this file should be compressed using zlib. xœ¥”=OÃ0@wÿŠ“ºÀà Z!¤n€U µbwœ+1ríp¶KïÇIi›”QeIä˽wþ8g³"Wf ʬÐxK%¬œ²†lS²ï(ý¦¥Ï­‰‘×Í×1 “k&ƒQrÃóp)”ÉÀ.ÀçÊÁBi—Û 3H¤]„ÎaÁ)ó_Z¥I¤‹Ú>d“H¯ï‰,ÅÐ× _M…_Âð"æ’ òbvIî—zЀ8›<ŸŒ?oÙ',O…wg¯B<•o@œÍËâdÁžá»}5ºdsµDüqã¨ÛØÄ8KK®ÂÁÈßô6úo²9 ¤«£%üè|‡¶@Ó­9ȯ]Τ52ÅvMÁ‡ØQÉCý®üR çÚò#ùunÏÑóœPdSkõýeð›]íW³¸Þ€ÉKõ쨰ɨ¥ÍdÎ RÆŸÿû¼êü_­/ªÖø“'RÉšãG4Hâ¯Õ·ØíÒÛÁ½„·ä·±fžªC{ÃÒÖß| åEª»;åö½¤-¿³ï2½ä{ÉÁÌ]iäÄÇMë;û–¨«HÏãm‹8û‹´RKspectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/local/python2_local_links.txt0000644000077000000240000000314613100750165032061 0ustar adamstaff00000000000000# Sphinx inventory version 2 # Project: Python # Version: 2.7 and 3.5 # The remainder of this file should be compressed using zlib. # python2 IndexError py:exception -1 2/library/exceptions.html#IndexError - IOError py:exception -1 2/library/exceptions.html#IOError - KeyError py:exception -1 2/library/exceptions.html#KeyError - ValueError py:exception -1 2/library/exceptions.html#ValueError - TypeError py:exception -1 2/library/exceptions.html#TypeError - # python3 only TimeoutError py:exception -1 3/library/exceptions.html#TimeoutError - bytes py:function -1 3/library/functions.html#bytes - urllib.request.urlopen py:function -1 3/library/urllib.request.html#urllib.request.urlopen - concurrent.futures.Future py:class -1 3/library/concurrent.futures.html#concurrent.futures.Future - concurrent.futures.ThreadPoolExecutor py:class -1 3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor - queue.Queue py:class -1 3/library/queue.html#queue.Queue - print() py:function -1 3/library/functions.html#print - # python3 only collections.abc collections.Generator py:class -1 3/library/collections.abc.html#collections.abc.Generator - collections.ByteString py:class -1 3/library/collections.abc.html#collections.abc.ByteString - collections.Awaitable py:class -1 3/library/collections.abc.html#collections.abc.Awaitable - collections.Coroutine py:class -1 3/library/collections.abc.html#collections.abc.Coroutine - collections.AsyncIterable py:class -1 3/library/collections.abc.html#collections.abc.AsyncIterable - collections.AsyncIterator py:class -1 3/library/collections.abc.html#collections.abc.AsyncIterator - spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/local/python3_local_links.inv0000644000077000000240000000122213100750165032030 0ustar adamstaff00000000000000# Sphinx inventory version 2 # Project: Python # Version: 3.5 # The remainder of this file should be compressed using zlib. xœ­ÕÁŽÚ0Ð{¾b$.í!YTUâÖîa·ª*!Qq­{h\;µ¶Ù¯ïgÉšlÏÇvÆ ØV…ÔAê#jolG´N Ëdk~#÷kØ4¾0šZv]ï–Ù'`ZÀ*ûHÍ? ‹%“Z ³_H{©\aj% Gব,:‡j'õ/xU2Ï(ºjõ%­PR\Â7’(ºjÖ¥5ýJ?Àò,³ÍCÕöf…/Õ¢ëOûˆ4©µäF´±ûZszš ºouÐO“Ü ‘Ïål;ƒ”Ýð4’û¬`îç›6éyDí¼ðM…ç”AÄ-}Jwq‰!dÞj´!#âÈT·"ç Ú«hè2£oS¡žßˆ~`· “¨t8«¤²Rûwï§ÒjnKÛñcŸRxÂržŒÿ?íéèÒÉ%Ÿ+æ\ˆOb»ÓÆ ’ø3sË»£ü¿ö`„ôV¾Ò»áv@ˆ>2¥bç;!ý•I,=Whª_ÑôÉé'Ôñö™l!þ©Qó¨%Œþ^ûÓBÝ#Ô }inuüÜÂD¹#e³ªŠ\‹ž¸:ï{ø¡tuþ;‰/wx†–yó®–.–¿ !þn²X{0Bz×Þo±øH ùÏ/LúØòxA&UÝXS{ºžâ®¸™ÌÜ5šß£¸‡Ð\’ÈRBiòJFü?spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/local/python3_local_links.txt0000644000077000000240000000540413100750165032061 0ustar adamstaff00000000000000# Sphinx inventory version 2 # Project: Python # Version: 2.7 and 3.5 # The remainder of this file should be compressed using zlib. # python2 only links cPickle py:module -1 2/library/pickle.html#module-cPickle - unicode py:function -1 2/library/functions.html#unicode - bsddb py:module -1 2/library/bsddb.html#module-bsddb - dict.has_key py:method -1 2/library/stdtypes.html#dict.has_key - dict.iteritems py:method -1 2/library/stdtypes.html#dict.iteritems - dict.iterkeys py:method -1 2/library/stdtypes.html#dict.iterkeys - dict.itervalues py:method -1 2/library/stdtypes.html#dict.itervalues - urllib2.urlopen py:function -1 2/library/urllib2.html#urllib2.urlopen - # python3 print() py:function -1 3/library/functions.html#print - # python3 collections.abc collections.Container py:class -1 3/library/collections.abc.html#collections.abc.Container - collections.Hashable py:class -1 3/library/collections.abc.html#collections.abc.Hashable - collections.Sized py:class -1 3/library/collections.abc.html#collections.abc.Sized - collections.Callable py:class -1 3/library/collections.abc.html#collections.abc.Callable - collections.Iterable py:class -1 3/library/collections.abc.html#collections.abc.Iterable - collections.Iterator py:class -1 3/library/collections.abc.html#collections.abc.Iterator - collections.Generator py:class -1 3/library/collections.abc.html#collections.abc.Generator - collections.Sequence py:class -1 3/library/collections.abc.html#collections.abc.Sequence - collections.MutableSequence py:class -1 3/library/collections.abc.html#collections.abc.MutableSequence - collections.ByteString py:class -1 3/library/collections.abc.html#collections.abc.ByteString - collections.Set py:class -1 3/library/collections.abc.html#collections.abc.Set - collections.MutableSet py:class -1 3/library/collections.abc.html#collections.abc.MutableSet - collections.Mapping py:class -1 3/library/collections.abc.html#collections.abc.Mapping - collections.MutableMapping py:class -1 3/library/collections.abc.html#collections.abc.MutableMapping - collections.MappingView py:class -1 3/library/collections.abc.html#collections.abc.MappingView - collections.ItemsView py:class -1 3/library/collections.abc.html#collections.abc.ItemsView - collections.KeysView py:class -1 3/library/collections.abc.html#collections.abc.KeysView - collections.ValuesView py:class -1 3/library/collections.abc.html#collections.abc.ValuesView - collections.Awaitable py:class -1 3/library/collections.abc.html#collections.abc.Awaitable - collections.Coroutine py:class -1 3/library/collections.abc.html#collections.abc.Coroutine - collections.AsyncIterable py:class -1 3/library/collections.abc.html#collections.abc.AsyncIterable - collections.AsyncIterator py:class -1 3/library/collections.abc.html#collections.abc.AsyncIterator - spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/setup_package.py0000644000077000000240000000044413126505434027441 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst def get_package_data(): # Install the theme files return { 'astropy_helpers.sphinx': [ 'local/*.inv', 'themes/bootstrap-astropy/*.*', 'themes/bootstrap-astropy/static/*.*']} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/themes/0000755000077000000240000000000013261442571025541 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/0000755000077000000240000000000013261442571031255 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/globaltoc.html0000644000077000000240000000011112340434262034075 0ustar adamstaff00000000000000

Table of Contents

{{ toctree(maxdepth=-1, titles_only=true) }} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/layout.html0000644000077000000240000000655112657374605033501 0ustar adamstaff00000000000000{% extends "basic/layout.html" %} {# Collapsible sidebar script from default/layout.html in Sphinx #} {% set script_files = script_files + ['_static/sidebar.js'] %} {# Add the google webfonts needed for the logo #} {% block extrahead %} {% if not embedded %}{% endif %} {% endblock %} {% block header %}
{{ theme_logotext1 }}{{ theme_logotext2 }}{{ theme_logotext3 }}
  • Index
  • Modules
  • {% block sidebarsearch %} {% include "searchbox.html" %} {% endblock %}
{% 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 %} spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/localtoc.html0000644000077000000240000000004212340434262033732 0ustar adamstaff00000000000000

Page Contents

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white-space: nowrap; background-color: rgb(238, 238, 238); border-radius: 0 3px 3px 0; -webkit-border-radius: 0 3px 3px 0; } ././@LongLink0000000000000000000000000000015100000000000011212 Lustar 00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/static/copybutton.jsspectral-cube-0.4.3/astropy_helpers/astropy_helpers/sphinx/themes/bootstrap-astropy/static/copybutto0000644000077000000240000000532113100750165034511 0ustar adamstaff00000000000000$(document).ready(function() { /* Add a [>>>] button on the top-right corner of code samples to hide * the >>> and ... prompts and the output and thus make the code * copyable. */ var div = $('.highlight-python .highlight,' + '.highlight-python3 .highlight,' + '.highlight-default .highlight') var pre = div.find('pre'); // get the styles from the current theme pre.parent().parent().css('position', 'relative'); var hide_text = 'Hide the prompts and output'; var show_text = 'Show the prompts and output'; var border_width = pre.css('border-top-width'); var border_style = pre.css('border-top-style'); var border_color = pre.css('border-top-color'); var button_styles = { 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', 'border-color': border_color, 'border-style': border_style, 'border-width': border_width, 'color': border_color, 'text-size': '75%', 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em', 'border-radius': '0 3px 0 0' } // create and add the button to all the code blocks that contain >>> div.each(function(index) { var jthis = $(this); if (jthis.find('.gp').length > 0) { var button = $('>>>'); button.css(button_styles) button.attr('title', hide_text); button.data('hidden', 'false'); jthis.prepend(button); } // tracebacks (.gt) contain bare text elements that need to be // wrapped in a span to work with .nextUntil() (see later) jthis.find('pre:has(.gt)').contents().filter(function() { return ((this.nodeType == 3) && (this.data.trim().length > 0)); 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This script adds * in .sphixsidebar, after .sphinxsidebarwrapper, the #sidebarbutton * used to collapse and expand the sidebar. * * When the sidebar is collapsed the .sphinxsidebarwrapper is hidden * and the width of the sidebar and the margin-left of the document * are decreased. When the sidebar is expanded the opposite happens. * This script saves a per-browser/per-session cookie used to * remember the position of the sidebar among the pages. * Once the browser is closed the cookie is deleted and the position * reset to the default (expanded). * * :copyright: Copyright 2007-2011 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ $(function() { // global elements used by the functions. // the 'sidebarbutton' element is defined as global after its // creation, in the add_sidebar_button function var bodywrapper = $('.bodywrapper'); var sidebar = $('.sphinxsidebar'); var sidebarwrapper = $('.sphinxsidebarwrapper'); // for some reason, the document has no sidebar; do not run into errors if (!sidebar.length) return; // original margin-left of the bodywrapper and width of the sidebar // with the sidebar expanded var bw_margin_expanded = bodywrapper.css('margin-left'); var ssb_width_expanded = sidebar.width(); // margin-left of the bodywrapper and width of the sidebar // with the sidebar collapsed var bw_margin_collapsed = 12; var ssb_width_collapsed = 12; // custom colors var dark_color = '#404040'; var light_color = '#505050'; function sidebar_is_collapsed() { return sidebarwrapper.is(':not(:visible)'); } function toggle_sidebar() { if (sidebar_is_collapsed()) expand_sidebar(); else collapse_sidebar(); } function collapse_sidebar() { sidebarwrapper.hide(); sidebar.css('width', ssb_width_collapsed); bodywrapper.css('margin-left', bw_margin_collapsed); sidebarbutton.css({ 'margin-left': '-1px', 'height': bodywrapper.height(), 'border-radius': '3px' }); sidebarbutton.find('span').text('»'); sidebarbutton.attr('title', _('Expand sidebar')); document.cookie = 'sidebar=collapsed'; } function expand_sidebar() { bodywrapper.css('margin-left', bw_margin_expanded); sidebar.css('width', ssb_width_expanded); sidebarwrapper.show(); sidebarbutton.css({ 'margin-left': ssb_width_expanded - 12, 'height': bodywrapper.height(), 'border-radius': '0px 3px 3px 0px' }); sidebarbutton.find('span').text('«'); sidebarbutton.attr('title', _('Collapse sidebar')); document.cookie = 'sidebar=expanded'; } function add_sidebar_button() { sidebarwrapper.css({ 'float': 'left', 'margin-right': '0', 'width': ssb_width_expanded - 18 }); // create the button sidebar.append('
«
'); var sidebarbutton = $('#sidebarbutton'); // find the height of the viewport to center the '<<' in the page var viewport_height; if (window.innerHeight) viewport_height = window.innerHeight; else viewport_height = $(window).height(); var sidebar_offset = sidebar.offset().top; var sidebar_height = Math.max(bodywrapper.height(), sidebar.height()); sidebarbutton.find('span').css({ 'font-family': '"Lucida Grande",Arial,sans-serif', 'display': 'block', 'top': Math.min(viewport_height/2, sidebar_height/2 + sidebar_offset) - 10, 'width': 12, 'position': 'fixed', 'text-align': 'center' }); sidebarbutton.click(toggle_sidebar); sidebarbutton.attr('title', _('Collapse sidebar')); sidebarbutton.css({ 'color': '#FFFFFF', 'background-color': light_color, 'border': '1px solid ' + light_color, 'border-radius': '0px 3px 3px 0px', 'font-size': '1.2em', 'cursor': 'pointer', 'height': sidebar_height, 'padding-top': '1px', 'margin': '-1px', 'margin-left': ssb_width_expanded - 12 }); sidebarbutton.hover( function () { $(this).css('background-color', dark_color); }, function () { $(this).css('background-color', light_color); } ); } function set_position_from_cookie() { if (!document.cookie) return; var items = document.cookie.split(';'); for(var k=0; k= 7: eggs = glob.glob(os.path.join(path, '.eggs', '*.egg')) else: eggs = glob.glob('*.egg') return eggs spectral-cube-0.4.3/astropy_helpers/astropy_helpers/tests/test_git_helpers.py0000644000077000000240000002013213245574455030032 0ustar adamstaff00000000000000import glob import imp import os import pkgutil import re import sys import tarfile import pytest from warnings import catch_warnings from . import reset_setup_helpers, reset_distutils_log # noqa from . import run_cmd, run_setup, cleanup_import from astropy_helpers.git_helpers import get_git_devstr PY3 = sys.version_info[0] == 3 if PY3: _text_type = str else: _text_type = unicode # noqa _DEV_VERSION_RE = re.compile(r'\d+\.\d+(?:\.\d+)?\.dev(\d+)') ASTROPY_HELPERS_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) TEST_VERSION_SETUP_PY = """\ #!/usr/bin/env python import sys from setuptools import setup NAME = 'apyhtest_eva' VERSION = {version!r} RELEASE = 'dev' not in VERSION sys.path.insert(0, r'{astropy_helpers_path}') from astropy_helpers.git_helpers import get_git_devstr from astropy_helpers.version_helpers import generate_version_py if not RELEASE: VERSION += get_git_devstr(False) generate_version_py(NAME, VERSION, RELEASE, False, uses_git=not RELEASE) setup(name=NAME, version=VERSION, packages=['apyhtest_eva']) """ TEST_VERSION_INIT = """\ try: from .version import version as __version__ from .version import githash as __githash__ except ImportError: __version__ = __githash__ = '' """ @pytest.fixture def version_test_package(tmpdir, request): def make_test_package(version='42.42.dev'): test_package = tmpdir.mkdir('test_package') test_package.join('setup.py').write( TEST_VERSION_SETUP_PY.format(version=version, astropy_helpers_path=ASTROPY_HELPERS_PATH)) test_package.mkdir('apyhtest_eva').join('__init__.py').write(TEST_VERSION_INIT) with test_package.as_cwd(): run_cmd('git', ['init']) run_cmd('git', ['add', '--all']) run_cmd('git', ['commit', '-m', 'test package']) if '' in sys.path: sys.path.remove('') sys.path.insert(0, '') def finalize(): cleanup_import('apyhtest_eva') request.addfinalizer(finalize) return test_package return make_test_package def test_update_git_devstr(version_test_package, capsys): """Tests that the commit number in the package's version string updates after git commits even without re-running setup.py. """ # We have to call version_test_package to actually create the package test_pkg = version_test_package() with test_pkg.as_cwd(): run_setup('setup.py', ['--version']) stdout, stderr = capsys.readouterr() version = stdout.strip() m = _DEV_VERSION_RE.match(version) assert m, ( "Stdout did not match the version string pattern:" "\n\n{0}\n\nStderr:\n\n{1}".format(stdout, stderr)) revcount = int(m.group(1)) import apyhtest_eva assert apyhtest_eva.__version__ == version # Make a silly git commit with open('.test', 'w'): pass run_cmd('git', ['add', '.test']) run_cmd('git', ['commit', '-m', 'test']) import apyhtest_eva.version imp.reload(apyhtest_eva.version) # Previously this checked packagename.__version__, but in order for that to # be updated we also have to re-import _astropy_init which could be tricky. # Checking directly that the packagename.version module was updated is # sufficient: m = _DEV_VERSION_RE.match(apyhtest_eva.version.version) assert m assert int(m.group(1)) == revcount + 1 # This doesn't test astropy_helpers.get_helpers.update_git_devstr directly # since a copy of that function is made in packagename.version (so that it # can work without astropy_helpers installed). In order to get test # coverage on the actual astropy_helpers copy of that function just call it # directly and compare to the value in packagename from astropy_helpers.git_helpers import update_git_devstr newversion = update_git_devstr(version, path=str(test_pkg)) assert newversion == apyhtest_eva.version.version def test_version_update_in_other_repos(version_test_package, tmpdir): """ Regression test for https://github.com/astropy/astropy-helpers/issues/114 and for https://github.com/astropy/astropy-helpers/issues/107 """ test_pkg = version_test_package() with test_pkg.as_cwd(): run_setup('setup.py', ['build']) # Add the path to the test package to sys.path for now sys.path.insert(0, str(test_pkg)) try: import apyhtest_eva m = _DEV_VERSION_RE.match(apyhtest_eva.__version__) assert m correct_revcount = int(m.group(1)) with tmpdir.as_cwd(): testrepo = tmpdir.mkdir('testrepo') testrepo.chdir() # Create an empty git repo run_cmd('git', ['init']) import apyhtest_eva.version imp.reload(apyhtest_eva.version) m = _DEV_VERSION_RE.match(apyhtest_eva.version.version) assert m assert int(m.group(1)) == correct_revcount correct_revcount = int(m.group(1)) # Add several commits--more than the revcount for the apyhtest_eva package for idx in range(correct_revcount + 5): test_filename = '.test' + str(idx) testrepo.ensure(test_filename) run_cmd('git', ['add', test_filename]) run_cmd('git', ['commit', '-m', 'A message']) import apyhtest_eva.version imp.reload(apyhtest_eva.version) m = _DEV_VERSION_RE.match(apyhtest_eva.version.version) assert m assert int(m.group(1)) == correct_revcount correct_revcount = int(m.group(1)) finally: sys.path.remove(str(test_pkg)) @pytest.mark.parametrize('version', ['1.0.dev', '1.0']) def test_installed_git_version(version_test_package, version, tmpdir, capsys): """ Test for https://github.com/astropy/astropy-helpers/issues/87 Ensures that packages installed with astropy_helpers have a correct copy of the git hash of the installed commit. """ # To test this, it should suffice to build a source dist, unpack it # somewhere outside the git repository, and then do a build and import # from the build directory--no need to "install" as such test_pkg = version_test_package(version) with test_pkg.as_cwd(): run_setup('setup.py', ['build']) try: import apyhtest_eva githash = apyhtest_eva.__githash__ assert githash and isinstance(githash, _text_type) # Ensure that it does in fact look like a git hash and not some # other arbitrary string assert re.match(r'[0-9a-f]{40}', githash) finally: cleanup_import('apyhtest_eva') run_setup('setup.py', ['sdist', '--dist-dir=dist', '--formats=gztar']) tgzs = glob.glob(os.path.join('dist', '*.tar.gz')) assert len(tgzs) == 1 tgz = test_pkg.join(tgzs[0]) build_dir = tmpdir.mkdir('build_dir') tf = tarfile.open(str(tgz), mode='r:gz') tf.extractall(str(build_dir)) with build_dir.as_cwd(): pkg_dir = glob.glob('apyhtest_eva-*')[0] os.chdir(pkg_dir) with catch_warnings(record=True) as w: run_setup('setup.py', ['build']) try: import apyhtest_eva loader = pkgutil.get_loader('apyhtest_eva') # Ensure we are importing the 'packagename' that was just unpacked # into the build_dir assert loader.get_filename().startswith(str(build_dir)) assert apyhtest_eva.__githash__ == githash finally: cleanup_import('apyhtest_eva') def test_get_git_devstr(tmpdir): dirpath = str(tmpdir) warn_msg = "No git repository present at" # Verify as much as possible, but avoid dealing with paths on windows if not sys.platform.startswith('win'): warn_msg += " '{}'".format(dirpath) with catch_warnings(record=True) as w: devstr = get_git_devstr(path=dirpath) assert devstr == '0' assert len(w) == 1 assert str(w[0].message).startswith(warn_msg) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/tests/test_openmp_helpers.py0000644000077000000240000000232013242700737030533 0ustar adamstaff00000000000000import os import sys from copy import deepcopy from distutils.core import Extension from ..openmp_helpers import add_openmp_flags_if_available from ..setup_helpers import _module_state, register_commands IS_TRAVIS_LINUX = os.environ.get('TRAVIS_OS_NAME', None) == 'linux' IS_APPVEYOR = os.environ.get('APPVEYOR', None) == 'True' PY3_LT_35 = sys.version_info[0] == 3 and sys.version_info[1] < 5 _state = None def setup_function(function): global state state = deepcopy(_module_state) def teardown_function(function): _module_state.clear() _module_state.update(state) def test_add_openmp_flags_if_available(): register_commands('openmp_testing', '0.0', False) using_openmp = add_openmp_flags_if_available(Extension('test', [])) # Make sure that on Travis (Linux) and AppVeyor OpenMP does get used (for # MacOS X usually it will not work but this will depend on the compiler). # Having this is useful because we'll find out if OpenMP no longer works # for any reason on platforms on which it does work at the time of writing. # OpenMP doesn't work on Python 3.x where x<5 on AppVeyor though. if IS_TRAVIS_LINUX or (IS_APPVEYOR and not PY3_LT_35): assert using_openmp spectral-cube-0.4.3/astropy_helpers/astropy_helpers/tests/test_setup_helpers.py0000644000077000000240000004355313245574455030423 0ustar adamstaff00000000000000import os import sys import stat import shutil import contextlib import pytest from textwrap import dedent from setuptools import Distribution from ..setup_helpers import get_package_info, register_commands from ..commands import build_ext from . import reset_setup_helpers, reset_distutils_log # noqa from . import run_setup, cleanup_import ASTROPY_HELPERS_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) # Determine whether we're in a PY2 environment without using six USING_PY2 = sys.version_info < (3,0,0) def _extension_test_package(tmpdir, request, extension_type='c'): """Creates a simple test package with an extension module.""" test_pkg = tmpdir.mkdir('test_pkg') test_pkg.mkdir('apyhtest_eva').ensure('__init__.py') # TODO: It might be later worth making this particular test package into a # reusable fixture for other build_ext tests if extension_type in ('c', 'both'): # A minimal C extension for testing test_pkg.join('apyhtest_eva', 'unit01.c').write(dedent("""\ #include #ifndef PY3K #if PY_MAJOR_VERSION >= 3 #define PY3K 1 #else #define PY3K 0 #endif #endif #if PY3K static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, "unit01", NULL, -1, NULL }; PyMODINIT_FUNC PyInit_unit01(void) { return PyModule_Create(&moduledef); } #else PyMODINIT_FUNC initunit01(void) { Py_InitModule3("unit01", NULL, NULL); } #endif """)) if extension_type in ('pyx', 'both'): # A minimal Cython extension for testing test_pkg.join('apyhtest_eva', 'unit02.pyx').write(dedent("""\ print("Hello cruel angel.") """)) if extension_type == 'c': extensions = ['unit01.c'] elif extension_type == 'pyx': extensions = ['unit02.pyx'] elif extension_type == 'both': extensions = ['unit01.c', 'unit02.pyx'] extensions_list = [ "Extension('apyhtest_eva.{0}', [join('apyhtest_eva', '{1}')])".format( os.path.splitext(extension)[0], extension) for extension in extensions] test_pkg.join('apyhtest_eva', 'setup_package.py').write(dedent("""\ from setuptools import Extension from os.path import join def get_extensions(): return [{0}] """.format(', '.join(extensions_list)))) test_pkg.join('setup.py').write(dedent("""\ import sys from os.path import join from setuptools import setup sys.path.insert(0, r'{astropy_helpers_path}') from astropy_helpers.setup_helpers import register_commands from astropy_helpers.setup_helpers import get_package_info from astropy_helpers.version_helpers import generate_version_py if '--no-cython' in sys.argv: from astropy_helpers.commands import build_ext build_ext.should_build_with_cython = lambda *args: False sys.argv.remove('--no-cython') NAME = 'apyhtest_eva' VERSION = '0.1' RELEASE = True cmdclassd = register_commands(NAME, VERSION, RELEASE) generate_version_py(NAME, VERSION, RELEASE, False, False) package_info = get_package_info() setup( name=NAME, version=VERSION, cmdclass=cmdclassd, **package_info ) """.format(astropy_helpers_path=ASTROPY_HELPERS_PATH))) if '' in sys.path: sys.path.remove('') sys.path.insert(0, '') def finalize(): cleanup_import('apyhtest_eva') request.addfinalizer(finalize) return test_pkg @pytest.fixture def extension_test_package(tmpdir, request): return _extension_test_package(tmpdir, request, extension_type='both') @pytest.fixture def c_extension_test_package(tmpdir, request): return _extension_test_package(tmpdir, request, extension_type='c') @pytest.fixture def pyx_extension_test_package(tmpdir, request): return _extension_test_package(tmpdir, request, extension_type='pyx') def test_cython_autoextensions(tmpdir): """ Regression test for https://github.com/astropy/astropy-helpers/pull/19 Ensures that Cython extensions in sub-packages are discovered and built only once. """ # Make a simple test package test_pkg = tmpdir.mkdir('test_pkg') test_pkg.mkdir('yoda').mkdir('luke') test_pkg.ensure('yoda', '__init__.py') test_pkg.ensure('yoda', 'luke', '__init__.py') test_pkg.join('yoda', 'luke', 'dagobah.pyx').write( """def testfunc(): pass""") # Required, currently, for get_package_info to work register_commands('yoda', '0.0', False, srcdir=str(test_pkg)) package_info = get_package_info(str(test_pkg)) assert len(package_info['ext_modules']) == 1 assert package_info['ext_modules'][0].name == 'yoda.luke.dagobah' def test_compiler_module(capsys, c_extension_test_package): """ Test ensuring that the compiler module is built and installed for packages that have extension modules. """ test_pkg = c_extension_test_package install_temp = test_pkg.mkdir('install_temp') with test_pkg.as_cwd(): # This is one of the simplest ways to install just a package into a # test directory run_setup('setup.py', ['install', '--single-version-externally-managed', '--install-lib={0}'.format(install_temp), '--record={0}'.format(install_temp.join('record.txt'))]) stdout, stderr = capsys.readouterr() assert "No git repository present at" in stderr with install_temp.as_cwd(): import apyhtest_eva # Make sure we imported the apyhtest_eva package from the correct place dirname = os.path.abspath(os.path.dirname(apyhtest_eva.__file__)) assert dirname == str(install_temp.join('apyhtest_eva')) import apyhtest_eva._compiler import apyhtest_eva.version assert apyhtest_eva.version.compiler == apyhtest_eva._compiler.compiler assert apyhtest_eva.version.compiler != 'unknown' def test_no_cython_buildext(capsys, c_extension_test_package, monkeypatch): """ Regression test for https://github.com/astropy/astropy-helpers/pull/35 This tests the custom build_ext command installed by astropy_helpers when used with a project that has no Cython extensions (but does have one or more normal C extensions). """ test_pkg = c_extension_test_package with test_pkg.as_cwd(): run_setup('setup.py', ['build_ext', '--inplace', '--no-cython']) stdout, stderr = capsys.readouterr() assert "No git repository present at" in stderr sys.path.insert(0, str(test_pkg)) try: import apyhtest_eva.unit01 dirname = os.path.abspath(os.path.dirname(apyhtest_eva.unit01.__file__)) assert dirname == str(test_pkg.join('apyhtest_eva')) finally: sys.path.remove(str(test_pkg)) def test_missing_cython_c_files(capsys, pyx_extension_test_package, monkeypatch): """ Regression test for https://github.com/astropy/astropy-helpers/pull/181 Test failure mode when building a package that has Cython modules, but where Cython is not installed and the generated C files are missing. """ test_pkg = pyx_extension_test_package with test_pkg.as_cwd(): run_setup('setup.py', ['build_ext', '--inplace', '--no-cython']) stdout, stderr = capsys.readouterr() assert "No git repository present at" in stderr msg = ('Could not find C/C++ file ' '{0}.(c/cpp)'.format('apyhtest_eva/unit02'.replace('/', os.sep))) assert msg in stderr @pytest.mark.parametrize('mode', ['cli', 'cli-w', 'deprecated', 'cli-l', 'cli-error']) def test_build_docs(capsys, tmpdir, mode): """ Test for build_docs """ test_pkg = tmpdir.mkdir('test_pkg') test_pkg.mkdir('mypackage') test_pkg.join('mypackage').join('__init__.py').write(dedent("""\ def test_function(): pass class A(): pass class B(A): pass """)) test_pkg.mkdir('docs') docs = test_pkg.join('docs') autosummary = docs.mkdir('_templates').mkdir('autosummary') autosummary.join('base.rst').write('{% extends "autosummary_core/base.rst" %}') autosummary.join('class.rst').write('{% extends "autosummary_core/class.rst" %}') autosummary.join('module.rst').write('{% extends "autosummary_core/module.rst" %}') docs_dir = test_pkg.join('docs') docs_dir.join('conf.py').write(dedent("""\ import sys sys.path.append("../") import warnings with warnings.catch_warnings(): # ignore matplotlib warning warnings.simplefilter("ignore") from astropy_helpers.sphinx.conf import * exclude_patterns.append('_templates') """)) if mode == 'cli-error': docs_dir.join('conf.py').write(dedent(""" raise ValueError("TestException") """)) docs_dir.join('index.rst').write(dedent("""\ .. automodapi:: mypackage :no-inheritance-diagram: """)) test_pkg.join('setup.py').write(dedent("""\ import sys sys.path.insert(0, r'{astropy_helpers_path}') from os.path import join from setuptools import setup, Extension from astropy_helpers.setup_helpers import register_commands, get_package_info NAME = 'mypackage' VERSION = 0.1 RELEASE = True cmdclassd = register_commands(NAME, VERSION, RELEASE) setup( name=NAME, version=VERSION, cmdclass=cmdclassd, **get_package_info() ) """.format(astropy_helpers_path=ASTROPY_HELPERS_PATH))) with test_pkg.as_cwd(): if mode == 'cli': run_setup('setup.py', ['build_docs']) elif mode == 'cli-w': run_setup('setup.py', ['build_docs', '-w']) elif mode == 'cli-l': run_setup('setup.py', ['build_docs', '-l']) elif mode == 'deprecated': run_setup('setup.py', ['build_sphinx']) stdout, stderr = capsys.readouterr() assert 'AstropyDeprecationWarning' in stderr def test_command_hooks(tmpdir, capsys): """A basic test for pre- and post-command hooks.""" test_pkg = tmpdir.mkdir('test_pkg') test_pkg.mkdir('_welltall_') test_pkg.join('_welltall_', '__init__.py').ensure() # Create a setup_package module with a couple of command hooks in it test_pkg.join('_welltall_', 'setup_package.py').write(dedent("""\ def pre_build_hook(cmd_obj): print('Hello build!') def post_build_hook(cmd_obj): print('Goodbye build!') """)) # A simple setup.py for the test package--running register_commands should # discover and enable the command hooks test_pkg.join('setup.py').write(dedent("""\ import sys from os.path import join from setuptools import setup, Extension sys.path.insert(0, r'{astropy_helpers_path}') from astropy_helpers.setup_helpers import register_commands, get_package_info NAME = '_welltall_' VERSION = 0.1 RELEASE = True cmdclassd = register_commands(NAME, VERSION, RELEASE) setup( name=NAME, version=VERSION, cmdclass=cmdclassd ) """.format(astropy_helpers_path=ASTROPY_HELPERS_PATH))) with test_pkg.as_cwd(): try: run_setup('setup.py', ['build']) finally: cleanup_import('_welltall_') stdout, stderr = capsys.readouterr() want = dedent("""\ running build running pre_hook from _welltall_.setup_package for build command Hello build! running post_hook from _welltall_.setup_package for build command Goodbye build! """).strip() assert want in stdout.replace('\r\n', '\n').replace('\r', '\n') def test_adjust_compiler(monkeypatch, tmpdir): """ Regression test for https://github.com/astropy/astropy-helpers/issues/182 """ from distutils import ccompiler, sysconfig class MockLog(object): def __init__(self): self.messages = [] def warn(self, message): self.messages.append(message) good = tmpdir.join('gcc-good') good.write(dedent("""\ #!{python} import sys print('gcc 4.10') sys.exit(0) """.format(python=sys.executable))) good.chmod(stat.S_IRUSR | stat.S_IEXEC) # A "compiler" that reports itself to be a version of Apple's llvm-gcc # which is broken bad = tmpdir.join('gcc-bad') bad.write(dedent("""\ #!{python} import sys print('i686-apple-darwin-llvm-gcc-4.2') sys.exit(0) """.format(python=sys.executable))) bad.chmod(stat.S_IRUSR | stat.S_IEXEC) # A "compiler" that doesn't even know its identity (this reproduces the bug # in #182) ugly = tmpdir.join('gcc-ugly') ugly.write(dedent("""\ #!{python} import sys sys.exit(1) """.format(python=sys.executable))) ugly.chmod(stat.S_IRUSR | stat.S_IEXEC) # Scripts with shebang lines don't work implicitly in Windows when passed # to subprocess.Popen, so... if 'win' in sys.platform: good = ' '.join((sys.executable, str(good))) bad = ' '.join((sys.executable, str(bad))) ugly = ' '.join((sys.executable, str(ugly))) dist = Distribution({}) cmd_cls = build_ext.generate_build_ext_command('astropy', False) cmd = cmd_cls(dist) adjust_compiler = cmd._adjust_compiler @contextlib.contextmanager def test_setup(): log = MockLog() monkeypatch.setattr(build_ext, 'log', log) yield log monkeypatch.undo() @contextlib.contextmanager def compiler_setter_with_environ(compiler): monkeypatch.setenv('CC', compiler) with test_setup() as log: yield log monkeypatch.undo() @contextlib.contextmanager def compiler_setter_with_sysconfig(compiler): monkeypatch.setattr(ccompiler, 'get_default_compiler', lambda: 'unix') monkeypatch.setattr(sysconfig, 'get_config_var', lambda v: compiler) old_cc = os.environ.get('CC') if old_cc is not None: del os.environ['CC'] with test_setup() as log: yield log monkeypatch.undo() monkeypatch.undo() monkeypatch.undo() if old_cc is not None: os.environ['CC'] = old_cc compiler_setters = (compiler_setter_with_environ, compiler_setter_with_sysconfig) for compiler_setter in compiler_setters: with compiler_setter(str(good)): # Should have no side-effects adjust_compiler() with compiler_setter(str(ugly)): # Should just pass without complaint, since we can't determine # anything about the compiler anyways adjust_compiler() # In the following tests we check the log messages just to ensure that the # failures occur on the correct code paths for these cases with compiler_setter_with_environ(str(bad)) as log: with pytest.raises(SystemExit): adjust_compiler() assert len(log.messages) == 1 assert 'will fail to compile' in log.messages[0] with compiler_setter_with_sysconfig(str(bad)): adjust_compiler() assert 'CC' in os.environ and os.environ['CC'] == 'clang' with compiler_setter_with_environ('bogus') as log: with pytest.raises(SystemExit): # Missing compiler? adjust_compiler() assert len(log.messages) == 1 assert 'cannot be found or executed' in log.messages[0] with compiler_setter_with_sysconfig('bogus') as log: with pytest.raises(SystemExit): # Missing compiler? adjust_compiler() assert len(log.messages) == 1 assert 'The C compiler used to compile Python' in log.messages[0] def test_invalid_package_exclusion(tmpdir, capsys): module_name = 'foobar' setup_header = dedent("""\ import sys from os.path import join from setuptools import setup, Extension sys.path.insert(0, r'{astropy_helpers_path}') from astropy_helpers.setup_helpers import register_commands, \\ get_package_info, add_exclude_packages NAME = {module_name!r} VERSION = 0.1 RELEASE = True """.format(module_name=module_name, astropy_helpers_path=ASTROPY_HELPERS_PATH)) setup_footer = dedent("""\ setup( name=NAME, version=VERSION, cmdclass=cmdclassd, **package_info ) """) # Test error when using add_package_excludes out of order error_commands = dedent("""\ cmdclassd = register_commands(NAME, VERSION, RELEASE) package_info = get_package_info() add_exclude_packages(['tests*']) """) error_pkg = tmpdir.mkdir('error_pkg') error_pkg.join('setup.py').write( setup_header + error_commands + setup_footer) with error_pkg.as_cwd(): run_setup('setup.py', ['build']) stdout, stderr = capsys.readouterr() assert "RuntimeError" in stderr # Test warning when using deprecated exclude parameter warn_commands = dedent("""\ cmdclassd = register_commands(NAME, VERSION, RELEASE) package_info = get_package_info(exclude=['test*']) """) warn_pkg = tmpdir.mkdir('warn_pkg') warn_pkg.join('setup.py').write( setup_header + warn_commands + setup_footer) with warn_pkg.as_cwd(): run_setup('setup.py', ['build']) stdout, stderr = capsys.readouterr() assert 'AstropyDeprecationWarning' in stderr spectral-cube-0.4.3/astropy_helpers/astropy_helpers/tests/test_utils.py0000644000077000000240000000135712657374605026676 0ustar adamstaff00000000000000import os from ..utils import find_data_files def test_find_data_files(tmpdir): data = tmpdir.mkdir('data') sub1 = data.mkdir('sub1') sub2 = data.mkdir('sub2') sub3 = sub1.mkdir('sub3') for directory in (data, sub1, sub2, sub3): filename = directory.join('data.dat').strpath with open(filename, 'w') as f: f.write('test') filenames = find_data_files(data.strpath, '**/*.dat') filenames = sorted(os.path.relpath(x, data.strpath) for x in filenames) assert filenames[0] == os.path.join('data.dat') assert filenames[1] == os.path.join('sub1', 'data.dat') assert filenames[2] == os.path.join('sub1', 'sub3', 'data.dat') assert filenames[3] == os.path.join('sub2', 'data.dat') spectral-cube-0.4.3/astropy_helpers/astropy_helpers/utils.py0000644000077000000240000006476513242700737024477 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst from __future__ import absolute_import, unicode_literals import contextlib import functools import imp import inspect import os import sys import glob import textwrap import types import warnings try: from importlib import machinery as import_machinery # Python 3.2 does not have SourceLoader if not hasattr(import_machinery, 'SourceLoader'): import_machinery = None except ImportError: import_machinery = None # Python 3.3's importlib caches filesystem reads for faster imports in the # general case. But sometimes it's necessary to manually invalidate those # caches so that the import system can pick up new generated files. See # https://github.com/astropy/astropy/issues/820 if sys.version_info[:2] >= (3, 3): from importlib import invalidate_caches else: def invalidate_caches(): return None # Python 2/3 compatibility if sys.version_info[0] < 3: string_types = (str, unicode) # noqa else: string_types = (str,) # Note: The following Warning subclasses are simply copies of the Warnings in # Astropy of the same names. class AstropyWarning(Warning): """ The base warning class from which all Astropy warnings should inherit. Any warning inheriting from this class is handled by the Astropy logger. """ class AstropyDeprecationWarning(AstropyWarning): """ A warning class to indicate a deprecated feature. """ class AstropyPendingDeprecationWarning(PendingDeprecationWarning, AstropyWarning): """ A warning class to indicate a soon-to-be deprecated feature. """ def _get_platlib_dir(cmd): """ Given a build command, return the name of the appropriate platform-specific build subdirectory directory (e.g. build/lib.linux-x86_64-2.7) """ plat_specifier = '.{0}-{1}'.format(cmd.plat_name, sys.version[0:3]) return os.path.join(cmd.build_base, 'lib' + plat_specifier) def get_numpy_include_path(): """ Gets the path to the numpy headers. """ # We need to go through this nonsense in case setuptools # downloaded and installed Numpy for us as part of the build or # install, since Numpy may still think it's in "setup mode", when # in fact we're ready to use it to build astropy now. if sys.version_info[0] >= 3: import builtins if hasattr(builtins, '__NUMPY_SETUP__'): del builtins.__NUMPY_SETUP__ import imp import numpy imp.reload(numpy) else: import __builtin__ if hasattr(__builtin__, '__NUMPY_SETUP__'): del __builtin__.__NUMPY_SETUP__ import numpy reload(numpy) try: numpy_include = numpy.get_include() except AttributeError: numpy_include = numpy.get_numpy_include() return numpy_include class _DummyFile(object): """A noop writeable object.""" errors = '' # Required for Python 3.x 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 if sys.platform == 'win32': import ctypes def _has_hidden_attribute(filepath): """ Returns True if the given filepath has the hidden attribute on MS-Windows. Based on a post here: http://stackoverflow.com/questions/284115/cross-platform-hidden-file-detection """ if isinstance(filepath, bytes): filepath = filepath.decode(sys.getfilesystemencoding()) try: attrs = ctypes.windll.kernel32.GetFileAttributesW(filepath) assert attrs != -1 result = bool(attrs & 2) except (AttributeError, AssertionError): result = False return result else: def _has_hidden_attribute(filepath): return False def is_path_hidden(filepath): """ Determines if a given file or directory is hidden. Parameters ---------- filepath : str The path to a file or directory Returns ------- hidden : bool Returns `True` if the file is hidden """ name = os.path.basename(os.path.abspath(filepath)) if isinstance(name, bytes): is_dotted = name.startswith(b'.') else: is_dotted = name.startswith('.') return is_dotted or _has_hidden_attribute(filepath) def walk_skip_hidden(top, onerror=None, followlinks=False): """ A wrapper for `os.walk` that skips hidden files and directories. This function does not have the parameter `topdown` from `os.walk`: the directories must always be recursed top-down when using this function. See also -------- os.walk : For a description of the parameters """ for root, dirs, files in os.walk( top, topdown=True, onerror=onerror, followlinks=followlinks): # These lists must be updated in-place so os.walk will skip # hidden directories dirs[:] = [d for d in dirs if not is_path_hidden(d)] files[:] = [f for f in files if not is_path_hidden(f)] yield root, dirs, files def write_if_different(filename, data): """Write `data` to `filename`, if the content of the file is different. Parameters ---------- filename : str The file name to be written to. data : bytes The data to be written to `filename`. """ assert isinstance(data, bytes) if os.path.exists(filename): with open(filename, 'rb') as fd: original_data = fd.read() else: original_data = None if original_data != data: with open(filename, 'wb') as fd: fd.write(data) def import_file(filename, name=None): """ Imports a module from a single file as if it doesn't belong to a particular package. The returned module will have the optional ``name`` if given, or else a name generated from the filename. """ # Specifying a traditional dot-separated fully qualified name here # results in a number of "Parent module 'astropy' not found while # handling absolute import" warnings. Using the same name, the # namespaces of the modules get merged together. So, this # generates an underscore-separated name which is more likely to # be unique, and it doesn't really matter because the name isn't # used directly here anyway. mode = 'U' if sys.version_info[0] < 3 else 'r' if name is None: basename = os.path.splitext(filename)[0] name = '_'.join(os.path.relpath(basename).split(os.sep)[1:]) if import_machinery: loader = import_machinery.SourceFileLoader(name, filename) mod = loader.load_module() else: with open(filename, mode) as fd: mod = imp.load_module(name, fd, filename, ('.py', mode, 1)) return mod def resolve_name(name): """Resolve a name like ``module.object`` to an object and return it. Raise `ImportError` if the module or name is not found. """ parts = name.split('.') cursor = len(parts) - 1 module_name = parts[:cursor] attr_name = parts[-1] while cursor > 0: try: ret = __import__('.'.join(module_name), fromlist=[attr_name]) break except ImportError: if cursor == 0: raise cursor -= 1 module_name = parts[:cursor] attr_name = parts[cursor] ret = '' for part in parts[cursor:]: try: ret = getattr(ret, part) except AttributeError: raise ImportError(name) return ret if sys.version_info[0] >= 3: def iteritems(dictionary): return dictionary.items() else: def iteritems(dictionary): return dictionary.iteritems() def extends_doc(extended_func): """ A function decorator for use when wrapping an existing function but adding additional functionality. This copies the docstring from the original function, and appends to it (along with a newline) the docstring of the wrapper function. Examples -------- >>> def foo(): ... '''Hello.''' ... >>> @extends_doc(foo) ... def bar(): ... '''Goodbye.''' ... >>> print(bar.__doc__) Hello. Goodbye. """ def decorator(func): if not (extended_func.__doc__ is None or func.__doc__ is None): func.__doc__ = '\n\n'.join([extended_func.__doc__.rstrip('\n'), func.__doc__.lstrip('\n')]) return func return decorator # Duplicated from astropy.utils.decorators.deprecated # When fixing issues in this function fix them in astropy first, then # port the fixes over to astropy-helpers def deprecated(since, message='', name='', alternative='', pending=False, obj_type=None): """ Used to mark a function or class as deprecated. To mark an attribute as deprecated, use `deprecated_attribute`. Parameters ---------- since : str The release at which this API became deprecated. This is required. message : str, optional Override the default deprecation message. The format specifier ``func`` may be used for the name of the function, and ``alternative`` may be used in the deprecation message to insert the name of an alternative to the deprecated function. ``obj_type`` may be used to insert a friendly name for the type of object being deprecated. name : str, optional The name of the deprecated function or class; if not provided the name is automatically determined from the passed in function or class, though this is useful in the case of renamed functions, where the new function is just assigned to the name of the deprecated function. For example:: def new_function(): ... oldFunction = new_function alternative : str, optional An alternative function or class name that the user may use in place of the deprecated object. The deprecation warning will tell the user about this alternative if provided. pending : bool, optional If True, uses a AstropyPendingDeprecationWarning instead of a AstropyDeprecationWarning. obj_type : str, optional The type of this object, if the automatically determined one needs to be overridden. """ method_types = (classmethod, staticmethod, types.MethodType) def deprecate_doc(old_doc, message): """ Returns a given docstring with a deprecation message prepended to it. """ if not old_doc: old_doc = '' old_doc = textwrap.dedent(old_doc).strip('\n') new_doc = (('\n.. deprecated:: %(since)s' '\n %(message)s\n\n' % {'since': since, 'message': message.strip()}) + old_doc) if not old_doc: # This is to prevent a spurious 'unexpected unindent' warning from # docutils when the original docstring was blank. new_doc += r'\ ' return new_doc def get_function(func): """ Given a function or classmethod (or other function wrapper type), get the function object. """ if isinstance(func, method_types): func = func.__func__ return func def deprecate_function(func, message): """ Returns a wrapped function that displays an ``AstropyDeprecationWarning`` when it is called. """ if isinstance(func, method_types): func_wrapper = type(func) else: func_wrapper = lambda f: f func = get_function(func) def deprecated_func(*args, **kwargs): if pending: category = AstropyPendingDeprecationWarning else: category = AstropyDeprecationWarning warnings.warn(message, category, stacklevel=2) return func(*args, **kwargs) # If this is an extension function, we can't call # functools.wraps on it, but we normally don't care. # This crazy way to get the type of a wrapper descriptor is # straight out of the Python 3.3 inspect module docs. if type(func) != type(str.__dict__['__add__']): deprecated_func = functools.wraps(func)(deprecated_func) deprecated_func.__doc__ = deprecate_doc( deprecated_func.__doc__, message) return func_wrapper(deprecated_func) def deprecate_class(cls, message): """ Returns a wrapper class with the docstrings updated and an __init__ function that will raise an ``AstropyDeprectationWarning`` warning when called. """ # Creates a new class with the same name and bases as the # original class, but updates the dictionary with a new # docstring and a wrapped __init__ method. __module__ needs # to be manually copied over, since otherwise it will be set # to *this* module (astropy.utils.misc). # This approach seems to make Sphinx happy (the new class # looks enough like the original class), and works with # extension classes (which functools.wraps does not, since # it tries to modify the original class). # We need to add a custom pickler or you'll get # Can't pickle : it's not found as ... # errors. Picklability is required for any class that is # documented by Sphinx. members = cls.__dict__.copy() members.update({ '__doc__': deprecate_doc(cls.__doc__, message), '__init__': deprecate_function(get_function(cls.__init__), message), }) return type(cls.__name__, cls.__bases__, members) def deprecate(obj, message=message, name=name, alternative=alternative, pending=pending): if obj_type is None: if isinstance(obj, type): obj_type_name = 'class' elif inspect.isfunction(obj): obj_type_name = 'function' elif inspect.ismethod(obj) or isinstance(obj, method_types): obj_type_name = 'method' else: obj_type_name = 'object' else: obj_type_name = obj_type if not name: name = get_function(obj).__name__ altmessage = '' if not message or type(message) == type(deprecate): if pending: message = ('The %(func)s %(obj_type)s will be deprecated in a ' 'future version.') else: message = ('The %(func)s %(obj_type)s is deprecated and may ' 'be removed in a future version.') if alternative: altmessage = '\n Use %s instead.' % alternative message = ((message % { 'func': name, 'name': name, 'alternative': alternative, 'obj_type': obj_type_name}) + altmessage) if isinstance(obj, type): return deprecate_class(obj, message) else: return deprecate_function(obj, message) if type(message) == type(deprecate): return deprecate(message) return deprecate def deprecated_attribute(name, since, message=None, alternative=None, pending=False): """ Used to mark a public attribute as deprecated. This creates a property that will warn when the given attribute name is accessed. To prevent the warning (i.e. for internal code), use the private name for the attribute by prepending an underscore (i.e. ``self._name``). Parameters ---------- name : str The name of the deprecated attribute. since : str The release at which this API became deprecated. This is required. message : str, optional Override the default deprecation message. The format specifier ``name`` may be used for the name of the attribute, and ``alternative`` may be used in the deprecation message to insert the name of an alternative to the deprecated function. alternative : str, optional An alternative attribute that the user may use in place of the deprecated attribute. The deprecation warning will tell the user about this alternative if provided. pending : bool, optional If True, uses a AstropyPendingDeprecationWarning instead of a AstropyDeprecationWarning. Examples -------- :: class MyClass: # Mark the old_name as deprecated old_name = misc.deprecated_attribute('old_name', '0.1') def method(self): self._old_name = 42 """ private_name = '_' + name @deprecated(since, name=name, obj_type='attribute') def get(self): return getattr(self, private_name) @deprecated(since, name=name, obj_type='attribute') def set(self, val): setattr(self, private_name, val) @deprecated(since, name=name, obj_type='attribute') def delete(self): delattr(self, private_name) return property(get, set, delete) def minversion(module, version, inclusive=True, version_path='__version__'): """ Returns `True` if the specified Python module satisfies a minimum version requirement, and `False` if not. By default this uses `pkg_resources.parse_version` to do the version comparison if available. Otherwise it falls back on `distutils.version.LooseVersion`. Parameters ---------- module : module or `str` An imported module of which to check the version, or the name of that module (in which case an import of that module is attempted-- if this fails `False` is returned). version : `str` The version as a string that this module must have at a minimum (e.g. ``'0.12'``). inclusive : `bool` The specified version meets the requirement inclusively (i.e. ``>=``) as opposed to strictly greater than (default: `True`). version_path : `str` A dotted attribute path to follow in the module for the version. Defaults to just ``'__version__'``, which should work for most Python modules. Examples -------- >>> import astropy >>> minversion(astropy, '0.4.4') True """ if isinstance(module, types.ModuleType): module_name = module.__name__ elif isinstance(module, string_types): module_name = module try: module = resolve_name(module_name) except ImportError: return False else: raise ValueError('module argument must be an actual imported ' 'module, or the import name of the module; ' 'got {0!r}'.format(module)) if '.' not in version_path: have_version = getattr(module, version_path) else: have_version = resolve_name('.'.join([module.__name__, version_path])) try: from pkg_resources import parse_version except ImportError: from distutils.version import LooseVersion as parse_version if inclusive: return parse_version(have_version) >= parse_version(version) else: return parse_version(have_version) > parse_version(version) # Copy of the classproperty decorator from astropy.utils.decorators class classproperty(property): """ Similar to `property`, but allows class-level properties. That is, a property whose getter is like a `classmethod`. The wrapped method may explicitly use the `classmethod` decorator (which must become before this decorator), or the `classmethod` may be omitted (it is implicit through use of this decorator). .. note:: classproperty only works for *read-only* properties. It does not currently allow writeable/deleteable properties, due to subtleties of how Python descriptors work. In order to implement such properties on a class a metaclass for that class must be implemented. Parameters ---------- fget : callable The function that computes the value of this property (in particular, the function when this is used as a decorator) a la `property`. doc : str, optional The docstring for the property--by default inherited from the getter function. lazy : bool, optional If True, caches the value returned by the first call to the getter function, so that it is only called once (used for lazy evaluation of an attribute). This is analogous to `lazyproperty`. The ``lazy`` argument can also be used when `classproperty` is used as a decorator (see the third example below). When used in the decorator syntax this *must* be passed in as a keyword argument. Examples -------- :: >>> class Foo(object): ... _bar_internal = 1 ... @classproperty ... def bar(cls): ... return cls._bar_internal + 1 ... >>> Foo.bar 2 >>> foo_instance = Foo() >>> foo_instance.bar 2 >>> foo_instance._bar_internal = 2 >>> foo_instance.bar # Ignores instance attributes 2 As previously noted, a `classproperty` is limited to implementing read-only attributes:: >>> class Foo(object): ... _bar_internal = 1 ... @classproperty ... def bar(cls): ... return cls._bar_internal ... @bar.setter ... def bar(cls, value): ... cls._bar_internal = value ... Traceback (most recent call last): ... NotImplementedError: classproperty can only be read-only; use a metaclass to implement modifiable class-level properties When the ``lazy`` option is used, the getter is only called once:: >>> class Foo(object): ... @classproperty(lazy=True) ... def bar(cls): ... print("Performing complicated calculation") ... return 1 ... >>> Foo.bar Performing complicated calculation 1 >>> Foo.bar 1 If a subclass inherits a lazy `classproperty` the property is still re-evaluated for the subclass:: >>> class FooSub(Foo): ... pass ... >>> FooSub.bar Performing complicated calculation 1 >>> FooSub.bar 1 """ def __new__(cls, fget=None, doc=None, lazy=False): if fget is None: # Being used as a decorator--return a wrapper that implements # decorator syntax def wrapper(func): return cls(func, lazy=lazy) return wrapper return super(classproperty, cls).__new__(cls) def __init__(self, fget, doc=None, lazy=False): self._lazy = lazy if lazy: self._cache = {} fget = self._wrap_fget(fget) super(classproperty, self).__init__(fget=fget, doc=doc) # There is a buglet in Python where self.__doc__ doesn't # get set properly on instances of property subclasses if # the doc argument was used rather than taking the docstring # from fget if doc is not None: self.__doc__ = doc def __get__(self, obj, objtype=None): if self._lazy and objtype in self._cache: return self._cache[objtype] if objtype is not None: # The base property.__get__ will just return self here; # instead we pass objtype through to the original wrapped # function (which takes the class as its sole argument) val = self.fget.__wrapped__(objtype) else: val = super(classproperty, self).__get__(obj, objtype=objtype) if self._lazy: if objtype is None: objtype = obj.__class__ self._cache[objtype] = val return val def getter(self, fget): return super(classproperty, self).getter(self._wrap_fget(fget)) def setter(self, fset): raise NotImplementedError( "classproperty can only be read-only; use a metaclass to " "implement modifiable class-level properties") def deleter(self, fdel): raise NotImplementedError( "classproperty can only be read-only; use a metaclass to " "implement modifiable class-level properties") @staticmethod def _wrap_fget(orig_fget): if isinstance(orig_fget, classmethod): orig_fget = orig_fget.__func__ # Using stock functools.wraps instead of the fancier version # found later in this module, which is overkill for this purpose @functools.wraps(orig_fget) def fget(obj): return orig_fget(obj.__class__) # Set the __wrapped__ attribute manually for support on Python 2 fget.__wrapped__ = orig_fget return fget def find_data_files(package, pattern): """ Include files matching ``pattern`` inside ``package``. Parameters ---------- package : str The package inside which to look for data files pattern : str Pattern (glob-style) to match for the data files (e.g. ``*.dat``). This supports the Python 3.5 ``**``recursive syntax. For example, ``**/*.fits`` matches all files ending with ``.fits`` recursively. Only one instance of ``**`` can be included in the pattern. """ if sys.version_info[:2] >= (3, 5): return glob.glob(os.path.join(package, pattern), recursive=True) else: if '**' in pattern: start, end = pattern.split('**') if end.startswith(('/', os.sep)): end = end[1:] matches = glob.glob(os.path.join(package, start, end)) for root, dirs, files in os.walk(os.path.join(package, start)): for dirname in dirs: matches += glob.glob(os.path.join(root, dirname, end)) return matches else: return glob.glob(os.path.join(package, pattern)) spectral-cube-0.4.3/astropy_helpers/astropy_helpers/version.py0000644000077000000240000000104412672322551025001 0ustar adamstaff00000000000000# Autogenerated by Astropy-affiliated package astropy_helpers's setup.py on 2016-03-16 19:26:49.214985 from __future__ import unicode_literals import datetime version = "1.1.1" githash = "fedd7c52ba80e2246f83b8d1752baa3a153a33e2" major = 1 minor = 1 bugfix = 1 release = True timestamp = datetime.datetime(2016, 3, 16, 19, 26, 49, 214985) debug = False try: from ._compiler import compiler except ImportError: compiler = "unknown" try: from .cython_version import cython_version except ImportError: cython_version = "unknown" spectral-cube-0.4.3/astropy_helpers/astropy_helpers/version_helpers.py0000644000077000000240000002313213245574455026536 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ Utilities for generating the version string for Astropy (or an affiliated package) and the version.py module, which contains version info for the package. Within the generated astropy.version module, the `major`, `minor`, and `bugfix` variables hold the respective parts of the version number (bugfix is '0' if absent). The `release` variable is True if this is a release, and False if this is a development version of astropy. For the actual version string, use:: from astropy.version import version or:: from astropy import __version__ """ from __future__ import division import datetime import imp import os import pkgutil import sys import time from distutils import log import pkg_resources from . import git_helpers from .distutils_helpers import is_distutils_display_option from .utils import invalidate_caches PY3 = sys.version_info[0] == 3 def _version_split(version): """ Split a version string into major, minor, and bugfix numbers. If any of those numbers are missing the default is zero. Any pre/post release modifiers are ignored. Examples ======== >>> _version_split('1.2.3') (1, 2, 3) >>> _version_split('1.2') (1, 2, 0) >>> _version_split('1.2rc1') (1, 2, 0) >>> _version_split('1') (1, 0, 0) >>> _version_split('') (0, 0, 0) """ parsed_version = pkg_resources.parse_version(version) if hasattr(parsed_version, 'base_version'): # New version parsing for setuptools >= 8.0 if parsed_version.base_version: parts = [int(part) for part in parsed_version.base_version.split('.')] else: parts = [] else: parts = [] for part in parsed_version: if part.startswith('*'): # Ignore any .dev, a, b, rc, etc. break parts.append(int(part)) if len(parts) < 3: parts += [0] * (3 - len(parts)) # In principle a version could have more parts (like 1.2.3.4) but we only # support .. return tuple(parts[:3]) # This is used by setup.py to create a new version.py - see that file for # details. Note that the imports have to be absolute, since this is also used # by affiliated packages. _FROZEN_VERSION_PY_TEMPLATE = """ # Autogenerated by {packagetitle}'s setup.py on {timestamp!s} from __future__ import unicode_literals import datetime {header} major = {major} minor = {minor} bugfix = {bugfix} release = {rel} timestamp = {timestamp!r} debug = {debug} try: from ._compiler import compiler except ImportError: compiler = "unknown" try: from .cython_version import cython_version except ImportError: cython_version = "unknown" """[1:] _FROZEN_VERSION_PY_WITH_GIT_HEADER = """ {git_helpers} _packagename = "{packagename}" _last_generated_version = "{verstr}" _last_githash = "{githash}" # 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 """[1:] _FROZEN_VERSION_PY_STATIC_HEADER = """ version = "{verstr}" githash = "{githash}" """[1:] def _get_version_py_str(packagename, version, githash, release, debug, uses_git=True): epoch = int(os.environ.get('SOURCE_DATE_EPOCH', time.time())) timestamp = datetime.datetime.utcfromtimestamp(epoch) major, minor, bugfix = _version_split(version) if packagename.lower() == 'astropy': packagetitle = 'Astropy' else: packagetitle = 'Astropy-affiliated package ' + packagename header = '' if uses_git: header = _generate_git_header(packagename, version, githash) elif not githash: # _generate_git_header will already generate a new git has for us, but # for creating a new version.py for a release (even if uses_git=False) # we still need to get the githash to include in the version.py # See https://github.com/astropy/astropy-helpers/issues/141 githash = git_helpers.get_git_devstr(sha=True, show_warning=True) if not header: # If _generate_git_header fails it returns an empty string header = _FROZEN_VERSION_PY_STATIC_HEADER.format(verstr=version, githash=githash) return _FROZEN_VERSION_PY_TEMPLATE.format(packagetitle=packagetitle, timestamp=timestamp, header=header, major=major, minor=minor, bugfix=bugfix, rel=release, debug=debug) def _generate_git_header(packagename, version, githash): """ Generates a header to the version.py module that includes utilities for probing the git repository for updates (to the current git hash, etc.) These utilities should only be available in development versions, and not in release builds. If this fails for any reason an empty string is returned. """ loader = pkgutil.get_loader(git_helpers) source = loader.get_source(git_helpers.__name__) or '' source_lines = source.splitlines() if not source_lines: log.warn('Cannot get source code for astropy_helpers.git_helpers; ' 'git support disabled.') return '' idx = 0 for idx, line in enumerate(source_lines): if line.startswith('# BEGIN'): break git_helpers_py = '\n'.join(source_lines[idx + 1:]) if PY3: verstr = version else: # In Python 2 don't pass in a unicode string; otherwise verstr will # be represented with u'' syntax which breaks on Python 3.x with x # < 3. This is only an issue when developing on multiple Python # versions at once verstr = version.encode('utf8') new_githash = git_helpers.get_git_devstr(sha=True, show_warning=False) if new_githash: githash = new_githash return _FROZEN_VERSION_PY_WITH_GIT_HEADER.format( git_helpers=git_helpers_py, packagename=packagename, verstr=verstr, githash=githash) def generate_version_py(packagename, version, release=None, debug=None, uses_git=True, srcdir='.'): """Regenerate the version.py module if necessary.""" try: version_module = get_pkg_version_module(packagename) try: last_generated_version = version_module._last_generated_version except AttributeError: last_generated_version = version_module.version try: last_githash = version_module._last_githash except AttributeError: last_githash = version_module.githash current_release = version_module.release current_debug = version_module.debug except ImportError: version_module = None last_generated_version = None last_githash = None current_release = None current_debug = None if release is None: # Keep whatever the current value is, if it exists release = bool(current_release) if debug is None: # Likewise, keep whatever the current value is, if it exists debug = bool(current_debug) package_srcdir = os.path.join(srcdir, *packagename.split('.')) version_py = os.path.join(package_srcdir, 'version.py') if (last_generated_version != version or current_release != release or current_debug != debug): if '-q' not in sys.argv and '--quiet' not in sys.argv: log.set_threshold(log.INFO) if is_distutils_display_option(): # Always silence unnecessary log messages when display options are # being used log.set_threshold(log.WARN) log.info('Freezing version number to {0}'.format(version_py)) with open(version_py, 'w') as f: # This overwrites the actual version.py f.write(_get_version_py_str(packagename, version, last_githash, release, debug, uses_git=uses_git)) invalidate_caches() if version_module: imp.reload(version_module) def get_pkg_version_module(packagename, fromlist=None): """Returns the package's .version module generated by `astropy_helpers.version_helpers.generate_version_py`. Raises an ImportError if the version module is not found. If ``fromlist`` is an iterable, return a tuple of the members of the version module corresponding to the member names given in ``fromlist``. Raises an `AttributeError` if any of these module members are not found. """ if not fromlist: # Due to a historical quirk of Python's import implementation, # __import__ will not return submodules of a package if 'fromlist' is # empty. # TODO: For Python 3.1 and up it may be preferable to use importlib # instead of the __import__ builtin return __import__(packagename + '.version', fromlist=['']) else: mod = __import__(packagename + '.version', fromlist=fromlist) return tuple(getattr(mod, member) for member in fromlist) spectral-cube-0.4.3/astropy_helpers/astropy_helpers.egg-info/0000755000077000000240000000000013261442571024435 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/astropy_helpers.egg-info/dependency_links.txt0000644000077000000240000000000112672322551030503 0ustar adamstaff00000000000000 spectral-cube-0.4.3/astropy_helpers/astropy_helpers.egg-info/not-zip-safe0000644000077000000240000000000112672322551026663 0ustar adamstaff00000000000000 spectral-cube-0.4.3/astropy_helpers/astropy_helpers.egg-info/PKG-INFO0000644000077000000240000000563212672322551025540 0ustar adamstaff00000000000000Metadata-Version: 1.1 Name: astropy-helpers Version: 1.1.1 Summary: Utilities for building and installing Astropy, Astropy affiliated packages, and their respective documentation. Home-page: http://astropy.org Author: The Astropy Developers Author-email: astropy.team@gmail.com License: BSD Download-URL: http://pypi.python.org/packages/source/a/astropy-helpers/astropy-helpers-1.1.1.tar.gz Description: astropy-helpers =============== This project provides a Python package, ``astropy_helpers``, which includes many build, installation, and documentation-related tools used by the Astropy project, but packaged separately for use by other projects that wish to leverage this work. The motivation behind this package and details of its implementation are in the accepted `Astropy Proposal for Enhancement (APE) 4 `_. ``astropy_helpers`` includes a special "bootstrap" module called ``ah_bootstrap.py`` which is intended to be used by a project's setup.py in order to ensure that the ``astropy_helpers`` package is available for build/installation. This is similar to the ``ez_setup.py`` module that is shipped with some projects to bootstrap `setuptools `_. As described in APE4, the version numbers for ``astropy_helpers`` follow the corresponding major/minor version of the `astropy core package `_, but with an independent sequence of micro (bugfix) version numbers. Hence, the initial release is 0.4, in parallel with Astropy v0.4, which will be the first version of Astropy to use ``astropy-helpers``. For examples of how to implement ``astropy-helpers`` in a project, see the ``setup.py`` and ``setup.cfg`` files of the `Affiliated package template `_. .. image:: https://travis-ci.org/astropy/astropy-helpers.png :target: https://travis-ci.org/astropy/astropy-helpers .. image:: https://coveralls.io/repos/astropy/astropy-helpers/badge.png :target: https://coveralls.io/r/astropy/astropy-helpers Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Intended Audience :: Developers Classifier: Framework :: Setuptools Plugin Classifier: Framework :: Sphinx :: Extension Classifier: Framework :: Sphinx :: Theme Classifier: License :: OSI Approved :: BSD License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 3 Classifier: Topic :: Software Development :: Build Tools Classifier: Topic :: Software Development :: Libraries :: Python Modules Classifier: Topic :: System :: Archiving :: Packaging spectral-cube-0.4.3/astropy_helpers/astropy_helpers.egg-info/SOURCES.txt0000644000077000000240000000654012672322551026326 0ustar adamstaff00000000000000CHANGES.rst LICENSE.rst MANIFEST.in README.rst ah_bootstrap.py ez_setup.py setup.cfg setup.py astropy_helpers/__init__.py astropy_helpers/distutils_helpers.py astropy_helpers/git_helpers.py astropy_helpers/setup_helpers.py astropy_helpers/test_helpers.py astropy_helpers/utils.py astropy_helpers/version.py astropy_helpers/version_helpers.py astropy_helpers.egg-info/PKG-INFO astropy_helpers.egg-info/SOURCES.txt astropy_helpers.egg-info/dependency_links.txt astropy_helpers.egg-info/not-zip-safe astropy_helpers.egg-info/top_level.txt astropy_helpers/commands/__init__.py astropy_helpers/commands/_dummy.py astropy_helpers/commands/_test_compat.py astropy_helpers/commands/build_ext.py astropy_helpers/commands/build_py.py astropy_helpers/commands/build_sphinx.py astropy_helpers/commands/install.py astropy_helpers/commands/install_lib.py astropy_helpers/commands/register.py astropy_helpers/commands/setup_package.py astropy_helpers/commands/test.py astropy_helpers/commands/src/compiler.c astropy_helpers/compat/__init__.py astropy_helpers/compat/subprocess.py astropy_helpers/sphinx/__init__.py astropy_helpers/sphinx/conf.py astropy_helpers/sphinx/setup_package.py astropy_helpers/sphinx/ext/__init__.py astropy_helpers/sphinx/ext/astropyautosummary.py astropy_helpers/sphinx/ext/autodoc_enhancements.py astropy_helpers/sphinx/ext/automodapi.py astropy_helpers/sphinx/ext/automodsumm.py astropy_helpers/sphinx/ext/changelog_links.py astropy_helpers/sphinx/ext/comment_eater.py astropy_helpers/sphinx/ext/compiler_unparse.py astropy_helpers/sphinx/ext/docscrape.py astropy_helpers/sphinx/ext/docscrape_sphinx.py astropy_helpers/sphinx/ext/doctest.py astropy_helpers/sphinx/ext/edit_on_github.py astropy_helpers/sphinx/ext/numpydoc.py astropy_helpers/sphinx/ext/phantom_import.py astropy_helpers/sphinx/ext/smart_resolver.py astropy_helpers/sphinx/ext/tocdepthfix.py astropy_helpers/sphinx/ext/traitsdoc.py astropy_helpers/sphinx/ext/utils.py astropy_helpers/sphinx/ext/viewcode.py astropy_helpers/sphinx/ext/templates/autosummary_core/base.rst astropy_helpers/sphinx/ext/templates/autosummary_core/class.rst astropy_helpers/sphinx/ext/templates/autosummary_core/module.rst astropy_helpers/sphinx/ext/tests/__init__.py astropy_helpers/sphinx/ext/tests/test_autodoc_enhancements.py astropy_helpers/sphinx/ext/tests/test_automodapi.py astropy_helpers/sphinx/ext/tests/test_automodsumm.py astropy_helpers/sphinx/ext/tests/test_docscrape.py astropy_helpers/sphinx/ext/tests/test_utils.py astropy_helpers/sphinx/local/python3links.inv astropy_helpers/sphinx/themes/bootstrap-astropy/globaltoc.html astropy_helpers/sphinx/themes/bootstrap-astropy/layout.html astropy_helpers/sphinx/themes/bootstrap-astropy/localtoc.html astropy_helpers/sphinx/themes/bootstrap-astropy/searchbox.html astropy_helpers/sphinx/themes/bootstrap-astropy/theme.conf astropy_helpers/sphinx/themes/bootstrap-astropy/static/astropy_linkout.svg astropy_helpers/sphinx/themes/bootstrap-astropy/static/astropy_linkout_20.png astropy_helpers/sphinx/themes/bootstrap-astropy/static/astropy_logo.ico astropy_helpers/sphinx/themes/bootstrap-astropy/static/astropy_logo.svg astropy_helpers/sphinx/themes/bootstrap-astropy/static/astropy_logo_32.png astropy_helpers/sphinx/themes/bootstrap-astropy/static/bootstrap-astropy.css astropy_helpers/sphinx/themes/bootstrap-astropy/static/copybutton.js astropy_helpers/sphinx/themes/bootstrap-astropy/static/sidebar.jsspectral-cube-0.4.3/astropy_helpers/astropy_helpers.egg-info/top_level.txt0000644000077000000240000000002012672322551027157 0ustar adamstaff00000000000000astropy_helpers spectral-cube-0.4.3/astropy_helpers/CHANGES.rst0000644000077000000240000004140013245574455021332 0ustar adamstaff00000000000000astropy-helpers Changelog ************************* 2.0.6 (2018-02-24) ------------------ - Avoid deprecation warning due to ``exclude=`` keyword in ``setup.py``. [#379] 2.0.5 (2018-02-22) ------------------ - Fix segmentation faults that occurred when the astropy-helpers submodule was first initialized in packages that also contained Cython code. [#375] 2.0.4 (2018-02-09) ------------------ - Support dotted package names as namespace packages in generate_version_py. [#370] - Fix compatibility with setuptools 36.x and above. [#372] - Fix false negative in add_openmp_flags_if_available when measuring code coverage with gcc. [#374] 2.0.3 (2018-01-20) ------------------ - Make sure that astropy-helpers 3.x.x is not downloaded on Python 2. [#363] - The bundled version of sphinx-automodapi has been updated to v0.7. [#365] - Add --auto-use and --no-auto-use command-line flags to match the ``auto_use`` configuration option, and add an alias ``--use-system-astropy-helpers`` for ``--no-auto-use``. [#366] 2.0.2 (2017-10-13) ------------------ - Added new helper function add_openmp_flags_if_available that can add OpenMP compilation flags to a C/Cython extension if needed. [#346] - Update numpydoc to v0.7. [#343] - The function ``get_git_devstr`` now returns ``'0'`` instead of ``None`` when no git repository is present. This allows generation of development version strings that are in a format that ``setuptools`` expects (e.g. "1.1.3.dev0" instead of "1.1.3.dev"). [#330] - It is now possible to override generated timestamps to make builds reproducible by setting the ``SOURCE_DATE_EPOCH`` environment variable [#341] - Mark Sphinx extensions as parallel-safe. [#344] - Switch to using mathjax instead of imgmath for local builds. [#342] - Deprecate ``exclude`` parameter of various functions in setup_helpers since it could not work as intended. Add new function ``add_exclude_packages`` to provide intended behavior. [#331] - Allow custom Sphinx doctest extension to recognize and process standard doctest directives ``testsetup`` and ``doctest``. [#335] 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. spectral-cube-0.4.3/astropy_helpers/CONTRIBUTING.md0000644000077000000240000000216512412505144021746 0ustar adamstaff00000000000000Contributing to astropy-helpers =============================== The guidelines for contributing to ``astropy-helpers`` are generally the same as the [contributing guidelines for the astropy core package](http://github.com/astropy/astropy/blob/master/CONTRIBUTING.md). Basically, report relevant issues in the ``astropy-helpers`` issue tracker, and we welcome pull requests that broadly follow the [Astropy coding guidelines](http://docs.astropy.org/en/latest/development/codeguide.html). The key subtlety lies in understanding the relationship between ``astropy`` and ``astropy-helpers``. This package contains the build, installation, and documentation tools used by astropy. It also includes support for the ``setup.py test`` command, though Astropy is still required for this to function (it does not currently include the full Astropy test runner). So issues or improvements to that functionality should be addressed in this package. Any other aspect of the [astropy core package](http://github.com/astropy/astropy) (or any other package that uses ``astropy-helpers``) should be addressed in the github repository for that package. spectral-cube-0.4.3/astropy_helpers/ez_setup.py0000644000077000000240000003037113126505434021732 0ustar adamstaff00000000000000#!/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()) spectral-cube-0.4.3/astropy_helpers/LICENSE.rst0000644000077000000240000000272312412505144021331 0ustar adamstaff00000000000000Copyright (c) 2014, Astropy Developers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the Astropy Team nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. spectral-cube-0.4.3/astropy_helpers/licenses/0000755000077000000240000000000013261442571021325 5ustar adamstaff00000000000000spectral-cube-0.4.3/astropy_helpers/licenses/LICENSE_ASTROSCRAPPY.rst0000644000077000000240000000315413242700737025156 0ustar adamstaff00000000000000# The OpenMP helpers include code heavily adapted from astroscrappy, released # under the following license: # # Copyright (c) 2015, Curtis McCully # All rights reserved. # # Redistribution and use in source and binary forms, with or without modification, # are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, this # list of conditions and the following disclaimer in the documentation and/or # other materials provided with the distribution. # * Neither the name of the Astropy Team nor the names of its contributors may be # used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. 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By copying, installing or otherwise using Python, Licensee agrees to be bound by the terms and conditions of this License Agreement. spectral-cube-0.4.3/astropy_helpers/licenses/LICENSE_NUMPYDOC.rst0000644000077000000240000001350712412505144024456 0ustar adamstaff00000000000000------------------------------------------------------------------------------- The files - numpydoc.py - docscrape.py - docscrape_sphinx.py - phantom_import.py have the following license: Copyright (C) 2008 Stefan van der Walt , Pauli Virtanen Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 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By copying, installing or otherwise using matplotlib 0.98.3, Licensee agrees to be bound by the terms and conditions of this License Agreement. spectral-cube-0.4.3/astropy_helpers/MANIFEST.in0000644000077000000240000000030013100750165021240 0ustar adamstaff00000000000000include README.rst include CHANGES.rst include LICENSE.rst recursive-include licenses * include ez_setup.py include ah_bootstrap.py exclude *.pyc *.o prune build prune astropy_helpers/tests spectral-cube-0.4.3/astropy_helpers/README.rst0000644000077000000240000000503313242700737021210 0ustar adamstaff00000000000000astropy-helpers =============== * Stable versions: https://pypi.org/project/astropy-helpers/ * Development version, issue tracker: https://github.com/astropy/astropy-helpers This project provides a Python package, ``astropy_helpers``, which includes many build, installation, and documentation-related tools used by the Astropy project, but packaged separately for use by other projects that wish to leverage this work. The motivation behind this package and details of its implementation are in the accepted `Astropy Proposal for Enhancement (APE) 4 `_. The ``astropy_helpers.extern`` sub-module includes modules developed elsewhere that are bundled here for convenience. At the moment, this consists of the following two sphinx extensions: * `numpydoc `_, a Sphinx extension developed as part of the Numpy project. This is used to parse docstrings in Numpy format * `sphinx-automodapi `_, a Sphinx extension developed as part of the Astropy project. This used to be developed directly in ``astropy-helpers`` but is now a standalone package. Issues with these sub-modules should be reported in their respective repositories, and we will regularly update the bundled versions to reflect the latest released versions. ``astropy_helpers`` includes a special "bootstrap" module called ``ah_bootstrap.py`` which is intended to be used by a project's setup.py in order to ensure that the ``astropy_helpers`` package is available for build/installation. This is similar to the ``ez_setup.py`` module that is shipped with some projects to bootstrap `setuptools `_. As described in APE4, the version numbers for ``astropy_helpers`` follow the corresponding major/minor version of the `astropy core package `_, but with an independent sequence of micro (bugfix) version numbers. Hence, the initial release is 0.4, in parallel with Astropy v0.4, which will be the first version of Astropy to use ``astropy-helpers``. For examples of how to implement ``astropy-helpers`` in a project, see the ``setup.py`` and ``setup.cfg`` files of the `Affiliated package template `_. .. image:: https://travis-ci.org/astropy/astropy-helpers.svg :target: https://travis-ci.org/astropy/astropy-helpers .. image:: https://coveralls.io/repos/astropy/astropy-helpers/badge.svg :target: https://coveralls.io/r/astropy/astropy-helpers spectral-cube-0.4.3/astropy_helpers/setup.cfg0000644000077000000240000000015313100750165021331 0ustar adamstaff00000000000000[tool:pytest] norecursedirs = .tox astropy_helpers/tests/package_template python_functions = test_ spectral-cube-0.4.3/astropy_helpers/setup.py0000755000077000000240000000364013245574455021251 0ustar adamstaff00000000000000#!/usr/bin/env python # Licensed under a 3-clause BSD style license - see LICENSE.rst import ah_bootstrap import pkg_resources from setuptools import setup from astropy_helpers.setup_helpers import (register_commands, get_package_info, add_exclude_packages) from astropy_helpers.version_helpers import generate_version_py NAME = 'astropy_helpers' VERSION = '2.0.6' RELEASE = 'dev' not in VERSION generate_version_py(NAME, VERSION, RELEASE, False, uses_git=not RELEASE) # Use the updated version including the git rev count from astropy_helpers.version import version as VERSION add_exclude_packages(['astropy_helpers.tests']) cmdclass = register_commands(NAME, VERSION, RELEASE) # This package actually doesn't use the Astropy test command del cmdclass['test'] setup( name=pkg_resources.safe_name(NAME), # astropy_helpers -> astropy-helpers version=VERSION, description='Utilities for building and installing Astropy, Astropy ' 'affiliated packages, and their respective documentation.', author='The Astropy Developers', author_email='astropy.team@gmail.com', license='BSD', url=' https://github.com/astropy/astropy-helpers', long_description=open('README.rst').read(), classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Framework :: Setuptools Plugin', 'Framework :: Sphinx :: Extension', 'Framework :: Sphinx :: Theme', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Topic :: Software Development :: Build Tools', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: System :: Archiving :: Packaging' ], cmdclass=cmdclass, zip_safe=False, **get_package_info() ) spectral-cube-0.4.3/CHANGES.rst0000644000077000000240000002315713261442567016114 0ustar adamstaff000000000000000.4.3 (2018-04-05) ------------------ - Refactor spectral smoothing tools to allow parallelized application *and* memory mapped output (to avoid loading cube into memory). Created ``apply_function_parallel_spectral`` to make this general. Added ``joblib`` as a dependency. (https://github.com/radio-astro-tools/spectral-cube/pull/474) - Bugfix: Reversing a cube's spectral axis should now do something reasonable instead of unreasonable (https://github.com/radio-astro-tools/spectral-cube/pull/478) 0.4.2 (2018-02-21) ------------------ - Bugfix and enhancement: handle multiple beams using radio_beam's multiple-beams feature. This allows `convolve_to` to work when some beams are masked out. Also removes ``cube_utils.average_beams``, which is now implemented directly in radio_beam (https://github.com/radio-astro-tools/spectral-cube/pull/437) - Added a variety of stacking tools, both for stacking full velocity cubes of different lines and for stacking full spectra based on a velocity field (https://github.com/radio-astro-tools/spectral-cube/pull/446, https://github.com/radio-astro-tools/spectral-cube/pull/453, https://github.com/radio-astro-tools/spectral-cube/pull/457, https://github.com/radio-astro-tools/spectral-cube/pull/465) 0.4.1 (2017-10-17) ------------------ - Add SpectralCube.with_beam and Projection.with_beam for attaching beam objects. Raise error for position-spectral slices of VRSCs (https://github.com/radio-astro-tools/spectral-cube/pull/433) - Raise a nicer error if no data is present in the default or selected HDU (https://github.com/radio-astro-tools/spectral-cube/pull/424) - Check mask inputs to OneDSpectrum and add mask handling for OneDSpectrum.spectral_interpolate (https://github.com/radio-astro-tools/spectral-cube/pull/400) - Improve exception if cube does not have two celestial and one spectral dimesnion (https://github.com/radio-astro-tools/spectral-cube/pull/425) - Add creating a Projection from a FITS HDU (https://github.com/radio-astro-tools/spectral-cube/pull/376) - Deprecate numpy <=1.8 because nanmedian is needed (https://github.com/radio-astro-tools/spectral-cube/pull/373) - Add tools for masking bad beams in VaryingResolutionSpectralCubes (https://github.com/radio-astro-tools/spectral-cube/pull/373) - Don't warn if no beam was found in a cube (https://github.com/radio-astro-tools/spectral-cube/pull/422) 0.4.0 (2016-09-06) ------------------ - Handle equal beams when convolving cubes spatially. (https://github.com/radio-astro-tools/spectral-cube/pull/356) - Whole cube convolution & reprojection has been added, including tools to smooth spectrally and spatially to force two cubes onto an identical grid. (https://github.com/radio-astro-tools/spectral-cube/pull/313) - Bugfix: files larger than the available memory are now readable again because ``spectral-cube`` does not encourage you to modify cubes inplace (https://github.com/radio-astro-tools/spectral-cube/pull/299) - Cube planes with bad beams will be masked out (https://github.com/radio-astro-tools/spectral-cube/pull/298) - Added a new cube type, VaryingResolutionSpectralCube, meant to handle CASA-produced cubes that have different beams in each channel (https://github.com/radio-astro-tools/spectral-cube/pull/292) - Added tests for new functionality in OneDSpectrum (https://github.com/radio-astro-tools/spectral-cube/pull/277) - Split out common functionality between SpectralCube and LowerDimensionalObject into BaseNDClass and SpectralAxisMixinClass (https://github.com/radio-astro-tools/spectral-cube/pull/274) - added new linewidth_sigma and linewidth_fwhm methods to SpectralCube for computing linewidth maps, and make sure the documentation is clear that moment(order=2) is a variance map. (https://github.com/radio-astro-tools/spectral-cube/pull/275) - fixed significant error when the cube WCS includes a cd matrix. This error resulted in incorrect spectral coordinate conversions (https://github.com/radio-astro-tools/spectral-cube/pull/276) 0.3.2 (2016-07-11) ------------------ - Bugfix in configuration 0.3.1 (2016-02-04) ------------------ - Preserve metadata when making projections (https://github.com/radio-astro-tools/spectral-cube/pull/250) - bugfix: cube._data cannot be a quantity (https://github.com/radio-astro-tools/spectral-cube/pull/251) - partial fix for ds9 import bug (https://github.com/radio-astro-tools/spectral-cube/pull/253) - preserve WCS information in projections (https://github.com/radio-astro-tools/spectral-cube/pull/256) - whitespace stripped from BUNIT (https://github.com/radio-astro-tools/spectral-cube/pull/257) - bugfix: sometimes cube would be read into memory when it should not be (https://github.com/radio-astro-tools/spectral-cube/pull/259) - more projection preservation fixes (https://github.com/radio-astro-tools/spectral-cube/pull/265) - correct jy/beam capitalization (https://github.com/radio-astro-tools/spectral-cube/pull/267) - convenience attribute for beam access (https://github.com/radio-astro-tools/spectral-cube/pull/268) - fix beam reading, which would claim failure even during success (https://github.com/radio-astro-tools/spectral-cube/pull/271) 0.3.0 (2015-08-16) ------------------ - Add experimental line-finding tool using astroquery.splatalogue (https://github.com/radio-astro-tools/spectral-cube/pull/210) - Bugfixes (211,212,217) - Add arithmetic operations (add, subtract, divide, multiply, power) (https://github.com/radio-astro-tools/spectral-cube/pull/220). These operations will not be permitted on large cubes by default, but will require the user to specify that they are allowed using the attribute ``allow_huge_operations`` - Implemented slicewise stddev and mean (https://github.com/radio-astro-tools/spectral-cube/pull/225) - Bugfix: prevent a memory leak when creating a large number of Cubes (https://github.com/radio-astro-tools/spectral-cube/pull/233) - Provide a ``base`` attribute so that tools like joblib can operate on ``SpectralCube`` s as memory maps (https://github.com/radio-astro-tools/spectral-cube/pull/230) - Masks have a quicklook method (https://github.com/radio-astro-tools/spectral-cube/pull/228) - Memory mapping can be disabled (https://github.com/radio-astro-tools/spectral-cube/pull/226) - Add xor operations for Masks (https://github.com/radio-astro-tools/spectral-cube/pull/241) - Added a new StokesSpectralCube class to deal with 4-d cubes (https://github.com/radio-astro-tools/spectral-cube/pull/249) 0.2.2 (2015-03-12) ------------------ - Output mask as a CASA image https://github.com/radio-astro-tools/spectral-cube/pull/171 - ytcube exports to .obj and .ply too https://github.com/radio-astro-tools/spectral-cube/pull/173 - Fix air wavelengths, which were mistreated (https://github.com/radio-astro-tools/spectral-cube/pull/186) - Add support for sum/mean/std over both spatial axes to return a OneDSpectrum object. This PR also removes numpy 1.5-1.7 tests, since many `spectral_cube` functions are not compatible with these versions of numpy (https://github.com/radio-astro-tools/spectral-cube/pull/188) 0.2.1 (2014-12-03) ------------------ - CASA cube readers now compatible with ALMA .image files (tested on Cycle 2 data) https://github.com/radio-astro-tools/spectral-cube/pull/165 - Spectral quicklooks available https://github.com/radio-astro-tools/spectral-cube/pull/164 now that 1D slices are possible https://github.com/radio-astro-tools/spectral-cube/pull/157 - `to_pvextractor` tool allows easy export to `pvextractor `_ https://github.com/radio-astro-tools/spectral-cube/pull/160 - `to_glue` sends the cube to `glue `_ https://github.com/radio-astro-tools/spectral-cube/pull/153 0.2 (2014-09-11) ---------------- - `moments` preserve spectral units now https://github.com/radio-astro-tools/spectral-cube/pull/118 - Initial support added for Air Wavelength. This is only 1-way support, round-tripping (vacuum->air) is not supported yet. https://github.com/radio-astro-tools/spectral-cube/pull/117 - Integer slices (single frames) are supported https://github.com/radio-astro-tools/spectral-cube/pull/113 - Bugfix: BUNIT capitalized https://github.com/radio-astro-tools/spectral-cube/pull/112 - Masks can be any array that is broadcastable to the cube shape https://github.com/radio-astro-tools/spectral-cube/pull/115 - Added `.header` and `.hdu` convenience methods https://github.com/radio-astro-tools/spectral-cube/pull/120 - Added public functions `apply_function` and `apply_numpy_function` that allow functions to be run on cubes while preserving important metadata (e.g., WCS) - Added a quicklook tool using aplpy to view slices (https://github.com/radio-astro-tools/spectral-cube/pull/131) - Added subcube and ds9 region extraction tools (https://github.com/radio-astro-tools/spectral-cube/pull/128) - Added a `to_yt` function for easily converting between SpectralCube and yt datacube/dataset objects (https://github.com/radio-astro-tools/spectral-cube/pull/90, https://github.com/radio-astro-tools/spectral-cube/pull/129) - Masks' `.include()` method works without ``data`` arguments. (https://github.com/radio-astro-tools/spectral-cube/pull/147) - Allow movie name to be specified in yt movie creation (https://github.com/radio-astro-tools/spectral-cube/pull/145) - add `flattened_world` method to get the world coordinates corresponding to each pixel in the flattened array (https://github.com/radio-astro-tools/spectral-cube/pull/146) 0.1 (2014-06-01) ---------------- - Initial Release. spectral-cube-0.4.3/docs/0000755000077000000240000000000013261442571015225 5ustar adamstaff00000000000000spectral-cube-0.4.3/docs/.ipynb_checkpoints/0000755000077000000240000000000013261442571021016 5ustar adamstaff00000000000000spectral-cube-0.4.3/docs/.ipynb_checkpoints/SpectralCube Demo-checkpoint.ipynb0000644000077000000240000546010313252227514027436 0ustar adamstaff00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from spectral_cube import SpectralCube\n", "from astropy import units as u\n", "from radio_beam import Beam" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "path = '/Users/adam/work/sgrb2/alma_lb/FITS/SgrB2_N_SiO_medsub_cutout.fits'\n", "cube = SpectralCube.read(path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "VaryingResolutionSpectralCube with shape=(90, 600, 600) and unit=Jy / beam:\n", " n_x: 600 type_x: RA---SIN unit_x: deg range: 266.832160 deg: 266.833484 deg\n", " n_y: 600 type_y: DEC--SIN unit_y: deg range: -28.372359 deg: -28.371194 deg\n", " n_s: 90 type_s: VRAD unit_s: km / s range: 0.610 km / s: 120.634 km / s" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cube" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "Beam: BMAJ=$0.08259665220975876^{''}$ BMIN=$0.042222000658512115^{''}$ BPA=$80.50083923339844^\\circ$" ], "text/plain": [ "Beam: BMAJ=0.08259665220975876 arcsec BMIN=0.042222000658512115 arcsec BPA=80.50083923339844 deg" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cube.beams[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -2.495e-02 [aplpy.core]\n", "INFO: Auto-setting vmax to 1.334e-02 [aplpy.core]\n" ] }, { "data": { "image/png": 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0K22eEwVe+2nb5j54WicZT1fFWmBLHINHsweRI899eNkCoRYccj7JlyZHW/Zn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vJVm39yZn7RzPzImfm0mflLM/Mb9n3/dej/R83Mb973/R979f0zZuZXz8w/MzM/aGZ+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\nQJ3lCmH6JW2ryg2rl6zk8OczZvrx+h4v1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ku2bfvgzHz5zPxs9PVi3/e/8UapOYAzMHwk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\n5LZM03YElKXwj5Wy9MF5cMKi6cXITasouZpDXMhLy2qji/Ld+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ChFYQSxcQsU4doREm9y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tINdr75oT7iCC7YkD0JRh68tco31lPwxC+G7MlPM8Ntk1tfsbsQ7aActp+kB9xPKT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\nXj29mHyt52dDk1F8+/b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peymfvMY/H0xFZ87riYEhr/XJscz8WGbi1FFum1Pv9WuykxSH\n8/7+/sKZa3NFMEJw2Bxw9odgm38OZng+uS58L4Gxye0ZQOQaeU29lj43R9Y63PPLsXGNmidbzjZ/\nc0CLMpexWgZdl3kbsg02D2lHmj42OIwOavU2W505bJkgtkc9x75Qvia+kNoYJ4BB0ODnqtPm9Eyr\nd3TQ9lh3sB+cR/ObvzdA6h0hfH7QQVuDHPPGMsr7zN/2zDn7k/qmnUHURW7LOqTZpjZ3+S1lWiA6\n1ICi/ULPUdOzW/7ilk/3RWgHn890/cn/nXbaaaeddtppp5122mmnnW6a9ozhjZCzTYnQT9EZErdB\n5HrbKsLtComOtyjZFKH09oeXjqtFbRk9TfTM2xlaG45Etahm4xGfmWH2hH05HA5PWSpec7S5RYKT\nUfV93KbpKP+1Z2VapsFbcFw2suD5v7v78HLwH/qhH1pv3rxZb968qQfaJOLqOh1hzO/tj2Va5iBy\nnixP21aUQz18ymvLXKZcIp9ZN35+jplW8jrX8vyHs9qMgiaTm77kPso1yzJyPGXiKM8tuzFlYZ1N\nalF2j9XyMmVp1loXEf8WiZ/aouxP2RbKQNpjhJzbj9tYTU3nMbMxrTeXpQ6mrnh4eBi3mzkrSlni\nvHh3QZsz/m1lubxm3P9c8zPHU/aBmZj8Ns2xs6vMUKac9W2IW6DNF67btmuGuw04R84gb+lTj3/K\nWFB/tXqY/eSLyWnLW90tU+5s2CTvU520Yc7apn7L4VZWrI033zmnlAHukEgbHgO3+Vrvpb7GG+r8\nrX56V4h1MNdvfA636d0fLfvVfBPyv/kNrMu7fmLr2vr1Izoct/W3x86sZNMX+d9jM+9au01XWLev\ntT9f+AdBOzC8ETIwzPNpVBTNGQoZUDYnaa3zwzEIPNNWA4hc9JOySP+suKkg2R9uJYpTwneW2eB7\nK5yVF9tq/Xz16tUZOGyGtz1TQL63g15aeSrc1BenN2X4/M3xeKz1ckwGXPwzcPBzcKyH75N88+bN\nWmtdOLANfOb7NaPp3zmPvq8ZTl+zc8HtbZOB9nYcboO07NrZ4HdujWIfpmc6KIuTk8c+ZDyWNzqc\nLJ/72ms7WO/k9LUx8jp5R343MMJnXbdA+vScqJ/xSZ2UGT4XyDGz3lzLemIghn1h2Rw2wzat48zv\nBgCybrLNlLrm1atXZ0AyxOeRJxlqYN26zZT+c3t4xuDtXdGzrCtbxieZYZ/p+FtfOOg2rdFWJ3Wn\nD+thPXd3z6fkpi0/k+Ux2Ibws/F7rctXHRmA8LlABjdyWAjnk31t4NT21XzKPQQW7BvlhLLV7BjJ\n4J+0ZeutT/g77aD1bsAYT0913w3UDNocuOXnVoCA8m3eWEYtEw5SWH82nrVAS9af7YztvPtC2Sew\nbv5QiPW1gHUDhh6fA16ps60J+122aRNt8fOL0Meo41ZoB4Y3QhMYyzXSlE0IZaHZkchinRz13GOl\n5gU/AVQ7iTYYNJJRTHY8U09AUzvUIvVZGTaQ6Pcr2sHIvXyGiIagOWbuh9vmOPjnDMjkdEfht4xJ\nDGvua3PAsZnoOKeeN2/ePJ1Y2sAQ+W3j5bkm/8lDy4KNT4iyaYcwc91AXjPs/r85/+ZNcyynNtZ6\nPsHOUXLWQ1lNPckuxzlufMx8pJ6ccGdwxX7SKZn42kDctG4tT9N8N0fu/fsPB620dw4S+BGk8QAY\nHjLCZ4Lye8o9Pj4+BTzevn27Hh8fn2Tv8fHx7HUHuf74+HhRb8bJA33IX2dgeMrgmzdvznRN/qYj\n/XOf9TPnK3LCa5yHFixaqz9f1IBa+Bf59fOIrjP1BvC29Wf5Yh/4fxzkZuMM5NprKkIEltQPlks/\nC23eTACgfWfdlBGPccoYpQ5eJ2CxDrKucvCm2ap8TvZqsv8sz0zdBCr5mbLUsx4/A9fWFwTSBnjm\nw0tAAOvNqaDtmdZrdW6BTfarASrbd+phZ1o5b26D62vyy5qNZf9afz0mzkGoBSZaUJJ9oU1klnin\nT0M7MLwRasqG4KAZihgfL+zmLLayVDKOQtEhNsCZjE0bk/tFpRblwZMy2e9mkLYcHRsiK/7mROT3\nOOgt4j5lFNoYJ7LTkfv9Cglei+PETAUBgeuj0p6ycafT5daylKMTOxlhO7I0SDS63rbJ780QNZ7a\ngNLoXjNuJGdtWN5ZktTFfhM0tDnMGjTAi8xYDtPG3d3dxWsnmqPCTJSDFwwScL23gz3IS4P25jB7\nXltAyvW3MRAY0kE3uGUm0MELv8uOBzIdj8f12WefnWUDCSgJTvlqipRlhoI89uErlv2sywRWeDQ9\n75+coibD+T2yk3FwLhngC0/57tIGOEicu4y/ZTfs6EYHvX///klu838DVK1tBpZYhjqL4D999G4P\nO8fcDm9+Zf5aNplz0exY083ePmzdlrrTL9vRZjN4b2vTPMxc8NAQ93/KWuUzcn8N7DhIzHomoE0A\nyHLT+PjIAB8xcL8JTtY612Nbsp7X/DhQbDlnW40fLGPbZXDkA6t8f9rZeiTBY2m2ePIfrvGcfWgg\n37yJjNLmuU2OgWshunGnT0M7MLwRaot+2gpiQ0QlOm2JSDlGl6hgaZibU7BlYPjp33xiXFNgbQum\nwQGVzATGcp8VLykKrQEjKjYr+8l4kl+NPF46Q3Rq+NyMyzPiFplgxqUpc/av1Umg+vj4eOHwkug8\n0LFyVo9thS80IJNhYv9pxCwv7EPuo2NAkMGydjqZiQkYYDsNiDYHgmMiiDDP6BCyjfTHzzVyvATY\nzBpFbuggZWwej50Ryz/ngfqDAKTV6zVKR8eZfm/7pNy0LaGez2SYAuycTWQWkdlEZwvZfur1PBFk\ncE4d1Y+Dzr+11lmAhU6k59dE2Wl6g7qI8kQ+Zitea6/NudfM1C+CVK4hrqn0s63pfHJsliWu18z3\nWuti3jhHth0tU5O2yLu04/kxvzkGjsPtNp6RHylnveR5muxk6px4yzYbaG5ghPqz2TfK1zRnLGeQ\nyHfXTnZ/rWfdSV+BgHoCRI03Te81XcMdG7aT1uOWJf/PccSW588Bi4k4JgcT0g8H0tyfLRDqPoRX\nU4CvBfl4P3nr8ukLt3s/PDyMY9/yn74IfYw6boV2YHgj5AjZWufbFx2ZCnkh0xA1ZUSFnPvTfgOE\naYN1Tg5wGxP7Nzkpx+PxAozZ4aQjNznnKdMedm5GnAaNv9EJYP/tQNnQN8NqXtEwuw5G4DkfvN8O\nkLcThncNqKW8DWGcsMPhcJbtYHupn1kcOvEGHJNjs/W/ycYlbRrAkBwcSF/CrxjvOJ0BZc15Cq/4\nDCadncxRfmtZii0nzw59+sM5mnhEcJi+xtmb1jHlqgEj66DIBR1hO/mce+og/hk08nlBOmu8xvKs\nM9k+Zv34O0HEtb5wjikn5CkBmAEHHUsfusFrW07h5ODaIWUfPRecry0b0sbLdls2vBHXPQNV6YP5\nzKBW1tK0hq8FoLyN/v3792dZ98lWeqxsj4BxGrPr5J91vK+THADlmuF197cFYFyvgVHLalK3TU79\n1hhoY/id/OQ8tQyY+881Rxs7+RysNy+ht31jm023se7mG1AOuRYtA+w7gaG3rrYMm4nzxfaov60T\nWa4Bwcn2tPnnddpzg/0mv1zrCSImS7gVDNvp66EdGN4IccGvda547Mw3hyXf22EWpCz6toWOCz2O\nJont0Dk2iHGdoS3FyLacsSI1x46Kx0CXSixjbo4JlSCdJDu+Bsk2LJPDY+PE7zZMjmROgJN9cF+Z\nYSCv+Mn6eL8Bh53hKcroNsgfgzXW1X7n4Ut23FvUlNcti3aqaOhjxAgOze8ANz9P+Pr167PsV/qc\nMYSPdrAM4PNbPjn3Nswckx1+jn3KAEQ2uE1vkm+22eTCINTgwIDM1xowbGXXWheZQgJDPifog3k8\nrklPUYZCk7MV4lxvZY8monPdrjm4E2ogvQHAXDNodXsMOtl5bs6lQSR56mxwrocaqFrrfNvZNQDv\nbLTnzHO8pfem4Ab77bVq/pCHtDvmffSHn3VjXwxUp/42/efsVPpA+bTdDl/bfE3gtgHQ9jvJ89vI\n9s6BafK2tZ/vlP0JHKYePkbi/vrPvAlfuVMgzx0nCEKfbfLNms30eDyXuUY5uTZX/CQfnS11kIH2\nJ/bS7dm+el6u0ZZO/ph0OBz+3Frr311r/dha6/9aa/1bp9Pp/9i4/x9fa/3ltdY/tNb6tbXWXzqd\nTv+17vln11p/ca31x9dav7TW+vdOp9Pf+irtflXaofhOO+2000477bTTTjvttFOhw+Hwz60PIO8/\nWGt9a30AaH/7cDj8seH+P77W+h/XWv/LWusfXmv9tbXWf3k4HP407vn2Wuu/WWv9F2utf2St9d+v\ntf67w+HwD37Zdj8G7RnDGyFG8UOJMDma5WcPGZlhVGorc5JyPIGPkVhG9hgV2ooCObPByB0zNI2m\nSHWLdjGCxyioo/ktk9WeDXE00H3hczNbkexW1vxhG4mMt+0+zLSZT1OUm31zpNMZsRa15LZQRv34\nwHk7YGUab8pvEbd3so70M1m5tdbTu558SEnuZ3TWUWlHPd0HZhI5ny2j0U59tLyRV8wccmwtE8Ws\nwVYEPs+kOjsQcjY84+OWzLXWRfaOMsT55LjSHmXJcs/MDzN/zBKyXY69yWeTa2YT/Z7S1Jl2KDNT\nRsljJl8dXacMMJrf6mtR+6abpog/6wlxzUyZHfZt2n661vOhStfWcsjz1TJRlr/0hZkq95N1erzO\nSjkD3fjKcrmX1y3z7RrroA3z2NNXnnB7OBzOsoPJKKVcyxpmLNZDzKZNerbZTP/PsUTf5blUZ4E5\nL/6d/SK/nd1ieWaCmx/Q9I7r9XrKWDg+rgvvjLKtbJm49K/tavBYnaHMusy8s2+U3eZDTPbA8+lM\navNbzFPbx5YtNL+3+uk1Yd3KOW670/6A6N9ea/3np9Pp59da63A4/GtrrT+z1vqX11r/Sbn/X19r\n/crpdPoLn///i4fD4R/7vJ7/+fPf/vxa62+dTqe/8vn///7nwPHfXGv9G1+y3a9MOzC8EfIip3Pr\nFH5zYAl8uFXSxt7GrDks+d+KMt+bEkufmjPbjISdUBOVLsFIc2DNw7yTiycXcuzNkSNIc53te8pd\nAxG+nzyhE8u2PSckA0POjbdY8nCSACw+D0HFnXmLguf2GIJpzi9Bo51BUsZqQBWZoTFvjg+d8Xaw\nSsae/y0z4Rs/Wd4yz7XUxrJl5AxAM27yngbWTkHbDtjGZufaDs7hcP5+TMuagRNBXJNf6yePuema\nOLqUUR8gYyDa1mnm4t27d5vbNid94rFbhlPWcxi+crsZtxHzeVzKcHvezU5ZvjeQOm3N8xzYOUt9\nDhZZtzTeua0WcKF+23IsJ9DdqAVwrEso1wGx1Ovsk/tggBOZTNsGKpbFXDPgaFsz80mQTV06Oen5\nvAZuG789T5Yv8zZE2YyOMmiyLks589SAmY8ltG2Hk/2+ti4yRvtJk42mrmh8ow9knUJZo+/Ffjcg\n2vjL/y1faav1wXW2eaE8N6Ic226TJ57L+AJtDthn6jyCQ/tXW48HTXbli9JWHYfD4X6t9Y+utf5D\n3H86HA6/sNb6k0OxP7HW+gX99rfXWn8V///J9SEb6Hv+7Fdo9yvTDgxvhCaDvdal8QxNRpmZIUa7\nmpNBpU6l2wyMlRKvERy6P1SgdDLa80cslzE2o9kUbPqQevMOs7XWmZOce2mokp0zcPB4zBcan+aU\nUSF7fuPomg9sOwqXfJmeseF4zMf375+fgZsizwF4zF7zcBY7iJ4jAz/yohlJ1sFyzTFPO2udnwDa\nwGhziCaHqpXlkeZum/U3MEZDOa1pO3ETb9payvykzbYe7+7uzg7JyXgZ/Xa2jcDQzq77kE86Jk3u\n2Wc6yjwh1BF4t2NnhqArvOb6mKLc7nMo9VmPeK74/FDK8YAJz5ufXfL80lFswQrLjXUMAw3pj/U9\necl5MChpbRKge/00veG60mePfSL20Xr4dDpdvPdz0rENqE0Bg63slXlB/dyCE5R3j5NBGOsKO9qN\nzwTikSsCWdpYO+Suq42z6SADp1wL6Gv2J/0yOGQ77M+WDXMbrGMCEs3fIZhsuvUaKLEO9no2v+mD\nTOur2V/PgfW6x7jW5W6PiZpO9M4pjpPfrZ85Jo4999J3eMm6/4T0x9Zar9Zaf0+//7211j8wlPmx\n4f4fORwOb06n02cb9/zYV2j3K9MODG+EcpJVUwiT0YvBbsaI2//olNk5SjlmGunw857JaE9gsfU9\n/8fQJ7PHe+gkxFGnAmI/m2Jndix1B2DZQHCc5KtBlnnJ37xN006J2yHvnEEhHxKp5HbK1q9Wv4FT\njHZT0gwS0LCxDy1bx7LNsOevHYTjOshTjq9RM+pbAQNS6z8d6Wb4/F463susDf/fym7SYWlOWcpP\nPJucf89/Gyvlba3zF8BvOdFTXyirBJt0LCmH+U5waFDrsbqttS4PmWA5Zz3N47TFegwefR8d87XW\n2cmDbV0bKDTeZa2xbYOA5lwFYMQBu7+/f/rN/H7//v06Ho9P/c1uCvK7OeEGkuS32zA/OR/OFFD2\n7VQaHNre5VAP9nfKongcfkzDY23keWm6f8rytHFab2XOUsb6oskQwbHvc3tcT86Iun6Ol9cngEIy\nqCA4DFmWt3h2zca0MfA+AifW7/Zcp2WI9Zmn5HUD4pG5abv3NE5/ty9momw2nc9+sU7ukOJ6ok/D\nAESIY2/2L3bAmdWdPi3twPBGiNHotS5BEsnGoxlKKjNvc2mLlkquXSNgaNvNqEit/JohPJ2etxZa\nATmSSyVKA9OcIPa7GYMpOhwlZ0fDRv0aYLFxyB/bCIUHyTo8Pj6utfpzKo6Qt0ww65+yBh5z+m0g\nY0c+v3GMljv35SXtN2fGL2+eyLx2VJ/X+JvHwE+C+chrcwJYDw0lKXxthtGgsPGL85M+UuYPh/Nn\nKb0WnVXgOvQL4Lm1k/z1HHltc11SBgJC2X6+G+zxPtZLeWG2swUBfB/fuednZx0gcfTcMs3+c06c\nMWQ51vXq1fnrUTj2pr9Op/MMmWUg93D9U0dYFhJIS7CMepdyOIFDr3u2b51PvrS+v8Q5Nj9CDTA3\n55V8so5oAGcCPdZZDvhRb3qsdK7Nb9oSZle4lqwrLVu2zW283tLM8XAteawE0w1wuG+kzI9lm+M3\nP1l2Wv8k+yjX5o9jmOwTx0W5d18je217OWWRujbX0u40Z9N6oD+Xts23a7uIGuhs7aWv9AW37HbT\nJQSebrvRz//8z68f/uEfPvvt29/+9vr2t789lvnOd76zvvOd75z99nu/93tbzfy/a613a61v6Pdv\nrLX+7lDm7w73/3+nD9nCrXtS55dp9yvTDgxvhGJQQ1zQXmDNOfPCbwuTzpUd+QYOJqXPOt0nK5Io\nZCuuKDo7rGudR/wN1NLXybk36PWzATZW7Cf77/YasCNfOFb+zjI26DZWLJeMTviUoIHn0tkjg9vU\nnawdAwZtHN4q40DDlAFtTpfBVnNCwge2G0DGLC/HkHLkbeqZnEry8nR6zq4xs94AXjO2W88KspyD\nC403Uzt2APO7ee65p4wT4HIOeC118M/tNfltOqKt7+k5wuaotO3UDlDEYfVzZnbi8n87rMgAKn/T\nOzyb82SZaXNM8M46DEJb0IdtuD3LqddE6we/Uy6tS6k7ud49L5wP64TondTNwAxB1eTI5j7ziIHF\n9sxS06dZ69QPDQBvAaAGELluXr16dRbUzbveDAQJJFoQ0WvZ4+K9JAeOqPPJk6ZLmh1J4Mi+QspN\nOs+6yX5J5r0FOfi/ee414XunOljW8pb+hj9sr/Gajwj4j+svu77MN64B2wXzg+Px2Cg3ab/ptmt1\nTpT58S6olKd8WYYp1wxURbdO9LM/+7PrJ37iJ672jdSA4/e///31cz/3c/X+0+l0PBwO/+da60+t\ntf6HtdY6fOj8n1pr/adDM39nrfVP6bd/4vPfeY/r+NO550u2+5VpB4Y77bTTTjvttNNOO+200x8q\ncuDlq9Rzhf7KWutvfA7U/vf14bTQH15r/Y211jocDv/RWuvvO51O/+Ln9/9na60/dzgc/uO11n+1\nPoC5f2at9U+jzr+21vpfD4fDv7PW+p/WWv/8+nDYzL/60na/DtqB4Q2RI4POhngLQIsOM/rZMoYt\nWpprbLfVzT6mb22rgetukT9Gf72thdEvHoqRe7nNihEt1uPIIg9nmCLHvJ91po1rfEmdzp7697QT\nPiZjljFmW57nO9fyzGHLxmT7j9t0dDh8ZD/z5+wHswNT1jTkrTQun/n1NjTX6X7ntzy3wf6bp9Pc\ntgxHsqSM8FuGWW7KVPBe9pXz7DKOqBjs17UAACAASURBVPP3aRvPtDXVEXX+xjGn7inr6W1azlBN\nGQKS12TrSzJ/zl4ej8ezjDd1AjNHznCwbc8J59DZRfOYrxeg/vUc5n+f2ktqOpIZLPaBfSef2Ddn\ntSP/W/JrXqz1vEWbW/6aPXHWcIs4TynrOrk1cSt71NZusjCTDLJeX6fubxkQy8Q0NmZ6KIstE9Xm\n17sAWtaQOoz1UUbIo7bu3XevYV7zjoG11lOGnYdSOWvl7OfUhyZTa51vl512d7Q1bP3F+afeaHzg\nPG/t4uD82Cby0Y9khlsWkzuVrpF1S/On2m/hA3Wi22P/Jr6YmOl2vyY9yvn0HF3LGH4qOp1O/+3h\nw7sD/+L6sJXzu2utf/J0Ov3W57f82Frr78f9v3o4HP7M+nAK6Z9fa/3GWutfOZ1Ov4B7/s7hcPgX\n1lp/6fO//2et9WdPp9P//QXa/ej0B8/tnT4KXdvGRfICpEKK8eQ2KyuVa9ulbMBo5CcgSAdj6i/L\n0YDbQfZ4GshoTpDBUnPmfHANiQqXfU9fmwPIOjxOO6W+1k44I78NLHMtSjfbNQia7XCzXLaH5BAH\nvpKCfwYfrZ611oVjQHK/Jx403lPevA2v8T2UrYs2kg18+fnF5pD5mttugC//+yCplzxrmDo573yG\nhc9u0SHKJ+sx+GXf8w7EtdaZYzuBAM5zcwKnMaQ9BwUC/vy8cr43hzSgIp9+ttLj2HLy/AxX6rBO\nsINkh5QOd9MZuc/tUL9Oepb383MrONXqsC6yjHh8k26b2pm2m7179+7pmWmul7dv31bH0zLU9Gpk\nhqDYfDUwtBNtW0I9tbVl0rYtY09ZPkuWv/SF4I3tUZeyr5MdnciAlzzxvNvWMAAbnWgd2p71NhDI\nb1lnGf+k4wl2vfYMGDkvBsLkYfNPpuBVky9SW5cZI8Gh17318URTm1trro09Y4yc2eY1nfJFx0+A\nR7vMNdN02BeR4U9Fp9Ppr6+1/vpw7V8qv/1v60MGcKvOv7nW+ptftt2vg3ZgeCNkJUqj1RYYHVk6\nnck8WVGEWtQ7v9NwTVmXBmLS/xatspLfotSVSFxTdIxIW+nT2TQw9El8Bqm5xmcEWr/d7mTUfN3O\nx7t3784OaGjlPB7Wt9Z5xNB9bYA02bZ2CIUBo40tHd1ca6+yME0gPHXzk5S+B+w1MjhJFpoAMXXR\nWTHobc/psQ/OUuV5IgIpOmBrfQAyOVAq4zBP892AY8pU3d19eNaUpzOaX5STOM8BY3HKOV98VoYZ\nnfTtJYbeeopjsmykn4+PjyMwpKParjVwGGq/TSAmfSe4Izi6uzs/YMZrxm2wzoytBU7MX88F52hr\njdBhv7t7PqXUgQODnsYfBpvY3hQQoZPo+fVY7TzGPjnQEP2bcbf1Qr3I/rO9KYPs/m7VST424J+1\n7x0qrC/r3mC3HRiVYE3jV+qhjjEom/Ssfzc/mr1nmVYfgcEEZFqdnmvLjYGs62PfyKPISgKllEGu\naa+nrSCIdZmBoQOYHkPIZzI0XdrA88RXyjd5unW/yQGP6T7PQ8bNgIfLb9n7ib7o/Vv17PSBdmB4\nI2QFNCkqEheuF/BalxE3KmQrZ4KBOJIGR3YIQlEWzRhMZAVvpR2Adk1pOJrofjAyOoHDxsMGeB2R\nZqRuMmrksQ/VoeNg543t+yChLcfydHrOiLjOGBXWnXH4IIW2VSdE+eKhC80BJig2T1rQg867twwG\nvATgcDtygATXjw8nMS/IA7effqb9AHBm21rEOOSoO8t5jUfe13oOipBXdkoCOJk1cdSfPKdjFABB\nsBkAlDXvuWpOTFubXvcGXJwXglWfXhrwl8wix5P+eS2xDy0YxjlrxH5uZdL5jkvy3vrL4HAC1Q5S\nUT8biDpQwPZDCf5Y1sjfVo46PGOfylrP8T7KZAParV+NJ1xbky7LGJremYDwVgCLQSi26VNWqa+d\nfXbfSQYRBPaZ7/DVgRbqTPNmAhlbazP1tu/XHGyufernBDu5dZf8XOs808gA25YdpS5v4Ms8dBbW\nctjGO9XHOtJXg0LKN+ub9Lh1Ygsy0VfhmK2P6RdM4L6Ny+Bw0t3Wl+lbggNs3wGPph92+jS0A8Mb\noZ/6qZ9aP/MzP7N+93d/d/3mb/5mBYheYAaTvCeKx8CJ5VrEtkW4G9Cy0ozjbCBFhWXFxfsNcKyU\nmqGnAiVNCjH9t3Il4OWY0q6fwWB9VKwNANlocDxRrJ5rGtGAIWZOeF8zWq39drqc55LjsFPCcbbI\n+pRJYb2eE8pnc4DzLCUzITZq7md4Sz7yvXoci8fc+hdyNmut52CKAwWh3Ov5Jfg1uEkf29bTa0EB\nAj/zewuYJBjgOSG13zivbI/rpZXLtYA/bmEj3xpo3OoLPx0safqUvGn6jzoya9EgfdLTdOSn7Baz\nQBmXg2ue3wCUtp7a+uS6oh52ubb9mgCEa4b9sxwGqJl36cPknLcyzF7GQZ7GYJ5zbFsgkXPPtWon\nl4FFrl/LPsFJy/p6/Olb1uwURKBsOuM/BadY1uujAa9mm1u5pl/zaRBt/WbZWesDQDQ4NDkI1tZ7\nk6/Mx8TXyTYZ1DTfa/LHuE5aPa3sRLZxrS/2pdK2g6GmxjMCUAeLsra5nq0Tou9S74//+I+vH/mR\nH1m/8zu/86Lx7vRxaAeGN0K/9Eu/dLGFbwIWTflNSoqGMovWTnGuxfg1pdWeocj/ExhtRtwRxGZc\nOa5JcVF50VmL8iKYSt8ytvSRxougmRTlTqPFwxscJfS2RPaRvLXh9dw56+d5amDGin5yQuhUkNfN\nCPG654lAqTnOdLwaSIjz6bHbwDYygM39k7HzM2jNmSIA5e+Rtbdv3z5tDc346ayzrGWT64DX7OS3\nZ2TdVwdU2hwadBrYRk79zkwD29aux0Ne+X7PO/vEQy7If/OefJmIzq9/J5+nLXDUKQ108JP3Zw6Y\nvW462eNJO9aflheWs+4lT9P3fDY95DGwby1AYXvQAGzqJ2gisa+T8954y/r5XC3livOQOhxQbNs2\n3T7rp24kX1k+19vapq7fAiQev3XKlqzTT3hJO7m2BZIN+Lx+m71u9ie6hTqRbUTuqYMMeCdfoOkm\nz38D39TR0xqethHzXpdra5rysgUmbUetxxtNPkrq4zg8byzX/Cev20lWaENZJ+c+AZbD4bB+8IMf\nrMPhsH7913+9jsn17vRxaD4ecKeddtppp5122mmnnXbaaac/ErRnDG+EfNgCt4YlyjZlE/PbWpcZ\nuESF1jrP8CRbw0gVT4xj9ouRIG6rSj+d+fH2I2/ZWes5U8HxtSwVM30hR6sdveLzS46YOWvYaCvC\ny/oSkXOWiPU6k+MMY9vuQb643N3d3frss8/GbOOrV8+Hy5A3LcofcqTccnEtipnnmvxM1Pv3789e\n9tyivC17Qz63qGtkwgfMOPPlyG3LjLl+zgVlOv08Ho9nkd5swWzPGnFLnLfluY/tWts2lr5krbYI\neNZ321aUP27fdEaxZQpTr+tkhNxEfbOVqfCa4qezMltR84nCQ+qzlgFp+jLU5CRrv2W+2zhNznIy\nQ9wySc5KNV2U8ZFvE68oL86YxN4wG0W5ZxlmWVOuZTbTDx9w43YtM9GH026WjIXtcH1PuqfdR747\ny2+asnReuy1z1r5b9s2X3Hs8Hs90Qjut2ONr5MxXG0fuM/+oF8gj9tPPZVrnuc7wzf3iujCv2hz5\nk7sz7Ctw54Xlwv5XG/+UveO9HEPjKfkSPrhetuk5sg5jmSa/zlxO19xnj4+ZRs+NZWrPCH5a2oHh\nDRGNP41hFBQP4Zgc26ZU8j+3aqSNRqkj132QBvtJp6gpEbZNp4tANGMN2an2QQhpN4qZRjKKMs8n\nHY/HpzFYOZmHzUjSAFo5E0QTdBpUEeSZ6GBzmwYNJQ1avh+Px6exeStLeGrgwbFw3KnDBoJzyjF5\nC03Gwe10Kcu5JZ9ZV9uWw+vuF+WjPQvE+Vjr+dUR5A+dVIP1yVByrBwzZdCUa2yHctJALK+nbPhJ\nx4mBj4zNQG+ty2f+zG/KIMnOX+pf6/mZqEmH0MHnc4S837Ll+iwL5v8WUX6pJwyE2la5qS7Ps3nK\nNWq5sexPfY7Oa0CI+qcdDjMFi9wHkgMJtA1bW/A49rbWuBW+9YlBTo6NMpA5Oh6PF8EV9tMO8BZI\nZH/s2DcA1PQdwYSBfGuz6b8W+DDgajY+17ntPfyZAlAERqxnAhSpt5GDJSxnG2u7ZbvIOvOXYMQk\ny02GOZ7mfzQA13jisTkwl3uaHmU/8mldRvuz1XfbbequtS4fx2l6JmXZNuvkJ8s5IG359e+sI/bY\nj/Fs2UeO/6vSDj6faQeGN0R2tG2QsrgYEZ5oAgdrdSA0OYcmA8N37949nbjoZx3cfyoIvpKiKTo6\n/8xENUNKRRdnKsYz4CnO6dYYGw/SxpZTnYfn6biHrFxdb667zbXWhbPFa6w71I7xt3MRubCspY/O\nHDRgOxkiU8o2w0wD0+TQY03f4xDFGWXmmnJkWXNfefKkD5MweLEz6r7mj3NvwOV5o0PJNZPvzlay\n/3bmSZF7HtxCJz6Gm/1sfUtdBOe8RlnxfBEU5i/rkE5Dc6otv56HJiftvgnMrbUu3uNIHeQxUg/Z\nESK/owfXmrPuGd//z97bx+q6dWdd497n7LWbVEqh0dISTSFKxY+3IRAIBAwEo6FBxWgCGoIpaqKE\nihAEqpYYpCIG5SNSA2poE+MfgN9YQC2mMYAYFWlQAjXyYSkvfX1pQCjvWWvvdfvHPtdav+e3rnmv\ntc/Z55Ru75GsPM+6P+Ycc8wxxxjXGPO+n3xfvZzk2bNnF28GDi8teMt9masjO+4A0UQ5hD8mKNmO\nkw8rEB8Z8zj9WQN6+d8VXr7Ugjxzjblv22ufa5Wg9v8R0HdQHx5tT/yiKgfP9IdtTdpmRjb84/OH\nTlx6vRiImhcmc7N+ae9o3z13GYd5cf+5j8+lrWTc1rvPNSBOnvg/ZWrQTGpALWu++e0jfs0jr3dM\nYD6Z3I3crFsc+8qWOjHZxso+mzybXBireZfIYzsqTnq7dALDd4QaqJh5aDRm7qttbWtNA2M2tg28\nuSroe54/f/4gQz7z2jixcrVyAA1w0QHR4LmCEx5yrgHbmcu3mYZnAoBU2VYPZrNdOxFXTjhvrBrx\n7WltDlaBhw2+idWmVAxWwYyDDhLl3YCOgVr6bs6E89f6YYBtMML7WzDQ5M2qlefQ7TBYY+Wj6ZMD\n1BbsrgIHZu/ZrgOdfb//XcF93y+caOaM88N2nOGljNwv5bP6werV2JxIsjxN23b/JkcHZ76vAbro\nyArcey5WOt0CMv9sggMekmXDhIxfGLUCewwSWTVbvRiEQZd3XPD4YyAy3/n7q01WDbg5kPRLW9IW\nwUWTPeVi0L9KiFlXokPRgRXAa3rIOVjRqsKTc7Q3bPOxtmKPQ5xDP2YRGdI+W3fdRyjzw/bNy9H4\nm31ssUWuZYKKVTMmllb+h3Lg/B5tBaYdbVU62/mV3HINx+F1Qxk0H+Q4xb6ExHVP3nIP5yRJljYf\nqySi5Ul7QX0NCDef9GsruRzJhGu42TGSE2nUcdpj09G6fhN6G228K3QCw3eEuIViZg0gcsxGh+fa\n4mabM+ufNuDr4W1Mwx+3YjHgY/+8jxUEXm8j6+DbQffM5RvqGojxmF+8eHEh3/feu/8tPL4VbVWF\nTdsJWBroolFn341a8Nz+XwUozWi3N4qlDTsiBmYG2JQlq0H54WAHe3a4dlTONrrC5DY8BmanfYxg\nJ226ckC9dUBGOVOmDro4N6t5dRCTY6zAOPDx7+MRVIQPZtIplyP9yjUrJ2n9zLw1O8FAvVWSySfH\nzgSDK3L53zsA0l/m0NV9999kbhl4vNQD6m0qfW6HWfAQbYavX21r9tumGZD5DZOUB8GaAzjLLrx6\nXXgtWDaWpcfDdUU5kl/amYyNuu2qEe2WKxwEUgbBq0CW1MAd7UbzpbzmSM9CXMvhNzyxOhobSHnZ\nxzd/T3mkP9pq6q+TyQZqlB/bNFnv8kmAGF74Q/K0z5SZQUxsHkFNS1YkRmgVauttGwN9Dqv8repl\nvWpg0z4huk1ddIKOc2XbyPa4Lhwjtbnz+BxfGZjnGsZyOee/FbX1m7GZ6EeaHz3p06MTGL4j5Awx\nDSEdV67lcZINaAu82dZRBsj80aAx0GM7rW1mNGkobUgcqDRwFMfUKljp2wGMZZyH9zkGgk1XLsJH\nHKLBcMba+rcMOJ4WlJNaRYjOIWNfVexWDsvjouN1UDEzd8+CtmC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OfNu2i2C86aGdvOec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4SScTnKzK4y0K0i5exr7m/35pyz7N6O5G1OM5fPOzozxSxwMqTcTse2t+3yh5CZ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\n9CjFodvpNUeahblysAFxL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dWU42J2kVu4qAttPjw2OkI7cfZH0OctN5SHf1sw31v7DCTs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+Zn3l/m3g2bJqfkSTCfmgvWny5hzK64RiW+wz5Frjn/WSd44j9+FqbuSaeXCgYaf3Rzsw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YA17qa/olefyxMZQReeSup7Sf/52Ay720ofZ1E4BtMYJl0uxv5tu+rQEz604DeG1Nh5zUaG1R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\nWpZrchgnyHMMIFz5bJl74+JsL/ufcLC8eJ6aI0yj1II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ZbHNbHOluW4ZI+zR/5MFa6+F9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\nWxaJiqItWBvmKPoYXwZArIYQh4y3Z9yiEOzo0chNmUXi2Jw0K/sWnLYAcPr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w1uVWMeJP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cube[:,300,300].quicklook()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -5.272e-01 [aplpy.core]\n", "INFO: Auto-setting vmax to 8.105e-01 [aplpy.core]\n" ] }, { "data": { "image/png": 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8PR2pTPMX+CVaNd5sHU8JPIhLXaOOpA7u1iDt5xqjC4pmCVXviwRez+qbAknr\nZ+JCOjh/4qv7Urd3flI3BuVpJn9p7m59iUM3bpJR4lPjkBcen3jU9xlvu3E6GenWoO77yC77JF9h\nBt08t4WHMcajAivwOyOwIvFmpkLtHIQah98dzKVqAv+3MrdC9rMn3fMhM+PIsa0Id7vdzbODpNe0\nWvmyAmUHmGMnZZwUmeex80aHPxk20m5FSnwYdBiSgemcZs5Juqqtg8KktG2skqFhRi85eqSbfCKw\nGkRc6NybXuLQGTZmVRNOzH5zPOM2Azpr3JNp3m6P2umaOYjEp3Avev1iFLZJ+Ho+Z+W93l0gkYKu\nJJfJgUiyn8DXS08kJ6cLLjxn8Sn1r3EdsFZby6XpMCT53wrEiLv3kPUsA5FUZenwTHy1AzhzwmfV\nIq5P4otxSHLkOThvCvr2+/3Bs+bEK+kewyzRxjksM5eXl1HfeP1stznmKXP7hEYKtna7B2+1JQ+6\nALnGSMGUaeY6pkqzIekbzpn2yow3lG3Tbpmx3+JAiv22ThRwnBkdnc3v8LRcWK6sgxPu7HdKW+PH\n77TpaQzS6QDPb9S1rPBzwcODFfidCXTKPAUUMyXGLJaNaAoct8Yt6LKKYzw4/pEU88whYWWD87uS\nUJl445RwZkY80TLDJ9E9CwBPCYC28LWjaejWZKttWgc7x4TuyBBpK/r9SvCOzouLi6PqB79XgJKq\nU936MEBPDpJfQkQakjOYkiizIIyBMYOnbm+ksUj3bK4a28Elq1L89It/0vdujetzi3YHjF3AwzXo\nHBnqKoKdC87T7c0x8o9hpyrN7GcG7BB2PHFwk2Sx2nFMB5edU5nmI04O0py1N7gPx3TQlugmj5Ke\nN19m9JTON1+6ZAxtjwMZv3Ha+KYgvwPuNR5t7vZ24bbb7aI8WTaYJGT/ujcDB1wJJzve1q1dIFJt\nk0wmfdPpfNJcbbkPZnvCYL65zxYOxD3xzXt3FgAn36z4lHRUSrBZ71LWKKNb+me2hoSZzHsNUyCY\nwLqbeHd2PNG+4NnDCvzOBMoYpmxzt3G3jDiVW7Wx8rHh8Ybl2HS4eY1v4STwmTsHFlTmNlj+YVeP\nmzLOBuPuQNqKvDPWdliTE52OtBEHj5M+TzGopH+WzSQuPCbk+Tw2ncNuTDph5lNS/CUbXWbcldbq\nR752mePkNFewyWRBGagt474FXv9Ed8HsGBllfhb8WR68Nqax+nT6gc6d93hHi/emcfN3juOfaUlt\nLffGme1Xz/4RAAAgAElEQVR9DDPxq1tX68XiW/o9zU438N5sj5bsOugtcCWmk41E2ywQTXyY4TtG\nrxeSnHd2IclwtU/Bx8zh5/gzfDsd3Dnfs8pa0rv7/YO3iHYBs+2ice/W3jqPtM0SXjOnPDnXrsjU\ntfS97Lhp9NjFF9Ka/ISEHyvLxsnJXlc4va7mve1Sjct5CdRrSZ6Jt2niWqeEOMed+QrmDfe47bZ5\n2UEKtpKuZ5uka0xr6p8CP9JecsKf5elwfhiB4QspuFyB35nAqYa6YOZMbY3NrGZyjPg67LrWzZEc\nu86R8TgMxjqD5OAvORIccytzaoWc7pk+B3/deMmYVz9/T04ux5wBeZZ+75BrULzyi2/sZNAwJQOV\nHLbkYBYQr3KCHZykwJT4pH41np1gGuii9RRDkObvZMM8KQNd+HRZ8y6gSngU3t2YXDPyuIKiVIlL\nMs3vNN4c17Lf8cbg/dIFMobkhBlP6xMCj8SxPx1LO2DmG/nAec1Xj594wPm8HqmalHAuqKRgck4T\nfzv8ZnpnNka3nxzEWM+Td0wyFn127ulMmzdJBqkLmWhIlbSZbJlX1nGn6PBqz3k8dmdP/BuNWwFJ\nF5gRXJllgN4FnzOe1Jgli3wcg9DpwcLVAWDhkSqiiVZ+Wqa495144dhbuqnTe50cVBvaqM4vSj4P\n5/A124bES/en3unGNV6mK43Z4evHbLjvU9J3wbOD+WHrBQsWLFiwYMGCBQsWLFjwyMOq+J0JVCUr\nleoLnIVM2RhnsLoKXFcZqms8juT5UnWksnx+1uGULGu1Sz+Eysz5GMdHZbYy6Smj3GXBXEVKfOWc\nHq/mTxnd9APAXRWIlbdUSStZ4R/H6I41sa/pYKXPWURmFmdZ4jGOXx5Q107N+jm73GUOk1yTZymz\n7gwt+3pv1Xdeq5c5ED/ibL7xPmWTOFCuDTMdwDaer/iQ7qU5SD+fiemy4Yk3xtFH5Ga6zLikvWl8\n0rHihGfxI+FX7dKRd85XlXXztNsH1GXpWFzK2htSxcTVkC3e+pp1cao6JKgKj08X1BhdRcw2pvic\nnhGmDq0TJ+ZLelsxn/ErHZCOEs4gyftMf9S1bp/Mqn4drwzdmqY9mSpObmvbypM0M71smfNpANNK\ne5fotr0ird0PvHsdrKMpXx0/uz3R7SH2SZXpZOOTb7Hln3Cu9Gwg+eE2Sd8nPdjZkXQ6xPObPt6f\n7VGO7+qnofPHbgsPY4xHBVbgdyaQApkxDo1nJ9h2cpKRT/P5Po9KUEE7oKMTUE5OXeNzijOFkhx0\nQnf0br/fH7x6vOOZjVLNzUAmQXqFfnfEhmPOjoqlV6+n42QOAhJfktPoteTazNbfBjPJzFZgS/yJ\ne+GRjujaOUvOYcIzBYXmhx2DGoOybZlJTl7iNw2ZeekjyVdXVwdOFXH1cyGkmXinAI6fSS75oiWu\nielN37tERCeHXnO/pGm329083zELUlJQZFnknk56w8fHOM/MuaVs7Pf7m5/3qHt0WI1fkjXTVM+4\nek9tOdxb4DVMiaTUJ+2LlLjwPH7+i4EWdcIsCLXuSHomvSSF91LwybXo9Gx3HNe84T50gOpHJNyH\nkJIeieaEU2e7kx7kHF3gQ91ku7bb7Q5+0qZwJx1e39nRQ+I0wyW9XZo0OeDsdLf7WZd7vE7+jEPy\nKeqzW0+3T2+fNb789Mu5TJN9iWRfZkEowbasSxZvQfJHZnvu+YbdbveTY4z/bIzxzWOMz4wx3rzf\n7/+0afuuMcYbxxj7MQaZ8bn9fv/Kr7Z54xjjXWrz/+73+xd/bSh4GlbgdyYwcwK6QKUztlZOvpcU\nbjIqNAQXFxc3zmz3oG7haQNCXDrnNymZdK1w4Y+esr2DZH92eBWkcRnwdEFR/V8vFhnjuHpiPEiP\nAyEHdAyMqo0DSjrerurZKXaVo67Nqmw1BmWly97S6U9BDfFI/ezUsz3xJO9Jr2U0ZaATTr5Wc9d4\npxpIrg2fPzGtMzmt/5NDNnO6kjGfOZMc384u9xT7z6q45UzWOlK+u8pmfVr+7GQmh5tzVDvSdGqA\nlXQgr3Mfpr1PejqHy7QkfdD9T7z8DK0rOuSf6XKlKgUw5GPaK6mKbdo6h95OfcEsIO4CbeI5e4EE\n1y0FBsRtjMPqU8KF9CS7wGdHZ2Ok/d7NW5CSY4l3Se92waTXNgV/CWY+RBeEdfe6sbeCMrZP47pq\n1uHfBXtst6Wv61rygZL8djSnsazbZv4T/TDP2flnxjONy2u0NfYNeO/vC+x2u/9ojPELY4wfG2N8\nYozx02OMP9jtdv9wv9//dejyU2OMt+D/yzHGk2OM96jd34wx/uF4EPh9zUuPK/A7E3BVbYxjB2Gm\nmHjNDttM8Xu+zomt+zZkSZFRsRQ+dugS/jYKdrLcb0u5JCeCypDjmIZaCwZcHX88pvnTOQcOKniv\nw48Ort+kWg5HctrquwO+usfjVXSWuXaFC4+r2Bk3TyiHKVtPPOq7DRr/J36zBISheNVlkhM4aEnO\nvp0M33MAznudAff6be1zzs8xXMmZ6ZAaiy926gJ202GdUN/5O2dOJDiILeCcszdumvZaWyYFCNRF\nM8fR+HR7ntechOh0Dr+n9Sd+vkc++y3K1SadLKigcEvvdxWxrf2Rvnftao1SwojrRh3H/imIpzwk\nfb9VCZ0FH8WX9HNDrIR1fOBern6dDHZ7qMONULR3/aiHukRp4kkC3ks/sWR+dKdl3IZ7uKvOpb3j\nOT2/f85hxtct+9VdTzo16QT7Pwk6/4L73/rCc3Gcbn0pM65OJz2U8Ex86YLGmUzN5rkNnDDGT48x\n/vv9fv/rX8XpPxlj/IdjjH8yxvhvwnhfGmN8qf7f7XbfN8b418cY//y46f7/fsaIPwNYgd+ZQLdx\naTRPcSw8nje+nUJuXBqzFJRRgTowSkcv6eT5qJQrgw5gkvKo/xOfEiQ+JQdtZvyr3Vab5NwlJ9p4\ns43XKfGBQZ8dPSt0G0vzJTkLDP5IC/Hhc0s1X619Zzw7x7L+ukpHZ1T5bE/Cs3OA0v2Zc+u5LT/m\nudsbhwS+R5nZ7/cHDlYysO7T0dQZxk6+U/XglCzu7BXylJHOiS8HvnPY/D8d2woWS0aJi2UmwRbP\n0n6o604QzPqlOelI7na7o7cm8rlBBjlcm/ScNMc2LvWZqkynOKqmN+07Vhod+HWBUtIj9f2UhI/l\nOB1Hpf5JPOiqrdWWdi05wB7DTrr7sW8KjjuaU4WNuJ56nfuy27Ns5zG3Kozkb1r34mtK0Nm36PSB\n59tqv6X7HWzV9y5pW9DZrZldIw1pj858j6RznLQk7R2f3NaVQ0Lii2n5+wK73e7uGOPfH2P813Vt\nv9/vd7vdvxhjfMeJw/yTMca/2O/3/4euf/1ut/tfx9Mv2/zUGOO/2O/3/9Ozx7qHFfidCZzikHCT\ndQaZm9qG28re36t95/zUhk6bmgaoM0SksZQNjcyWkuZ3jk0jdYqj0jnrM4WVlFpn4OwMdryp/+2Q\ndrSluRL+5OvMeaDjaEfGsthVUWre7mclEr7JqUhOmee0sU1BfJqvwMkK4+v+Nniey0FwopFrkvb3\nLBDgOPXdAYbxN+6kfYYH974TItXf41sf1OfWkTuvd8LHOqHr6zHoQI5xXElJAfNM37hPcthSgERe\nd+tEPFPfF73oRQdrnQKV4lX9b3rcJ9FXPKqfFGDfGnPLwU2OKnEjLgmof6iHODa/d89Pdfg4+DNv\nUkWMa5vsShcYWy6pNxwIJn6kI5uWac83O85YY6TgPgVrlVDsHHnb92S30vxss7UOxCnpSPY1/gYH\n2aQp7e2ZvmA76+H0admd2YGCdCqq8zVm87l9p+esU81v7rkuGVJ4d+u0lcSY+b4PCV46xrgzxviX\nuv4vxxj/7lbn3W73b44xvmeM8UO69T+PpwPCJ8cYLxlj/MwY4yO73e7f2+/3/+ezRbqDFfgtWLBg\nwYIFCxYsWLDgkYK3vOUt4yUvecnBtR/4gR8Yr3/969s+73nPe8Z73/veg2t/8zd/8zXB76vwj8cY\n/2qM8Tu8uN/vPzbG+Fj9v9vtPjrG+PwY48fHGP/l1wqZFfidCXRZHGbVfKSnMixd9qzG6ap+XZWq\nqwjwemWHZ9WKMR5kevy8DmkyHbPstDNgzmKm5zxMuysVs0rfFvBobHcGn3OkyoBxduYzzZcypNW3\nwNWSrjKasnis3jADz6x1VSu3KrxFd93jixPqPis8PP5qOk7JtnaVoU5mnPk1DXz+qI6ZGrpni9La\nkC8pK0x+pjFSNYRj+xmpornwShWyakNcumNJptHj+H/vCcJWNY3fvV9ZmeTeo87zszWpepDWPd03\nPl0VbFahSNWTjv9dRamO1fsIJfntSjE/U0Wh+OZqvPleQB3QrVONkXR5qg752UWCH0GwHuhw5ckH\n8pk4pJ8RsP1MstHZSt+jLKb9Zxp5ZLfa8FEKHvG3HTjlOGWa17zu9Gs3bgKP21Wcuv99r9NHXvtT\nTvJ4XTt7011Lx7pd8bJu4x/l3JX6pN+THuj8t3Sv0/kei7gkXdVV9fb7/c3+9XxjzE+BvP3tbx/3\n7t1r7yd4/etffxQYfvrTnx7f+Z3f2XX56zHG/THGN+n6N40x/q8TpnzTGOPX9/v91azRfr+/2u12\nfz7G+HdOGPMZwwr8zgRKoadN1RneU472JGPoNgWlDGfGzDh1tBDPmdEohZHwOxXsYNpoF8wM0dZ8\niQYbUht18tPj+yhadxTCL2ZwYNbxisemTB+PdiZIwQbpmBnRZIx8rxzMLiiq/1NQRx4nQ1vzkVfm\ni39mgnKSnDkbb1+v76afeKRgIPHfgbWfvzOfjFs68prkw0FUCtL43bh2x85Ib5KF9P+W01VgJ59J\npZqXTnZ9doEOYUuf1XxpP3V9OGdH48z5LZvg++atf5/TCZ4UFBiPMXJyLjmA6bdaTVNnn6oPX7aU\n8OmcZss016R4lZIeDpBqjMLh4uIivgSnC74T/9K1LkjxPfLZ+4c41jXLhWXAc6agKB0n5Xyk4VTd\nyntdgJjwm+kMy4XX12ub/CXT4bnSfj3lsRGDbYnHTLzk/DOd3c3X9WEA5+ulz5hwNS7p/zQex/Uc\n/t71m90/FTbmeGq32/3ZGOMfjTF+d4wxdk8T9o/GGP/dbNzdbvddY4x/e4zxP2zhsNvtLsYYrxxj\n/I+n4v1MYAV+ZwazYCQ548kRcDUlOYupX/VNwV+nUDvlPrtW84yx/WyW280UT7WnIvFv2nUKtQu+\n7HAk2qqff+S5nE478wV2IlM1sPrYcJ+qSOvTRnH2zArppRE3L6yw0/NUqR+z8DVHcvIMznLbibKx\nnjkpHJ887YLJgs6hJ51uZ3o9lnFx8MJ7XsMUsCZDz0zs7BkhX6ej6axtF/zx//QmV/40xha/DaX3\nOA8rJXW9xvHzoDP9YZ2YgmCuIX/zz/ui8EjX+H9aq+Qku2pnneAghvqv0yvkWQVifKEMn/lL8tU5\ndunt1ORH/V8/98I2lFGOP0vMcF/sdjmB6uCv5mQlze1rPtsUy9BMZtN+rf5dkpVrWbgU/q72sn0X\nFCbZJY1sx/GKtk5XJp65bQoC/b/XjzgXeFzfYz9/v21QwX21Ffx1/o/5NvMjPFYa5xQ6UmU6JcX4\nv3V/4rn3npNpBN5nv1nF7zmE/3aM8c+/GgDWzzm8eHz1LZ273e7nxxgv3+/3b1S//3iM8fH9fv95\nD7jb7X52PH3U838ZT7/x8z8fY/yDMcavfo1oGGOswO9sIWVxbHiSoiA4SHCQNQtm2C85s4bkANX/\npVzslNswJDzrXvdjul32j8Z1RmOiY3aPn2UgeeSNRsnzJIXZOVTVl7+FZno7Q+CAq3BxFdCVEFcs\nSUMy/slxcTBLmqj86cx0QWj9pQpDat8ZI66P+eI9kY45EewUjXFYKUmy47lSkEXwXkn9fc8Os/t7\nfc0rvinSDmFXmXTCI7WbvRSEfwlmFenEn7TnHUB09KcfTzb4OvukAI66wFXcTtdxvK56U5+UaQZ9\nXL+Szevr66MqW/VLwdZu9/RbRVPgzj1kfWk9XjqsaLd+TDCT4YQLv3dVTfLIezXxtAt8PFfha5gF\nLWM8WBcHQDW215dtuoBqRh9tCq/ZRphnSY/b90h0e5/NflaBiQvCzA85hW8cZ2YrjQ/tZ9I13Z4k\njcnWE48UnJOGtE7El/1Sosu0peScE55jHB/15Djcl2lfEAfb7WdSPX3YsN/v37Pb7V46xvivxtNH\nPD89xvgP9g9+iuGbxxj/Fvvsdrt/bYzx2vH0b/ol+IYxxq98te+/GmP82RjjO/b7/V8+fAoewAr8\nzgS6YxpjnJZhrGszY0SH1gomjW0HmH1ScJcCmA4X0t3RxL7pN4OMM5X1Fs9m45gGKl8HcjYSaezk\nAKZ5kqNTY/M17Q5uOwVc7cfIPxthRd05xdVny3h2FSH/z4ygq1icw287ncnRViDleVKbmsNOn50j\n/3h24lknK+leh29nrFOVPK1fV11KQdMYD5zR5DwY6EzYWeYcTg7M8D2FB8Y/0ePAakZT4VM/j5Jo\nTs5xpwfr086214TPznZ0cb4xDnWg5SfpauPV8bnaW5eU85b2Tu0D6sCOd8SR+rzTybMxzHfq+7qW\nKvzsm/S0K38pmO2CH8ppsjsz2TGt3XHTgqRjZ7rEc7rvLKiYjefvxpcBBK+TvoRb99M1XbCR1rwL\nEDv77nuce7avEux2T78dl29RTnP7/8QT3+v6Uocm/y3xYoxxkFTuigSczzqUPoXxSgm3Drbunwqn\njLHf7//ZGOOfNffeFK797Rjj6yfj/dMxxj89HcuHAyvwOxNwppbXxzg+j98pa2e7fc1BQVI0dp64\nMROedG46xZMUdqrW1RzOPlvxssrQVZnIv+Rg2lgUTgnvznmyA5syzkkhJcPQKa4thzBlKzkP29Dx\nSYHm7MUqs+DPUGN1OLPNzJgmh4sONfuarpQJnh1vqjbcN8mhS85G3e+yw6SDcp8ct1mlo+MXeeLK\nXY2RnG07DcmxKHxTwORjV7zH8VKbLVnqZDVBwpv9OpmufhcXhy8qKtrchzLQZe2rnYO/GpM87xJB\nYxw6Wjyy2QVNteZJRkqf+nhllxCxvJJW/p6gAxivvV/+VJ9XV1ebTnTSjdZvSQckvV73ugCI+BG6\na51TbR3Qfff4Tuh1wZ/lxgnj6+vro5/loCyS5k4/8f9TeOIgJQXP/D/pT1b9LFPEf6YHkrx5LOqD\nU6pQSV+aJst+6YZOx814neSbuG/pjm7PjJHlvNOb/D/ZB/o8xsU6/1ReLzgdVuC3YMGCBQsWLFiw\nYMGCRwpmSe/bjvNCgRX4nRmkKkJdT9nr+j9lcZyVcd/KdNdc7puyOZVVTse73NZZtpQB5FGiLrNl\nvGdHTFihcCbWn8yadscyiE+ikZCqH5yHP4DqI1Lu0x2BdDWLwKxvwq2qeT5O5PU27yl7W0fT0hER\n42daeS9lutOxwdSv1tAyzediZrIzq0AkGWZFyhnyTpbqGp97StXbGa9cKa/2XSbYcuS1r/Xtju0V\nX1NmmLI6y25vZc4TjeRN0lOd/JG/M3khP+rTxxHNK3/fOp7v6lzhzaqZx2R70nFxcXFzjIw0sHpT\nY3drZT1KHE2HK3qurCV+mk7PXzgmGduqcnHuWeXen0mfkgbOl/aJZYcy4/nTeN2+JA6sEs10gMep\nNeU49YIejpdo4GeHe8KjoztVyHmvq2huVWJrnqKT+7vDnTQk/Zramq6ZDp7NS/zTUdFU0XSFLOmM\n6+vrI33BdbKfYvpcpe6+28bb/6O/YPqS/e909YJnDivwO2PoDN8Yh86YA0T35/82WCkgspHwszZb\nx9lm583tjNMwmr7Eg1L+KfCZKRc6aFasqV9nGGdzdOMUT8gbOnQ2MHbcUlDKQGxmjAr8ogbOSac/\nKekU+NfcxIP3HESmozvkUXd0imMlB9AORJojORsGGzhe8/FXO5VMNMz2xixQJg3GeUtWO/wduBF/\n6gu2o2ORnMSUjElO5cyxpJyQZo/tdnZg2Y/BySn84pgz5zUFAB47Be8z3d0dX03OoMetZxG7PZSe\nrUpBc32WzHbPxXF84uHAnPeKtmqX5DDps1MDPz6PWfTVOpb8Jl1X4/me93OiJznjvN4ddfO+TGtq\nGlIA2Mkpky4em3xKumSL7hSoca4EswCcNKbHTNLcxTM+T1o+gNfBOMz2IGWS4yZ6tgJny3fiJ+fl\nmhtP40JZqsRxp3eN9wzsL5offtzG+M1omyUeFzwcWIHfmcCdO3eODNKWgrVCHuPQCXLAZocvOSvJ\nybu4uDh6c18KPJLj3ClC3tvKms+Cr64ClQKqTlEmg26nigZr1i8FPzVe55gzcBhjHD2nYOeThqPj\nhWmsV8+XAfGa1PVk4DxWCla6a5TLRLvHTAY8zdvRTkOW1jld6/ZZBX0M/tKeq3FoFGfOBukzTnbe\nZ0mfLWM6cwiSs1UOVpJl4kd60n45JYCZ4ZCSTCmg8T5IeoYOjPcv29jRrPGTXCZ+2LmxbPB63Ste\np2C7m5P8TPPZMSYNnezPHHyCA48UkFBP0ln1nGnPzao4rHK6bwVL1AGsqJPOdHqhc0yT853uJQeZ\ngZ+rNMmWFB5lazs5N74pSJoFUrPEcNePidZZUFPQ7VU+x2cwPcaP422tFcfs9AJlYWs+zpnsmvtz\nvyW8Es3e0xzLfdO9tLYzW2g8Olub1pB7ISXfzKNuzYnrlkydAg9jjEcFVuB3JlDHeAgOnLrqSucs\np82Z5nXfZMhSlcf3ZrjUPSqq5Gwk+rqAzpU/8sNORI2R3txnPs7oSUGqHUXzrv7shPE6oTv6ktp0\nP/BcePl7cq7r/2RkZnjMgpPCscZwgsBGlmttg5ac0U7uknPQrRdpr++UUf/eXHqjHmWnGysFP+Yh\nZZqv4Pf+pJPQ0WugUe4CNVYO/BpuVyNMXxeckkbj0uFcstAZcerDBJ28msdJPhJerpadghPXyPwu\n+ut3ES8vLw9k385jwiOtZSfj3Fun8KdLHqWgIAV/dvp83K1e6pL2Cz9Ni+UsVVsrYTZzen2aYiZr\nyTHnmLQXTojWPuI6dUkC8zTpBOvB9J24En+2S2tqGXF7V+dm/sQY+e2zna0nzl1w4EAj6TEH6fU9\n2Q7OS3m1Luf83L+k3/7QKUFM0g3Eo1vPxNPOxszssuUrAfW8EyVp3zLRk2jYOlGw4HawAr8zATsj\nNlidYrRiqE2ZDACVpo0Rs3FUcgXX1w+eS3GVbeb4E5/Cj581xsygUIk5yGI/K5vkKJyS/XJwSXqc\n/ezwq+8Mqu7fv3/z/IXXpDPK/l7jMpA1rqkKawNsAzmDVPkhrzpIc9b/lpuuQpaCHPOAtG/xkv3H\nOP6Bb14zHuxX7RgYuQ+D/gRJFu1MWxZnDlQ5tV6TSijNcCmwkU570d/peBee7Ms3UiYHk+D94+8O\nrgpc9Suau7dHGvfOQbTemslSuu72aY24Vy1rpnFLd7mfZSjBVuWra895qXvsQNOe3L179yCJ0u1t\ngm2F76XqJnH12Oa3kxsev7tOBz6tmROYpKWTGVf8E63e/16PLmFK6HSz2zi5muxNF1AlPBMvO5nz\nulueqs1Mbr1/PfZutztKBjBJm9ahSxQm2TQuaf29FqQn2aS0dpQbj0U+dfwxkK+Wp7qebEmdLPp7\n8oPtZwsr8DsTSFUQf+8qdjb0Hi8pICs1GtBq7xcQ0PB0NCQjkn7o2Nltjjk7ElL9/Or1mqfmrzFc\n6Sv6u8x2gR0nBn0pAO7GYFbQQXU5xKY/KW3yr+ja7XYHz3Fwrs6hTs6KabDspaoqIQW9DlYYqHn8\n9MB6F6B0Rp7t6WSQZt5PTlEyVilw7oKAciI4bicX/GMbvorfiYaS7/RbWHQ2jR+df+uLojHxKu2T\nzmEp/GoMrrXXxvg50Ewy3Dl9xDlVXrg3TB9x4p6Y0dj166BzqLkWDIqtd2a6PwXDDvRMr3+o3gFL\nCtBnSZ8uUOIc3QmKxJvu2ee0/wo3Bije+9TXHS2m6xSw083rnV7dqi7ObF76fT+Pbd04o5Xr1AVj\naSxfTzJAncV+tutJFpLcWqa8R7ugbhZkpwApBexOxph2+1nd2lsu3HZWlU/6qPpUWz4D6XFmvgDH\nqWu0jzMZcrWTdKV7M5jp0AXHsOqnCxYsWLBgwYIFCxYsWHDmsCp+ZwKpPM7MzCx7MsuGpewcP+vo\n4RiHxzi6DEyqJjnjyTF43p/j8piN8WbWKb3h0n3SDxIn+n3N93gMq8v8zrKgrPAVjT4WWkfQCu+t\no7IFrqCkDCTn6rKeaV3TGnTgo0tdtttr2GV6/RxM0Zeqb8zqF93d8RrO5yOSrgikF1DMxjNwXFea\nKPfdOnc85PHqukcZJR2W+ZTBJ03kt5/PIM3dUc+uolTX/NntSWe6XSnw8aIOuG+7dqbZuHActvcx\n4oT3bL4Es0piXbccUmdyH86qI939NDd1u/dKZ2PSEfyC2X6pveIKedfHRyA93+Xl5cEeT5Wwbs1J\n72zfELhvzAf28cuSDLYhxNXVslR14ydPVxh/07ZFZ2fjZ3vQNKQ5Oxw6XeH7hUuSN7fbqgyyqmXc\nfHR8tpe9L6iHkv/lft2R0oJubezLJRrr2mwvkhcz/Z942dG+jnp+bWEFfmcCdj7HyMdGCCnAqPZ8\nQUQyBEkpPvXUU9PSPo3v7OiR8UtOc9E2O/7CgIG0+vgF8eO8ppf/G5fOWFXfpFjpfNfLGoiHlaKP\netbcs2ManKf68bgeA18HnpxnRsuWcSsD2AVb5qXBr193kERcGPTZ2bBjanzNPz9zVrz20RbONTty\nPduPNHrmg9eFdHXHo4kXndguyeI5TXcX+HE/pbl53KmbIznjDCY7cJDH8blnyLMCO9R+jtD7pX7/\nznyeBYrFm3oJi+l3EJjkOe0rB2xbThn/p7zYWZvprurHYGvmLPqIepLPdC/trZnjymvdWte4RQP3\nAWQ5PigAACAASURBVIOd2Vo6GEy4zIKUJDeJbzMd0QV5dc9HIese31iaApQ0FvvPYMYz64ExHrxA\nJ+lhzt/hNAPq0LRv0p5ywGg9dkoAYr/BcuCgs5Pnma9WvEh7rttj7EeaPG9nE7knqEuTjG4F0L6X\n2vuYfcHW2m8F1qfCwxjjUYEV+J0J2AEdIwd2CdJ5airPThlwXgYRM+O59axJCozYv8A/D2FlQgfd\nhq4L/JLhTnwlznXPlUKOQ9wc9NLApWci0xpUP65vWpNEF9faRsRBRFLYCR8bls4wdE7CVjBmHpL+\ny8vL9gUcncPZVSHYnv2urq5u+tRaz5IDnCuBHYWizU4BA6Ax8jOEDJy2IAWhXSaee3XL8FbV0/xO\nz+ZurT/7pYx5crT9mRy7VC1m4Ljf7w/mrb14586dcXl5eRDEOYFgOjwnM/JeAzrsfsbRAXWB93uC\nFASloIfzJZ6mqmD3Eh4CeZAcOTp5KdFEfZHsT5J5j9sB75EX5Lf3N0+F2MYWnrMqbqcT0/p2+puf\nDvJ83cGeaUrz2d5aVhNNs33Y0UR7OaPVAeKWvHfVJdPo+7MERpe4SvaPpzXsexm3ZA9TwJr2YX1u\n0djxYsYT8+Xq6urmzcHd6Y0Es2CW697Rd+peXvDMYAV+ZwKdcqdDMcvKsD3/ZoECFReVY3JyaKgd\nKNS1pFj8EpbuJSudckjGsZyqjgcEZ0pnRxc6ug0MtOqH0TvD1gV+xCk5lDNHm9lv0pQyhylg7arA\n9b3jUZLRLmhJfHTGMTlpFxcXB9WbLWOVcPE6Wl47RyAZzkRXQXKIOa6TBAlmDpEdPzuonT6wY50C\nwOTIpECFOiQFcDVuWqcuePHaJIfUNPH/zgGf7TMmkqg/SD8dOtI2c3BTUm4WTG7JVEGSG+6/rrpI\nvDnOzMnzcXrj4KTYLBG5pY+Nrx1FjuuXPs3AgS/3SNmgOnlgPUT5SYFKWn/LmpN4PLnAhICTmQ6U\nCLOgyW2ZbHA/6iLyh/Q6GTajtehMiVnuw8421HwpuKh+XRLP/LCuT3hybI/DMQwlK8avO3VRbTsd\n1dmaUwOjTr8lfjPx9NRTTx34kcRnlhTc8jc7PVfrcepLlTq6bgsPY4xHBVbgdyZQP+DeOasJUrDl\n+90RgjHGkXJlNjwp1/rOjdo5g/W/jSqzzTPcOyVTCozHI09xqlwdSP1SJt4BC9ub/8lw06FOjkJn\nAMk7rwWrVs5+z6qHNPY2YLPjO+TT7P8UFJVcEboAJPHQMmrZJNiZKrCMzgzo7Jplyet6G3n2deLs\nREJy5Lp7HWwZ+K6K7nZpr8ySNqkKOsPVsuG3cs7Wj5DWbbfb3VSYOUe1n8mPx6JOOzWYcz/r5601\n5Nhbej3pO1Y0EszW0TR1wV8KSDwWZejUKsSW0+u+/F78uHPnzk31v9qUTBRPq1+quJF24kIa6jhx\nfdLmuYLun0bo5I77oNp6bga2aYw0Ju2L2zmQNZQ8pWBi5rt061v85z6f6VPiN6usM0lKfCwrTi4n\nv6HGTI/lFDBpyflMK+mrtetknLzhPqYeScGk9xd9ryTniX5ft95Lur3zrxY8PFiB35nA937v945X\nvepV46/+6q/GRz7ykTFGNjgFNMZJ0XgT8l7dt7NTRqrLgHGMNFYXtKZ7XTbZc9nAWClTuaWqZQc0\ndimrR7CR5HW294+XpuDJOCTje0owXfPTWJZBSgaE6zzLOtupnClwB60zZ55r49/wIqTqVMfHmbPU\njWtHxvSlta+5isZk9JMTWzgn2enwTUEe79lpTXCKIbfjxJ9CMT+S3iH+W3stOZ9dIFpyYj1BvFlV\nIQ6Fq50jJzXorM5ODhQ/0qmJMcbRaQY75MaL/SlL3ENb+rDw6vRFzZUcsgSdbkpBS9rrydG3I52c\nUesUB8PVnvRVQNXBLJlYDrlPn9QaMDisuRlAz/QM5y0nPuFS60v9O1trV2udBOn6FXi9UmDM/Usa\nusDedol72bYlyUbSoSm42tKXs0DP46afmWEb7yfyyPfq/6urqyNcvE6ez2vj9aGcb9lvju/kb+KL\n+U3Zph6zf+W9TFwMyc59z/d8z/i2b/u28eSTT453vOMdsd+C28MK/M4EPvCBD4zPfe5zzzcaCxYs\nWLBgwYIFCxY8K/jABz4wfv/3f3985Stfadt0lc7bwgupsrgCvzOClFlixmn2zAwz5Mw2p3P9nMuZ\nJL6dMoGfDdjK+M8qAc5+zvjArDGzVf5hW2a3SIerJQnn21YvXDFjBdH305jMkBbuNY6PrySlVkeH\nmDG9urpqj79YVny8hW1mWfduvMQrZiV5xC5ltP3d156pnHW4+ZMy5vm2suxJTgu6fdutb1XdZxnX\nlHlnNSutS8db/kSE974rV4k+45/mIJ+7Cjv3X9pLrvKxv6tenLt0BrPqzM4Xv5M8JvxIPytJnj9V\n352d9/F+42ocqLtmFREe6+rkuO511Z2yHXxZVeFAXAmpQsJ7/AmbtKdSxcYV3kTz7H618UttWOmg\nrqo21IVbOso0VL/OVm9V5c3b4sFM33YnOfi9kxnOl/Rg0peuDHa+i+eqv+5Uh/nW7WtfI87mS/Iz\nSm7SPc6T7KiPjPu++c23rBtP0medkfTtjA/eM8mWV3vamURDd416NMFMHy94OLACvzMBO/tUpsmY\nevNb+fJeMvqlGBg0lHKyY2aH1seTZgFTF1D4mo8NmjfJ0bNyTcfx/HwFeUm+2OlLuCTnwnxgHxvC\n1I/f02vWT1GwpP/u3bvj/v37R86s5z7VeSieciw7dSmAZLvr68Pf5Sujk3CwHMyOGdqJTUa6+vGI\n4CxI6ByJRFcaIxnomUNK/hLXLUct0Vh8rbe4dTKcAqvC005ArYGPUDrQoe6a8bjm6fb6GPmnbfhz\nCml8J3ncnwGV+eY+XHPTSL4UrgXeB7MEWtffzlXq18lGaj+TvdnRPgdmllHr+w6n7hga5YpQ6+tn\n1bgvOn1IvZXsoNd/dizRcm5nPvHA9zqbyPnTMcn6n+04Pq91829d8/0xDvWV6UsBE//vxkzXkz2x\nb2CYBQ/ep8YhBUFsk9Z/iy4GvoTZXP6JrS27XuMZH+LQ8bkSWvQtvF8T7WlPdPvce9GJAurTmR5a\ncHtYgd8ZQdpE3Hze+P4+xmE200EQ50iGqZSVHbm6RwVthzjhZkiZU/65Itdlvvh2ti1HzsqfuJjH\nxon8JI4djZ0TQ5oSrmM87dh2tHZBZWfsksJ24NzhYbzdxnzkfc/hazOj6ewv+Z2Mhmlg0mIr80/e\nbf3OE2XRdHq/blUk0tj8rLZVCZrtpepnufX39L/ntBOZAriUiXY1kPT7x+DNjzGOK2LGkePVm+kc\nnKSxtxxR8qN43f3uKfFNc1GHdOu/xf807uxa7R+ONdvfBuLc7ZdU/eneyDyb004s5SU5/4lXtgtd\nMivp5qR/XBVPjvQsKPS+Tfo0OcZbgSBp6OxkOgXQ2WDuVeOc6Hb/pJ/S2qQgtKM12a8aZ5ZM6HQJ\n/RL+P8Ohg7SeTHp5zhTA7Xa7A5vNxPoYo014si1p4P7sdFPSodYR5AWT/t43lq8tXdL5I7SHW4Hf\nLAi+DTyMMR4VWIHfmUApN28QGsTUhxmmMY6PsaTsem3MLWfQRrkg4Vj9bXyJfzJA1eY2CmaMB7/N\n5sqGFaGDO79JjThS2SYngbypa8n5c1vjRzp4nXjVsU07Ola0DlyKvvrtsoJuzUw/Aw/in4xoXUvH\nfcZ4EFS5kkKj2eHXZX8tK5Q9VqM7+ap5/aP3ib7OQKdAbYzjHxW3E+JxZwkUzuUxfJyn+tdamQen\nBh3Joa1xC1fiwt/NswzQyXC/Di/vWcKsGpd0C6vnszUwn7y2nt/9E86mlY5U0tUdJF4lWmqspEdn\n/9MO2PZwzd230wPpe6e/zBdWee0sJ92QghTq7LTW7pOCLM7NgCvRbR4kmO092wq+HCzNW8nOrjLY\nrT/tS+J/t67pGmU2nQ7xtQ4fA/VHkvWZf8D5UmDVHSlNfgPnL93lRxRMS9qTxQsnGWYv6OGa0D7R\nxnYnSsyfJHczOe72QMJ1thY8GXLq+i24PazA70zBBnNW3ar71a9zFK2YkrLj92RUUoWkCwqNU2c4\nHYjaGLut/6jAfdwg3fMc5EXHj64CU+N0So7OMyFVUGxU6/khP3dJ5Vr/p/k8b6d8jYf50Dmo3bqb\nTjv/zIjaIeGb9ooPyQCZFq678fO60cm0zMyCIuJZuNEwd7DljKd7VdU2Pfv9/uCV9N1RzQrMqg2D\nozTmKU5f59CVo+Jji5Rp78v6zjGrwm3c2M99iOfWvfRJfMlHz+9j2MyaW/YcDCZnOfHUkKo6pxyZ\nSmtMME8dhHXzsJ/1fCfjrgJ3ejYlFlJwl2hK+8l6L+nLy8vLoz1iG8Rx7MTXmN2a2FFP62waaQuT\nfef8iSek03Lg70wMm5+2jwTO7YpfClwTnYkHMxtqSDJddjrJXsLJ/PFYBZWE5Zid/BU/+YbtGoOf\n1nveBxzb9om8nJ2sqLWx/PsnJ0zXlq+WPlObrWu3ub/gEFbgdybA39Aj1Ca0Q2Sw0baBrTZd5prG\nrAvUaCwSWDHNnJSu8la4WPGn/ztj0hkcKkAbdRpeOzYOvNLRlmRsOQ6d7/rfFTkHovxuh5PVtLSm\n5fjXmHWUdPZ7QeRX4i2N/BakLKGDreQUOevtDC7XPtHB4Mc86ej1GA4k6rvH4T0fVbJRTgFPfSY5\nJJ9S8F5y0DnolC//LmCi1Xpgi0+mhTiWjLGd55zd97z1ufXSqeQQbwUzCdL62yGik239YP2xNVe3\nLrPrDuy2ggzib11HvLv+vF8yWeMlGe0qGtxHllEHkDN55Vze71tjGM/Cw0GMq39pn9beYoWm40uH\nQ31yjVICNcmloYIfj5nWpPBLgUNKWhCHmWx2stvRXt87uk6pHtl/Ma71l2y3x022xfvecxfYXrty\nl2g0v6yzuiB6jMNkhGn3XudRbb7Qy/iybZLfbi22fIoFDw9W4HcmkCoWY2QnKW2kTnFbGbi6ZUc2\nOao1Xhmk5Jzy/1OUflU0Oocz4VXjd8ea2HaWhU5gGujkVIUlOckJPzvnpDdV/owf18501PgpW5vW\nvF6KMcYDB6ayl6cqbR8zSQ5Q5xgnntIBTc48cU6Gtsaws11tu98J7IK3hGvRmWTS17hGnIPPjpn+\nOoptvqUKHpMKW3sr/Ugv5+oSN+nYW+Fq+TafZv+X/CRZ2NJnqU3Sk8Sx+FcyZPliUJqgCxqs1+p/\n7sMuqPFphjHm/DZN7Nc53jMd3PVJa5ocUFfKrHu8Tzr+VjvKQ60T7RF1jmnskjrus+Wcc07zutpw\njbqKm8dOb8X1vFyPVAlKQN4nXUqaO9y6AHnmT1AXejzbft6f6YYtW2N57fRIB0kG0l5LCecxDo+n\n170UYHXBY/JNnCzp7NrMtyBuhb+D9y39kfDtkrRbvLZvuuWTLXh4sAK/BQsWLFiwYMGCBQsWPFLQ\nBYzPZJwXCqzA70wgZWPTyyIMzrS44uWMYJfdTZkmZnN8lC2dO+8qCbOsZ+JBymAyU97xotrxL2Ua\nnYGaZa2YFUtrVOuUMsPVP1VsmFGuNolP7uc36/EopJ+T8eucq0+9+CU925aUMOmvjD3xnGUvu6NO\nXCPTno7ium9XCar7vMdsaGccuiw5cd3qu1UBc7uO/9wH3VHPwsdywqPANQ5/0iTJJ/ni6g3p95qk\najXHTLxwljllm/lX9/kTJTNdU/uxqtqkhzh0RzFnssx7bsc96v6sCLOK62c4a5zZkc1UnXJ/47DV\nZwvSs6auQlAvFc+7o7ldVa7GGuPwLcfV1j/x0I1bOPJ76k+Z7qphaT/Ufe5Tjt3R7QqM52SF0faJ\ne9uya/3Q6Z3ZkdhuLZI8WQfQvtXpgtlcHqPWx34E91rZNVeiio+psjWrnqU17Y7N896W/8G2rsB1\n/KYuTz5asrucp7PLqSrZze97qfLKdap2PhXA+W9zUmXB7WAFfmcCafOlZ714f4zt1+uncWdAI8Nr\nVMJjHL7swMrKz7X5mYMapwvAfM0KtAvoeOzBBiEdx7MT1z2X0gUODuRME8/R24nn+vpIn3nIz+qX\nfkA2BbB0QhygOjDo5IXXt55dSsGag/2i9fr6+uAHnY0717P6cz+ccryNfEm8YtuZg5dkk3gkZ9jA\nYCfJ9BgPjgTX2N3cybmqNknOO31Q9xMuNR7XbXbMN81L45/w5DqmPWYc/WPixc/6LOezeJiCXeNI\n/Ey/nU+PkY5EWT85kKsgxD8oPnPu0tuFC5xASv2rb7cOXkMeI5vJhPlE4FtfO950vCYuM6eSR5hn\ne7rbK6ap0wVcmy190h2L9zwO/oxrwpk/Ok+80r5PMp10ZLKxbMM5qZPTsXEfke18EycL7Bd0R0i7\no+qmzWMm2lKfopGBJsHPnHf2qcaZzeO16HyzzvYkWrb0WDd24o9t/xg5YO/o6xIxC549rMDvTOD6\n+vC1wZ3C5fcu487/0+ZMgVx6PiIZXhsQKxoHojamXeBk3DkunavkoJrWpPxmAW9n9Iijnwvhdzpn\nXZWqc7pTdtzBommsz8vLy4N+JS/EKTmrxnGrsmzHmnB1dXWwjq5ipTV2cJ7esjlzsqofg1/SkBwA\n4mf57xwj8iy1Jb1Fl2XDeKSxPb9/isVOUXKMOoNsPIt3dgrIc49dck19lNbUiYvZ/is6KI+k2+3J\nW1dUGPRw3Gp7586ddm9aPyZ5TfiQB8lxts6qtgxcy0neenEXedDpl+qb+JcSAFtz1X0HWAbrqXTf\nOn+2R+p6elkO97DtVreG/r9LXKR90QUMXusUTHXz+FoBg6mEs23azN7PriVIe7SzP/W93gyZ9r0T\nsDWH91aqCNVaMij0/koynGiYBUrs39maWVJnthf4bHcXfM1w6/RNp4M6fDx32ndJlmZzJpvET+q+\nLd2RcHs28DDGeFRgBX5nAn4degJuslQd6tpuzctxuqNczCpyTGaEy3AlBWcnb4zTMll0ZjhOtUtK\niGPwk05rZ8jTvc4IMzNI56TmsfFKhp8GkeO6Cmc+8vgexyyc6jvnrKNyXucCH2cpmB3N2go0TWMC\nVx9d6fJY7JN4NgMfp0lOcTqmxACtc2S9L0iTZYjGPckt1+g2ztzMIbEDVe1Jb/rNqgruHdhSJqtN\n4tcM34S7+eg23gOU9bSu5gNp5rWZnHaONsEV3cLNTh3n9akE6wXO150CSGA9TrkyP0gP91M5scmJ\nrz7WTxwrzek2hmTXPI7Xx1UotvG+M48LLBdJJjoZmOmpNEc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E43GMTmbS8+OnBO4Lnhms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ap6UlUfoOsfUFV/e+jzt6rqRzroewbfU1VbVf2iunnYy99+yTFfCbzHBX7btl1V1ZdW1SfWTan0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P09reEeeGs3nN1lkPW+el7UnnxSY1iszaO+5YhjzmmIMFPMdxpM7f6eeUa3bN62Rnp6fxpTzY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QzbKuPHaLsjVqvM3MzNTQ9aG5qc0jdNIVFukYGXerZlYiRmG0KM6rO9zgYcZTmy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88PV6/R2f+/8HLpfLL/lcO3/6c/e+eWa+/Xq9/oHP/f87L5fLL5uZf3lmfuvnh4zT8Xtv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\n+J611hbbbM6sFV+cUd8zQgVNee8ioBPZeDdQvDO8If43P61Mc/TolOa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PB6O112MELOsBmIBWRxnhvwffrpp4/vx+M9fr6Pb2fcDdLcP5ZpsmL5Bu4cUbYMJzBiZ5rXNoeW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\nUfLeZLDntSAkAGvOLunzw6oC1u1cV2fLM0ezK34iZUv4cE47o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R5G4+Vx2/pOQdvEl7Yu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0NXomZ52OU3McL5fLo/3rJFHG9ntImzxNa9z2YMZpdmbiVePBFDCttR7dI7TWwweF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\njjgeGE08CcA7xhjvvfGpauw1EB99ahN7rLF30Vs9GzcKIuLFWD3J7h8AuDo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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m0 = cube.moment0()\n", "m0.quicklook()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$\\mathrm{\\frac{Jy\\,km}{beam\\,s}}$" ], "text/plain": [ "Unit(\"Jy km / (beam s)\")" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m0.unit" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/spectral-cube/spectral_cube/utils.py:39: UserWarning: This function () requires loading the entire cube into memory and may therefore be slow.\n", " \"memory and may therefore be slow.\".format(str(function)))\n" ] } ], "source": [ "cube_K = cube.to(u.K)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] } ], "source": [ "m0 = cube_K.moment0()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -3.899e+03 [aplpy.core]\n", "INFO: Auto-setting vmax to 5.545e+03 [aplpy.core]\n" ] }, { "data": { "text/latex": [ "$\\mathrm{\\frac{K\\,km}{s}}$" ], "text/plain": [ "Unit(\"K km / s\")" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fWmB5KmcgGSwHgMSRfJg57qYvOWaek4aY83T0JyOU+JZoH+P497tmDr3H9jOX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ayH42nbxXsd+M7vnnf8JDU9Rl4pl7Yu7bdYb9oPYHDC8vV6tR7muLgM+aLc3ddXybyuQGkL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\ncohJlFs+2UbTeT6p16CR/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F/5TOwRKM54Elg2EHEXSIxtSHuUq22J2/B38uH1k/VAXeus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G4nMKa+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\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b3/rW2bWbm5tPBL/PAuyO4BOBFrlp0ahWr0U5uc3U5QOMYDkqnsgjoUXfvHWB95idchSU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\nbBU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hluYvHPAoEKNnFTmqNxMyQ1DRf55VTzT5PR5DY4TQkbIqs0gZsw0VhfS6aS7T566c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\nP7N5YCS3y34nvkiOlHRyvDNHyfmlo5O00MCmkUV52WW0E+97xsb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m0.quicklook()\n", "m0.unit" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] } ], "source": [ "blue_lobe_m0 = cube_K.spectral_slab(30*u.km/u.s, 55*u.km/u.s).moment0()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -1.590e+03 [aplpy.core]\n", "INFO: Auto-setting vmax to 3.309e+03 [aplpy.core]\n" ] }, { "data": { "image/png": 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cW+UYotPqZBu352jOOtT88rPdGHDQEefP6UGnl1UQxY311CZcuDyWI3hEwMi5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jvNf8qKJKcpga5VAMjJqDy51hTkJ9CWaCcI4HiqQ5cceNe/KUjHsdf5pSM5AVpnJyfqtjkvE2A7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\nN4ZO+yfpWVd+0hU6Dg4Q8rla7bfzMVJAoseQJx7c5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l3XZeZTVtGd0qLt2c3S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4Nr7vaRnXFrOCyqf2sYpOq8wpA8VINftwESuNHJOXqrp3FFM/eTzGRS4ZGeW4Nmlkms+0ueii\ne/6meUtZVpcVrHrvAW/lwWVWeKxL+XWRZWZ4HLkobLftIorTOkjRYR0P8qHZt2uygenaRFqeP8yc\n9hGPAut62Ml6tYwrcllG5Znz4SLzToZuY4pGq54g37o+KEcfh+9sT7f15MmTurl5aOLYL48wkqdp\nzi+X947iTRmuLqvj25k5Hh9dZaP0ua70kpNnz57Z42qaYXa6iUdK3frk8UDd/9yXeuTbPYfk5lOz\ngS4jmPR6Is00dfmWqZ8Xffbs2cvnMncdQtpXR9O6SmWm/5Nu4p5xejPtS2cfEq+83uvfZbid/Vnt\nZW23y7v7jlSum5ubBzZL1w/3XDoCvnM0nPrDHZnlOtF23Rpydrs/KYPz43bWzA6AUp8orXV3mqb3\n1K5tdPamdffrAnonZTqB4CMhvgDBKZ633367qvJxGFd35SAl0MS+mwgWqAydI9mfdDL0Po9OsF3e\n03HYcVwdEFJe9Lo7ItXtr464kBLgpixaRmV3xljrpaNNPU9ujPQYTCIaRndv4mvHIeN60L6SEW2Z\n3HjSWUh7YgKiyjf3iCPle3LO1fDrMUTn8Klc7+dIjQYImpdrHV7qF/10c8V7fLsijyeyjx6PnkcF\nhjpmaVxWICzV0blw8vBay9Hz7kDNpBuSTmA53acKnqiXekz0CGffUx2r958+fVp3d3d1e3tbt7e3\n9xxgXc8rR845gboHCe6dzpradwCl2+QYOlvijuVpe06GJjcGzp4lcvPu6nE/TPt0BRInm07ZErV8\nPCaegL/ysEPOrtOW6BzrmFN3E5xpoHcVHHTBjJXeb18tAUFnd7sv5Vu/J33Q9VbrLfluybbo9XQU\n2R3pZ/vU3Q7A6pyc9GboBIKPhFphTU6BOnbqhKRy/K4KwGXDpn7VMOhLRQhgWF+d3hR5Sg6YowS2\nyAvl3SECQG1DHT869K6drjsBrgkE0PBNYFfHfXJo1MlLIJvfCSAm0KL3d+eT7TkHl3JovwRMOnYK\nQhzI7v9Tm5TROSuMHCufnI/kAO+sg+6D5J7tSM/Dm83oKQAAIABJREFUTAB6Na+pvwTYu73OKDmH\nO8nTz4C538OjrN0fnfWUpXKkjtu0/tx33Sc7uiadGEjtksfb29uXPOp+7ug92ySA7sxZ1fO5URDY\nWUjtTwGk8kIZppMAdNT1+y6YYll15LV/59jTrqY+qUu7roJBx5MDe/rJOjo27Ee/T/s+2RXKkNay\n43uaC/JLG6V9c72sKMmZbFlaCwlgu3WmGUUHelbr0rVL/Zfsn1uzaS0xAzvZicSnk4f6SgP7Sk5f\nu32ifNCnXAHBVcBil15HGx9WOoHgI6F2fJjtc44BN9au0qUzps6y28AkzVrSYejvWt8pRueQOyVI\nY+qMqjorLJMcQuew3d3d3XM4tZ6OmYLCvqf9ODmePHlSt7e3DxxVUpLfKXItl16qksjdc4raGQby\nogDZOQXJiXb8OFDqjE06jtTk5oFjxzXAI1npu5sT13bV/WPIvW8mJ39yVNOcuWvMoOn3zgzqnlX5\nV5lfXfvO8XcyOMdXI/dpffX97ufm5uYeeOBYK2jh/Kbvynvvbz0KSRDEIJBSkoVjufo+OeeqazSz\n1zwrmOPROR0nrdd/zAhynShvbj1yPbk51nlNIHBywHX/6HcFThPA2eE/OazuVIwDqOQvyZB46z3m\njomubF6Szdlofp/0r2uPf6y30tPXkK4hyufWeRPHcQI1rQO6Xt9Lc+z8CO6R1ieOUqZN+9V2WpZ0\n8oB2zfE39aFt6bi4bCFti9bTtdaPMrnj/Se9XjpH+JHQl3zJl9Q777xTP/zDP1yf+tSnXioQGh0l\ndeCvVbpUbs6IMkLWPNAoJqUyOeCqYB3Y0bauBYiOf9aZHDUX/U5AbnI8+rOf03EK3lEbEAfQ6MRr\nP+SZ2Yd2+phFdsa127y9vR0NCvlTw6zypmeh6NR3G7qunJFxfEyZxEkGzrfj0zlS3W4KyiiQ0Dcd\nOgDpyI0NHRg6sJOcjjim1AlcSwlIkU9mNHTfa+BFg0tsW9dhj19fU757HHrfOIdsGhtdp1xHfV/B\nk8qt8qa2OVbuu/JK8My2CFD5rKI76qyOqdoWBYMKEp3Oc7w7h5/zrv/rsbo0Vivgds3eZjnKwLUx\nrZW+xqN8q/51rNJeZ1kXaHD6bdJ5ru2kS52zr5/an+oEt+51nJocoNB703VtbwVulJJMrizXpOu7\niWPv1ivHl/6P6m7t3+l8DUQxyEMdrbIT1Gnfqz3lxkh1kxszpdb/X/EVX1HvvvtuffrTn66v//qv\nt2VPev90AsFHQt/93d9dn/nMZ+ru7r1Xdje5oym6qV3EbMfRJLHOFP0m7QBRJ4NGEBMfyWGaZEiK\nLYHBBBqZaeA9/d85LqxH+d09Z6DpBCYZlX8t2w5yGxMHqlx704s4SGoYaKz04X/27+alne9pjtJ6\n7etdPv0AeZKZ1+hsufvJKPY9BRBTBLj7cWCAY6b7JmWB2Cblc2tg2m86ttpeWr9NevIg7XUFMg38\nOpqs11Jdd0ohgVy3Z92ecGWczLt7ZCIdzx2HV8f77u75sVFG3wkC+7PraUaQ45b0nPLQNB17dvzv\nAEG9PgWvrgGGyQnm+qbznPib+k7yO0d6ChgoaHC2aPX4gRsrrvPJfjsQqnw43UWafh+ZgR3yyhcD\ncdwIFKcxdTrQrbeU0XTz6/ZjItev6u6VTa+6f1pnF1y7zHx/pwxp3Ssf0zjqOPzpP/2n6xOf+MR2\nsOSkV6PzCcyTTjrppJNOOumkk0466aSPGJ0ZwUdCHYXRF1xoZsRF/VyqX6NULpKzE72beGQ9zUi6\nDFk6jqB86vN3lD1FHVOkWcm9LGSK+JP3Lu+ioF3GHSt0GcGUTUq8Mfrnyk9jsOovRdhXkbkUuXYR\ndM3waFas51uPFqXsYFqj16xbF/G/Rj6+vIHz5TJxrFNVD7KCKXLdfTC76rJQKpceuVQ+2S7vpXWi\n5PbN9AIAEn+6pclFtTXbqWuo/8ivjnV/d0eq3XeVjbomZc67jZ1sjMtAUVal6fgb1wv1kMtiqN7i\n8U/NJPZ37keuN8eL6n6u1en4IPW9jpfe1+turTLru9KLqU+9P83LTtZP21xl21jHZevSfqWtSbSz\nx1151dt9XdcS+U2ZpJSBIu+rdafPniqp/XGZr25bdbXyvtJlWk7X0sqfII+uXO+f6VlI1zbrXaOP\ntT2XRe1Pd3Tf2VP9dHr0VXg7aZ9OIPhIiEpLn6FpIsDqv2SI2ObKkUykx8+oyOn8JgXeZZNzSgXX\nZXk8VfmdDL5TqJPzyzHh/+7YmZZNRpgOUTqysXLanfM3zQWv0UF2jopr043damyb1DlVp57HGznP\nXLvJ2XHBDo6Dtploctgdf+6ZwG6/j+Ydx3svZ0nPdiSeOP50orgGkwxpzbj+kjPHcrr/Vr8zlYy/\n7hvnMEwArNvVe3SU+5pSWs9ubys/6Vhw0h/9fXLoeJ+8tVOXnGp3rXlPDrY+B5iuJXlXMiT+lC9X\n14Henb2Rgm8uaMhyJAdAtbzymQBG6lODGOQ1le2205u5+emO7SZZHU2AY9qDXYb7ZXrmd5pb994B\n8tj96f4neJrmqdt2NMnp5O6207ObCWA5mp5lnOaSezA9g5r8g0mn6JpPjxys/EDnMyU5rlmzUzsf\nVTqB4COhpCgUCDql33WnKJVzJJPzu1JYVMRsW/lzDpoS71GJqmJLn6k9p6BWRi85p0rOCVgZEWek\nCar1+SeXfXJyrwyfA1hap9vS+aQzRRA2Pd9GoECwqTLpH7Os5EPvTVm1FdjTMSNxPVPGJv2Bbr4A\nRvnR7+1gp7lzwCA5dnRclX+CfZ0LtuWc+GlvpXbSmCXnwTkTCkwUCHIfaqDJ6Q59DjbxSfl73jTD\nkfb+NDbXOCHUTcwK6R8BQsoYOp3R33tMXEbQPTussiddsHJw9brqFn3Oiw6/c0KVVFc4/qreyzy7\n/ZNshtpFd5/ZmklHrBzxxIsGwHoMqJOSY+3m+3VQz5GTxfFV9Xz8JzCoRFuRwCB1JNtQXTr1N9Gk\nz6agjPY5rb0uOz0rqUS7xjWvn6usIMfOjROBnNM3K13uAqQcg5PeDJ1A8JGRbhp17Oj0sexOZIjl\nUt9J6bZySJkApxjUuZoMIfunbDxy5EBsapft7AC3XSdQ21TetA4BWBOPpejnbhaLQJC8Nen4sVz3\n1Y6ayt/zrWXp5NNQKU3RRHXqOPcp6NE8TO07Y7hae2zXydF9uJ9cIQ80yupUqbxcE45PB95Ytss5\n583Jwf3lnGGCpSRr0heuLOsxuMDggL4YQdckAYoCnNX6UdK+CDKdrkjOqH7ScZ3GRmV3AIgO4Uon\nOJDQ46hgsOrhz25QFtee452O8nS8lb8r6T4JipyM6gQ7eZMtSeM2fV/ZOmebtVzSy+lYcTri6/rg\nuuXYrGgC8pfLxfoSx/H85V/98wBNuv9SuyubrXrT6RsHytIe2QWFkz/hbB0/ue81m+vkYpsru8R2\nlG+d6xXYo383BZU0AZGI9o26rPtTnePodQUvPsqA8wSCj4T66FwCebqx1ClKSsQZKudAqTJSB2Zy\nZCY+yYOCBGcgUv1knHkt3XPAS43JylluojFKjjL5T30qIKMD2n31D2n3NWdsJsOVjk7p2Og1fif/\nzAS7YzmJ0jgnZ829vp68OH5p2NwzYrr+yVeKpKock8PrxrkNqv5+X/Oqc+uAYJJ/AgMMLExlSLpX\nVf4puLAyvM4po9Om7fTa0p80UKeQWS+ng5jZmoBM3+efK0+dqP26cdN6VfOcrIAG/185ccoPQR8z\ngk4PJeK9CSC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LdXNzc+93Ncm/OnTkb+WAssxqzbrfGnNg1+3D5tcBhL6ur/enftLyrg7L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\ndmm+3NpwmQKVxx196n7cc203Nzd1e3u7PH6WnsV0NK0t3pvK6Sd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SVT+xqp5X1R/f9/3F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fArXrwcXzdBAOm8T0Aq2bkEhrrfrC/NEW+HfZOASxd8cvJtzWnepzHgsS6Im9qkrqaNoix+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1kuTtc6MFPshP0s08p3puAdFp7jRdOfG31pv+K9CzA4kck7Z1u7U31Z3AIn2BFOyRbkqy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\nqVs54O7epO9W4+7mIMnS8+l+5kF5pz4QXLv2qDNWfd+9P+2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Bov1jPZ2ba4Mduk84v/Rl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1l/AmPO8K1kVUWrmTHnjLmz+iznjGbip3PAQAGdjbu7u6r6xBlL7aUMjhoZBZqO2D86cqTES+cj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\nhMwZmb1cQI3YSA5FTrfjSabH7fHohS1a5kwRjUP7UJmlzuJ55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X+qIOH+RxntJzKcsvFq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "blue_lobe_m0.quicklook()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "masked_cube = cube_K.with_mask(cube_K > 50*u.K)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] } ], "source": [ "blue_lobe_masked_m0 = masked_cube.spectral_slab(30*u.km/u.s, 55*u.km/u.s).moment0()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -2.479e+02 [aplpy.core]\n", "INFO: Auto-setting vmax to 3.568e+03 [aplpy.core]\n" ] }, { "data": { "image/png": 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ez4sx/onb5ns0mplbk/SKGOMnKp7j0yR9u6TvmnIcf0vS10p6lt0WY3yHpMed4GXZPp8s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jHLstuyj2sA/nsAXwxgBuBHAfy1LMu6657LtgXK7wD48W22dTw8WMJCy3ur4tmZkmq1iqeeegrA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leV4ST9F0yzqfO7bBdY1Go6C0yfVEv99Ht9ut9axjzZ8KpJC0xVIxp9MpRqNRsBfQNFA9fsyy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\nJEmSpJqzFFOSJElSrVi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "blue_lobe_masked_m0.quicklook()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 } spectral-cube-0.4.3/docs/_templates/0000755000077000000240000000000013261442571017362 5ustar adamstaff00000000000000spectral-cube-0.4.3/docs/_templates/autosummary/0000755000077000000240000000000013261442571021750 5ustar adamstaff00000000000000spectral-cube-0.4.3/docs/_templates/autosummary/base.rst0000644000077000000240000000005212337446544023420 0ustar adamstaff00000000000000{% extends "autosummary_core/base.rst" %} spectral-cube-0.4.3/docs/_templates/autosummary/class.rst0000644000077000000240000000005312337446544023614 0ustar adamstaff00000000000000{% extends "autosummary_core/class.rst" %} spectral-cube-0.4.3/docs/_templates/autosummary/module.rst0000644000077000000240000000005412337446544023775 0ustar adamstaff00000000000000{% extends "autosummary_core/module.rst" %} spectral-cube-0.4.3/docs/accessing.rst0000644000077000000240000000560012551776560017730 0ustar adamstaff00000000000000Accessing data ============== Once you have initialized a :meth:`~spectral_cube.SpectralCube` instance, either directly or by reading in a file, you can easily access the data values and the world coordinate information. Data values ----------- You can access the underlying data using the ``unmasked_data`` array which is a Numpy-like array:: >>> slice_unmasked = cube.unmasked_data[0,:,:] # doctest: +SKIP The order of the dimensions of the ``unmasked_data`` array is deterministic - it is always ``(n_spectral, n_y, n_x)`` irrespective of how the cube was stored on disk. .. note:: The term ``unmasked`` indicates that the data is the raw original data from the file. :meth:`~spectral_cube.SpectralCube` also allows masking of values, which is discussed in :doc:`masking`. If a slice is not specified, the object returned is not strictly a Numpy array, and will not work with all functions outside of the ``spectral_cube`` package that expect Numpy arrays. In order to extract a normal Numpy array, instead specify a mask of ``[:]`` which will force the object to be converted to a Numpy array (the compulsory slicing is necessary in order to avoid memory-related issues with large data cubes). World coordinates ----------------- Given a cube object, it is straightforward to find the coordinates along the spectral axis:: >>> cube.spectral_axis # doctest: +SKIP [ -2.97198762e+03 -2.63992044e+03 -2.30785327e+03 -1.97578610e+03 -1.64371893e+03 -1.31165176e+03 -9.79584583e+02 -6.47517411e+02 ... 3.15629983e+04 3.18950655e+04 3.22271326e+04 3.25591998e+04 3.28912670e+04 3.32233342e+04] m / s The default units of a spectral axis are determined from the FITS header or WCS object used to initialize the cube, but it is also possible to change the spectral axis (see :doc:`manipulating`). More generally, it is possible to extract the world coordinates of all the pixels using the :attr:`~spectral_cube.SpectralCube.world` property, which returns the spectral axis then the two positional coordinates in reverse order (in the same order as the data indices). >>> velo, dec, ra = cube.world[:] # doctest: +SKIP In order to extract coordinates, a slice (such as ``[:]`` above) is required. Using ``[:]`` will return three 3-d arrays with the coordinates for all pixels. Using e.g. ``[0,:,:]`` will return three 2-d arrays of coordinates for the first spectral slice. If you forget to specify a slice, you will get the following error: >>> velo, dec, ra = cube.world # doctest: +SKIP ... Exception: You need to specify a slice (e.g. ``[:]`` or ``[0,:,:]`` in order to access this property. In the case of large data cubes, requesting the coordinates of all pixels would likely be too slow, so the slicing allows you to compute only a subset of the pixel coordinates (see :doc:`big_data` for more information on dealing with large data cubes). spectral-cube-0.4.3/docs/api.rst0000644000077000000240000000064713233661037016536 0ustar adamstaff00000000000000API Documentation ================= .. automodapi:: spectral_cube :no-inheritance-diagram: :inherited-members: .. automodapi:: spectral_cube.ytcube :no-inheritance-diagram: :no-inherited-members: .. automodapi:: spectral_cube.io.casa_masks :no-inheritance-diagram: :no-inherited-members: .. automodapi:: spectral_cube.lower_dimensional_structures :no-inheritance-diagram: :no-inherited-members: spectral-cube-0.4.3/docs/arithmetic.rst0000644000077000000240000000160113161003310020065 0ustar adamstaff00000000000000Spectral Cube Arithmetic ======================== Simple arithmetic operations between cubes and scalars, broadcastable numpy arrays, and other cubes are possible. However, such operations should be performed with caution because they require loading the whole cube into memory and will generally create a new cube in memory. Examples:: >>> import astropy.units as u >>> from spectral_cube import SpectralCube >>> cube = SpectralCube.read('adv.fits') # doctest: +SKIP >>> cube2 = cube * 2 # doctest: +SKIP >>> cube3 = cube + 1.5*u.K # doctest: +SKIP >>> cube4 = cube2 + cube3 # doctest: +SKIP Each of these cubes is a new cube in memory. Note that for addition and subtraction, the units must be equivalent to those of the cube. Please see :ref:`doc_handling_large_datasets` for details on how to perform arithmetic operations on a small subset of data at a time. spectral-cube-0.4.3/docs/beam_handling.rst0000644000077000000240000000567413242700604020534 0ustar adamstaff00000000000000Handling Beams ============== If you are using radio data, your cubes should have some sort of beam information included. ``spectral-cube`` handles beams using the `radio_beam `_ package. There are two ways beams can be stored in FITS files: as FITS header keywords (``BMAJ``, ``BMIN``, and ``BPA``) or as a ``BinTableHDU`` extension. If the latter is present, ``spectral-cube`` will return a `~spectral_cube.spectral_cube.VaryingResolutionSpectralCube` object. For the simpler case of a single beam across all channels, the presence of the beam allows for direct conversion of a cube with Jy/beam units to surface brightness (K) units. Note, however, that this requires loading the entire cube into memory!:: >>> cube.unit # doctest: +SKIP Unit("Jy / beam") >>> kcube = cube.to(u.K) # doctest: +SKIP >>> kcube.unit # doctest: +SKIP Unit("K") Adding a Beam ------------- If your cube does not have a beam, a custom beam can be attached given:: >>> new_beam = Beam(1. * u.deg) # doctest: +SKIP >>> new_cube = cube.with_beam(new_beam) # doctest: +SKIP >>> new_cube.beam # doctest: +SKIP Beam: BMAJ=3600.0 arcsec BMIN=3600.0 arcsec BPA=0.0 deg This is handy for synthetic observations, which initially have a point-like beam:: >>> point_beam = Beam(0 * u.deg) # doctest: +SKIP >>> new_cube = synth_cube.with_beam(point_beam) # doctest: +SKIP Beam: BMAJ=0.0 arcsec BMIN=0.0 arcsec BPA=0.0 deg The cube can then be convolved to a new resolution:: >>> new_beam = Beam(60 * u.arcsec) # doctest: +SKIP >>> conv_synth_cube = synth_cube.convolve_to(new_beam) # doctest: +SKIP >>> conv_synth_cube.beam # doctest: +SKIP Beam: BMAJ=60.0 arcsec BMIN=60.0 arcsec BPA=0.0 deg Beam can also be attached in the same way for `~spectral_cube.Projection` and `~spectral_cube.Slice` objects. Multi-beam cubes ---------------- Varying resolution (multi-beam) cubes are somewhat trickier to work with in general, though unit conversion is easy. You can perform the same sort of unit conversion with `~spectral_cube.spectral_cube.VaryingResolutionSpectralCube` s as with regular `~spectral_cube.spectral_cube.SpectralCube` s; ``spectral-cube`` will use a different beam and frequency for each plane. You can identify channels with bad beams (i.e., beams that differ from a reference beam, which by default is the median beam) using `~spectral_cube.spectral_cube.VaryingResolutionSpectralCube.identify_bad_beams` (the returned value is a mask array where ``True`` means the channel is good), mask channels with undesirable beams using `~spectral_cube.spectral_cube.VaryingResolutionSpectralCube.mask_out_bad_beams`, and in general mask out individual channels using `~spectral_cube.spectral_cube.VaryingResolutionSpectralCube.mask_channels`. For other sorts of operations, discussion of how to deal with these cubes via smoothing to a common resolution is in the :doc:`smoothing` document. spectral-cube-0.4.3/docs/big_data.rst0000644000077000000240000001244313242700604017506 0ustar adamstaff00000000000000.. _doc_handling_large_datasets: Handling large datasets ======================= .. currentmodule:: spectral_cube .. TODO: we can move things specific to large data and copying/referencing here. The :class:`SpectralCube` class is designed to allow working with files larger than can be stored in memory. To take advantage of this and work effectively with large spectral cubes, you should keep the following three ideas in mind: - Work with small subsets of data at a time. - Minimize data copying. - Minimize the number of passes over the data. Work with small subsets of data at a time ----------------------------------------- Numpy supports a *memory-mapping* mode which means that the data is stored on disk and the array elements are only loaded into memory when needed. ``spectral_cube`` takes advantage of this if possible, to avoid loading large files into memory. Typically, working with NumPy involves writing code that operates on an entire array at once. For example:: x = y = np.sum(np.abs(x * 3 + 10), axis=0) Unfortunately, this code creates several temporary arrays whose size is equal to ``x``. This is infeasible if ``x`` is a large memory-mapped array, because an operation like ``(x * 3)`` will require more RAM than exists on your system. A better way to compute y is by working with a single slice of ``x`` at a time:: y = np.zeros_like(x[0]) for plane in x: y += np.abs(plane * 3 + 10) Many methods in :class:`SpectralCube` allow you to extract subsets of relevant data, to make writing code like this easier: - :meth:`SpectralCube.filled_data`, :meth:`SpectralCube.unmasked_data`, :meth:`SpectralCube.world` all accept Numpy style slice syntax. For example, ``cube.filled_data[0:3, :, :]`` returns only the first 3 spectral channels of the cube, with masked elements replaced with ``cube.fill_value``. - :meth:`SpectralCube` itself can be sliced to extract subcubes - :meth:`SpectralCube.spectral_slab` extracts a subset of spectral channels. Many methods in :class:`SpectralCube` iterate over smaller chunks of data, to avoid large memory allocations when working with big cubes. Some of these have a ``how`` keyword parameter, for fine-grained control over how much memory is accessed at once. ``how='cube'`` works with the entire array in memory, ``how='slice'`` works with one slice at a time, and ``how='ray'`` works with one ray at a time. As a user, your best strategy for working with large datasets is to rely on builtin methods to :class:`SpectralCube`, and to access data from :meth:`~SpectralCube.filled_data` and :meth:`~SpectralCube.unmasked_data` in smaller chunks if possible. .. warning :: At the moment, :meth:`~SpectralCube.argmax` and :meth:`~SpectralCube.argmin`, are **not** optimized for handling large datasets. Minimize Data Copying --------------------- Methods in :meth:`SpectralCube` avoid copying as much as possible. For example, all of the following operations create new cubes or masks without copying any data:: >>> mask = cube > 3 # doctest: +SKIP >>> slab = cube.spectral_slab(...) # doctest: +SKIP >>> subcube = cube[0::2, 10:, 0:30] # doctest: +SKIP >>> cube2 = cube.with_fill(np.nan) # doctest: +SKIP >>> cube2 = cube.apply_mask(mask) # doctest: +SKIP Minimize the number of passes over the data ------------------------------------------- Accessing memory-mapped arrays is much slower than a normal array, due to the overhead of reading from disk. Because of this, it is more efficient to perform computations that iterate over the data as few times as possible. An even subtler issue pertains to how the 3D or 4D spectral cube is arranged as a 1D sequence of bytes in a file. Data access is much faster when it corresponds to a single contiguous scan of bytes on disk. For more information on this topic, see `this tutorial on Numpy strides `_. Recipe for large cube operations that can't be done in memory ------------------------------------------------------------- Sometimes, you will need to run full-cube operations that can't be done in memory and can't be handled by spectral-cube's built in operations. An example might be converting your cube from Jy/beam to K when you have a very large (e.g., >10GB) cube. Handling this sort of situation requires several manual steps. First, hard drive space needs to be allocated for the output data. Then, the cube must be manually looped over using a strategy that holds only limited data in memory.:: >>> import shutil # doctest: +SKIP >>> from spectral_cube import SpectralCube # doctest: +SKIP >>> from astropy.io import fits # doctest: +SKIP >>> cube = SpectralCube.read('file.fits') # doctest: +SKIP >>> # this copy step is necessary to allocate memory for the output >>> shutil.copy('file.fits', 'newfile.fits') # doctest: +SKIP >>> outfh = fits.open('newfile.fits', mode='update') # doctest: +SKIP >>> jtok_factors = cube.jtok_factors() # doctest: +SKIP >>> for index,(slice,factor) in enumerate(zip(cube,factors)): # doctest: +SKIP ... outfh[0].data[index] = slice * factor # doctest: +SKIP ... outfh.flush() # write the data to disk # doctest: +SKIP >>> outfh[0].header['BUNIT'] = 'K' # doctest: +SKIP >>> outfh.flush() # doctest: +SKIP spectral-cube-0.4.3/docs/conf.py0000644000077000000240000001525113161003310016507 0ustar adamstaff00000000000000# -*- coding: utf-8 -*- # Licensed under a 3-clause BSD style license - see LICENSE.rst # # Astropy documentation build configuration file. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this file. # # All configuration values have a default. Some values are defined in # the global Astropy configuration which is loaded here before anything else. # See astropy.sphinx.conf for which values are set there. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('..')) # IMPORTANT: the above commented section was generated by sphinx-quickstart, but # is *NOT* appropriate for astropy or Astropy affiliated packages. It is left # commented out with this explanation to make it clear why this should not be # done. If the sys.path entry above is added, when the astropy.sphinx.conf # import occurs, it will import the *source* version of astropy instead of the # version installed (if invoked as "make html" or directly with sphinx), or the # version in the build directory (if "python setup.py build_sphinx" is used). # Thus, any C-extensions that are needed to build the documentation will *not* # be accessible, and the documentation will not build correctly. import datetime import os import sys try: import astropy_helpers except ImportError: # Building from inside the docs/ directory? if os.path.basename(os.getcwd()) == 'docs': a_h_path = os.path.abspath(os.path.join('..', 'astropy_helpers')) if os.path.isdir(a_h_path): sys.path.insert(1, a_h_path) # Load all of the global Astropy configuration from astropy_helpers.sphinx.conf import * from astropy.extern import six # Get configuration information from setup.cfg try: from ConfigParser import ConfigParser except ImportError: from configparser import ConfigParser conf = ConfigParser() conf.read([os.path.join(os.path.dirname(__file__), '..', 'setup.cfg')]) setup_cfg = dict(conf.items('metadata')) # -- General configuration ---------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.2' # To perform a Sphinx version check that needs to be more specific than # major.minor, call `check_sphinx_version("x.y.z")` here. # check_sphinx_version("1.2.1") # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns.append('_templates') # This is added to the end of RST files - a good place to put substitutions to # be used globally. rst_epilog += """ """ intersphinx_mapping['astroquery'] = ('http://astroquery.readthedocs.org/en/latest/', None) # -- Project information ------------------------------------------------------ # This does not *have* to match the package name, but typically does project = setup_cfg['package_name'] author = setup_cfg['author'] copyright = '{0}, {1}'.format( datetime.datetime.now().year, setup_cfg['author']) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. __import__(setup_cfg['package_name']) package = sys.modules[setup_cfg['package_name']] # The short X.Y version. version = package.__version__.split('-', 1)[0] # The full version, including alpha/beta/rc tags. release = package.__version__ # -- Options for HTML output --------------------------------------------------- # A NOTE ON HTML THEMES # The global astropy configuration uses a custom theme, 'bootstrap-astropy', # which is installed along with astropy. 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List of tuples # (source start file, name, description, authors, manual section). man_pages = [('index', project.lower(), project + u' Documentation', [author], 1)] ## -- Options for the edit_on_github extension ---------------------------------------- if eval(setup_cfg.get('edit_on_github')): extensions += ['astropy_helpers.sphinx.ext.edit_on_github'] versionmod = __import__(setup_cfg['package_name'] + '.version') edit_on_github_project = setup_cfg['github_project'] if versionmod.version.release: edit_on_github_branch = "v" + versionmod.version.version else: edit_on_github_branch = "master" edit_on_github_source_root = "" edit_on_github_doc_root = "docs" nitpicky = True nitpick_ignore = [] for line in open('nitpick-exceptions'): if line.strip() == "" or line.startswith("#"): continue dtype, target = line.split(None, 1) target = target.strip() nitpick_ignore.append((dtype, six.u(target))) spectral-cube-0.4.3/docs/continuum_subtraction.rst0000644000077000000240000000416613233661037022423 0ustar adamstaff00000000000000Continuum Subtraction ===================== A common task with data cubes is continuum identification and subtraction. For line-rich cubes where the continuum is difficult to identify, you should use `statcont `_. For single-line cubes, the process is much easier. First, the simplest case is when you have a single line that makes up a small fraction of the total observed band, e.g., a narrow line. In this case, you can use a simple median approximation for the continuum.:: >>> med = cube.median(axis=0) # doctest: +SKIP >>> med_sub_cube = cube - med # doctest: +SKIP The second part of this task may complain that the cube is too big. If it does, you can still do the above operation by first setting ``cube.allow_huge_operations=True``, but be warned that this can be expensive. For a more complicated case, you may want to mask out the line-containing channels. This can be done using a spectral boolean mask.:: >>> from astropy import units as u # doctest: +SKIP >>> import numpy as np # doctest: +SKIP >>> spectral_axis = cube.with_spectral_unit(u.km/u.s).spectral_axis # doctest: +SKIP >>> good_channels = (spectral_axis < 25*u.km/u.s) | (spectral_axis > 45*u.km/u.s) # doctest: +SKIP >>> masked_cube = cube.with_mask(good_channels[:, np.newaxis, np.newaxis]) # doctest: +SKIP >>> med = masked_cube.median(axis=0) # doctest: +SKIP >>> med_sub_cube = cube - med # doctest: +SKIP The array ``good_channels`` is a simple 1D numpy boolean array that is ``True`` for all channels below 25 km/s and above 45 km/s, and is ``False`` for all channels in the range 25-45 km/s. The indexing trick ``good_channels[:, np.newaxis, np.newaxis]`` (or equivalently, ``good_channels[:, None, None]``) is just a way to tell the cube which axes to project along. In more recent versions of ``spectral-cube``, the indexing trick is not necessary. The median in this case is computed only over the specified line-free channels. Any operation can be used to compute the continuum, such as the ``mean`` or some ``percentile``, but for most use cases, the ``median`` is fine. spectral-cube-0.4.3/docs/creating_reading.rst0000644000077000000240000000602313161003310021224 0ustar adamstaff00000000000000Creating/reading spectral cubes =============================== Importing --------- The :class:`~spectral_cube.SpectralCube` class is used to represent 3-dimensional datasets (two positional dimensions and one spectral dimension) with a World Coordinate System (WCS) projection that describes the mapping from pixel to world coordinates and vice-versa. The class is imported with:: >>> from spectral_cube import SpectralCube Reading from a file ------------------- In most cases, you are likely to read in an existing spectral cube from a file. The reader is designed to be able to deal with any arbitrary axis order and always return a consistently oriented spectral cube (see :doc:`accessing`). To read in a file, use the :meth:`~spectral_cube.SpectralCube.read` method as follows:: >>> cube = SpectralCube.read('L1448_13CO.fits') # doctest: +SKIP This will always read the Stokes I parameter in the file. For information on accessing other Stokes parameters, see :doc:`stokes`. .. note:: In most cases, the FITS reader should be able to open the file in *memory-mapped* mode, which means that the data is not immediately read, but is instead read as needed when data is accessed. This allows large files (including larger than memory) to be accessed. However, note that certain FITS files cannot be opened in memory-mapped mode, in particular compressed (e.g. ``.fits.gz``) files. See the :doc:`big_data` page for more details about dealing with large data sets. Direct Initialization --------------------- If you are interested in directly creating a :class:`~spectral_cube.SpectralCube` instance, you can do so using a 3-d Numpy-like array with a 3-d :class:`~astropy.wcs.WCS` object:: >>> cube = SpectralCube(data=data, wcs=wcs) # doctest: +SKIP Here ``data`` can be any Numpy-like array, including *memory-mapped* Numpy arrays (as mentioned in `Reading from a file`_, memory-mapping is a technique that avoids reading the whole file into memory and instead accessing it from the disk as needed). Hacks for simulated data ------------------------ If you're working on synthetic images or simulated data, where a location on the sky is not relevant (but the frequency/wavelength axis still is!), a hack is required to set up the `world coordinate system `_. The header should be set up such that the projection is cartesian, i.e.:: CRVAL1 = 0 CTYPE1 = 'RA---CAR' CRVAL2 = 0 CTYPE2 = 'DEC--CAR' CDELT1 = 1.0e-4 //degrees CDELT2 = 1.0e-4 //degrees CUNIT1 = 'deg' CUNIT2 = 'deg' Note that the x/y axes must always have angular units (i.e., degrees). If your data are really in physical units, you should note that in the header in other comments, but ``spectral-cube`` doesn't care about this. If the frequency axis is irrelevant, ``spectral-cube`` is probably not the right tool to use; instead you should use `astropy.io.fits `_ or some other file reader directly. spectral-cube-0.4.3/docs/errors.rst0000644000077000000240000001047713242700604017275 0ustar adamstaff00000000000000.. doctest-skip-all Explanations of commonly-encountered error messages =================================================== Beam parameters differ ---------------------- If you are using spectral cubes produced by CASA's tclean, it may have a different *beam size* for each channel. In this case, it will be loaded as a `~spectral_cube.VaryingResolutionSpectralCube` object. If you perform any operations spanning the spectral axis, for example ``cube.moment0(axis=0)`` or ``cube.max(axis=0)``, you may encounter errors like this one: .. code:: Beam srs differ by up to 1.0x, which is greater than the threshold 0.01. This occurs if the beam sizes are different by more than the specified threshold factor. A default threshold of 1% is set because for most interferometric images, beam differences on this scale are negligible (they correspond to flux measurement errors of 10^-4). To inspect the beam properties, look at the ``beams`` attribute, for example: .. code:: >>> cube.beams [Beam: BMAJ=1.1972888708114624 arcsec BMIN=1.0741511583328247 arcsec BPA=72.71219635009766 deg, Beam: BMAJ=1.1972869634628296 arcsec BMIN=1.0741279125213623 arcsec BPA=72.71561431884766 deg, Beam: BMAJ=1.1972919702529907 arcsec BMIN=1.0741302967071533 arcsec BPA=72.71575164794922 deg, ... Beam: BMAJ=1.1978825330734253 arcsec BMIN=1.0744788646697998 arcsec BPA=72.73623657226562 deg, Beam: BMAJ=1.1978733539581299 arcsec BMIN=1.0744799375534058 arcsec BPA=72.73489379882812 deg, Beam: BMAJ=1.197875738143921 arcsec BMIN=1.0744699239730835 arcsec BPA=72.73745727539062 deg] In this example, the beams differ by a tiny amount that is below the threshold. However, sometimes you will encounter cubes with dramatically different beam sizes, and spectral-cube will prevent you from performing operations along the spectral axis with these beams because such operations are poorly defined. There are several options to manage this problem: 1. Increase the threshold. This is best done if the beams still differ by a small amount, but larger than 1%. To do this, set ``cube.beam_threshold = [new value]``. This is the `"tape over the check engine light" `_ approach; use with caution. 2. Convolve the cube to a common resolution using `~spectral_cube.SpectralCube.convolve_to`. This is again best if the largest beam is only slightly larger than the smallest. 3. Mask out the bad channels. For example: .. code:: good_beams = cube.identify_bad_beams(threshold=0.1) mcube = cube.mask_out_bad_beams(threshold=0.1) Moment-2 or FWHM calculations give unexpected NaNs -------------------------------------------------- It is fairly common to have moment 2 calculations return NaN values along pixels where real values are expected, e.g., along pixels where both moment0 and moment1 return real values. Most commonly, this is caused by "bad baselines", specifically, by large sections of the spectrum being slightly negative at large distances from the centroid position (the moment 1 position). Because moment 2 weights pixels at larger distances more highly (as the square of the distance), slight negative values at large distances can result in negative values entering the square root when computing the line width or the FWHM. The solution is either to make a tighter mask, excluding the pixels far from the centroid position, or to ensure that the baseline does not have any negative systematic offset. Looking at images with matplotlib --------------------------------- Matplotlib accesses a lot of hidden properties of arrays when plotting. If you try to show a slice with ``imshow``, you may encounter the WCS-related error:: NotImplementedError: Reversing an axis is not implemented. If you see this error, the only solution at present is to specify ``origin='lower'``, which is the standard for images anyway. For example:: import pylab as pl pl.imshow(cube[5,:,:], origin='lower') should work, where ``origin='upper'`` will not. This is due to a limitation in ``astropy.wcs`` slicing. An alternative option, if it is absolutely necessary to use ``origin='upper'`` or if you encounter other matplotlib-related issues, is to use the ``.value`` attribute of the slice to get a bald numpy array to plot:: import pylab as pl pl.imshow(cube[5,:,:].value) spectral-cube-0.4.3/docs/examples.rst0000644000077000000240000001475113242700604017576 0ustar adamstaff00000000000000.. doctest-skip-all Examples ======== Note that these examples are not tested by continuous integration tests; it is possible they will become out-of-date over time. If you notice any mistakes or inconsistencies, please post them at https://github.com/radio-astro-tools/spectral-cube/issues. 1. From a cube with many lines, extract each line and create moment maps using the brightest line as a mask: .. code-block:: python import numpy as np from spectral_cube import SpectralCube from astropy import units as u # Read the FITS cube # And change the units back to Hz # (note that python doesn't care about the line breaks here) cube = (SpectralCube .read('my_multiline_file.fits') .with_spectral_unit(u.Hz)) # Lines to be analyzed (including brightest_line) my_line_list = [362.630304, 364.103249, 363.945894, 363.785397, 362.736048] * u.GHz my_line_widths = [150.0, 80.0, 80.0, 80.0, 80.0] * u.km/u.s my_line_names = ['HNC43','H2COJ54K4','H2COJ54K2','HC3N4039','H2COJ54K0'] # These are: # H2CO 5(4)-4(4) at 364.103249 GHz # H2CO 5(24)-4(23) at 363.945894 GHz # HC3N 40-39 at 363.785397 GHz # H2CO 5(05)-4(04) at 362.736048 GHz (actually a blend with HNC 4-3...) brightest_line = 362.630304*u.GHz # HNC 4-3 # What is the maximum width spanned by the galaxy (in velocity) width = 150*u.km/u.s # Velocity center vz = 258*u.km/u.s # Use the brightest line to identify the appropriate peak velocities, but ONLY # from a slab including +/- width: brightest_cube = (cube .with_spectral_unit(u.km/u.s, rest_value=brightest_line, velocity_convention='optical') .spectral_slab(vz-width, vz+width)) # velocity of the brightest pixel peak_velocity = brightest_cube.spectral_axis[brightest_cube.argmax(axis=0)] # make a spatial mask excluding pixels with no signal peak_amplitude = brightest_cube.max(axis=0) # Create a noise map from a line-free region. # found this range from inspection of a spectrum: # s = cube.max(axis=(1,2)) # s.quicklook() noisemap = cube.spectral_slab(362.603*u.GHz, 363.283*u.GHz).std(axis=0) spatial_mask = peak_amplitude > 3*noisemap # Now loop over EACH line, extracting moments etc. from the appropriate region: # we'll also apply a transition-dependent width (my_line_widths) here because # these fainter lines do not have peaks as far out as the bright line. for line_name,line_freq,line_width in zip(my_line_names,my_line_list,my_line_widths): subcube = cube.with_spectral_unit(u.km/u.s, rest_value=line_freq, velocity_convention='optical' ).spectral_slab(peak_velocity.min()-line_width, peak_velocity.max()+line_width) # this part makes a cube of velocities for masking work temp = subcube.spectral_axis velocities = np.tile(temp[:,None,None], subcube.shape[1:]) # `velocities` has the same shape as `subcube` # now we use the velocities from the brightest line to create a mask region # in the same velocity range but with different rest frequencies (different # lines) mask = np.abs(peak_velocity - velocities) < line_width # Mask on a pixel-by-pixel basis with a 1-sigma cut signal_mask = subcube > noisemap # the mask is a cube, the spatial mask is a 2d array, but in this case # numpy knows how to combine them properly # (signal_mask is a different type, so it can't be combined with the others # yet - https://github.com/radio-astro-tools/spectral-cube/issues/231) msubcube = subcube.with_mask(mask & spatial_mask).with_mask(signal_mask) # Then make & save the moment maps for moment in (0,1,2): mom = msubcube.moment(order=moment, axis=0) mom.hdu.writeto("moment{0}/{1}_{2}_moment{0}.fits".format(moment,target,line_name), clobber=True) 2. Use aplpy (in a slightly unsupported way) to make an RGB velocity movie .. code-block:: python import aplpy cube = SpectralCube.read('file.fits') prefix = 'HC3N' # chop out the NaN borders cmin = cube.minimal_subcube() # Create the WCS template F = aplpy.FITSFigure(cmin[0].hdu) # decide on the velocity range v1 = 30*u.km/u.s v2 = 60*u.km/u.s # determine pixel range p1 = cmin.closest_spectral_channel(v1) p2 = cmin.closest_spectral_channel(v2) for jj,ii in enumerate(range(p1, p2-1)): rgb = np.array([cmin[ii+2], cmin[ii+1], cmin[ii]]).T.swapaxes(0,1) # in case you manually set min/max rgb[rgb > max.value] = 1 rgb[rgb < min.value] = 0 # this is the unsupported little bit... F._ax1.clear() F._ax1.imshow((rgb-min.value)/(max-min).value, extent=F._extent) v1_ = int(np.round(cube.spectral_axis[ii].value)) v2_ = int(np.round(cube.spectral_axis[ii+2].value)) # then write out the files F.save('rgb/{2}_v{0}to{1}.png'.format(v1_, v2_, prefix)) # make a sorted version for use with ffmpeg os.remove('rgb/{0:04d}.png'.format(jj)) os.link('rgb/{2}_v{0}to{1}.png'.format(v1_, v2_, prefix), 'rgb/{0:04d}.png'.format(jj)) print("Done with frame {1}: channel {0}".format(ii, jj)) os.system('ffmpeg -y -i rgb/%04d.png -c:v libx264 -pix_fmt yuv420p -vf "scale=1024:768,setpts=10*PTS" -r 10 rgb/{0}_RGB_movie.mp4'.format(prefix)) 3. Extract a beam-weighted spectrum from a cube Each spectral cube has a 'beam' parameter if you have radio_beam installed. You can use that to create a beam kernel: .. code:: python kernel = cube.beam.as_kernel(cube.wcs.pixel_scale_matrix[1,1]) Find the pixel you want to integrate over form the image. e.g., .. code:: python x,y = 500, 150 Then, cut out an appropriate sub-cube and integrate over it .. code-block:: python kernsize = kernel.shape[0] subcube = cube[:,y-kernsize/2.:y+kernsize/2., x-kernsize/2.:x+kernsize/2.] # create a boolean mask at the 1% of peak level (you can modify this) mask = kernel.array > (0.01*kernel.array.max()) msubcube = subcube.with_mask(mask) # Then, take an appropriate beam weighting weighted_cube = msubcube * kernel.array # and *sum* (do not average!) over the weighted cube. beam_weighted_spectrum = weighted_cube.sum(axis=(1,2)) spectral-cube-0.4.3/docs/index.rst0000644000077000000240000000463113242700604017063 0ustar adamstaff00000000000000Spectral Cube documentation =========================== The spectral-cube package provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. It provides the following main features: - A uniform interface to spectral cubes, robust to the wide range of conventions of axis order, spatial projections, and spectral units that exist in the wild. - Easy extraction of cube sub-regions using physical coordinates. - Ability to easily create, combine, and apply masks to datasets. - Basic summary statistic methods like moments and array aggregates. - Designed to work with datasets too large to load into memory. Quick start ----------- Here's a simple script demonstrating the spectral-cube package:: >>> import astropy.units as u >>> from spectral_cube import SpectralCube >>> cube = SpectralCube.read('adv.fits') # doctest: +SKIP >>> print cube # doctest: +SKIP SpectralCube with shape=(4, 3, 2) and unit=K: n_x: 2 type_x: RA---SIN unit_x: deg range: 24.062698 deg: 24.063349 deg n_y: 3 type_y: DEC--SIN unit_y: deg range: 29.934094 deg: 29.935209 deg n_s: 4 type_s: VOPT unit_s: m / s range: -321214.699 m / s: -317350.054 m / s # extract the subcube between 98 and 100 GHz >>> slab = cube.spectral_slab(98 * u.GHz, 100 * u.GHz) # doctest: +SKIP # Ignore elements fainter than 1K >>> masked_slab = slab.with_mask(slab > 1) # doctest: +SKIP # Compute the first moment and write to file >>> m1 = masked_slab.moment(order=1) # doctest: +SKIP >>> m1.write('moment_1.fits') # doctest: +SKIP Using spectral-cube ------------------- The package centers around the :class:`~spectral_cube.SpectralCube` class. In the following sections, we look at how to read data into this class, manipulate spectral cubes, extract moment maps or subsets of spectral cubes, and write spectral cubes to files. Getting started ^^^^^^^^^^^^^^^ .. toctree:: :maxdepth: 1 installing.rst creating_reading.rst accessing.rst masking.rst arithmetic.rst manipulating.rst smoothing.rst writing.rst moments.rst errors.rst quick_looks.rst beam_handling.rst spectral_extraction.rst continuum_subtraction.rst examples.rst Advanced ^^^^^^^^ .. toctree:: :maxdepth: 1 yt_example.rst big_data.rst api.rst spectral-cube-0.4.3/docs/installing.rst0000644000077000000240000000413413233661037020124 0ustar adamstaff00000000000000Installing ``spectral-cube`` ============================ Requirements ------------ This package has the following dependencies: * `Python `_ 2.7 or later (Python 3.x is supported) * `Numpy `_ 1.8 or later * `Astropy `__ 1.0 or later * `radio_beam `_, used when reading in spectral cubes that use the BMAJ/BMIN convention for specifying the beam size. * `Bottleneck `_, optional (speeds up median and percentile operations on cubes with missing data) Installation ------------ To install the latest stable release, you can type:: pip install spectral-cube or you can download the latest tar file from `PyPI `_ and install it using:: python setup.py install Developer version ----------------- If you want to install the latest developer version of the spectral cube code, you can do so from the git repository:: git clone https://github.com/radio-astro-tools/spectral-cube.git cd spectral-cube python setup.py install You may need to add the ``--user`` option to the last line `if you do not have root access `_. You can also install the latest developer version in a single line with pip:: pip install git+https://github.com/radio-astro-tools/spectral-cube.git Installing into CASA -------------------- Installing packages in CASA is fairly straightforward. The process is described `here `_. In short, you can do the following: First, we need to make sure `pip `__ is installed. Start up CASA as normal, and type:: CASA <1>: from setuptools.command import easy_install CASA <2>: easy_install.main(['--user', 'pip']) Now, quit CASA and re-open it, then type the following to install ``spectral-cube``:: CASA <1>: import pip CASA <2>: pip.main(['install', 'spectral-cube', '--user']) spectral-cube-0.4.3/docs/Makefile0000644000077000000240000001520212337446544016674 0ustar adamstaff00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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The pseudo-XML files are in $(BUILDDIR)/pseudoxml." spectral-cube-0.4.3/docs/manipulating.rst0000644000077000000240000001340113242700604020437 0ustar adamstaff00000000000000Manipulating cubes and extracting subcubes ========================================== Modifying the spectral axis --------------------------- As mentioned in :doc:`accessing`, it is straightforward to find the coordinates along the spectral axis using the :attr:`~spectral_cube.SpectralCube.spectral_axis` attribute:: >>> cube.spectral_axis # doctest: +SKIP [ -2.97198762e+03 -2.63992044e+03 -2.30785327e+03 -1.97578610e+03 -1.64371893e+03 -1.31165176e+03 -9.79584583e+02 -6.47517411e+02 ... 3.15629983e+04 3.18950655e+04 3.22271326e+04 3.25591998e+04 3.28912670e+04 3.32233342e+04] m / s The default units of a spectral axis are determined from the FITS header or WCS object used to initialize the cube, but it is also possible to change the spectral axis unit using :meth:`~spectral_cube.SpectralCube.with_spectral_unit`:: >>> from astropy import units as u >>> cube2 = cube.with_spectral_unit(u.km / u.s) # doctest: +SKIP >>> cube2.spectral_axis # doctest: +SKIP [ -2.97198762e+00 -2.63992044e+00 -2.30785327e+00 -1.97578610e+00 -1.64371893e+00 -1.31165176e+00 -9.79584583e-01 -6.47517411e-01 ... 3.02347296e+01 3.05667968e+01 3.08988639e+01 3.12309311e+01 3.15629983e+01 3.18950655e+01 3.22271326e+01 3.25591998e+01 3.28912670e+01 3.32233342e+01] km / s It is also possible to change from velocity to frequency for example, but this requires specifying the rest frequency or wavelength as well as a convention for the doppler shift calculation:: >>> cube3 = cube.with_spectral_unit(u.GHz, velocity_convention='radio', ... rest_value=200 * u.GHz) # doctest: +SKIP [ 220.40086492 220.40062079 220.40037667 220.40013254 220.39988841 220.39964429 220.39940016 220.39915604 220.39891191 220.39866778 ... 220.37645231 220.37620818 220.37596406 220.37571993 220.3754758 220.37523168 220.37498755 220.37474342 220.3744993 220.37425517] GHz The new cubes will then preserve the new spectral units when computing moments for example (see :doc:`moments`). Extracting a spectral slab -------------------------- Given a spectral cube, it is easy to extract a sub-cube covering only a subset of the original range in the spectral axis. To do this, you can use the :meth:`~spectral_cube.SpectralCube.spectral_slab` method. This method takes lower and upper bounds for the spectral axis, as well as an optional rest frequency, and returns a new :class:`~spectral_cube.SpectralCube` instance. The bounds can be specified as a frequency, wavelength, or a velocity but the units have to match the type of the spectral units in the cube (if they do not match, first use :meth:`~spectral_cube.SpectralCube.with_spectral_unit` to ensure that they are in the same units). The bounds should be given as Astropy :class:`Quantities ` as follows:: >>> from astropy import units as u >>> subcube = cube.spectral_slab(-50 * u.km / u.s, +50 * u.km / u.s) # doctest: +SKIP The resulting cube ``subcube`` (which is also a :class:`~spectral_cube.SpectralCube` instance) then contains all channels that overlap with the range -50 to 50 km/s relative to the rest frequency assumed by the world coordinates, or the rest frequency specified by a prior call to :meth:`~spectral_cube.SpectralCube.with_spectral_unit`. Extracting a sub-cube by indexing --------------------------------- It is also easy to extract a sub-cube from pixel coordinates using standard Numpy slicing notation:: >>> sub_cube = cube[:100, 10:50, 10:50] # doctest: +SKIP This returns a new :class:`~spectral_cube.SpectralCube` object with updated WCS information. Extracting a subcube from a ds9 region -------------------------------------- Starting with spectral_cube v0.2, you can use ds9 regions to extract subcubes. The minimal enclosing subcube will be extracted with a two-dimensional mask corresponding to the ds9 region. `pyregion `_ is required for region parsing:: >>> region_list = pyregion.open('file.reg') # doctest: +SKIP >>> sub_cube = cube.subcube_from_ds9region(region_list) # doctest: +SKIP If you want to loop over individual regions with a single region file, you need to convert the individual region to a shape list due to limitations in pyregion:: >>> region_list = pyregion.open('file.reg') #doctest: +SKIP >>> for region in region_list: #doctest: +SKIP >>> sub_cube = cube.subcube_from_ds9region(pyregion.ShapeList([region])) #doctest: +SKIP You can also create a region on the fly using ds9 region syntax. This extracts a 0.1 degree circle around the Galactic Center:: >>> region_list = pyregion.parse("galactic; circle(0,0,0.1)") # doctest: +SKIP >>> sub_cube = cube.subcube_from_ds9region(region_list) # doctest: +SKIP Extract the minimal valid subcube --------------------------------- If you have a mask that masks out some of the cube edges, such that the resulting sub-cube might be smaller in memory, it can be useful to extract the minimal enclosing sub-cube:: >>> sub_cube = cube.minimal_subcube() # doctest: +SKIP You can also shrink any cube by this mechanism:: >>> sub_cube = cube.with_mask(smaller_region).minimal_subcube() # doctest: +SKIP Extract a spatial and spectral subcube -------------------------------------- There is a generic subcube function that allows slices in the spatial and spectral axes simultaneously, as long as the spatial axes are aligned with the pixel axes. An arbitrary example looks like this:: >>> sub_cube = cube.subcube(xlo=5*u.deg, xhi=6*u.deg, # doctest: +SKIP ylo=2*u.deg, yhi=2.1*u.deg, # doctest: +SKIP zlo=50*u.GHz, zhi=51*u.GHz) # doctest: +SKIP spectral-cube-0.4.3/docs/masking.rst0000644000077000000240000002074513161003310017377 0ustar adamstaff00000000000000Masking ======= Getting started --------------- In addition to supporting the representation of data and associated WCS, it is also possible to attach a boolean mask to the :class:`~spectral_cube.SpectralCube` class. Masks can take various forms, but one of the more common ones is a cube with the same dimensions as the data, and that contains e.g. the boolean value `True` where data should be used, and the value `False` when the data should be ignored (though it is also possible to flip the convention around; see :ref:`mask_inclusion_exclusion`). To create a boolean mask from a boolean array ``mask_array``, you can for example use:: >>> from astropy import units as u >>> from spectral_cube import BooleanArrayMask >>> mask = BooleanArrayMask(mask=mask_array, wcs=cube.wcs) # doctest: +SKIP .. note:: Currently, the mask convention is opposite of what is defined for Numpy masked array and Astropy ``Table``. Using a pure boolean array may not always be the most efficient solution, because it may require a large amount of memory. You can also create a mask using simple conditions directly on the cube values themselves, for example:: >>> mask = cube > 1.3*u.K # doctest: +SKIP This is more efficient, because the condition is actually evaluated on-the-fly as needed. Note that units equivalent to the cube's units must be used. Masks can be combined using standard boolean comparison operators:: >>> new_mask = (cube > 1.3*u.K) & (cube < 100.*u.K) # doctest: +SKIP The available operators are ``&`` (and), ``|`` (or), and ``~`` (not). To apply a new mask to a :class:`~spectral_cube.SpectralCube` class, use the :meth:`~spectral_cube.SpectralCube.with_mask` method, which can take a mask and combine it with any pre-existing mask:: >>> cube2 = cube.with_mask(new_mask) # doctest: +SKIP In the above example, ``cube2`` contains a mask that is the ``&`` combination of ``new_mask`` with the existing mask on ``cube``. The ``cube2`` object contains a view to the same data as ``cube``, so no data is copied during this operation. Boolean arrays can also be used as input to :meth:`~spectral_cube.SpectralCube.with_mask`, assuming the shape of the mask and the data match:: >>> cube2 = cube.with_mask(boolean_array) # doctest: +SKIP Any boolean area that can be `broadcast `_ to the cube shape can be used as a boolean array mask. Accessing masked data --------------------- As mention in :doc:`accessing`, the raw and unmasked data can be accessed with the :attr:`~spectral_cube.SpectralCube.unmasked_data` attribute. You can access the masked data using ``filled_data``. This array is a copy of the original data with any masked value replaced by a fill value (which is ``np.nan`` by default but can be changed using the ``fill_value`` option in the :class:`~spectral_cube.SpectralCube` initializer). The 'filled' data is accessed with e.g.:: >>> slice_filled = cube.filled_data[0,:,:] # doctest: +SKIP Note that accessing the filled data should still be efficient because the data are loaded and filled only once you access the actual data values, so this should still be efficient for large datasets. If you are only interested in getting a flat (i.e. 1-d) array of all the non-masked values, you can also make use of the :meth:`~spectral_cube.SpectralCube.flattened` method:: >>> flat_array = cube.flattened() # doctest: +SKIP Fill values ----------- When accessing the data (see :doc:`accessing`), the mask may be applied to the data and the masked values replaced by a *fill* value. This fill value can be set using the ``fill_value`` initializer in :class:`~spectral_cube.SpectralCube`, and is set to ``np.nan`` by default. To change the fill value on a cube, you can make use of the :meth:`~spectral_cube.SpectralCube.with_fill_value` method:: >>> cube2 = cube.with_fill_value(0.) # doctest: +SKIP This returns a new :class:`~spectral_cube.SpectralCube` instance that contains a view to the same data in ``cube`` (so no data are copied). .. _mask_inclusion_exclusion: Inclusion and Exclusion ----------------------- The term "mask" is often used to refer both to the act of exluding and including pixels from analysis. To be explicit about how they behave, all mask objects have an :meth:`~spectral_cube.MaskBase.include` method that returns a boolean array. `True` values in this array indicate that the pixel is included/valid, and not filtered/replaced in any way. Conversely, `True` values in the output from :meth:`~spectral_cube.MaskBase.exclude` indicate the pixel is excluded/invalid, and will be filled/filtered. The inclusion/exclusion behavior of any mask can be inverted via:: >>> mask_inverse = ~mask # doctest: +SKIP Advanced masking ---------------- Masks based on simple functions that operate on the initial data can be defined using the :class:`~spectral_cube.LazyMask` class. The motivation behind the :class:`~spectral_cube.LazyMask` class is that it is essentially equivalent to a boolean array, but the boolean values are computed on-the-fly as needed, meaning that the whole boolean array does not ever necessarily need to be computed or stored in memory, making it ideal for very large datasets. The function passed to :class:`~spectral_cube.LazyMask` should be a simple function taking one argument - the dataset itself:: >>> from spectral_cube import LazyMask >>> cube = read(...) # doctest: +SKIP >>> LazyMask(np.isfinite, cube=cube) # doctest: +SKIP or for example:: >>> def threshold(data): ... return data > 3. >>> LazyMask(threshold, cube=cube) # doctest: +SKIP As shown in `Getting Started`_, :class:`~spectral_cube.LazyMask` instances can also be defined directly by specifying conditions on :class:`~spectral_cube.SpectralCube` objects: >>> cube > 5*u.K # doctest: +SKIP LazyComparisonMask(...) .. TODO: add example for FunctionalMask Outputting masks ---------------- The attached mask to the given :class:`~spectral_cube.SpectralCube` class can be converted into a CASA image using :func:`~spectral_cube.io.casa_masks.make_casa_mask`: >>> from spectral_cube.io.casa_masks import make_casa_mask >>> make_casa_mask(cube, 'casa_mask.image', add_stokes=False) # doctest: +SKIP Optionally, a redundant Stokes axis can be added to match the original CASA image. .. Masks may also be appended to an existing CASA image:: .. >>> make_casa_mask(cube, 'casa_mask.image', append_to_img=True, .. img='casa.image') .. note:: Outputting to CASA masks requires that `spectral_cube` be run from a CASA python session. Masking cubes with other cubes ------------------------------ A common use case is to mask a cube based on another cube in the same coordinates. For example, you want to create a mask of 13CO based on the brightness of 12CO. This can be done straightforwardly if they are on an identical grid:: >>> mask_12co = cube12co > 0.5*u.Jy # doctest: +SKIP >>> masked_cube13co = cube13co.with_mask(mask_12co) # doctest: +SKIP If you see errors such as ``WCS does not match mask WCS``, but you're confident that your two cube are on the same grid, you should have a look at the ``cube.wcs`` attribute and see if there are subtle differences in the world coordinate parameters. These frequently occur when converting from frequency to velocity as there is inadequate precision in the rest frequency. For example, these two axes are *nearly* identical, but not perfectly so:: Number of WCS axes: 3 CTYPE : 'RA---SIN' 'DEC--SIN' 'VRAD' CRVAL : 269.08866286689999 -21.956244813729999 -3000.000559989533 CRPIX : 161.0 161.0 1.0 PC1_1 PC1_2 PC1_3 : 1.0 0.0 0.0 PC2_1 PC2_2 PC2_3 : 0.0 1.0 0.0 PC3_1 PC3_2 PC3_3 : 0.0 0.0 1.0 CDELT : -1.3888888888889999e-05 1.3888888888889999e-05 299.99999994273281 NAXIS : 0 0 Number of WCS axes: 3 CTYPE : 'RA---SIN' 'DEC--SIN' 'VRAD' CRVAL : 269.08866286689999 -21.956244813729999 -3000.0000242346514 CRPIX : 161.0 161.0 1.0 PC1_1 PC1_2 PC1_3 : 1.0 0.0 0.0 PC2_1 PC2_2 PC2_3 : 0.0 1.0 0.0 PC3_1 PC3_2 PC3_3 : 0.0 0.0 1.0 CDELT : -1.3888888888889999e-05 1.3888888888889999e-05 300.00000001056611 NAXIS : 0 0 In order to compose masks from these, we need to set the ``wcs_tolerance`` parameter:: >>> masked_cube13co = cube13co.with_mask(mask_12co, wcs_tolerance=1e-3) # doctest: +SKIP which in this case will check equality at the 1e-3 level, which truncates the 3rd CRVAL to the point of equality before comparing the values. spectral-cube-0.4.3/docs/moments.rst0000644000077000000240000000352713233661037017447 0ustar adamstaff00000000000000Moment maps and statistics ========================== Moment maps ----------- Producing moment maps from a :class:`~spectral_cube.SpectralCube` instance is straightforward:: >>> moment_0 = cube.moment(order=0) # doctest: +SKIP >>> moment_1 = cube.moment(order=1) # doctest: +SKIP >>> moment_2 = cube.moment(order=2) # doctest: +SKIP By default, moments are computed along the spectral dimension, but it is also possible to pass the ``axis`` argument to compute them along a different axis:: >>> moment_0_along_x = cube.moment(order=0, axis=2) # doctest: +SKIP .. note:: These follow the mathematical definition of moments, so the second moment is computed as the variance. For linewidth maps, see the `Linewidth maps`_ section. The moment maps returned are :class:`~spectral_cube.lower_dimensional_structures.Projection` instances, which act like :class:`~astropy.units.Quantity` objects, and also have convenience methods for writing to a file:: >>> moment_0.write('moment0.fits') # doctest: +SKIP and converting the data and WCS to a FITS HDU:: >>> moment_0.hdu # doctest: +SKIP The conversion to HDU objects makes it very easy to use the moment map with plotting packages such as APLpy:: >>> import aplpy # doctest: +SKIP >>> f = aplpy.FITSFigure(moment_0.hdu) # doctest: +SKIP >>> f.show_colorscale() # doctest: +SKIP >>> f.save('moment_0.png') # doctest: +SKIP Linewidth maps -------------- Making linewidth maps (sometimes refered to as second moment maps in radio astronomy), you can use: >>> sigma_map = cube.linewidth_sigma() # doctest: +SKIP >>> fwhm_map = cube.linewidth_fwhm() # doctest: +SKIP These also return :class:`~spectral_cube.lower_dimensional_structures.Projection` instances as for the `Moment maps`_. spectral-cube-0.4.3/docs/nitpick-exceptions0000644000077000000240000000201613244140154020760 0ustar adamstaff00000000000000py:class spectral_cube.spectral_cube.BaseSpectralCube py:obj radio_beam.Beam py:obj Beam py:obj astroquery.splatalogue.Splatalogue py:class spectral_cube.base_class.BaseNDClass py:class spectral_cube.base_class.SpectralAxisMixinClass py:class spectral_cube.base_class.SpatialCoordMixinClass py:class spectral_cube.base_class.MaskableArrayMixinClass py:class spectral_cube.base_class.MultiBeamMixinClass # yt references py:obj yt.surface.export_sketchfab py:obj yt.show_colormaps # aplpy reference py:obj aplpy.FITSFigure # numpy inherited docstrings py:obj dtype py:obj a py:obj a.size == 1 py:obj n py:obj ndarray py:obj args py:obj Quantity py:obj conjugate py:obj numpy.conjugate py:obj numpy.lib.stride_tricks.as_strided py:obj x py:obj i py:obj j py:obj axis1 py:obj axis2 py:obj numpy.ctypeslib py:obj ndarray_subclass py:obj ndarray.T py:obj order py:obj refcheck py:obj val py:obj offset py:obj lexsort py:obj a.transpose() py:obj new_order py:obj inplace py:obj subok py:obj ndarray.setflags py:obj ndarray.flat py:obj arr_t spectral-cube-0.4.3/docs/quick_looks.rst0000644000077000000240000000116713161003310020266 0ustar adamstaff00000000000000Quick Looks =========== Once you've loaded a cube, you inevitably will want to look at it in various ways. Slices in any direction have ``quicklook`` methods: >>> cube[50,:,:].quicklook() # show an image # doctest: +SKIP >>> cube[:, 50, 50].quicklook() # plot a spectrum # doctest: +SKIP The same can be done with moments: >>> cube.moment0(axis=0).quicklook() # doctest: +SKIP PVSlicer -------- The `pvextractor `_ package comes with a GUI that has a simple matplotlib image viewer. To activate it for your cube: >>> cube.to_pvextractor() # doctest: +SKIP spectral-cube-0.4.3/docs/rtd-pip-requirements0000644000077000000240000000032713233661037021251 0ustar adamstaff00000000000000-e git+http://github.com/astropy/astropy-helpers.git#egg=astropy_helpers numpy Cython -e git+http://github.com/astropy/astropy.git#egg=astropy -e git+http://github.com/radio-astro-tools/radio-beam.git#egg=radio_beamspectral-cube-0.4.3/docs/smoothing.rst0000644000077000000240000000743513161003310017756 0ustar adamstaff00000000000000Smoothing --------- There are two types of smoothing routine available in ``spectral_cube``: spectral and spatial. Spatial Smoothing ================= The `~spectral_cube.SpectralCube.convolve_to` method will convolve each plane of the cube to a common resolution, assuming the cube's resolution is known in advanced and stored in the cube's ``beam`` or ``beams`` attribute. A simple example:: import radio_beam from spectral_cube import SpectralCube from astropy import units as u cube = SpectralCube.read('file.fits') beam = radio_beam.Beam(major=1*u.arcsec, minor=1*u.arcsec, pa=0*u.deg) new_cube = cube.convolve_to(beam) Note that the :meth:`~spectral_cube.SpectralCube.convolve_to` method will work for both :class:`~spectral_cube.VaryingResolutionSpectralCube` instances and single-resolution :class:`~spectral_cube.SpectralCube` instances, but for a :class:`~spectral_cube.VaryingResolutionSpectralCube`, the convolution kernel will be different for each slice. Spectral Smoothing ================== Only :class:`~spectral_cube.SpectralCube` instances with a consistent beam can be spectrally smoothed, so if you have a :class:`~spectral_cube.VaryingResolutionSpectralCube`, you must convolve each slice in it to a common resolution before spectrally smoothing. :meth:`~spectral_cube.SpectralCube.spectral_smooth` will apply a convolution kernel to each spectrum in turn. As of July 2016, a parallelized version is partly written but incomplete. Example:: import radio_beam from spectral_cube import SpectralCube from astropy import units as u from astropy.convolution import Gaussian1DKernel cube = SpectralCube.read('file.fits') kernel = Gaussian1DKernel(2.5) new_cube = cube.spectral_smooth(kernel) This can be useful if you want to interpolate onto a coarser grid but maintain Nyquist sampling. You can then use the `~spectral_cube.SpectralCube.spectral_interpolate` method to regrid your smoothed spectrum onto a new grid. Say, for example, you have a cube with 0.5 km/s resolution, but you want to resample it onto a 2 km/s grid. You might then choose to smooth by a factor of 4, then downsample by the same factor:: # cube.spectral_axis is np.arange(0,10,0.5) for this example new_axis = np.arange(0,10,2)*u.km/u.s fwhm_factor = np.sqrt(8*np.log(2)) smcube = cube.spectral_smooth(Gaussian1DKernel(4/fwhm_factor)) interp_Cube = smcube.spectral_interpolate(new_axis, suppress_smooth_warning=True) We include the ``suppress_smooth_warning`` override because there is no way for ``SpectralCube`` to know if you've done the appropriate smoothing (i.e., making sure that your new grid nyquist samples the data) prior to the interpolation step. If you don't specify this, it will still work, but you'll be warned that you should preserve Nyquist sampling. If you have a cube with 0.1 km/s resolution (where we assume resolution corresponds to the fwhm of a gaussian), and you want to smooth it to 0.25 km/s resolution, you can smooth the cube with a Gaussian Kernel that has a width of (0.25^2 - 0.1^2)^0.5 = 0.229 km/s. For simplicity, it can be done in the unit of pixel. In our example, each channel is 0.1 km/s wide:: import numpy as np from astropy import units as u from spectral_cube import SpectralCube from astropy.convolution import Gaussian1DKernel cube = SpectralCube.read('file.fits') fwhm_factor = np.sqrt(8*np.log(2)) current_resolution = 0.1 * u.km/u.s target_resolution = 0.25 * u.km/u.s pixel_scale = 0.1 * u.km/u.s gaussian_width = ((target_resolution**2 - current_resolution**2)**0.5 / pixel_scale / fwhm_factor) kernel = Gaussian1DKernel(gaussian_width) new_cube = cube.spectral_smooth(kernel) new_cube.write('newfile.fits') spectral-cube-0.4.3/docs/spectral_extraction.rst0000644000077000000240000000503413233661037022035 0ustar adamstaff00000000000000Spectral Extraction =================== A commonly required operation is extracting a spectrum from a part of a cube. The simplest way to get a spectrum from the cube is simply to slice it along a single pixel:: >>> spectrum = cube[:, 50, 60] # doctest: +SKIP Slicing along the first dimension will create a `~spectral_cube.lower_dimensional_structures.OneDSpectrum` object, which has a few useful capabilities. Aperture Extraction ------------------- Going one level further, you can extract a spectrum from an aperture. We'll start with the simplest variant: a square aperture. The cube can be sliced in pixel coordinates to produce a sub-cube which we then average spatially to get the spectrum:: >>> subcube = cube[:, 50:53, 60:63] # doctest: +SKIP >>> spectrum = subcube.mean(axis=(1,2)) # doctest: +SKIP The spectrum can be obtained using any mathematical operation, such as the ``max`` or ``std``, e.g., if you wanted to obtain the noise spectrum. Slightly more sophisticated aperture extraction ----------------------------------------------- To get the flux in a circular aperture, you need to mask the data. In this example, we don't use any external libraries, but show how to create a circular mask from scratch and apply it to the data.:: >>> yy, xx = np.indices([5,5], dtype='float') # doctest: +SKIP >>> radius = ((yy-2)**2 + (xx-2)**2)**0.5 # doctest: +SKIP >>> mask = radius <= 2 # doctest: +SKIP >>> subcube = cube[:, 50:55, 60:65] # doctest: +SKIP >>> maskedsubcube = subcube.with_mask(mask) # doctest: +SKIP >>> spectrum = maskedsubcube.mean(axis=(1,2)) # doctest: +SKIP Aperture extraction using regions --------------------------------- Spectral-cube supports ds9 regions, so you can use the ds9 region to create a mask. The ds9 region support relies on `pyregion `_, which supports most shapes in ds9, so you are not limited to circular apertures. In this example, we'll create a region "from scratch", but you can also use a predefined region file using `pyregion.open `_.:: >>> shapelist = pyregion.parse("fk5; circle(19:23:43.907,+14:30:34.66, 3\")") # doctest: +SKIP >>> subcube = cube.subcube_from_ds9region(shapelist) # doctest: +SKIP >>> spectrum = subcube.mean(axis=(1,2)) # doctest: +SKIP Eventually, we hope to change the region support from pyregion to `astropy regions `_, so the above example may become obsolete. spectral-cube-0.4.3/docs/SpectralCube Demo.ipynb0000644000077000000240000544757413252232557021544 0ustar adamstaff00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from spectral_cube import SpectralCube\n", "from astropy import units as u\n", "from radio_beam import Beam" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "path = '/Users/adam/work/sgrb2/alma_lb/FITS/SgrB2_N_SiO_medsub_cutout.fits'\n", "cube = SpectralCube.read(path)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "VaryingResolutionSpectralCube with shape=(90, 600, 600) and unit=Jy / beam:\n", " n_x: 600 type_x: RA---SIN unit_x: deg range: 266.832160 deg: 266.833484 deg\n", " n_y: 600 type_y: DEC--SIN unit_y: deg range: -28.372359 deg: -28.371194 deg\n", " n_s: 90 type_s: VRAD unit_s: km / s range: 0.610 km / s: 120.634 km / s" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cube" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "Beam: BMAJ=$0.08259665220975876^{''}$ BMIN=$0.042222000658512115^{''}$ BPA=$80.50083923339844^\\circ$" ], "text/plain": [ "Beam: BMAJ=0.08259665220975876 arcsec BMIN=0.042222000658512115 arcsec BPA=80.50083923339844 deg" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cube.beams[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -2.495e-02 [aplpy.core]\n", "INFO: Auto-setting vmax to 1.334e-02 [aplpy.core]\n" ] }, { "data": { "image/png": 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0K22eEwVe+2nb5j54WicZT1fFWmBLHINHsweRI899eNkCoRYccj7JlyZHW/Zn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vJVm39yZn7RzPzImfm0mflLM/Mb9n3/dej/R83Mb973/R979f0zZuZXz8w/MzM/aGZ+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\nQJ3lCmH6JW2ryg2rl6zk8OczZvrx+h4v1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ku2bfvgzHz5zPxs9PVi3/e/8UapOYAzMHwk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\n5LZM03YElKXwj5Wy9MF5cMKi6cXITasouZpDXMhLy2qji/Ld+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ChFYQSxcQsU4doREm9y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tINdr75oT7iCC7YkD0JRh68tco31lPwxC+G7MlPM8Ntk1tfsbsQ7aActp+kB9xPKT\n/Fv2aNco17bbrPeaz2c9lXJ5hQbv4xhIWzqjBch2+vpo+/SBnXbaaaeddtppp5122mmnnW6e9ozh\njVDbzuRsnzNRU/aC0SlHmBxdc2Ryirr7N5Zxtiz9THSYkaYWZcq2Q2+VdcamtZ8+mGeOACdLk98+\n++yzp8hie07SkePWdvrexh5ixJL89SEqfmUGs4bsG7NNLcPDOeH4mUF1NJcP7btcy4I2/rfsirOv\n/EzWpmX9OAb3l9HWtp1lrfMsrPkxyU/GYDkgOZLtLA0zXt4a5+1DzmjyvilrlHYoN9QXnj/Wbx61\nyL/batlfZ77Yj4zP232ZMXQGg1nPZDyYNeTzntcyohxPG3v6mGfd0kb+nB3hmvCcn07PB3RxS2fr\nm/Un9Vo7RZfkbMK1zCH7nN8yRzz0xP1ktjLj9DY0y0xb49T1PJAr5ZPpZpaOvCY12fQz3FyTrGvK\niJNahpj2gjLfsm5sl21bDztjGh44m0odOmWoPHctE+3MI585ZJbKeqYdGGI92x7taFkqz4l3QTjr\nZ56aMj7LpPvC//O4Avnadmg4m9zkhGvWNseZucPhcCbjHkPq27J7XpsszzKn0+liuyfLcS00n2ai\npiOoC9vjTO436SUH0Oz08WgHhjdEXmhUalZcUUJeUDRik5KdHCk6z3Zgcg//Z318ps/9jLPH7ZtW\nzHZk6ezYKdwynGmzbUOLY8L/11pPB2OY564v1+hI+Dkj8/ZweH4XHMdNkBPF3ra+eq9/6pzaC1/Y\nt4yDTpsBThR9cwb9vKaNGMsQ/BlIuPxkJD1vnBsGUJp8R1boPLYtnr7Ge9p2o/C7bXO2rKbeycCH\nR9Mzgu6jZZHzOB0mQ6ekbVFqBpz1p38GCOlXHB8+D0un03Pc5J6ghbwMX3wYCQ8CaeX8W/pi+aVu\nY6CBW1xTlrrHdaT8WuviJN8tPodXEzDhuvLct22R1NnUD24//GnXPC7Lo4OS+Y3tkreZL+ountqc\nMXhbPvvBOt0ng4qm0/K5pS/s6JM3PMCs6Zncx3GY9w0Yhg98DRTLcn17Xl5i012m8ZRAfrLr5B/n\nfDp9u9VhO0x+0hfwPKZvrovyu9bls5nUUa3NBkC9ZqwDqRP8CINtTcp4m2jaaXrPa5l1ZrwTcOb/\nW+A2fWc5UivntuyHXVujLOuD93b6emkHhjdCTcEQFDXl0RQ5lZgdRzubNsQ2avne9pO3dh2Rnpyc\n9I195WEcrsd9z/9bRnIiOopWauxbiPdODopfazCBHBsmOqT39/cX5eIsMeOS3+O4UA5oxP3weeqi\nUW7OnGXLRsdyk3vpuOb3SW5Op9PZs0aTwc6Ytv63Y0XZn+S9/U5nh/z0+Gn0HU33+JuzlvYNIJsz\n1JxzzmsDAM052spAGkQ2PZM/HirQwOYk99PYLYfhpQ8pYbl8Uq8ZDHrsrZ6UYVZzemZ1K5NnnbbW\nsxMUnTjxNP97/HQGGSxiVtiylvJeP+6/x8I1YX3r9d4cZ5flnOa7x96eAzRfU1dro60RAkASecox\nZRxcy16H5HU7LIp1N/01AcNXr149PddnG8y1an1lueY8sT3yZuvwlQZM8j95k+BPvjNw06gBscnu\nmZ9b8+z7UtavVbCMT/4Ax+9gsnUfZd99nfwIU+rhPPt67HoLkvjEZq/RyGn6+ZK1dDqdB8L5vfGa\nc5OxtDqn9nb6NLQDwxsiK2gqnxYNagqPysxb+Na63OJhRzbf7Xi0kzzZJo0A25oc94xvrXWmzPK7\nwcmkfCcA6T5NWaC0n9MgzVcqSY+RRq4ZSQKuCeDwdxtfOsE+eTXkbWN86bcNBYEknShHMc3vKdpO\nPrXDV0ycb4757u7yiPUtYzJl/yi3Nlq5vgXe6Qxy7FN2gM5onPmW/UldW47uZMSbw5a5tDO0BUQn\nHqbOyXmiA8AMxxbwZZtbDkHjb5xQO2vhNbM/dFy97i2/zmIx08d3Ldo5dHBl0pHOoNDBd1Yj5bd4\nEkBlHck+NUCQsnwnI+e4tTtl/ui8TrLt8TTHn+XS9waYTf6t2RgDhrbFzXaUOtFrrTnICWJ5twvb\ndPbLTnvmkLzz+Cag1sbbeOL2+H8LhvLPc8F6OBf85LZv98fBm4wvfzwZmDqAfWPbtivezTTturG+\nTz3ZZmpwyO+TTp54skW5x49z8DrXNtd79ECCkq1s5sLAsIFQj4trInWwLB8RMGinLeS4vggY/FjA\ncQefz7QDwxuhGBcrdSvvXKNRm5xHK/itLTU09Hb6vBX0GkgIWSFR4TkbQ8VvoNJAIsffnIXU6cyC\njUE+vf2jOQgtgkaHoD3DRyNJx7ONzU5gyADXr7Bo27HcXsbl92PmXoMcP1NxDaw1oEC+0fhEztv2\nY/aBc5Z6Aha2sn92vDKGBjjIY8qWx9RAHGUsc2nnecrI8B6/K7EBWo+PTrb5bb40R6vxzGUt7x57\ny+ybl9wCzTopb1sAnnUywMJyU+CF1+LUvH379uwZwzioeZ6R+iJzG/1HHexsUgPXljvybQKIBpcE\nV9b5LTAQvrNccwRNU9+tC3m/T2BkPVzHtiN8no7tsX8GQJ5fywrXmfWe790CXV5rKZ/gIftCfUHd\n5kCFAQQB7EvkoOkEU8bbZIRr3M+jbQGj1h/uWMi4CWLCEwK/tNN4QZ7ZLnOs1unmD/+fnlVtIHoK\nNrjOSdc036SBMfI1n9xZQLmf9HNr2/f4Gfdmt9sayP/JaLNNBtE8rwx6NB7kt62A8U4fn3ZgeCP0\n7t279fj4WLcCGBhG+VO5WWHSYNNJeIlysbE1MLTjHpqco1CL5hE0NHBoMNeii+4LgUNoenG2edUi\ncnR8GmiJkbcxsDNrZUk+8DcrcirnLQBmJ8A8yb15dibzkWzJ4fCcGWbbUzCBfTXItRwYbLK+JjOT\ngZn6YuPv59NCdvDzPeNn3S2y3xxlyg2v2cCyrzTEPIDFGUt+sn0a8MY7l2v/u07LTO53NoTlMk6+\nzzRzHT1Fh4XzGn0SkOa1Th636xynt0Zz7g38+EJ7AlTrtsxHrjlLS75Fh3keqAMyHvKj6UzPh3nq\na8y+RYa5w8A6owWgIkteM22tsb42ntTTbA23U27pSs49HXjrDPIh2/EdNGC/DdLbuDxG38sdHA1U\nZPxb+tIBVuqaLd3W5sK2IhS9w77bkW86eyvY0QJvtj9Z8y6bdR7+ROc1/b7lRxiMeV1MoK3JQgtU\nNx+grYvU07KAzWdjH6m7m1w3In89Dssr27PN9Bzaf7y7uzt7xyYf8cnrK+iPsE6OnfZga1w7fXza\ngeFOO+2000477bTTTjvt9IeKpiDKl6lnpw+0A8MboZ/+6Z9e3/rWt9Zv/dZvre9///sXGUEKPSNk\nW5lCR5YZ5f0iizGRXmYgpwzeWpeRM/bPhykwkt6eNfTWxrZtrEUFswWMx9JnG0QyY4xwJePBP/aT\nvHVmZCs6nLHnr0UoOV/kFTNx7TlRt8v5dnYgUe4W8WVkmZG/UJtj9qNlehLJbS8Q97xyC5+z0uST\nM4aN5xmDs8bMljsr58ipXxrveXGEnM+Duh+JDHMLF/vuZzN9aFDuZ7mWBbKeaJk139t4xzqTRXVm\nh/Vv7T5oUWKvBW7R29rqTnJEmr+7fDKGb9++fZLD6ZkwZjFTX15J4ePnk0lkJj5k+fL21MjmlJUh\nj/ysYMsGe916vjKPziCFX9RvHIP1k/Wgs2u+5nnimmhZw+ioln0PeX69FsmTtdbZDoiWVSEfyFPy\nsu0cSH9tn6zz2RfL/jTv1r+hllFjnV4TbX2wfxz/tX5O7blN8o32Lj5L+s8xPjw8PF1vW0onfUdq\nNujac6yRVT9DyueRyZ+JrBObHHqtUcf72cEma/zN/eH6tOw0f29LBlOf62af4z9xrFnX7OPpdFo/\n+ZM/uX70R390ffe7393k4U4fl3ZgeCP0i7/4i2ut82fV1rp0vvPbWpf77EN8gNkOprcUkGzEm2MQ\npeKtjW27JPuae61kAqw8lvQ/RtfOanNIU89knK1cucX2dDo9vcoi7ZAndBC4DYd8ZR88L96qxfbb\ng+MN2KdfWzxtAC7lAgwnx73Jhh1Jfua6gRzH9/j4uB4fH8+e62J7BIep71qd7iuJRs/OHetrBjTl\nJqfThpfyEJm0k9SAeJMVAhk7QpwnOkAch+XaWyu5DniITAsuhLhFqm2Z4zy6jql+zod5Gn0QOdmq\n13ykLLG/lK/379+vx8fHM7BAoMitpms9bz+P88gtv9FPLJNr2YZlpzB1NgeVPM18cY3ynib34WNz\nhrfWPMGIAxjuS+SB7+Br9VJHtjmkvvOWRm/1zdjIGx6CYRkP6CRtPfNHfhKUeHsi77O8OZhAfhmw\nUzcZXFNPNId9rX7yrPvP/3nPNBe2WZQFAw7zLn1iO7bBvL+BG+pR6y72c9KJbIeU4HDT7fmk/FEP\nHY/Hp/5MemiyM54XzwHHxPHbj+C1Bvxy/xZf2N8WKGA/8hvtrZ9/TwD9cDhcHEbT6vrVX/3V9Su/\n8ivrBz/4wUW/Gl++Cn2MOm6FdmB4I8TToNbqityRqcnpoiKx4jKgoqKx8qBTwggvlcX0brG1LrN7\nBEY0nlPkn4qOQGWrPfOO5ZnZsRKmAvZBAylvxRceM2LMeYqyz6cdARsbPmfmh/wbgAjQa884NKfH\n/GzlWJ71UDba/XSU0j/y2RFZg0s78i176+zOZLADMPi7jXJzxm2UyUPXwev5zrXWDPbkzHiewq/m\nPBoA2cEiP72WM26Ck/xG0E3KtfZ8MD9Jdpq3wIgdMjqblBOuQwMRy4OfAeMBM/lO8Jcyzv7wvlev\nXj29mHyt52dDk1F8+/b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peymfvMY/H0xFZ87riYEhr/XJscz8WGbi1FFum1Pv9WuykxSH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\nHKLBcMba+rcMOJ4WlJNaRYjOIWNfVexWDsvjouN1UDEzd8+CtmC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OfNu2i2C86aGdvOec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4SScTnKzK4y0K0i5exr7m/35pyz7N6O5G1OM5fPOzozxSxwMqTcTse2t+3yh5CZ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\n9CjFodvpNUeahblysAFxL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dWU42J2kVu4qAttPjw2OkI7cfZH0OctN5SHf1sw31v7DCTs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+Zn3l/m3g2bJqfkSTCfmgvWny5hzK64RiW+wz5Frjn/WSd44j9+FqbuSaeXCgYaf3Rzsw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YA17qa/olefyxMZQReeSup7Sf/52Ay720ofZ1E4BtMYJl0uxv5tu+rQEz604DeG1Nh5zUaG1R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\nWpZrchgnyHMMIFz5bJl74+JsL/ufcLC8eJ6aI0yj1II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ZbHNbHOluW4ZI+zR/5MFa6+F9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\nWxaJiqItWBvmKPoYXwZArIYQh4y3Z9yiEOzo0chNmUXi2Jw0K/sWnLYAcPr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w1uVWMeJP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cube[:,300,300].quicklook()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -5.415e-01 [aplpy.core]\n", "INFO: Auto-setting vmax to 7.872e-01 [aplpy.core]\n" ] }, { "data": { "image/png": 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YXz7G+JExxuOHw+FvH4/Hf/50ke5gBX4nAl2QkjY3lUT3HAQVDK9tKYu0oZOC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\n4lMKQmdOctHhgNC8MW31P70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i/XRKzU2HO1Unq30Ffwlvy2lXcS48SY/lgCdV+Eyl8S9c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PVObd1kPUa1nAFzJClFQh2BYtkpOjVl8Bhldz/T6yZSZmXaZ84xpQMqEqyijdO2H0beUgR3yuqt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\ndN2GtO2p8SStiGfV0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6mtVDf1dU7ZpqNlu9tbvQMvGcCeL+Li67x3juxLusuBSLe5ast9\nTrKx71n2GMwT3Q+3RzqYyACj5QlxdLCADk5yxjzO9J9ro/H3eFjWQTrrhEmPTLxuWTnh3/q3wdu2\nJ71sOXXOukl4WhasZNABHzscjt89AkfGLURX+6+TUd3/kzA8ZRy5H75A1H23ovc4nmXhG5+VMZgM\n7Kqngnffn75k1AqLZTzGtMc+OR6sY+eF5R1tTHV7rDRGW4j7mYtuZzIqeS0Z2Cka2P/97AJ5sZVw\n6i8dbtPtdL/pfrfb10knKmuOl8/xPKsjTKMyBUqSA8qxNL38kDrnMDlppuVEH9dLuJiXPA7O8dXV\n1Z17Xde8N8kB9jXJFRocDgwkg8xtrbIRpNv19fUTA9tOoA3qBORxrvXpubDGgcbaBF4zDtp1n/yd\nZDDxMD5TdtNtrwz5c9pNY3OddGBJ80BnnPuzwtv9EM9pHa5wrMrjn/pMspp9TPXcr2VpcgKJW+JT\nr/uU5V85nxxTooHrrXjmnD7o5HEdOhCQ7JKW6daHlNGkAZ0+H+7C/5RpHkfLDcsarsEpkLOiVxoH\nT5hNOCVdOq37NJ+Jj6b2LJtd1rbEpOd5L/Wf5n6F3wEfGxyO3z2ByfDgQrRhwbqT8KbhzHZpfCRI\n7Vt49r12ChvffpebjT3iMhlbPU4bhXbgeI313C8VRCssj9vCbnX89zm0WglRKoIWymnu0oELvD9t\nRSG+0xjTdwttRldZr68nI9gOrnGxIqeCtVNJXrEi7H6mtWKwk9KQjGbynDMbE6+yXIpEm49twPFj\nRywdlMMxWQkTj4uLi3rllVduOeXT6zESfqTNpOzpeCZHmmOf3l/oa6d+P3r06JZs4fqhkZN4vXH0\nOJPBbhpPuw84VkPCIf0mPitnZzK4ul5aQ6syqZ3UN2VNCkK209dtORNrWWqnhtfZrsvYyZr01uRQ\nuB8Ccec16qqE96nMburTciDdm9ZjkomGlZzjmNhGckZWjkj6JLokGnhXgMfW42cgJTl9dArtLLoP\nBymIT1p7k7OW2uz7vNenoPrEXutIywfaR26zcW/d4HtpnvzaKa7LJA88/omn07xyaz/7IZ4TJF5/\nHngRbbxW4HD87gkwalp1W3mnaCTLTRmMyTieDFkrArc1Ree7Tit5R/z7kwTBpMAsXE7hnsbJ3zTi\nJ+HPMoxg8zqVSj/bRKHOsfU4kiC1sk30blpSiVrQpyzwKYdhCgSk0yvtCHJ+SUdmKBPY4CGudpg8\nni6/epH0SoGZD0l3OgR0uho83z0fvmelmI4YT2NzRsW8noxYO5Bd1rzEOTNdklO1ysaQXn2N46TB\n3bLAdDYOk3xq4NH8Sf55/fb20+TceF2Q35hFNk173pLstRyyUW2ZRXxSG4lHCDaUvZ6S3GP/Pcd+\nv6Vx7XsOfBAPbvO1vOtP1zddkmNDHTEZpMnZ4JrmWL0uJrqecmLcflXemUMnwW2l+eF198X/7s/y\nwnrSc9k87ICnMzSWM+xn0j8G4j/JEsthXidfeE5bjlpfrJw+04U0nfi+qqL8dwCGdDRwbvzsfApm\nE1qGsX3PgQPrLDfZReY920ArSI5eKpPkXd9b2WsHPDscjt89gVamVjC9oFPmZyVkU0aIAicp877X\nYKOJBpL7tMNg/JLSs5LuNi8vL5+8xy8piFPKOtWz8eqonfGk0UwjNimR5GCYJt5+kubO9LYB1eV9\njf1MRtNEf9bn+7f83qYGOz5TVC8ZZ1SiPFCGgQsaqAZvnyHu7D85GTQsVg7qSkF5ztL68vpdPZdC\nviDtnClMRl6a+1PAjCnH5Dm2Ak/rxU5domkyUFMU3XyY1rSfX0m8lnjRTlviRfbt57V6DloeWT46\ns5HGQ5iyzcbPfbUMYrR+WiNVd7fAO9q/wp/3kqE3ORAeV6pHHKe1fMoo9ZrY9/1OUMiGumVol5l0\nicfg+jToV0HZ1f+07ihj0vymvixr2R4dpAmSM8k1xbWXMkyk5yqjR5zMk0lXJjwTz1Bfun4HOtKa\ndF3203gm2+KUI2Q9MJUlTeiAG/929JL8YVuUgUkvpEyhdx0l3L2WXJ79UX4d8OrB4fgdcMABBxxw\nwAEHHHDAAa8pmBzl52nnkwUOx++eAF8kXJW3QjWsIi8NKZPQ4MhPl3fkzX0yAs4Ij+s4e0F8Cc5A\nOmI/RfHY9rR9NGU2+t60LWKK2jIiR2D2i5F4boX02NPvFK2dIs9dzhE2gyOZqy0pq4zLdAx9A/lh\nordx4XxfXFw8yfB2e1Nf7tc4p8xJ+u/sG+c28QT5LLXLeika3fjymZUuO9EmZfyIk/GeyplGxpNy\nojO901pbzbGBdOD89jyxPcu8XqMeH/uaMldJbq3AW31T5qNp7W1bfoZm9c2KiAAAIABJREFUysBN\nmTOO2TSzPORYyBusZx5n2R6Ln8HjAUuJVq2TOutAungOEp6k2bmZAGYTPZZUNt33XEw7Ewhp7vnb\n274TTlP2jbimDBkzzc72JbqtdIXXhfHjPWf3UrkVj6Y+WM//U1te003btEvGtDDuad2w3ERDgnfO\nrHRw0jGW3z0WXiOe7o88Qr7rOt6qvVpX3NpNPLmNm2NcyXfbQMn28LqpurtD44CPHQ7H754AjaFz\nynLBU4FYaEzGfy9Yv2/L2zr6Hq9Z2VJRW8D2teRAUBkQp65HepyjXKyITBfW88l0NP5Y3v36v2lt\nAW78V86MDY1kACclb5ySgrQj5n6JlxVFl6ej6/5O/U6GTM+DtwdNbRCfpCyJa3KCbOB3Pa87tpEc\n0ek5En97q6efHfHD//1tA9HjSGvHBpCVNHkqtcc54fYwB5CSM5kMtEn29DX2Mck8jmdlkHB8HIfL\nTwZSz033NR2F7oAWD+CZDBsb8av1w3VIA3Gq67KUs3Qi+n8fOkE4ZciRdkl/JMN/kj12rKe+TAvL\nJ+Pl68SVdEiByS7j9cH1tO/7k8BsVd1a06cc30TTvudDrho363SO03ravOtx+b/lg/Vgg50L4+5x\n9jflbKJng3Wa+YKPAnj++8TuqrvyYyUn3EeSn6meaUg9YvtlCj7aKeQ923OmdfMbn3luGvJVNGn9\nTHotfSY6UMYl28rQ5VIgIcG0Rp4VXkQbrxU4HL97AlOkvWF1r+o8o3TVhg2Hfsi46q4BRgHn/egJ\nksHQ0IpsciaTwLCTSyU4OWo2bm3QTQ7aSjASPwpzKx/Xn8aVFGMLUM5FKpvaTLReGdked2oz0WIS\n3OaH6QW3bYCkOgRmp1nHY+A4WJ7tMMPo50YSnHL2UiZ8MggY/e37ae7cZsqCUxGvMkuO7vP6SvG2\nTOi+UoYi9Uu+5xi6vV73K+OKNGZfDpKRTqbLyuBoMC/6RF33bei5eta+EkyOe3IeE8/QWXGZZCSv\n6NRt2Jm8vr6uq6urWzxqvmB7dGps8Fvme9673LS2E7BdylC3Ncl008A4tbM0rZuVHKJ+5f3uJ/EH\nr1tPUybyv/s+tc4mXlrpYTsGXNMOAqe595ys8Nm2rR48eHAHB+JiHkqOjuswMJOCCYn3kjNXddsu\nSbSZ+GwVOEl9e/2kNcw58FwQl3MDhMS169kRnWyHVXsHPB8cjt89AUcAq86Lxq6UoJ2O9J99Wcml\nF6VTGTTYcLZgOmf7ng1hOpbT2LsMtxN1ZJaKxfWqbm9hpBCccLNw5PgSTlX5sBMb3ZMRZMHtLARh\npSwTva3U0/3JaPA8UdgnpZgyrqYT+6DhbkOVdEljN40nY8iZG2dlJkh0o/Hr1xbQEPK89/Wp72Q8\nEJo3OEZHYx2sIY3Nd5NB3PRKePogFOJuOnj8fdx5WlvODvJ3O402kvs9f6RrolePicEiOpLX19d3\n+je/8HfTecrKJ4dwtV5X2e++1rSf1t4k9zj/hOSsOdtLOqRdD5PsdDnzYsKzx+R1bLp6DK7fsG1P\nM1tJjne96Z7x6fJJL3kd2fFjmbSmbKRzTKlPy7jJEfV4KJc9/4kPz8mSpuveDj3JGUNaT9Y3/c1X\nPJDm7mvqh7iTholPky3FoEfLk2RPUf4kmk3ZXtOvgXOd1nRf9/q1TPO68LVE8zT3dExXa+mAjx0O\nx++ewJd+6ZfW2972tvrgBz9Y73nPe6pqjqCkDFlDMuQIU2R6FeVKuKT6SUEnYTrhzMgZldzKME+O\nyMOHD+9Eo6wQiAP7S0LQBhPbcUbR88I5Wzkspq3vd5vTe/0apswsaWmD1PiuHG86+X7XnMs5K5x4\nwHOSIsXJGF4ZOF2XxqDrOErKcfse6ZbwT2P17xSJT8atx8Pr3pbtdUolb0OdhkUy1EnnlRNsvkjP\nb9jYMd3tsNjBWBkWnAfTLt0jv5AGbDc5UU1vzkGSZ13O2Ve3leiTvqeMX/c58fCEJ/Fw0IM0TuuB\npxnT+GdwLc3RytCjs78yqskXpmXSVaeMS44zZUg8T6t5m/53Pa9J8pbfNemM3aNHj+44YlxH0/s9\nOUbLtAlXZxHtGHB9TYa8ZbV5yjsSrM/Yjtc3oevyZPGqu1tSLbua56eAQLednDTiZT030dRjcV/T\nCaOm0dXVVWyb65cB7svLy8ifnMspw5qcbLfDcad7DVyjFxcX9Za3vKXe8IY31Pvf//44ni478fSz\nwIto47UCh+N3T+Cbvumb6r3vfe+dTBR/J4GRjOmVEjxljLD+KmPmMjYqUtvuxw4egVuIVk6fx2un\nxMrMzkl/T8/1TILNtOi2jS+dyTTGqrsRVRtnxpnt2Qhim0kJpuxrcsCcffUhDWkc0z3OtftNc9u0\n7GeSbBASfI/09rhpUCZHtvtmPRsrdgATP3iMNkiaX3ouycP9vA63ciWjNLVJx9WOQNPSW2ppMKV+\nJmW67zcHd3SbzP5N/Epcu3zK/HKddjukk/F69Oj2eya5nnx4SX97Gyfb4tpvuiSjOo21oecwrUNC\nCgY5Y9v9MFOcZO3k1LD91ZwmGcRvvtu05y+9oN1tOvCT3hdKHDlPHMvKAD01ZgJlHdci75kHV/hM\nOJiHV3KWkHQXHYdpZ0E6xCPpdUIKIDXN2P+0+8f0WAUu7Gxw3Vjf9G/TNOGaxtvtNj1dNgVA3GYK\n1pknUrm0fk+tkf6m82eaJVx7Xa4OUEnOXXI6+9vrzN/8bR4wDh/60Ifqwx/+cP3Ij/xIxO2A54Pj\nqJwDDjjggAMOOOCAAw444IB7DkfG756Asz7T1q/+duRplXWZ6k/303VHxLztcep3lX3sSB8j9myH\nETVHAVftT5GtFOnq9hsfZwTchyOKKXLoyHZH/r31hWXTdgtnKrpPl3d2KWVS2G7T3Jm3dDqcM2Up\ny5Qi8c7mOvLNsUzPuviZ16478ZyfrUjZQG9nTAeXEAdGxPszZeM4T8aL4Og9o/i9RZmnnXociR8a\nnyna3G0mPMm3aQ1Pa89zapnQbTvDM0Wzu463oFU9zYKkLAH5Os2jecH0nsAylfhwrJYp+77f2SbK\nCDnreK64vjk2tkE5xbIpQ9TgLYXGtX9PGUhmFFZbPT3WJC9IyylLSh6d9I3HNsGUbUk4eK2xrDOF\nbjv163am8i7nrcNTFoa/Sbuul7JmEy2II+fCY+J6ZtvebWL5aNlloM7wWK2Duq/edeDrrE85kXYo\npaxa3/ccNg5pTrss9RDx8Zrh3PgedUTjPWXzmfXzXKXyrEdcSRfLTI/deHKuLStXa6Rh4okDMhyO\n3z0BO35Vd9PpXNjeAmfnhfWtsJLQ8hZCKkQ+60HhyfoUqF7E05aJ6Vradub+Tt3zvvZ0gIF/JyWZ\nxkfw1is7ht5Oatq3gc+tOh6HwYbKZKx4HFRiFvb96S2dNlQnSEqv8fbrCjy/k5Nk5WrFY+M40aXq\n9jN+btcO4PX19R3espPdipLHunNLGLcJcuuvDSgb7TTykjFKWtuItXJm2cQ73Qa3fNqoNb0TpPlc\nbWlsoCFjHD3ffJ6J40w4kx4rI7dp2/32oTCpbI+t6u7W6eTITbLF/5Oc4u+VA9c4sZ63+U3tW/7T\n4Wv+96EQNgy7/MR7PT9pq99EO+Lsw0ASHZLsSga18V0FUlZbTlN7ic7GbeqPfVhvJ73o4MDUZpIB\nEw+lsRBfyyi3y3WTdKT7tYNPvC2bPV8O0nnMlLPUGxPfVtWtg6BIq0Q386l1bqo3ySDen+aUYB3q\n4LHHZ1m22vrddEg2BPthH5w7j6HXbJLPKx484NnhcPzuEXghpWhRAw8vscFtoej2JkWU9vtX3X3H\nENuwwk2Kd1r0FCx8b49xW9GF7bjPZDDwWsqKTJCMHEc1bXjYQPGD+XSo2UfjlgyZpHCSITLVS047\nDUiP0W2luSQNpkyI6c4IrZU8s8C8Z7ySo2GDgtf7mtvkHK2UlMfUY51exUJ804m9ae5pgDlQYEeU\n7dJgct/OGHht8TQ604r/V3zB+xPvNb7NL3Z0OO+mmZ0Nnr7JTIczbS03V8Z7orHB82dDltdsbKU1\nx/827LpOMqoS2JAmndop9NqrqltBJxpsfY9tT3RpPJNcSrCSI5NBzHYt8+z4NT5Jlk/yIxn/xMkH\nivCe26q6u0PFss3rr+u4fMJ1Aq5ZrpHOaJ8zJ2lcXnfpXpqLqtvvfSVNUtaZzluyS0g3Bz6nDKPH\nlOwBZ7cSDRr/xuUcx64PW0ljNo+3DFrpn7Yf2CbHxXLEn0GYc50v8/KUkCBYdjlwezh+LxYOx++e\nQFpMViCpTjI2+rqVuYXNKYFhZd5CJGUoWPfc8XGcU2R6yu4QEg2S00RcbUixfKrThmrqu/GcjgxP\nxnHXmZzWKeJGmnlMBmcBiCcVCOllx8gON5VCchRs8PMlvMlocJTWDh+dKs7FZKywnVO4EhfTlW12\n+ZVx0IELK7vmG4/DNPF4UibMToLHbaPIdKPRkjIUia4s4/nzmpmMcuNrvBINmicYDGo6dtnJ0fJ6\nc1aNZUmnCab1yzUzZXSJuyEZo112tWPBBrfHM/XruU96YnLI+J1klx1G8wTxIf6nDG62tdp1wfan\ngy4m5yXhu3IGWCfh7MyV+0j1enwtn204r/Sf7YTU7lTfTgR/T/zJuhONrAdXcpeyi/qHtkhfm+wI\nHy5FWiT52XzSvz0Gy7PEF8mBZZnLy8sYADCtSGfLp8Rb5+j+HleyHz2+lCxIfGFeSPqo20u4TDDp\nnWeFF9HGawUOx++eQBIMyYhzHS/UyYFJ4EXc31Y0NshShofCZDJG3Y5x8bYXK1CWTeOjUHSk03h7\nC4UFqJUH2/C89H2/VHfbbkeLiW86ltuC2PQkbn3P71KzYWGDu/EnvRNdE781XukZNzoXxLOfpzI/\nuVy3SeXPZzdIX/ZnHqaBkV6STPoZF0YsOR80OCY+8T07s7zv66uI6qRcJ8Og5yAZSb2uJ6fBvO12\niZvX+8pgT0GkyeHh+rTDxVcBeA7t0HDu+p4Nnqqnjkp6ntRjMa78TuPu9m18ntNP4+wMYOLJtBbI\nY8ywElp+8PRR1qMxyjns63a4XC/dc3Y0gXmtf0/P/FbdfSE6+yZfJL63TOMYXTb1bSeBfE/54myf\neZG0tmxNYyNekzwgTnYsVo5DCj709Yku1h2WJ848WQbb8XO7SY96zXOtJdqZTlMmfdIrfT2dSpz0\nbnoswPLP64r0Ts/tpfJJnjLbR/zcHvmAY+11cSqgzrYYqCKsHL8Dnh0Ox++ewGQIVeUteJNT5YU5\nGZHug78tDLqfFC1vg4L92oHpMSRhbhqk9idHZHIIqXDs+KRxJEM0OTST8KJipcHqdwwZ38mY6bIv\nvfTSEwfW8zkZnJMi97zYmem5OpVh5TykrSfsbwIatTYq22Hjdj8bCMmIdmbS38mYSGN79OjRHZp7\njMlwmcabjhLv9my09j3PsZ0mlk1rxcbDgwcPnrTXGbRz1sApZb0yANimnRSXmcBGclV2lsm/kxHE\nsqYPHaCpTuPgdpMhS+gMThpXojmfPUpOQYMdgykYdcp56ba6jnmauBp/ZrE5L+m55dQn66WMDceT\nrhMfl+WYzY+JHtYzq3WW6hNH6zzSaMrMVd11lFL76V76n8C8umrDjh9pmAII/c01OOmjvu41Pa31\nU7ZC613KAbeX+NiQdMiKNr0G3E+yI/xyd/bH/3a23RbnkNnttN7Zlu0k98t7Ho+TCml8KbjDMRwZ\nvxcLhxt9wAEHHHDAAQcccMABBxxwz+HI+N0T4J5zgrdB8Poq48AoF6Oxjr6k7Qks22WmzMAUner/\nzJhV1Z1ndKbM5MXF01MhOb60pYdRpykyyDFzy5j7dDSMkes+ep/bS0zztL0l0c31mG145ZVX6urq\n6hZejmozauqMwdSHgfN7TtQtzRV5jLyWoreOfLPMahvXhMsqIs77jMyuoq7m1WmLWNqSnaK3foF3\noknaOsWo6xShdlYoZdSYTaEcqbq9LZmRZG6NIz6NZ9pum3jHmaC+71NjOcZTfEu6eKt2Z2ot36Zj\n0tkmI9TcIuxyq+yF8We2q+WGx9N0c6Zh2g657/vJzFjPs7ODafuYeW3axpfWodeXx8XMX8q4ef2t\n1nrPrzObrO+tpqQJ73sMxsv9pvEmujj72t/MCHGrp2V3074PbfNYrYuNbzoRehpHKsc5dPaGvDzZ\nAb5GuWE8Jp1smk78N61hyziP2/csuxJtUpbQY/TOkgQccxqPs35cF2k7ayrb19zftj195GQah/Hk\nDoCJf4wb6yfb6oAXB4fjd0+gF1FaOFQiVbefGbLR3gu1BQwXZ9oWemoh9z2WTUAhwnJ2fPr/gwcP\n7jizPWYqmT4wI9HDwsZCfTI6kgHB9pNw5nH/7tcOOGnYypPbOFiX9EpbJpsek0GWDOMW9ITkONo4\nZ1kbNTao2FbPEesxWNFbN+l0PHz48Mk7mJoXuh6f7XObHH9V3qYyzW1yMJMTODnuk9HUtOZJu24/\nvdpitV03BWn6vh1U1nO5xoGGD3Fsvp6Mg8aDfMxxTU6fjVeWSe/sTHQ12DGh0e8PwQYOyyS5ldbX\ntDa7XOLDacwTsB6fCer+GURIzqK3e/J3O2E2Ejn3fj9b4zQZk3ZG+jfpMhnNhmn7INtJ+sdOiWXA\nysjmHE1yOeljwqQTE+9YRk5OWs+lA0+TDJ5kRRrPOfgzsJjw7L6s0/pemuN0bVr7lvuTvk1jnbZr\nWoeSnqfaNST7pn87cDTxdRp7sr1Wr6Hi2k3z4DlMAfrVekzBhwmXrjPZOCtIOuR54EW08VqBw/G7\nR2CBMi1QR8emCLXbTQvMAtcOWirH/0mY2xGjQEjtpKPufTJbZwouLy+fHOW+GgsNqL5nPFImZqXs\nur0u322RrnRwHPVO7U8KkU6TjdYJaAQl44IK1Y4fx5Gchh4b26ZzZyeeQQqOp+/R6eM8XV9fPylL\nh5LzMyn3iTZUWMxwdZ3kTHAuXCYpbfPBtm1P+LS/k+PKcfme55z17cBNmZAGZtqs0MnHXBcs33zc\n/JVeAZGCBQmf7oN8R7qm+fA6mSLdqS/T2wZ0wr3q9nH0E7SBbicnOX8cG/HyuuHLmJPxn/qruvt6\nGI7FbTRQHiaZnzI2bG+6Rho3DsSPPEd6PS9wjZvuKbhIvNI989fk0Lq/pi+/3cZkbBMPBu66fQZz\nCdataU5ch870KZuAurb5MzlOqzFZNtpOSLrYcmEFkyPl4Kb1Y+Nh/lvpWzranCvqsVWgiO2zzXP0\nV5KDXL+rte/1uHLSmh4MBnLtTrjSxlrJjgM+Njgcv3sEVlZcmBS+NARtxDhayHpW+gQLDfbTMBlp\nFpgW4nReus0+QIN13M6+70+2bzVubcCmF24T52TMWCFMTpKV2cXF7eP4nVm0AZcUs3FoRZ6icP2f\nWwVJL+OajDv3bQPdyjZl/XiP9RI/JaOSTmTiHTqQ3gLsUz45BvI26caMRTIckuKk4qZj6P+njLUu\nbx7mNj8boKRXMkiaRww2mliPtEjGnp13O3pVeauXx0c6Jx5MtOlxNk2nkxhJ7zTXpgmzD5eXl3dk\nJfms23WwiY4IacrA0TSuKRM9yVuOI637xtXGctMvGeoTrUjTqf+Ej2Wh3wW4Mozdp9cb+2U/K900\nZbtMh9QWM1ipjYRnX3dQxHUSDiyfZDH/T/iYZv1Nmcr+Vs5Xkr3Gw/wxBVy6DX+MK9fcKZjW1SQX\n3M/kxCZniG2Zp8zTU7DatHEb27Y9CWCuINkqbjMFzZ3hdl2Oxfo60XeSQRP92K/nm3Rb8dABHxsc\njt89gRSt8XYPK2Mr/IYkGPr/FAUiJIVlQesoUIMN/5T16z4uLi7q6urqSdYnRfs4jv7No5QnunQ7\nEziiSjyTAKXxTofWddgWt1hNp9vZqDJMin5qq6GV2urZuckYOKXs00vDU5uTcdCONIF8Tscg4bE6\nTTQZonbUE8+kKKZ5NbVL5cZ27Rwn46DbJU95TM0/HJuDJN1P02RlcNCRY700bmZoPIfEh+PnGFaG\nBWlqfBPdbeAY/Cyf5ab5csU7lpHJ4GTbTfckZ1M/pkv322PoYIGNM4+d87SKtE/zZFpPBu7FxcWd\n18acMuqsL3zP4+txNEzBCOtF06bb9FqhrHYmz2u3r9PJTnRnWePS7bEvB3NPtTn1c0r+exxJzpN3\nSS+3Mz07OOnqcyCV55qc5oPjNy08Nrdt26Cq7pzebHBmz+2Zbl2neeeUfuIacuauIdk9yd5xAJmy\nhRlo0i/xqf+zv5RdXM3RufA8PDS188kCh+N3T6CZ38ZH2iZYddfZIFgwpnpJcCaDgLi4bt+zIEqC\n18ZrC7Iec8oUWWD5/mRQJKDB4CwLDXCPaWrH16byfp6JCpPvGkyOR9VTY8FbVifcbOTy3jQGO2Ur\nPvAYpnnyPQOzNDaapzFyjibnZvUs1TkOalLwxiEZ+FSkDT2W5vOeb2aekhHLMXC7obddpQyieZlg\nY4AwbYfz/KRDYUgvt7eSXZQrCU8bHX0tyaFkoBnIk6ec33QvtX99fX3HIKPRl+SUZTNlY5KnHGMa\nJ681Lskgdf+s32Umg89t2ABNDqqNbV/zWqKsIF36d3p3Gvvv70l+Vd3eQWA+SvxMZ23iEd7jGjSu\niQ5pHOyfv5OzxTasYxOk9htPOw10ENJ4uZ76+hRkmOyM6doKJtlPB6//OzjXYF5POCXHj+OecO46\nfEVM45aCwCu+4P/WBdwdYr5LMOmDac3wHnescGyTvP1kcr4+nnA4fgcccMABBxxwwAEHHHDAawqO\njN+zw+H43SOYsjUNjJ45Jc8yjnydijSm+ilzUZW3Q/V/RgKr7j6nkqKf3gLabTGLlJ756s+prIwj\n+x01m6KiXZ6ZDV5Pz8alMZhGnRlwX1dXV3FOpm1d6XmiFBleCUFn4rr8tEXJEXHyAF+5kYC8aj7s\n6GV6DoFl+rlOj4/R5pRhSlFxj8nbcqd5YLbYEWS2laL4/fEW7G7fW1r5Amy35fopy9btpuxo1+Or\nBrq8y67gnG22nIsEaQ26jjN9/Xs1p742Zair6k5EPrVHuZb6bZnijMKU5ZjaStc8X8ar6vYWQrdh\nOTHRbSV/UvYwZSb4mxmBdM+6yFsiiRN3vXQb5ntnraYsHsuk7f0TfdL8OTs7fac2T+li820aW1Xd\n2cLKHQGmk2X3lBml/JzWo8u6DbdrHpiyTA3c5WKZbxvD4B0Sic9sfyQ8mPVbran0u2ltWyit0TSm\nvk/Z4t0XtsUm3ZP6m3Bf2YbOKE9zn9patXvAs8Ph+N0TOLVYTjl4yfhPfVgYJOPDSo0Gv+tMCrB/\nt/CzE8j2bOgmp4+OnxWCt2j2AQ/Gpx0IKsmqbFQlxdJ9uf9uxwfRmD6el8vLyzttNT50RPZ9v4X7\ntH3FBq6vWRGl7X7e1rUyZLpNb01saNzTFkif2krYtqdbTEhfj48HmPAVEGk9nDLs2G4yFNN2GRuQ\nqd3u8/Ly8tYW457ffd/jqze4XujU8uPnSFL5qqdbCPse67388svjdtxED5czrRqm7UWsN81J4t+q\nuuMg21moyq/U6PvT9su09dO4TtswE21Mf5eho5LmnuvLNOVBWS53eXl55yAhGvupzW532lqdgkF2\nQJLOWhnM3LJMGtGwbfp0P+nDMXFLdKJ3MnIpWxJNeS3p15WxbDr0uC0Hk7E+6V+3nejsspNT4/5P\nrcNpjFPAw+35UCLSIskfjmOlYzwu8k233f27nMeSYLJvkq71da+9lYyh7nV/XAcMhE76kXhaB6Ry\n5i3PG9vwul31ObV5wMcOh+N3T2DKpHExrYR2w8pxoUCelDjL9rXpmQ2WSwLAESrjYuFOo2UliPt7\nEuLX19dPnD87hVVPnYQkaJND5P7toDYObLPq9pH3fkj+lVdeeWL0M3vZffA496Q86JAkQ5e06t9+\nFyF5hO1Q0TLa2EC6MeNJw5z121l2Vmnf9yfzxHp8/UFSkF03RYVXDhqB/E8jb1Li3Tbbt5M89dt1\nfJItHVjXbdoy0GDcq+4aNOYl0o3BBDocPsSCv6nsbbhNNDV9vO4bl17z5DvTf3KiiGfP377v9cor\nr0QnJmWVTa+UISVeXsc0fFkn0XPlJBHMc/xPuZiMM67FFLhJspr3/BqJdqIT33Pd2NngWCcj0ONL\np/p2fx3g6TEmPux1ZEh1TEM6mx6HHRvOzaQnJkPX68h0Y33LY47Vjp7HZKdz0r/Ea2qj75MvyPek\nj//7t/H1nDArlmwA6hsCAymTk+Zn1SabhbilteJ2p6AZ6dFlew2lMXB9mzaUwbanuO6TTWNZ3vdY\nJvF9Gi+BuHaZie8nOrrMAefD4fjdE5gcHQqZyQlkuSQ0UpnJ4UtAQZEUIcutBEjV7WjdqUhzUlQ0\nOlYOWrqXjOu+fioqzvo2sjk2RsgoHH28/rZt9eDBg1vtJaMu4c1sWVJ0/dvO3TTnVBqtJCeDxP+v\nrq6eGMTttLHNybjh9s0u37hdXV09uW/FymACjSPSOUX4raScSeFvGlx2PDx+4zAZCn4nnPmYvEVH\nhA7jymAkDn4ZN+mVeIl8Qd7vdWhHsaruOKurtTgFJ2wEmUdPrfEpY+x7Nv5T5J192UGkQ8X2naFq\n3Lk9a+KblcGVHF7ft7zq+U1ZL0bpLYO4NtrZ6zq9rvnp8VFeeB4m/dC4cm7T2nHAIwVGCMn4Ju/b\n8DwH35Wz1ePgfdOVNE1OWgpCJPlK8CEbCez4eX7dbzL+Wb7/92++LHzl+E1jofNL2iadZB4mX0z0\nm3Seeap353T5hDN5NdFr4oFz9DiDSJ63iSeSveR1TVwm28B9euzUj147/G/9yM85tuABzw+H43dP\noJVu2sY4GXn+3eAFZwNwMmaTkGObNKydhbLQ6/qMqBtvGrw21BjlohFLxWGD2sYz8aRQZSS527Si\ncZkEHpeFrZVkitgzmt3OjsukbBtp4fKTEvH8OhqZ5tC8ko44p5FKCLfOAAAgAElEQVRIsINF6Gyd\nDUDi7Ai5HW3+f/To5oXv/SHuzpAlI448l8r3eHif5ZPTQIVs2iQFzjHRuJmMS6+pqqf8+tJLL92i\nQ9M4Gb38JKfIhmBfs5OdMmXpepIF3eZUzn1465gNDW85Tv2ybpJRqYxxTAGY1AZpwcDNZByunCeP\nr9tLzxqyf2YGiBPL0DDe95sgTMtgn0JMfTJtl9/326fZdn3Ppx01r+3JuLZ8nXiR/Xg3A3dmUPYk\n3cFxJf70mtn3/U6WmOUIvpacWfN5clg45lNOrh2+/jaPrmRgA3W2dQrHlGR+co4TnuQ9lmdbKSBl\nnde04StrkpNEfvQ4qMMN0zgm+ZpsEPfTYDk88ZJtA/Nzqmt70W1Qf04yz3Rs+fGJANu2/dqq+req\n6o1V9YGq+jf3ff++M+r901X13qr6C/u+/zxc/5VV9Q1VtVdVT9Tf3/f9dS8Y9VtwOH73BOgkVN12\nKibl0r9TZGWKtjBCTUjC2MB6jjgno8lCYWUAJoHYkDIVdnpYLhkAydAmPWlQGodkRLJt9pGMwxaY\nbrdxbXyvrq6e9OetG2yLPJIio/3N650FSk46jSYbuJzb5BzY+WP/5jUayen5NPbHa6Qz58rbwdr5\no0PZODLDZnrRIbKj12WomPt6epY0OTQcA+nW7Zlf+Xwjx7HKejS0oc656P+nMifTq1BIA9Okn79N\n7SY+SUZUQ5JlEw29c2ByhLtv8m0KXK0gOY7kxfSus95W3f04w028uJ5JhxS4orFGw7N5eBoLs2cc\nt2UAadTz2rqpdyiQFxwsYSDkpZdeqsvLy1u7AJhpSXNNGvu57mRcJnlr+WW6sc++blmaHP1pbSe+\nS7olZdAmnc4xEiYnL/GF2z1Vf+ovOUWUmRyX6W3eJk4O1FoHsE9/DMkGcZvumzYE5X46hI39JDvG\n/SWwjEqOZqKzacD+m/Y+pKvrkKfTK6ESDoYpSOV75pO0BhJMcvtZ4VQb27Z9cVX9zqr6N6rqf62q\nr6yq92zb9pZ9339sUe9Tq+oPVtWfqao3hCJ/u6reUk8dv1c9vTnvdTnggAMOOOCAAw444IADDvjk\nhq+sqt+37/sf2vf9L1bVu6rq71XVrzpR7/dW1R+uqu8d7u/7vv/1fd//2uPPX39xKGc4Mn73BLbt\n9sl/jMA6AuZ6U8RkqjdFGR2xYpSGWyJSZDlF3Byd8xaNKUrqrFSKzqfoH7NS3lqQsmepTQOzLh3F\nTlk/9sH7vUXKdRvPFBXtCHfzwBTlZDtV84lqXa7xmCKO5AHPE3nG0eoJnM2aIuYE4mAe2fenB6F4\nax6vTdtzUtTY2dwHDx48ybx2xoJ05QEtnd3w9pfGlWtzFfHs5yR7HMz2ndoi4ywR++MzWcwiep2T\nH9Lpe32vx0x6dX1uLU1R7dSm5UjaGuY6hBS1Ttkk9z/JgdQXcfI2SfLQ1KbvcV5TxqTrNDjDmsp7\njCkD5gxKl/fOCfJhf5quzlj1dW4LvLy8fJLVa77uNdO8xa37zvJY1hB/8zrp4rXn+bYeMR1MQ/aT\nyqV+EiTZSRmUxm/8Em7pepL5SYa6zAr3NPd9vfVdykC5n9VuH+rXRNNEL7e1qr+ij8s0rs7CkRdX\nkOSr+7N86vanLbQPHjx40i4PqUk7jPioELduJ7ntDF2XW8kVyxHLKu+KIU0+nrBt21VV/fyq+g/7\n2r7v+7Ztf6aqPntR78uq6udU1ZdW1VcPxX7atm0/VDeJuP+tqv7dfd//9xeEeoTD8bsnMDl1FBZc\ngMnInNr0VoUkQG2gJqeQ2wrS/n+3sTJUrJT8e9qW2MLOStllWuil7Wa97ZHj4zhXRsRksE1zYKOP\nuNAgmRTwpOxXzuCEC+tZgNNJoMPRyob1zRt21nwvPQs2bZdhP2zD1/uejQ6eksrnORKNuj06N2m7\no7d6+kRMG0amE/tI/dtI5RpMJ4FO67dp7K2u5HcaSKbhSgak58doFJKfUhsrAzPB5OhxXu2IcLxu\nh/3bMFttkaQs87qyw2TjKjkNNpzNf6k8ceHhK6RDt9nXJ8N7NW7Pd5+um5zadva8JvqVEldXV0+C\nXo1z/05bnJPBbBmQgiDedur1wd/mX29dTs5hoo1xnByI1OdkRPt3ais5ch7jhMtKvvqawe2Qztav\nE+4eV8KtrydHwc4X+bkDW2k7sB0b37MeTjRywGcVIOeYprlK+ND2SM/RX19f36E5eSvZhCwzbX/u\nb8tSrivjzL4mOZNwmyC18zxwoo3XV9VLVfVRXf9oVX1mqrBt2z9WN47i5+77/mgYw1+qm4zhn6+q\nT62qX19V371t28/d9/3/fqYBPAMcjt89gSSYLMQmgb8yXFK2bBJKp6IyfIeUDW72YwFKJ40OBoXH\nsyg0G/RJWa8Ezbbdza42nsnRoCFlGtH4oqIgXVIUjP2ynNt1m6txMuPoKP9KKG7b3VMnjWuKYpIm\nVtitnH1Me9/rayvHIBnPNhLMh21YXl9fx4AHy/I3edHGvpVZ47B6Js59UJnbYeD47WT7PUorh5hZ\nGtL+4cOHt57981zwehv7fY/tTtFd4soxtVG24iuCnadES2dMeK0PJLFzw/bTOiCfnHIAOUY7XBxj\n0zStfRr9PDSl6rYMMq1ooE607zYI3b+fM6RcXjlX7J+0IN27/6urqydO38svv3wr+Pbo0c0zuOmA\npeav5AQnnO0E2Fhe0S45eymocUo+pes2vifDOTlIXIervlN9r4kVzsSNQNonOvc9yyzKsqnPFHhI\nkMZmHCb55YAiHTv+J85Vd+0Xl/VY7fytaG59RXuC9Zt2fS/ZFS1L7dSt5pk22zl2mfUt58P6x2V5\nPdHstQTbtl3UzfbO37Lv+1/uyy637/v3FraAbtv2PVX1A1X1a6rqt7xa+B2O3z2BpKxpZFhQp4hZ\n/+aCs+CjIZ4EO/vnby9sC63+fcpBmSJyaTyTs8T7K0ViY5vCim13lsjOKOnSddPJlTYmbLwkIc6+\n7fgxi2Th7sgc22pIjlWiLX+zHypQZogcUXWfjsROvGU80neiX3IsDI7+ExfzHulAA5gnY3K9VNUt\nI5ftUDmyz8nwY500TwSv50SL7ptjZZZvMiqb76fTetuB5u8GZ78diLFT0/fIZ1NGxMEslp0CPT1/\nk/N2iienOgTyRhtryfCajEy3bfmZZG3CY2o/rfeeWzt1q1NA0wFJ3iXR8pBbPa+ururll1++9ZqX\nbqPfr8pDkdgeZY3HZ1lnw32lH6tuOyWUJ5bNCS+3l4xft51wSOvdeq8/KUjhfn1v0pfTPd43WKf7\nJNSVLZLucd1N6yHJ4q5j2k06jjKM29s9VupX453m13IxZf7cT8poN15J7tl2YF/+zzli+TQPq+/E\nv8w8prYnPmqYgl0TfN3XfV391J/6U29de/vb317veMc7xjrf8R3fUd/5nd9569pP/uRPjuWr6seq\n6mHdPZzlDVX1V0P5T6mqX1BVb9u27eseX7uoqm3btleq6p/b9/29rrTv+/W2bX+uqt68QuZjhcPx\nuyfgaFUDBVOKuiXlYCVFQcHnvFZGEvtmW9N2G5dL3xRGKUu5UkxJ8Tp7QXzsLPh4fAqmFrpN1/R8\nIL8b2kFjJC8JTN6fsoGrbB9/ryJnNhqtEJOx4fqT0rUhSCAv+XUkLpPo6PE1jza/2klM9fwsgxXk\nOc6Vadx84khrgx0d0i05+x5/80qiG+fBfNtrh/xkgyEZh9MYuh0bMk3/dgy5ZvmqiL7uk2u5niaD\n3ddIg+nVIQlsUKbsgPsgTHzbMBnICZLBuDKUedrehKfbpFPtZ+ZsGPe89nYxO5qUTcSNW7Tt8Df0\nevVaa3nx8ssv3wmGUN56rCmTwrXE7wbKXo6L9xvIJ82vfm7bfZgn7ECs6pJuls3JaWAfdhqSLHHZ\nBMlBXTl7K8cirfOpP5fxuC0L7KxwnjroNK3PRPP0njzy1PRalQn/LsfHCVgmBdbSvQn4/F5yxDwv\nxHN6NnvKdnp9eK4ZRDQtWt54HToofW6W7yu+4ivqLW95y1llG97xjnfccQw/9KEP1bve9a5Yft/3\nB9u2fX9VvaOq/vhjPLfH/782VPk7VfVZuvZrq+oXV9WvqKofSv1sN5nCf6Kq/tSZQ3kuOBy/ewJ2\nOKyUU1RtMvJZruq2wqQTkqJHNOIsHCwQeM/GKeuwnJ+/sXBp8HNJVhaTM9BGx2SkMWKfHCEbOnbI\nbRiRro8ePaqrq6to6PNgjcabDijplrbBJmPNtEvKwZHRlbJusBI81QZxTAbnqT5StqiNQ9KF7xFz\nO+ZLH8JCA9tgx23lMNHwYWQzGRZWhOaXnufV8eGTcWx6si4VdF87FeBJssAZkYuLp0f6V9Wd9yU2\n8L1yTZNE+8lg5vhOGeAu39/TM42e4+QorRw8BpJaRqV5YEY2yVHim5xCj63q9isheJgOgQ5e1+fa\nJL42HM2HTZsU9CGe5B3W699dr2VjZ/6SMeo1X3V7nfm1R2ntrX6Tp/t3OpwpBUmsLxKfnGPsTjJ4\n0q/U914TyQlOdkGyI2gPJOeYmTM7EH3f9RxUJaR12t/u022k3TumZc+j1zJxTQ6e537loFJ++KwA\ntpuu0d6hjWL9QgeQz8SmuVo5kqR599NjmIII59gJlE1cW91X0tPnOoGvMvyuqvrGxw5gv87hdVX1\njVVV27b99qr69H3ff+V+Q4BbB7Rs2/bX6uYdfT+Aa19dN1s9P1JVP72qfkNV/cNV9QdezYEcjt89\ngpQBSwLUi3P6TWHW/90GhY+Nb9az8zgZ//27vxmpTIufmQbj2u+AslBO9GB/pN1UngasnSziwoyG\nFQGNoqqnzqqzPcSLNOjyXYZG5QrsVCRFZ1qdUhAcD/G00+fyXSYZK40flW9fI52mbBFp0+XaYGwc\n+RybjVMqJRtPk9FmZUXeNU81Phyn++Zc0RltPNOpazQEjJP7T0bASj70/0kR2ymqesqnaYsrM3qm\nN8vZQPS1vu4IfJIpp4Br9dGjR7ccIY6TuPC6ebchHQrVcoq40yGc5J77avC4vQ6Z+WC7LQc6S0en\nPMk1j73XY8K119XEs1wHfNdfkof93fg7QNMGtaHL+jClFHwhMIPS4+M8MeuXgKcrk252GrgeSDfT\nkQ5AArblLKp1W//mN3973h0QncbAcaZgUF+3HE26h/ixrRSg6vaTI9ayzrrGOo/8Y3tiWofT2Kkr\nfD9d4zqZdJFtC36zLNda8/2ES7dxanwO7vfvZBeS5rx3yoEjX0w6zLByNJ8FTrWx7/u3btv2+qr6\nbXWzxfP9VfX5+9PXL7yxqn72M3b7M6rq9z+u+7eq6vur6rP3m9dFvGpwOH4HHHDAAQcccMABBxxw\nwAED7Pv+9VX19cO9LztR92uq6mt07auq6qteGIJnwuH43RPoSKS3yqUouqP3vucIWgMjaYzqELjN\nk31PUUFH39g3t1v1fx+Fn9rhePqEOD+Dk6KYjgZ6+xHHmLZ/mQ4ehyN1pCWj8I4+TlnSjkL21qeU\nCXFEd9oO7Gses2lsPAzO9E0RZdPhnEh21e0sEjNcTcPuk5kGz9O01WSKRK+y1c7sdb2OtHYGgxkD\nbhPj9s80ZvK6M7pTlsnbCE0nfjvLZRmR+jWkSLXxJHhb3pRNch/Monue/Lxaqp/wcUSd0XRvQT7V\nttch5Qvnl+NoXnB7nZVOGSpvwXUU3pnTHhcPlul7jUfLSr9TMdHNGY3Ex1zblOfbtj3ZjcFsSo+V\np3eSZr3mmeWvqjvryLJiyuoQ79UaTBk//k96reubd/p3yuikLXSmt9tyG8Sb40l4rDIpHk+S4QlI\nL9La7bkt2xfOFnk8lEfNU8wmm2edvTYeq/G4bLfn7fd9zx/LAstst2sbwzgn+cz/fiSlT1tO9hz/\nn5PV9I6hZPukDGI6jbfBbXJcLyKbd8BtOBy/ewK/7Jf9snrb295WH/7wh+u9733vrUVD5dPXGiyQ\nLIRoPE77ug2T4Tw5Gt4WkZy/7t94rx4Ub6FEoeIDCrj9is/V2Mg1Dbsc+zbtEl2m737mJj0wTnom\ngzg9/9ff/fJWlrXBl4wU0s+4pvGyvWTQVN1+/q4hbYHi9RWfTcZ/16dx2df6enoGdXrmrGreytX8\nxXasICfD0W1OQRbTgXPmcRN/bu/pNiYeTPw+zXNy7pKjbbDBQuMmbSPzuAlTH9wevSpPvlk50jYu\nVw4gnStDyxne62s+ubTb6va5proP9jXNaXJweo7Mp8SLp696zKQt65juSRaQVm2I9vg8B147poUN\nY9LQ2y/phCSHOK1Zby9NB7lMOPY35yzNYZoz4tTXk2Fv4LiSjuV32hrIvlayheCtvXZeVzgQjyQ/\nm37JPkltU6+b71ZA+ls/ObCRtjX6lHNDj2V6ls/ygte49rwek9yZ7I+2L9zXFODo/7xO6Hn3Wmeb\nEx+x7OR0ks5vetOb6tM+7dNOzufhHD4bHI7fPYFv//Zvrw984AP14MGDOwLAUae+nozyXpx+f1Lf\na6CQZxn+plPmqJf7XikdKojJ+aNQs1JndLgdLGYBJ+cxGXF2NFhvJXxscLB8O9d+7iEdZZ/6TeXp\ntCd6t4KcnMrJyU3AbFf/Xh3GYT7gKV98dtKK0QrZGVr2wzFSOfMERCuvHqef9+O4kvOaFKPp4vXC\nMdqwbXDWlP0lgz/hkyK4XGureeX6evTo0a3TUhMfW6Y4oJNw6HLJYew5JJ7+TmMzDdL1zioRVk7r\nVM7GocdFfvEpo53d8jOOlpWm3+S0Ea92UCzjSA/yvB0XjpVyZDJwKU94jf35Outwrf1/7L1dqLVb\ne9815rOftV9SPQikkhgQkhP19E1P8mqLAREP64mI8aAoKOIXVC0VBFMELTmwaC2FFsEgpFChIAUp\nAQ88CCQoSk1QaxJpmo+XHeyhmmSv9TzTg73/a/3mb/2vMeez99qpz3rvCyZzzvseH9e4xjWuz3GP\n+3w+X2QD2R/ftWgddT6f193d3eN40yflXJNBputkhO4OhskYOfdeZ5N+MD2bg5o2fa3Va/ebo0Cc\nea05sZ5782rL2OwyofnNNeJMtfH03LSDkqY1TJ0QWvKb9DWuxiXXdvaNnc9mJ9HByzjI1w7ARO43\nXvLaCy5cI5N83vF9+nQ9OrQOWDbH3/177dj+zO/f/u3fXt/97nfXb/7mb64DXg4Ox++VQDN4bBxf\nU3ZrPSnjGA1NmLh8YHIWdsci01jKh8KnGeu5Z+FCAdeiaGs9KYmHh4dHB3AyXH2ASHMy3EeLADdl\nmfZThwKdxrq3H9moTD0KfmdmfRx+MovEyWMhPSdo5ZrzvDMayQ+n0+nReZ0Ol2g4TEaVHTg7EW6T\nNDEPhffatjGPx/+dbWg051ima1SSxNvbjPi9w4lGV+jDNlv5KRLNPsmrlj87vAwc987ZaHw6Zc3J\ng1zbNhi5Rht9SGfLJ5ZzJtN9eyz8PW2Nmhw/ysfg1jJ+hswT56fRlEZ+o0nLREwyhPrDW/aJk9ev\nZZ2d97bOHDB0P00HEW/uwmg0Sb+WJ3YCm9zxnDTHbVr3hqZT039zIgktoMkxWC/RUWh81RxB9sXx\nky4MsrF/OoT5T1sgfJSAblvPab85QAw8TLRl/zw104FJBvya3rLTzXmadJV5Z7K12D9pzy2ppC37\nT4CP43Wd/M8nh6VxLvxIg4FrfrJFm8w84OXgcPxeEXABOovVFEFbmM6+7RRNyrPvWw07Gy5N4VFI\n27E9nZ6267Usk41OCnYrFz+Dx3GxXivn8TSh5UimlR4VUsuUtcwU+/BcUYE2A5PC1Uqw0aABhTcj\nljZ8jVejWxP4PNGvbcWzMzQ5oc1J4XWOMYos8+B75DkrxYl/g2ObQ9KKzlHKTEZ4o3NzDq4ZHNfA\nyp6OjV+VYoOdfXpdEgcaS7tAg3nRDg/bn6L+HEdrM+Np/WRN2dlsRiRh19fEu45+74wot92e4XVb\nActqGvcJwNio3dG23WsBgrWeTsRsOoLrya8CoIHJbzqSqdMyMju+bw4w9Ye3Tje82RZ5f7fm2jpu\num/C2b+tZ+x0+tuyxw6eneRcN5+2deB7bMM0afrRAc/oTAc68z/rmwGBa4EzO8RN95tuuZcty3Sc\nHSgwv1BGcc2EX6Ygs8tO+nTnBDaZsIPovLSVa9GV/HDszI5zrU68ZfrY2dzBrfrslna+V+Bw/F4J\nWLFTUDaHxYuF5dlWy6ZEeNvBsYJtEaMmbKbXENhAbkYl24rwMR6OSlv4ZHuhny20wppo5+t2VD3m\nFglNXTsbrY/csxPbjDwLW0fr2WczitscW2BbkTbnb8rKcY6ak5hxTO87o2LyuJoC9X8b2/xuBs9a\nz99PF3o3R4T0bFmHFqBxGeNK46eBy01OcauTsU4OkIEBi4Y7DWAaR3YG+coErx33FzAfOmuxc1Ca\nUZG5tMzga1NSnzy3czLZ3+l0erZ9+1bji+t+4pXgOuHitd34Ne3S2IsOaQ6qg2t2NmzIs4/wMNdw\nMxxbHRuHDphwDs0zXIuWo8Yn88Mshtc3P02nciv7NG+mP+tMMsHXp/65DvlZa7+rw5mbxifW9QRn\n8tba6zTOrXnVcnsX4AjOHhv1s3d3NCfEwaWmr+OMcX7J1xOwfnulkHmYhy2ZHm19NR5tMAVQ6LS1\nYCfX6CeffPIo1/jeQNslxsdz6nVEnL6XnLI/CDgcvwMOOOCAAw444IADDjjgo4Ij4/fhcDh+rwSc\n8UuUltkIbsmbwNH2th1tredRLUZ42tYKRn9aRDXlWiQr5RgF4paI4DJlqfzNKGK2LLhMxtGOjA5t\n2Qe3CDEbk+gtadCyY44m5psRSeLubTYNf9KEmQBHnlt2h/Rhm4zIMQPC6KRp7CwMeYzbXUIrgyPf\nLTPsZwa8rdNlvaUr9ZjtMDgKa3p5HpoiaVk/Z0laeUfynZVjuzyp1uV2Ef6UZ2ScbUxrk/PvzNC7\nd+/W/f39sxMtp/Gu9bSV1Vkdg2nJ+UxdZ6dcfqKBM+PZAuy2UjYR+SZ7EhVfaz3jU8pFP+uWzKKf\nNWYZZ7YoeziG8HWTId6O6ewF5WHLbJA32kFKHGd+R+4mW5BDWe7u7h63gTrDRh2T+sElv61nWI9Z\nilavZfxSp21Nbeu9ZRlznbKJdWy48lrLTres3u7etGancrze9Abr2+bgd4Br3zsfOE/OZk3yOf/z\nmwcBTeB2+D3pQfOQ5UKbM7Zpfel61+a+jWGX8XOfHotx9nppazTX/boTXrdMcbvmO5/5kOu2FYjL\ntVcJHfBhcDh+rwRi6ExOkxent+U0x8O/DVzEFDh54DfQBEq71wRk/rdXFlhBti0PvkZDygZXDj3h\n0dA06E6n08WpqbzHU+RCm4xvepaFeLfXVjThTdrxwWrTkXWaAmsOY1OA3vLG+Ws8tlOeVube0sLg\nRHMSPIe8tgsWGJ/QuClSO7Wu2xzJ1DVdrMCzXibj49qzcG1rHgMhvjc5a9OWTdbbGUQEzlf6y7rw\niXJeTx4ncWG7liema+NtGwqNr3YOvusZyEf8JtjA9asA8t22WaVvB9MarjSepsNc3HYzVOk4eq29\nffv2MZDYDFz3n0O0aIzyOaA4XHH0Pv3000f8co1GZO7RUeSzRXEi6eDR2WafdBi9tXTqszmFnAM7\nfN4iSP1E/Uv5wfm0TiNM+pJrbdKB1KfG3e0F7ymI5/F7HTZg8ID2CmnX2vGYTU/KSV5nn9MYI18Y\nTM69zPlu7E3HcB1N9UiTfHO9EBJcmhw+ttsc9NBosikYnEp5Xs+aXOv5+/i4nkizZo9Qz7VHNCiD\nOL5rAcsDPgwOx+8VAYX0WpdGYYtSN8FhsBNhozcQgWMDJ3hYIOyEO9v0//SfrA0zDJOx3iLfVsJs\nM/cZaf/kk0/W/f39xclhVIhUGFQ0NKIseIO/M5fEhweokB6h8bXI+I7ewc8wOVGJrFqwN4fOc0D6\ncr4cgEjd3bw2x6q1YbwINtBaZL312Zw/ttfqed00x77hGh50fQYufGpkYHKgORbiQKfUBtnu1Rzk\n29Yf1wbnK44iFb1xD+QABfbbcLLhYrzs7PAACK9Lj4HfLh/jqBmA5H3Kotyjk9FOumPGbcKnOZOu\nw3ne8Ut+E0+OjdlkGtwpR6POaz1O2t3d3UVmL4Zl7tHZoqPF63Hk0ua3vvWti3usx7LN8UtZ/va8\nTc/4MejDwFXqTJk1y8smx7xmdnrO8zzp9MwZdZd5d+LjxlPtegPi7hMmm2PZHBTzsPUNr7G8A93N\nqWK/tnF2OAVv08/Oo+s0Hcy15HVHnbbW82ccU2/S56GZZe2ES3idGfGsJ64f9j+1OfFWO0At7Tea\nTtCc768CL9HGxwKH4/dKIMzfjG5vF6LwtTNyLTo1CT9GiKgoU6ZF8/I9CVb2EeFiA5/OnxUrBXPL\niKz1XOmez+fH6Pb5fJm9iCLPqyAyJj943RyVZlBTsdPhIW4BbjULOELdom63CMw2p025pqwFtuk5\nKW3T51oUb3KunCk03+f/u3fvLt6F5LZIM/NAU4Yt6EHeYlmOm+3vMtN2jNp6nmjjdrhezHOTEXft\nHvHJ/bu7u8fyCYw0Z9pOjqPck4NKI9E0I35rPT8wxkEA/jefTrJnkqsEO02BhrMzjs2xs3PRjKjM\niQ25nQHI8UxjdqCC0X3LL+Jjh8KBKuL06aefrrdv365PP/30YlvnWuvCQbOznPu55+1nzuzRiE1G\ncFevydKUmYKWGWsLFvBjWcz5m8A6z7zV9AzruS3qav4mvuY18+Uk35rMuAV2+p/9UzZZhjQ5Z76h\nY95o9+7du3V3d3fxfl+vlTY+z1Hqpa/J0WtrmoEwz0+Tq+yzBddZv8l102q6b1owSGh6WEdYB9q5\nNQ6mVepNuzMO+GpwOH6vBCanbypLwWGB2RZsIFEoKnz3aeVpIdEMUV6fFMEumuVtZVZk3Nri+nYm\nmU2kcxenh5HltJ13Aq41n54Yh9gOBLdQ7TImLTLsbRGGyTwiFqYAACAASURBVOmajEnSuvFFw2Uy\nIPNNfmr0vuacpo/J8XK2da3LZ0CZ2aFh7G2XaYPvV2R/uxeYOwLrbxswrMu2mrO7c/LcBnk/vOw6\npIHp2HCmgWuDI/zH52QbbdKe8ZkCUB6Tt4iyLDNlO0eNhlOj88THUwbQv1uWNTwUucE+uIXS0f3m\naLRxO8vpNcx6k9OWss3gZLuWpRxr5BpfA2FeS5vJ+MUBtK6wfHUm085fxhc+bG0ya2HHj/echeIc\nWP/YuSNNafzzQ2j8aJp5XbCuMy/GbeeItXV0Td9OPNF4hm3a1phwJu75pp1hB4VyqdkU5NfQt+m1\nOBZ+GTll/TQXbov95zeDM208bT05wBa+b6+pcHtsk/Wbc7ezG3yfNLQDbf51n/m/k2c7PK/xzAEf\nBofj90ogRkWLDq7VHZwWKcuCZpSTbRiasqACZTlHfthuMzSncbpMjMtm9Dc8KZho2FFgN0MwOD48\nPDxuSQpOEcp2Qtd6LsRpVHBLVMtOWXgSGAm7Za48xkm4OmJnfMw3zeC148f2JyelGabEpRki1wxz\n9ksnI0rdz0TR+aNSM509ds5pizT7d6NpfptubetLc8JI04mnec3za1oQP69dj7sB6ewtqTTAgs+U\nOfMcO7t0S1Yw4/PR+im/C2Jw3jwPHi9xpSzlOIPLWk/PFZum12ShHRP+psyYnBWD5QwNVI6Bznvo\n5nXlfuisrfWU1YsDyFdc2CFjf7mfe35/mPkr9+zYMcPoLZx2UpsMbPQkr6/15DS0QBFpxN9tjohL\n+L4505RflguWbe7PbRvIV00/2lB3WTtC/m76pukDbsd1f9QhO2fC9Lb+mQKRrkeaNvmVtnKdO46i\n8xt/p6+0bZ1p56/Ru80Neciyr2Umr9kddmgbrbyF2DxKsEPvMVzTM5NM/hB4iTY+Fjjc6AMOOOCA\nAw444IADDjjggFcOR8bvlUAil+05l7We7/Hn/RapTRSoZQQCU5bO2cJWZorsTVEmjin1nVlh5L8d\nHz+158hksnqO1ifDligu+z+fn54NJExRyLWeb5m6u7urmbQWGWSE0tFaRkDTz7UMQiDReY6r0cxb\nPb2dpUWXueWP9/iM5hQhbFmm8ACjoMwGtdc+7A4WYH1eS795no3QtuQ480T6O6vSsqWN7tyuxEiw\nt5Elyuq1QXpyqw7LtEgraZd6XjOMXnMOyc9TxpKZrzZ+vgrFNDeOpEHLXude2m1tcLyB4MBj4y2z\nWgaWWSBu6WSb4cmWVfVWR4+zRfqJ+7VslcfbZG/jC9NoOozBsjr48XAXnuDJssyWcu4oC1mPmbwp\nA+UypiGzmy2TZnnQsn3t3rUsq/XDJEf9mAXLWlcRWsaN91iG8q/RwPPUsoGNJwncbdRwYjmuU8oC\n6sLGd00OU0+2E5Sdnb2WBWTbja7mF44r+EyZLMsZ8mj03W4ngbNlzmjnXpM7xGFaM7Q7rM+dReRc\n7ewQ21M729PwvZStewk4HL9XAlGGFlpZQFxINtJsSDblz374e9pC4LJrXTpWdlKmejECDFROHh8N\n/1u2AdD4tnD1s4HZ9kel5PaJc1Nu7dmVNn5vHSGNJ8WX/6zbjAres4Lnb85T2rZjyVNjHTDYGUA0\nNFxvMnY9XiszO0d0LvJMFfuaDONmmJB/iacNcTvs05hY/tpWltPp9HjyocdJXtwFO5oR0MoQd16n\nkWvez9zbOeba8qFApDEddfPvNCZft3HC8aQ8j//3FtGp7WwR5TOeloNp07TxllvjRbnlbxpRDT+P\nlb8nHJs8tePj9cv1RWjPJbq//LZ+iOMWx89jd7CEdWj88555uskyGqwZX8O3OTEZp9cede3k4E0y\nmGuK13iP15uRnTanOaYd4HapOz0HLXjkMU1lprkwfh6f+yAkmOvAlfVX1qCffzX91urPV3q7bu6x\nTeLqgORO959Op4vnlSeHvfES5yeyhv1NQJ3VgpCTLgh+LkO8m7wgru1au8e1Od074OXgcPxeCXhx\nNUeuOX4WHlTmzSDZCSca8qy3i0DawCAw2tzwpKNG4UNDfCc0JqWf6xSuaz0d+tGUh9ujQcKooQWo\nT5MjPpxDOkcpM81Lw8sOej6mDRXDTinbEGhGfXAj/gYrcdanwXYtAOA5aTyW68yetWikx+tT3hqf\nNpzsALagivttWXU6R7zGDw0SO6qmCd//2KDNdTMMQxtGmf2fa/J8Pq/PP//8sU2uX9LLuDSaMdBA\nw8lGGIHPCK21Hh3pZmA0uk33mJ0iPnQKTO8EkHjda7QZ4JRBjV4OCJkerT86WxPdWK7Nh8fiupZv\nHGOcOLdtZy74kX/Zfsr7cBcbrJbPdGLcZ9qh/G0GfYCyc7fuKdvWmk+ovNXoJb94HuzQND1OnOxI\nXXMOdjRo/dFRnoIQ+c2gnWnTbAfLJvZpmqcuM3rN4eK16UXicQxNA9OCOmSnF6dAAv9P8nuSTw03\nynauGTt8bU00x4/0dZs7nDnmdr3x2QFfHQ7H75UA33u21qVTwWv5tjFhJ8NOYoCRf0KLIrIOwUqE\nhltb+NN4r0UbJ6eCUUMCD1qJEWun0GMm3lTizTFqYzAtfFIdjXxvObHgbveCHzNCFNrtZEsaLW7z\nmtGTMqZbrrXAAcv6VEDzJetNSphlGuyyh3au1rrkeePcnBYbJLzH9WT8zAvNwGE7HE+LGjd8glMz\n8m1sNsNqgtCLgZ9cz+FHjKBn22TGwjH4vaNT9orjJ112TlGyfl5zdJCIJ+nSAmbk+ZaB/BAacrx2\nzqaxTwGTZpDZ4XIbcZpcj99TAObaFjO3les+ZZNble2I0PGzE095ZueuOZx2sl3P+i9lyIvJLDkz\ntPvmXARaFrEFb7y2m55phrXXpp0wjrnp1J3jZyeV3zt+p9PT+Ml8SOc7NJnsEjqWjS7s0xlb4s8x\nTLqk6QTrwDbftBWa07Rz+JoMIB7NTkhAzoeWWVa47eb4Tf+n3zsZZn1HB70F9Bp8iN24g5do42OB\nw/F7RTAZrla8a11Gl5nlo/BInSbAW5uT00A8dgokQCXY2me5GCLNyfVYCG17TjPubeAbV47r/v7+\nmdFKo6E5OjQ6bJCdz+cL48YGAgUkcaVjZPxNj8YTzB41R6Q5uteEZpsL0zttma7BjWXS587583Nj\nDSeethZ+alF/KtRm4DdoCprrgnRrfEangmCn2HO/w6MZfSyX+3SGUrb1xzanteZAQxs759e0aHRi\n3zaqUtfO3VQnDghlBrO8dkJJCzqw7tdylGMKDadMWWQaM4O+b5lBZ6o5fs0pSr04xHaMSKN8Epwh\nL1Huh97urzkppEH6IZ/YKUz5BBMcJGttUtc1o9W7LpocNo05Bwm6tmABnYCm49Jm7jFA52wt5WTb\n2p15tG7mmO2gWt5wnqZ2jAvb4287P7zfnBSuFT6zTfwyBp6ATR2a/wyExznfyclp7gzNIWuOr/mZ\n/ZgXJnui0Y28YruK9oXnMDLtzZs3j7TlYw9N/oSOTQdN42Wf1hPWZ6Rdu3fANweH4/dKwIp+racF\nSYdsredRHAsmG+PN2LLicJusRwEfRUugQG/GYxPWVBbOGJgOrW4TPm27TVNKjpz6oXtmI/0cTHOS\nWuSWtGV5j2syLHaKjUa9651OT9HBpvha22zHdEv5pug511RaHCeNfxqVk8NCZRUlZ+UyGeY06GhU\n5n74bJq/KSo5OYbX6Mm5bXXidNg4Ik0aTk252vlqdXeGXoyr9j7NHV0yX8Td4whPNmi0cYZnrSdH\nyrizj2as0WCioek2Hh4enm0NN1+wDzsaa60Lp5h4sC86g7yW7yl67+CS19C0nZNry21O2SWWtQOY\nes5q+jczgV73vM+5dj8cE6+bZ+hEmw7WoR4/dVeTpQSuLTt7pg1xu8UI9jojTS1Xd4Y6ofFRc2jz\nf8p6GjJXWVfekpnva3KTzq+f6SPetxzkRJ2+C/JO1ycnN/hxS/KkL5pNQr3W7rPMTs5MNlH4xvd2\ntCe/Nlna7Ba2aXzXunx0h/2k/C7Ae8CHw+H4HXDAAQcccMABBxxwwAEfFdgR/jrtfK/A4fi9EmiZ\nNGfFpmitr7esjculfUaWGO1pWURG8xxd97aZtJ8+GHUlPi3L5CyIcW6ZPf5nOUenuGWkRbcZxW20\nnbIXadfR3rTpaHLu57TBJrRSz9HuRFoTSfO8MhrrSPG1LNKUeeR1R/KmjFdwbFkdzrH7dBa4beMx\nNN4kPdIPs4KBPI/C7YLuzxnptMsMGee/Za9buy0TQf40T5Febo9j573M13QE+rt37x4/3ILF7bgT\nz5Cek7w5nS5fMRJINNvzm/FRBnCboDNCjtZbloYPmbH3nDHbl3FwHVn+EpdbIvK8ttvZEHz5zbIt\nM8YxNtnZZJ37oh7It/kwc0j6OkuS/ltWr2VJW3+m5+4eYdr25jlo2TPKK5ZJffJY/rf11OjdMiRt\na14bj/F1pm7Cd7rn37YBCM2O8Lw04DZXZrzIS21OsjZ28nL33P6UqfR8BEffZ33iwHvk/cjFhqeh\nZW2nbeQel3Vl66tl/jyuAGWF11LjD/Nmm3fO3W7+DngZOBy/VwJeNDT6mtGRbwt3C5MJXI+O304J\nRqiz3vS8A/FJnxQK3F5DAeMtV41W7VkIb0NsRrwNQALpP20r8z07HN4aNm1x4Pju7++rMZY2moNO\nhRrgyWkOInDLHOnRfrctgzSO0laUcJ7ZYD9soykt9mlDknQ7nU4Xz/rZSeK4Mqc09FNmwiF1+Vzg\nhKeBDgK3QhNCH+LaghFt2xwN/VbPuNB4YvkWkOHY4/w9PDysd+/erfv7+2djbAYA589bvoxXoPGv\njRmva5bzFriUoyHjutyCafkV4NrlvNhhnRwW05Vyhs5PM+YdBGh8FB5hmRaIaI6Yf7OeDXLfy307\ncM0JyT1u88xzh8HP21nN98SV9RqunmcCecOOSKNN5p5rzDqXbUyvGvI2X+vKydDm3LYx7vRDcGqO\nm//bofH/tEldOeFroK1ip9FjYxvsy2WI+84maE6qxzLRxNDGm769PZmPIkz9+555n7K74eOAxm6e\nzTOWma1+gzYHDm63rblcx7tAxwFfDw7H75VAe+aBxsIUdWvODTMyO8Xoelms3M/e2iE+zdAg3lRo\naYvQ9rXHobBg4TeNNBqRjGbRoA5dmpETpZnyifSz7zZm4sExTwrbRp6NkVuMNUfW2vMHBAvchufk\nsBhvGnueOxvNbcwNaGQTx9DHh+Pcwj8OJDSjo42TYwmwblPMNO4m58jGw86IyT07RMHNc+U13HjB\n+HMcfL0AM968x7GaLsSBPMt3XbFuk28tKJHrXms7njUNGpgHmjOd+5YD7H8XSDDPuw3zdTNym+HU\n1hbxsnNEXNo91o3ca9fDGz6kxTjkm84fn7WlQzjxd76b3GLb5gPzhp0K/m4GuHm46cfWjgM+TU9b\nlnp+CG29NbkVmHiCv6dAB3872Mf2m9yjHgpk58rkFDenZK3nr9tpwWNn6t2m54RgWcgx0Lk1vfwh\nrmzLMmRyxFy+0afpCv7nWGh7TDq82Tup2/jQZSe+a+PjGrU+3rUzObwfCi/RxscCh+P3SiDKlYqU\nWZBbo0k08taa31vDdlOPQsRZiBhUuwhZU9ZTdi1A5RlI396u2Pq1kkibqW9jm06inQYKUG7nWOvy\n4WUrFp/4uTOI2b/L8oXUNA45xmQh7HBw3BwvFQlpye2lzfh1m83xdTbkmnHSIoCTQs7Yk41KnR0f\neUuis4g+qCf1UrdlUKY5Nz5TNmwX9WxOIaOzzcnLWGzENoOT/ZFXJkMu1zivjGhzDMyinU6nZwYR\njcB2wEfjFcuXZoBkDDbWKbMYDCFO16CVoXHFdptz3gy+ADPYtzifdGh4j7zYnOj85xzawGvGWjss\nxo4f596OpOc3Tl9z/JoDSjo04zN4pj0brK5LsIx0HQfrQls7MCxvB8f82ZwG8kc7fMuywHPW+JO6\np61p1iPveLwZc+pZ1jend6Jz003uyzLFenrnKDVZvHMsmv7xOE2/Jk/z20FTts92Jn3V7APyTOoS\nL/IaZQLlHG22D4GJD42Lx2N8zfPm5wNeDg7H75VAMwppADfjkwu2RfdpsBi82Cm0LIQi6CJkfNJi\n8LdRGHxaJoj3p73yO2HOvu0A0wjh+KyQbKwEovTzm3XaMwZWcE1hUWCTpgFmV7L1M/O/1qUxPhkW\nHlcT6M3oIp4tshr8rdgmJeBrzfFvL8sNONLdMkKNN6KYbDSv9bRFZ5oLQnDlXO94MX21zPwEHpOz\nX1M/OzCuXJt3d3cXWzhNS+LjZ7ny3B/nnmvLL9xmwCcOoI34RlfzlZ07rolpvdFQClgGXqMj26Xc\naJkI4nbLfLNtGsrTvZ2DToPTp34GqD+8kyO4U2577eU+M37532S+M36u56yAx0a8rFvSp7edki7N\n0M4Y7AhRFkzyZBe05Bw1x7NlYvif/Vp27ehhaDi0/qZdHs1BnHSJ9cDk2Da7o+kN4z+VafYK+7oG\nzXls/aavdo2/SSPrT/ZzS+bM/TT5yLZtNzQ+tOxqa43rd+L/nW72d8v2Te0Yl2mNfQi8RBsfCxyO\n3yuBaRtbU9h20NZ6Hrlr7xPaGbHOztl4cGS4vWONi58wGTD5vr+/r0LzmjCiEnI/k+A+nU71QJLT\n6fQsk7bW06sd0h6NTn639zLZ6TFdOS/cVmaDwXPRDKp8M0JvIyRteY6o+HPIB2FSyFRupJNpTsM5\n47az3ObaSjB1m5HDthv/0lj0eGwMtowKyzQF7Dm8BVLnVmfRdU3DwLTW2uFFvJ+MiueV6518Ttk0\nzaeNw5YlCnB+fM3j2fGkI9828igTk1GeDilKmXwbxzhd5jkavjbkdjSgg2nHYKpDBy1zQplgp9A0\ndf21njtynkM6Ye35Px6YZPnUxrJzctgOx0h6eo5Zl7S1Ac15scNE8PyyTuQ8601B0Gbku/+Jv685\nfzt5MN0nH7P/nYEfHWpZ2wJ0TV80/U7nxY4KcbFszlomTxmPCQfywxSYvibPdwFEyoDG743nQ4cm\n97xWrMMt1zJPHrvlI/X2zvYyHzqA1NbHdO+ArwfHE5MHHHDAAQcccMABBxxwwAGvHI6M3ysBZo3W\neopy5p4zHy1rwXuOZq7Vn2VwZJ4RNAIjUT6lztk/R42nSF7aZLZprfXs2YopMtpo6Kg+o1y85khe\ni2JNtN5lJhhR9kPrbpPfiZITV0bIc4+HJiSqzoirI5+eZ4+ZY29Zxrxk3HTK70Q1+TxcG7PnkHQy\nMOvKKG+ymK0uI8G7Zzk8hqlO6nnsKZ/7vJ6MMK9d6//rRkm9fslXLdLdouKs56wt6zibzeyaX2Id\nWniumCGznOJ/yiPj4bE2nvMzdZYLzFxabkxgPqF881z5+ZsWHZ9kTq5F1qa+dcBui5tlAjNm17I5\n7s945j/lEDNcTQ55XOljx+Ou1w6G8XPQ0R2Nb6gH19o/Y8X/5J/gteMn07LpWv73mL2joGX8uH53\nfECc2uMUXEvsx5miJhPcvnWa9ab1ZctOpizlie2WD5H/u8cTiBfbtF3l3QCNt9J34zOOmbTzXDiD\nx/adoQtwZw9l4rQrhTRsL3InPUgD43VLZnCa3wYfUvaAw/F7VUBDx8K3GRZrdWGb+xEmTZE0gzPG\nWo51dx90NPyMz2Q8+UH8tsCzRa5tFUn/bU+/hRaduzikpke2dVkJGkcKc5ZthtdkrGYOYxg042Gt\nJyO1GUg2WkL7GECZE9Iy39PWy+aI8DfnltvYaPD4Xhu7t/w0Gjd+aMZw2nBbfpaL3972wnbthMZx\nZT/GveFrBd2UOu+3Me6M3x20tT31zyBEW18eZ8DBGzq93sZGZyvOYIPppEzTd2ektfttTAFv6+Jc\nk08aPYLbRDc6s2utZ84JwVsVvZ2T2zEZ8GnbLe1ApZ6Nz53jZdwCPsDFjivHz/FwC2jukYbmU9Nl\nKmPaEJfIJcqEFpyx08T7/k1css6m4JB1V9OvhFvWu2XFZIg3PjJ+TbZZTjdHzn0HSD86RZb3zfGj\ns8n2cp36cqIHx2GZ5TmyU7sL7NhWYFCMMsL2wKSbP0Sus41pbbs/P/Pqeu062+Aaabhy2zlp12SB\nr7FN2xsHfH04HL9XBJMittGVe3Fs2qLaRdO8MNd6bvBT8DZBzAhvlEfK2fGh8KAQsUBjmzRU+d0M\nMI+Pzh0FNqPBbT982uf40qadnkbjFkm3QWA8+b9lE6zQmF2xwdO+abjnuyl71jPQ+Gl8QxyT/SXN\n2G+jG/tm8MDR2+astmzJZAD5wJ61Lg+0YNagQRu78WoG565uG0NbL2tdHlDEtUL8vK5SL/eNF2nh\nPjkXqTPxGscRHIjrNI8tSNTWGGVWc15Nl4Znm6f2rLINHT+Hadz9jB/xmIyu1OP9rFd+M9PP69QH\ndIj8bC/5O31Ydpu+xMVOpdtpBucuaEXa87edTPNwGyvBMrfpCOIVoDPSHHvSZZIr1EnOMLV6zZky\nns3RmQISppnXDPu1w5e1em3HQ5P51P1t7F7HobFlkOs7a0ucM0633XiCuJqG/t9kJm0FXrcuJ29O\nu7ba+jFeXFMOnHDtmNcdbFnr+VkA0xrN/wnPBg4M8LdtkgO+GTgcv1cCWXztsAQLOyp1H2Gf+1RY\n00K2EFnryWD3QRUU0rvFTicxCpj4WJhnHBZuwaU5TMbX444AptBmlm8y4mhMOZPCuq7flCPxyRiN\nf2hMRyfQotaGKEmewkjDiOMhX7U5IM6cJzsBTeGybnChA0gl2mC6bue/OTNupxmcxJfvckwfNJjX\nunRgvF33miE3GRKu0zIiqU/8nenezR3x5FxxDNMhJpNhO72T65Yx2hDl9WY4uc3m6E8GuueV91o/\nlK0Z5+QwUJ5Na/39+/6ydzsblAc02nLNxl/4JK8xYPbGTi7ruZ/m9DSHwc6Wt26mHvFsbdqhNZ4s\n7+/m3LGfaR48nrWe84uN8ZSZ5p9Zn4DXg9tsWSHSjf9bYKPp5NxvTpXbbnIyeFG2ZRs/T4bkGAke\nL50GB5YaPdm+9TDth3YoHXXzzqlIGdMt+NJ2sV3R5GKzV4i7M2INn9Tjbqm2HiynWkAk/73ew3/W\nYZQhljMO6LS13eQpeYhzQV1ju2WaL9JpFwi5FV6ijY8FDsfvlUBbgJMiWOsywt0UwmSgBiIkKWDo\nMNDoZVS8GWUTtK0BVBJ0CBh1Ml52RJoDwjHsBFeD0JL9WHkQ59Zvo43xpIBvxgPveX49/szPmzdv\nHueHAj3OL7dg+WTL5kTQIWI5ZkyJp+lFg9NO0y6TZgOIxo7pzXuNH23UT3Qmvm3d0GBw25ynHZ9d\nM3K55lofuzU8RenP53Pd5rybg+C6M/7c/2S4rPXcWfIa2q3L1h7nh+vVuDTD0AY2v7M+pih26Mb1\nyHIOtKRNy6JAc+6aQeYoPg04BnomXGw48p6DDtYBrufXdWSO3Gfrj06R6TvxtuewbWec6pIn2dbE\nF+xjWucT7MpYnudao7nHPDkIGZczUJafxM/OF9t5+/btenh4eLYGaHdM67TND3V6+qUc8C6iRsfo\nDfNM05cGB28bztYJzXaibGkONvWLedK83+RF+x3wNQfFWc/OXeaU5Zz5W6u/fse8575Iq8lRo+72\nuA54WTgcv1cCNDDW6ovO0AxHbsfzwnPWKW2sdSlEUobRqhj9TSlNxkN7xcFu7MarGd7OvNn4p8Cb\ntpE15y7/7dyx3XzsvKb95qw0pRuIwG0OpcfUlJPHmchtoosPDw8Xzwi1rEHamvDMvZ1RdG1+gz+j\nzVa4kyO/a9u09j3/btem+/kfnJ19JN40gBsObS0yYOP5Di+1sUXBezsU26KjbtzdX8PT9IgTyTpv\n37694JnT6Slaz3XbHD1HzSmDWmaa9CYdrslE16Nj5udqSe+2Jdi0ajDxUfokbeyMcfx2qoLf+/f9\nfX02widHzAZxw8V0bJlJ4954phngjb8mhzxt3ALk6clZae1yh0ALNHht8Z636dtY3gU3yO+81hyB\nlMtcNB3k5/EtP22Q5150xcPDw9Wgj3mDNLdjy9+U+eQV4kxaZZzcyUJZ1xx44uvxe96t6xukTsv2\nWrat9fy5ejt/jWbsx2vPgZtGW9YLTjloiThR5zOA4md4P0QfUHYbdoHOA14ODsfvgAMOOOCAAw44\n4IADDvioYApyfpV2vlfgcPxeCfzET/zE+rEf+7H1W7/1W+uXfumXnkVLHHmaUuqM3F277/Yd/WEk\nKxmEtrWPHy8+ZgoJjsq3KGfbiuBXPqz1fItK2+qRKJ2fHSAtuX1yingyC8DIbyLcpF8i9MHX90iv\naxmMFu3jGDjORHCdCQiETrvDZZxpIL+1vqfMHOeD/Bh6tGyQx2N+ZjlHlLk9tvEcvydgufaMissx\nG+OoqNeH67dovLcUEtwmt2nxOVZm/HYZVkaMm7xIViDrhm0weu/sVLZrMQNJWuV7GudazzNuLXru\ne61s403vfri7u1sPDw/PnmFx2y1rElwbtHXrT5OlLRKfOeJOjFwnLzljED5xhtHbTt1X6jtj2LKO\nltXWIR4f2+b9lhFp1wItI+s+duvda4I6hRlp8n7bIt/0YvRm639Hi0nm8zqfJ/U687P5zJR5PTEz\n64z3pI8sq2yX5BofEfGjFNR3xKtlHcljOxnccKbe9a6VnaNgGeh7bd6YGW/nFkxr23NPoF3h++fz\n+dnzei376MNdWrav8Rrx9u+2XrxeT6fT+uEf/uH1/d///evzzz8faX3Ah8Ph+L0S+Pmf//n12Wef\njVtvqPyaEWfB164FJkWYxev7FBCTom140rjnVg/WnQw7tm1jJQql4WPDjIeNULianhl7lLWVuoUe\nr1tppVxec8E+b41K0YnJf7ZjmpFeNgSJGw1uOqU2eJqwT/2m6FOXMBnjoc1uq0gzVJqz0IwvG1ue\nN+MUJ6U5Is24nxyoaX6Djw27ydFyv3a2aKz5fXVxgFyQCgAAIABJREFU/BvwwB2PkYcD0ME3PWzg\npkyMkJShMUoeCY1y8m5rc60nw9C4Ejevg0ZPl/V36nGOGv3atvXwTniZ/OHABOs0HHPNxhrLTXzN\nurvr3K7Z+mPZ3Pen1W0y6NqYLdfamDhubk/nXE8BJz/HaBmWewyKeMvktIUyv1vfDsxYB0zGvw14\n02IyzCkLOCe5NzmedNgmp8J4r3X5eALXN+s0neAgY+tvt7WW0MbTnEKOnbYI+2h0oDxrr5UgzVOe\ntLHzxzKsMwVLGo2ok3dgmeigTegyBUmpm9pYie8uABD47LPP1meffba++93vbnG+1S7awUu08bHA\n4fi9Eri7u1t3d3fPDCo7J4Fd9MpGvh2TqWyL4PK3DdZc8z0KNO/PJ9AxIzgSTRyoWB8eHh6j9GmP\nyo6GzN3d3cXpZU1hRTFEeAdi2FFhk75+LyDxSfkW8ZwcRkYLaehyztocvHnz9F6/5jDa6GAbMXgn\n5ZmPFWXaY2TcY2rGO8fVHK70Yz7k9eb05950QIB51E7VFF0lnpPBNxmhqd8MQP4PeA02x4j9rrUe\nT+jLGEy79+/fX2TuXN80CIRPU9eH/mStsD8bz7yW7xwq8f79+2eOlmmVtoyr59BGDGHKvnCskSWf\nfvrpWmutzz//fD08PKzPP//8wpE1vTw/cVSIt/mM5Ql2HKaMmWW19YXvUabY4GT7pkvLCrDs1Cbl\nt8uyLa8J0scyxPLH9CXt6Hg3wzrzxN/kbzqEOwevZarsgFsG00Foc9jmYwLKV88913MzrqdAHeVs\n0z8Grm3SiwHFneMc2nsu3cfkGJlOt/BU+pue3W66jXRq89T42/XanNrWabJuWqcTTs0Zb3RygIq0\nix4NBMfIN2dtdweHHfCycDh+rwTevn277u7uHv9boa41bzHxgm6KM2UpYG20WFjQkKNin7J+FpK8\nNkWE7OCkDxsNxCUOcu61I/cnxRwBdUsWgL9bND0GFbfC0uFohrSNY5YjTTL2nWNk8JaOZnSlrnE6\nn58OFYmDzPE3fuR190HwHNMQsYNCxWmjyzza6DApudRP/xMtOQaX2xmBXmu857G0gAb755ohtPXI\nNlm+GWqmO9tq9CIPNhw4Jq5f4mFnM3VyYFQ7xMFzTJjwnZwpy5DmGGXOiVPKZK7u7++fOagMdkyG\nMaPqxosOQPpr+PNecGL2jsGe5qS1bV+mGesbv3bP64RbzFjGNJ+czSYrA+Qz6zY7C3ZUTOMJmsNh\nfbmDycnjmLye2j3Sdddno4dxnRwbjzcy2AHLtZ6vST+yYAeFNGvB6dRtwafIyUbvScdM67617WAv\n1zrLEoddMK/xs/EyPzY52XjGuHhd5veOXzyG5sS34CLthNYeM6Gkf5P1B3wzcDh+rwQmo5mGsZVi\ne4F7E8SOcsVgcVkqASvznXFjI9wGanPuLMh9bxLkjjKxP25law5CPi3TYGVkhdJwcnQsv5vh6owB\njZHQ3EZQoCmXFnFshtrkiDShTqXt50QaDh4/nX4rrEkJmK92yqLR1bwd+lM5tePvW/3maO0cWyvn\nXR/kkdCJjhhpwOwdYXLyApz7FoDJf0bi29h20WzTvs2vjRwGEix7uHU7c+bgVNpkNobOlNeK8eb1\nfNPZ4ngs92g8tYg2eS6QVy7QcWT/zL75tEzSms4KnTy3yW2CdnCas9hoYhyaLKFTx495NThy/MaJ\nuE1ghyDXms6bHED210619v/GS01fmu/bWvP4iQdpZ3objylo2sZAHePdNnYCdg7ONPbmdLX/1mFN\njlIXRh8358d9TDrN9aadDbzWnOO00QLtlkkeJ9udnELjExp7jTWgHjEPsV3LSOsY2gGW95yn5ji2\nse+Ccbfw7ks4id9Ljubh+L0SoEOW/2tdZlwCNoZ2URYLsGbgGqKYKES4JdOGJ5/t4TchuFqxThFe\nj7Pds/F2Ol0eE+0xRWjaIXC0yzScHB8CX1jr7ULGMWPaKduJJs72cLtQylGRcAws0wynaVzGsRkn\n7Jtjn8p6zIHd2OkMNJydCefznY1OxMu8wbFbue0c1ckBbP+tjN1O48PgRUMn5XcvZyc4uu/+dvKh\nBUcm/Nkf69hJzzy1+TSeHnOutcxSfofOk4PDYFK2dqb/bBF3uTY35BM6eDlSnWXfvHmz7u7unhn/\nrGca8znMN2/eXBzuYkfTa785aXZKzIssbwfVR9fbCZ2cm8ivWwxO8kFke2jfHEF+mtNBB8Fzl7b8\nO3Sx85X7PsSDdXif/VCOmGeJK4Mjk1PLMbTrdPq8xhnoyzXTNzKR9A6upmdbdwEHaij7uM4jq3dG\n/K26rI2L5VieOFi+u03zlvub9O7OCaT9wPG3wIH5aHKwJqevBb+MW/jWfM9gAnnbNgfr7PTEAV8N\n5o3XBxxwwAEHHHDAAQcccMABB7wKODJ+rwS87WCtpyhZi47m29Gaa9Et79l3Vu18Pj9uuUhbiUBl\nC48zAsFvylilrSnCm8iVo7zXMhCJMPoghbWeZ2u8LaHhR7x2mbopMh1cGQVzFJp98xmhliFhW+SL\naQtLInvOqqx1GcFzPY7DtDHPtC2gjpTnmxnARkvPt3+37MouEsx+Gz352oGWUeF8TDRqpwu2zKaj\n977HuZqiwh7/FNElLu0wJeLoOTG/Etq6JVzbSuVs1Vprm/VPtJ+ZV8ol8tvEW6Hp7tAol8/H29oi\nC3eH4rSdD8nM5YXKyfywP2YDmUljdo608cuZPRa223ZSMGvnrCfpYTo6s5d7fj2Ex+CsF3HIGJw9\nIS7EjYcH8XEFzsW0RtzetMPA65hyrckFjtFZzeBs/dyye87mEOcmC3bbD1vmhmX90nTvAJrkDXV+\nW3vGsY2H1yiLOFZm1a/tSmKbzpBapjfeoFwx/t5l0Ppr/5t9w/m1PjCQz2xDNF3hdj0GyjRC9Fjj\nGfOrdzmEX6wbpvV1K1zT6wdcwuH4vRLwtotcu+b8tK0HNNRtyFBIui9v0+A783IsdTv22/UbjjTY\nJrBBuHufUMoTl+DGkz6tsNrYU5f9TcY4/1voRzDSWbPxTeU0GT8pxy20k1PN/1TQniMfaGO+YJ2m\nYNpc+L2OO8dk4t/mbGT87Vh908t98h6d1Pv7+2f0WevyPWPBpxn4pH8zqptx19YJ74Vf2nYe/vb2\nWcoEG23Zauwtup73Sa7Yuc+6aEaCDS0bfjRimlGY65wLrnuffJv/Xks2fJvB5nK+TllL5yQHvjQj\n3rQL3jl8KvUZnGK7dAzXunw2zg6Vncp8cs392eBs2wv5m/Rhvcnxa+0avG3N870zglswpRnZbVt7\nA25VZR9so91r6968F/3Gvho/+pv95Hr6MU0ZhPUzszsZGPAjIc0BnMCOBfWcdXbmzQEej9e4cA1a\npk1zw3uUN23ubAulTgt++N3DlEPsy+vQgRfzawsGckxNjlrfec1O9LXsbTzOsTS9Q4eTbTf9QRsv\ndJ3m74CvD4fj94qAQssGGsHGRzOG85kMQP+3sGy4MYvWlLkFRe5Pij4CgsqO9+JstmcM+Ns4+9m9\nlG309dhtqFpoWYD6vo0uO++T8G0KPPd2xyZbcdGZaMZKU5Csa+OEDm1zFK1Q7YhOPHiND8kbLSLt\n9dGe/6Fx5OP8zUNsj/1YsRH83CDb5dxcU3zsZ+e8pJxfqZDfzuY3h9GGR+q1OeQ9OyA2BpsxS97l\nfLM/OuPkNQdFvEYnQ3UyjLw2AmwrbfMZ3WbAE3anRdIQZLaOTpodOJYh3Wlo2plkH+wzONDobDzb\nsiw0Uj12z3eu8XcCTaFj2rwVOI92OtjmVDfQnC3e261Nyjcasi2YSMds6o9gGdXWJulGWjJIGVnu\ntT712U6xnP6TPhxXe7edf++y5JazDFb5HvGwjriGQ4CZ4iZPPK7QdGov9ODzrBlz/odnqEenLNtO\nH/r+LXUn2livNQeU66WtHWd/G43IA+2+4ZYx3AIv0cbHAofj90rBRhxhWpRrPQkYRv0tRFLOyt59\nGGwUT/ds8E5ZpEB7mJsOC4Vyw9n9RKgxI+UXuNMJSJaBisFbzTzW6RqF/du3b5+9NLs5sFRMa106\niDTYg3sc4kZPZlatMOwEToKSRk76YfbRTkpTSlMmkv2z/eZgt60qzOaannZCbVx5jAH3b0VpvFv7\npkXGwG3I5tvmGNpYsFHHqLi3bsWwy3VvD25OsXml4Wmnmdc5DvJXM745PjtchNz3tmL+No+SDtOa\nbf1xDrJW800cnLWzI8qtmCxLh22t9ejsxZh3xqDV5ZhpbLoef/s/6zW53Byq9Nnkt+nZHHtnhSiD\n2Ee+KdNakIZ9tzVqvmv3fc1O3A5It7Zu3F7DZae7J5nOQIhlomVuc5oIdsZ3+PC+HZh2iFrri/qU\ncovBXGYjb8lg2jGbcOf18Jfnuzl4lC8ef661dcHrjc/ZngNUTV/nevvv/poO873Gj5O8nOi54y2C\nAzdt6/0BXx0Oar4SaFmNLMwmzLmoqWS4LakZ3JMiYNtNMFmANMXj/8w67YRhU8gWqPkdB64Z9RbS\ncf74m0qDNHdmgriabp4jj4f/7+7uHpWdo2WMunkcdvh4r9F9wpXXvWVtoruV6s6BDK2aY8t2Wsbw\nmqG11uVx/2zPc9mUJsG0vr+/f8SdNHE2qAGNPK6FphQn5Zr5aOWNN8ccXrKhYOOJTosdxB00A5h0\ntfNEg7QZkB53jFjzoO/bcOY8ux8GCTLehj/XVOMR8zXpZic3/EQHi1k9P99nJ42ncLJ/ZgTznbHS\ngaM8dYaBc8HnAZ2haI6facYxBywLHagin0zyuemN9k083Sf/U2ZOxnyTN5bHbZzBtY0p/5vMJK+z\nLB04Bh7a2Nke8W33pwzbTr5Na6yNf63n2znZ50528H8cP2bWGdDayXFf49y3PptzRXrxd+MP49KC\ndcavyT/LIMox2xq8Z3nnbL7lwe5e68/0Cm8ah1au1SMdAofT9/JwUPSVwDUnImVY1kp3rUtB05RE\nM+AN7XkIKg47cVZsjIxP77OiUGkKm+OxUqTx6/45hqYkGIWmIPQ21iYcKRR5fa2ukImzx51xTM5C\n/jcDaecw0ZCblOE15Txdy/VdW4G2HYSGOQ31ppSa82uaTa8vuHbNztRaTzzfnDQb1I2vgl+gKT7O\nr5/DJT6eP/KYnT/WowFFw+rh4eHZdmnKiUkOsHxwbNuhdobF5BSzfzsZPISHfVjWNT5ugbJ2fRrz\nRIMmLzluOlR3d3ePDtfd3d3F6xwmIy19nE6n+lwgZSgdZ9+z/NqtdTuJbexTdr3NK+my1uxINGO0\nORmBNu8tYNCCAl5fxoe6x2tuhxPrGw/yhA184mWn37gST8ul5ny6nnk3coHXGMBIG40W7ot1XW+y\nO0jnr+r4pa02fs9H6LaTGaZPytiZNS/QHmq8ubvX+Ne8MY3dW8MtD7izgO22QP4UrGvjJjjAwOsN\npldsua+vCy/RxscCX+0InQMOOOCAAw444IADDjjggAM+Gjgyfq8Ipmiso0Rt21Orm+iPT0dklK4d\nTtC2ZaYv4+KIkaOYjpS3cs5ctshre2h+ii6aHm6zZdp4YqDrtwyhaTI9B7XWU/bR0V9naRj5b8/a\nuE+3OUXYA4zkOnNJOjnCTTo7wpvvRIBbdNjbA9d6/nqKKUPBKC6/vRbSfouaOtvXIowp7wfSg7+z\nL2xvreeHOoQXWoaZ0elGM0f6W1bBzwAnit4i57nPSK23Q7b1RXzNUy2rZpqsdf0Uumncjtb78B7j\nyYxCe7YxbZgXPX8eI8EywFm4u7u7x7a8ZZPbMtlX2wXBvpgVZJSe9zxPTXY5C5WxmzcNrU3Txmu8\nbXt0e94G2XTElGVpcnrKpHCttQxd6u6yVYG2W6W1Sb7w/Ob/u3fvnvGC+7hl3ViuTDSZslpth5Dn\ncKIZ+2AbU2aL29DZH5//c5seY8bSdF3TW6QNbSJmZtvYLGO97n1651rPD3yxPmpyPLhNWcK2BjJn\nfF64jd/3pjnc7fiwXbbb1hv+sv6xTD7g68Ph+L0S8OKkwlprFrBtwVPINyNprfnAlWaE5L9xYF9U\nyjbi6WjECXW9SSk3vKI4fRrhWv0kT+MyKfhm6Poe2yJYIbEe58NOO2lgelkx+HebC+PI/nhQhQ+7\n8VisUOnYXTMEDKYnDeL2/B5x4rHgqTcZXN5mRdwmg47X8zwht2amnRgBNJ64frg9MeDtj+bh9M9x\nNIeMc0m+8eFFk8FFaPN0Op0uXvcxGdx2Nhrf5F6jb8OFfGlH0vM2OZrpp60Nbi3j9d///d+/qJ/X\nfbQ1NK0TPsPn9+y9efPmgo9snLftjh47y9twbfI+fTf6cM2xH2+RNB7Gp7VNCB95/RHnrCOe/On7\n7s9812QgA1E7mLZMNt1BHeo5tKNF3Jvj157PdF06xO6nHXA2bbtr0OwE6/G0GV3W1nTDzWDjP/93\ngdpreo56tM0bHag29rUuA1wOfJj/eAgN2+RpvHwshg4hX83C/shPDup4Hkhryt9G+yaP7bQ1B67J\nfP/3PE12AHWI1//O8dvpqw+Bl2jjY4HD8XslMAlQZ9DWeh7xt6FAwXI+nx8NkF3mrzmH/p+26dw1\nvJ2tyjVHHJsxOUFTUDEeaOTxJE2Pz4aMcZkiXxNYSDaj08qUdenctZNNQ+sY5qybNtkf7+0Mfx+M\nEmjR+l2GIYrcxqlxa85IaNf6XKu/nqGNy7xNmk/jdzQy+CQKPzncrW077uTN0MPGngMe7Ccnthrf\ntdZFtDrfNuQfHh4u2iOeNK52673BNQOPfdI42TncNgZteLdTS3mfbeUaAxQsOxnLDT86T+SLjC30\nMD/TqaNB2Awt0s3OIIFry8af6/G5HjobfAVEc/xaf5aNjfYTD9lhmBwm/2bbnKMm30wn885O/lk/\nNdnfjGjCVN48zbbpLETOOAOV1yBY3lp/kTa3BuSarmo082+vJztJXldN5nO9+/nkOEy5xucQW5/N\njuDvJqsomzgvvO61nXXs8ed61lQy/QzkNKcwY7OdYB3RgkHNQSX9yUukRfjKDqPlc1ubU1CK9Qie\nl2tZ4wO+OhyO3ysBOwJeKF6Qaz0JSCuayUCajGz2MQkYCtdJCRic3XLfduaaYEo5Gs1rrQuBbKMq\nx01HALMNOqA2YGzA8p7x5bgm54Y0mKDV8Vxz/FTgpqmdsF2fdhomXKaXVwe3qV+/x2nXF50tZgF2\nRkdT3DRArmVS2F47TMQ4577foej33E2HxZi/r2V8Mg5fp5HEMbOv0N1rJjBFX+3Yku9Ig4yh8T/x\nJM3bOvD8uiwdP2fsmhzifV7fOaFxMNd6cvhMnzdv3qzPP//8ImNGOTEZQU12cJdCvr1G+FL3gJ0G\nzkvw4svbOfc8YZQ8Tud65zQQN9+3vOTa3ckiGsIZA9ttRiPbbHKl8eKujdThGCantMl1yuHWHzNn\nucYP5Rd1VZNFHB/xiSPF9u0UGge3ybrttwMl07yy/yYTmizPeAm2UyYHsNG06XXrTLbDe8Yh5Sa7\njGturcvTdfPxnOaU70ZTBwRvHXvaT9CPY6Dz18bWaMyy7LOt97a2uSuHuB7wcnA4fq8EePLbWpcC\nLQrCJ09SKFP4c0vfZKC0SE8zTvPbQpTC1QKJgmMyQo0TjczWZovSrjUbDMxwpNwkfCajlff5vXMo\n7BwY/50AbA5Ofhs/9td+G88YFs6Qsi/TgWVsULJdGjAB80C7N9GE/4lTo+s0J81pmoxCv8qjzRG3\nnLLNZujmf+p4GygdvqaEbSwZWsbSQZZGA+IwOX9tnORFjpdrz+3ZEdvNXZNPkyPSjEo7UoYYaOnD\nxmvWRebJ2ykzz1zblq3GOWMxb71//74aaNOYm+HXXssQB4/PF6Z/P29og9PPGhuXaY21cVNHWTaE\nZ5rBz7Khd9NXDljtHNIJz2ls1jnmYddr/GaH/BoO1lf+TzzI+17bzqixP+K6k3GkceRXc/ya/m/j\nbP1x7r1+eW2yhSgzTc9Jvje6E3Y8ZN2W63aGMiY6b+6Pa4Lz5PW/49/pHvmG11pwlr8nXdfwsE1F\n2nEOrUM8Z9O4vi68RBsfCxyO3ysBP+htId+cL/9f6/mLUptgboYhM2g2Ru0QGiaHjcK8CZF823il\ngGwGvQWJHQkqKuOZLRE20m51IDKufFspTAZ1MxImJ3UnCDOGpgBo1PEacWYbk3JqRnVz0A3XspvG\niYYocWmvJiCezUD13DdHir/Jz54bzouDDy0g4cM6OE627ci/aZv6XvvNaGkZDxuF5qFrBrzrnU5f\nvO+Qa5HOho2gW7J6hMlRazi7vdbflOFM/4m25/UWaz0ZmJF/LfhFJ64ZNO6vGbzmwTxLunNQCJbF\ndPxysEzLCKYcjVJmUfNc67t379b9/X0NmJjWlrcNf+Lr9cE1Y/41zdnfLYbdzsDPdc9FC1w1fTfJ\n/ZYl57owP/G79TM5U4FGd66l5ii2ca/VAzJN5njsEw6UE00PNyeg6fSmg+wAcqyTnGW//mYd9+d6\n3v49zc/OwWG/BG9tJX0nJ3Mak+813BqtTbPGN+ZX/rc91mTgAS8Hh+P3SoBKL7BTrK7jaHk+VGrN\nkWoKuAnra1Gb5qjtHJmd4c1x2DmJcTYpWBuiFj7cluG+iBvLT8YQx0rHuT2P18bIKK/7NExRzmvO\nnx0R1t85ta2vFm3Pp23Fm/rgARCNL6g4WvSa0W3jyns7A6A9D+E54hpiXePb/jeedNk2BjuwLUM7\nGaBu2+Wao9kUvdvhWOj48RAKG1H+trPGjJkDUOZT40J+43U6aHROLAO5puP0UL7xO2O2w8JDHNr2\ny8DEH4SW5WnriLKasjb9x/nLuwNTlh87sn7uO3T1c1gtQNfGEJzTH7MOWc/t0BvTiOMj3zvo1Yxh\nyyni1dbFpGfbeiWN0jczHKGTM/psj2XJa+Yfyh2OnXNhXTDpX7YTmLaI5hqzUtZjE/3ZdwucUQ5d\nsyeIS5u7lHP/U7tNltOpnHCY+JTBFJa3XGQbzVFy/83ZmmTipF/aAUH8PWUmmThojt9kx7GNrHOO\n9ZrzdziHHwYfdhrFAQcccMABBxxwwAEHHHDAAR8dHBm/VwJtW0TL5K31PKvTotUtK8TfU7SKZRkV\nd5atQcuEtf8py6j/lHFzJJNbLlpk11E1b4lr24ZCT9OH/beMCDMpqcP7U+a00czlmPloh00wMtey\ncI4IMwKdiDSj/W0OPA5nnhxZbRHBNsbg19ppkcmWzZ628zIjxKyPtzH7kJa03TJXnl/SwVuZ27qb\n+D/fbasg12E7xGlqb7oXPtkdktJ43GMiP2X83iJpPmlrom0xY1/TOpnGSZpda9tyLwcRtflea9UM\nlV/l0LI3LVPsKLvnOvPNLb8EXmP7WSt3d3cXGb+2/fVWvjSvWfaavrk36Ye2vbxlqCzDMl88Ln7K\n+Ew813iwZZD43FXLTrvNSV/kmvXFJCOoj52NyTg9do6htTn16W/LFfJho1/w4bfvNXti0jFNxk+7\nOawzaPsYH6+1W7OD3jHT+vC88XfKW87w8Bfrh+mQJeqHSZ6QDo0ungvzmXcB8FGL4EB8SEvzv3Ff\n6/KQrgNeBg7H7xVBM7jaVkMDFzmVs4UIDZmmXCyQdgqmKdu00QRUM9hiCDQjr+HA8ba+01ZowHfV\nsey0FaQ52e6P96zgbMRT4Xo7p+tP23yypY59n8/ni2coLKBDAxpa6ZvP1fFVCM04IC52eFKOBk/b\nttgMEv9uc9jozf6bAbNzfmhIu30bCO3kztR3XdKCjpXntRlDxN9wbZ2xT5bnONt7v/x/MgjZn7ec\nrfWkzDOuZpA2OjV8CO05uqnNJi+bgdd4JPe5TnzPxprXot/Z1Qy0ZgDS+JscqPAcn0XMds9mhPPD\nbad3d3ePbdqwnAzIjGHHGxM0Z6UZyc0Ab84By/K0Zq7Rti69jXFy/CY+JK3u7+/rQS2W722MNvDz\nbcPZYw1+vuYxeDt4k+PcttfWYdMjrjvJHuO7o+k0R9QhdmKNT8ZnB30KYje+Jj5TWTs3be7SHvmT\nhym1epQHlKWkoenMuWiyZFrHTd+4zYD5m3pl59TZfmT7t+rma/dvhZdo42OBw/F7JWBh1xRjgM97\n+FmMpuBaGzRq2F/bj21jmb8dBZ6ici1rNQkm9tOcJbY9RSMztvaMWOuT9LchwfaJNw1GCmFmlayw\ndkrRQrRFmtn3+/fvH188zfHFsWtGIjOgNqjYd6MZ57jRg7SajAiPN7gSPzoUdvKiiKZnCpsBROPA\nY7TTZ7CT24w1luFzWB5bo9lO6bXrNiSnusSxGX7+bv2S1+yo0CFua4nlWjaBfXA8Ex9yXM0R2Tmd\npAH/s722fmmoZZzM/vGeDX6/94/4s7756eHh4fEQmilrzd+kqX/n/0Qzt9louFsTjd6TDON/X2fd\niac5TxM/kccpc/x/B4126bPpK+ujxhOsZwObfGi9yXscK2VwTqO1bOUYWnCQ6996O7zHPif6mHa3\nyjLLfOPLOtRrGd9kjxC8ll3WDlwry3UamGyXBIb8Dk/3NTmPkZW32ggNrP+mIHecZx5q1fqyLOf9\n1iZ5defQHvD14HD8Xgk0h4RKcBIGzai1Ap0UVTNyGjgz1wSfldUO5+neZIw358fGDr9pJBCvOEJN\nuFJQWYlaoBFfC1fjOglyO9OTczk54flkmxrxttJhPToBE90mw9xZxGsGvrNhHqszLaw3OfQ7R4ft\nGh/+3ynuqf3g5MyODdL04ZMUSYPdurimIElXl+fc+UXBPin1mqMUmBzYnfHvjAr7a+1YrqQ8X3tg\nYynl2U7WomnkOaJDxTLNuSNefiFzDD3L0uDjw1gC5A32k77syKS99++/OJG0vVQ6ZfhairWedj2Q\n92zg03GYZIJh51DSKeZp1T4QhzRo88S++NuBn3bQjw3TD5UnuT8Z21MQyHLF8rMZ1Mbnmnxojj6d\nQvNhyvF/c/rpTLIOx+Prk/zZ4b3bbm4nlPVD8k3JAAAgAElEQVSntc95anaM7+ea7Q2vd/Jxk0O2\nryhDJluKY+F4smvAr3ppc3DN+WMfWS9uc6cr2S/L+lUtza5sbTCQcMDLwOH4vRKI8J0WtRXhznjN\nNQuYJiydBZkcDpexIKQDdC2qynG07yaA2d+EW4RUE2S5NhkFNHCb8mrCjsqMdDD+pMlEIypsOhmk\nRxt/E8Atij8peY4lzoLHTofoFv5MOb6Y3e1ORoYNgGn8no82XuPHiCoj7x6TX+Rtp42/W+YidYNL\nMwIm4yn4TUZgxuhtUryXNnhC48PDQ43ut3k139HRJdBA55prhuk0bjvn0zoPTfhOOs615ZwdFzv+\nxIGBEvfLrZ42FvOc3y6qvjPS2v2MxQ4qjdBE6jO/dAabY0tn2I6fnYYA1xWd0dxzRsjjoFNM/snL\n5P3eWsvVJsN5n/Ra69IoJT7kCTt/TcY3aNs8U34yxsn/pCXLEZ9mqDenKr+91ihvvN18asP6wA50\nk78eP8c6jcl9ey7Mg5MObvLUzhtliLOuzW5p8pt1wsfN8TMd3K/BAdpWv829aey+m6MYGdLwcPlW\nbnLem/1hXOwYX4M2118FXqKNjwUOx++VQNtmsNbzLUkBGju8F6HTDOdJIOWe27OgTr+JiK91Kdzt\nTDbFzXtNuQUiuNprF6xQDE1xWFE2g7QpqYajjVPSi8awaduOON692Jrz5/HujOnUcXn25y3CdiiM\nf4Odgx9eS5sOXOyMsOa4cgzkRTssjR+cbWltOtNCsDG541lDM57MJ82os2PW6GJjvRlQa61Hpy/Z\nHfaX/zaQSTca4rc6/m19pT+Os7Wxk1G7b9LMeHoN2anl73avGZWT4d+MKI6tHZFPvBr+lkmUs8kU\nBCdnSm3Q+7AgBh3dR5OJ5k3jxrHEcCYNbYyvtS52Y0zOVK6dz09bqh8eHh75O/fpFLf/uzW7C5wa\nj2uwk4+TA0FD3LLezlKuNblAHNkucfM8eh1x/RtH6jvLRcvttOUgT8DrI7JxF5zyb84bt1k3XmMd\nryXPDdsgftP8NyeTNJ3+myaEXX9NLvFea/+a85ayDJY2fIiXaTn1fcDLwOH4HXDAAQcccMABBxxw\nwAEfFRwZvw+Hw/F7JeBISb6z97ttxeDvtn1pl1Fbax+FYTTHkR+flslosU9bcwR52oaygxZZb4vc\nWS2XcwbNUTJmGG+JahsXZ0pdrkXBvI2KY+bvhoe3GvLUO29/I+2T5cnrHLzly3j4usczRXIZAZye\nI2oZMfKaeTjQsqLMMkzbadxmm8MJj0CukVfMU5mDloV0GfKsI/fOwHKeWlbN2/gatLXX5tBZTq95\nlmlZKuPM/w8PD4/ja5kGR9rJ0y2jzu+2fZSvAjAtnHni/JxOp3V3d/fYjg/vIT1bRH3KhO0y6+Sb\nu7u7x2tsy9uos52XGcXcs5xhxo/Pfnqrp9e855d6wevGNFzr8jRp0mKt+TUKLkdasv3T6fTIUyzP\nuWlZ5oZz6plfrMvI+55v0tDZLJ8MS1qmbdI07TOz17J30xZJ4m88W3nLz7aumZ112YnWu50jxp/1\nrVNb5p1rz7w3ZenYxm7L51S26fVdZi76eZKV3r3B3xxj01XBjf3t1iTlQcOF64o8Oq0vlvXL443b\nAV8fDsfvlcAklNZ6vgUpCptbLZrwacqIQmQS6K7jNi3UbRTbKM8YmrCkQGR7k4Hg/naOrZUHvydh\n1xwmbyFrirVdn7bStDGZJh6jhW/as6Hx9u3bx61e3s7p7RvEZ3rWzgrnmjHIsVNpvH379sLxDL2a\n4UieZsCDitxGzs7pT3/NOGDZNoc0giYlSUPHcxjD+u3bt9V59lzwuS2Pg7+9jprRkHvNWWEZB5Um\ng4bO3865+VDgWBl8WevS+I+coNPDct6ORUgdB61cnzyba3xWzbKYYJltxzllJtlKPgg/hzbeSsVD\nU/K8XMbH/timgwHtwB/LncYzXmu7QEuTcXaaCJPxS3xae6SNt9TzlRjs85qDyX7twDng4bXg5zPT\nH9c767Etjos4ZK4cXGWwx/SbHMHdNtQdUF5bdl8LIHKMba247hTEct1p3XNNM/jhdhxA9bxMPDc5\nlNP4fd9rhDzUyre28v+aA9xg4nXOE2k20S8wOZ6UqQe8DByO3yuB3eL3wp8UeYDPS7DtXRZkwoFA\ng3t6mL45WzEuE/Fle82Qas5hEypNyRB3j9dKmm1OtKTwm5y+tN0M4F3mys5zo8Fal1kICt+mvM/n\n8/rWt7716PxRKfKADyq69m6sBlYEdHY4J8SfzoLfR7hzFDzngRYJJy5s33Rxpi79kJ6TQdTwbQqZ\nbZEu9/f3F3OY616jue7nkthHA7/X0QECvgeOY4xDTufLxgMNC8JknLTrbW7Mb8GvyZLIHmeNCM1Y\n8Rh8Imbwo2Nnxy/t2DjkWPzbdPKanYzMRlvSJwe58N1ypAnXudvyXLRMn+Vxw5PyP793a4MyqNHE\ntJrwnua89duchmlt2wE0/nS61nqSl83op0MXGdCcOztALZgx4Wr6EzfzozODjVbGxXPQ9JlpbPza\nnFmWNIfimi6f1krjGzt9a/UgGPEKMBjQ+GKaf95vDmCz3RzwMXAdNH7b0dqfqZ0W3NzJAOPGcTh4\n7nKGRqevAi/RxscCh+P3SoBGyVrPD67wotstxixeO4B2dNbqxridudxj35OSJG5T+TYGGwW7tvJt\n4WrjpDmFrSzp4Do2Ett3MwLZrnFq+HhsVi5NMcRxaM5HozeNIhoPwZP8sjOWzBdUtCwzKef8dqYn\nbViBMBvGCD7pQSO2zQfp6HfFnU7Xnd70wXHQiLfB3JzipvycLbGxzGs7Yyf9NRrRUM090i2nLDY5\nYj7yfzsyBvNZ/jejzvIpp2audfly5MhKtjEZro1ulnms35w74tQi1zaAJxmaMe7k3Q5v9peDetZ6\nesE4jVvP0/n8tL3W+DYZRGdtMqia0xB+bi+x5vibDnEZy6CGB+V8MpikDWVec1RM17bW2A7LkV8o\nE6hL2Jb1CnmtGcttjJbPWUscG50L03fnGDZ56TFQ5xoXX5v0UnNUJl4wrk33Tmufc+RgkXmwOX0u\nG8hceQ7Zv2lg3vE4d/bTdJ26kvqGY2f77GN6vzHHt6PthL+z0Ws9P1DtgK8Ph+P3SuDb3/72+va3\nv71+53d+Z/3Kr/zKo4C4ppytzJrQaoogQEOTSr4ZVufz03Yrtmkjp+HA75RhnVsNNyqd1PE2yGas\nTLRk+xkz8fIpiBZ2k7J2GY4516m0LRxD4+aouF8b0ZOh4WislV3K0AnwdtFmODgjSZo2Oqdcozfb\nSJ3mOHh9cHw7ZzXjtvPW6tn4acYMadd4uJVnWTvuzVhq42hGddYCn/MMmI8bjfwy3/Tfsga7QE7D\nmc+VcQ6bMdGMtfBLy/hxPZiXJgeM825nzxnPZpSaFya+s2NgmReZyjb433i14B2f7ws0wy4ZQ46d\n45gcNN/jf/J+rvOVG862ZB537dnxMx/uZLnHFEew3WvGaMPL65Bzaf5xveYUNDCPTEY159Jlm2Pr\n+XGbdOQMLMOgxs42meQXaT85JdccnhacZd3GN7uMvfm76a6m3037XRCnydtJTq51/Vk485oDdnbk\nmwO3o/ckDyZ8XS+yPnrkR3/0R9cP/MAP3JTN/oOA0+n0r6+1/t211g+ttf6Xtda/eT6f/8eh7D++\n1vrptdY/utb6Q2utv7PW+kvn8/k/Vbl/dq31H661fmSt9StrrX/vfD7/jW9qDGsdjt+rgV/+5V9e\nv/d7v7fWWo8v5aaxZCEaaII9i65F6ydD10aOhTn/00mhUraRxTa98OlgpN5kTDZjyTjmXntekL8Z\nrZsUP8fHrVOTYefnOUgbOnDub4pyNkfO2SX3E5rmm0Yh701K3gY96W0Fz+9mLLI+adLmzXNmRdnm\nOPzUeHSXuWO2a1J6Npo4FirSAJWct5Da+SKNJx5u421ODu8H6LjRgbMB1/qc6LFbv7sx0EB1tiWO\nSuNDvuT77u7ukaZ3d3cXW8XtAHFNcG20TIhxbZF70paGk40s8y37DJ7NAG0ymmOIQ9iy6J7XOHS8\nzt8ZYzJhfn6UsqU5mpYvlkEcR3OWOWdcJ82obg5T+o48JVCupH3za5MXrj85dxzPbo0EwqNN5u8M\n/skZ4u/IN9/z2mx6P7B7fdC0jqfyjfcNHDNlesuccx69nkwbts/y0Sfsm0GM3bw2B24aG+VKXm/V\n5A7btr3V5HPDY5JL0/+0aTrZhpr0SOuT9ZoeDDio+au/+qvr137t19Zv/MZvPCtrfL9pOJ1O/9xa\n6z9Za/0ra63/Ya31J9daP3c6nf7h8/n8d0uV/2et9Z+vtX7py99/dK31l0+n0/99Pp//iy/b/MfW\nWn9lrfWn11r/7VrrX1hr/Ten0+nb5/P5f/umxnIclXPAAQcccMABBxxwwAEHHNDhT64vMnb/1fl8\n/ltrrX91rfX/rrX+pVb4fD7/zfP5/FfP5/P/fj6ff+N8Pv+VtdbPrbX+GIr9W2utv3E+n//c+Xz+\nP87n83+w1vqf11r/xjc5kCPj90rAUVLfa9vQ1nqK7jrFzyg2I7Yu63qpM0X0W0aHWxVbFGitvs/b\nWR1HyVqGwtkj4+KMHdv2lldHsjP2HKCQ+owgOsLKbXXTNkduQSV4zjmmKXPFbVQGZ3/aoQu7bCfb\nIN1api9lORctq9KioLssV+jL7xbdTpTXdGJ035FU0mOKaDKrMGUzd5kDlue36TStk/RLWjlK7X79\n21lUZpl2EeSW0Xak3nV2eDAz4G2JzFxw3bx79+7x9Qk8uTLbPLlNcMpSex5Ib2ZPTOvwFPF39oBj\n9JbjJqfSN/mBmQ0+1+2Msbd457rlRrJ59/f3F31yvMkKej6NN7dlNhpnDpnlmzJMzmgQF9Niyvy5\nTfZP/NgO6zr7zTqUTcxkNHx4rcleZzWdLSWOxpffTdaRz1qGz3rX/BQeN1+4Df/3WjGddrtgdvPn\n7CrX3S7LuJsT0nuXATO9mXnfzQX78/1k0H1qrMfRsn6NHr7HfluWbtpGaVk26STKk5b5m9ZEmyfL\n0KncHzScTqe7tdYfWWv9x7l2Pp/Pp9Ppv1trfefGNr79Zdl/H5e/s77IIhJ+bq31x78WwlfgcPxe\nCUwCi4prEg4U6Db22/YFP/yeNuLABZfJ4LYwsBIO2OD3uFxuUkY7o3JqNwdDTIdcWChONKPy9IP0\nud7okms0TPi8HIVqm3cfzBPIaZ3NsbfD7PYy7myxtXO3o21zpPyfRmRTojsFMCmXGKxuP2Xbcx8T\n7Wg4TQ4qjf1cn+apjdUGevBea37+cTKuDOZTz0nmvRkcHDeNgLa11TTZ9cf7DqjsYDJiP/nkk/Xw\n8PD4/rq2TdDPGdsYJf872GXepVHdwE6WZVrG4GfqKBdMGwfbmtNjI/iaYRwcmuMX2Z77fuYtfXI+\nowuID8GvgvB4myPCfjlXHvtalwfU2EAm32Q8CShMzkjrI2Dj19ddpxnElgHtgJ20yfHvZK31L8ds\nOWhZtNaTg2x6uQ/zjGnV8DQ+AR/qM8l109t6eZIhE46T7URodJvwM063QOg91TmdTo/bQhte1Ok7\n8P0WXGzyogUTA97unTFY37B/0i9lo1smW7DBNdvgVrjSxh9ea32y1vodXf+dtdY/sqt4Op1+c631\nD3xZ/8+cz+f/Erd/aGjzh25A+SvD4fi9EuDCWet5NHQyENa6PAq6GWuOUPL5Az7vsdZ6fE/WzsAw\n+HkOGzbttwWNx2uBPGUdaMSmXOozO8Bod3PQbARN2TnSczKyWYfHx1tZEkcblzSkWI+G7Zs3by5O\nPqTxn7ZpeLKcnVAHDG510kw3KpnmYJFuLN8Mi9CMZWw8EhfzrRViy14Q7yjmaZ35uZ2mgI1PrrcM\nrp0Q4tqMf/fZnMv8bg5V6Ec8/byuDXYbP5PhlHESFwatvN6ZkSRvB6e2tkKLyChet/PVMjz8zTEz\nO07HjEaz75nGTV5ynXKezCfkA/ZHXrtmHIUuCZS0Nv18GN/DZ3nLuWtOWjOqORfs188U0rjkGm19\nE7KO3r9/v+7v7x/HcH9//zgfcf7a6w4aDbnW7DBTv5jnJmeD7Tbd2+Rarrtc03mUYx6DgfxKnFq5\nyTGj8+g2Wgbr/fv36+7u7ibHL+DdFNecAOsp8tKunwmmrP4U0HbfBK9v6kMfgsYy4b1ra5zf1gW+\nZoeP42JfnCfi5WTDNJe5Rno1HfsRwx9da/39a60fX2v99Ol0+rXz+fxX/14idDh+rwi8QJsT53ve\nmmMj14KaRu5kLNo4ZBRncgabkLQjwDFQaNJAIp7N4GT/zlpaeFmghRZtOwJxagZOcGzjZ1ucE9Ka\n+IUeNsRdz/Rk3cDDw8NjFNEZwMkpNW19CqQNIDtbAQt7fpMO0xgmWgbXKYNkHlvri8M/bPC1Pt2v\nDfBpvm7ZvpX7+W0jcne6oH9PW31Jl7Z9mmu/ZZauZfpYxnS7Nmd8UXbwsOFr58PGY/rh0fzBK5/2\n6gmuabZPg9+OFfGnA0Ja5Fo7VCTtM/tFfMIDNPiCf+jEfpqx7Iwn2zWfetzENQ6ReY3ysQXQIpfN\n93RkzRfNkVxrPW6hpx5ywCvOxiRn6MittS6cXfIM5dDuZGb+t06gs08+jUM3jZM0sxHceLHNF68R\n/yY7QvNGz50NYfq2+fWWX+M8rSPyqmnsPixzG87TOLhmmuxyXfM++ZBtOphNnJvjNQHXF9szTwea\ns39LHy6f9iODPW+THvOcEhj49LjsMHoMuxe4/+zP/uz6vu/7votrP/7jP76+8515B+Yv/MIvrF/8\nxV+8uPa7v/u7Y/m11t9da71ba/2grv/gWuuzXcXz+fx3vvz5v55Opx9aa/2ZtVYcv8++SptfFw7H\n7xWBBZQFkJ25JqgDLdNiYem+aZzSaKPDYCVq3BrYCPM9Cinjf0s/jNA3Gq61nhkcNsRtFE3O3+7E\nUP9uThi/Gf2dFEpTmFT+b9++fYyoJ9LKMgbixv52isY0peNBpUueSZ0o1WtBDV5nhsz8TwO/Kej0\n5fExa8MxmB6+Z6UfGqS/ySnl3Afn6d1J17JHxLE5c56XtNl4zjS1ErdDEbrYuSG9SH+OJ/TzWOMM\nrfX8vU/53N/fXxiOp9Pp8dm/jJXBLhvmXkNeb4QWKCK96OTwXpwMO6kZF9+P6KALnUGfcvnmzZvH\n0yH5kna+DoH3HJzi3NuRbnNGGRTwuuB9G3+WD1ybXEM26s3Paz3tzLD8ZV2upYeHh/Xw8PCY9Wvv\nteO4J2PX1wzkjcghyr8An3XP2Gkcsx/Pk9dJoDnsXtPRAXSaPd6Mo43Lcm6Sa8R7kutsg/+J+043\ncb2QXtf0bYMJPwY2pvMJJjqar43n5KDuoAVdDKYl+5hwb3aMy1kXt8Bqrpu32Y+fcbw2nsBP/uRP\nrh/5kR8Z7zf4zne+88wx/PVf//X1Uz/1U7X8+Xy+P51O/9Na659ca/31L3E+ffn/z39A15+stb6F\n/79Q2vinvrz+jcHh+L0SmAwPCli/3oF16ShNwjWL1ovXeFhYWYBT8U7GFttLuWYkssxEl7TvsUx1\nJ6EXR4lOwFrPX3ng9jmGdrjERHM7RTuFyj7tuDYnIHWZ8bu/v794fq8ZFg1StkUbm9HMKHDmsQl9\n9tmMhFwjP5vuNqpMu5Zd3Rk3MZCc+co6a7T2eG0ATpH99jLb/J4ObLARzfHQ4AjddllVgtcL69kw\nN/04Rq/39rwY+2pjMJ1IbzpU3LJ4Op3W559/vtZajw4V8cv8NSfMa6bBjm/4v2Wa6Bh7XF5zxtvG\nUxwKflI2zh/fZ5h76cuOgzN9lgPWN03/TPRxBtLlaAyyr4k2XAeWI8aLjk6yfXTCm0O1k3/t2sQr\ndsCpR9sBUpQjphNpMQWGOG+eQ+uIyemwvqfh7uwc59DtGg8Hg/zd9HOzFZqcaPWabUK4JgfDN8y6\n7/RG68O0s3xpOEw85nbTpsfZ+Jfy1W02GTzxv+1HyzHLmcYfnnPbkW0+/x7An1tr/cyXDmBe5/CH\n1lo/s9Zap9Ppz661fvh8Pv+JL///a2ut31hr/a0v6/8Ta61/Z63F9/j9Z2ut//50Ov3b64vXOfzz\n64tDZP7lb3Igh+N3wAEHHHDAAQcccMABB3xUsAvKfGg7V+7/16fT6Q+vL162/oNrrb+51vqnz+fz\n//VlkR9aa/1DqPJmrfVn1xcvZn9Ya/2fa60/dT6f/zLa/IXT6fSTa63/6MvPr661/vj5G3yH31qH\n4/dqIFFsblFx9NRbqQK77WiOPjn64/ZbWt4RXEccvb1tFyFrbbZIMsfge97KF+BWQLfdMh285uzd\nBI6WexsO8eJ2t4ku0zNpnr8pYsgoMV/gvdblMfrmA2dvWjaHkGxK267EV0y0Z7J4kEzq8dMyV7ln\nvne2y89Jteg+Dygwjztq2niGUU/OBTNUDRw5ZWaZJyy2DIyjppyXtp78HAfXQa636Lu3PxN4PfPr\neUodHuDB7YMt88kMaYvYt61GWWvv379fn3766Xr//v3Fs61pi4fYcOyWWflt2dm2ULZsJtv0HLrd\nZAY5TzsZ1TI0ob2za4bz+bzu7+8fx5+MWJMfoanx5X/eN61IW2YymQkiX1H3tIwGYdqhQJmx1nrM\n9mXLp+Uxy7EN06/15fWb8uRhZszYZstwJIPdtgrudo+0LFvL+FlXWD5M+jAf7qjgdl7reB6ERGiy\nlPfctuu23y7T7rWDniZo691zwUzuNVyanpyybFwPbS3txj7ZRJbzuzXTrhtH7u7wGP3fu1/Y1ks4\nct8EnM/nv7jW+ovDvX9R///CWusv3NDmX1tr/bUXQfBGOBy/VwLcBrJWPzHLjkozHgK5f6sByLab\nUJ4cSSqGpgRp5DUjIH1Obfo+jbsm4KmgbOCTtnw2jsaIDQtCc852CsxOPA1nG5ONrqk7CdHQlEYe\nDUZueWvt7LbpsQ/S0u2QZjYeTB+Pl+P2NifiTDqaf8kjVExtrOfz5YmsoTOdz6aEpzVDoBFNCA3s\n/HjOXTdb+SaDaHpYvhkIpDevpTydu2vjNC6hl9cTx+nxhVfyHFzAr2gg/6bc+/fvH7d8Bux02JFu\nRlX7bfnF+21+Oa9tfI0PbdS7H8qItv2Oc9SMSr7aYK11cegJZW7aP532R8xz/ojX5OjS6Y88ovPq\ntcD6u0DUJC+9Zbo94zeNiXzRnCYbsjaQw69+9UScozbGlGlO/DTG5vi1AFkbV75zKqkDNoHGF3Rg\nyHN2egNtfVj/Uja4nOXPtEYaeA1PQD7Ntmk7vm0bZ3O0r+FivDxHjTaND0yn1raB68G2JMfosZh3\nmn3Tgk+263b0OeDrw+H4vRKI0g9Q0e6gGTXtfyCKuS3wJpxShot5clKaUTNFlJshQaVgo5hZLAtm\nG5fJQu3o9ebNm8d3hd3f318oHhvpFsY2KqwcrXBTj4atX67b5s+Oo4GZo+BBusU4Sd8pMwl3KgrT\nvvGiHWXzbObIRpDHYiOAbdt58McKf+e82KB0n+0027TtzCrrOtrvdu04cFzNQPRhH+26HTwbBObX\nSZHn/g4YvLHD7LF6/M3pZFa4ORB0Gjg3Wa/BmU4X5z6Zn9SbTkgN3Mo76SvfXmuciykDxPYjp1pW\nz6dGOuNnPqXTxzHz+TfiSrrl2k5mkn/Nhzxd1tkgrg8fPNKMSvKuae05z3fGzjKT4cy6k4HNMU8Q\nmoZf2yuTMqYAg7F2UCb5zvEHL64n04ptsYz1vuk/3bNO9wmjphm/PSaO1+tlokHDtenaRsvm2Dio\nQuePay11LWcZQKHT3cAy2XLZthfp4kAEadHos6Nbk3/k2ebIBQfKfAc3Wn+T0zfhSDy/LrxEGx8L\nHI7fK4EIIDtNXICBOIkW+IRm6DVDa1K+LGvnxAvfip7GobMWhMkAb/9pLNLQ41gdNTYdMiY7mox4\nN2FpRduM38kAd3aW37nfjA8qe9OX433z5unAktShknIGxQaacbaB0GjFNti26Z96zAZ5DOyD9wmm\nvZUwaWSnzNAMpXaf9e0U8MRRRr/b+mp9NoeY42g8EvAYm0NOw97t74wol21ZE7bNeh43ZRcdZ48t\n/LHW01blh4eHx6w8eT0ZwmaMhf/Mm6S1ZZhp5ixMAwerOMZdm3YEMo90uHh4C41Sl/f4OQfMDuVa\nc5ZI09COMqFlCQJxwGmksu3pZEnLMY+h3V/r+QEuzfE5n8+PZThGtm3eJ60aPhmjceO4fMp1yrXM\nmnXq5Pjldwswsv98W1d43IQ2p874Njrx9+SYWHaxnTgafhdig2kummzlvGZtmMZtDrme7OT5t7/b\nAULEpckCO3YTn+W3ec30NJ0aH3udcAyUC+ZDzqPHGf3fHrGgLvS9A14WDsfvlcAuwsTvtfozLV7w\nXuRpg8bKpJRt7Fu4T5nIJpzsEFwr7/vtNwXspEimDMWktByxJc1sxEzZFkfGOMYYRDGaWn+ec+PB\ntqkcorhjcLXTX6N0GTU33ZvxYeeH9IsSsMNpyDzZGI8CmbIyViQ0TjMX5hEbYgGO2ScwEjg+bgv1\n+mSbdlzZzuR87YC8SyPSmRMbkvk2fzdna2dETYGUlrklzVM2z5Tt+Ln1QfpSvuUegw7E0/jxXuaA\nPNgMM9OQ3+yDdGMm1Maa1yf5xgan12hO7WS9OIVtC7ANXa9jjruNK3SanDD/p6OV/rjN1/9pRIdP\nHBAjXpQ1az05fty26gCTZbvHRlrZYPY6Ndh5b468g3TO3LBsc0J3sMtYU5fn0/iYcnyt52uU20DT\nLr953U6FHaYmm1M3W05ZL9CcdeJg58i/yT/5b7k9OfJt/pudk7YcYEkbdoTIH+7bfOisI+nGuXXA\n0HYd69jOM7QMfcboIMrpdHp8bYoDbbYfJxl1wMvA4fi9EnDErilaChEKNRp7az0ZyBbejtZdM2za\nfd+zk9OyW80hani5bSrIZpQ4a8I+0hTGS6gAACAASURBVKe3OJFWNmwSZXcmiYcj2CG28T0J3+Bx\nf3//iJ+FY4usWmhze1HwbQqqCea1nozQyQFsvGSeND67eQrwGVY7a8x60IlIhJGvYMiL2u0Utnlh\nlrHRN9/Gk469s32Exgc2BHK/GXrNKDfNW3t+TjFtTQ7Azsg03Uwvr+mJ3jacwl87B7vx2vv3Xxze\nQocnYw4PtXXMtdIctBhMDrg42zEZSDRqUy+8k34dBSeudhp4nYbw3d3dhZPHA2z4bj/iGQestUn8\nbRxOmQXTgrJjrecZVsoSthm8LKObUxEaNueUeGcbb9a2HTnjzjasZ1KXMsf8bWd1rcttt+lrkpF2\ngKwrCOHpD3EKJ0e24RV5ttZl5ik0sAM14dHkRNokrzeZ0AK1tCWmPied1OahySiXbeUtr9mG13TW\nIIMG7svX2nznm32ShrxPHlxrXdh4Bs6teds63fLdvO82Qyu3k287k00XN3wPuB3+f/FyjAMOOOCA\nAw444IADDjjggAO+OTgyfq8IpqxW20bniBSzAImscZtT2mxRVkKLRLaoYotu+WAQR7Fcb4quG5+W\nFWMUi322rA7LN2B0rh1w4MxY20o3RbGnSFaidVNm55ZIdjJiHIfnh3WzLdhbl64dvsLybHvKOLW2\nPAa2w9eYcDtnIGN8eHi4iLS26PL5/LR9tGUoQveWJU3ZFq3l+KbtQqyz4xFn7KdDNVr2w20GnNn2\nWktZP9fR1uOtvHgLr+/WHeczfQUPZ1q99ZP4WuZwrih/slW0ZZYYHW/0bnI2fNuywhyXMw3J5nGc\nHGOye874te2hGUPGl+wbs9a5zjGSNp5vAvt0ufTV6DnRxWB51uY0fWXdMtPB3RhTP8yYtOwj53fK\nzrlOvqc+W9ak6b9JPk7ytOEZedjKEqYysRfaoVK3ZGIaDayXLQf9MvvWDvvOOmgy2NmxgHc9ULbv\nZHgyoOE3nr0QGvEAmGn8bV1M4+O1yUaKLdj0heVss7fcR9Pjt4zNOx2C22SnBO8JdnbSh8D3Utbw\ncPxeCUQpWUlMBirrWfBxvz4XJw3Ntu2gGYr5zS0DdiwsqCKYdkaf67TtBs1ApxFj5XSrgWFoNPQR\n8aQFy9mQmgSthR9p017lYSONfVrYsgyvuT87VPnNg4Iyf1QKnmvzE7ddebvMTuFNAp+Kh1tE4tBF\nobO+x92UY65TSZGXmlHFcXFrW/okHdNerjFow3Yb7oadwRDaeCtve1ifdXwYT4D05Bg8T21bJu+7\nflP44S+fNmu8m5NHelle0blynxzHbotww9W8Hsia4VZUGpV25rjWMjb+Tlkf7OLthc0oz5i4fts4\n+IxtxtucP/9/eHgYX/nQnpmMYW+HN303eZ02uAWb7dFxowM3bTNtffhZ1FsMxSbHpt8pb1lCHtr1\nSRpMRnRz+vhtmRqeDx7eHp1vH+zjMXl8k8HvOh570yuRZROdbnGwJtnNcpYnfkyCvMd1QfvAzuMt\nNprpkHa9DpszNsk0jr3NgZ8DbzKRwVPyRdZTW/Osz2/i5GvcDnvAy8Dh+L0SoFOV/zakGjTlwkVM\nJ28nHJsjZUE4Kc2dcuP/ZuxOhlUbZ/s9jSGwE5wEO38U7nSk/WC1FU/DO3PQjGuWc1Rz5zgTh2k8\n/KYTTceK5ams7RQar5R78+aL55J4OmDKeK53/MTxNKON/dlwTx800tqznZNS2hm/NMTaHDq7lu+W\n/W5Gbeo7Ej3xk++xjE+n3RkkpPHEY54LGgh+PcjEq663k3NcT27r/v5+fetb36q8QQOuGf7EMU5J\nYPcMsI1RjsNHvnseaBDyN7PnKTed6mnHyUYe+dBG5OQ4sB7pP8l1PsProEbKtsNHuC4br7Z7dkTM\no3T6GDhgWfO85ZGdqkneWDZar5JPHbgzPxMmfehrU3DCfexkKcdh56mtkwTZeJpu699ri9d3md5r\nOr7h63uTTNs5hXTyvA65fhtNW9uWvxOtdjKc/TFAaJiCWAE7beR3fjc7ML+91vKdZ3TZV5sT/7ZO\nvMUxPuDD4HD8XhlM0UhHzynUrJRT3w//MmMxReqYxqegYIS1KSyClYuVHcfZBEdrswlhKke2QaFz\nyxYYtuvydvw4dm4/Is0mHH3duExGrcu1qC3HEQXA+U/UzQ6G+6WB7P5NN44rxg8PYvEWWTqToZ2j\ngeY1RvTtbBB29Nzh3rJyk7JmhLT1Tcg42jg9l9721LLDuce1bsPZxqnHy62AbNvjNq0mA5Zj8viI\nc6NZO5iAjkTwDTjbmmv5psHjTGhzLtv9ZtikjLNzKc9gil+9kOts169rcBvZ3skTPjNGy1HS22t9\nx/tcT1zvLNeM1yYz2tyxDeOZ6+GfSUfQuaPDFj4z36VO2z1hHJuT5fJ02tN2cwAciGqyv2XQTPMp\nMMI22/psjr6Bzk3LSqfN5gyadqSJ8SM+XvdtXbZHDBo/2NZpWeSGJ+81x5d0WetyG6fX0KQTnDFs\n9kyr0xz7xo87sL3R+N42Y+pNfTT9YWeyjYtz3mg9Pc7AcXxdeIk2PhY4HL9XBJMxa0OwRVCsQKgo\nbRBQ6XoRUyj5XjspNL+nRedsi5WuI9r8bjThNbc5ObMTWNHFMbKwZMbuzZs39Z10FMITfo66TVG+\nZqhS+NoZdBs+Cjq48Cj0XZSa42fm6haFlDYbrzSFkbZpjNrIsyPY6ESn1oamHS/3T+fPxomzdFaI\nE27p18ZvynpNNPrvlLfXqI2xyTDzujav+j75f60n3uK2wem9VjSq2phsYBFCT2834vw4yzdda46G\naTMZLByzZQ379LoP7nHw2Fayfbmftpzxa7g045DO0CRPsh7pUGVtTc5ZxjbJm8mwNX8TD8/JhCvX\nU07y5FqxnuPH/NAcNtbNNToj4U/LdNKtZWatzyhDvObdP+ewzeVEb+tf8ox3QhCaQ7rW09rePbLh\nbJnb8fwzC2R8vAWZtCKelPHTnDbZxuxec1J5n31zHq7xvPE0vfnftDR/sUwLAphOk+2R+Wlru11v\nvzn3dt4az1mf8N4BLweH4/eKgAY+o2E2mihw7VxMyiT/LXSb4xWgIrCBarxZpxmTrZwF3q0CgvWa\n8U8h3pzkJvB8j335mci8i293XD2h4Zrx23Bhuck4anTIWC2kqVxTPsYf+YlzZaXAg2iM57WxNuFP\nJTFFt2NcObo/vaIheBqPlNll3EhnKz7SxYazn4W0MZ7xNGPUa9p8wTZudbzt4Jg30j752Q6ztw7Z\n6aEx9e7du8f3Ok18wTZyb2fAGV87tqw78aGNO8uLKbPX1ty0Tck8tNaloUyD3/c8Tmf12rqn02p6\new05AJYxO8uaepTvAfOSebatfePZskrWQV535/P5cY3n1TdZc7sgQeD+/v7CEZucE+Nk3HOPOpnj\npwwlj9vRb2ufvGz5ReevyaIWCOB/gnWAaef2HVxucsT48BUpzdm3bMn42zomfYx30x/Gs42f68nr\nqtk3aX9yipu+mHirBQKNP2FHazqTbX01GyL3+Aw/8eA6nvplO82Wm2wW0/kWO+mA2+Fw/A444IAD\nDjjggAMOOOCAjwqmhMJXaed7BQ7H7xUBIz7ePpRrKeeo0S4zxGhV/reo9NRHXqLdIoeOPk2Lj1s9\niMMO/117LZOSdhmZddR9l12aou3EP+UJLVLa/rdMJbe7tS0vBmc9Gj4cLyN0p9Pp8Qh9QoumetsK\ncfZ/ZuRcr2X9zEfMRHBLmrevkX8dvTQOxttjaRFzZvYafZI5CTjCya2fgfAVo9Ipa/qF7rtMgzNg\nlhf53Y6tZ3ue57Uun3Hh2Ns8pb3wr7NJU4T+3bt3j4cBuQ5xbNuK2DdpxszLFNXf4dTGGjDt3V57\n9ov4Zz7bASjh07aVlDRnO+Rzr4cmD52FaZnEfIgjn0P0uFOWc2UZRH1BnbJ7rpD8+fnnn9ctqWmn\nyafgyb5Ttm31m+Qax+mt3+wzc8/t/+mL/Ovxs3/OJ7fdTjLdmXBnzVqmhRm5psOaHDINCaYtcZt0\nNuVV5qHpRWbUKI84R9Z/bat1vi2fp/knH9lWIa7E2bTJ3LeMrcHZOtLJOAZ3Z7xJq9aW1xbXTFs3\nponBYzcYF+uqA14ODsfvlUBT3DbuCL7XtplNBuy0cKnMvIC5XcBCcbd1atfvNcdvd32tJ4PD4+X3\nZAT6d8bWjLsJbwvltbpTSIdiJ1iND8vR6Fjr8rkIj2VSzLk/bZmiAqUS5OsaiAMN2mY8+Pkh9uMg\nBNum82dFZ8OoGdR5Joj/Gy2DS9oOP1GJeg2wndSnw5LyxqttqabxRgMu/3PNc8h55Vgc2CCNWd7P\nmnqc7itlEgAy7qaLx8r+Q5vgQccpZaeDAHKfDkGuhzcaXWwkcrwOoDSji31xnrz1j/yVz8PDw+PW\ncPZBp9Hzm9P0eM/OEreZ2yj2OJpDTGg6htu78zoHOje5bjq5veaMkhbm18gGr3Fu9eRYHIhp44kz\nyDUVunAeXY9rZaeXHZAJP1g3tK2NTXdN8jl87iCkHRvjYhnLsTaa+YAef0/OBrdzWh86gDA5CE2e\nUNdZR0duc6wBys9GG4/b4yCtCNQtpsPESyw/yVzydq7lQC7Ss9l1TbdNMPU7Adu+tmWTweZbgtNp\n/yUcw+8l5/Jw/F4J2ED24rEwSpm2aBjxp1LaRRKDAx0g4kBDxjhE6DYnw+Ny1qEZxHSUGr5WpjbO\nLFgDLULO8g1f4sU2WruO9PH35Bi3MhGwprX7a4LY+Ld3FxHXtZ4fwOPx0kC3kzaNZQeO+k9GXnPE\nm6ImrqRli67vHOU8U9QMp2ZYsQz/h+42YFs23TQn30/9sY/GO80B47U2T1xzNuh2c2saZR4MnN/J\nueccrfWF0ZM54SEvNAA9B3nGi/d2Ge4mV1murQfSi2MnfWz4twNS8nwU13PGljabER462QGejM5J\nvvlam+PQ2Wvr/v7+Qs4yw5l6t8hvOz4t65Wx0hklj7S5Ik1boID05pqzDg5/8JUhppPnyDqRDkjT\nVTv6+Drx92/TxvJjF9RqJ0AHrEM4Nq9D0yb3nJVvcobryQ5E+vFadqbfGdppbRtHrxffm2ShZTnH\n1xw0y3P31+yFFqxY6zJz19p1O7426UTjdqtTuNbTPLeAxwEvB4fj98JwOp3+2FrrT621/sha6x9c\na/0z5/P5r+P+37fW+um11h9fa/3AWutvr7X+/Pl8/kso87fXWn9irXVaa/3M+Xz+0Vv7t9BpRp4V\najPGKYACUWAU7E3INUHACPS7d++eOSVUcBYsxGtyPJ31yPfkAE4nV7Je2iWQPjvjqBnG/u3xRQhP\nr74gjqa1FXjG2MZu57nhSUMq/VER/n/svX2sr21217Xu/Zx9ZoQCMkCYlMAE5K2VTjOKkhIUiEa0\nURISIkgICEZFtMQ20ICmalBoNCDCH1UJSDEEDRBBrOVFwSDhJSp02mBbSgMzpUzo0NZghM5z9nnO\n7R/n+e7z2Z/9ve699zl7Zphn7pX88vv97pfrWmtd61qv133dNvw53qo3L168uM74N1rIg4YbjYJ5\nT76Rp87ws8+VkWryHojz7eCijbOX19nhMF3ezCHg4IYyeZcxdPaa9LVgi/j7tx1e8sbJEPPQTqD1\nDnFzNYxzycv2IksORGZmPvWpT82TJ0/qPGZATBll/37/FHnWklpOTLUAqo0vnU469TyXeykrDAhW\nsr3SP5xzLeHU5IHj4WXVDKiaruE4cXw5l/z+yACDfdPQHOq2PDnfLSgOfpxPq4CTfbkC3ID3WxaZ\nPGsQfILHfR3fVVU+x/ht2hreR2A75HbyP0Gh7S11GgPbmVdJD9MWXuS+5qvwWEuUHfFjZfOPdH2+\nVzbYOnHlH6zA/gZ1BftbJV54fXwv60vq7pWc8FzmMmm2rTC95qX5xPO2N20+nvBmcAZ+jw8/eGY+\nOjO/e2b+h3L+t83Mz5mZXzIzH5+Zf25m/stt2/7Wvu/fUK6/l4bw8rSj4MVOM481R5kKlEbUTiKd\n4+aQtfdU2XFozswqu5lzNJKmtylYOn9pJ/itlIwdPSvd9LWivdHAwC3G0JnToyV/xKudy3nz1Aa3\ntdey1/yOM7PikZ2Bfd+vl504oHr+/PlyZ0A6p34VQAsOLy4ubr3IfmXEKOstWAtkzPNplTKCM/t0\nSlp175133rl+6TH5z6CZwW3rq4EdErfP+9nfXe2t6I6TZ2hOSQsUVng62GAbni+hN/owskCHh9WX\n3MdlunY+Mj7sk/LC9+U1ehyw5D46ZK5QsErE+cv38/ldfc3BNTCIMq7N0eLYeel15m6OWUapmxn4\nhddMIvJc6HUQ1mTP+rXNE/53QORggUFuzvtY08FOOtI+Jrg8qhzzPs7Nu+ac+7O9vgtss1xBXi2b\nbjoy0JI7bSdjB0HNLjtAXwXC90mGrcDzpvH7KPizDFIPOkHhPiybOWb/qiWuzf82V0wfbfMqSWXa\njuyn7YhXBN0VsJmv6Y/j7Ec2Gtw3kD7hJZyB3yPDvu9/fGb++MzM1jXvl83M7933/c+++/93bdv2\nq2bmn5yZFvjdt9/lsp6myGio26RnO1aAR22zHRvDXNeWfgQn3hcjxmqR+2v9NsXJczM3nxMynkcO\ncAwTNydwu1bYLQjyfQ4S2N+qYkJe8ZwNJ5W3A4FGX5y01YYbXorBcYkcrgIDG+c4RHTMQ0szCjMv\nnYCrq6vrDQTIs7v4ZSe8OYErQ8cAsLXv9ujEkbe811WmlfOd9vjt6wl2HnNNcKRzHeByXLZx5LgY\nX+ohzuvmUDe8yavwx8Hk6pUcxocylbmeucv5aweKTmRzUinHlBcnWHJ/+ubcc+DHwGDbtut39OV3\nzuU3A06/zoFjZFm9yxkzfbzPchC6/Nwm22l48DiDwPRlJ97B/iqgYXKm2awkq1bJiRU/8uFGSzN9\noxQndhhUsa8W0Js/LZBftZlzR9XIlhh1G82upd1mY9PealzaPD8KTFnNIv2Zt+G/A9zGU+OQ304O\nUB82XNwG6beOy+9mXwjtXEtkuk8nU3Jt86PYbltVYrvQzvH8Sl/eNba0Y01mGz8o3w5yT3hzOBfP\nfubhz8/Mz9+27QtnZrZt+7kz85Nm5k/gmjN9ccIJJ5xwwgknnHDCCSc8GpwVv888fMXM/M6Z+e5t\n257PzDsz86/v+/7ncsG+7z8B1/+EeUNIRmsFLdvCc4FkwFylym9mb1oGtWVzk81xBdL4eSkVs13J\naLVqErNkMzd3m8t/9uPspbcoT79eMkQaG/2t4sYKTDLSrUK1yh7yWMtWur8cD36uMHr8nDX3b1Y9\nKAfM7HE8j3hCetl2u+7y8vJ6rFL14xI24scl0K262njZeOrsc9pMFaHR0DahIW3M3pN3BOLC36mE\ntbEgrJ6XaTQRZ4KX4921fIf3ka62hK7RR7rb0iW2SXp4vdt7++23r6tjXpbUxtVVPOqR1RJpy1XG\niVUdnuNugl4KmGOp/KXPVPyiL3I+bZJuz2FXw63nmz5Lm8GPY0j6LEdctteqGOY15735ZmhVeePc\nlsi349YXK3kjH6y/W2UubbkKx369tJHnzDues84IT7hMs431Sg+7ekUescLU8AhvXCU/8hNWyzXT\ntyvhAc/JAOcofZ02D9I//3tJtat4bYw519uYHI29+UhYrRAxXkfzyv6Hzzc/yVVQ3tfkxues8/Kb\nevrIx3P7M682Xlrp3MCqvYfCY7TxuQJn4PeZh18zMz9jZv7FmfmumfmnZ+brtm37xL7vf/p1G23l\n8JXh5TfX3hOs1Nlern/x4sUth5uK3U7UKkhsy6qMQxydLCuigfazee3Bdrdl3Ah2RNIfjRLvNf5+\n3nLlpLQlMnYsGDT42oa3+Ra4K7gi3c0I2Ni0ftv/tOelnDM3n2nwMkG31WjxGDfaGExz+/gWePgc\n217NodXOqcGhLStjYEi6vBTGTimB42FnP/fZAbLjcIQ3ceH3il8tmLTsNufzrmWHbIf4rV5HstIl\nbRlqfh89r5oAjbrU4+KlrV6CFt1M3iQIZVDF++jsJADMbwaweTa08d7OIgONo7lmPRN8omsbcCfd\n0MdgyXrT85nP7VIvenxzvelzW55LDNpae3aWOXcs4+YZEwnExxuqWSZX4GBipZs8flymeXRfk/Wj\n5ZotQG8JJj9r2uiN7mkBldvms7UtEHbQa73eEn6ev5wXTh4zAA1d7LsFZeZbk1P7P0c8Xfl0K+Cc\nY5u0F054mR+GJq+my/RFLzU72pLOnl/m+10B9AkPgzPw+wzCtm3vn5nfNC93+vxj7x7+K9u2fWRm\nfu3MvHbg98Vf/MXzkY985Max7/u+75uPfexjh4bGmcFVpvFd/JfnWN1YKYNmdFeO2qrfGAI63M6c\nOqPH9u+rQKKg2nr5mdvZ+1SX/L4h8oa8yHcLXhp/27r+1h7bYiav8caGkRtWtCCkBanpi86KFTqD\nP/OF9zrYaHKZ30wA2Mjzvx2zOEeuQHnTC/dPY2k8TbfHL9f4eSYHQi0wynWkg0GBnVLj0rKlbY77\nN8+3JI6vbXLZjq/oM9+aLLPNlTPAyk6rBOY3s+mZty0AtNwZTzqpucb359vBnIM+P6vnimDOBVdW\nKoxr5Jh6kmNhfbQKwInHixe3d+CkY855cdSmeUPg/GvJTCdamtxZ1zSdTfrswB/ZPvdFOnwNdeIK\nWtBp2lZzpdmDowRdq2ZGvjJfWmCf64/OzdyeA0d2bAUrfWE+Rp/yWgaVrkC2QDM4M5nCZ2aPdHqj\nn7BKch7d6z5W51dyah9o1a+vz5wjT03DUeLAOFIf8Tu8Zn8c5xz/oi/6ovngBz94o92PfvSjyz5P\neDicgd9nFi7f/bgM8M684fOW3/7t316rKg1WDlH+twx1gIqZSnxVvWIfLfgJtKCQONnotheT5neU\n/iojZwVnx87HSQsN7iqo3Lbt+qXLOcfXC9Cg2wjxHirIVoXjODrYiTPRAmCPA4OetmMmgXLhNh3k\npR0ed7t2/hwI8R7LbT6UezufK5lygGgcSUPabfMmPGuVNt6XayxjdC44F+PgsmrQqofGx3xtcmoH\ngtDOkabmaFNuWj8J1Ek75y/llW06oA0PvDycMp5ApDn5TmyQV3QYyU8nSlYOYPp3lclgnrKayAQF\ng0BWIhi0s42cYyBhR948I62UQY9trru8vLx+z+HMq2WnT548uZZvznvOfdoG4m59RB3eZM1JG89v\nyonP8R1hPJZ+OQ6r4GVlE9L3Kjhsetd48TrbiXzbPh054bYPjc/eYTWBvtvPHF8F63biVzo+fThZ\nm/7uCrAo++Y15zx5xbm9Cpia3qeMHgVlPm+ba/zNwxXN1mGc7x5768+GQ2htOrXZYdtG00PZaPJu\n3nGpuO0V7e9f+2t/bb7jO77jxvh+93d/d+WRcXkTeIw2PlfgDPweGbaX7+n7iTOTmfATtm370pn5\n/n3f/+a2bX9mZn7Ltm1fMS9f5/BzZuaXzcy/+yb9uoLhb07Au4LDo4DJyt3LO1oQEcXqYCHA46ah\nBS4BKkBn9O2oE+w8NXpXjqeNIXGKsnOgSseWW8G7LdNBJ87O9czN5yHo6OS+uwxXU3Rui8eNF/lN\n/JuDQZw5DuSDjQSryK2Cw0DNDqf7Iu5sw04VafA4tyDFwSdpCO+Na4I6Opor45pnGB2s5P7mdAUv\ny38CCL9XK/fZmbDjmLHy3KUz537JCy5dyu9WTbQT4yCAVdu28yz7ZBtPnz69QU+Tf1fRTMeRY9qC\nEH5a5c6VP56LfqWzmDY4vxue1kPepp9g+fO8YL8zc53UilzzlQ5cTp3XtBzZG9NgmW4OfrNj1DvW\nQZwf5HkboxWOR/hbF7Zz1idH8k28eS2Dk4bXKvBsTjnvSdvtdRPUzcTF8sW2jAuTBRyHXL8Ksh0Y\n+vgRrR5P7prsdqnzU/1PPysfwv2xzRXeub4FvWyLck/e2KY3vqxkw23neo6t7Xrzq3iOPOZ9Kx3F\nal9bBUUZO/I/TnhzOAO/x4efPjP/28zs735+67vHf+/M/MqZ+UUz87Uz8/tm5gPzMvj7Dfu+/87H\n6JyKnMeaw9aUmiftUcDgyckMrp1RGq2WLaIy8jJO4txotdMbaJtfUGnSCJlXzQlYZfybsTdOcVh5\nzg7WKuA4evfWyumPo+nnEldGlrjHgLhtB2rkmftvOF9cXNTnNFe45Du4XF1dXbeZ4+yn9e2gYOXY\nMrBuAbiNNfuz02kjuKKRAao3LzIfmrFvsrI6ZvAmR6vf7Cv4OLDj3LLR9isVfK8DcZ5rDv6+v3qm\nN9d4u386eKkA2mFyv2nPOtGJH75ChHh6LhJy/sWLFzfw4Xyjk8ln+HwuVTY/d8i+yIeWCGh4ktdt\nLkSuGEDm+svLy2t5yn15PivBn5MwDmw5fpx/5jUrUwk4cy54siJsCO1eznfXKyNa4iXB0pGtaE58\nS1K2IMbzibqGOpX3ZQyIbwt4+M3j7TnBhhv7dlLXQQHlwvJKu0Lfw3zKeepi8r0lwdgHeeYAmf1R\nFlpFstFP3E2HebeSLV5jvWBc2piv/A7y0UkPyiSTrDzHMV79brDyIT32zQewLWx+KMF2/nXhMdr4\nXIEz8Htk2Pf9z8zBss193z85M//aZw6jE0444YQTTjjhhBNOOOHzHc7A7z0CLZvu6kfLcrWsVruO\n4IxervU9zNwkk5jMkpfYMWPI+1bVJWeOGn7GLW2udsJzxatBw4XZL+OXVw/4k/v5fFj6JW9a2/nP\nvtvyOmb9V2PMjCurWs4cOpvKcYms8dtZRWZpuVTM0I6lCswKmSsRLdPpcWLGt1WmSCev9zf57Yfh\nj2S1ZUlblcy4e7kf23Rlg32yCkL8grOXBxs8LzxPzQtex/ZzzFnr6AXKTXAhz1uVwPxIf2yfrzq4\nuHi51JN9tCop22J/rn40flkmLi4ublTuiE87nvu4yUSqUz63bduNjVU4Xq2ycVR5znlWfdK2VyJY\nl/hcqvmp9D1//rxW/VhRaDg1h7HPggAAIABJREFUuxVglc1yEtmhfBxVbWIL8szpkW3z7tHBk3K4\n0l/u2zamVfpZKQsvmv1kH7yOOKa9VmGlbqY+Oaq0tH5Xc4NjSNnMf8+JtOu+raObD5AqbAOObbOb\nR7iuqle0b36edrXKiPqBwD5axZB2wNW5lb8TWv1ITuvP9oC84MZXd6004LevcYXPNrH5Jfx9wuPA\nGfi9R6BNJC6NmOmTsiksKi07sjbyXjLDNgJRFDYsbtO0tN0PrVDcHs+t7jsKiJpDn/sdGLFtGlfT\nc7QFumm0Q2BnqSnUFqwdOWp0nNhm+o6R8fIwG/Sm1MMnB1mNhhbIkt88R2cmAUN7FtF4WgZCUwwd\nZY3LXew0O1gz/9O+DRTnl52l8OkoICQdzfit+La6PgF0c+g8VqaX/KBzxM2giAPb9ZxJO+G7nwXj\nUq6mHyizPG5Z4xLJjHvwb4mAfBgcHwHx4GfmZdInm5+E1gRUXrJJ5yy/HRzOvHKO6cC3ObriGZ1U\n62vzJe0waGt6YOaVLIeGLP+8urq6EQDmWvbZnG86t03nNTvgXYLbHPVvAp146xCC5+tqSWmgbcvP\na450FttvtiW/VxvEEBgQt6AkPGMQlfYMnP8rXtOmtTHMNd5gh3hRxnNtghDiYF/GdBlII/0TnvP4\nkG/2kRxQkReUN+tKn3eSwDq3yW4ba49B8G945vvy8vJGcjX8WfHQY8N+WxKlBYr2TXzevxusdNJD\n4THa+FyBM/B7D4Ed+ihJTsLASqE4eLACzccKgm3l2tWEZdZrZip+uc4OrI0/+6EyjyEwDq46OBuX\nb993pDAJwac5A+GzAxG2b4PC8+YpeeOsM50Y009Dtco6hzctE9fA48CxIB2Wwbb7p6EFQcx821Gz\nk+vnAI03vx2cmC/5z/mykgc7UI0Wn2/3zNzcxp5OcfDlOLlaYrl2cG+c7cyRXuJAelYOvH+vDLrx\nDg7mF53aNlbkBxMrq6DT/YXX+R2dysokcbfT5iAtwV+CPgZ+LcAjbjzXNn5pwTR19CpAs86g3lkl\nvPhp/OacnHn1bGd2/GTgxyDfOpj609Ut4hT++hhxavrZz+Q1G0Letf5575FTeh9HlrJqnPybdNpW\ntD6aLT4K4lZ4t3lGaHPwCDx3ef/Kdkdf+X2WOceA3RW1u/A5wr3ZslyXvpocMMiyj3EUzK3kxDbn\nyJb5PMfQskedRjw5T5o+sQ4mfWx3pS8sV/QJj4LMEx4HzsDvPQKenA7I7BzS4HKJRXPwm7FpTllz\nEN3GqnriKpFxp6PDNqnsGy4rh7NVKv2/BVMrA2j88k1niLTwfzZBaEbWx1ZBSXPw0/aRg0Ieud3m\nzDdl3gz/XYEi+2+8tTysDN0qULq4eLVJUHOALBd0HlKFCvDVA6tgLm2sjGM+zv6v8DEvZm4Hf8bf\n54hHw9nOhPFeGWA7JKbVfa8co9zv5Wfc8TNznHMm96wql+EzZYB02vnnsTb+0XW+J+1ahhjc8ZMg\nkOfiwNrpYiBm/bwKeDxmbU40/UwHtOlYzg3OATtw3jmUCSguV007bRONtJVj2Y0x93nOt/mb+12Z\nu7i4uO5ztZlS6LItanbEjjadaAfgR0GZZc38aeAq1UODHPKEeFEnsDLbAgfSSTtnHdbkMBt12cYG\nXBlL/++88871PMr1sXHkCQPAlWwHR1b9jGfOrRKT7Ic8tZy2BJSXCa+qeTnn3aDNX/5v/hznZOvP\n40vfzLJIvdx8CLbV5Nj8vis5sVo1dcLrwRn4vUeAGdUAlU9TojO3nxHjZJ+ZaugZ9K0CqBzjN6E5\nmivn0IqO9OSeq6urQ6PnLOZqJ8WVgSOeK4ez0WzjH0VJvBKg0dDmXFvOyPtaEMExbMp9FYxxeVtb\nvkfHz1W21e/wmv3aYaKBXQW2dmjtWAXooDZ8zGMHFJRryz2TFk4ysC337cC9OUi+LufpyJiOldNw\nFNw6+G0O4CpwtGzOvMoME0+3s5pTnAt2/lN1i2PFXQEdYFBuI2/BMdcxyLi6uroVTNExbHimXY9R\n6EoQx+eHU/G7vLycp0+fzsXFxfXzrawAOvBjny3wy2/qI/5fBScMsk2nx8/zg/qrOZOWj7ZMn88N\nJtHF/g0JGNvrb3LeyUzi3RzcRhd5tQr4mi0zPtaJbNP3OSjlN/VskzX+9lxlEsVjyYDkSPeQpw7O\nW2DbEgJNX7Gd5uSvZID3MVHIecDvmZtVeydcV1VtBmf8Xuk32osG0QW0a+ZR0+NtXINPm7u2P6Zx\n1Z5tYWxx65NJk32//aou6/o2Jqbd4x3ZXfk7R7DyaU7ocAZ+7xHIcho7eW2SWRE0g8mM+sq48Ryd\n+MCRkmzOL/HhN3GzU51PcwKak84ghMo+7TmgNS00dC1bGqVIXJrTz/vsGLd2TQerIc1gt3aNzwqn\nZqjYvjPSdJj8n5vXtP6ak51vV4UYbNyVDTe/2HdzVM0by9PKmLn9FhzxHVKr6y1vKwOYc2k/97ds\neaNxVeHl3G0OGNs2LnQOVrhatlsQ4bbpUJLm0O2qn52QXD8zN6q/WX5ovUKnK0FKdKqrSyvH9eg5\nvvyfmeuAkAFjC7rSpvnW5hD1HoOl8DeOPF8pEcj4r2ScuuQoqdISNbmX5+xItoDJAcTRHHRQYJlv\n7fLelWPJOdoCsdzf5rb5wXtbBWXm7pUOzWHm8SOcApTllRPO61ZBJu0dgwEGrY2vDv7YbmQ9+sCB\nXwt4Ut0lvyzbnN8r+8OEp8/5euoh02q76I1RHER7rnKVzkreGn5tnPx/JU+2I+YfjzWfoF2zsr8r\nWuxLNtpPeBw4A7/3CGSiWSkcGYqjLBfBjmYznrmu9UejTOMccCaSRnCVIeT1voYGoVVvYphaJtDv\nQGp8ceBDpWn6jxSWnRsrRSvH1dIMZ4fJm9V4NufiPlm19EX6XdFjWzSKzRA0nHI/AwpnFTOmq35N\nL/v0Muh2r9txkGIHYCUzfkjesHI02vMOzchavo6yx3Q+TIfvtSH2XCEvGNSSD8zg2pjz2EpfHNFp\nPUEavISKsHJiPSesE4iT5TaVKQZ6uTfncp4Vv3aP6VvpD+LSeOPkiOeAxzd8PHKMc69/NxxcteI1\nDviaXmhjzj6brg2+K32Z803X8rmwFkAc6XHbpPymrlwFQaSFNPpY69MBEwMc4uX7bMvCE+sJ4rJa\njeEEYL6T8Gu00WbTNnuPgpzPOdr5BFBs18k1yoplwr5C00etUkwInW0e0ubt+36DjpWPkOPZNG/V\np207/5vfLTlm2W5+go81O9Pk2b5bKxS0+cXvlgA8mgsnPBzOwO+EE0444YQTTjjhhBNO+JyCowTZ\nQ9v5fIEz8HuPQDJMzJwwK8aM3szcyu6tMrktY5f2230t43ifikyrQiTrxIyWs9O5jvR5yRKzrc5O\nsjqQzBzpbhUUPlO0ook8afwjMDPopZvM8JlnptV9ssJDXq2W4rTsvCFbu3OL9+DfMnPsI3S0ytp9\n8HE22DLBe1oWvmVQXUVz9pX8Jh65z9+uTjPr2fBw9ZEVeOPblqz5XONLk2GPVcu8Ehqv2TZlLEsJ\nyYPwaJVFbvLmygShVbMyl1eVRPbF9to4ux9XRi0nlDtm91n1866erPpx7NPukey21R2pdrb52+aE\nebCqsrVxZ7sraP0Q/9WSUd7b5hOfZbeed3Uhv1cb4ljH8tvtNv10F+0rfb+6ZtVXgKsfrIe45JHz\nrs2/1dxb0bSq7nh35Rx3tdf3cbWR6aM8czOkVME4v3gfwXM6/VovuHJ+pDdY5TpaPnzXyhmvZFpV\nldu8s53idayGNtvgFSBsc7Vksz2iwHP09wiuRofu1Uqd1Xw/4dMDZ+D3HoE24TmR2jIuOkzNIfO7\n0u4TwPDbeGRyNyXdJvtqqYPpsLGeuf1MgI19W3pofI1jcDf+WdZiB8XnjQeVH40o+e8lTU0hrxw2\nOwW5j/zyEkXj0QIj8y94rp7ZsZPsNhmE+AFyOhEBbguf3eFsdCkXK4eE0PA1XyhvDl5XywA9brx3\n5WBmnLIskO2xXV/Pa5pjQcgSIOPL78BqyRzPNz1DXua6Ng8CTW/weZ9cQ6fXPPByW7YVxyPP8qwC\nnyb3xs36NLSb337GL2PK3T7bBi6r15ysggSea+NtZ400hB9NFqLX2jLAI/m1M5353Ghwf9Z3bUlf\nkyMvY1s5rNaXR85ms5XWhQ1W8kQ6WnDEsW9jnvFoS10jQ3bw+Z+bcqS/6HIvL2y+RJu35nWbV7bD\n/G+bn7lCfEJDNknymPHT9PAKn/DNY2L6DavANfet5ib5aJmjDsk4OqFHPA0O/sg3B83E1UsyzQvi\nlX5y3oFh43XAtuHI/zpqt11/NFb3hcdo43MFzsDvPQLM9gXuMmgzffODmVeONw3d0fMDzZG2E5D7\nMrFzrgVTxrUpczp8q90vaQh9b8uOWyETb9LegtdW9WFAYCfO19MIm6cx+OYBDYUDKTpIDgJXvIqT\nbd5cXV3d4EXL2JtOnnPVj/e0QDxt8OXe7o+B8X2csZnbFd/GBxq13GP+mq92YAIZGyYGWnW7jXl2\nn2zBI//zd6v8ka6csyN3FDTk2naO/WUcms45av/oeAtOV/isEk78n3Flsotj3Rw1zj06lzM3t523\nw82NW7KRSyp+/M+XywcSmCahQnlb6db7OK2k1fPpyLmik+e+Gdh5jjgptBqnld2a6YmHjMldgRr7\nILQk1V2yGcfZ+ju8ccBjnUFcwp+VviJfPe/5m3qDFe/I1Myryhnl17Yn966eLWPQaDx5jWkk//yb\ngarvJw/YvueajzdcyF/LsOXFY2bbnHb5yhW2y9885nnZkmUO3FjxvE+id8XjtHn03KDl1z4fA91m\nV0infTP3lWuzk3njj/XB+TqHx4Uz8HuPgJ0tKpG2Ta6DwqbU6XTzGNu2E08cCJzQTUGvjO5RNsfb\nuh9VHJuCdtafeNrhPMJp5YA0up4/f37LiQu0AMFOlh189s9MPg2VaeR1NLxpn0p29f4eO5/BowWc\nq+oJncPmAIVXeVWJx5dy2/jUjEgcJB9vDqPlfnVdrm1y4CqODT353trzTnONHo49+7X8NuPs8WWi\npMmwHScnEhxE8frmuDbHsTnCOeYd8yg31kFtLCJPDY6ST60qxn6YUeccjfOWCkZ7x197nUPadpCb\nsW7JB/LF/GhBSJszK6eOuqnpRc9h4ugEIts1mF4nEuh8Hr3qxvqx8Yf/my7I73zaWASn+yaUgt/R\n5idHemgFwY8634HYCky7edd4ZJ3fAr1me1o1suHjNinvTU5tF9kud9VMu+6Xmzi1oJABX9q3rlz5\nGA3XyE2ravIattkCXNKe3/QhWrDY9DrnG/snz8L/Vj2kbLQkJsFzukGj4YTHgzPwe49AU9j+9g6e\n7V479VQAq8wk2zkKmHJ9M/rt/UMz622v87sthfAunvxO5soOrPuwUW7G2ee4ZMKKsAUirmKlDTrO\nq6x8M5i+zw56A74gmbzLh89rXV1d3Qgam8MQmognnwc031jZs6HP/xb40QlYBRUGjs3KQbNxJH0O\nKHO9ZZTzzIENcV85/ORTCzQtS21O3MdZbPQHP76PjvxoDpaDBtNz5ACQnlYxWS0PXI0T6XDWn69m\naJloyq3pzjUO2ukA+hydu7feeuu6yjczN17o7mofaXN1sjljDvQaf/gs8ypIoR5r8naXA+Z5tZqX\n/GZQNXPTaTRfjpJYxJXLgo2/5wf7twwSmlO/sg35ti1sCa+Z9aMALTFpfAyrObg6x0SCaWgJoNWc\nbTxouEemWzDV8KRNm7m9+iJ4Zu5ybpPOlkQlPsY9kPYiV2w7SR0GZeRb47nPueLpJOxKHzV/LzSu\nArKmG1pAap4dzds2F1b3GahvV35Yvo9e32Q987rwGG18rsAZ+L1HgEqJQCV7VF3zsRbANCXRMkW5\nd+UA2tCvAlI6WisDmH68JXoLYBvuR8qrZdXv61CzP74mgsbSzu3MTcPQHNSV8+Ugw1UB8tHKfRXQ\n2+jToVrJhr9XdDZazG8GhDlm2iwfLXsfoAO9MiZN+Tee04m20+iXHs/czBS39u9yqts9R051a5PV\nuaPgrDkbdniMx31oIM4etyOj2+SajiDPte+Z29uoHwU3djjtrF5cvHrPH5/PiyPYdA2f85u5/Y4/\nO6b8vrh4taGJHSrywnrX+s9V61XywIEmeXKfMfZ9q6C1Je2aPrK+Mn8b0AFm0Od5Y1wDLcDMONhe\nNX1IncCXiRtYEW3nLIv5cFln+OIApjne5h8ThK7YpY3Gb/Ky2QLanRaMrebhURXNtJEvq8ph69PX\nOJAhjVzSSRvohE/mPnngudDONZ3UdEG73v5U0/1Odvr+1Vxqet3yvSo0WCbua1NX134+BWSfKViv\nbTnhhBNOOOGEE0444YQTTjjhPQFnxe89AsxG5X/LFvE/sy7OxLdzzBh5uQaX+DHb1MDLZZLBbNn2\nZKy4jNK0MGOWY2zDFTXTQZ4RJ2dUs1QsGc22PGbF69XSnLbDIHFd3WecWwWIvPOzUbwubXgnQS5B\nYibSGW5WHFyRCbQqTBtDtulqTssWN2DGkf15aYkrSUfQMuXsry0j5ZLOBlxW6+tWeM/0MW8VvFa5\nc2Z4de758+fXO1CGPlfByBvqD+O3epH4Ec/vykQHJy6ZI+3OSHPzivTNipD74TE/v9eWdZkfM3NL\nH7cKDe9nG6udlkk7lwqGpm179exby6pT17hNzocms1z21vjNsfbcXtHSzrHq5wqF5Wy1EsM4rvRS\n5CkybLtmfL3MkOcaDmnLu4G6QmYdfKRrWG2zTlzZdlb2mqwerWhpY77qnzxpy2N5/+r5N1fS2D5p\nMQ3ey4C4kU7KF5dAG1f7E/Sxtm27sSnTqhJt+ff8J79W/CTd1lntftLruWPZI45trq30SHhJPvE+\nt9v6a/g1O2oZMTSaXgc+nyqLZ+D3HgEHfjzObx5vyoP/7azYwW/v0WmOvXGhMqFCbU5ClDR3cEwb\naSfKnMuvmlINfm3J5V1AJWTDFjza80irwCl4un3e24zJysFpgREdMToWTdkSj9zXFCHHxw4kAz87\nEuzHyzKPFDedeBoaL4ehQ//OO+/M8+fP646CrX/zsi2vPUpiEFfPC9LQnH/30ZY2pQ07E6v5Sxlv\n7bTfaZdjQXnmki0vK2fCx/Rnbh89o2GnvM0F43zX8rqA6fdyyJm55cARole8u2rTfeEfnUoGaZ5T\n1ncr2ryck/xq5xpQV66c9abjDGyf8hJcmi4Jj3Iuu/lZJxMntn20JLvp7jbmq/9HwVj7zesajyx7\nrU0uGzXO5FOzh+zXOLYdgImz53OuYyBJYFC0CgAciBKf+zjunnNe5tmCr4ZD5vAqyOJv2oRVoNjw\n5rVMAjU73doyjdGVq4RB88mSKM5/42ZdYn/FesU+WdOnTd6sB0ib+17ZO/Mp88J60oH+CW8OZ+D3\nHoKVgo2iaROyBUZW8n62i9+r5/N4LO01J2dleO8COhtp04Z0xQsaVjvUaZsZ0oanHYAYU59rz7U1\nZ3xVvfKYOthywMBzrZoZnBrdb7311o3nURwYrcaIONkBtLw0R9UOZHD0hi/NaTZw58Rt2+bq6up6\nDCgf6dtVofDNhrxVdkg773NQ0ZyYXM//qw2ObJAZaK0CO357nFvCozkudpAj33wmyYHqtm03ztn5\nJQ1Nn6SvNs+IW66zjLpSyvtahjpgOvjNwNBBvQNGy3dkODxjlc4BdAvunLzIRkdpk3PtaCOs/M94\nkB8rRzB849i2gILzvvVpHvv+hmvwo1z4fLsvdLiflQ5aObyrOWr5slOd6xhYZJyjf+Lc5jc3ruL8\nSXu2sSs9vApEyCcnJ+kbmBfWAQ2araBdbXx0YMVzfBb2LpvYZKa9cuAooFvtKO2+DJkrq/5WQW9o\nSlDtzdU4Drw3/KWckW/Bx36LbUTjC6EFeZY384p262iumgftOtOb648Cv4f6jkftfL7AGfi9R4AK\ng9Ccu5ZBcRBDJ7kpsVxjZzlKp1VTVpPXGU7jGwVrBc3/dOL3/VUFyJVIGqqcd3Uwv+lI+T7jSYei\nGaXGw/DsKODgPQ4QbaBbNj7tkl6Pr2HlIBGXVUBKp5X/+dtL1PKbS0vjDDUczB9XpV+8eHHj5ecc\nu9BNnPid60kbt/pu70LyC4ADwctVslXAatk4ck7TjoN8Ol2mP+17oxNek988H+ck9DnQybffbZXj\nnF8eS8qLx2KVSGi4ko/WCzxunDge3GjFlYaMoenjOTp1+X7x4sVcXV3date/7Vj5w8CPc8U7Qq7G\nlP34uhZgtUpLsy/Rk9Rj7r8FaSvHkte6zxacObAILaQn/xkoW7fbOW/OOnmY35El21Tq3PRPHClH\n7I8VHW/gQr3bnN3gYseevAj+SY6Zjx7fBEYrG9kcfeq9ZtM5Z5ioSx/c9KjJGz88FvpJm2my7Yrc\nrmS0BYvGpZ1fBTsZO66CsA4Nv+1vZTz8apnwdJUMMA0rH6bNL/pyq+CYlWEHdcaLc3rlq7IiSlxO\neDw4A7/3CHhnsCMD0RwnKlEuq/Rkd5aeL363YWqBgTOSVGxelmglYmXRFES+aZAbBD8bbCsYZ3hX\nBjLHbGyS3bUTT3z9f5VhN088jqYvfGj0hTctsMpY+DUMdoDM27bDpSsSzela9edgthksB0wxqvn2\nmDHz3vi2Ajpmll/yxgaZspjr7ATkt4OK4NeMMuee8WFWuMkpx6g5R+1ezynKaq718zKhlztSNmcn\nbTu4cd+53hURB3QJbhtdpmXm5WtKGBATmvPZdFDuc/abcp0AMPft+z7ve9/7qnPH+1Ply7H8ZzWb\n49oc7rQb+XDwy4CJ42QaVwGb5/0RPrZNbDfz1fMr54hTC3yaPIX2zBkHhRzTFmzm/6ryQL3khEjj\nFa8Lf5igMA8JLdjNb+q6lgzLJ3M17ZHnKwfbOoH2nPwjPkyIGOfMNyZM+F47J1PIqxaAJWgin5qt\nXM3v5ifZpzGdKxk+8ieIF32XAG3oKpj0fZzPDV/aFtvxFd45Rzy8xNQy3fB0f8GX7fC3dWvOuY0T\n3gzOwO89As1Y28G08qMDt8qyWyFaSayyxM2pbYrCSsBr2FeGjritjFXAS+jioFrpEpcWAJIm84SG\n96h60ZwKOiLOKNvpopNhHDgmdAJc3aMxaEEaZYJONoMe9v3kyZMbz9S1YKoZCzr6aZ9BIvljR4bj\nZIMfB/fJkyc35IkB4dEzZ3T0fCx8sSxy/DwnHCzxHIMp3rsK8EljxtjLhVaJA+Lf5r3p5L1+Bjd9\nZClrPg7WTdsqScNgL9Ve6iGCk08tudN0QpvTwfHZs2fXstHu5bzk3AoOcVJb8JuA4+LiYq6urm60\nF4fV7fOT4G9mroO9q6ur6w95ynni5xDtDJJvdmKJfxszAvWp72VAHKAsrdokLq3NpqOb3uI9q0qH\ng1le47E2zpZjtms942v9MX1ph/QHB1dY0h/tPnHJ8fCNvPKySgavLRHFa8gry5dfV5L7vFkS9bOr\nhU0PrvQazx0FsasAZxX4HQUddwVBllP6NPZ9TMeq3xV9jV7LqP0EB3cre8Fzba6ucLQ9sY+1asv6\n6S5oOJywhjOMPuGEE0444YQTTjjhhBNOeI/DWfF7DwGrfi17RXA1h20w0+wMJzNxzpIxQ7PaqCLt\nHy0bbBuiOHPGrHXLOKZdL/natm0uLy9vZLpcOVtVXe7KQK0y0avqBbOurZqQTGlbatWWVHnMW4Wl\n4dwqfi3zPTM3ZGIlYy0bGXlyNdTP3bAauNqsx9nJVYWxyVG+s5GNlw02aNVG8rVlb0l3eJpKXXv+\nNHJ31E5rM9dxyTUrG6bJc61VfTzvWbVj5SCQZ2rcHvFqNPA3x4ty4aWhAVZxmg568uTJckxdLZm5\nucTu2bNnt15lQT1CufLSTj5b6qVRfPn31dXVsuJHnrAanuOurgdv8jrVJlaEXAkmbygXR1U/0sN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oZLW0pKcLY3919eXt6YQ6SB7dHR53FnsnNdNp1o4+65bGchxxr/7FRzPvD5FzoZ\nLTPsICXJFeqSVIeIrx18QpZehg46eU3+vCTXeoeVL46RnRO+XsbOFat4+W7yw7nU5r77YZAQWghu\n30mdbduuEye+h/xykonfLTDic4LkW+TDm7Q0nU04StiQd7nv8vLyRhtetttsQc4xweJKim3cKuFm\nGWKgTJqIA20D26OsWLcRJ29wwkRle3a+jVvuC/3mG5McjQ+cv61d2jDf63Yp21xCbz6vbEzTv+Fn\n+uI1LQnqJJ37s3/k36b/KMBrweaKBtqg3NN4av8jPM0cvE8wxjbbHOL1niMMQo9sfxsz+mjmCwsD\nq2BuNd8+27Bt2y+al4HcvzEz/8fMfOXM/Ilt237yvu/fW25538x8cmb+43evXcHfnZmfPK8Cv087\nwQ8No/+TmfnGmfmSmfltM/NPzMwfnpl/b9/3L973/b/aXz4HeMIJJ5xwwgknnHDCCSec8GkBBvxv\n+rkDvnJm/ut93//bfd+/fWZ+1bx8fd2vXOD18X3fv3Lf9983M//vMQn739n3/ZPvfv7O6/DhIfDQ\npZ5fMjO/et/3b9227WvmJSO+et/3//HxUTvhIZDsuqFl95JlZEZ9lUkluEK4Ktfn2tW9rEY4890m\n4CrDmetb9t1L3ZzVaxnDXE8amc3jErBVhp5Z4BwnPa0a5MzeipZGe74zlvxPnFgZYCaubSrjbHfw\n4hIiZnEjdy1bnPaYQST+V1dX19nN9lwVd3z0va36wvFJppvVD/OzZZF9zapaGmCVM5UY0pC+U8Vr\nGd4m48nCrzLNbUOUBpRF9uN5wOqdqw6cu6Qh7bSltWmHGwIRl4uLixtLCJ89e3bNT8p2WwLEahp1\nCceHFTZu6uKlcKxouR9n2C1v3Dwh/RJCp/njJb3tmeW0z3mY+ULZp+7m3LecWWfzfPiRsXUFlOcJ\ntAWuqqSvtiz1iF/BISsiuIzN1QuOC/UWn/G1XFI3spoT/Fq1pQHvoU0NDqz00T67imhdk/Hl98zc\noMnfrvRZvmfWr8MgX9uGVK1C43443v5eLeGzrcy4WxfnHHneKrXN12l08Lr4P65k59v+DP83nuQa\n49lWlLhS6HFvVS+eb3rHY2sdR94ejZnHlnOItrpV/oiD/RCvqGpycWTLPluwvXyc7R+fmd+cY/u+\n79u2/a8z82Vv2PwXbNv2sXlZiPvL87KQ9q1v2OYhPDTw++Ez870zM/u+/8C2bX9/Zv7Ko2N1woPB\nS8w8+QiZeO3ZjJVj4PNNGfAeG9IV2Oiusi6rwIHQlu+4fSs28ynBiPtw4GeHj9fasbAB93Vsl06H\njR+PBYemvFc8JJ7EpSn9I8VsnBjw7Pt+y0EhHXFcc+6tt96aq6ur+pxT/tNhcpt2Kr3U0/LsZUF2\nuhhIOGCiI3DEW/6m4acTS3war+3UNuBSxzZ/HMA6AeC2yBMvFXMwwD74Idg5oWPMd0nmnIHnWiAY\n+hx459s4Zfwio3SoueOn5Sv3eifdmVdLE5t+4n8uow2uOZ72yO/QkOCB45oAOddQX4Unlu0mX9bP\nDvY9DpQN0pB7m51h/9RrvrYFjE7UeBm5+d3kM9e14C9AftleBI8V3qaNS/jch+9pS+GCf+STYzLz\nKhHR8GnBm3nD4NDnzSPbFicuHPDZ+V8FewTKvYP6BmyXtjj3tGfZiKP1JING87PZmdZOCwCZILJ8\neIMn0nbEA/O6+TJuM9c2sI5c+QLUlTzX5gqDdwewzSdtPk6Or+b5Zwl+5My8NTPfo+PfMzM/5Q3a\n/avzsmL4LTPzw2bm183Mn9+27Yv3ff/EG7R7CK+zq+cXby8fWpx5uSb1p2zb9oN5wb7v3/LGmJ3w\nIMgzUSun38HVXddlovGhbZ/LsQacuFaYxsUGdxWctUAt33ZyW7/kVRRnM0QtEHWQxiCAhrQ54L6e\nPKKjZufyCKKQV9nUVeYw987MLcMdWlbPVbnaQRlh4oHBU/A0zek/hvCdd96Zq6ur6/uurq6ug0K3\n3161wGcZ6XzRAXPl13zJ+XY80AKcmR5UsR1WNnOdDaqBW/PbkSc+djKJt/G6KwmTsbq6uqpt2rkO\nH5ssBQf2ybEJ5H469uQzA1zOj33vr0FJm6zUWcapG3Idr28BuvENDmy/8WDlxLMKzConn3/MXKLs\nN/nnN4Mwj9PKKU2wqGMAACAASURBVOccJR+aI3zknDsQs44LnpbZVXKL1+R50dYf5XLlRK7wZhKU\nzvpdYFodvHPDkYc4rq4AN2d8lXTM76a/PX7Bm9e2YI/Xms/eyMzQeE1bT1ltjv4qQKA+jG11cLWi\nv+HVVnhwN0z7BWkrfYUW86rpbOqt5oMYV7bX5LzRtBrjFlAdyWbT65xPR8ketsH7jnzGFQ1HcNTm\nQ+Ax2niNPv/izPzF/N+27S/MzLfNzL85M//hp6vf1wn8/tTMcES+4d3v/d3j+7yMjE/4DMJR4OFr\ncn6ldHg/g8k4YFEGVpb876U3VPDNCVhl/I+UKw2InSUHjabrrsoN218db/2RH6s2G43BlXyKsj5y\n+mmAk/mPI3OfJEAz7uF5U8Q0hAEv37i4eLWEj8bGDhADXW5rP/OykpLgL+Nr5zfHr66ubji/3DDH\nwR6/OU5sO46V5wtln44InRHync5kPqz60UGzvFxcXNygn9CyyXZYm1Pd7gkdDKxyrC0db44c5yiv\nCQ8ZrPH+7NxpcLWJDr/HZOXQe5xYKYnjxfuePHlyYxloIAFRdFQCYieEApS3VXIp7cY59XKpy8vL\nG5siPX369PoaLiP2MlAn2ohnw8l8s5yYl+YxoelKJsZIJ//bsbducrtHVUwGFcbzyImMnNDh9/xp\nVVTeGxmy3Wyyz9dhtODHgY836CJeq0QM6WW1+ShQbvKwsnHUWw4egzd/8z7abffbEg3mt8fYSRy2\nSTpWMmB+G3d/Zl7p4ODTll3SJpqnbY6aB43fM69WGbhyuOKpP83e239s5xu/8+3EGOnlvGdC2mNg\nfD0nVvCN3/iN8/73v//GsQ9/+MPzpV/6pct7vvmbv3m+5Vtu1qc+9alPLa+flysd35mZH63jP3pm\n/vbRjQ+Bfd+fb9v2TfNyF9BPGzw08PvxnxYsTnhj8KS2opq5/awSJ9aRwggcBYvNgYvBokNPhZX7\nrLjorMQwrnBuNOTeVfDVFBlhpXCoBFcZRisoOve8Nr9DH7evJ56B1bNeOWc8zXNDGy8rawcYzHqu\nKnAOwlulo/XrHQPzMuo4weyPyYJUTYgLKwrNSK34SN40x6I56YTQyKV3DgrYf5xCZ87JtwTELeBp\nFb0Vf2deBTF22HydZYPLcBnkkxbrDfKNCSOC5+8qQ58geObme/yOxpGOaWhP5Tj9MhDkOPj5sNyT\nBEMC+PCKiQi2mzazW2mrKgQ3Bx7kEennmLiamnEi/YG2O6aDKCY9+NwuHbf2LNQqMOM1BjvqLWER\n+WFlx/OS7bXVCDO3lybb3lFXtGpk+moBdtowvym3kZHgd3l5ect+tH7MOzrN/OZ97V7y0Xosxx2g\nkK4jWM1B6wefa/6J/YCVfrIfYv2e3052cJw4v5uNaH36v/ndAjgHMZ6zBuLy4sXN1/nMvFo94CW/\nK9vmIKzxk/whnuTJyo42uv3bS5WpI1f+Uju3gi//8i+fL/zCL7x1/Mg+fPjDH54Pf/jDN4594hOf\nmK/7uq+r1+/7frVt21+amX9mZv7ou/ht7/7/HfdC9B6wbdvFvNxL5X9+rDYbPCjw2/f9458uRE54\nM4iTQafy6NqZmwbMRowKgwqC/48UN69ruDQHoSlXGgIaSyqxZpAbbu6vKZimvPObhsSK8cjBId+8\npIrGoW3w0Bz+RqOdjjjaDn7YdnNwHGzTENBBsvPQ+m88NL/jWIcfDKzyUu9sW8+qVAxoHHoHfuRP\nc6xynEbWTl8b0xbA2jEOxFlvDlp+53yMIRMErh6Rx156tHJGeYzHwzvP7aOsvZdZrqAZcy4hTj/m\nJSsbdGzJ14xvez45cp5g+cmTJ9dBVwKfy8vLW4HW06dP6+YvwcU6KM/YkS+sCDngCj58XcXMzVd2\nkBd8pULo4RJcOlReYu357mXWLSBylbrpzbTNZ5vt+Fnn8r4WUFjPBye2R/ooM7Y1xLX9XznG7is0\nrJ6dZYDJc82ZD762zaxgx7lfrUC5K1AwUL9Rt5EHTjAy+GFfGetVEjXttISlVxe0seb1xL/5F03v\nkIduxwHPqupnObQ8+Tn6hs+KxjYfia+fJ+b5ph+ZsOOYtGc2TSNpPbILnu/NnpBG8iK4W7bMX/OG\nOBqvf8DgP5+Zr383AMzrHH7QzHz9zMy2bV87M1+47/svzw3btn3pzGwz8wUz86Pe/f9s3/dve/f8\n18zLpZ7fOTP/8Mx89cz8uJn5XZ9OQu6vUWZm27aftG3bf7dt2w8t537Ytm2/f9u2n/p46J1wwgkn\nnHDCCSeccMIJJ3x2YN/3PzAvX97+G2fmm2bmwzPz8/ZXr1/44Mz8WN32TTPzl2bmH5uZXzIvd+1k\nNe+Hz8zvnJlvfff4F8zMl+0vXxfxaYOHLvX8dTPzN/d9v/VOin3f/+62bX9zZn79zPyrj4DbCa8B\nrqo4Iz5zu7TvbFXudxbI7bricFT1chvOpPI649Aykr7GuHAN/lH/pMntsT/T5mycKwQBV1BamznG\nKhrv99bJ+e0KlftqmVDj4aof22KWeubmMkove0x1y1lALhlj36QjbXkJcCodebYqbXmJJ2llRahl\n5sm3i4tXO4Y2HreM/lF2P7KzWg4aOr1kNXxeLQXMteShZYXVw9zfsvgZi/Tt5Z+tQkMd0TK0q3nv\nCjllKeMdHJqMcnc8LnXi8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B9NQ9Oxto1NholbC5JbwLjyyXI+35z7tPUM8MjrmdsbNxmf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ul9mdmeevsCB/qA/beuEcbWu78YPl/FiE54J5vcmOtNu2drZ7lh3e3sn6bUumeeA5x7l2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v9YZsqgJELZnNgGKtn/FhV2HQQPbCty81gRaE1bIb29kk/LZFbP2b6W8SNIaVk9Oh48R75Yp5/c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\nhjnXxj3H2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TMq8ulx+Ybcz+Pr0/d7cbXyo+H02AximE0nMDNvo7zWU8d5yuhS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\nK8/1dN/xd54Xl0XN/thQr/p1Gfsl8LFAruf6qWQXYyZPs/1qTej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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m0 = cube.moment0()\n", "m0.quicklook()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$\\mathrm{\\frac{Jy\\,km}{beam\\,s}}$" ], "text/plain": [ "Unit(\"Jy km / (beam s)\")" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m0.unit" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/spectral-cube/spectral_cube/utils.py:39: UserWarning: This function () requires loading the entire cube into memory and may therefore be slow.\n", " \"memory and may therefore be slow.\".format(str(function)))\n" ] } ], "source": [ "cube_K = cube.to(u.K)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] } ], "source": [ "m0 = cube_K.moment0()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -3.917e+03 [aplpy.core]\n", "INFO: Auto-setting vmax to 5.696e+03 [aplpy.core]\n" ] }, { "data": { "text/latex": [ "$\\mathrm{\\frac{K\\,km}{s}}$" ], "text/plain": [ "Unit(\"K km / s\")" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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08NZuJ9aqP+Na89Tp+q3AJW27o6wlnc9AhMnH1Id51QWGPJ7haxqrrZ8VrGtp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kxbj9HZH24XY5/Eldd5H72ra6sKIZUA+033BXr81Ta13o6Q7ufqsadMW6p8uCpE2jke+5rucWK/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\n3vDDxzEnXLpPHjc+DXTsEyTDOjltlFdfN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caZZ35PMK1pOju+F26q3LlNO+58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\nk37qSn6S/caRW2qrnr+vM23j47Eey68OcdXeeLFvbu32mNaT5C3lomVs0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f7YfjMYVChH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\nrSSNubX5ktx2hsC7VJgdJK340nhuYWVZ1ueauL6+vtUnD+bJNdJ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stuuO6GW6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Uaa2kChMUfDm/WD/0rhmb+bQicEn2zRWhl5zTloz4KRR9IOecXBGTpHoYnlBIMn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\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gruYVaWFgElw9l00LnczILIM+bojCbDlpnSqR/PX50Mcgh3qDdGSMOAfu7u7Wzc3NWcDE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EsaEZyDOBAvXrwYt+o1HvMx/cSP+CRQtXNA2nyzP6+Z/GewrG1f5VzQcCXP2tHzWvNc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JsN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\nB4gMlogUDqV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T17rqn9lzXdlPZq2rEw95LddD5eRXuqhyHtmBdPzlMW7TH/NC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\nZ2mnSeeuUvZnAj1mUgVDswDKyfGMRifPfJ1p4ntjDFx00UUHAkEXbDjaHdJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m0.quicklook()\n", "m0.unit" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] } ], "source": [ "blue_lobe_m0 = cube_K.spectral_slab(30*u.km/u.s, 55*u.km/u.s).moment0()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -1.579e+03 [aplpy.core]\n", "INFO: Auto-setting vmax to 3.398e+03 [aplpy.core]\n" ] }, { "data": { "image/png": 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L4QwX1a6aG+YlYUahY183btw4d9SzkY4EoeORjI12CHGNuGOZig80iJNxmpwQ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2tP5II9zhmegJbKuAKsaUy5jhjcOo4XnxVe9T8FsnZ1hNN70x7Sfe30Xt/nnFPPt+zt2LKOAq/0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\nxm2KiLkosbvv+PDIMsdQ5dHMS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M5neibaEfcudQf1hfJMOtr1YeU77NCr4PF2pf3XRZ50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\n52z81B7vcU9pIHtlvzm2bn1q28ln4NpgmSlI5AKpr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LbzofpsarP5n+Dv40cnEHwkRMDU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y6TLyMe1htkn+uQ84j00TyGkeuNYIUlZEfTmVSTwoL7yX5mZH308Ah+Oqz7RpHfdMmwuM6vpb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\n8cYbD8r0WPF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vcRy/oKp+9DzPf3IcxzdX1R84juML69nPR/zSqvoNVfU730q/\n3Kz92YEXB/wYkX0rUUh1LrWOM5iO3yQTAd7KQXZOpFPsdPidAWhw0EacQFT/v/baa2/+1IEaDAKh\nZEDd67sTTUZYAYwDrlwfPT6teBn5bnJZNgIQAsH+S1H8W5SwHtVZOcL634FiHvnVOg4INSVnQveE\nA9HKD+87ObVdykPQrTLs/G6jkgJROk7kyd1zjpbuH/09RJVByQGHFQB0IJtgsOvd3d1F2ZhVJE/J\n2dA95AIIKTubdFrVi3rTkTpTznHVPtgnZVs5hud5PjjloE6qgoG0Pxqwcm8l0OJ4TXzp5526O06w\nsw+uXq8VZ2O1ry6ne5LPg7P/Cbxp27pPCJqTnSUQ1uyRy9Rre7qfE58k5xN0fd1v1HNsQ+XeBTNV\nL/70D+s2H2l/NFFvqB5KACjpMfVR9DQF29sBhE5fJn6cfXJ8OrDp/CQnm1Jas9SLk2/nfL2LXi1d\nQPDV06dX1d+oqvP53x98fv1rquo3VtXnVdXvq6o/XVU/rZ6Bwd9znudXvJVOk7PhDFNTisKuHBBH\nVKZ0UOg0OAXTnycwSEVF5ZLKTgpSr6VjUG081QGjYlSe2bfWZ8Yg9a3ZBFJS9I5cG8mxrcpZzVv6\n08zP5ERPvBA0u/V9HEfd3d3F+Z2cd0e6VifHwDmKzoCtwJ6WJbDptlmfe4ugPck5GVRmtRNYohPp\ngLjORcqOqiPkeJvWmI7VcRwPIuq83/295z3veTNwMGXbEohqcr/12aRrp8rrBfedAKudw5TxTuC0\nx3MV9edaXTlaqpedjteMV9/TDHOvXQW4bMfZgFvBoPvOewxkTGs3jUPqi3uUe6/XqQvSrE7RVFXd\n39+/MqdY90z/iPwOiKDeUXtIWVwbTe4Egq7dKdvY/bP9BNjJVzrpw7HlOmfwQj9TryQ+HHhT/tLa\nTL6Va5d1Ew/dLtetk2HyySZKvOq4sy3np+7op7dKr6KNdypdQPAV03me31zDMc/zPH+kqn7TR4+j\niy666KKLLrrooosuuuiih3QBwUdGjPZMGaUU+d9J+bv2NJLLaJJmLjSyphkj9jtF+DRKp30o/xqt\n1Oc60vGv6ahPR3RdxqijdZoh1CNp3WcfjWQmVtvUaLGLtKbxmJ5xcdlLRnYp62rcea2v39/fvyCf\nRlv5nB0jjfzujqcyenie54PxXmWWXDtTxJPjkqLkqR/NjK2OUU2k0V5dU65c3+MYMsuUxoJ1+nrX\n01eC83iXku6Fu7u7B/29zFisns2hnBqlZ0Zwl1yGSvcTdU+K1rPNdI26reVxbWl/3ONuL1DXaft6\nze1LJ6s+K6Z9M6ujZVwWj/rWZeSnMWM7pCRbGk/ueZfxckey9d7O+k4y6tH6RG4du6yXUsvD44j9\nmcdB3X9ec3oy+RBOpzv92uvHZV21L21n119ZnWhw5NaFK6N8unFjBo7EMeA9/Z/mqftxdVN7zh4k\nf6D5X9m+5Cto38ku3qqjL3prdAHBR0RuU6YHexUIpk2XjgA4gKXOUqrX/KRjEawzHU+Y+KVxogJX\nJUWFrfec86cKjCC0/wh2eGSk29Rn7bqe8kNnYjrKm5S18qfX1aFOBsU5Twl49L27u7sHYJB9ERQl\nQLJzrM7Jp2vaGarJuZycDl0XdJ7IL53h/vmSKejCtTbxPNHqWFbiQ53DJgcAqz7yvF9/dsEVref2\nWted9EVypDkflNUBAjo903FatrVDk3wtS1qLO33QwXe6vvvQ43V8EYXbhwqak4PbY0qdpAEJPiOo\n+sUBBpXbHRmmPZnGzQGMNH66/nvM0viqDAR7Xd/ZPnckfgWKeI9yErxNQHO6p8dA9Ugvgyy0oUoT\nUGFd3nO/l8t+ej26uabdYjsTONnRh06n8DttJP0I/c5A2Q5/zp5Psjh++nqyayT6LemxjtR34nOy\nsRMlPkmT7byFXkUb71S6gOAjogkIuLJq/N2zTM4ZT4ZNlbOL5E28pqjQSnnSANNxJ2/62TlEzYMa\nfu2LfRPsaAaGYMf9lqF+VkeRQDAZ5Ekh63c6KCQ3tjQo+jwIZVDj0bKq09LfXeaxwTAzC46f5Ohx\njLSOEtfENC50xNj3TsSynyfT/jh/7k9lcs5n1+32nXO+chJdZpBjnJ6pYVabz/qk/tx6TXvW8UP5\nV+TGW0lfkqGOMMvSmXKOlTqt7pSCW4vsM/GdZGtihr3HjkFAOtkEgr13HT+9Tx1Q6Db1nj53RiDY\na3sFHNJLuVz5lGXimPbecAC5y2lbfA0/x71l4VtA077t8SY5fp29UFmn37WbyLVH0N6U7M3Ub9rT\nrj3ys9MH7+868Kk9gsEJ4Lp76kdpGReMpC+ilPZqt9Xf07pKfejcOt2ie4PtONvkeNyxh26PN61O\nr1309tIFBB8pOYdYNyCBkFNO09GzSfm6+1Qm6iCpItYyTqGlNl3bUzsu+t2OUHIu+j8NUF9TZZoc\nACpcddzcOKrinwzULqlhmY5RTXWno1HJAXNAT+V2kdmUoVJgMTlQXcetbQXtWpfyKDmgz6y6a6Md\nT2YbuHbJywoM7gZcppdUaLn+YznnKE1rZwoWuIzW5NjxdzSZveL8sn/NXGrbKdPeZSbQQXnTcTy3\nhnhf5VNAxz6mNpqYpWUbyclXnch7TQzmVD3MBjpnUMeaoJQgR3lpPvVnK5y8KxvEcuoIu6ORSU8k\nubo9fYFQeguxtss2k67pe0nPs23OzUQcd/15EJcVpQxKzh7qPQcgklxuPSR5XR/0X94KOZ8j8ctx\nY6ZwB7yqHG49aPvMqqa6E/AkP+n+FNSoevHtstO4JdCvunDajxe9PXQBwUdC7UhMIIXklJjec0cD\nknPjQEpSas4hqHrxWbuVIiD4ckrWOT/uaJ/rh0clJkeK/SXApQa7edcspDum0tedE7Qy+I4UhGsb\n01g4RycdkWKGrmXkelKQy36bFwde3Bw4R4DAMzkPKp+2lUiddgWCCZyqE84AhBsv5ceNm9YjP9Pe\n1Gf0OB7q0BN8dR/uqKjLVOocqvOt5fW3/Ny+ov5woIUApj/zO4n9TQEN/TztNQ1o0bnudkgqYwLQ\nk/OWqLNxLlBAAKLEOda+HE8Tb9Sdrs1d4lxQfzgeEiiaZHb3W0erTk96Qk8A9P2djCDloD5M+loz\nm/zufp5iAmBa32UcnT3Q6zs+wiQz26YdncDoFNR05ZMvNAEY18cUIOh+EhBkv64+54q6tWlawzz9\n8SpAlbNFyteOzLxOuzjZ6sTTRS9PFxB8JPT06dMHjodGcvq7c7YSmKqanSLSyjlwCq9qfuBdKR0f\ndU4BI1ROofa9dByC5Iy/G09G3qeoZDtq6oQ7x21yZPqaA4lOJld/OqrkfvdPQUpfS0BQjy9yPfUY\nOBmab5ehXTnHbq5X5Sjb1IaTvQGIq9fPTLIM98PkzJzni78N6MY7OZ9JZiWXfdulrkfHtNvRcar6\nyOvjJ4fBzZXqLNbnEUfOB/kj//3fgb+0fqf11BmtpD8p08scjUpAIf3YPF9G1eWbB2bgHN3idLX+\nok7ofpNN0fWuc0E9Np28aNJssCvP9T61yf2dsuzsizqPQQPyokSbzswJ+52cfnfNZem1LfKvZVVf\nTnZTy6/sUeqPwEE/uyDcDgCk7knj0zpF+XM6qUmDkKt5mGy3ftfj2Zz/KVunMrwdpLy40yMr/dX/\n304eL5rpAoKPhNT4Vz008k1O6U3KStuu8o5MivYoKUCqyj+m6xyiyWAQpOr1SR6ttzpOw3YTYKGs\n2iazeaxLUOH6TkZfiVkhGitXx5HLLhEAkk919tRhU2ePjh15ndZXWi+JtP+dLEfzr46dWxtpzPte\n2g/6Y+rOOKc1lebdjS/5WdHKgCfaGfcuRzCpYF7Hg7rE6ahVnwr0bnF4lTcHBPUa1yjXlOvTZXXd\n3pxkc07ttN6a99Se05c7jjJlWDnyaT3qmExHlpntnsru8N5gN/WVrjErqOQAZtKhykcam12g7eZf\n9/EEakhpDU6Ak/X4OQFEglW3JtNY35Jx2uWV5MZ00iuJnA2a+OB4cewUdCd9ye9TFnPyfZKvM/W1\nW44y9v/+PAVkLnp76AKCF1100UUXXXTRRRdddNE7iqaA1K3tvFvpAoKPhJ48eVJvvPHGg99T6+hZ\nimJX5azeFAVMUe2d4w4pSts8Vz08yqMZpvN8+HKRVYbORaC0DqPDLkrpZJ+yexwLF9Vr0mOknY1y\nWc5Vf66MZt+mzEwize51O8wMK38duZ/qad30WcvuZmtXa5VHFN24uSyQe1ZH16OSviVx4kX/83iY\n23fHcbzwohM9WreiaS+rzCkTT+JedfpjN2LcxEwps10p8s3j1D03x/GR37PUl4Jodn7KIqS1rn/T\nEXWX5XV6Q/l32ZTUPutq2bQmqFv0VIbLyJJS9tqdQiDPqzXo5GNbrm4iZmC63W5b32rMPeB4n7Iq\n3V//d3uIczxlW7S91J/KyJfe9D528mu9aW9p1n4nczZlfnZot7z6NM6mJeJY35rVaz+BWb2Vfabd\n6Xouo6fk5mTSWU6HTLZxoslvS/bYZZl1Tt2zlMln4zPeXf6WkwAX3U4XEHwkRCexKRlwGiYaqabp\n+EFqz7VDcmCN/FY9dDTogDtHaNU+23J1k0Pe19kvndak+JwjmHjTek6uJvd8oXNiaBimB+z1yJZ7\nRoq8kV8esUxAiO2l41Vax12nUeUckU83jromeIR1MqY0xroWpmc/KQvnY2VckwPt5pVlEpjRvh25\ncdBrasR3iPuM8p/n+YJTwM+cX1139/f3dXf3zMTpWxGdjDomqmf0+jRm5Evl0Ps8aujWb19zekif\nPZpAoFtv3Kd6RDm9rEHHdjW3yYlMekzXMftz+rR5cMcukw5t+Zze7rIrJ5M6ygGCCRCTUh29l/aF\nkl5nkEPvKeifgCJ5diCTdXbI6bJp7lzb9A90XXAtcf6Vd7enHE9dj/plep6eMilf1JP6X9f6BA7d\nOOn9RKt56rF0+oMAljKw/clfZL20RzmfqvMcrXzNXXoVbbxT6QKCj4ScotWFrVEpOjk0AgkMqjJI\n4JI8VL0IeNgfjR4V/ARaVverXvwtqKoXI+Qr6n6c0neyqMJTQJKcNY3uuvuJ2I/yoNf5PBbrNrXS\npZOiGb9ElKfr9b1kvKZ2V4CTMtGQrMCmkz/xND3M3m3TYLo3cLJN/exAbnLqbzWArmxazy7gkII1\nt1zbJTpyU4ZD++M4u3VRlQNXU8BjouQkKXjRNXXL/Lk9kMqxDuV1WVECQacXtO7k7HGvTePonMKp\n7dUzu2kNuxfgdP0OdjFAMI2ze+EOZdzJ6rEOZWD72pYL9qicejpBgw0EinpqyMnDn19ZOffKtwMR\nK33gwJTWczrIga4UGFgBppU/w3EjaGFd8nMcRwxKtZ6jrlJaBWOSvZhoxy7qfrolKOD0oq7RqQ2d\n653TLxe9PF1A8JGQ20TccDTSSWmoAUn3V4ovGcWdjAGVdopK6XcFaXovRd+6PKOqJBpIZ9AUcLjy\njGIn58g5aMkxSI6n8uQiaVToBJJtqHrN8JjQCpQ3P67MZCySc0MHvXlya1n7cG8K7M87YJBOe8vF\ndaWOddWLGfl2yJx8LkDhMlU6TlpOM7aujVuMp+MtrR3uw9XRQndd9US6X/URJ16zEwQuOk/Mftzf\n37/Ax3n63+10+3iXXOay6sVsTJd78uRJ/MF0d037cWBVZXCf1fFkwIKZwelYJR1wF/BS4t7VNrXv\nppTNXtE0Flxn5FPHc5XBd7yuAGrVi2CU+sKNT1pT3SeDJQkI975Iben1tBddEKD5uKWtruPWOHWB\n82kYJHZ9q27pa6uAnKPkd1S9OKaTDuNn11bVw+PraZwnkNj3J32ggfbVXqP/5YIFiQ83p7f4gRMg\nv+jV0wUEHwm1stPvzthXebCm97p+cogm500NqiMqOmccHZ8kVZrKL9tM353BcE7NpLxclHECz5TT\nZQUIMhzf2l6aC6UJoDqlrtlJXnPRdR1D/paWOuNJoafAAgHzLqDrtUFD1rxNzpcDnto3neUGKW5+\n9TXvzvC55wrprDZP6vjx9fFpDayO+2h/qayWSX1MQCHVU74nQ8/90XPhfh5CAaMDgd3XkydP6u7u\nzgJdyu7kVdqRVeeH2aRexyvnLRH3RZUHKP2Mk9NnGtzoz87J5Vx1uZVu0z1HcnuD8lAPaF+UeaIE\nrFTf3Zp5mOZnB3g43db8cWx7ftJPfKT1PNnGqb726dpc1XX33GcFCs5OKTHw1mWm77uUgJCS2//p\n5MPKD3Dtdb2Ufedf4lPbSnZU6ya/RcvpXkv9Ox3qAPW0L5w+uMVGvCy9mwHnBQQfEa0iNTQqU2bH\ngUU6p0rOoPF+imTR0aAB0/+UocvRAV0ptf7vAAP7dveqctbJ9dc8Mns1GQtn8NKYJXDtHId2opOR\n777VsVY51MHjePWLGDTz4bIYlGGKUjqHkDzrNXdEs683H+5oMMeCwEjXOCPsGphQ4Hl3dxd/u67L\n9HwkYOocnR4z/lZdr2l9aVQKWOg9l9UgL13OOSd6b8dB0bWUyBl4B4CVd3Xqq+oBGFS5lTg/iaeV\nY5j0m64dtxZcFmqlTx2vnDf3Xcsz2OPAtcv26vhrppZ9Nv8OwHU7unYcr64eM3i7lIJ6UwZ9lTWh\nvdJxcqdAUtvu9AHXtzud4eRwvDJoyuu0Id0eQccOEXimupSr+bjVIVed0N9dmVtBvhsLvcex2Tnt\n5NrfBdUJCCqPbEP3YH+f7OcOeE3lUpuk1Thdx0A/+nTbyr3ooosuuuiiiy666KKLLnqX0HEcHzyO\n4y8dx/GDx3E8PY7jV8m9u+M4fv9xHB8+juNfPC/zNcdx/Cy08fHHcfyx4zj+7+M4/vlxHH/2OI6f\ngTI/9TiOrz2O4/85juPHjuP4k8dxfOLbKduVEXxE5LJlVR+JCqWoWYq670ShlRgBnaL56UFhx+NO\nhFDb1ohZijCmrFsqtxOpnPrsflxGQOVUfqZov7bLjILL0qRMHPl3Gbhupyn9MHyqSxn1mss0uM88\nWqbR8Z1xUh41K0j+VtFPzfZVPTy+xePKVWWPJ7r20/Oc2peSPsOZnq/UzEGXVV5SBjIdV+o2VA7O\n4S10S+TfzSv1VGcD3Z7WeWB2ruskHTRlOKlzHOm+YgaWmVny2LJP+yrttcSvywroHkvPCLrMYNd1\nzy1yv1EGl6Hi2uLeno5hOlndODW/rizXjj7jST3rskzM0LkTJ6uMe8s5ZSq7bJfpt+NOe3HKCuo1\nzofLCqusqZ6TT9thJs2dXHDtUccmvTn5L84e9j2n27j++T09U80+VvokyeROuXCfu9MdPX9dfyc7\nqG1pe8l2TXqP3+mXcG+sMueOXiaLnNpZ0CdW1XdV1VdW1Z/DvU+oqk+tqv+uqj5cVT+1qv5IVf3F\nqvqAlPvDVfXLq+pXV9WPV9Ufq6r/pao+KGW+rqo+uao+q6o+rqq+uqq+vKp+3W0S7dMFBB8JJUXV\nlBwId/SIxo6f3cZUx8Ztaiq/lUIgnytwROeFyivxNCnvCSQ6I+F+m8q1N7WZrqX/VS++mVLXAl+2\noQCBSpzOmzMabl2og8Y1ou1MYLD/J+dHneV24hVA6Vyo88n+eDTUgSweuWLbymd/7u9pHWrZaV9q\n2bQ/lRhY0HF3z++tjjDrnLrjon0UVYGoHi/j2EwAaeJD/7syBG/Kc4Or+/v7N+vqOPV88V4fjdQ+\nnRPEfZNk1LXM/eb0NIEf/3eZCQy5vbWiaT02f+k5qOaX/RMEatsEVslu0XF14E3H2NkO/TwBKtLk\nFJIfZ5N6vLgG+l4CIytwqX2Ql2Tz+rMCYAfi3HrR+XHHIwlGSKu97/aw8uvGIQFFldv5MwTmXZbt\npGtpz7vASaLd/ZhAl+4hznN/T8+QqixsbwJbac2o7Z54Tz6fs4XOP5oeY/po0nmeH6qqD1VVHRDy\nPM8fr6rP0WvHcfyXVfW3j+P4lPM8f+A4jk+qqt9YVb/2PM9vfl7mC6rqHxzH8YHzPL/jOI6f/7yd\nTzvP8zufl/nCqvorx3F80XmeP/x2yHYBwUdCVAZuM66cv24nGVR9oJ79TVHO/qwGRa+rQkgGezLk\nrv9W/JShy05OqjPQPU6Tg6BOJEEhnQD2tzKYVJLO8aHT0VFKBTus53ihE5quJ7DHZ18SmCQx4q9r\njGvJXe865GtaN86R7Dr6HGB6FqxBxRQ1V7DIzJOOh9ZnJmEykApgVvKuSJ2mJuWvQaCuKQWB/Z3/\nOTarvcS+3TU64gS6aW27ZzJbNuqopE8ncsEAOqDdh9v7ur+a5/SCI9Zzn7VPdWoJzKpeDABMsrkT\nIO4ncJJj3n9OJ+pcpmClK+9k6jacfvvIZVEAACAASURBVOq6OjasR9pZxwzEUMbV2t/RWVw7T58+\nrbu7uxeCaQl0ahs6Lho0UfulOlHbSuBp+s/+Wy7yQlrtR+rWxGvKCJM36hq+gT2NS+LBrYHUtwsA\nuAAf6+k9p3dJyeZyzTDwrmUSSFZSvp1Np15I9t/JvGNLVvQq2gD9lKo6q+qfPf/+afUMc/116fMf\nHsfx/VX1GVX1HVX1i6rqxxoEPqdvfN7OL6xnGcZXThcQfCTUSoLRmqZpszjl7P6zzORsuGsrZ4qO\npCrsNqY7UV0dBx0P/nf9qVxUxiqDU/AaOXPAtOvzRSMps6mKllk1x2/K1BJg6ItSXCCg70/ReL2m\nbTo+Uz1HDjSqo+/GwoHELrfaA26eet7bqb27u3vT2HPNJPDT93TOmSVIPHU7ytv0o9fT3nIOoK5h\nB4SdA9PlFQx2WWZBNGCUwI7jUcvo+Kbf1+T/doSbJxcEodza7+uvv/7mj9CnNbU6rjfd23VYdH2n\nPa/fldx+Ju8roqO8A4IJAqdMID9Pv/M3EfUx55tHv3VdOqd2tYd27ae758aemRa9Pp1GIV/umuqZ\nlb5M9t8BZh4fTsBs1+azjtblOlwFKXpfT8BUZXEyTjJ1H8ojr+/QZFObNw3YkCfuF8rMPc+52OGV\n62WlwxMx8LkKDLxMH/8m0nEcH19V/0NVfd15nv/i+eWfWVX/+nyWPVT6p8/vdZkf0ZvneT45juNH\npcwrpwsIPhJKxnsyYiuF5yJnSYkQYO0agZVzRCWUjuyxjmvH9esiX3qfxtplSmh81WApsHJ80yAx\nupgcAip+Zvpo7FMkn9FANVIN8NK4kTQDR15uXQsOlE/1dgIgE5hScmvFgQFXVq/trMPJOPe88dX2\nq6Njyncit7dZbwe8aTnHl3NMtd7kdE5OqtZRp1Qj5m5dNNCrevG5wab7+/sHb11VfUZn9GWPLbkx\ndoDP7SeVfaUbGJyiY0aekgPebbB8/7mfZnH6U9tb8c97ei0BkTQfzVcfF+Y66vl1R2CnvdL3uVZd\nO45XR+5kiaMVAE3gWuWhrXF1NNjDve5OOlDGaQ+nQEaSLYHkrqNzOflAab2kQGx/T4DS7eO0txNo\na1Kgp+2n9cM95rKdad83eOb86n1n/97KiYkVgKbtf6fRcRx3VfU/17Ms3n/xMWZniy4g+IgoAcGV\no+Ic2ynaqOVcGSqOCQjopndKtflLip/8KznDtAMu1BEhwNW2XJtVL2Yk6VxNY1FVLxy7I1/aJ4+Q\naTspEtfgwgFIbU9/sJzAOYFnBYMqg/Lgji5xHOksTICd/+n8uvZvIe2/nQ33bB6DAGwjgejJ8WUf\n2lbz4TIq2p4D+ykTOjlFOhYOhJFS5NqVdyAoge9Ezb9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jA1FpLpwumcARZdT97/rS0wDa\nT3KaXICDfCZyOsHJ4dZMynpNOkjrpuDYlCFiOde3tpHA306QT4NDOqZcO8yaMKjk5FCantfS8U37\nTgMZifRe/9ai2lPVfxOAcdlxbcsFMLVsmi/9Pu1Lvb+SOZV1wGQHwDJInnwp5zORL/3sbLL+T4Au\n9ePkc35Z1bMAlNpV9a8oUwfbklzpGmXdpVvLT+28W+nWjODvrmc/blhVVcdx/IdV9d9X1X9bVf+g\nqr6knoHC3/2qGLxon3azeQ5oqdO7c1ys6+72kcBd99n8a7SewE7L6v1WWg60OeVKpz4pxpWCUSDI\no4ppLBJpX+qQEAyqfFX5aEsr5Ol4HF9Aoo6wc0JvlWlVj8cik1FcrUM3NiqDA4QcM53LdMRvylKm\nTIoDgDtAkPecsU7kfoCdsk/Oidt/u0fBOG49nqvMCoFDcvLd/iW5vbRLyYGt8lk8V5ayqDw9jjuA\nUPvlj9y7I+E8GumIR1yVF/2sazYBRgU7CZj0GiCpY5vWxs7YkPSkgAN5TanPnaAF+1N+09pk8ELL\n6/5yfabjdtyr6pxrmwyIuBMFWi4BkqT33PpIgJGyJnCjwZBEDoTo+E/33VqeTjlN69SRO3rq+HZr\nZrXu07i8FX9tpy/9n/Rk3yNQ3+l/0ksXvb10KxD89+shyPvPqup/P8/zS6qqjuP4/6rqf6wLCH7U\nySkHGmkq/1XEy21EGjM1MGnjtjPBP21TFQhlUFnonLYcrKsGT2WdImP6XY0j+WR0rh21yfl0TpMr\nk44ppUypji3lUvlTn2xXwSBf6Z6cEBpKZ9iTMeB4Ooc/GfRpnLvc3d3dm8+S9Wc3JgTNzAISBO4c\n/6GjTv5W2QjWSUc2dSzcvnT1yK9z/F0/K/mpS8jj5GjvOCqJt8R7U88F5en/DdjS0VDyy3FIe1Hl\ndb8xmHh1n6sePtfaffEZSf3sjvWmjKDqUbe3eU/bSOt/Jaur4/S8ypTWkc7JLvBj3em60zetN/So\nZ9fbdejdWtOf4rnVuad+TPafcuj/qhf1BH9+YzouqvO4C3amdU/eun8neyLtO/lC6c2zL0tTIDbx\n+CqAENfUyu9wpEEV7nsNsHKtqU9Bef5/9t4/Vrs2q+9a+7znvNiK02IVCKa0tMqAqThDWoiGDFE0\ntZAaWioFBMKY+gMsNIQ4tlUaAvanFQlBE0M1tG8YmhmQGUr5YQAZW0CbCulbpwPYgLQSmZG2gabg\nPOc8z/aP5/2e53s+57vW3vd5zjNv3+fdK7lz3/fe176uta4fa63vWtfeu5svWzIc9GzpVCD4T1XV\n37P/n1ZV77T/762qj3lapg46nZKD7d9b4KNzVjtgRkAo8nsKkiHqgGBn0LxNd8DcqWIk3I0reUnA\nY1K8KQuQgGDnnPi1ncKlkiWvCfh0oH+L3HgTwCbnMVH3ePLEW+p3EceL51N9um5rvntZbXHSKwfW\n9UmmjiDYqXPquq3FLpN+sy84T9La8+9pTjET1J1LvHXUOVe+7vY6pgQOBGrTmtmTRewcijSHJJdv\na/Lx1asotpwm9iv1Q9JtDnQTMEvEdeKZxO6deR746YB62irLeZrOOSBMa20CG9RNWw4uzzvP02tU\nyC8zYnRQ07Vp3nGXTMrWqU13jgmUuA40jj5HNA8TMejR0ZRd7I6lsVIZt7Vdpr5bM3tsSlq/CahO\nOyv2gK3ORtKuJz8i1cXrOx6STthDycbuWUeTb9Wt+64d73OW5W0QOpf8L/JDXZl8gVOA4NOA5oOq\nTg15/HxVfWJV1bIsH15V/3JV/aid/w1V9Sv3w9pBBx100EEHHXTQQQcddNBBz4JOzQi+s6q+YVmW\nP1FVn1lVv1BV/tK6315VP3VPvB10Ik1bK50Y5WX5KbrSZRo9stntEe8iyFtR+CQLt2Gl7EuKhHn0\ndY+sKaPTRc+2Ingps5eOp29mNvdQN/aMwnX9wWieE+9983vy2Mde9xSJTvwzYp7k8e9Ul87p1RGP\nHj268RoJlZnGTsT+YH+leTdltzy6PW3Ldv72RErFR3d/0JQh8jZFft0pWUbPJPDeIM/Kdms7ZYDS\n9k2XpWtPL21/4YUXbrzA3XcxTJmNtPXS2046sctmUUemPkv9zfY9++fHuqh6t57Ttm6R6yefh3u2\nW046NmXLtvSb1tL0ugD2GbNJzhP7h+vV1wm30as+kV7boPJ+3LfSSU5mAv14erAL+yvxw3LOe8qk\nqa4pM5Tmka7RjpKUoXcZPTPYZfOTLu22OO/ZAZH6pFtTPq/T/bjkNfHLttI58rLlr+3J+k3lUvnu\nNoDks/k1e9eoykxb9dM8SP7IXvkOejo6FQh+bVX9c1X1jfUYBH7huq5uqT+/qv7SPfF20AmkhUXH\nW7/9e1JUVE5JUWxtgaQCcOXS8bIFCJMsVCYszy1TnYxJhgT6knKsugmE9gCTTqZ0nkZ4Gks6N9w2\nluqng5rAoL5Zhzs93raDxM5Z6+T2elz+BJTdSXE+/Tpv3x2sztAlfrfAEh3vvYAtGf1u61vaApiM\ne9VtQNDRtL0ylUtgaQ8orXqyvSw5e+xz9uGeNei/p7V3dnZW5+fn9cILL1w73w4KSdzW2QH29CTX\n9B4/fxKs9EbnmHodqoegPt2DmMadY9ZtfyS4IP9p7bgcBIl07pw6G7UFMPeS+opBnz0ANjnLaRuc\n18V76HRN0gtbjvxk27yOCcAlcJLGztch65rWufsDyd4TEKo98rcX/HidDropz0T0k+h3cO7zlTLT\nFtS0lTb1X6c/U/9PPsLkJyVKa5E6JM0jlZl0fWcbvU6W8TrZt5yPU9u0K3el1zPYPAkIruv6q1X1\nxcP5f+2pOTroTtQBwSm64oqPhi8tQtWfot/+e4qsq86ksLeMn5MbrwkMTo5RckwJLhKwTnyrjDvL\nqlPn/JvH5IQxcjsZti2Q6OcnQ+L8d1lHKmq2q4i3+s1lOEXBJn63jFuag04pO9AZFzqye3neY4C7\ntlJ7rM+BBrN86f4ZJzrleyjd16Hfydl16vo1OYya4wQR1Dl85YPOJUDsPJJvfZ+fn18DQQHDBGQc\nHCW9OWXSHOyRmNHrnG85YZ699Ezq1j1zHfFJut7nHWgTTeNNADs5mdOamQCOju+59y3xzPWadkR0\n98IxW9jpkmSTaB9TGerurey7v6OVbXFdqFzqu5SZJ9FRJ3Vgp6M0H7r+d5Dlc0zXbWV61Z8PHz68\nkZ0lEEwANenXLf+ms/sTkHSfZBqLCXRN7VM+yTONpc/DPXZkyx5P/h1t/l4f4KCnp3t5obxoWZZ/\noqr+4Lquf/Y+6z1oH9Hh9YidL3g3BEkxdQZMv7uF7I7ZlqL034kvry9d50DNI9Ist2UgWH4q0znu\nzouDU5dD11AeV9By8iaaHKEJ9CW+u/q9HtaV5osc087pVRkaxQkkb/FK8N3N2c5hJNBnvczmcO7x\n3BQxpSElj2q328K2x6lKdTMIktZKVy/XYVcnwVgXfFiW5dZDJ3zep8xW11ecPw6S9H12dnYj06ff\nAoDaHurn9D89jIWA1Z2iLSeFkW99u2MqPiiryvFdmvrmdsOur/TbeZmA2R4At0f27vouEOOgrAsW\n+u/E5ylBnL3Xuwypz6tu20aCFp9T+uh/Z3MIWjjGiZfOse90Xsd/V4Y8Jt07Ad0949PJIFvT2XsC\nn04u381Cfej6ruuvFLjZI4vrva6/tT7S6528zQ4QJv+qs+l7gjMpKLEnQE39nPje47NsBSi2zu+l\n1zPYPBkILsvyz1bVp1bVg6r6wXVdHy7LclFVX1ZVf+SVOg8g+CqRLz53/Kpy9Lpb0EnJ0MlnZkJl\nkpKi4kw8uZKceCNPNDpUhmkriNokEKUC23JMKFsCs122ZzLY/ptGLdWdALXLN/EwOQYJvCUDr7FL\n26N0zuekju+Zi+yrZXmScWRbW4ZtamPqa5IDUf3fA1y5HrXFiX2qvuR4+/lujXYgRePAb5d1ilh3\n1PX5BJ4d+E4OOPlK85HOt4NAB3sOAv2c5qbrLp+jSSclfvb0Q3Ig6XByjbFu3xa35YSSXDeQJ3fg\nRd5OB+j2ACgvRwe304N7gpQpyDbpXFG3VmkjE8jx31yfCVDw2+vpwJJec8Nthh6I8Pa7rZJOqf09\nQQxm7NkPJNpRvzbZq6mezm6noCPnWjc2XTtJ12wB53T9FNhJQRTOqRQQ7mQmL6m9jn/6DM67KAW0\nU3vT3Ei2ugOESab7AnoH9XQSEFyW5dOq6rur6g1VtVbVX1+W5a1V9a6quqqqr6mqP3/PPB60gzrF\nnqJiWw7uZCQ7xcB6qYj3OixU/t21DnB8u5TKE8RwS0cCOW7AuOVH5ZORoaJi9jWBT2/X60/yEqCk\nMqxXn+QcJPDLvnGAIplE3Mrm9VKuqifv79NYdVtMCGzJH9txvpi14nmXew9gS0CrM+LJ0enqTmBP\nr7RwWdd1vX7IxBZxHXZ9vAXyuiCOqHv1wp5+SvNFfCbgMwWoOr0jXs7Pz6+B3YsvvnjdlmcCVaaq\nrn87Hy6T86j/SRbqgaQr/JyvL88QCYgmXvy6xAcfMsL+92uTc57613eXeF1OyXH1jwBF4on92GXX\nyRsDGQRILl9ytl029kNqg5TGlgBvy+6xTx1ETG1PDvuWfdf8cjkSret6q7+7dZ/mPu8V93PcqeN8\nT/be2+5skH9UnjshnNy2J7Cyx6P6bgAAIABJREFUB4QkWR4+fHhrC/bWnNA5353gbSQ5Jr+AOjgB\nuGk9Os9caxP/Xp8DZOlJJhD8d8fnQc+OTs0I/hdV9T1V9cer6q31+MXx31lVf3Rd12+/Z94OOuig\ngw466KCDDjrooINu0X1lDF/PYPNUIPgvVdWXrev6t5Zl+eqq+sqqetu6ru++f9YOuguliHSXJVQZ\nRr+nyLffszC1n6I4KVqXrk8ZwSkDNkXIKeOUwdB3l5nySKZTJ2tVf1+gl+n48ogkz3vkeYq0p36Z\noomMaF9dXVXVzRvzmaFUuSnyqAip94f+e/YqbRlOmVLKujXHU5l0zdZ9SZ18bLMjZVur6lY2sOp2\nBtOfMknya+6ypXOiFBn3ubEnI+jf3VbetLWPmWMvN61j8aXPxcXF9XUvvvhinZ+f18XFxY0tolW3\nnxpKWSUDo+JensckB/Uxr1E5ktYf+5LbqlN21nmn/ur05GQrko6hDNyamNYf29q71b7Ti8z6SWZu\nm/RsIZ8gqmvZN93amtY453B6II8Td12kcuu6Xj/UaCLKr99d2fQqiz32mv2i+cHrtzKpXj+P+/h2\nepz2x+XQsSRPl2307FiX5U00ZbBZbk9dom5LutfR6cLOr0g2hDrGefDx4LZnL7MFoDgvEm/0Kcjj\nQc+WTgWCH1FVv1hVta7rry7L8itV9X/cO1cHnUxboI+LzA13p/y6+w6osLv2Jh7Tlr3O6HhbndPJ\nbStOdCg75Si+/J4tr5+8T0qrcwDp9CfjR5lT/QkIpnJVtx++MW29qqq6uLiI2+GSA+V8+nxyua6u\nrq77k+9SczDo/b3nPp8JCO0FZ1tGzOUUX36dr50OAHHbKseOY+W8uYOS+Jmc++QIqk5tW0rvPZPj\n2d3T5E5Bd4+Tvt256nST5hnP+3bLbl2k+7J8G6jLor7Vb/4n/+yzRHscu6TP3NFLINfbpb7kWGsM\n/YEkXV2do8zt7C67675uDRIwbtHechMl3TgBb/GXdMV0f90WsHLietJ89DmU9In/9z71OrXW05zz\n4+lhS07deeoxjvuWrkxghPeydoAy1ZH+dyCyCxaQ2LdbNAXFvYzPvYmXyW4LoKs8bYvL63026WBv\nk3qE50mdPC4v7fakKyVDClZ0eouyHPRs6C5PDf0Xl2X56Fd+L1X1xmVZ/kkvsK7ry0/N2UFPRcnp\n6gzjRHzSn767eyC2lEoiKpJ0TXJkWIbAJDmfroCSM5TKOZ9JQRHEdbImp07/k2PocvK/y6d78LxO\n540GJY2piI6Ijznv63BHVw82OTs7u5Ud9Be5u3zixcHg5IQ4Lw6Q0jwm+OrGg2PWOQ/uxHXjk7IJ\nLste58PrSPcy8dH8e58027Wh+uWIXF1d3QBGezIi03pKziT7O4EUl5Hl3dFOQLALBPh5d5y7e6E0\nv9zpSZkjb4d1pfu9tLZ8fSZnKIEbrm8HDGqP4CN9MxubdjukMXRe/DfB5JYD1zny3W9mKziGvKe5\nc6KnNii3t+V2z6/rwILPXwKFLjPi9UqnpqdHsu1kg05xoFM/u73qgp++libglQIIWlt34ZF1TsDW\nf7OOlB3t2iNtBR+Tbqt6ErDxQJd42etT0E/w9raCAHvA4ATsOFeo2yZAzDonu93xRjp1rk/1vF7p\nLkDwB+sxABR99yvf6yvH16rq9w4e9MwoOVpOXKxd1EWLk1tbJuDYbSnwtvYstOT4bFGKDnaRUxr1\nqmzAVU8XDeY1VNwdwHDj5wZWMrj87owmQ0An0t8plQyv16UyKTos4+hzRGNPB9hl8mwqAaQDPd4A\nL+PEV0zQsexAKR33BDJSv6XtNomS8yDivCCfLM9ruAb3ZEpSuc6BSRmH6TplCtlGylQmosMjZ5br\nIQGLtHboLOl8AjT6rWzflLn0OhJ4TP07bat0SpF+6hrP3qk/U3R8WZYbQJXBBc5FZlF9LegYQZOv\nddY5gVDnJ61XAhjqjk63U39vOYIin2/abeC8dW13ujKVJ3Dgk2c7eSbnmYDd56WfS0EAtpEc6D3g\nfMtf4Dr09lJQOM2bVP/eYIH3T/JdJgBH/bMFenwc+eRmp8kOTH7ApD8ZqJ5sWDfftsaYr6Ihz3vJ\n9cz0FONT6k3+1tRfB90PnQoEP+6ZcHHQM6O9ho/K0Bd3ohQForLu2nQl5lH2PY7wpBBo8DplOTl1\nrCs51ZMTPEXqUhsCQP4+seS8dsbOZfUoW+ew6RofI84RHxsZDWZDCKKcBz9PIJicYvUDM5W8P87P\n6bzqS4CRxDFn5maibr5MGUJfQz4WndHcMuwuv/53a9mdTbXj4GOi1Ie+Rt2Box7gnGW/TWArlUly\nJqdZbRP8cYue6tMxr9O3ijL7zH7hepoc9G4bmMjbdVm4pll3AqvikcBgCg5Qf3jdE/hLfeI2w4Fh\nly3bAk9Jnyagqjnpfd0BoRRgEnBN59Um54+3meRgsCIFL7qAJftKPBA8pcxiys677FuUgkdc5wng\ndACrk2kC+mktTTQBkE5fiAg+fP54P07+zgQKU3tTIFrHuswveSGwm/Qq9VQC9nuIPoQf35Ms6GjP\n/Oz4OejudBIQXNf1554VIwc9PaUo2eQsJqBGJZiUv8iNvkfgU3v+PQFMRu+2wEqnjGg8OyO1B1iq\nHr+vyOXsHEDK0xk8ORLaUklHlgDPr03yO08J3MnIdOPgwKHr1wQGElCUc8hx9TrcgaNj48e6PqXj\nluagyrpT0/VbNyemLLPOP3r06Pq+NN0bSTDB61yOVHcy8Il3r0v8THJwDncOvvcVMyJedgJ3HZ/T\nWKXfaXyYlXH5/YEw/iFApv5S33HLtcACA1kEG04eREky8b8DC+e/qm6spa0HCXlfdfOu48HLcl0S\nJHK9+3XdPGX5KcjGLEnKWLMN72/qcY2jH2PGkLsWnA+/7vz8/Hrep1cF+O/Jqeda9br2OOfeR0mn\nJSDOuim7r6fU56ltr8vLsw89qNSt9cn3mPS06p+CIInnaS5uAc0tkEP7lHRlt47JO/9P/kUK6E3t\ndW119Uj3uZ7xdTfZy67du4LBg+5OJz1qblmWf2FZlm9bluUN4dyvW5bl7cuyfML9sXfQQQcddNBB\nBx100EEHHXTQfdOpW0P/k6r6u+u6/jJPrOv6S8uy/N2q+sNV9SX3wNtBT0GMtKfILDMkXjZtL9I1\nrEPXeARxigI7MXPFutN2Bp5n1JvlJxlS1GqSV/J55Mojn9zSNd2blOTw++R0zrMC6cmcHFvPpCX+\nq/I2o9QHLofX6Vk4ZvM8o+EPPFBdzER6Fsb5ZUbK/3sWyMmzEF3Wm+26DFOUOc0lZiB9jPQQH/Yd\n+UkRa8nSRY8Tb6zLs5N7ruN9QCLPQnX9w/nXnWPbU3R44jdF/D17wWy6juvVEf76CJ1PuoRbo5kZ\n4T15fi11Sxchd72pOv0/H9yjjD6j5+n1DUm/KiPmWTlmb7rrmH2XvvLfXpb1OW/+m9kYfxn3nowY\nefXrPJvKnRBal7x20pP+lFbxrdfATNmZVP9WVmeqJ8nbbTns+obHUjbbeUy7ATiffO5NWcR0C0Wn\nZzgenS/R2W7alpT1S/3WZaj8el+TSRbx5n3S2RLnybPMU2Ywyb2164rXp7b33KrQ+TLdedaR5KC9\n35NtPdWO7OXp9UKnAsFPr6ovHM6/o6refnd2DrpPSkrG/yel6cCr27qRFnda9HsX1uTkJjBI2WhE\nt4DgHpoAIMEut0B5ex3w47eX2zI8KuMOTFLAqd9cFl3XAWj2g5xebfcUyWh0AEzt+Ta7qicAw9uU\n7Hw4ideXQDcBnTutyTnVf/bLXsDF/wSDkk8Oovefzxu27/XTgXX5Xe7uvZ50hLdAqP53Tlgnt+rr\n1vt0nOPX8TbdS+ZrQ/8FBiX7+fn59X+BwAQqOX/TE22dH384CYMCnT5I/cCthdQJ/JYsDr58WyDB\nqY75A6X0++rq6lpGvtszrSOOEwGh6p+AgPehkzugmvfs60RTO+y/ye6RL3dKE3hw3cdzE22BxhRY\n6MqRun7obLrTdDz1W9L5pzrUnR3sgj3cbprq6fjXeHa6kHUSUCUQtK756adpe6+vj+SbdDKk/66j\n9tx/R+p8AB2b1hN9j8TnNGeSzic/p/psB92dTgWCH1tVHxjO/2JV/ca7s3PQXSk5VH68qs++uXKd\nHHAuZK/LDSYp1UEF4UbqLjJMirNzKE+9n8l/03mkozgZJZahEhdgolOX7jFKRndZnmRFeH+PX+PA\nrmoGoCIHWSniT4dQZTWG7syJPz/GOab7Jn3OOaBixF79JOeU8ndgz0HTHgejc2KdPJrrcvFdY5PR\nkzyeTXQZ0nUERul4oq210c01UsoMTOUTbTnW7kT5+Pi9fuldgf5hf/ic8jVxdXV1/UAnf6BI1w/J\nwUokPpX96uZPklvzgg4h9VD67bJIRn9gVQJ0nZxaax6A6YjnGNBJx329UF+67JMO8/oE3BhkSUEZ\nXSs5E00OOOVyfjyznDJjBJguL+9xZV9M/DoP4nFr3XuQIgFqrpnUD6S99rEDFlo3TgS51IMJeO0B\nr2kNUT/RBrkOTIGF1GYKBLJ/uONI5bgOEu8dQN4CZnehzv9imyTv1wMIfujoVCD4S1X1W6vq55rz\n/3xV3do2etCHjjrni8pCRptKU0pfxmgPiHNKCjUpmK6OzpikdpIyTt8u597tBuSbj9VPDiiBs851\nINYVcOJHgNC3w3SOBRUrDTbH0Q3UZAw7ZZ2yBuKFjrTm1MOHD6+3SvIc+04OoL9agsAzGTBvV33m\nDq8cLI6h18F+SX1DcKnv5JwSCBJ4qOxWRpLGfyqTnNAtw+sOX4pmp77yMmkMp/a4hliuk4Vz28vp\n49tDdc6PpWxDAoLKljl48rlIORPwTmPrDh3nW3pvHPUDA1E6xkANdU0iOvEJ6CZH0x26BGZ8S3Gq\ncw+l+cS1q3PexuTYkxhocXK7uQe8eJ0TeHTd1e0M6AJzbi+67YwJEIl36iAHo5MO8nFOAI02tquj\nA3deb5pP4le/Hcx3bU1t6DftiWgKtnF+ue7rdLzz4uPA+rWdP43hls7kfEh8dHbBbVe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gDYN94XXT/wuAMrGg2WI3UydA6+2u+cerazLLffhTU58P5KAJHf2ycwqOvdQU4ZWh+vtH7S\nfOH16VjXjvPDvklPGnQgKRnUD+naBKI6h8fnXlpb7rxz3ut7CwTSqXXZ+E5FB0XpmimQlXiio646\n07bAlIHu9NAWSW7J40DJ+eK5KahEALDVDy5vOj/ZkwRAybuvh04PdW104+v3/nWBiM7GOY90kpP8\n7ngnG8SyXb+msUyUdCt561683tnGZBecpoyVf3drOP3uyqR60v90zO1cBwq31uJWv5GfDsgk0D31\nL9tzMDhR2l1wir7xuUu+2TYB/7TeWVc339LYu37y86cGmw86je4KBL+uqv7HZVl+a1X90CvHPqOq\nPr+qjvsDXyWaFAe3gdHgdc5AWoxb27k65U5nQN+d0kjydQZnciY7eabyvC5lWGgU5NzI8a26qdy7\nCJc7RZSRYFDUbTHz61K/pi0u3naSi8dOMajs0wmUdsQgQjLwac45cNIxGT4aQY/s80XPPpY0iATy\nU18QRPq1+s9XcDjQEpEf51WZkmTcE9DjWhY/Dr4Y3OjGkHPF55bq9r7tnEwHVFuvffD74DxT1OmK\nDqx7eV+PHB/1Q/eOSO9Tz1rpfHL8yA/ncwdM9du3hupeQM5H8e2vVEkZhQ5wd0DK+ziNkXjtruGc\nYeZp68FLHfG+qATwEnX6W785VpwbfLjOnoBH4iHpbs6drUyk23bKPdlw6o6tsZtoCiRsgcnUB8ke\neXmuN157ClBKdneyM4k6sJj48YCdynb+WXq4lvNKXT35EEkuPz71W9ppMck8rTt9b/ks6do9c3GL\n7qOO1yrdCQiu6/qXlmX57Kr6o1X1+6rqV6vq5ar6N9Z1fc898nfQQQcddNBBBx100EEHHXTQPdOd\nXx+xrutfrqq/fI+8HPSU1EXB9M2oSxft6TIKHpn0LVB+rsuqdVvrpgiSZ0PSfRQkjzQyUjVlBruM\nFetm9DdFMBnRrrodNZsyR2mrhj6eGUn9PI2/t91tQUmZFJe5i9539VO+UzKCzIR5pmxrG5XPma4M\ns3ucAxwHZYEkm0f+tyKSlDNFecmr6kwPDPD5lLKU7Ls0nv4KAq/Ls4Aemda1PJbWS5f5Tv3Auchz\nnhlkexoX9Ye/PoFzdM/WO9W5LLcf1ODjrHZVH+95IX9TW9TFqrPLJnn22beBVj1+Mujl5eWt+wu9\nzzV3J/3JTFOaJ17u4cOHdX5+fssmeH9Rf5OSzp2yVyw/9TOzLswypjXk5X2O8h53ZlN9a+qeDAMz\nWBMv1Kt8kBYzXz4f09pN/NPGJntG/vmbfgWzYMm3SJRsRcrK7qFTMoFpx89WfVtZwW5+Jh1Ju+/H\n9dvX2imypT7fyrZNx7lGXXdN+p+kOdplzU+R8aDT6c5AcFmWX1+Ps4G/par+7Lquf39Zlk+uqvev\n6/rz98XgQfuo2z5Fh0LfSdk4dQqehp7Gx9v131SubmCoODqQxq1we5TUHkDINro63djTuHU8UE4/\nR2NJ+V3J0iFODnRqP/Enw8Q+Fb+dI09nmOccMKR+P0XJ67gHEBIY7ObBFDyg4+V9orFlGZfPZeO9\nSO48UR6vK/HqPG4BwOQ8et84X13/0CH0+tK6cJAsXqZ7Rdgv3Thr+6fmtv/Xh7z59Wy7A33TfOho\na2uj6wOuJ86bxFu39VHbPbut9ASDzqs//ZbvvXSg4fqI4GPaSul9oYDB5eXl9Vj6dQSDzqfq5dru\nKK3bdK4jzuuqJwERBnZYd/pWHyd9zt8qn3hiWwKRCZBvAYq0zghSu77iHEi6fgKESd49/HaUxsHX\n2ClAQ8T5nABwx2fSO2l+s62q/IRZkvjwrflpPFwfs52qfus2gw7O5571k/xH2c0JnPt1U8DG62Z/\nb82T6fxeuo86Xqt01/cIflJV/UBV/VJV/eaq+nNV9fer6vdW1cdW1RffE38H7SQph87Z9sWSnIsU\nJeoW5N4IUgI0yTgkx5jXsy4vm3hyo9Yp384hJ9j1diYA7aCWTlaKMu51ONw4eFnPqCTyMe+MuYPL\n5GAm8JocY9ZPR7KLcpM6oN4BaV6XxoblOqfd+XSnSwYsGf2zs8cv2KbjLvL/CXyyjztjNIHAVHZd\n1xsOJc/rOt6P4nIzC5fm3AQUBEoSsS6CS/8kB7hzJP24O4sETeSFMvg56snOWRYococ7PbzD6530\nqQMN71MHebxH0s8RlFP2pF9dTpbTOZI7uVxP03UJZHtZB1xep/RLp/u6HQks4+c01zqnPtXFzD77\n29dPt64pB0GqH6M949xkJjnJnmTbeu3HqRk479s0TmlNpXOJF+8L6oAO6JBo//xY0ic6xgdT+fWp\nj5KvwyBMN0/V7hZ/SSby5ztZ6JuwL6Z1T/9R17icCSCnPuH8daLunubEQfdDd80Ifn1Vfcu6rm9b\nluUf2vHvqaq3Pz1bB92V9jiVnRJlHfy9p90twEVlRH5YXudZhjJ0EaROoSb+aGhYhsppD1AVdaCK\nQCHVK0fFnUoavAQIUpvOI6OOCXB18rjT505t4kHlJ/kSpXkq6tojiO8ya13bAnXJQLNvqp44B35d\n6j83onSUnNfJ4E3OA8F8cihZj7bzqYwDL44TM4I853L5nL64uLgxVg72zs/P64UXXrj+9ozgNEYO\nIBzgODASGGIWiiBeMlAvUZZpvTt/3vdXV1djtF7tUDdxDtMJY8ZPc1wvjJeM+u/yO91Fzzt1jnjV\nza3F1HM+ZqmONEYigTVmIRJI9Gu55pi15TUpcEe5PXOTAP+kP51SkC9dx/mqteftdcGqxJtfswU0\nSATre8nneFdH8mFSX07lurIdMPXfrMv7cgr0dT5Ux+vkixDUU/dTJ3SZNvLN+eJ6L/G/FWj2oJco\n2TenTrdTp58agDjo7nRXIPg7quo/DMd/vqo++u7sHHRXSgtnjxOdFH9yBL3s5LizbT/u3wn40bnf\nAledTOm4O6IdnyLf6sV65FD5+VTn5HRRXjfGbtx9HGjc5WCrfSd3ZtyR7fqGhvCUfvfrJqMtY0Ww\nfUobbvj8f+prAY1lufluQO/nrv3z8/MI0NmGvuWQ0yGk802Dq3p9vhG4sD1RMuh+bhp3z7LJAfV7\nvLhViPOSQHBZnrxDL1HSG7rGgeD5+WNzJCA4bY/Si9H1knT1mQNB14nen+ovAgl3itifHAvfAukf\nl1ftOt88l5wi9iXloKx75WebexzSNH5cb6qT986xDjrNzGR6OV3rQRkHXnTove706gh3ln19qG1m\no6f+6OqljCLx2AHj1F73aib/zXnqdkJgmeVTPV12NdmXSa8QzKbv7nySwXnw30luP0+7Rl2VgjEd\nP92YTgEC58vnrMvn1zj/lC0FolI9e0BT0k+djaE+7NbE1A9pXChzCpRRVyZ/Yg8fp9J91PFapbsC\nwQ9WfnH8x1fV/3t3dg66K11dXd3KZHQLXv+niU9nsOqmkmYUM7Xh1C1kN8rJUeK1VFwdoCRfqivd\nS5nKJ37lNJBPd55VLimxbgyS06LfBIJJ3s4R3QK8kocGvBtH56GLhKZrvF+Sg+i8Tkay236TeE7G\ni8GSvSDUnS6159cSBNLJFP8cQ/GozwsvvHDjVQDsawYIWK/a9P5zOZnV6+7L66LAWyDRv1U+gVT/\nLVDlvKR2nCSXQJA/LEV9LqCY+sXHVe0xI5Scdy+fynVzTt+cfxwfAWH2GXWif9I5z4y6/F1fcLz5\nCpPOAaeczgszBQ78WHfnxCZ9rgw8M4JVdSM7l4JjXeZEvEkXco52/BFo+TFmHWlHCUY5nxIo8PPd\n8e6/jnUO+F5KWX/W5zZLlGwSy1C2ZO8cnCT50nxKgVWnbvu2U7K/HdFO8MFbe9ridVMmc4unac3y\nepX1ddbNldTfyUZ2INh5OIXvg+6f7qYNqr6rqv7YsiwXr/xfl2X52Kr601X1HffC2UEHHXTQQQcd\ndNBBBx100EHPhO6aEfyqqvr2qvpAVf2aqnpPPd4S+mNV9Z/dD2sHnUJ6mfC0xWHK9DgpWpki3B6R\nS4+Y7zJWrKOLZnlGxaNwU2ag+99lfxgZTjynTJOiX7yXyzM7zjd56Pj36K9n6JgRY73KHnX3gHUR\nNfKetp+k7RmiFEnU9XsjplvETFi37Tn9ZuTXM5Ke0Z3mQeKX299YRn3JbW5qk9lkzm2fQ3opeMpu\nJN66tcAIsjJwvC/P/0/bp1Pfcnukb/GkbClzwXHqtoR6nyVZldmZtkaKl6QPks7rsp/cJjutNT7Q\nRd/TTgBt1dWaSnrIM1l+3O8V9KeGcs77uiDPrme4M+OukXofJ2ZGky5Jv/2aZOO8PHV5yky7nld/\nUe927XY7FMgPM07cBcPrmLXbK7/Kdpk2b7/L0uwd22SjuE6cV/oCyVawLm5J5c6IvTz79t8py5r+\n0x5yfFN9tOtuN6YMm1/vPpYfS7tCJlnYD7SltPMcN/kj67q2Tzj2sUw2dBpzzkO26Tx2tDdTu0X3\nUcdrle76Qvlfqqp/c1mWT6uqT6qqD6+qH1/X9Qfuk7mD9pOM/x5lrP+J5Mw6qEnOJIl1Ezh02822\nyJXDdL0bjtQ+y07G3q9LDljalkUjQwPfGW7yk5xQHe+MDg2UOwNTYIB9Mzl6NNAcj2RI+H/LaU7l\n6TCn8tPcTs5fAurse7+227qsY9z24+0IyCVA4u1o3Xk/89q985T8uyEWCNSn2xqa7gua2pHcBJdq\nI20p7QAd652cXn4nRyb1V7ddr3PCHcjyWslHp8rHU3NBwRs6OV6nO17c7p36SzJ2D8fROVH3gAf1\ntY9zCmBMeqJzcqdATkfTfXITuEmkoCXHXm3w1TTdFlKeSzpfdXS6X3MpOc9pvnaBvqTntrZuuk3h\ntalv7+JkpzXON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9bLXcDtQU9PBxB8TkiO6HTvhCuHCaTRQdpDHShj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\nfBonfw+o+HTn1PuUxiHpXer1pgdSHVxvUyCE5HOCvKZgXqqf2zr5W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Jzdjxf1NezYEhn779r/GRHAInz1wlfhNw8TFmFtllmOTgBnkH+tK55gzy3rNknB3Q6Vjj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\njIaDGl/s5NeBGUFHAymMZCY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l9JIMUgYq1ZmyV8rEpddH6OXtzAhOkXjy6xF4ZQOnraEp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\ndw1Nem3Xdu/w9qHSCQRvhNJCd1s6tDwdSafMHRggqROhL03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JBAYnILsCi8nY8L4zwGw7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "blue_lobe_m0.quicklook()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "masked_cube = cube_K.with_mask(cube_K > 50*u.K)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam/repos/radio_beam/build/lib.macosx-10.6-x86_64-3.5/radio_beam/multiple_beams.py:240: UserWarning: Do not use the average beam for convolution! Use the smallest common beam from `Beams.common_beam()`.\n", " warnings.warn(\"Do not use the average beam for convolution! Use the\"\n", "WARNING: BeamAverageWarning: Arithmetic beam averaging is being performed. This is not a mathematically robust operation, but is being permitted because the beams differ by <0.01 [spectral_cube.spectral_cube]\n" ] } ], "source": [ "blue_lobe_masked_m0 = masked_cube.spectral_slab(30*u.km/u.s, 55*u.km/u.s).moment0()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -2.579e+02 [aplpy.core]\n", "INFO: Auto-setting vmax to 3.679e+03 [aplpy.core]\n" ] }, { "data": { "image/png": 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DptO57g8hfGo6ZezKcY935QO7EMKGpB+X9PppGaV5tiYljX8t6UCTsstnxhj/zG3zYk0y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WtpW/g8qSqC71GlUkG9Xke5XI6TI3yeU5E3OlpyH8z7A6bklTl0dqKu3W4jz3N0Op0YxXZTIYfj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\n2SumJEmSJNWcGTtJkiR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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "blue_lobe_masked_m0.quicklook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cube.convolve_to(...).spectral_smooth()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 } spectral-cube-0.4.3/docs/stokes.rst0000644000077000000240000000040212377070030017255 0ustar adamstaff00000000000000:orphan: Stokes components ================= We plan to implement the `~spectral_cube.StokesSpectralCube` class and will update the documentation once this class is ready to use. .. TODO: first we need to make sure the StokesSpectralCube class is working.spectral-cube-0.4.3/docs/writing.rst0000644000077000000240000000040012551776560017445 0ustar adamstaff00000000000000Writing spectral cubes ====================== You can write out a :class:`~spectral_cube.SpectralCube` instance by making use of the :meth:`~spectral_cube.SpectralCube.write` method:: >>> cube.write('new_cube.fits', format='fits') # doctest: +SKIP spectral-cube-0.4.3/docs/yt_example.rst0000644000077000000240000001525013161003310020110 0ustar adamstaff00000000000000Visualizing spectral cubes with yt ================================== Extracting yt objects --------------------- The :class:`~spectral_cube.SpectralCube` class includes a :meth:`~spectral_cube.SpectralCube.to_yt` method that makes is easy to return an object that can be used by `yt `_ to make volume renderings or other visualizations of the data. One common issue with volume rendering of spectral cubes is that you may not want pixels along the spectral axis to be given the same '3-d' size as positional pixels, so the :meth:`~spectral_cube.SpectralCube.to_yt` method includes a ``spectral_factor`` argument that can be used to compress or expand the spectral axis. The :meth:`~spectral_cube.SpectralCube.to_yt` method is used as follows:: >>> ytcube = cube.to_yt(spectral_factor=0.5) # doctest: +SKIP >>> ds = ytcube.dataset # doctest: +SKIP .. WARNING:: The API change in https://github.com/radio-astro-tools/spectral-cube/pull/129 affects the interpretation of the 0-pixel. There may be a 1-pixel offset between the yt cube and the SpectralCube The ``ds`` object is then a yt object that can be used for rendering! By default the dataset is defined in pixel coordinates, going from ``0.5`` to ``n+0.5``, as would be the case in ds9, for example. Along the spectral axis, this range will be modified if ``spectral_factor`` does not equal unity. When working with datasets in yt, it may be useful to convert world coordinates to pixel coordinates, so that whenever you may have to input a position in yt (e.g., for slicing or volume rendering) you can get the pixel coordinate that corresponds to the desired world coordinate. For this purpose, the method :meth:`~spectral_cube.ytcube.ytCube.world2yt` is provided:: >>> import astropy.units as u >>> pix_coord = ytcube.world2yt([51.424522, ... 30.723611, ... 5205.18071], # units of deg, deg, m/s ... ) # doctest: +SKIP There is also a reverse method provided, :meth:`~spectral_cube.ytcube.ytCube.yt2world`:: >>> world_coord = ytcube.yt2world([ds.domain_center]) # doctest: +SKIP which in this case would return the world coordinates of the center of the dataset in yt. .. TODO: add a way to center it on a specific coordinate and return in world .. coordinate offset. .. note:: The :meth:`~spectral_cube.SpectralCube.to_yt` method and its associated coordinate methods are compatible with both yt v. 2.x and v. 3.0 and following, but use of version 3.0 or later is recommended due to substantial improvements in support for FITS data. For more information on how yt handles FITS datasets, see `the yt docs `_. Visualization example --------------------- This section shows an example of a rendering script that can be used to produce a 3-d isocontour visualization using an object returned by :meth:`~spectral_cube.SpectralCube.to_yt`:: import numpy as np from spectral_cube import SpectralCube from yt.mods import ColorTransferFunction, write_bitmap import astropy.units as u # Read in spectral cube cube = SpectralCube.read('L1448_13CO.fits', format='fits') # Extract the yt object from the SpectralCube instance ytcube = cube.to_yt(spectral_factor=0.75) ds = ytcube.dataset # Set the number of levels, the minimum and maximum level and the width # of the isocontours n_v = 10 vmin = 0.05 vmax = 4.0 dv = 0.02 # Set up color transfer function transfer = ColorTransferFunction((vmin, vmax)) transfer.add_layers(n_v, dv, colormap='RdBu_r') # Set up the camera parameters # Derive the pixel coordinate of the desired center # from the corresponding world coordinate center = ytcube.world2yt([51.424522, 30.723611, 5205.18071]) direction = np.array([1.0, 0.0, 0.0]) width = 100. # pixels size = 1024 camera = ds.camera(center, direction, width, size, transfer, fields=['flux']) # Take a snapshot and save to a file snapshot = camera.snapshot() write_bitmap(snapshot, 'cube_rendering.png', transpose=True) You can move the camera around; see the `yt camera docs `_. Movie Making ------------ There is a simple utility for quick movie making. The default movie is a rotation of the cube around one of the spatial axes, going from PP -> PV space and back.:: >>> cube = read('cube.fits', format='fits') # doctest: +SKIP >>> ytcube = cube.to_yt() # doctest: +SKIP >>> images = ytcube.quick_render_movie('outdir') # doctest: +SKIP The movie only does rotation, but it is a useful stepping-stone if you wish to learn how to use yt's rendering system. Example: .. raw:: html SketchFab Isosurface Contours ----------------------------- For data exploration, making movies can be tedious - it is difficult to control the camera and expensive to generate new renderings. Instead, creating a 'model' from the data and exporting that to SketchFab can be very useful. Only grayscale figures will be created with the quicklook code. You need an account on sketchfab.com for this to work.:: >>> ytcube.quick_isocontour(title='GRS l=49 13CO 1 K contours', level=1.0) # doctest: +SKIP Here's an example: .. raw:: html

GRS l=49 13CO 1 K contours by keflavich on Sketchfab

You can also export locally to .ply and .obj files, which can be read by many programs (sketchfab, meshlab, blender). See the `yt page `_ for details.:: >>> ytcube.quick_isocontour(export_to='ply', filename='meshes.ply', level=1.0) # doctest: +SKIP >>> ytcube.quick_isocontour(export_to='obj', filename='meshes', level=1.0) # doctest: +SKIP spectral-cube-0.4.3/ez_setup.py0000644000077000000240000003037113233661037016510 0ustar adamstaff00000000000000#!/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()) spectral-cube-0.4.3/LICENSE.rst0000644000077000000240000000272612377070030016112 0ustar adamstaff00000000000000Copyright (c) 2014, spectral-cube developers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.spectral-cube-0.4.3/PKG-INFO0000644000077000000240000000062013261442571015370 0ustar adamstaff00000000000000Metadata-Version: 1.0 Name: spectral-cube Version: 0.4.3 Summary: A package for interaction with spectral cubes Home-page: http://spectral-cube.readthedocs.org Author: Adam Ginsburg, Tom Robitaille, Chris Beaumont, Adam Leroy, Erik Rosolowsky, and Eric Koch Author-email: adam.g.ginsburg@gmail.com License: BSD Description: This is an Astropy affiliated package. Platform: UNKNOWN spectral-cube-0.4.3/README.md0000644000077000000240000000267213161003310015542 0ustar adamstaff00000000000000About ===== [![Join the chat at https://gitter.im/radio-astro-tools/spectral-cube](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/radio-astro-tools/spectral-cube?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) This package aims to facilitate the reading, writing, manipulation, and analysis of spectral data cubes. More information is available in the documentation, avaliable [online at readthedocs.org](http://spectral-cube.rtfd.org). ![Powered by Astropy Badge](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat) Credits ======= This package is developed by: * Chris Beaumont ([@ChrisBeaumont](http://github.com/ChrisBeaumont)) * Adam Ginsburg ([@keflavich](http://github.com/keflavich)) * Adam Leroy ([@akleroy](http://github.com/akleroy)) * Thomas Robitaille ([@astrofrog](http://github.com/astrofrog)) * Erik Rosolowsky ([@low-sky](http://github.com/low-sky)) * Eric Koch ([@e-koch](https://github.com/e-koch)) Build and coverage status ========================= [![Build Status](https://travis-ci.org/radio-astro-tools/spectral-cube.png?branch=master)](https://travis-ci.org/radio-astro-tools/spectral-cube) [![Coverage Status](https://coveralls.io/repos/radio-astro-tools/spectral-cube/badge.svg?branch=master)](https://coveralls.io/r/radio-astro-tools/spectral-cube?branch=master) [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.11485.png)](http://dx.doi.org/10.5281/zenodo.11485) spectral-cube-0.4.3/setup.cfg0000644000077000000240000000135613233661037016122 0ustar adamstaff00000000000000[build_sphinx] source-dir = docs build-dir = docs/_build all_files = 1 [upload_docs] upload-dir = docs/_build/html show-response = 1 [pytest] minversion = 2.2 norecursedirs = build docs/_build doctest_plus = enabled addopts = -p no:warnings [ah_bootstrap] auto_use = True [metadata] package_name = spectral_cube description = A package for interaction with spectral cubes long_description = There are lots of things you wanna do with spectral cubes and this does some of them author = Adam Ginsburg, Tom Robitaille, Chris Beaumont, Adam Leroy, Erik Rosolowsky, and Eric Koch author_email = adam.g.ginsburg@gmail.com license = BSD url = http://spectral-cube.readthedocs.org edit_on_github = False github_project = radio-astro-tools/spectral-cube spectral-cube-0.4.3/setup.py0000755000077000000240000000666313261442567016032 0ustar adamstaff00000000000000#!/usr/bin/env python # Licensed under a 3-clause BSD style license - see LICENSE.rst import glob import os import sys import ah_bootstrap from setuptools import setup #A dirty hack to get around some early import/configurations ambiguities if sys.version_info[0] >= 3: import builtins else: import __builtin__ as builtins builtins._ASTROPY_SETUP_ = True from astropy_helpers.setup_helpers import ( register_commands, get_debug_option, get_package_info) from astropy_helpers.git_helpers import get_git_devstr from astropy_helpers.version_helpers import generate_version_py # Get some values from the setup.cfg try: from ConfigParser import ConfigParser except ImportError: from configparser import ConfigParser conf = ConfigParser() conf.read(['setup.cfg']) metadata = dict(conf.items('metadata')) PACKAGENAME = metadata.get('package_name', 'packagename') DESCRIPTION = metadata.get('description', 'Astropy affiliated package') AUTHOR = metadata.get('author', '') AUTHOR_EMAIL = metadata.get('author_email', '') LICENSE = metadata.get('license', 'unknown') URL = metadata.get('url', 'http://astropy.org') # Get the long description from the package's docstring __import__(PACKAGENAME) package = sys.modules[PACKAGENAME] LONG_DESCRIPTION = package.__doc__ # Store the package name in a built-in variable so it's easy # to get from other parts of the setup infrastructure builtins._ASTROPY_PACKAGE_NAME_ = PACKAGENAME # VERSION should be PEP386 compatible (http://www.python.org/dev/peps/pep-0386) VERSION = '0.4.3' # Indicates if this version is a release version RELEASE = 'dev' not in VERSION if not RELEASE: VERSION += get_git_devstr(False) # Populate the dict of setup command overrides; this should be done before # invoking any other functionality from distutils since it can potentially # modify distutils' behavior. cmdclassd = register_commands(PACKAGENAME, VERSION, RELEASE) # Freeze build information in version.py generate_version_py(PACKAGENAME, VERSION, RELEASE, get_debug_option(PACKAGENAME)) # Treat everything in scripts except README.rst as a script to be installed scripts = [fname for fname in glob.glob(os.path.join('scripts', '*')) if os.path.basename(fname) != 'README.rst'] # Get configuration information from all of the various subpackages. # See the docstring for setup_helpers.update_package_files for more # details. package_info = get_package_info() # Add the project-global data package_info['package_data'].setdefault(PACKAGENAME, []) package_info['package_data'][PACKAGENAME].append('data/*') # Include all .c files, recursively, including those generated by # Cython, since we can not do this in MANIFEST.in with a "dynamic" # directory name. c_files = [] for root, dirs, files in os.walk(PACKAGENAME): for filename in files: if filename.endswith('.c'): c_files.append( os.path.join( os.path.relpath(root, PACKAGENAME), filename)) package_info['package_data'][PACKAGENAME].extend(c_files) os.chdir('spectral_cube/tests/data') os.system('make') os.chdir('../../..') setup(name='spectral-cube', version=VERSION, description=DESCRIPTION, scripts=scripts, install_requires=['astropy','numpy>=1.8.0', 'radio_beam'], author=AUTHOR, author_email=AUTHOR_EMAIL, license=LICENSE, url=URL, long_description=LONG_DESCRIPTION, cmdclass=cmdclassd, zip_safe=False, **package_info ) spectral-cube-0.4.3/spectral_cube/0000755000077000000240000000000013261442571017110 5ustar adamstaff00000000000000spectral-cube-0.4.3/spectral_cube/__init__.py0000644000077000000240000000123313233661037021217 0ustar adamstaff00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This is an Astropy affiliated package. """ # Affiliated packages may add whatever they like to this file, but # should keep this content at the top. # ---------------------------------------------------------------------------- from ._astropy_init import * # ---------------------------------------------------------------------------- if not _ASTROPY_SETUP_: from .spectral_cube import SpectralCube, VaryingResolutionSpectralCube from .stokes_spectral_cube import StokesSpectralCube from .masks import * from .lower_dimensional_structures import (OneDSpectrum, Projection, Slice) spectral-cube-0.4.3/spectral_cube/_astropy_init.py0000644000077000000240000001223513161003310022327 0ustar adamstaff00000000000000# 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_: import os from warnings import warn from astropy import config # 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: config.configuration.update_default_config( __package__, config_dir, version=__version__) except TypeError as orig_error: try: config.configuration.update_default_config( __package__, config_dir) except config.configuration.ConfigurationDefaultMissingError as e: wmsg = (e.args[0] + " Cannot install default profile. If you are " "importing from source, this is expected.") warn(config.configuration.ConfigurationDefaultMissingWarning(wmsg)) del e except: raise orig_error spectral-cube-0.4.3/spectral_cube/_moments.py0000644000077000000240000001122213161003310021260 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import numpy as np from .cube_utils import iterator_strategy from .np_compat import allbadtonan """ Functions to compute moment maps in a variety of ways """ def _moment_shp(cube, axis): """ Return the shape of the moment map Parameters ----------- cube : SpectralCube The cube to collapse axis : int The axis to collapse along (numpy convention) Returns ------- ny, nx """ return cube.shape[:axis] + cube.shape[axis + 1:] def _slice0(cube, axis): """ 0th moment along an axis, calculated slicewise Parameters ---------- cube : SpectralCube axis : int Returns ------- moment0 : array """ shp = _moment_shp(cube, axis) result = np.zeros(shp) view = [slice(None)] * 3 valid = np.zeros(shp, dtype=np.bool) for i in range(cube.shape[axis]): view[axis] = i plane = cube._get_filled_data(fill=np.nan, view=view) valid |= np.isfinite(plane) result += np.nan_to_num(plane) * cube._pix_size_slice(axis) result[~valid] = np.nan return result def _slice1(cube, axis): """ 1st moment along an axis, calculated slicewise Parameters ---------- cube : SpectralCube axis : int Returns ------- moment1 : array """ shp = _moment_shp(cube, axis) result = np.zeros(shp) view = [slice(None)] * 3 pix_size = cube._pix_size_slice(axis) pix_cen = cube._pix_cen()[axis] weights = np.zeros(shp) for i in range(cube.shape[axis]): view[axis] = i plane = cube._get_filled_data(fill=0, view=view) result += (plane * pix_cen[view] * pix_size) weights += plane * pix_size return result / weights def moment_slicewise(cube, order, axis): """ Compute moments by accumulating the result 1 slice at a time """ if order == 0: return _slice0(cube, axis) if order == 1: return _slice1(cube, axis) shp = _moment_shp(cube, axis) result = np.zeros(shp) view = [slice(None)] * 3 pix_size = cube._pix_size_slice(axis) pix_cen = cube._pix_cen()[axis] weights = np.zeros(shp) # would be nice to get mom1 and momn in single pass over data # possible for mom2, not sure about general case mom1 = _slice1(cube, axis) for i in range(cube.shape[axis]): view[axis] = i plane = cube._get_filled_data(fill=0, view=view) result += (plane * (pix_cen[view] - mom1) ** order * pix_size) weights += plane * pix_size return (result / weights) def moment_raywise(cube, order, axis): """ Compute moments by accumulating the answer one ray at a time """ shp = _moment_shp(cube, axis) out = np.zeros(shp) * np.nan pix_cen = cube._pix_cen()[axis] pix_size = cube._pix_size_slice(axis) for x, y, slc in cube._iter_rays(axis): # the intensity, i.e. the weights include = cube._mask.include(data=cube._data, wcs=cube._wcs, view=slc, wcs_tolerance=cube._wcs_tolerance) if not include.any(): continue data = cube.flattened(slc).value * pix_size if order == 0: out[x, y] = data.sum() continue order1 = (data * pix_cen[slc][include]).sum() / data.sum() if order == 1: out[x, y] = order1 continue ordern = (data * (pix_cen[slc][include] - order1) ** order).sum() ordern /= data.sum() out[x, y] = ordern return out def moment_cubewise(cube, order, axis): """ Compute the moments by working with the entire data at once """ pix_cen = cube._pix_cen()[axis] data = cube._get_filled_data() * cube._pix_size_slice(axis) if order == 0: return allbadtonan(np.nansum)(data, axis=axis) if order == 1: return (np.nansum(data * pix_cen, axis=axis) / np.nansum(data, axis=axis)) else: mom1 = moment_cubewise(cube, 1, axis) # insert an axis so it broadcasts properly shp = list(_moment_shp(cube, axis)) shp.insert(axis, 1) mom1 = mom1.reshape(shp) return (np.nansum(data * (pix_cen - mom1) ** order, axis=axis) / np.nansum(data, axis=axis)) def moment_auto(cube, order, axis): """ Build a moment map, choosing a strategy to balance speed and memory. """ strategy = dict(cube=moment_cubewise, ray=moment_raywise, slice=moment_slicewise) return strategy[iterator_strategy(cube, axis)](cube, order, axis) spectral-cube-0.4.3/spectral_cube/analysis_utilities.py0000644000077000000240000002751313261203122023373 0ustar adamstaff00000000000000import numpy as np from astropy import units as u from astropy.extern.six.moves import zip from astropy.extern.six.moves import range as xrange from astropy.wcs import WCS from astropy.utils.console import ProgressBar from .cube_utils import _map_context from .lower_dimensional_structures import OneDSpectrum from .spectral_cube import VaryingResolutionSpectralCube def fourier_shift(x, shift, axis=0, add_pad=False, pad_size=None): ''' Shift a spectrum in the Fourier plane. Parameters ---------- x : np.ndarray Array to be shifted shift : int or float Number of pixels to shift. axis : int, optional Axis to shift along. pad_size : int, optional Pad the array before shifting. Returns ------- x2 : np.ndarray Shifted array. ''' nanmask = ~np.isfinite(x) # If all NaNs, there is nothing to shift # But only if there is no added padding. Otherwise we need to pad if nanmask.all() and not add_pad: return x nonan = x.copy() shift_mask = False if nanmask.any(): nonan[nanmask] = 0.0 shift_mask = True # Optionally pad the edges if add_pad: if pad_size is None: # Pad by the size of the shift pad = np.ceil(shift).astype(int) # Determine edge to pad whether it is a positive or negative shift pad_size = (pad, 0) if shift > 0 else (0, pad) else: assert len(pad_size) pad_nonan = np.pad(nonan, pad_size, mode='constant', constant_values=(0)) if shift_mask: pad_mask = np.pad(nanmask, pad_size, mode='constant', constant_values=(0)) else: pad_nonan = nonan pad_mask = nanmask # Check if there are all NaNs before shifting if nanmask.all(): return np.array([np.NaN] * pad_mask.size) nonan_shift = _fourier_shifter(pad_nonan, shift, axis) if shift_mask: mask_shift = _fourier_shifter(pad_mask, shift, axis) > 0.5 nonan_shift[mask_shift] = np.NaN return nonan_shift def _fourier_shifter(x, shift, axis): ''' Helper function for `~fourier_shift`. ''' ftx = np.fft.fft(x, axis=axis) m = np.fft.fftfreq(x.shape[axis]) # m_shape = [1] * x.ndim # m_shape[axis] = m.shape[0] # m = m.reshape(m_shape) slices = [slice(None) if ii == axis else None for ii in range(x.ndim)] m = m[slices] phase = np.exp(-2 * np.pi * m * 1j * shift) x2 = np.real(np.fft.ifft(ftx * phase, axis=axis)) return x2 def get_chunks(num_items, chunk): ''' Parameters ---------- num_items : int Number of total items. chunk : int Size of chunks Returns ------- chunks : list of np.ndarray List of channels in chunks of the given size. ''' items = np.arange(num_items) if num_items == chunk: return [items] chunks = \ np.array_split(items, [chunk * i for i in xrange(int(num_items / chunk))]) if chunks[-1].size == 0: # Last one is empty chunks = chunks[:-1] if chunks[0].size == 0: # First one is empty chunks = chunks[1:] return chunks def _spectrum_shifter(inputs): spec, shift, add_pad, pad_size = inputs return fourier_shift(spec, shift, add_pad=add_pad, pad_size=pad_size) def stack_spectra(cube, velocity_surface, v0=None, stack_function=np.nanmean, xy_posns=None, num_cores=1, chunk_size=-1, progressbar=False, pad_edges=True, vdiff_tol=0.01): ''' Shift spectra in a cube according to a given velocity surface (peak velocity, centroid, rotation model, etc.). Parameters ---------- cube : SpectralCube The cube velocity_field : Quantity A Quantity array with m/s or equivalent units stack_function : function A function that can operate over a list of numpy arrays (and accepts ``axis=0``) to combine the spectra. `numpy.nanmean` is the default, though one might consider `numpy.mean` or `numpy.median` as other options. xy_posns : list, optional List the spatial positions to include in the stack. For example, if the data is masked by some criterion, the valid points can be given as `xy_posns = np.where(mask)`. num_cores : int, optional Choose number of cores to run on. Defaults to 1. chunk_size : int, optional To limit memory usage, the shuffling of spectra can be done in chunks. Chunk size sets the number of spectra that, if memory-mapping is used, is the number of spectra loaded into memory. Defaults to -1, which is all spectra. progressbar : bool, optional Print progress through every chunk iteration. pad_edges : bool, optional Pad the edges of the shuffled spectra to stop data from rolling over. Default is True. The rolling over occurs since the FFT treats the boundary as periodic. This should only be disabled if you know that the velocity range exceeds the range that a spectrum has to be shuffled to reach `v0`. vdiff_tol : float, optional Allowed tolerance for changes in the spectral axis spacing. Default is 0.01, or 1%. Returns ------- stack_spec : OneDSpectrum The stacked spectrum. ''' if not np.isfinite(velocity_surface).any(): raise ValueError("velocity_surface contains no finite values.") vshape = velocity_surface.shape cshape = cube.shape[1:] if not (vshape == cshape): raise ValueError("Velocity surface map does not match cube spatial " "dimensions.") if xy_posns is None: # Only compute where a shift can be found xy_posns = np.where(np.isfinite(velocity_surface)) if v0 is None: # Set to the mean velocity of the cube if not given. v0 = cube.spectral_axis.mean() else: if not isinstance(v0, u.Quantity): raise u.UnitsError("v0 must be a quantity.") spec_unit = cube.spectral_axis.unit if not v0.unit.is_equivalent(spec_unit): raise u.UnitsError("v0 must have units equivalent to the cube's" " spectral unit ().".format(spec_unit)) v0 = v0.to(spec_unit) if v0 < cube.spectral_axis.min() or v0 > cube.spectral_axis.max(): raise ValueError("v0 must be within the range of the spectral " "axis of the cube.") # Calculate the pixel shifts that will be applied. spec_size = np.diff(cube.spectral_axis[:2])[0] # Assign the correct +/- for pixel shifts based on whether the spectral # axis is increasing (-1) or decreasing (+1) vdiff_sign = -1. if spec_size.value > 0. else 1. vdiff = np.abs(spec_size) vel_unit = vdiff.unit # Check to make sure vdiff doesn't change more than the allowed tolerance # over the spectral axis vdiff2 = np.abs(np.diff(cube.spectral_axis[-2:])[0]) if not np.isclose(vdiff2.value, vdiff.value, rtol=vdiff_tol): raise ValueError("Cannot shift spectra on a non-linear axes") pix_shifts = vdiff_sign * ((velocity_surface.to(vel_unit) - v0.to(vel_unit)) / vdiff).value[xy_posns] # May a header copy so we can start altering new_header = cube[:, 0, 0].header.copy() if pad_edges: # Enables padding the whole cube such that no spectrum will wrap around # This is critical if a low-SB component is far off of the bright # component that the velocity surface is derived from. # Find max +/- pixel shifts, rounding up to the nearest integer max_pos_shift = np.ceil(np.nanmax(pix_shifts)).astype(int) max_neg_shift = np.ceil(np.nanmin(pix_shifts)).astype(int) if max_neg_shift > 0: # if there are no negative shifts, we can ignore them and just # use the positive shift max_neg_shift = 0 if max_pos_shift < 0: # same for positive max_pos_shift = 0 # The total pixel size of the new spectral axis num_vel_pix = cube.spectral_axis.size + max_pos_shift - max_neg_shift new_header['NAXIS1'] = num_vel_pix # Adjust CRPIX in header new_header['CRPIX1'] += -max_neg_shift pad_size = (-max_neg_shift, max_pos_shift) else: pad_size = None all_shifted_spectra = [] if chunk_size == -1: chunk_size = len(xy_posns[0]) # Create chunks of spectra for read-out. chunks = get_chunks(len(xy_posns[0]), chunk_size) if progressbar: iterat = ProgressBar(chunks) else: iterat = chunks for i, chunk in enumerate(iterat): gen = ((cube.filled_data[:, y, x].value, shift, pad_edges, pad_size) for y, x, shift in zip(xy_posns[0][chunk], xy_posns[1][chunk], pix_shifts[chunk])) with _map_context(num_cores) as map: shifted_spectra = map(_spectrum_shifter, gen) all_shifted_spectra.extend([out for out in shifted_spectra]) shifted_spectra_array = np.array(all_shifted_spectra) assert shifted_spectra_array.ndim == 2 stacked = stack_function(shifted_spectra_array, axis=0) stack_spec = \ OneDSpectrum(stacked, unit=cube.unit, wcs=WCS(new_header), header=new_header, meta=cube.meta, spectral_unit=vel_unit, beams=cube.beams if hasattr(cube, "beams") else None) return stack_spec def stack_cube(cube, linelist, vmin, vmax, average=np.nanmean, convolve_beam=None): """ Create a stacked cube by averaging on a common velocity grid. Parameters ---------- cube : SpectralCube The cube linelist : list of Quantities An iterable of Quantities representing line rest frequencies vmin / vmax : Quantity Velocity-equivalent quantities specifying the velocity range to average over average : function A function that can operate over a list of numpy arrays (and accepts ``axis=0``) to average the spectra. `numpy.nanmean` is the default, though one might consider `numpy.mean` or `numpy.median` as other options. convolve_beam : None If the cube is a VaryingResolutionSpectralCube, a convolution beam is required to put the cube onto a common grid prior to spectral interpolation. """ line_cube = cube.with_spectral_unit(u.km/u.s, velocity_convention='radio', rest_value=linelist[0]) if isinstance(line_cube, VaryingResolutionSpectralCube): if convolve_beam is None: raise ValueError("When stacking VaryingResolutionSpectralCubes, " "you must specify a target beam size with the " "keyword `convolve_beam`") reference_cube = line_cube.spectral_slab(vmin, vmax).convolve_to(convolve_beam) else: reference_cube = line_cube.spectral_slab(vmin, vmax) cutout_cubes = [reference_cube.filled_data[:].value] for restval in linelist[1:]: line_cube = cube.with_spectral_unit(u.km/u.s, velocity_convention='radio', rest_value=restval) line_cutout = line_cube.spectral_slab(vmin, vmax) if isinstance(line_cube, VaryingResolutionSpectralCube): line_cutout = line_cutout.convolve_to(convolve_beam) regridded = line_cutout.spectral_interpolate(reference_cube.spectral_axis) cutout_cubes.append(regridded.filled_data[:].value) stacked_cube = average(cutout_cubes, axis=0) hdu = reference_cube.hdu hdu.data = stacked_cube return hdu spectral-cube-0.4.3/spectral_cube/base_class.py0000644000077000000240000002352613261015477021572 0ustar adamstaff00000000000000from astropy import units as u from astropy import log import numpy as np from . import wcs_utils from . import cube_utils from .utils import cached, WCSCelestialError __doctest_skip__ = ['SpatialCoordMixinClass.world'] DOPPLER_CONVENTIONS = {} DOPPLER_CONVENTIONS['radio'] = u.doppler_radio DOPPLER_CONVENTIONS['optical'] = u.doppler_optical DOPPLER_CONVENTIONS['relativistic'] = u.doppler_relativistic class BaseNDClass(object): _cache = {} @property def _nowcs_header(self): """ Return a copy of the header with no WCS information attached """ log.debug("Stripping WCS from header") return wcs_utils.strip_wcs_from_header(self._header) @property def wcs(self): return self._wcs @property def meta(self): return self._meta @property def mask(self): return self._mask class SpatialCoordMixinClass(object): @property def _has_wcs_celestial(self): return self.wcs.has_celestial def _raise_wcs_no_celestial(self): if not self._has_wcs_celestial: raise WCSCelestialError("WCS does not contain two spatial axes.") @cube_utils.slice_syntax def world(self, view): """ Return a list of the world coordinates in a cube, projection, or a view of it. SpatialCoordMixinClass.world is called with *bracket notation*, like a NumPy array:: c.world[0:3, :, :] Returns ------- [v, y, x] : list of NumPy arrays The 3 world coordinates at each pixel in the view. For a 2D image, the output is ``[y, x]``. Examples -------- Extract the first 3 velocity channels of the cube: >>> v, y, x = c.world[0:3] Extract all the world coordinates: >>> v, y, x = c.world[:, :, :] Extract every other pixel along all axes: >>> v, y, x = c.world[::2, ::2, ::2] Extract all the world coordinates for a 2D image: >>> y, x = c.world[:, :] """ self._raise_wcs_no_celestial() # note: view is a tuple of view # the next 3 lines are equivalent to (but more efficient than) # inds = np.indices(self._data.shape) # inds = [i[view] for i in inds] inds = np.ogrid[[slice(0, s) for s in self.shape]] inds = np.broadcast_arrays(*inds) inds = [i[view] for i in inds[::-1]] # numpy -> wcs order shp = inds[0].shape inds = np.column_stack([i.ravel() for i in inds]) world = self._wcs.all_pix2world(inds, 0).T world = [w.reshape(shp) for w in world] # 1D->3D # apply units world = [w * u.Unit(self._wcs.wcs.cunit[i]) for i, w in enumerate(world)] # convert spectral unit if needed if hasattr(self, "_spectral_unit"): if self._spectral_unit is not None: specind = self.wcs.wcs.spec world[specind] = world[specind].to(self._spectral_unit) return world[::-1] # reverse WCS -> numpy order def world_spines(self): """ Returns a list of 1D arrays, for the world coordinates along each pixel axis. Raises error if this operation is ill-posed (e.g. rotated world coordinates, strong distortions) This method is not currently implemented. Use :meth:`world` instead. """ raise NotImplementedError() @property def spatial_coordinate_map(self): view = [0 for ii in range(self.ndim - 2)] + [slice(None)] * 2 return self.world[view][self.ndim - 2:] @property @cached def world_extrema(self): lat, lon = self.spatial_coordinate_map _lon_min = lon.min() _lon_max = lon.max() _lat_min = lat.min() _lat_max = lat.max() return u.Quantity(((_lon_min.to(u.deg).value, _lon_max.to(u.deg).value), (_lat_min.to(u.deg).value, _lat_max.to(u.deg).value)), u.deg) @property @cached def longitude_extrema(self): return self.world_extrema[0] @property @cached def latitude_extrema(self): return self.world_extrema[1] class SpectralAxisMixinClass(object): def _new_spectral_wcs(self, unit, velocity_convention=None, rest_value=None): """ Returns a new WCS with a different Spectral Axis unit Parameters ---------- unit : :class:`~astropy.units.Unit` Any valid spectral unit: velocity, (wave)length, or frequency. Only vacuum units are supported. velocity_convention : 'relativistic', 'radio', or 'optical' The velocity convention to use for the output velocity axis. Required if the output type is velocity. This can be either one of the above strings, or an `astropy.units` equivalency. rest_value : :class:`~astropy.units.Quantity` A rest wavelength or frequency with appropriate units. Required if output type is velocity. The cube's WCS should include this already if the *input* type is velocity, but the WCS's rest wavelength/frequency can be overridden with this parameter. .. note: This must be the rest frequency/wavelength *in vacuum*, even if your cube has air wavelength units """ from .spectral_axis import (convert_spectral_axis, determine_ctype_from_vconv) # Allow string specification of units, for example if not isinstance(unit, u.Unit): unit = u.Unit(unit) # Velocity conventions: required for frq <-> velo # convert_spectral_axis will handle the case of no velocity # convention specified & one is required if velocity_convention in DOPPLER_CONVENTIONS: velocity_convention = DOPPLER_CONVENTIONS[velocity_convention] elif (velocity_convention is not None and velocity_convention not in DOPPLER_CONVENTIONS.values()): raise ValueError("Velocity convention must be radio, optical, " "or relativistic.") # If rest value is specified, it must be a quantity if (rest_value is not None and (not hasattr(rest_value, 'unit') or not rest_value.unit.is_equivalent(u.m, u.spectral()))): raise ValueError("Rest value must be specified as an astropy " "quantity with spectral equivalence.") # Shorter versions to keep lines under 80 ctype_from_vconv = determine_ctype_from_vconv meta = self._meta.copy() if 'Original Unit' not in self._meta: meta['Original Unit'] = self._wcs.wcs.cunit[self._wcs.wcs.spec] meta['Original Type'] = self._wcs.wcs.ctype[self._wcs.wcs.spec] out_ctype = ctype_from_vconv(self._wcs.wcs.ctype[self._wcs.wcs.spec], unit, velocity_convention=velocity_convention) newwcs = convert_spectral_axis(self._wcs, unit, out_ctype, rest_value=rest_value) newwcs.wcs.set() return newwcs, meta @property def spectral_axis(self): # spectral objects should be forced to implement this raise NotImplementedError class MaskableArrayMixinClass(object): """ Mixin class for maskable arrays """ def _get_filled_data(self, view=(), fill=np.nan, check_endian=False): """ Return the underlying data as a numpy array. Always returns the spectral axis as the 0th axis Sets masked values to *fill* """ if check_endian: if not self._data.dtype.isnative: kind = str(self._data.dtype.kind) sz = str(self._data.dtype.itemsize) dt = '=' + kind + sz data = self._data.astype(dt) else: data = self._data else: data = self._data if self._mask is None: return data[view] return self._mask._filled(data=data, wcs=self._wcs, fill=fill, view=view, wcs_tolerance=self._wcs_tolerance) @cube_utils.slice_syntax def filled_data(self, view): """ Return a portion of the data array, with excluded mask values replaced by :meth:`fill_value`. Returns ------- data : Quantity The masked data. """ return u.Quantity(self._get_filled_data(view, fill=self._fill_value), self.unit, copy=False) def filled(self, fill_value=None): if fill_value is not None: return u.Quantity(self._get_filled_data(fill=fill_value), self.unit, copy=False) return self.filled_data[:] @property def fill_value(self): """ The replacement value used by :meth:`filled_data`. fill_value is immutable; use :meth:`with_fill_value` to create a new cube with a different fill value. """ return self._fill_value class MultiBeamMixinClass(object): """ A mixin class to handle multibeam objects. To be used by VaryingResolutionSpectralCube's and OneDSpectrum's """ def jtok_factors(self, equivalencies=()): """ Compute an array of multiplicative factors that will convert from Jy/beam to K """ factors = [] for bm,frq in zip(self.beams, self.with_spectral_unit(u.Hz).spectral_axis): # create a beam equivalency for brightness temperature bmequiv = bm.jtok_equiv(frq) factor = (u.Jy).to(u.K, equivalencies=bmequiv+list(equivalencies)) factors.append(factor) factor = np.array(factors) return factor spectral-cube-0.4.3/spectral_cube/conftest.py0000644000077000000240000000243212643464660021316 0ustar adamstaff00000000000000# 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 __future__ import print_function, absolute_import, division 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['scikit-image'] = 'skimage' # del PYTEST_HEADER_MODULES['h5py'] # 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 # from . import version # # try: # packagename = os.path.basename(os.path.dirname(__file__)) # TESTED_VERSIONS[packagename] = version.version # except NameError: # Needed to support Astropy <= 1.0.0 # pass spectral-cube-0.4.3/spectral_cube/cube_utils.py0000644000077000000240000003336213261015477021630 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import contextlib import warnings try: import builtins except ImportError: # python2 import __builtin__ as builtins import numpy as np from astropy.wcs import (WCSSUB_SPECTRAL, WCSSUB_LONGITUDE, WCSSUB_LATITUDE) from . import wcs_utils from astropy import log from astropy.io import fits from astropy.io.fits import BinTableHDU, Column from astropy import units as u import itertools import re from radio_beam import Beam def _fix_spectral(wcs): """ Attempt to fix a cube with an invalid spectral axis definition. Only uses well-known exceptions, e.g. CTYPE = 'VELOCITY'. For the rest, it will try to raise a helpful error. """ axtypes = wcs.get_axis_types() types = [a['coordinate_type'] for a in axtypes] if wcs.naxis not in (3, 4): raise TypeError("The WCS has {0} axes of types {1}".format(len(types), types)) # sanitize noncompliant headers if 'spectral' not in types: log.warning("No spectral axis found; header may be non-compliant.") for ind,tp in enumerate(types): if tp not in ('celestial','stokes'): if wcs.wcs.ctype[ind] in wcs_utils.bad_spectypes_mapping: wcs.wcs.ctype[ind] = wcs_utils.bad_spectypes_mapping[wcs.wcs.ctype[ind]] return wcs def _split_stokes(array, wcs): """ Given a 4-d data cube with 4-d WCS (spectral cube + stokes) return a dictionary of data and WCS objects for each Stokes component Parameters ---------- array : `~numpy.ndarray` The input 3-d array with two position dimensions, one spectral dimension, and a Stokes dimension. wcs : `~astropy.wcs.WCS` The input 3-d WCS with two position dimensions, one spectral dimension, and a Stokes dimension. """ if array.ndim not in (3,4): raise ValueError("Input array must be 3- or 4-dimensional for a" " STOKES cube") if wcs.wcs.naxis != 4: raise ValueError("Input WCS must be 4-dimensional for a STOKES cube") wcs = _fix_spectral(wcs) # reverse from wcs -> numpy convention axtypes = wcs.get_axis_types()[::-1] types = [a['coordinate_type'] for a in axtypes] try: # Find stokes dimension stokes_index = types.index('stokes') except ValueError: # stokes not in list, but we are 4d if types.count('celestial') == 2 and types.count('spectral') == 1: if None in types: stokes_index = types.index(None) log.warning("FITS file has no STOKES axis, but it has a blank" " axis type at index {0} that is assumed to be " "stokes.".format(4-stokes_index)) else: for ii,tp in enumerate(types): if tp not in ('celestial', 'spectral'): stokes_index = ii stokes_type = tp log.warning("FITS file has no STOKES axis, but it has an axis" " of type {1} at index {0} that is assumed to be " "stokes.".format(4-stokes_index, stokes_type)) else: raise IOError("There are 4 axes in the data cube but no STOKES " "axis could be identified") # TODO: make the stokes names more general stokes_names = ["I", "Q", "U", "V"] stokes_arrays = {} wcs_slice = wcs_utils.drop_axis(wcs, wcs.naxis - 1 - stokes_index) if array.ndim == 4: for i_stokes in range(array.shape[stokes_index]): array_slice = [i_stokes if idim == stokes_index else slice(None) for idim in range(array.ndim)] stokes_arrays[stokes_names[i_stokes]] = array[array_slice] else: # 3D array with STOKES as a 4th header parameter stokes_arrays['I'] = array return stokes_arrays, wcs_slice def _orient(array, wcs): """ Given a 3-d spectral cube and WCS, swap around the axes so that the spectral axis cube is the first in Numpy notation, and the last in WCS notation. Parameters ---------- array : `~numpy.ndarray` The input 3-d array with two position dimensions and one spectral dimension. wcs : `~astropy.wcs.WCS` The input 3-d WCS with two position dimensions and one spectral dimension. """ if array.ndim != 3: raise ValueError("Input array must be 3-dimensional") if wcs.wcs.naxis != 3: raise ValueError("Input WCS must be 3-dimensional") wcs = wcs_utils.diagonal_wcs_to_cdelt(_fix_spectral(wcs)) # reverse from wcs -> numpy convention axtypes = wcs.get_axis_types()[::-1] types = [a['coordinate_type'] for a in axtypes] n_celestial = types.count('celestial') if n_celestial == 0: raise ValueError('No celestial axes found in WCS') elif n_celestial != 2: raise ValueError('WCS should contain 2 celestial dimensions but ' 'contains {0}'.format(n_celestial)) n_spectral = types.count('spectral') if n_spectral == 0: raise ValueError('No spectral axes found in WCS') elif n_spectral != 1: raise ValueError('WCS should contain one spectral dimension but ' 'contains {0}'.format(n_spectral)) nums = [None if a['coordinate_type'] != 'celestial' else a['number'] for a in axtypes] if 'stokes' in types: raise ValueError("Input WCS should not contain stokes") t = [types.index('spectral'), nums.index(1), nums.index(0)] result_array = array.transpose(t) result_wcs = wcs.sub([WCSSUB_LONGITUDE, WCSSUB_LATITUDE, WCSSUB_SPECTRAL]) return result_array, result_wcs def slice_syntax(f): """ This decorator wraps a function that accepts a tuple of slices. After wrapping, the function acts like a property that accepts bracket syntax (e.g., p[1:3, :, :]) Parameters ---------- f : function """ def wrapper(self): result = SliceIndexer(f, self) result.__doc__ = f.__doc__ return result wrapper.__doc__ = slice_doc.format(f.__doc__ or '', f.__name__) result = property(wrapper) return result slice_doc = """ {0} Notes ----- Supports efficient Numpy slice notation, like ``{1}[0:3, :, 2:4]`` """ class SliceIndexer(object): def __init__(self, func, _other): self._func = func self._other = _other def __getitem__(self, view): return self._func(self._other, view) def __iter__(self): raise Exception("You need to specify a slice (e.g. ``[:]`` or " "``[0,:,:]`` in order to access this property.") # TODO: make this into a proper configuration item # TODO: make threshold depend on memory? MEMORY_THRESHOLD=1e8 def is_huge(cube): if cube.size < MEMORY_THRESHOLD: # smallish return False else: return True def iterator_strategy(cube, axis=None): """ Guess the most efficient iteration strategy for iterating over a cube, given its size and layout Parameters ---------- cube : SpectralCube instance The cube to iterate over axis : [0, 1, 2] For reduction methods, the axis that is being collapsed Returns ------- strategy : ['cube' | 'ray' | 'slice'] The recommended iteration strategy. *cube* recommends working with the entire array in memory *slice* recommends working with one slice at a time *ray* recommends working with one ray at a time """ # pretty simple for now if cube.size < 1e8: # smallish return 'cube' return 'slice' def try_load_beam(header): ''' Try loading a beam from a FITS header. ''' try: beam = Beam.from_fits_header(header) return beam except Exception as ex: # We don't emit a warning if no beam was found since it's ok for # cubes to not have beams if 'No BMAJ' not in str(ex): warnings.warn("Could not parse beam information from header." " Exception was: {0}".format(ex.__repr__())) def try_load_beams(data): ''' Try loading a beam table from a FITS HDU list. ''' try: from radio_beam import Beam except ImportError: warnings.warn("radio_beam is not installed. No beam " "can be created.") if isinstance(data, fits.BinTableHDU): if 'BPA' in data.data.names: beam_table = data.data return beam_table else: raise ValueError("No beam table found") elif isinstance(data, fits.HDUList): for ihdu, hdu_item in enumerate(data): if isinstance(hdu_item, (fits.PrimaryHDU, fits.ImageHDU)): beam = try_load_beams(hdu_item.header) elif isinstance(hdu_item, fits.BinTableHDU): if 'BPA' in hdu_item.data.names: beam_table = hdu_item.data return beam_table try: # if there was a beam in a header, but not a beam table return beam except NameError: # if the for loop has completed, we didn't find a beam table raise ValueError("No beam table found") elif isinstance(data, (fits.PrimaryHDU, fits.ImageHDU)): return try_load_beams(data.header) elif isinstance(data, fits.Header): try: beam = Beam.from_fits_header(data) return beam except Exception as ex: warnings.warn("Could not parse beam information from header." " Exception was: {0}".format(ex.__repr__())) else: raise ValueError("How did you get here? This is some sort of error.") def beams_to_bintable(beams): """ Convert a list of beams to a CASA-style BinTableHDU """ c1 = Column(name='BMAJ', format='1E', array=[bm.major.to(u.arcsec).value for bm in beams], unit=u.arcsec.to_string('FITS')) c2 = Column(name='BMIN', format='1E', array=[bm.minor.to(u.arcsec).value for bm in beams], unit=u.arcsec.to_string('FITS')) c3 = Column(name='BPA', format='1E', array=[bm.pa.to(u.deg).value for bm in beams], unit=u.deg.to_string('FITS')) #c4 = Column(name='CHAN', format='1J', array=[bm.meta['CHAN'] if 'CHAN' in bm.meta else 0 for bm in beams]) c4 = Column(name='CHAN', format='1J', array=np.arange(len(beams))) c5 = Column(name='POL', format='1J', array=[bm.meta['POL'] if 'POL' in bm.meta else 0 for bm in beams]) bmhdu = BinTableHDU.from_columns([c1, c2, c3, c4, c5]) bmhdu.header['EXTNAME'] = 'BEAMS' bmhdu.header['EXTVER'] = 1 bmhdu.header['XTENSION'] = 'BINTABLE' bmhdu.header['NCHAN'] = len(beams) bmhdu.header['NPOL'] = len(set([bm.meta['POL'] for bm in beams])) return bmhdu def beam_props(beams, includemask=None): ''' Returns separate quantities for the major, minor, and PA of a list of beams. ''' if includemask is None: includemask = itertools.cycle([True]) major = u.Quantity([bm.major for bm, incl in zip(beams, includemask) if incl], u.deg) minor = u.Quantity([bm.minor for bm, incl in zip(beams, includemask) if incl], u.deg) pa = u.Quantity([bm.pa for bm, incl in zip(beams, includemask) if incl], u.deg) return major, minor, pa def largest_beam(beams, includemask=None): """ Returns the largest beam (by area) in a list of beams. """ from radio_beam import Beam major, minor, pa = beam_props(beams, includemask) largest_idx = (major * minor).argmax() new_beam = Beam(major=major[largest_idx], minor=minor[largest_idx], pa=pa[largest_idx]) return new_beam def smallest_beam(beams, includemask=None): """ Returns the smallest beam (by area) in a list of beams. """ from radio_beam import Beam major, minor, pa = beam_props(beams, includemask) smallest_idx = (major * minor).argmin() new_beam = Beam(major=major[smallest_idx], minor=minor[smallest_idx], pa=pa[smallest_idx]) return new_beam @contextlib.contextmanager def _map_context(numcores): """ Mapping context manager to allow parallel mapping or regular mapping depending on the number of cores specified. The builtin map is overloaded to handle python3 problems: python3 returns a generator, while ``multiprocessing.Pool.map`` actually runs the whole thing """ if numcores is not None and numcores > 1: try: from joblib import Parallel, delayed from joblib.pool import has_shareable_memory map = lambda x,y: Parallel(n_jobs=numcores)(delayed(has_shareable_memory)(x))(y) parallel = True except ImportError: map = lambda x,y: list(builtins.map(x,y)) warnings.warn("Could not import joblib. " "map will be non-parallel.") parallel = False else: parallel = False map = lambda x,y: list(builtins.map(x,y)) yield map def convert_bunit(bunit): ''' Convert a BUNIT string to a quantity Parameters ---------- bunit : str String to convert to an `~astropy.units.Unit` Returns ------- unit : `~astropy.unit.Unit` Corresponding unit. ''' # special case: CASA (sometimes) makes non-FITS-compliant jy/beam headers bunit_lower = re.sub("\s", "", bunit.lower()) if bunit_lower == 'jy/beam': unit = u.Jy / u.beam else: try: unit = u.Unit(bunit) except ValueError: warnings.warn("Could not parse unit {0}".format(bunit)) unit = None return unit spectral-cube-0.4.3/spectral_cube/cython_version.py0000644000077000000240000000007213256754067022544 0ustar adamstaff00000000000000# Generated file; do not modify cython_version = '0.23.4' spectral-cube-0.4.3/spectral_cube/io/0000755000077000000240000000000013261442571017517 5ustar adamstaff00000000000000spectral-cube-0.4.3/spectral_cube/io/__init__.py0000644000077000000240000000010112643464660021626 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division spectral-cube-0.4.3/spectral_cube/io/casa_image.py0000644000077000000240000001263712643464660022161 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import warnings from astropy.io import fits from astropy.extern import six from astropy.wcs import WCS import numpy as np from .. import SpectralCube, StokesSpectralCube, BooleanArrayMask, LazyMask from .. import cube_utils # Read and write from a CASA image. This has a few # complications. First, by default CASA does not return the # "python order" and so we either have to transpose the cube on # read or have dueling conventions. Second, CASA often has # degenerate stokes axes present in unpredictable places (3rd or # 4th without a clear expectation). We need to replicate these # when writing but don't want them in memory. By default, try to # yield the same array in memory that we would get from astropy. def is_casa_image(input, **kwargs): if isinstance(input, six.string_types): if input.endswith('.image'): return True return False def wcs_casa2astropy(casa_wcs): """ Convert a casac.coordsys object into an astropy.wcs.WCS object """ from astropy.wcs import WCS wcs = WCS(naxis=int(casa_wcs.naxes())) crpix = casa_wcs.referencepixel() if crpix['ar_type'] != 'absolute': raise ValueError("Unexpected ar_type: %s" % crpix['ar_type']) elif crpix['pw_type'] != 'pixel': raise ValueError("Unexpected pw_type: %s" % crpix['pw_type']) else: wcs.wcs.crpix = crpix['numeric'] cdelt = casa_wcs.increment() if cdelt['ar_type'] != 'absolute': raise ValueError("Unexpected ar_type: %s" % cdelt['ar_type']) elif cdelt['pw_type'] != 'world': raise ValueError("Unexpected pw_type: %s" % cdelt['pw_type']) else: wcs.wcs.cdelt = cdelt['numeric'] crval = casa_wcs.referencevalue() if crval['ar_type'] != 'absolute': raise ValueError("Unexpected ar_type: %s" % crval['ar_type']) elif crval['pw_type'] != 'world': raise ValueError("Unexpected pw_type: %s" % crval['pw_type']) else: wcs.wcs.crval = crval['numeric'] wcs.wcs.cunit = casa_wcs.units() # mapping betweeen CASA and FITS COORD_TYPE = {} COORD_TYPE['Right Ascension'] = "RA--" COORD_TYPE['Declination'] = "DEC-" COORD_TYPE['Longitude'] = "GLON" COORD_TYPE['Latitude'] = "GLAT" COORD_TYPE['Frequency'] = "FREQ" COORD_TYPE['Stokes'] = "STOKES" # There is no easy way at the moment to extract the orginal projection # codes from a coordsys object, so we need to figure out how to do this in # the most general way. The code below is still experimental. ctype = [] for i, name in enumerate(casa_wcs.names()): if name in COORD_TYPE: ctype.append(COORD_TYPE[name]) if casa_wcs.axiscoordinatetypes()[i] == 'Direction': ctype[-1] += ("%4s" % casa_wcs.projection()['type']).replace(' ', '-') else: raise KeyError("Don't know how to convert: %s" % name) wcs.wcs.ctype = ctype return wcs def load_casa_image(filename, skipdata=False, skipvalid=False, skipcs=False, **kwargs): """ Load a cube (into memory?) from a CASA image. By default it will transpose the cube into a 'python' order and drop degenerate axes. These options can be suppressed. The object holds the coordsys object from the image in memory. """ try: from taskinit import ia except ImportError: raise ImportError("Could not import CASA (casac) and therefore cannot read CASA .image files") # use the ia tool to get the file contents ia.open(filename) # read in the data if not skipdata: data = ia.getchunk() # CASA stores validity of data as a mask if not skipvalid: valid = ia.getchunk(getmask=True) # transpose is dealt with within the cube object # read in coordinate system object casa_cs = ia.coordsys() wcs = wcs_casa2astropy(casa_cs) unit = ia.brightnessunit() # don't need this yet # stokes = get_casa_axis(temp_cs, wanttype="Stokes", skipdeg=False,) # if stokes == None: # order = np.arange(self.data.ndim) # else: # order = [] # for ax in np.arange(self.data.ndim+1): # if ax == stokes: # continue # order.append(ax) # self.casa_cs = ia.coordsys(order) # This should work, but coordsys.reorder() has a bug # on the error checking. JIRA filed. Until then the # axes will be reversed from the original. # if transpose == True: # new_order = np.arange(self.data.ndim) # new_order = new_order[-1*np.arange(self.data.ndim)-1] # print new_order # self.casa_cs.reorder(new_order) # close the ia tool ia.close() meta = {'filename': filename, 'BUNIT': unit} if wcs.naxis == 3: mask = BooleanArrayMask(np.logical_not(valid), wcs) cube = SpectralCube(data, wcs, mask, meta=meta) elif wcs.naxis == 4: data, wcs = cube_utils._split_stokes(data.T, wcs) mask = {} for component in data: data[component], wcs_slice = cube_utils._orient(data[component], wcs) mask[component] = LazyMask(np.isfinite, data=data[component], wcs=wcs_slice) cube = StokesSpectralCube(data, wcs_slice, mask, meta=meta) return cube spectral-cube-0.4.3/spectral_cube/io/casa_masks.py0000644000077000000240000000611013161003310022153 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import numpy as np from astropy.io import fits import tempfile import warnings from ..wcs_utils import add_stokes_axis_to_wcs __all__ = ['make_casa_mask'] def make_casa_mask(SpecCube, outname, append_to_image=True, img=None, add_stokes=True, stokes_posn=None): ''' Outputs the mask attached to the SpectralCube object as a CASA image, or optionally appends the mask to a preexisting CASA image. Parameters ---------- SpecCube : SpectralCube SpectralCube object containing mask. outname : str Name of the outputted mask file. append_to_image : bool, optional Appends the mask to a given image. img : str, optional Image to be appended to. Must be specified if append_to_image is enabled. add_stokes: bool, optional Adds a Stokes axis onto the wcs from SpecCube. stokes_posn : int, optional Sets the position of the new Stokes axis. Defaults to the last axis. ''' try: from taskinit import ia except ImportError: print("Cannot import casac. Must be run in a CASA environment.") # Get the header info from the image # There's not wcs_astropy2casa (yet), so create a temporary file for # CASA to open. temp = tempfile.NamedTemporaryFile() # CASA is closing this file at some point so set it to manual delete. temp2 = tempfile.NamedTemporaryFile(delete=False) # Grab wcs # Optionally re-add on the Stokes axis if add_stokes: my_wcs = SpecCube.wcs if stokes_posn is None: stokes_posn = my_wcs.wcs.naxis new_wcs = add_stokes_axis_to_wcs(my_wcs, stokes_posn) header = new_wcs.to_header() # Transpose the shape so we're adding the axis at the place CASA will # recognize. Then transpose back. shape = SpecCube.shape[::-1] shape = shape[:stokes_posn] + (1,) + shape[stokes_posn:] shape = shape[::-1] else: # Just grab the header from SpecCube header = SpecCube.header shape = SpecCube.shape hdu = fits.PrimaryHDU(header=header, data=np.empty(shape, dtype='int16')) hdu.writeto(temp.name) ia.fromfits(infile=temp.name, outfile=temp2.name, overwrite=True) temp.close() cs = ia.coordsys() ia.close() temp2.close() mask_arr = SpecCube.mask.include() # Reshape mask with possible Stokes axis mask_arr = mask_arr.reshape(shape) # Transpose to match CASA axes mask_arr = mask_arr.T ia.newimagefromarray(outfile=outname, pixels=mask_arr.astype('int16')) ia.open(outname) ia.setcoordsys(cs.torecord()) ia.close() if append_to_image: if img is None: raise TypeError("img argument must be specified to append the mask.") ia.open(outname) ia.calcmask(outname+">0.5") ia.close() ia.open(img) ia.maskhandler('copy', [outname+":mask0", outname]) ia.maskhandler('set', outname) ia.close() spectral-cube-0.4.3/spectral_cube/io/class_lmv.py0000644000077000000240000007151313161003310022042 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import numpy as np import struct import warnings import string from astropy.extern import six from astropy import log from .fits import load_fits_cube """ .. TODO:: When any section length is zero, that means the following values are to be ignored. No warning is needed. """ # Constant: r2deg = 180/np.pi # see sicfits.f90 _ctype_dict={'LII':'GLON', 'BII':'GLAT', 'VELOCITY':'VELO', 'RA':'RA', 'DEC':'DEC', 'FREQUENCY': 'FREQ', } _cunit_dict = {'LII':'deg', 'BII':'deg', 'VELOCITY':'km s-1', 'RA':'deg', 'DEC':'deg', 'FREQUENCY': 'MHz', } cel_types = ('RA','DEC','GLON','GLAT') # CLASS apparently defaults to an ARC (zenithal equidistant) projection; this # is what is output in case the projection # is zero when exporting from CLASS _proj_dict = {0:'ARC', 1:'TAN', 2:'SIN', 3:'AZP', 4:'STG', 5:'ZEA', 6:'AIT', 7:'GLS', 8:'SFL', } _bunit_dict = {'k (tmb)': 'K'} def is_lmv(input, **kwargs): """ Determine whether input is in GILDAS CLASS lmv format """ if isinstance(input, six.string_types): if input.lower().endswith(('.lmv')): return True else: return False def read_lmv(filename): """ Read an LMV cube file Specification is primarily in GILDAS image_def.f90 """ log.warning("CLASS LMV cube reading is tentatively supported. " "Please post bug reports at the first sign of danger!") with open(filename,'rb') as lf: # lf for "LMV File" filetype = _read_string(lf, 12) #!--------------------------------------------------------------------- #! @ private #! SYCODE system code #! '-' IEEE #! '.' EEEI (IBM like) #! '_' VAX #! IMCODE file code #! '<' IEEE 64 bits (Little Endian, 99.9 % of recent computers) #! '>' EEEI 64 bits (Big Endian, HPUX, IBM-RISC, and SPARC ...) #!--------------------------------------------------------------------- imcode = filetype[6] if filetype[:6] != 'GILDAS' or filetype[7:] != 'IMAGE': raise TypeError("File is not a GILDAS Image file") if imcode in ('<','>'): if imcode =='>': log.warning("Swap the endianness first...") return read_lmv_type2(lf) else: return read_lmv_type1(lf) def read_lmv_type1(lf): header = {} # fmt probably matters! Default is "r4", i.e. float32 data, but could be float64 fmt = np.fromfile(lf, dtype='int32', count=1) # 4 # number of data blocks ndb = np.fromfile(lf, dtype='int32', count=1) # 5 gdf_type = np.fromfile(lf, dtype='int32', count=1) # 6 # Reserved Space reserved_fill = np.fromfile(lf, dtype='int32', count=4) # 7 general_section_length = np.fromfile(lf, dtype='int32', count=1) # 11 #print "Format: ",fmt," ndb: ",ndb, " fill: ",fill," other: ",unknown # pos 12 naxis,naxis1,naxis2,naxis3,naxis4 = np.fromfile(lf,count=5,dtype='int32') header['NAXIS'] = naxis header['NAXIS1'] = naxis1 header['NAXIS2'] = naxis2 header['NAXIS3'] = naxis3 header['NAXIS4'] = naxis4 # We are indexing bytes from here; CLASS indices are higher by 12 # pos 17 header['CRPIX1'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CRVAL1'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CDELT1'] = np.fromfile(lf,count=1,dtype='float64')[0] * r2deg header['CRPIX2'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CRVAL2'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CDELT2'] = np.fromfile(lf,count=1,dtype='float64')[0] * r2deg header['CRPIX3'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CRVAL3'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CDELT3'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CRPIX4'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CRVAL4'] = np.fromfile(lf,count=1,dtype='float64')[0] header['CDELT4'] = np.fromfile(lf,count=1,dtype='float64')[0] # pos 41 #print "Post-crval",lf.tell() blank_section_length = np.fromfile(lf,count=1,dtype='int32') if blank_section_length != 8: warnings.warn("Invalid section length found for blanking section") bval = np.fromfile(lf,count=1,dtype='float32')[0] # 42 header['TOLERANC'] = np.fromfile(lf,count=1,dtype='int32')[0] # 43 eval = tolerance extrema_section_length = np.fromfile(lf,count=1,dtype='int32')[0] # 44 if extrema_section_length != 40: warnings.warn("Invalid section length found for extrema section") vmin,vmax = np.fromfile(lf,count=2,dtype='float32') # 45 xmin,xmax,ymin,ymax,zmin,zmax = np.fromfile(lf,count=6,dtype='int32') # 47 wmin,wmax = np.fromfile(lf,count=2,dtype='int32') # 53 description_section_length = np.fromfile(lf,count=1,dtype='int32')[0] # 55 if description_section_length != 72: warnings.warn("Invalid section length found for description section") #strings = lf.read(description_section_length) # 56 header['BUNIT'] = _read_string(lf, 12) # 56 header['CTYPE1'] = _read_string(lf, 12) # 59 header['CTYPE2'] = _read_string(lf, 12) # 62 header['CTYPE3'] = _read_string(lf, 12) # 65 header['CTYPE4'] = _read_string(lf, 12) # 68 header['CUNIT1'] = _cunit_dict[header['CTYPE1'].strip()] header['CUNIT2'] = _cunit_dict[header['CTYPE2'].strip()] header['CUNIT3'] = _cunit_dict[header['CTYPE3'].strip()] header['COOSYS'] = _read_string(lf, 12) # 71 position_section_length = np.fromfile(lf,count=1,dtype='int32') # 74 if position_section_length != 48: warnings.warn("Invalid section length found for position section") header['OBJNAME'] = _read_string(lf, 4*3) # 75 header['RA'] = np.fromfile(lf, count=1, dtype='float64')[0] * r2deg # 78 header['DEC'] = np.fromfile(lf, count=1, dtype='float64')[0] * r2deg # 80 header['GLON'] = np.fromfile(lf, count=1, dtype='float64')[0] * r2deg # 82 header['GLAT'] = np.fromfile(lf, count=1, dtype='float64')[0] * r2deg # 84 header['EQUINOX'] = np.fromfile(lf,count=1,dtype='float32')[0] # 86 header['PROJWORD'] = _read_string(lf, 4) # 87 header['PTYP'] = np.fromfile(lf,count=1,dtype='int32')[0] # 88 header['A0'] = np.fromfile(lf,count=1,dtype='float64')[0] # 89 header['D0'] = np.fromfile(lf,count=1,dtype='float64')[0] # 91 header['PANG'] = np.fromfile(lf,count=1,dtype='float64')[0] # 93 header['XAXI'] = np.fromfile(lf,count=1,dtype='float32')[0] # 95 header['YAXI'] = np.fromfile(lf,count=1,dtype='float32')[0] # 96 spectroscopy_section_length = np.fromfile(lf,count=1,dtype='int32') # 97 if spectroscopy_section_length != 48: warnings.warn("Invalid section length found for spectroscopy section") header['RECVR'] = _read_string(lf, 12) # 98 header['FRES'] = np.fromfile(lf,count=1,dtype='float64')[0] # 101 header['IMAGFREQ'] = np.fromfile(lf,count=1,dtype='float64')[0] # 103 "FIMA" header['REFFREQ'] = np.fromfile(lf,count=1,dtype='float64')[0] # 105 header['VRES'] = np.fromfile(lf,count=1,dtype='float32')[0] # 107 header['VOFF'] = np.fromfile(lf,count=1,dtype='float32')[0] # 108 header['FAXI'] = np.fromfile(lf,count=1,dtype='int32')[0] # 109 resolution_section_length = np.fromfile(lf,count=1,dtype='int32')[0] # 110 if resolution_section_length != 12: warnings.warn("Invalid section length found for resolution section") #header['DOPP'] = np.fromfile(lf,count=1,dtype='float16')[0] # 110a ??? #header['VTYP'] = np.fromfile(lf,count=1,dtype='int16')[0] # 110b # integer, parameter :: vel_unk = 0 ! Unsupported referential :: planetary...) # integer, parameter :: vel_lsr = 1 ! LSR referential # integer, parameter :: vel_hel = 2 ! Heliocentric referential # integer, parameter :: vel_obs = 3 ! Observatory referential # integer, parameter :: vel_ear = 4 ! Earth-Moon barycenter referential # integer, parameter :: vel_aut = -1 ! Take referential from data header['BMAJ'] = np.fromfile(lf,count=1,dtype='float32')[0] # 111 header['BMIN'] = np.fromfile(lf,count=1,dtype='float32')[0] # 112 header['BPA'] = np.fromfile(lf,count=1,dtype='float32')[0] # 113 noise_section_length = np.fromfile(lf,count=1,dtype='int32') if noise_section_length != 0: warnings.warn("Invalid section length found for noise section") header['NOISE'] = np.fromfile(lf,count=1,dtype='float32')[0] # 115 header['RMS'] = np.fromfile(lf,count=1,dtype='float32')[0] # 116 astrometry_section_length = np.fromfile(lf,count=1,dtype='int32') if astrometry_section_length != 0: warnings.warn("Invalid section length found for astrometry section") header['MURA'] = np.fromfile(lf,count=1,dtype='float32')[0] # 118 header['MUDEC'] = np.fromfile(lf,count=1,dtype='float32')[0] # 119 header['PARALLAX'] = np.fromfile(lf,count=1,dtype='float32')[0] # 120 # Apparently CLASS headers aren't required to fill the 'value at # reference pixel' column if (header['CTYPE1'].strip() == 'RA' and header['CRVAL1'] == 0 and header['RA'] != 0): header['CRVAL1'] = header['RA'] header['CRVAL2'] = header['DEC'] # Copied from the type 2 reader: # Use the appropriate projection type ptyp = header['PTYP'] for kw in header: if 'CTYPE' in kw: if header[kw].strip() in cel_types: n_dashes = 5-len(header[kw].strip()) header[kw] = header[kw].strip()+ '-'*n_dashes + _proj_dict[ptyp] other_info = np.fromfile(lf, count=7, dtype='float32') # 121-end if not np.all(other_info == 0): warnings.warn("Found additional information in the last 7 bytes") endpoint = 508 if lf.tell() != endpoint: raise ValueError("Header was not parsed correctly") data = np.fromfile(lf, count=naxis1*naxis2*naxis3, dtype='float32') data[data == bval] = np.nan # for no apparent reason, y and z are 1-indexed and x is zero-indexed if (wmin-1,zmin-1,ymin-1,xmin) != np.unravel_index(np.nanargmin(data), [naxis4,naxis3,naxis2,naxis1]): warnings.warn("Data min location does not match that on file. " "Possible error reading data.") if (wmax-1,zmax-1,ymax-1,xmax) != np.unravel_index(np.nanargmax(data), [naxis4,naxis3,naxis2,naxis1]): warnings.warn("Data max location does not match that on file. " "Possible error reading data.") if np.nanmax(data) != vmax: warnings.warn("Data max does not match that on file. " "Possible error reading data.") if np.nanmin(data) != vmin: warnings.warn("Data min does not match that on file. " "Possible error reading data.") return data.reshape([naxis4,naxis3,naxis2,naxis1]),header # debug #return data.reshape([naxis3,naxis2,naxis1]), header, hdr_f, hdr_s, hdr_i, hdr_d, hdr_d_2 def read_lmv_tofits(filename): from astropy.io import fits data,header = read_lmv(filename) # LMV may contain extra dimensions that are improperly labeled data = data.squeeze() bad_kws = ['NAXIS4','CRVAL4','CRPIX4','CDELT4','CROTA4','CUNIT4','CTYPE4'] cards = [fits.header.Card(keyword=k, value=v[0], comment=v[1]) if isinstance(v, tuple) else fits.header.Card(''.join(s for s in k if s in string.printable), ''.join(s for s in v if s in string.printable) if isinstance(v, six.string_types) else v) for k,v in six.iteritems(header) if k not in bad_kws] Header = fits.Header(cards) hdu = fits.PrimaryHDU(data=data, header=Header) return hdu def load_lmv_cube(filename): hdu = read_lmv_tofits(filename) meta = {'filename':filename} return load_fits_cube(hdu, meta=meta) def _read_byte(f): '''Read a single byte (from idlsave)''' return np.uint8(struct.unpack('=B', f.read(4)[:1])[0]) def _read_int16(f): '''Read a signed 16-bit integer (from idlsave)''' return np.int16(struct.unpack('=h', f.read(4)[2:4])[0]) def _read_int32(f): '''Read a signed 32-bit integer (from idlsave)''' return np.int32(struct.unpack('=i', f.read(4))[0]) def _read_int64(f): '''Read a signed 64-bit integer ''' return np.int64(struct.unpack('=q', f.read(8))[0]) def _read_float32(f): '''Read a 32-bit float (from idlsave)''' return np.float32(struct.unpack('=f', f.read(4))[0]) def _read_string(f, size): '''Read a string of known maximum length''' return f.read(size).decode('utf-8').strip() def _read_float64(f): '''Read a 64-bit float (from idlsave)''' return np.float64(struct.unpack('=d', f.read(8))[0]) def _check_val(name, got,expected): if got != expected: log.warning("{2} = {0} instead of {1}".format(got, expected, name)) def read_lmv_type2(lf): """ See image_def.f90 """ header = {} lf.seek(12) # DONE before integer(kind=4) :: ijtyp(3) = 0 ! 1 Image Type # fmt probably matters! Default is "r4", i.e. float32 data, but could be float64 fmt = _read_int32(lf) # 4 # number of data blocks ndb = _read_int64(lf) # 5 nhb = _read_int32(lf) # 7 ntb = _read_int32(lf) # 8 version_gdf = _read_int32(lf) # 9 if version_gdf != 20: raise TypeError("Trying to read a version-2 file, but the version" " number is {0} (should be 20)".format(version_gdf)) type_gdf = _read_int32(lf) # 10 dim_start = _read_int32(lf) # 11 pad_trail = _read_int32(lf) # 12 if dim_start % 2 == 0: log.warning("Got even dim_start in lmv cube: this is not expected.") if dim_start > 17: log.warning("dim_start > 17 in lmv cube: this is not expected.") lf.seek(16*4) gdf_maxdims=7 dim_words = _read_int32(lf) # 17 if dim_words != 2*gdf_maxdims+2: log.warning("dim_words = {0} instead of {1}".format(dim_words, gdf_maxdims*2+2)) blan_start = _read_int32(lf) # 18 if blan_start != dim_start+dim_words+2: log.warning("blan_star = {0} instead of {1}".format(blan_start, dim_start+dim_words+2)) mdim = _read_int32(lf) # 19 ndim = _read_int32(lf) # 20 dims = np.fromfile(lf, count=gdf_maxdims, dtype='int64') if np.count_nonzero(dims) != ndim: raise ValueError("Disagreement between ndims and number of nonzero dims.") header['NAXIS'] = ndim valid_dims = [] for ii,dim in enumerate(dims): if dim != 0: header['NAXIS{0}'.format(ii+1)] = dim valid_dims.append(ii) blan_words = _read_int32(lf) if blan_words != 2: log.warning("blan_words = {0} instead of 2".format(blan_words)) extr_start = _read_int32(lf) bval = _read_float32(lf) # blanking value bval_tol = _read_float32(lf) # eval = tolerance # FITS requires integer BLANKs #header['BLANK'] = bval extr_words = _read_int32(lf) if extr_words != 6: log.warning("extr_words = {0} instead of 6".format(extr_words)) coor_start = _read_int32(lf) if coor_start != extr_start+extr_words+2: log.warning("coor_start = {0} instead of {1}".format(coor_start, extr_start+extr_words+2)) rmin = _read_float32(lf) rmax = _read_float32(lf) # position 168 minloc = _read_int64(lf) maxloc = _read_int64(lf) # lf.seek(184) coor_words = _read_int32(lf) if coor_words != gdf_maxdims*6: log.warning("coor_words = {0} instead of {1}".format(coor_words, gdf_maxdims*6)) desc_start = _read_int32(lf) if desc_start != coor_start+coor_words+2: log.warning("desc_start = {0} instead of {1}".format(desc_start, coor_start+coor_words+2)) convert = np.fromfile(lf, count=3*gdf_maxdims, dtype='float64').reshape([gdf_maxdims,3]) # conversion of "convert" to CRPIX/CRVAL/CDELT below desc_words = _read_int32(lf) if desc_words != 3*(gdf_maxdims+1): log.warning("desc_words = {0} instead of {1}".format(desc_words, 3*(gdf_maxdims+1))) null_start = _read_int32(lf) if null_start != desc_start+desc_words+2: log.warning("null_start = {0} instead of {1}".format(null_start, desc_start+desc_words+2)) ijuni = _read_string(lf, 12) # data unit ijcode = [_read_string(lf, 12) for ii in range(gdf_maxdims)] pad_desc = _read_int32(lf) if ijuni.lower() in _bunit_dict: header['BUNIT'] = (_bunit_dict[ijuni.lower()], ijuni) else: header['BUNIT'] = ijuni #! The first block length is thus #! s_dim-1 + (2*mdim+4) + (4) + (8) + (6*mdim+2) + (3*mdim+5) #! = s_dim-1 + mdim*(2+6+3) + (4+4+2+5+8) #! = s_dim-1 + 11*mdim + 23 #! With mdim = 7, s_dim=11, this is 110 spaces #! With mdim = 8, s_dim=11, this is 121 spaces #! MDIM > 8 would NOT fit in one block... #! #! Block 2: Ancillary information #! #! The same logic of Length + Pointer is used there too, although the #! length are fixed. Note rounding to even number for the pointer offsets #! in order to preserve alignement... #! lf.seek(512) posi_words = _read_int32(lf) _check_val('posi_words', posi_words, 15) proj_start = _read_int32(lf) source_name = _read_string(lf, 12) header['OBJECT'] = source_name coordinate_system = _read_string(lf, 12) header['RA'] = _read_float64(lf) header['DEC'] = _read_float64(lf) header['LII'] = _read_float64(lf) header['BII'] = _read_float64(lf) header['EPOCH'] = _read_float32(lf) #pad_posi = _read_float32(lf) #print pad_posi #raise ValueError("pad_posi should probably be 0?") #! PROJECTION #integer(kind=4) :: proj_words = 9 ! Projection length: 9 used + 1 padding #integer(kind=4) :: spec_start !! = proj_start + 12 #real(kind=8) :: a0 = 0.d0 ! 89 X of projection center #real(kind=8) :: d0 = 0.d0 ! 91 Y of projection center #real(kind=8) :: pang = 0.d0 ! 93 Projection angle #integer(kind=4) :: ptyp = p_none ! 88 Projection type (see p_... codes) #integer(kind=4) :: xaxi = 0 ! 95 X axis #integer(kind=4) :: yaxi = 0 ! 96 Y axis #integer(kind=4) :: pad_proj #! proj_words = _read_int32(lf) spec_start = _read_int32(lf) _check_val('spec_start', spec_start, proj_start+proj_words+2) if proj_words == 9: header['PROJ_A0'] = _read_float64(lf) header['PROJ_D0'] = _read_float64(lf) header['PROJPANG'] = _read_float64(lf) ptyp = _read_int32(lf) header['PROJXAXI'] = _read_int32(lf) header['PROJYAXI'] = _read_int32(lf) elif proj_words != 0: raise ValueError("Invalid # of projection keywords") for kw in header: if 'CTYPE' in kw: if header[kw].strip() in cel_types: n_dashes = 5-len(header[kw].strip()) header[kw] = header[kw].strip()+ '-'*n_dashes + _proj_dict[ptyp] for ii,((ref,val,inc),code) in enumerate(zip(convert,ijcode)): if ii in valid_dims: # jul14a gio/to_imfits.f90 line 284-313 if ptyp != 0 and (ii+1) in (header['PROJXAXI'], header['PROJYAXI']): #! Compute reference pixel so that VAL(REF) = 0 ref = ref - val/inc if (ii+1) == header['PROJXAXI']: val = header['PROJ_A0'] elif (ii+1) == header['PROJYAXI']: val = header['PROJ_D0'] else: raise ValueError("Impossible state - code bug.") val = val*r2deg inc = inc*r2deg rota = r2deg*header['PROJPANG'] elif code in ('RA', 'L', 'B', 'DEC', 'LII', 'BII', 'GLAT', 'GLON', 'LAT', 'LON'): val = val*r2deg inc = inc*r2deg rota = 0.0 # These are not implemented: prefer to maintain original units (we're # reading in to spectral_cube after all, no need to change units until the # output step) #elseif (code.eq.'FREQUENCY') then #val = val*1.0d6 ! MHz to Hz #inc = inc*1.0d6 #elseif (code.eq.'VELOCITY') then #code = 'VRAD' ! force VRAD instead of VELOCITY for CASA #val = val*1.0d3 ! km/s to m/s #inc = inc*1.0d3 header['CRPIX{0}'.format(ii+1)] = ref header['CRVAL{0}'.format(ii+1)] = val header['CDELT{0}'.format(ii+1)] = inc for ii,ctype in enumerate(ijcode): if ii in valid_dims: header['CTYPE{0}'.format(ii+1)] = _ctype_dict[ctype] header['CUNIT{0}'.format(ii+1)] = _cunit_dict[ctype] spec_words = _read_int32(lf) reso_start = _read_int32(lf) _check_val('reso_start', reso_start, proj_start+proj_words+2+spec_words+2) if spec_words == 14: header['FRES'] = _read_float64(lf) header['FIMA'] = _read_float64(lf) header['FREQ'] = _read_float64(lf) header['VRES'] = _read_float32(lf) header['VOFF'] = _read_float32(lf) header['DOPP'] = _read_float32(lf) header['FAXI'] = _read_int32(lf) header['LINENAME'] = _read_string(lf, 12) header['VTYPE'] = _read_int32(lf) elif spec_words != 0: raise ValueError("Invalid # of spectroscopic keywords") #! SPECTROSCOPY #integer(kind=4) :: spec_words = 14 ! Spectroscopy length: 14 used #integer(kind=4) :: reso_start !! = spec_words + 16 #real(kind=8) :: fres = 0.d0 !101 Frequency resolution #real(kind=8) :: fima = 0.d0 !103 Image frequency #real(kind=8) :: freq = 0.d0 !105 Rest Frequency #real(kind=4) :: vres = 0.0 !107 Velocity resolution #real(kind=4) :: voff = 0.0 !108 Velocity offset #real(kind=4) :: dopp = 0.0 ! Doppler factor #integer(kind=4) :: faxi = 0 !109 Frequency axis #integer(kind=4) :: ijlin(3) = 0 ! 98 Line name #integer(kind=4) :: vtyp = vel_unk ! Velocity type (see vel_... codes) reso_words = _read_int32(lf) nois_start = _read_int32(lf) _check_val('nois_start', nois_start, proj_start+proj_words+2+spec_words+2+reso_words+2) if reso_words == 3: header['BMAJ'] = _read_float32(lf) header['BMIN'] = _read_float32(lf) header['BPA'] = _read_float32(lf) #pad_reso = _read_float32(lf) elif reso_words != 0: raise ValueError("Invalid # of resolution keywords") #! RESOLUTION #integer(kind=4) :: reso_words = 3 ! Resolution length: 3 used + 1 padding #integer(kind=4) :: nois_start !! = reso_words + 6 #real(kind=4) :: majo = 0.0 !111 Major axis #real(kind=4) :: mino = 0.0 !112 Minor axis #real(kind=4) :: posa = 0.0 !113 Position angle #real(kind=4) :: pad_reso nois_words = _read_int32(lf) astr_start = _read_int32(lf) _check_val('astr_start', astr_start, proj_start+proj_words+2+spec_words+2+reso_words+2+nois_words+2) if nois_words == 2: header['NOISE_T'] = (_read_float32(lf), "Theoretical Noise") header['NOISERMS'] = (_read_float32(lf), "Measured (RMS) noise") elif nois_words != 0: raise ValueError("Invalid # of noise keywords") #! NOISE #integer(kind=4) :: nois_words = 2 ! Noise section length: 2 used #integer(kind=4) :: astr_start !! = s_nois + 4 #real(kind=4) :: noise = 0.0 ! 115 Theoretical noise #real(kind=4) :: rms = 0.0 ! 116 Actual noise astr_words = _read_int32(lf) uvda_start = _read_int32(lf) _check_val('uvda_start', uvda_start, proj_start+proj_words+2+spec_words+2+reso_words+2+nois_words+2+astr_words+2) if astr_words == 3: header['MURA'] = _read_float32(lf) header['MUDEC'] = _read_float32(lf) header['PARALLAX'] = _read_float32(lf) elif astr_words != 0: raise ValueError("Invalid # of astrometry keywords") #! ASTROMETRY #integer(kind=4) :: astr_words = 3 ! Proper motion section length: 3 used + 1 padding #integer(kind=4) :: uvda_start !! = s_astr + 4 #real(kind=4) :: mura = 0.0 ! 118 along RA, in mas/yr #real(kind=4) :: mudec = 0.0 ! 119 along Dec, in mas/yr #real(kind=4) :: parallax = 0.0 ! 120 in mas #real(kind=4) :: pad_astr #! real(kind=4) :: pepoch = 2000.0 ! 121 in yrs ? code_uvt_last=25 uvda_words = _read_int32(lf) void_start = _read_int32(lf) _check_val('void_start', void_start, proj_start + proj_words + 2 + spec_words + 2 + reso_words + 2 + nois_words + 2 + astr_words + 2 + uvda_words + 2) if uvda_words == 18+2*code_uvt_last: version_uv = _read_int32(lf) nchan = _read_int32(lf) nvisi = _read_int64(lf) nstokes = _read_int32(lf) natom = _read_int32(lf) basemin = _read_float32(lf) basemax = _read_float32(lf) fcol = _read_int32(lf) lcol = _read_int32(lf) nlead = _read_int32(lf) ntrail = _read_int32(lf) column_pointer = np.fromfile(lf, count=code_uvt_last, dtype='int32') column_size = np.fromfile(lf, count=code_uvt_last, dtype='int32') column_codes = np.fromfile(lf, count=nlead+ntrail, dtype='int32') column_types = np.fromfile(lf, count=nlead+ntrail, dtype='int32') order = _read_int32(lf) nfreq = _read_int32(lf) atoms = np.fromfile(lf, count=4, dtype='int32') elif uvda_words != 0: raise ValueError("Invalid # of UV data keywords") #! UV_DATA information #integer(kind=4) :: uvda_words = 18+2*code_uvt_last ! Length of section: 14 used #integer(kind=4) :: void_start !! = s_uvda + l_uvda + 2 #integer(kind=4) :: version_uv = code_version_uvt_current ! 1 version number. Will allow us to change the data format #integer(kind=4) :: nchan = 0 ! 2 Number of channels #integer(kind=8) :: nvisi = 0 ! 3-4 Independent of the transposition status #integer(kind=4) :: nstokes = 0 ! 5 Number of polarizations #integer(kind=4) :: natom = 0 ! 6. 3 for real, imaginary, weight. 1 for real. #real(kind=4) :: basemin = 0. ! 7 Minimum Baseline #real(kind=4) :: basemax = 0. ! 8 Maximum Baseline #integer(kind=4) :: fcol ! 9 Column of first channel #integer(kind=4) :: lcol ! 10 Column of last channel #! The number of information per channel can be obtained by #! (lcol-fcol+1)/(nchan*natom) #! so this could allow to derive the number of Stokes parameters #! Leading data at start of each visibility contains specific information #integer(kind=4) :: nlead = 7 ! 11 Number of leading informations (at lest 7) #! Trailing data at end of each visibility may hold additional information #integer(kind=4) :: ntrail = 0 ! 12 Number of trailing informations #! #! Leading / Trailing information codes have been specified before #integer(kind=4) :: column_pointer(code_uvt_last) = code_null ! Back pointer to the columns... #integer(kind=4) :: column_size(code_uvt_last) = 0 ! Number of columns for each #! In the data, we instead have the codes for each column #! integer(kind=4) :: column_codes(nlead+ntrail) ! Start column for each ... #! integer(kind=4) :: column_types(nlead+ntrail) /0,1,2/ ! Number of columns for each: 1 real*4, 2 real*8 #! Leading / Trailing information codes #! #integer(kind=4) :: order = 0 ! 13 Stoke/Channel ordering #integer(kind=4) :: nfreq = 0 ! 14 ! 0 or = nchan*nstokes #integer(kind=4) :: atoms(4) ! 15-18 Atom description #! #real(kind=8), pointer :: freqs(:) => null() ! (nchan*nstokes) = 0d0 #integer(kind=4), pointer :: stokes(:) => null() ! (nchan*nstokes) or (nstokes) = code_stoke #! #real(kind=8), pointer :: ref(:) => null() #real(kind=8), pointer :: val(:) => null() #real(kind=8), pointer :: inc(:) => null() lf.seek(1024) real_dims = dims[:ndim] data = np.fromfile(lf, count=np.product(real_dims), dtype='float32').reshape(real_dims[::-1]) data[data==bval] = np.nan return data,header spectral-cube-0.4.3/spectral_cube/io/core.py0000644000077000000240000000472112643464660021033 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division def read(filename, format=None, hdu=None, **kwargs): """ Read a file into a :class:`SpectralCube` or :class:`StokesSpectralCube` instance. Parameters ---------- filename : str or HDU File to read format : str, optional File format. hdu : int or str For FITS files, the HDU to read in (can be the ID or name of an HDU). kwargs : dict If the format is 'fits', the kwargs are passed to :func:`~astropy.io.fits.open`. Returns ------- cube : :class:`SpectralCube` or :class:`StokesSpectralCube` The spectral cube read in """ if format is None: format = determine_format(filename) if format == 'fits': from .fits import load_fits_cube return load_fits_cube(filename, hdu=hdu, **kwargs) elif format == 'casa_image': from .casa_image import load_casa_image return load_casa_image(filename) elif format in ('class_lmv','lmv'): from .class_lmv import load_lmv_cube return load_lmv_cube(filename) else: raise ValueError("Format {0} not implemented. Supported formats are " "'fits', 'casa_image', and 'lmv'.".format(format)) def write(filename, cube, overwrite=False, format=None): """ Write :class:`SpectralCube` or :class:`StokesSpectralCube` to a file. Parameters ---------- filename : str Name of the output file cube : :class:`SpectralCube` or :class:`StokesSpectralCube` The spectral cube to write out overwrite : bool, optional Whether to overwrite the output file format : str, optional File format. """ if format is None: format = determine_format(filename) if format == 'fits': from .fits import write_fits_cube write_fits_cube(filename, cube, overwrite=overwrite) else: raise ValueError("Format {0} not implemented. The only supported format is 'fits'".format(format)) def determine_format(input): from .fits import is_fits from .casa_image import is_casa_image from .class_lmv import is_lmv if is_fits(input): return 'fits' elif is_casa_image(input): return 'casa_image' elif is_lmv(input): return 'lmv' else: raise ValueError("Could not determine format - use the `format=` " "parameter to explicitly set the format") spectral-cube-0.4.3/spectral_cube/io/fits.py0000644000077000000240000001614613233661037021045 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import warnings from astropy.io import fits from astropy import wcs from astropy.wcs import WCS from astropy.extern import six from collections import OrderedDict from astropy.io.fits.hdu.hdulist import fitsopen as fits_open import numpy as np import datetime try: from .. import version SPECTRAL_CUBE_VERSION = version.version except ImportError: # We might be running py.test on a clean checkout SPECTRAL_CUBE_VERSION = 'dev' from .. import SpectralCube, StokesSpectralCube, LazyMask, VaryingResolutionSpectralCube from ..spectral_cube import BaseSpectralCube from .. import cube_utils warnings.filterwarnings('ignore', category=wcs.FITSFixedWarning, append=True) def first(iterable): return next(iter(iterable)) # FITS registry code - once Astropy includes a proper extensible I/O base # class, we can use that instead. The following code takes care of # interpreting string input (filename), HDU, and HDUList. def is_fits(input, **kwargs): """ Determine whether input is in FITS format """ if isinstance(input, six.string_types): if input.lower().endswith(('.fits', '.fits.gz', '.fit', '.fit.gz', '.fits.Z', '.fit.Z')): return True elif isinstance(input, (fits.HDUList, fits.PrimaryHDU, fits.ImageHDU)): return True else: return False def read_data_fits(input, hdu=None, mode='denywrite', **kwargs): """ Read an array and header from an FITS file. Parameters ---------- input : str or compatible `astropy.io.fits` HDU object If a string, the filename to read the table from. The following `astropy.io.fits` HDU objects can be used as input: - :class:`~astropy.io.fits.hdu.table.PrimaryHDU` - :class:`~astropy.io.fits.hdu.table.ImageHDU` - :class:`~astropy.io.fits.hdu.hdulist.HDUList` hdu : int or str, optional The HDU to read the table from. mode : str One of the FITS file reading modes; see `~astropy.io.fits.open`. ``denywrite`` is used by default since this prevents the system from checking that the entire cube will fit into swap, which can prevent the file from being opened at all. """ beam_table = None if isinstance(input, fits.HDUList): # Parse all array objects arrays = OrderedDict() for ihdu, hdu_item in enumerate(input): if isinstance(hdu_item, (fits.PrimaryHDU, fits.ImageHDU)): arrays[ihdu] = hdu_item elif isinstance(hdu_item, fits.BinTableHDU): if 'BPA' in hdu_item.data.names: beam_table = hdu_item.data if len(arrays) > 1: if hdu is None: hdu = first(arrays) warnings.warn("hdu= was not specified but multiple arrays" " are present, reading in first available" " array (hdu={0})".format(hdu)) # hdu might not be an integer, so we first need to convert it # to the correct HDU index hdu = input.index_of(hdu) if hdu in arrays: array_hdu = arrays[hdu] else: raise ValueError("No array found in hdu={0}".format(hdu)) elif len(arrays) == 1: array_hdu = arrays[first(arrays)] else: raise ValueError("No arrays found") elif isinstance(input, (fits.PrimaryHDU, fits.ImageHDU)): array_hdu = input else: hdulist = fits_open(input, mode=mode, **kwargs) try: return read_data_fits(hdulist, hdu=hdu) finally: hdulist.close() return array_hdu.data, array_hdu.header, beam_table def load_fits_cube(input, hdu=0, meta=None, **kwargs): """ Read in a cube from a FITS file using astropy. Parameters ---------- input: str or HDU The FITS cube file name or HDU hdu: int The extension number containing the data to be read meta: dict Metadata (can be inherited from other readers, for example) """ data, header, beam_table = read_data_fits(input, hdu=hdu, **kwargs) if data is None: raise Exception('No data found in HDU {0}. You can try using the hdu= ' 'keyword argument to read data from another HDU.'.format(hdu)) if meta is None: meta = {} if 'BUNIT' in header: meta['BUNIT'] = header['BUNIT'] wcs = WCS(header) if wcs.wcs.naxis == 3: data, wcs = cube_utils._orient(data, wcs) mask = LazyMask(np.isfinite, data=data, wcs=wcs) assert data.shape == mask._data.shape if beam_table is None: cube = SpectralCube(data, wcs, mask, meta=meta, header=header) else: cube = VaryingResolutionSpectralCube(data, wcs, mask, meta=meta, header=header, beam_table=beam_table) if hasattr(cube._mask, '_data'): # check that the shape matches if there is a shape # it is possible that VaryingResolution cubes will have a composite # mask instead assert cube._data.shape == cube._mask._data.shape elif wcs.wcs.naxis == 4: data, wcs = cube_utils._split_stokes(data, wcs) stokes_data = {} for component in data: comp_data, comp_wcs = cube_utils._orient(data[component], wcs) comp_mask = LazyMask(np.isfinite, data=comp_data, wcs=comp_wcs) if beam_table is None: stokes_data[component] = SpectralCube(comp_data, wcs=comp_wcs, mask=comp_mask, meta=meta, header=header) else: VRSC = VaryingResolutionSpectralCube stokes_data[component] = VRSC(comp_data, wcs=comp_wcs, mask=comp_mask, meta=meta, header=header, beam_table=beam_table) cube = StokesSpectralCube(stokes_data) else: raise Exception("Data should be 3- or 4-dimensional") return cube def write_fits_cube(filename, cube, overwrite=False, include_origin_notes=True): """ Write a FITS cube with a WCS to a filename """ if isinstance(cube, BaseSpectralCube): hdulist = cube.hdulist now = datetime.datetime.strftime(datetime.datetime.now(), "%Y/%m/%d-%H:%M:%S") hdulist[0].header.add_history("Written by spectral_cube v{version} on " "{date}".format(version=SPECTRAL_CUBE_VERSION, date=now)) try: hdulist.writeto(filename, overwrite=overwrite) except TypeError: hdulist.writeto(filename, clobber=overwrite) else: raise NotImplementedError() spectral-cube-0.4.3/spectral_cube/lower_dimensional_structures.py0000644000077000000240000010744513250025071025500 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import warnings import numpy as np from numpy.ma.core import nomask from astropy import convolution from astropy import units as u from astropy import wcs #from astropy import log from astropy.io.fits import Header, Card, HDUList, PrimaryHDU from radio_beam import Beam from .io.core import determine_format from . import spectral_axis from .utils import SliceWarning from .cube_utils import convert_bunit from . import wcs_utils from .masks import BooleanArrayMask, MaskBase from .base_class import (BaseNDClass, SpectralAxisMixinClass, SpatialCoordMixinClass, MaskableArrayMixinClass, MultiBeamMixinClass ) from . import cube_utils __all__ = ['LowerDimensionalObject', 'Projection', 'Slice', 'OneDSpectrum'] class LowerDimensionalObject(u.Quantity, BaseNDClass): """ Generic class for 1D and 2D objects. """ @property def header(self): header = self._header # This inplace update is OK; it's not bad to overwrite WCS in this # header if self.wcs is not None: header.update(self.wcs.to_header()) header['BUNIT'] = self.unit.to_string(format='fits') for keyword in header: if 'NAXIS' in keyword: del header[keyword] header.insert(2, Card(keyword='NAXIS', value=self.ndim)) for ind,sh in enumerate(self.shape[::-1]): header.insert(3+ind, Card(keyword='NAXIS{0:1d}'.format(ind+1), value=sh)) if 'beam' in self.meta: header.update(self.meta['beam'].to_header_keywords()) return header @property def hdu(self): if self.wcs is None: hdu = PrimaryHDU(self.value) else: hdu = PrimaryHDU(self.value, header=self.header) hdu.header['BUNIT'] = self.unit.to_string(format='fits') if 'beam' in self.meta: hdu.header.update(self.meta['beam'].to_header_keywords()) return hdu def write(self, filename, format=None, overwrite=False): """ Write the lower dimensional object to a file. Parameters ---------- filename : str The path to write the file to format : str The kind of file to write. (Currently limited to 'fits') overwrite : bool If True, overwrite ``filename`` if it exists """ if format is None: format = determine_format(filename) if format == 'fits': try: self.hdu.writeto(filename, overwrite=overwrite) except TypeError: self.hdu.writeto(filename, clobber=overwrite) else: raise ValueError("Unknown format '{0}' - the only available " "format at this time is 'fits'") def __getslice__(self, start, end, increment=None): # I don't know why this is needed, but apparently one of the inherited # classes implements getslice, which forces us to overwrite it # I can't find any examples where __getslice__ is actually implemented, # though, so this seems like a deep and frightening bug. #log.debug("Getting a slice from {0} to {1}".format(start,end)) return self.__getitem__(slice(start, end, increment)) def __getitem__(self, key, **kwargs): """ Return a new `~spectral_cube.lower_dimensional_structures.LowerDimensionalObject` of the same class while keeping other properties fixed. """ new_qty = super(LowerDimensionalObject, self).__getitem__(key) if new_qty.ndim < 2: # do not return a projection return u.Quantity(new_qty) if self._wcs is not None: if ((isinstance(key, tuple) and any(isinstance(k, slice) for k in key) and len(key) > self.ndim)): # Example cases include: indexing tricks like [:,:,None] warnings.warn("Slice {0} cannot be used on this {1}-dimensional" " array's WCS. If this is intentional, you " " should use this {2}'s ``array`` or ``quantity``" " attribute." .format(key, self.ndim, type(self)), SliceWarning ) return self.quantity[key] else: newwcs = self._wcs[key] else: newwcs = None new = self.__class__(value=new_qty.value, unit=new_qty.unit, copy=False, wcs=newwcs, meta=self._meta, mask=(self._mask[key] if self._mask is not nomask else None), header=self._header, **kwargs) new._wcs = newwcs new._meta = self._meta new._mask=(self._mask[key] if self._mask is not nomask else nomask) new._header = self._header return new def __array_finalize__(self, obj): #log.debug("Finalizing self={0}{1} obj={2}{3}" # .format(self, type(self), obj, type(obj))) self._wcs = getattr(obj, '_wcs', None) self._meta = getattr(obj, '_meta', None) self._mask = getattr(obj, '_mask', None) self._header = getattr(obj, '_header', None) self._spectral_unit = getattr(obj, '_spectral_unit', None) super(LowerDimensionalObject, self).__array_finalize__(obj) @property def __array_priority__(self): return super(LowerDimensionalObject, self).__array_priority__*2 @property def array(self): """ Get a pure array representation of the LDO. Useful when multiplying and using numpy indexing tricks. """ return np.asarray(self) @property def quantity(self): """ Get a pure `~astropy.units.Quantity` representation of the LDO. """ return u.Quantity(self) def to(self, unit, equivalencies=[], freq=None): """ Return a new `~spectral_cube.lower_dimensional_structures.Projection` of the same class with the specified unit. See `astropy.units.Quantity.to` for further details. """ if not isinstance(unit, u.Unit): unit = u.Unit(unit) if unit == self.unit: # No copying return self if ((self.unit.is_equivalent(u.Jy / u.beam) and not any({u.Jy/u.beam, u.K}.issubset(set(eq)) for eq in equivalencies))): # the 'not any' above checks that there is not already a defined # Jy<->K equivalency. If there is, the code below is redundant # and will cause problems. if hasattr(self, 'beams'): factor = (self.jtok_factors(equivalencies=equivalencies) * (self.unit*u.beam).to(u.Jy)) else: # replace "beam" with the actual beam if not hasattr(self, 'beam'): raise ValueError("To convert objects with Jy/beam units, " "the object needs to have a beam defined.") brightness_unit = self.unit * u.beam # create a beam equivalency for brightness temperature if freq is None: try: freq = self.with_spectral_unit(u.Hz).spectral_axis except AttributeError: raise TypeError("Object of type {0} has no spectral " "information. `freq` must be provided for" " unit conversion from Jy/beam" .format(type(self))) else: if not freq.unit.is_equivalent(u.Hz): raise u.UnitsError("freq must be given in equivalent " "frequency units.") bmequiv = self.beam.jtok_equiv(freq) # backport to handle astropy < 3: the beam equivalency was only # modified to handle jy/beam in astropy 3 if bmequiv[0] == u.Jy: bmequiv.append([u.Jy/u.beam, u.K, bmequiv[2], bmequiv[3]]) factor = brightness_unit.to(unit, equivalencies=bmequiv + list(equivalencies)) else: # scaling factor factor = self.unit.to(unit, equivalencies=equivalencies) converted_array = (self.quantity * factor).value # use private versions of variables, not the generated property # versions # Not entirely sure the use of __class__ here is kosher, but we do want # self.__class__, not super() new = self.__class__(value=converted_array, unit=unit, copy=True, wcs=self._wcs, meta=self._meta, mask=self._mask, header=self._header) return new @property def _mask(self): """ Annoying hack to deal with np.ma.core.is_mask failures (I don't like using __ but I think it's necessary here)""" if self.__mask is None: # need this to be *exactly* the numpy boolean False return nomask return self.__mask @_mask.setter def _mask(self, value): self.__mask = value def shrink_mask(self): """ Copy of the numpy masked_array shrink_mask method. This is essentially a hack needed for matplotlib to show images. """ m = self._mask if m.ndim and not m.any(): self._mask = nomask return self class Projection(LowerDimensionalObject, SpatialCoordMixinClass): def __new__(cls, value, unit=None, dtype=None, copy=True, wcs=None, meta=None, mask=None, header=None, beam=None, read_beam=False): if np.asarray(value).ndim != 2: raise ValueError("value should be a 2-d array") if wcs is not None and wcs.wcs.naxis != 2: raise ValueError("wcs should have two dimension") self = u.Quantity.__new__(cls, value, unit=unit, dtype=dtype, copy=copy).view(cls) self._wcs = wcs self._meta = {} if meta is None else meta self._mask = mask if header is not None: self._header = header else: self._header = Header() if beam is None: if "beam" in self.meta: beam = self.meta['beam'] elif read_beam: beam = cube_utils.try_load_beam(header) if beam is None: warnings.warn("Cannot load beam from header.") if beam is not None: self.beam = beam self.meta['beam'] = beam # TODO: Enable header updating when non-celestial slices are # properly handled in the WCS object. # self._header.update(beam.to_header_keywords()) return self def with_beam(self, beam): ''' Attach a new beam object to the Projection. Parameters ---------- beam : `~radio_beam.Beam` A new beam object. ''' meta = self.meta.copy() meta['beam'] = beam self = Projection(self.value, unit=self.unit, wcs=self.wcs, meta=meta, header=self.header, beam=beam) return self @property def beam(self): return self._beam @beam.setter def beam(self, obj): if not isinstance(obj, Beam): raise TypeError("beam must be a radio_beam.Beam object.") self._beam = obj @staticmethod def from_hdu(hdu): ''' Return a projection from a FITS HDU. ''' if isinstance(hdu, HDUList): hdul = hdu hdu = hdul[0] if not len(hdu.data.shape) == 2: raise ValueError("HDU must contain two-dimensional data.") meta = {} mywcs = wcs.WCS(hdu.header) if "BUNIT" in hdu.header: unit = convert_bunit(hdu.header["BUNIT"]) meta["BUNIT"] = hdu.header["BUNIT"] else: unit = None beam = cube_utils.try_load_beam(hdu.header) self = Projection(hdu.data, unit=unit, wcs=mywcs, meta=meta, header=hdu.header, beam=beam) return self def quicklook(self, filename=None, use_aplpy=True, aplpy_kwargs={}): """ Use `APLpy `_ to make a quick-look image of the projection. This will make the ``FITSFigure`` attribute available. If there are unmatched celestial axes, this will instead show an image without axis labels. Parameters ---------- filename : str or Non Optional - the filename to save the quicklook to. """ if use_aplpy: try: if not hasattr(self, 'FITSFigure'): import aplpy self.FITSFigure = aplpy.FITSFigure(self.hdu, **aplpy_kwargs) self.FITSFigure.show_grayscale() self.FITSFigure.add_colorbar() if filename is not None: self.FITSFigure.save(filename) except (wcs.InconsistentAxisTypesError, ImportError): self._quicklook_mpl(filename=filename) else: self._quicklook_mpl(filename=filename) def _quicklook_mpl(self, filename=None): from matplotlib import pyplot self.figure = pyplot.imshow(self.value) if filename is not None: self.figure.savefig(filename) def convolve_to(self, beam, convolve=convolution.convolve_fft): """ Convolve the image to a specified beam. Parameters ---------- beam : `radio_beam.Beam` The beam to convolve to convolve : function The astropy convolution function to use, either `astropy.convolution.convolve` or `astropy.convolution.convolve_fft` Returns ------- proj : `Projection` A Projection convolved to the given ``beam`` object. """ self._raise_wcs_no_celestial() if not hasattr(self, 'beam'): raise ValueError("No beam is contained in Projection.meta.") pixscale = wcs.utils.proj_plane_pixel_area(self.wcs.celestial)**0.5 * u.deg convolution_kernel = \ beam.deconvolve(self.beam).as_kernel(pixscale) newdata = convolve(self.value, convolution_kernel, normalize_kernel=True) self = Projection(newdata, unit=self.unit, wcs=self.wcs, meta=self.meta, header=self.header, beam=beam) return self def reproject(self, header, order='bilinear'): """ Reproject the image into a new header. Parameters ---------- header : `astropy.io.fits.Header` A header specifying a cube in valid WCS order : int or str, optional The order of the interpolation (if ``mode`` is set to ``'interpolation'``). This can be either one of the following strings: * 'nearest-neighbor' * 'bilinear' * 'biquadratic' * 'bicubic' or an integer. A value of ``0`` indicates nearest neighbor interpolation. """ self._raise_wcs_no_celestial() try: from reproject.version import version except ImportError: raise ImportError("Requires the reproject package to be" " installed.") # Need version > 0.2 to work with cubes from distutils.version import LooseVersion if LooseVersion(version) < "0.3": raise Warning("Requires version >=0.3 of reproject. The current " "version is: {}".format(version)) from reproject import reproject_interp # TODO: Find the minimal footprint that contains the header and only reproject that # (see FITS_tools.regrid_cube for a guide on how to do this) newwcs = wcs.WCS(header) shape_out = [header['NAXIS{0}'.format(i + 1)] for i in range(header['NAXIS'])][::-1] newproj, newproj_valid = reproject_interp((self.value, self.header), newwcs, shape_out=shape_out, order=order) self = Projection(newproj, unit=self.unit, wcs=newwcs, meta=self.meta, header=header, read_beam=True) return self def subimage(self, xlo='min', xhi='max', ylo='min', yhi='max'): """ Extract a region spatially. Parameters ---------- [xy]lo/[xy]hi : int or `astropy.units.Quantity` or `min`/`max` The endpoints to extract. If given as a quantity, will be interpreted as World coordinates. If given as a string or int, will be interpreted as pixel coordinates. """ self._raise_wcs_no_celestial() limit_dict = {'xlo': 0 if xlo == 'min' else xlo, 'ylo': 0 if ylo == 'min' else ylo, 'xhi': self.shape[1] if xhi == 'max' else xhi, 'yhi': self.shape[0] if yhi == 'max' else yhi} dims = {'x': 1, 'y': 0} for val in (xlo, ylo, xhi, yhi): if hasattr(val, 'unit') and not val.unit.is_equivalent(u.degree): raise u.UnitsError("The X and Y slices must be specified in " "degree-equivalent units.") for lim in limit_dict: limval = limit_dict[lim] if hasattr(limval, 'unit'): dim = dims[lim[0]] sl = [slice(0, 1)] sl.insert(dim, slice(None)) spine = self.world[sl][dim] val = np.argmin(np.abs(limval - spine)) if limval > spine.max() or limval < spine.min(): pass # log.warn("The limit {0} is out of bounds." # " Using min/max instead.".format(lim)) if lim[1:] == 'hi': # End-inclusive indexing: need to add one for the high # slice limit_dict[lim] = val + 1 else: limit_dict[lim] = val slices = [slice(limit_dict[xx + 'lo'], limit_dict[xx + 'hi']) for xx in 'yx'] return self[slices] def to(self, unit, equivalencies=[], freq=None): """ Return a new `~spectral_cube.lower_dimensional_structures.Projection` of the same class with the specified unit. See `astropy.units.Quantity.to` for further details. """ return super(Projection, self).to(unit, equivalencies, freq) # A slice is just like a projection in every way class Slice(Projection): pass class OneDSpectrum(LowerDimensionalObject, MaskableArrayMixinClass, SpectralAxisMixinClass): def __new__(cls, value, unit=None, dtype=None, copy=True, wcs=None, meta=None, mask=None, header=None, spectral_unit=None, fill_value=np.nan, beams=None, wcs_tolerance=0.0): #log.debug("Creating a OneDSpectrum with __new__") if np.asarray(value).ndim != 1: raise ValueError("value should be a 1-d array") if wcs is not None and wcs.wcs.naxis != 1: raise ValueError("wcs should have two dimension") self = u.Quantity.__new__(cls, value, unit=unit, dtype=dtype, copy=copy).view(cls) self._wcs = wcs self._meta = {} if meta is None else meta self._wcs_tolerance = wcs_tolerance if mask is None: mask = BooleanArrayMask(np.ones_like(self.value, dtype=bool), self._wcs, shape=self.value.shape) elif isinstance(mask, np.ndarray): if mask.shape != self.value.shape: raise ValueError("Mask shape must match the spectrum shape.") mask = BooleanArrayMask(mask, self._wcs, shape=self.value.shape) elif isinstance(mask, MaskBase): pass else: raise TypeError("mask of type {} is not a supported mask " "type.".format(type(mask))) # Validate the mask before setting mask._validate_wcs(new_data=self.value, new_wcs=self._wcs, wcs_tolerance=self._wcs_tolerance) self._mask = mask self._fill_value = fill_value if header is not None: self._header = header else: self._header = Header() self._spectral_unit = spectral_unit if spectral_unit is None: if 'CUNIT1' in self._header: self._spectral_unit = u.Unit(self._header['CUNIT1']) elif self._wcs is not None: self._spectral_unit = u.Unit(self._wcs.wcs.cunit[0]) if beams is not None: self.beams = beams # HACK: OneDSpectrum should eventually become not-a-quantity # Maybe it should be a u.Quantity(np.ma)? self._data = self.value return self def __repr__(self): prefixstr = '<' + self.__class__.__name__ + ' ' arrstr = np.array2string(self.filled_data[:].value, separator=',', prefix=prefixstr) return '{0}{1}{2:s}>'.format(prefixstr, arrstr, self._unitstr) @staticmethod def from_hdu(hdu): ''' Return a OneDSpectrum from a FITS HDU or HDU list. ''' if isinstance(hdu, HDUList): hdul = hdu hdu = hdul[0] else: hdul = HDUList([hdu]) if not len(hdu.data.shape) == 1: raise ValueError("HDU must contain one-dimensional data.") meta = {} mywcs = wcs.WCS(hdu.header) if "BUNIT" in hdu.header: unit = convert_bunit(hdu.header["BUNIT"]) meta["BUNIT"] = hdu.header["BUNIT"] else: unit = None beams = cube_utils.try_load_beams(hdul) self = OneDSpectrum(hdu.data, unit=unit, wcs=mywcs, meta=meta, header=hdu.header, beams=beams) return self @property def header(self): header = self._header # This inplace update is OK; it's not bad to overwrite WCS in this # header if self.wcs is not None: header.update(self.wcs.to_header()) header['BUNIT'] = self.unit.to_string(format='fits') header.insert(2, Card(keyword='NAXIS', value=self.ndim)) for ind,sh in enumerate(self.shape[::-1]): header.insert(3+ind, Card(keyword='NAXIS{0:1d}'.format(ind+1), value=sh)) # Preserve the spectrum's spectral units if 'CUNIT1' in header and self._spectral_unit != u.Unit(header['CUNIT1']): spectral_scale = spectral_axis.wcs_unit_scale(self._spectral_unit) header['CDELT1'] *= spectral_scale header['CRVAL1'] *= spectral_scale header['CUNIT1'] = self.spectral_axis.unit.to_string(format='FITS') return header @property def spectral_axis(self): """ A `~astropy.units.Quantity` array containing the central values of each channel along the spectral axis. """ if self._wcs is None: spec_axis = np.arange(self.size) * u.dimensionless_unscaled else: spec_axis = self.wcs.wcs_pix2world(np.arange(self.size), 0)[0] * \ u.Unit(self.wcs.wcs.cunit[0]) if self._spectral_unit is not None: spec_axis = spec_axis.to(self._spectral_unit) return spec_axis def quicklook(self, filename=None, drawstyle='steps-mid', **kwargs): """ Plot the spectrum with current spectral units in the currently open figure kwargs are passed to `matplotlib.pyplot.plot` Parameters ---------- filename : str or Non Optional - the filename to save the quicklook to. """ from matplotlib import pyplot ax = pyplot.gca() ax.plot(self.spectral_axis, self.filled_data[:].value, drawstyle=drawstyle, **kwargs) ax.set_xlabel(self.spectral_axis.unit.to_string(format='latex')) ax.set_ylabel(self.unit) if filename is not None: pyplot.gcf().savefig(filename) def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): newwcs, newmeta = self._new_spectral_wcs(unit, velocity_convention=velocity_convention, rest_value=rest_value) newheader = self._nowcs_header.copy() newheader.update(newwcs.to_header()) wcs_cunit = u.Unit(newheader['CUNIT1']) newheader['CUNIT1'] = unit.to_string(format='FITS') newheader['CDELT1'] *= wcs_cunit.to(unit) if self._mask is not None: newmask = self._mask.with_spectral_unit(unit, velocity_convention=velocity_convention, rest_value=rest_value) newmask._wcs = newwcs else: newmask = None return self._new_spectrum_with(wcs=newwcs, spectral_unit=unit, mask=newmask, meta=newmeta, header=newheader) def __getitem__(self, key, **kwargs): try: beams = self.beams[key] except (AttributeError,TypeError): beams = None new_qty = super(OneDSpectrum, self).__getitem__(key, beams=beams) if isinstance(key, slice): new = self.__class__(value=new_qty.value, unit=new_qty.unit, copy=False, wcs=wcs_utils.slice_wcs(self._wcs, key, shape=self.shape), meta=self._meta, mask=(self._mask[key] if self._mask is not nomask else nomask), header=self._header, wcs_tolerance=self._wcs_tolerance, fill_value=self.fill_value, **kwargs) return new else: if self._mask is not nomask: # Kind of a hack; this is probably inefficient bad = self._mask.exclude()[key] new_qty[bad] = np.nan return new_qty @property def hdu(self): if hasattr(self, 'beams'): warnings.warn("There are multiple beams for this spectrum that " "are being ignored when creating the HDU.") return super(OneDSpectrum, self).hdu @property def hdulist(self): with warnings.catch_warnings(): warnings.simplefilter("ignore") hdu = self.hdu beamhdu = cube_utils.beams_to_bintable(self.beams) return HDUList([hdu, beamhdu]) def spectral_interpolate(self, spectral_grid, suppress_smooth_warning=False, fill_value=None): """ Resample the spectrum onto a specific grid Parameters ---------- spectral_grid : array An array of the spectral positions to regrid onto suppress_smooth_warning : bool If disabled, a warning will be raised when interpolating onto a grid that does not nyquist sample the existing grid. Disable this if you have already appropriately smoothed the data. fill_value : float Value for extrapolated spectral values that lie outside of the spectral range defined in the original data. The default is to use the nearest spectral channel in the cube. Returns ------- spectrum : OneDSpectrum """ assert spectral_grid.ndim == 1 inaxis = self.spectral_axis.to(spectral_grid.unit) indiff = np.mean(np.diff(inaxis)) outdiff = np.mean(np.diff(spectral_grid)) # account for reversed axes if outdiff < 0: spectral_grid = spectral_grid[::-1] outdiff = np.mean(np.diff(spectral_grid)) outslice = slice(None, None, -1) else: outslice = slice(None, None, 1) specslice = slice(None) if indiff >= 0 else slice(None, None, -1) inaxis = inaxis[specslice] indiff = np.mean(np.diff(inaxis)) # insanity checks if indiff < 0 or outdiff < 0: raise ValueError("impossible.") assert np.all(np.diff(spectral_grid) > 0) assert np.all(np.diff(inaxis) > 0) np.testing.assert_allclose(np.diff(spectral_grid), outdiff, err_msg="Output grid must be linear") if outdiff > 2 * indiff and not suppress_smooth_warning: warnings.warn("Input grid has too small a spacing. The data should " "be smoothed prior to resampling.") newspec = np.empty([spectral_grid.size], dtype=self.dtype) newmask = np.empty([spectral_grid.size], dtype='bool') newspec[outslice] = np.interp(spectral_grid.value, inaxis.value, self.filled_data[specslice].value, left=fill_value, right=fill_value) mask = self.mask.include() if all(mask): newmask = np.ones([spectral_grid.size], dtype='bool') else: interped = np.interp(spectral_grid.value, inaxis.value, mask[specslice]) > 0 newmask[outslice] = interped newwcs = self.wcs.deepcopy() newwcs.wcs.crpix[0] = 1 newwcs.wcs.crval[0] = spectral_grid[0].value if outslice.step > 0 \ else spectral_grid[-1].value newwcs.wcs.cunit[0] = spectral_grid.unit.to_string(format='FITS') newwcs.wcs.cdelt[0] = outdiff.value if outslice.step > 0 \ else -outdiff.value newwcs.wcs.set() newheader = self._nowcs_header.copy() newheader.update(newwcs.to_header()) wcs_cunit = u.Unit(newheader['CUNIT1']) newheader['CUNIT1'] = spectral_grid.unit.to_string(format='FITS') newheader['CDELT1'] *= wcs_cunit.to(spectral_grid.unit) newbmask = BooleanArrayMask(newmask, wcs=newwcs) return self._new_spectrum_with(data=newspec, wcs=newwcs, mask=newbmask, header=newheader, spectral_unit=spectral_grid.unit) def spectral_smooth(self, kernel, convolve=convolution.convolve, **kwargs): """ Smooth the spectrum Parameters ---------- kernel : `~astropy.convolution.Kernel1D` A 1D kernel from astropy convolve : function The astropy convolution function to use, either `astropy.convolution.convolve` or `astropy.convolution.convolve_fft` kwargs : dict Passed to the convolve function """ newspec = convolve(self.value, kernel, normalize_kernel=True, **kwargs) return self._new_spectrum_with(data=newspec) def to(self, unit, equivalencies=[]): """ Return a new `~spectral_cube.lower_dimensional_structures.OneDSpectrum` of the same class with the specified unit. See `astropy.units.Quantity.to` for further details. """ return super(OneDSpectrum, self).to(unit, equivalencies, freq=None) def with_fill_value(self, fill_value): """ Create a new :class:`OneDSpectrum` with a different ``fill_value``. """ return self._new_spectrum_with(fill_value=fill_value) def _new_spectrum_with(self, data=None, wcs=None, mask=None, meta=None, fill_value=None, spectral_unit=None, unit=None, header=None, wcs_tolerance=None, **kwargs): data = self._data if data is None else data if unit is None and hasattr(data, 'unit'): if data.unit != self.unit: raise u.UnitsError("New data unit '{0}' does not" " match unit '{1}'. You can" " override this by specifying the" " `unit` keyword." .format(data.unit, self.unit)) unit = data.unit elif unit is None: unit = self.unit elif unit is not None: # convert string units to Units if not isinstance(unit, u.Unit): unit = u.Unit(unit) if hasattr(data, 'unit'): if u.Unit(unit) != data.unit: raise u.UnitsError("The specified new cube unit '{0}' " "does not match the input unit '{1}'." .format(unit, data.unit)) else: data = u.Quantity(data, unit=unit, copy=False) wcs = self._wcs if wcs is None else wcs mask = self._mask if mask is None else mask if meta is None: meta = {} meta.update(self._meta) if unit is not None: meta['BUNIT'] = unit.to_string(format='FITS') fill_value = self._fill_value if fill_value is None else fill_value spectral_unit = self._spectral_unit if spectral_unit is None else u.Unit(spectral_unit) spectrum = self.__class__(value=data, wcs=wcs, mask=mask, meta=meta, unit=unit, fill_value=fill_value, header=header or self._header, wcs_tolerance=wcs_tolerance or self._wcs_tolerance, **kwargs) spectrum._spectral_unit = spectral_unit return spectrum def __getattribute__(self, attrname): # This is a hack to handle dimensionality-reducing functions # We want spectrum.max() to return a Quantity, not a spectrum # Long-term, we really want `OneDSpectrum` to not inherit from # `Quantity`, but for now this approach works.... we just have # to add more functions to this list. if attrname in ('min', 'max', 'std', 'mean', 'sum', 'cumsum', 'nansum', 'ptp', 'var'): return getattr(self.quantity, attrname) else: return super(OneDSpectrum, self).__getattribute__(attrname) def __array_finalize__(self, obj): #from astropy import log #log.debug("in OneDSpectrum, Finalizing self={0}{1} obj={2}{3}" # .format(self, type(self), obj, type(obj))) self._fill_value = getattr(obj, '_fill_value', np.nan) self._data = self.view(np.ndarray) self._wcs_tolerance = getattr(obj, '_wcs_tolerance', 0.0) super(OneDSpectrum, self).__array_finalize__(obj) class VaryingResolutionOneDSpectrum(OneDSpectrum, MultiBeamMixinClass): pass spectral-cube-0.4.3/spectral_cube/masks.py0000644000077000000240000006756513242700604020614 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import abc import numpy as np from numpy.lib.stride_tricks import as_strided from astropy.wcs import InconsistentAxisTypesError from astropy.io import fits from astropy.extern.six.moves import zip from . import wcs_utils __all__ = ['MaskBase', 'InvertedMask', 'CompositeMask', 'BooleanArrayMask', 'LazyMask', 'LazyComparisonMask', 'FunctionMask'] # Global version of the with_spectral_unit docs to avoid duplicating them with_spectral_unit_docs = """ Parameters ---------- unit : u.Unit Any valid spectral unit: velocity, (wave)length, or frequency. Only vacuum units are supported. velocity_convention : u.doppler_relativistic, u.doppler_radio, or u.doppler_optical The velocity convention to use for the output velocity axis. Required if the output type is velocity. rest_value : u.Quantity A rest wavelength or frequency with appropriate units. Required if output type is velocity. The cube's WCS should include this already if the *input* type is velocity, but the WCS's rest wavelength/frequency can be overridden with this parameter. """ def is_broadcastable_and_smaller(shp1, shp2): """ Test if shape 1 can be broadcast to shape 2, not allowing the case where shape 2 has a dimension length 1 """ for a, b in zip(shp1[::-1], shp2[::-1]): # b==1 is broadcastable but not desired if a == 1 or a == b: pass else: return False return True def dims_to_skip(shp1, shp2): """ For a shape `shp1` that is broadcastable to shape `shp2`, specify which dimensions are length 1. Parameters ---------- keep : bool If True, return the dimensions to keep rather than those to remove """ if not is_broadcastable_and_smaller(shp1, shp2): raise ValueError("Cannot broadcast {0} to {1}".format(shp1,shp2)) dims = [] for ii,(a, b) in enumerate(zip(shp1[::-1], shp2[::-1])): # b==1 is broadcastable but not desired if a == 1: dims.append(len(shp2) - ii - 1) elif a == b: pass else: raise ValueError("This should not be possible") if len(shp1) < len(shp2): dims += list(range(len(shp2)-len(shp1))) return dims def view_of_subset(shp1, shp2, view): """ Given two shapes and a view, assuming that shape 1 can be broadcast to shape 2, return the sub-view that applies to shape 1 """ # if the view is 1-dimensional, we can't subset it if not hasattr(view, '__len__'): return view dts = dims_to_skip(shp1, shp2) if view: cv_view = [x for ii,x in enumerate(view) if ii not in dts] else: # if no view is specified, still need to slice cv_view = [x for ii,x in enumerate([slice(None)]*3) if ii not in dts] # return type matters # array[[0,0]] = [array[0], array[0]] # array[(0,0)] = array[0,0] return tuple(cv_view) class MaskBase(object): __metaclass__ = abc.ABCMeta def include(self, data=None, wcs=None, view=(), **kwargs): """ Return a boolean array indicating which values should be included. If ``view`` is passed, only the sliced mask will be returned, which avoids having to load the whole mask in memory. Otherwise, the whole mask is returned in-memory. kwargs are passed to _validate_wcs """ self._validate_wcs(data, wcs, **kwargs) return self._include(data=data, wcs=wcs, view=view) def _validate_wcs(self, new_data=None, new_wcs=None, **kwargs): """ This method can be overridden in cases where the data and WCS have to conform to some rules. This gets called automatically when ``include`` or ``exclude`` are called. """ pass @abc.abstractmethod def _include(self, data=None, wcs=None, view=()): pass def exclude(self, data=None, wcs=None, view=(), **kwargs): """ Return a boolean array indicating which values should be excluded. If ``view`` is passed, only the sliced mask will be returned, which avoids having to load the whole mask in memory. Otherwise, the whole mask is returned in-memory. kwargs are passed to _validate_wcs """ self._validate_wcs(data, wcs, **kwargs) return self._exclude(data=data, wcs=wcs, view=view) def _exclude(self, data=None, wcs=None, view=()): return ~self._include(data=data, wcs=wcs, view=view) def any(self): return np.any(self.exclude()) def _flattened(self, data, wcs=None, view=()): """ Return a flattened array of the included elements of cube Parameters ---------- data : array-like The data array to flatten view : tuple, optional Any slicing to apply to the data before flattening Returns ------- flat_array : `~numpy.ndarray` A 1-D ndarray containing the flattened output Notes ----- This is an internal method used by :class:`SpectralCube`. """ return data[view][self.include(data=data, wcs=wcs, view=view)] def _filled(self, data, wcs=None, fill=np.nan, view=(), **kwargs): """ Replace the excluded elements of *array* with *fill*. Parameters ---------- data : array-like Input array fill : number Replacement value view : tuple, optional Any slicing to apply to the data before flattening Returns ------- filled_array : `~numpy.ndarray` A 1-D ndarray containing the filled output Notes ----- This is an internal method used by :class:`SpectralCube`. Users should use the property :meth:`MaskBase.filled_data` """ # Must convert to floating point, but should not change from inherited # type otherwise dt = np.find_common_type([data.dtype], [np.float]) sliced_data = data[view].astype(dt) ex = self.exclude(data=data, wcs=wcs, view=view, **kwargs) sliced_data[ex] = fill return sliced_data def __and__(self, other): return CompositeMask(self, other, operation='and') def __or__(self, other): return CompositeMask(self, other, operation='or') def __xor__(self, other): return CompositeMask(self, other, operation='xor') def __invert__(self): return InvertedMask(self) @property def shape(self): raise NotImplementedError("{0} mask classes do not have shape attributes." .format(self.__class__.__name__)) def __getitem__(self): raise NotImplementedError("Slicing not supported by mask class {0}" .format(self.__class__.__name__)) def quicklook(self, view, wcs=None, filename=None, use_aplpy=True, aplpy_kwargs={}): ''' View a 2D slice of the mask, specified by view. Parameters ---------- view : tuple Slicing to apply to the mask. Must return a 2D slice. wcs : astropy.wcs.WCS, optional WCS object to use in plotting the mask slice. filename : str, optional Filename of the output image. Enables saving of the plot. use_aplpy : bool, optional Try plotting with the aplpy package aplpy_kwargs : dict, optional kwargs passed to `~aplpy.FITSFigure`. ''' view_twod = self.include(view=view, wcs=wcs) if use_aplpy: if wcs is not None: hdu = fits.PrimaryHDU(view_twod.astype(int), wcs.to_header()) else: hdu = fits.PrimaryHDU(view_twod.astype(int)) try: import aplpy FITSFigure = aplpy.FITSFigure(hdu, **aplpy_kwargs) FITSFigure.show_grayscale() FITSFigure.add_colorbar() if filename is not None: FITSFigure.save(filename) except (InconsistentAxisTypesError, ImportError): use_aplpy = True if not use_aplpy: from matplotlib import pyplot figure = pyplot.imshow(view_twod) if filename is not None: figure.savefig(filename) def _get_new_wcs(self, unit, velocity_convention=None, rest_value=None): """ Returns a new WCS with a different Spectral Axis unit """ from .spectral_axis import convert_spectral_axis,determine_ctype_from_vconv out_ctype = determine_ctype_from_vconv(self._wcs.wcs.ctype[self._wcs.wcs.spec], unit, velocity_convention=velocity_convention) newwcs = convert_spectral_axis(self._wcs, unit, out_ctype, rest_value=rest_value) newwcs.wcs.set() return newwcs _get_new_wcs.__doc__ += with_spectral_unit_docs class InvertedMask(MaskBase): def __init__(self, mask): self._mask = mask @property def shape(self): return self._mask.shape def _include(self, data=None, wcs=None, view=()): return ~self._mask.include(data=data, wcs=wcs, view=view) def __getitem__(self, view): return InvertedMask(self._mask[view]) def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): """ Get an InvertedMask copy with a WCS in the modified unit """ newmask = self._mask.with_spectral_unit(unit, velocity_convention=velocity_convention, rest_value=rest_value) return InvertedMask(newmask) with_spectral_unit.__doc__ += with_spectral_unit_docs class CompositeMask(MaskBase): """ A combination of several masks. The included masks are treated with the specified operation. Parameters ---------- mask1, mask2 : Masks The two masks to composite operation : str Either 'and' or 'or'; the operation used to combine the masks """ def __init__(self, mask1, mask2, operation='and'): if isinstance(mask1, np.ndarray) and isinstance(mask2, MaskBase) and hasattr(mask2, 'shape'): if not is_broadcastable_and_smaller(mask1.shape, mask2.shape): raise ValueError("Mask1 shape is not broadcastable to Mask2 shape: " "%s vs %s" % (mask1.shape, mask2.shape)) mask1 = BooleanArrayMask(mask1, mask2._wcs, shape=mask2.shape) elif isinstance(mask2, np.ndarray) and isinstance(mask1, MaskBase) and hasattr(mask1, 'shape'): if not is_broadcastable_and_smaller(mask2.shape, mask1.shape): raise ValueError("Mask2 shape is not broadcastable to Mask1 shape: " "%s vs %s" % (mask2.shape, mask1.shape)) mask2 = BooleanArrayMask(mask2, mask1._wcs, shape=mask1.shape) # both entries must have compatible, which effectively means # equal, WCSes. Unless one is a function. if hasattr(mask1, '_wcs') and hasattr(mask2, '_wcs'): mask1._validate_wcs(new_data=None, wcs=mask2._wcs) # In order to composite composites, they must have a _wcs defined. # (maybe this should be a property?) self._wcs = mask1._wcs elif hasattr(mask1, '_wcs'): # if one mask doesn't have a WCS, but the other does, the # compositemask should have the same WCS as the one that does self._wcs = mask1._wcs elif hasattr(mask2, '_wcs'): self._wcs = mask2._wcs self._mask1 = mask1 self._mask2 = mask2 self._operation = operation def _validate_wcs(self, new_data=None, new_wcs=None, **kwargs): self._mask1._validate_wcs(new_data=new_data, new_wcs=new_wcs, **kwargs) self._mask2._validate_wcs(new_data=new_data, new_wcs=new_wcs, **kwargs) @property def shape(self): try: assert self._mask1.shape == self._mask2.shape return self._mask1.shape except AssertionError: raise ValueError("The composite mask does not have a well-defined " "shape; its two components have shapes {0} and " "{1}.".format(self._mask1.shape, self._mask2.shape)) except NotImplementedError: raise ValueError("The composite mask contains at least one " "component with no defined shape.") def _include(self, data=None, wcs=None, view=()): result_mask_1 = self._mask1._include(data=data, wcs=wcs, view=view) result_mask_2 = self._mask2._include(data=data, wcs=wcs, view=view) if self._operation == 'and': return result_mask_1 & result_mask_2 elif self._operation == 'or': return result_mask_1 | result_mask_2 elif self._operation == 'xor': return result_mask_1 ^ result_mask_2 else: raise ValueError("Operation '{0}' not supported".format(self._operation)) def __getitem__(self, view): return CompositeMask(self._mask1[view], self._mask2[view], operation=self._operation) def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): """ Get a CompositeMask copy in which each component has a WCS in the modified unit """ newmask1 = self._mask1.with_spectral_unit(unit, velocity_convention=velocity_convention, rest_value=rest_value) newmask2 = self._mask2.with_spectral_unit(unit, velocity_convention=velocity_convention, rest_value=rest_value) return CompositeMask(newmask1, newmask2, self._operation) with_spectral_unit.__doc__ += with_spectral_unit_docs class BooleanArrayMask(MaskBase): """ A mask defined as an array on a spectral cube WCS Parameters ---------- mask: `numpy.ndarray` A boolean numpy ndarray wcs: `astropy.wcs.WCS` The WCS object shape: tuple The shape of the region the array is masking. This is *required* if ``mask.ndim != data.ndim`` to provide rules for how to broadcast the mask """ def __init__(self, mask, wcs, shape=None, include=True): self._mask_type = 'include' if include else 'exclude' self._wcs = wcs self._wcs_whitelist = set() #if mask.ndim != 3 and (shape is None or len(shape) != 3): # raise ValueError("When creating a BooleanArrayMask with <3 dimensions, " # "the shape of the 3D array must be specified.") if shape is not None and not is_broadcastable_and_smaller(mask.shape, shape): raise ValueError("Mask cannot be broadcast to the specified shape.") self._shape = shape or mask.shape self._mask = mask """ Developer note (AG): The logic in this following section seems overly complicated. All of it is added to make sure that a 1D boolean array along the spectral axis can be created. I thought this was possible previously, but experience many errors in my latest attempt to use one. """ # If a shape is given, we may need to broadcast to that shape if shape is not None: # these are dimensions that simply don't exist n_empty_dims = (len(self._shape)-mask.ndim) # these are dimensions of shape 1 that would be squeezed away but may # be needed to make the arrays broadcastable (e.g., mask[:,None,None]) # Need to add n_empty_dims because (1,2) will broadcast to (3,1,2) # and there will be no extra dims. extra_dims = [ii for ii,(sh1,sh2) in enumerate(zip((0,)*n_empty_dims + mask.shape, shape)) if sh1 == 1 and sh1 != sh2] # Add the [None,]'s and the nonexistant n_extra_dims = n_empty_dims + len(extra_dims) # if there are no extra dims, we're done, the original shape is fine if n_extra_dims > 0: strides = (0,)*n_empty_dims + mask.strides for ed in extra_dims: # all of the [None,] dims should have 0 stride assert strides[ed] == 0,"Stride shape failure" self._mask = as_strided(mask, shape=self.shape, strides=strides) # Make sure the mask shape matches the Mask object shape assert self._mask.shape == self.shape,"Shape initialization failure" def _validate_wcs(self, new_data=None, new_wcs=None, **kwargs): """ Check that the new WCS matches the current one Parameters ---------- kwargs : dict Passed to `wcs_utils.check_equality` """ if new_data is not None and not is_broadcastable_and_smaller(self._mask.shape, new_data.shape): raise ValueError("data shape cannot be broadcast to match mask shape") if new_wcs is not None: if new_wcs not in self._wcs_whitelist: if not wcs_utils.check_equality(new_wcs, self._wcs, warn_missing=True, **kwargs): raise ValueError("WCS does not match mask WCS") else: self._wcs_whitelist.add(new_wcs) def _include(self, data=None, wcs=None, view=()): result_mask = self._mask[view] return result_mask if self._mask_type == 'include' else ~result_mask def _exclude(self, data=None, wcs=None, view=()): result_mask = self._mask[view] return result_mask if self._mask_type == 'exclude' else ~result_mask @property def shape(self): return self._shape def __getitem__(self, view): return BooleanArrayMask(self._mask[view], wcs_utils.slice_wcs(self._wcs, view, shape=self.shape, drop_degenerate=True), shape=self._mask[view].shape) def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): """ Get a BooleanArrayMask copy with a WCS in the modified unit """ newwcs = self._get_new_wcs(unit, velocity_convention, rest_value) newmask = BooleanArrayMask(self._mask, newwcs, include=self._mask_type=='include') return newmask with_spectral_unit.__doc__ += with_spectral_unit_docs class LazyMask(MaskBase): """ A boolean mask defined by the evaluation of a function on a fixed dataset. This is conceptually identical to a fixed boolean mask as in :class:`BooleanArrayMask` but defers the evaluation of the mask until it is needed. Parameters ---------- function : callable The function to apply to ``data``. This method should accept a numpy array, which will be a subset of the data array passed to __init__. It should return a boolean array, where `True` values indicate that which pixels are valid/unaffected by masking. data : array-like The array to evaluate ``function`` on. This should support Numpy-like slicing syntax. wcs : `~astropy.wcs.WCS` The WCS of the input data, which is used to define the coordinates for which the boolean mask is defined. """ def __init__(self, function, cube=None, data=None, wcs=None): self._function = function if cube is not None and (data is not None or wcs is not None): raise ValueError("Pass only cube or (data & wcs)") elif cube is not None: self._data = cube._data self._wcs = cube._wcs elif data is not None and wcs is not None: self._data = data self._wcs = wcs else: raise ValueError("Either a cube or (data & wcs) is required.") self._wcs_whitelist = set() @property def shape(self): return self._data.shape def _validate_wcs(self, new_data=None, new_wcs=None, **kwargs): """ Check that the new WCS matches the current one Parameters ---------- kwargs : dict Passed to `wcs_utils.check_equality` """ if new_data is not None: if not is_broadcastable_and_smaller(new_data.shape, self._data.shape): raise ValueError("data shape cannot be broadcast to match mask shape") if new_wcs is not None: if new_wcs not in self._wcs_whitelist: if not wcs_utils.check_equality(new_wcs, self._wcs, warn_missing=True, **kwargs): raise ValueError("WCS does not match mask WCS") else: self._wcs_whitelist.add(new_wcs) def _include(self, data=None, wcs=None, view=()): self._validate_wcs(data, wcs) return self._function(self._data[view]) def __getitem__(self, view): return LazyMask(self._function, data=self._data[view], wcs=wcs_utils.slice_wcs(self._wcs, view, shape=self._data.shape, drop_degenerate=True)) def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): """ Get a LazyMask copy with a WCS in the modified unit """ newwcs = self._get_new_wcs(unit, velocity_convention, rest_value) newmask = LazyMask(self._function, data=self._data, wcs=newwcs) return newmask with_spectral_unit.__doc__ += with_spectral_unit_docs class LazyComparisonMask(LazyMask): """ A boolean mask defined by the evaluation of a comparison function between a fixed dataset and some other value. This is conceptually similar to the :class:`LazyMask` but it will ensure that the comparison value can be compared to the data Parameters ---------- function : callable The function to apply to ``data``. This method should accept a numpy array, which will be the data array passed to __init__, and a second argument also passed to __init__. It should return a boolean array, where `True` values indicate that which pixels are valid/unaffected by masking. comparison_value : float or array The comparison value for the array data : array-like The array to evaluate ``function`` on. This should support Numpy-like slicing syntax. wcs : `~astropy.wcs.WCS` The WCS of the input data, which is used to define the coordinates for which the boolean mask is defined. """ def __init__(self, function, comparison_value, cube=None, data=None, wcs=None): self._function = function if cube is not None and (data is not None or wcs is not None): raise ValueError("Pass only cube or (data & wcs)") elif cube is not None: self._data = cube._data self._wcs = cube._wcs elif data is not None and wcs is not None: self._data = data self._wcs = wcs else: raise ValueError("Either a cube or (data & wcs) is required.") if (hasattr(comparison_value, 'shape') and not is_broadcastable_and_smaller(self._data.shape, comparison_value.shape)): raise ValueError("The data and the comparison value cannot " "be broadcast to match shape") self._comparison_value = comparison_value self._wcs_whitelist = set() def _include(self, data=None, wcs=None, view=()): self._validate_wcs(data, wcs) if hasattr(self._comparison_value, 'shape') and self._comparison_value.shape: cv_view = view_of_subset(self._comparison_value.shape, self._data.shape, view) return self._function(self._data[view], self._comparison_value[cv_view]) else: return self._function(self._data[view], self._comparison_value) def __getitem__(self, view): if hasattr(self._comparison_value, 'shape') and self._comparison_value.shape: cv_view = view_of_subset(self._comparison_value.shape, self._data.shape, view) return LazyComparisonMask(self._function, data=self._data[view], comparison_value=self._comparison_value[cv_view], wcs=wcs_utils.slice_wcs(self._wcs, view, drop_degenerate=True)) else: return LazyComparisonMask(self._function, data=self._data[view], comparison_value=self._comparison_value, wcs=wcs_utils.slice_wcs(self._wcs, view, drop_degenerate=True)) def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): """ Get a LazyComparisonMask copy with a WCS in the modified unit """ newwcs = self._get_new_wcs(unit, velocity_convention, rest_value) newmask = LazyComparisonMask(self._function, data=self._data, comparison_value=self._comparison_value, wcs=newwcs) return newmask class FunctionMask(MaskBase): """ A mask defined by a function that is evaluated at run-time using the data passed to the mask. This function differs from :class:`LazyMask` in the arguments which are passed to the function. FunctionMasks receive an array, wcs object, and view, whereas LazyMasks receive pre-sliced views into an array specified at mask-creation time. Parameters ---------- function : callable The function to evaluate the mask. The call signature should be ``function(data, wcs, slice)`` where ``data`` and ``wcs`` are the arguments that get passed to e.g. ``include``, ``exclude``, ``_filled``, and ``_flattened``. The function should return a boolean array, where `True` values indicate that which pixels are valid / unaffected by masking. """ def __init__(self, function): self._function = function def _validate_wcs(self, new_data=None, new_wcs=None, **kwargs): pass def _include(self, data=None, wcs=None, view=()): result = self._function(data, wcs, view) if result.shape != data[view].shape: raise ValueError("Function did not return mask with correct shape - expected {0}, got {1}".format(data[view].shape, result.shape)) return result def __getitem__(self, slice): return self def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): """ Functional masks do not have WCS defined, so this simply returns a copy of the current mask in order to be consistent with ``with_spectral_unit`` from other Masks """ return FunctionMask(self._function) spectral-cube-0.4.3/spectral_cube/np_compat.py0000644000077000000240000000145013261015477021443 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import numpy as np from distutils.version import LooseVersion def allbadtonan(function): """ Wrapper of numpy's nansum etc.: for <=1.8, just return the function's results. For >=1.9, any axes with all-nan values will have all-nan outputs in the collapsed version """ def f(data, axis=None): result = function(data, axis=axis) if LooseVersion(np.__version__) >= LooseVersion('1.9.0') and hasattr(result, '__len__'): if axis is None: if np.all(np.isnan(data)): return np.nan else: return result nans = np.all(np.isnan(data), axis=axis) result[nans] = np.nan return result return f spectral-cube-0.4.3/spectral_cube/spectral_axis.py0000644000077000000240000003751713242700604022331 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division from astropy import wcs from astropy import units as u from astropy import constants import warnings def _parse_velocity_convention(vc): if vc in (u.doppler_radio, 'radio', 'RADIO', 'VRAD', 'F', 'FREQ'): return u.doppler_radio elif vc in (u.doppler_optical, 'optical', 'OPTICAL', 'VOPT', 'W', 'WAVE'): return u.doppler_optical elif vc in (u.doppler_relativistic, 'relativistic', 'RELATIVE', 'VREL', 'speed', 'V', 'VELO'): return u.doppler_relativistic # These are the only linear transformations allowed LINEAR_CTYPES = {u.doppler_optical: 'VOPT', u.doppler_radio: 'VRAD', u.doppler_relativistic: 'VELO'} LINEAR_CTYPE_CHARS = {u.doppler_optical: 'W', u.doppler_radio: 'F', u.doppler_relativistic: 'V'} ALL_CTYPES = {'speed': LINEAR_CTYPES, 'frequency': 'FREQ', 'length': 'WAVE'} CTYPE_TO_PHYSICALTYPE = {'WAVE': 'length', 'AIR': 'air wavelength', 'AWAV': 'air wavelength', 'FREQ': 'frequency', 'VELO': 'speed', 'VRAD': 'speed', 'VOPT': 'speed', } CTYPE_CHAR_TO_PHYSICALTYPE = {'W': 'length', 'A': 'air wavelength', 'F': 'frequency', 'V': 'speed'} CTYPE_TO_PHYSICALTYPE.update(CTYPE_CHAR_TO_PHYSICALTYPE) PHYSICAL_TYPE_TO_CTYPE = dict([(v,k) for k,v in CTYPE_CHAR_TO_PHYSICALTYPE.items()]) PHYSICAL_TYPE_TO_CHAR = {'speed': 'V', 'frequency': 'F', 'length': 'W'} # Used to indicate the intial / final sampling system WCS_UNIT_DICT = {'F': u.Hz, 'W': u.m, 'V': u.m/u.s} PHYS_UNIT_DICT = {'length': u.m, 'frequency': u.Hz, 'speed': u.m/u.s} LINEAR_CUNIT_DICT = {'VRAD': u.Hz, 'VOPT': u.m, 'FREQ': u.Hz, 'WAVE': u.m, 'VELO': u.m/u.s, 'AWAV': u.m} LINEAR_CUNIT_DICT.update(WCS_UNIT_DICT) def unit_from_header(header, spectral_axis_number=3): """ Retrieve the spectral unit from a header """ cunitind = 'CUNIT{0}'.format(spectral_axis_number) if cunitind in header: return u.Unit(header[cunitind]) def wcs_unit_scale(unit): """ Determine the appropriate scaling factor to get to the equivalent WCS unit """ for wu in WCS_UNIT_DICT.values(): if wu.is_equivalent(unit): return wu.to(unit) def determine_vconv_from_ctype(ctype): """ Given a CTYPE, say what velocity convention it is associated with, i.e. what unit the velocity is linearly proportional to Parameters ---------- ctype : str The spectral CTYPE """ if len(ctype) < 5: return _parse_velocity_convention(ctype) elif len(ctype) == 8: return _parse_velocity_convention(ctype[7]) else: raise ValueError("A valid ctype must either have 4 or 8 characters.") def determine_ctype_from_vconv(ctype, unit, velocity_convention=None): """ Given a CTYPE describing the current WCS and an output unit and velocity convention, determine the appropriate output CTYPE Examples -------- >>> determine_ctype_from_vconv('VELO-F2V', u.Hz) 'FREQ' >>> determine_ctype_from_vconv('VELO-F2V', u.m) 'WAVE-F2W' >>> determine_ctype_from_vconv('FREQ', u.m/u.s) # doctest: +SKIP ... ValueError: A velocity convention must be specified >>> determine_ctype_from_vconv('FREQ', u.m/u.s, velocity_convention=u.doppler_radio) 'VRAD' >>> determine_ctype_from_vconv('FREQ', u.m/u.s, velocity_convention=u.doppler_optical) 'VOPT-F2W' >>> determine_ctype_from_vconv('FREQ', u.m/u.s, velocity_convention=u.doppler_relativistic) 'VELO-F2V' """ unit = u.Unit(unit) if len(ctype) > 4: in_physchar = ctype[5] else: lin_cunit = LINEAR_CUNIT_DICT[ctype] in_physchar = PHYSICAL_TYPE_TO_CHAR[lin_cunit.physical_type] if unit.physical_type == 'speed': if velocity_convention is None and ctype[0] == 'V': # Special case: velocity <-> velocity doesn't care about convention return ctype elif velocity_convention is None: raise ValueError('A velocity convention must be specified') vcin = _parse_velocity_convention(ctype[:4]) vcout = _parse_velocity_convention(velocity_convention) if vcin == vcout: return LINEAR_CTYPES[vcout] else: return "{type}-{s1}2{s2}".format(type=LINEAR_CTYPES[vcout], s1=in_physchar, s2=LINEAR_CTYPE_CHARS[vcout]) else: in_phystype = CTYPE_TO_PHYSICALTYPE[in_physchar] if in_phystype == unit.physical_type: # Linear case return ALL_CTYPES[in_phystype] else: # Nonlinear case out_physchar = PHYSICAL_TYPE_TO_CTYPE[unit.physical_type] return "{type}-{s1}2{s2}".format(type=ALL_CTYPES[unit.physical_type], s1=in_physchar, s2=out_physchar) def get_rest_value_from_wcs(mywcs): if mywcs.wcs.restfrq: ref_value = mywcs.wcs.restfrq*u.Hz return ref_value elif mywcs.wcs.restwav: ref_value = mywcs.wcs.restwav*u.m return ref_value def convert_spectral_axis(mywcs, outunit, out_ctype, rest_value=None): """ Convert a spectral axis from its unit to a specified out unit with a given output ctype Only VACUUM units are supported (not air) Process: 1. Convert the input unit to its equivalent linear unit 2. Convert the input linear unit to the output linear unit 3. Convert the output linear unit to the output unit """ # If the WCS includes a rest frequency/wavelength, convert it to frequency # or wavelength first. This allows the possibility of changing the rest # frequency wcs_rv = get_rest_value_from_wcs(mywcs) inunit = u.Unit(mywcs.wcs.cunit[mywcs.wcs.spec]) outunit = u.Unit(outunit) # If wcs_rv is set and speed -> speed, then we're changing the reference # location and we need to convert to meters or Hz first if ((inunit.physical_type == 'speed' and outunit.physical_type == 'speed' and wcs_rv is not None)): mywcs = convert_spectral_axis(mywcs, wcs_rv.unit, ALL_CTYPES[wcs_rv.unit.physical_type], rest_value=wcs_rv) inunit = u.Unit(mywcs.wcs.cunit[mywcs.wcs.spec]) elif (inunit.physical_type == 'speed' and outunit.physical_type == 'speed' and wcs_rv is None): # If there is no reference change, we want an identical WCS, since # WCS doesn't know about units *at all* newwcs = mywcs.deepcopy() return newwcs #crval_out = (mywcs.wcs.crval[mywcs.wcs.spec] * inunit).to(outunit) #cdelt_out = (mywcs.wcs.cdelt[mywcs.wcs.spec] * inunit).to(outunit) #newwcs.wcs.cdelt[newwcs.wcs.spec] = cdelt_out.value #newwcs.wcs.cunit[newwcs.wcs.spec] = cdelt_out.unit.to_string(format='fits') #newwcs.wcs.crval[newwcs.wcs.spec] = crval_out.value #newwcs.wcs.ctype[newwcs.wcs.spec] = out_ctype #return newwcs in_spec_ctype = mywcs.wcs.ctype[mywcs.wcs.spec] # Check whether we need to convert the rest value first ref_value = None if outunit.physical_type == 'speed': if rest_value is None: rest_value = wcs_rv if rest_value is None: raise ValueError("If converting from wavelength/frequency to speed, " "a reference wavelength/frequency is required.") ref_value = rest_value.to(u.Hz, u.spectral()) elif inunit.physical_type == 'speed': # The rest frequency and wavelength should be equivalent if rest_value is not None: ref_value = rest_value elif wcs_rv is not None: ref_value = wcs_rv else: raise ValueError("If converting from speed to wavelength/frequency, " "a reference wavelength/frequency is required.") # If the input unit is not linearly sampled, its linear equivalent will be # the 8th character in the ctype, and the linearly-sampled ctype will be # the 6th character # e.g.: VOPT-F2V lin_ctype = (in_spec_ctype[7] if len(in_spec_ctype) > 4 else in_spec_ctype[:4]) lin_cunit = (LINEAR_CUNIT_DICT[lin_ctype] if lin_ctype in LINEAR_CUNIT_DICT else mywcs.wcs.cunit[mywcs.wcs.spec]) in_vcequiv = _parse_velocity_convention(in_spec_ctype[:4]) out_ctype_conv = out_ctype[7] if len(out_ctype) > 4 else out_ctype[:4] if CTYPE_TO_PHYSICALTYPE[out_ctype_conv] == 'air wavelength': raise NotImplementedError("Conversion to air wavelength is not supported.") out_lin_cunit = (LINEAR_CUNIT_DICT[out_ctype_conv] if out_ctype_conv in LINEAR_CUNIT_DICT else outunit) out_vcequiv = _parse_velocity_convention(out_ctype_conv) # Load the input values crval_in = (mywcs.wcs.crval[mywcs.wcs.spec] * inunit) # the cdelt matrix may not be correctly populated: need to account for cd, # cdelt, and pc cdelt_in = (mywcs.pixel_scale_matrix[mywcs.wcs.spec, mywcs.wcs.spec] * inunit) if in_spec_ctype == 'AWAV': warnings.warn("Support for air wavelengths is experimental and only " "works in the forward direction (air->vac, not vac->air).") cdelt_in = air_to_vac_deriv(crval_in) * cdelt_in crval_in = air_to_vac(crval_in) in_spec_ctype = 'WAVE' # 1. Convert input to input, linear if in_vcequiv is not None and ref_value is not None: crval_lin1 = crval_in.to(lin_cunit, u.spectral() + in_vcequiv(ref_value)) else: crval_lin1 = crval_in.to(lin_cunit, u.spectral()) cdelt_lin1 = cdelt_derivative(crval_in, cdelt_in, # equivalent: inunit.physical_type intype=CTYPE_TO_PHYSICALTYPE[in_spec_ctype[:4]], outtype=lin_cunit.physical_type, rest=ref_value, linear=True ) # 2. Convert input, linear to output, linear if ref_value is None: if in_vcequiv is not None: pass # consider raising a ValueError here; not clear if this is valid crval_lin2 = crval_lin1.to(out_lin_cunit, u.spectral()) else: # at this stage, the transition can ONLY be relativistic, because the V # frame (as a linear frame) is only defined as "apparent velocity" crval_lin2 = crval_lin1.to(out_lin_cunit, u.spectral() + u.doppler_relativistic(ref_value)) # For cases like VRAD <-> FREQ and VOPT <-> WAVE, this will be linear too: linear_middle = in_vcequiv == out_vcequiv cdelt_lin2 = cdelt_derivative(crval_lin1, cdelt_lin1, intype=lin_cunit.physical_type, outtype=CTYPE_TO_PHYSICALTYPE[out_ctype_conv], rest=ref_value, linear=linear_middle) # 3. Convert output, linear to output if out_vcequiv is not None and ref_value is not None: crval_out = crval_lin2.to(outunit, out_vcequiv(ref_value) + u.spectral()) #cdelt_out = cdelt_lin2.to(outunit, out_vcequiv(ref_value) + u.spectral()) cdelt_out = cdelt_derivative(crval_lin2, cdelt_lin2, intype=CTYPE_TO_PHYSICALTYPE[out_ctype_conv], outtype=outunit.physical_type, rest=ref_value, linear=True ).to(outunit) else: crval_out = crval_lin2.to(outunit, u.spectral()) cdelt_out = cdelt_lin2.to(outunit, u.spectral()) if crval_out.unit != cdelt_out.unit: # this should not be possible, but it's a sanity check raise ValueError("Conversion failed: the units of cdelt and crval don't match.") # A cdelt of 0 would be meaningless if cdelt_out.value == 0: raise ValueError("Conversion failed: the output CDELT would be 0.") newwcs = mywcs.deepcopy() if hasattr(newwcs.wcs,'cd'): newwcs.wcs.cd[newwcs.wcs.spec, newwcs.wcs.spec] = cdelt_out.value # todo: would be nice to have an assertion here that no off-diagonal # values for the spectral WCS are nonzero, but this is a nontrivial # check else: newwcs.wcs.cdelt[newwcs.wcs.spec] = cdelt_out.value newwcs.wcs.cunit[newwcs.wcs.spec] = cdelt_out.unit.to_string(format='fits') newwcs.wcs.crval[newwcs.wcs.spec] = crval_out.value newwcs.wcs.ctype[newwcs.wcs.spec] = out_ctype if rest_value is not None: if rest_value.unit.physical_type == 'frequency': newwcs.wcs.restfrq = rest_value.to(u.Hz).value elif rest_value.unit.physical_type == 'length': newwcs.wcs.restwav = rest_value.to(u.m).value else: raise ValueError("Rest Value was specified, but not in frequency or length units") return newwcs def cdelt_derivative(crval, cdelt, intype, outtype, linear=False, rest=None): if intype == outtype: return cdelt elif set((outtype,intype)) == set(('length','frequency')): # Symmetric equations! return (-constants.c / crval**2 * cdelt).to(PHYS_UNIT_DICT[outtype]) elif outtype in ('frequency','length') and intype == 'speed': if linear: numer = cdelt * rest.to(PHYS_UNIT_DICT[outtype], u.spectral()) denom = constants.c else: numer = cdelt * constants.c * rest.to(PHYS_UNIT_DICT[outtype], u.spectral()) denom = (constants.c + crval)*(constants.c**2 - crval**2)**0.5 if outtype == 'frequency': return (-numer/denom).to(PHYS_UNIT_DICT[outtype], u.spectral()) else: return (numer/denom).to(PHYS_UNIT_DICT[outtype], u.spectral()) elif outtype == 'speed' and intype in ('frequency','length'): if linear: numer = cdelt * constants.c denom = rest.to(PHYS_UNIT_DICT[intype], u.spectral()) else: numer = 4 * constants.c * crval * rest.to(crval.unit, u.spectral())**2 * cdelt denom = (crval**2 + rest.to(crval.unit, u.spectral())**2)**2 if intype == 'frequency': return (-numer/denom).to(PHYS_UNIT_DICT[outtype], u.spectral()) else: return (numer/denom).to(PHYS_UNIT_DICT[outtype], u.spectral()) elif intype == 'air wavelength': raise TypeError("Air wavelength should be converted to vacuum earlier.") elif outtype == 'air wavelength': raise TypeError("Conversion to air wavelength not supported.") else: raise ValueError("Invalid in/out frames") def air_to_vac(wavelength): """ Implements the air to vacuum wavelength conversion described in eqn 65 of Griesen 2006 """ wlum = wavelength.to(u.um).value return (1+1e-6*(287.6155+1.62887/wlum**2+0.01360/wlum**4)) * wavelength def vac_to_air(wavelength): """ Griesen 2006 reports that the error in naively inverting Eqn 65 is less than 10^-9 and therefore acceptable. This is therefore eqn 67 """ wlum = wavelength.to(u.um).value nl = (1+1e-6*(287.6155+1.62887/wlum**2+0.01360/wlum**4)) return wavelength/nl def air_to_vac_deriv(wavelength): """ Eqn 66 """ wlum = wavelength.to(u.um).value return (1+1e-6*(287.6155 - 1.62887/wlum**2 - 0.04080/wlum**4)) spectral-cube-0.4.3/spectral_cube/spectral_cube.py0000644000077000000240000043204113261015477022302 0ustar adamstaff00000000000000""" A class to represent a 3-d position-position-velocity spectral cube. """ from __future__ import print_function, absolute_import, division import warnings from functools import wraps import operator import re import itertools import copy import tempfile import astropy.wcs from astropy import units as u from astropy.extern import six from astropy.extern.six.moves import range as xrange from astropy.extern.six.moves import zip from astropy.io.fits import PrimaryHDU, BinTableHDU, Header, Card, HDUList from astropy.utils.console import ProgressBar from astropy import log from astropy import wcs from astropy import convolution from astropy import stats import numpy as np from radio_beam import Beam, Beams from . import cube_utils from . import wcs_utils from . import spectral_axis from .masks import (LazyMask, LazyComparisonMask, BooleanArrayMask, MaskBase, is_broadcastable_and_smaller) from .ytcube import ytCube from .lower_dimensional_structures import (Projection, Slice, OneDSpectrum, LowerDimensionalObject, VaryingResolutionOneDSpectrum ) from .base_class import (BaseNDClass, SpectralAxisMixinClass, DOPPLER_CONVENTIONS, SpatialCoordMixinClass, MaskableArrayMixinClass, MultiBeamMixinClass) from .utils import (cached, warn_slow, VarianceWarning, BeamAverageWarning, UnsupportedIterationStrategyWarning, WCSMismatchWarning, NotImplementedWarning) from distutils.version import LooseVersion __all__ = ['SpectralCube', 'VaryingResolutionSpectralCube'] # apply_everywhere, world: do not have a valid cube to test on __doctest_skip__ = ['BaseSpectralCube._apply_everywhere'] try: from scipy import ndimage scipyOK = True except ImportError: scipyOK = False warnings.filterwarnings('ignore', category=wcs.FITSFixedWarning, append=True) SIGMA2FWHM = 2. * np.sqrt(2. * np.log(2.)) _NP_DOC = """ Ignores excluded mask elements. Parameters ---------- axis : int (optional) The axis to collapse, or None to perform a global aggregation how : cube | slice | ray | auto How to compute the aggregation. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default='auto' """.replace('\n', '\n ') def aggregation_docstring(func): @wraps(func) def wrapper(*args, **kwargs): return func(*args, **kwargs) wrapper.__doc__ += _NP_DOC return wrapper def _apply_function(arguments, outcube, function, **kwargs): """ Helper function to apply a function to a spectrum. Needs to be declared toward the top of the code to allow pickling by joblib. """ (spec, includemask, ii, jj) = arguments if any(includemask): outcube[:,jj,ii] = function(spec, **kwargs) else: outcube[:,jj,ii] = spec # convenience structures to keep track of the reversed index # conventions between WCS and numpy np2wcs = {2: 0, 1: 1, 0: 2} class BaseSpectralCube(BaseNDClass, MaskableArrayMixinClass, SpectralAxisMixinClass, SpatialCoordMixinClass): def __init__(self, data, wcs, mask=None, meta=None, fill_value=np.nan, header=None, allow_huge_operations=False, wcs_tolerance=0.0): # Deal with metadata first because it can affect data reading self._meta = meta or {} # must extract unit from data before stripping it if 'BUNIT' in self._meta: self._unit = cube_utils.convert_bunit(self._meta["BUNIT"]) elif hasattr(data, 'unit'): self._unit = data.unit else: self._unit = None # data must not be a quantity when stored in self._data if hasattr(data, 'unit'): # strip the unit so that it can be treated as cube metadata data = data.value # TODO: mask should be oriented? Or should we assume correctly oriented here? self._data, self._wcs = cube_utils._orient(data, wcs) self._wcs_tolerance = wcs_tolerance self._spectral_axis = None self._mask = mask # specifies which elements to Nan/blank/ignore # object or array-like object, given that WCS needs # to be consistent with data? #assert mask._wcs == self._wcs self._fill_value = fill_value self._header = Header() if header is None else header if not isinstance(self._header, Header): raise TypeError("If a header is given, it must be a fits.Header") # We don't pass the spectral unit via the initializer since the user # should be using ``with_spectral_unit`` if they want to set it. # However, we do want to keep track of what units the spectral axis # should be returned in, otherwise astropy's WCS can change the units, # e.g. km/s -> m/s. # This can be overridden with Header below self._spectral_unit = u.Unit(self._wcs.wcs.cunit[2]) # This operation is kind of expensive? header_specaxnum = astropy.wcs.WCS(header).wcs.spec header_specaxunit = spectral_axis.unit_from_header(self._header, spectral_axis_number=header_specaxnum+1) # Allow the original header spectral axis unit to override the default # unit if header_specaxunit is not None: self._spectral_unit = header_specaxunit self._spectral_scale = spectral_axis.wcs_unit_scale(self._spectral_unit) self.allow_huge_operations = allow_huge_operations self._cache = {} @property def _is_huge(self): return cube_utils.is_huge(self) def _new_cube_with(self, data=None, wcs=None, mask=None, meta=None, fill_value=None, spectral_unit=None, unit=None, wcs_tolerance=None, **kwargs): data = self._data if data is None else data if unit is None and hasattr(data, 'unit'): if data.unit != self.unit: raise u.UnitsError("New data unit '{0}' does not" " match cube unit '{1}'. You can" " override this by specifying the" " `unit` keyword." .format(data.unit, self.unit)) unit = data.unit elif unit is not None: # convert string units to Units if not isinstance(unit, u.Unit): unit = u.Unit(unit) if hasattr(data, 'unit'): if u.Unit(unit) != data.unit: raise u.UnitsError("The specified new cube unit '{0}' " "does not match the input unit '{1}'." .format(unit, data.unit)) else: data = u.Quantity(data, unit=unit, copy=False) wcs = self._wcs if wcs is None else wcs mask = self._mask if mask is None else mask if meta is None: meta = {} meta.update(self._meta) if unit is not None: meta['BUNIT'] = unit.to_string(format='FITS') fill_value = self._fill_value if fill_value is None else fill_value spectral_unit = self._spectral_unit if spectral_unit is None else u.Unit(spectral_unit) cube = self.__class__(data=data, wcs=wcs, mask=mask, meta=meta, fill_value=fill_value, header=self._header, allow_huge_operations=self.allow_huge_operations, wcs_tolerance=wcs_tolerance or self._wcs_tolerance, **kwargs) cube._spectral_unit = spectral_unit cube._spectral_scale = spectral_axis.wcs_unit_scale(spectral_unit) return cube @property def unit(self): """ The flux unit """ if self._unit: return self._unit else: return u.dimensionless_unscaled @property def shape(self): """ Length of cube along each axis """ return self._data.shape @property def size(self): """ Number of elements in the cube """ return self._data.size @property def base(self): """ The data type 'base' of the cube - useful for, e.g., joblib """ return self._data.base def __len__(self): return self.shape[0] @property def ndim(self): """ Dimensionality of the data """ return self._data.ndim def __repr__(self): s = "{1} with shape={0}".format(self.shape, self.__class__.__name__) if self.unit is u.dimensionless_unscaled: s += ":\n" else: s += " and unit={0}:\n".format(self.unit) s += (" n_x: {0:6d} type_x: {1:8s} unit_x: {2:5s}" " range: {3:12.6f}:{4:12.6f}\n".format(self.shape[2], self.wcs.wcs.ctype[0], self.wcs.wcs.cunit[0], self.longitude_extrema[0], self.longitude_extrema[1],)) s += (" n_y: {0:6d} type_y: {1:8s} unit_y: {2:5s}" " range: {3:12.6f}:{4:12.6f}\n".format(self.shape[1], self.wcs.wcs.ctype[1], self.wcs.wcs.cunit[1], self.latitude_extrema[0], self.latitude_extrema[1], )) s += (" n_s: {0:6d} type_s: {1:8s} unit_s: {2:5s}" " range: {3:12.3f}:{4:12.3f}".format(self.shape[0], self.wcs.wcs.ctype[2], self._spectral_unit, self.spectral_extrema[0], self.spectral_extrema[1], )) return s @property @cached def spectral_extrema(self): _spectral_min = self.spectral_axis.min() _spectral_max = self.spectral_axis.max() return _spectral_min, _spectral_max def apply_numpy_function(self, function, fill=np.nan, reduce=True, how='auto', projection=False, unit=None, check_endian=False, progressbar=False, includemask=False, **kwargs): """ Apply a numpy function to the cube Parameters ---------- function : Numpy ufunc A numpy ufunc to apply to the cube fill : float The fill value to use on the data reduce : bool reduce indicates whether this is a reduce-like operation, that can be accumulated one slice at a time. sum/max/min are like this. argmax/argmin/stddev are not how : cube | slice | ray | auto How to compute the moment. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default='auto' projection : bool Return a :class:`~spectral_cube.lower_dimensional_structures.Projection` if the resulting array is 2D or a OneDProjection if the resulting array is 1D and the sum is over both spatial axes? unit : None or `astropy.units.Unit` The unit to include for the output array. For example, `SpectralCube.max` calls ``SpectralCube.apply_numpy_function(np.max, unit=self.unit)``, inheriting the unit from the original cube. However, for other numpy functions, e.g. `numpy.argmax`, the return is an index and therefore unitless. check_endian : bool A flag to check the endianness of the data before applying the function. This is only needed for optimized functions, e.g. those in the `bottleneck `_ package. progressbar : bool Show a progressbar while iterating over the slices through the cube? kwargs : dict Passed to the numpy function. Returns ------- result : :class:`~spectral_cube.lower_dimensional_structures.Projection` or `~astropy.units.Quantity` or float The result depends on the value of ``axis``, ``projection``, and ``unit``. If ``axis`` is None, the return will be a scalar with or without units. If axis is an integer, the return will be a :class:`~spectral_cube.lower_dimensional_structures.Projection` if ``projection`` is set """ # leave axis in kwargs to avoid overriding numpy defaults, e.g. if the # default is axis=-1, we don't want to force it to be axis=None by # specifying that in the function definition axis = kwargs.get('axis', None) if how == 'auto': strategy = cube_utils.iterator_strategy(self, axis) else: strategy = how out = None log.debug("applying numpy function {0} with strategy {1}" .format(function, strategy)) if strategy == 'slice' and reduce: out = self._reduce_slicewise(function, fill, check_endian, includemask=includemask, progressbar=progressbar, **kwargs) elif how == 'ray': out = self.apply_function(function, **kwargs) elif how not in ['auto', 'cube']: warnings.warn("Cannot use how=%s. Using how=cube" % how, UnsupportedIterationStrategyWarning) if out is None: out = function(self._get_filled_data(fill=fill, check_endian=check_endian), **kwargs) if axis is None: # return is scalar if unit is not None: return u.Quantity(out, unit=unit) else: return out elif projection and reduce: meta = {'collapse_axis': axis} meta.update(self._meta) if hasattr(axis, '__len__') and len(axis) == 2: # if operation is over two spatial dims if set(axis) == set((1,2)): new_wcs = self._wcs.sub([wcs.WCSSUB_SPECTRAL]) header = self._nowcs_header return self._oned_spectrum(value=out, wcs=new_wcs, copy=False, unit=unit, header=header, meta=meta, spectral_unit=self._spectral_unit, beams=(self.beams if hasattr(self,'beams') else None), ) else: warnings.warn("Averaging over a spatial and a spectral " "dimension cannot produce a Projection " "quantity (no units or WCS are preserved).") return out else: new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis]) header = self._nowcs_header return Projection(out, copy=False, wcs=new_wcs, meta=meta, unit=unit, header=header) else: return out def _reduce_slicewise(self, function, fill, check_endian, includemask=False, progressbar=False, **kwargs): """ Compute a numpy aggregation by grabbing one slice at a time """ ax = kwargs.pop('axis', None) full_reduce = ax is None ax = ax or 0 if isinstance(ax, tuple): assert len(ax) == 2 # we only work with cubes... iterax = [x for x in range(3) if x not in ax][0] else: iterax = ax log.debug("reducing slicewise with axis = {0}".format(ax)) if includemask: planes = self._iter_mask_slices(iterax) else: planes = self._iter_slices(iterax, fill=fill, check_endian=check_endian) result = next(planes) if progressbar: progressbar = ProgressBar(self.shape[iterax]) pbu = progressbar.update else: pbu = lambda: True if isinstance(ax, tuple): # have to make a result a list of itself, since we already "got" # the first plane above result = [function(result, axis=(0,1), **kwargs)] for plane in planes: # apply to axes 0 and 1, because we're fully reducing the plane # to a number if we're applying over two axes result.append(function(plane, axis=(0,1), **kwargs)) pbu() result = np.array(result) else: for plane in planes: # axis = 2 means we're stacking two planes, the previously # computed one and the current one result = function(np.dstack((result, plane)), axis=2, **kwargs) pbu() if full_reduce: result = function(result) return result def get_mask_array(self): """ Convert the mask to a boolean numpy array """ return self._mask.include(data=self._data, wcs=self._wcs, wcs_tolerance=self._wcs_tolerance) def _naxes_dropped(self, view): """ Determine how many axes are being selected given a view. (1,2) -> 2 None -> 3 1 -> 1 2 -> 1 """ if hasattr(view,'__len__'): return len(view) elif view is None: return 3 else: return 1 @aggregation_docstring @warn_slow def sum(self, axis=None, how='auto', **kwargs): """ Return the sum of the cube, optionally over an axis. """ from .np_compat import allbadtonan projection = self._naxes_dropped(axis) in (1,2) return self.apply_numpy_function(allbadtonan(np.nansum), fill=np.nan, how=how, axis=axis, unit=self.unit, projection=projection, **kwargs) @aggregation_docstring @warn_slow def mean(self, axis=None, how='cube', **kwargs): """ Return the mean of the cube, optionally over an axis. """ projection = self._naxes_dropped(axis) in (1,2) if how == 'slice': # two-pass approach: first total the # of points, # then total the value of the points, then divide # (a one-pass approach is possible but requires # more sophisticated bookkeeping) counts = self._count_nonzero_slicewise(axis=axis, progressbar=kwargs.get('progressbar')) ttl = self.apply_numpy_function(np.nansum, fill=np.nan, how=how, axis=axis, unit=None, projection=False, **kwargs) out = ttl / counts if projection: if self._naxes_dropped(axis) == 1: new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis]) meta = {'collapse_axis': axis} meta.update(self._meta) return Projection(out, copy=False, wcs=new_wcs, meta=meta, unit=self.unit, header=self._nowcs_header) elif axis == (1,2): newwcs = self._wcs.sub([wcs.WCSSUB_SPECTRAL]) return self._oned_spectrum(value=out, wcs=newwcs, copy=False, unit=self.unit, spectral_unit=self._spectral_unit, beams=(self.beams if hasattr(self, 'beams') else None), meta=self.meta) else: # this is a weird case, but even if projection is # specified, we can't return a Quantity here because of WCS # issues. `apply_numpy_function` already does this # silently, which is unfortunate. warnings.warn("Averaging over a spatial and a spectral " "dimension cannot produce a Projection " "quantity (no units or WCS are preserved).") return out else: return out return self.apply_numpy_function(np.nanmean, fill=np.nan, how=how, axis=axis, unit=self.unit, projection=projection, **kwargs) def _count_nonzero_slicewise(self, axis=None, progressbar=False): """ Count the number of finite pixels along an axis slicewise. This is a helper function for the mean and std deviation slicewise iterators. """ counts = self.apply_numpy_function(np.sum, fill=np.nan, how='slice', axis=axis, unit=None, projection=False, progressbar=progressbar, includemask=True) return counts @aggregation_docstring @warn_slow def std(self, axis=None, how='cube', ddof=0, **kwargs): """ Return the standard deviation of the cube, optionally over an axis. Other Parameters ---------------- ddof : int Means Delta Degrees of Freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of elements. By default ``ddof`` is zero. """ projection = self._naxes_dropped(axis) in (1,2) if how == 'slice': if axis is None: raise NotImplementedError("The overall standard deviation " "cannot be computed in a slicewise " "manner. Please use a " "different strategy.") if hasattr(axis, '__len__') and len(axis) == 2: return self.apply_numpy_function(np.nanstd, axis=axis, how='slice', projection=projection, unit=self.unit, **kwargs) else: counts = self._count_nonzero_slicewise(axis=axis) ttl = self.apply_numpy_function(np.nansum, fill=np.nan, how='slice', axis=axis, unit=None, projection=False, **kwargs) # Equivalent, but with more overhead: # ttl = self.sum(axis=axis, how='slice').value mean = ttl/counts planes = self._iter_slices(axis, fill=np.nan, check_endian=False) result = (next(planes)-mean)**2 for plane in planes: result = np.nansum(np.dstack((result, (plane-mean)**2)), axis=2) out = (result/(counts-ddof))**0.5 if projection: new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis]) meta = {'collapse_axis': axis} meta.update(self._meta) return Projection(out, copy=False, wcs=new_wcs, meta=meta, unit=self.unit, header=self._nowcs_header) else: return out # standard deviation cannot be computed as a trivial step-by-step # process. There IS a one-pass algorithm for std dev, but it is not # implemented, so we must force cube here. We could and should also # implement raywise reduction return self.apply_numpy_function(np.nanstd, fill=np.nan, how=how, axis=axis, unit=self.unit, projection=projection, **kwargs) @aggregation_docstring @warn_slow def mad_std(self, axis=None, how='cube', **kwargs): """ Use astropy's mad_std to computer the standard deviation """ if int(astropy.__version__[0]) < 2: raise NotImplementedError("mad_std requires astropy >= 2") projection = self._naxes_dropped(axis) in (1,2) if how == 'ray': # no need for fill here; masked-out data are simply not included return self.apply_function(stats.mad_std, axis=axis, unit=self.unit, projection=projection, ) else: return self.apply_numpy_function(stats.mad_std, fill=np.nan, axis=axis, unit=self.unit, ignore_nan=True, how=how, projection=projection, **kwargs) @aggregation_docstring @warn_slow def max(self, axis=None, how='auto', **kwargs): """ Return the maximum data value of the cube, optionally over an axis. """ projection = self._naxes_dropped(axis) in (1,2) return self.apply_numpy_function(np.nanmax, fill=np.nan, how=how, axis=axis, unit=self.unit, projection=projection, **kwargs) @aggregation_docstring @warn_slow def min(self, axis=None, how='auto', **kwargs): """ Return the minimum data value of the cube, optionally over an axis. """ projection = self._naxes_dropped(axis) in (1,2) return self.apply_numpy_function(np.nanmin, fill=np.nan, how=how, axis=axis, unit=self.unit, projection=projection, **kwargs) @aggregation_docstring @warn_slow def argmax(self, axis=None, how='auto', **kwargs): """ Return the index of the maximum data value. The return value is arbitrary if all pixels along ``axis`` are excluded from the mask. """ return self.apply_numpy_function(np.nanargmax, fill=-np.inf, reduce=False, projection=False, how=how, axis=axis, **kwargs) @aggregation_docstring @warn_slow def argmin(self, axis=None, how='auto', **kwargs): """ Return the index of the minimum data value. The return value is arbitrary if all pixels along ``axis`` are excluded from the mask """ return self.apply_numpy_function(np.nanargmin, fill=np.inf, reduce=False, projection=False, how=how, axis=axis, **kwargs) def chunked(self, chunksize=1000): """ Not Implemented. Iterate over chunks of valid data """ raise NotImplementedError() def _get_flat_shape(self, axis): """ Get the shape of the array after flattening along an axis """ iteraxes = [0, 1, 2] iteraxes.remove(axis) # x,y are defined as first,second dim to iterate over # (not x,y in pixel space...) nx = self.shape[iteraxes[0]] ny = self.shape[iteraxes[1]] return nx, ny @warn_slow def _apply_everywhere(self, function, *args): """ Return a new cube with ``function`` applied to all pixels Private because this doesn't have an obvious and easy-to-use API Examples -------- >>> newcube = cube.apply_everywhere(np.add, 0.5*u.Jy) """ try: test_result = function(np.ones([1,1,1])*self.unit, *args) # First, check that function returns same # of dims? assert test_result.ndim == 3,"Output is not 3-dimensional" except Exception as ex: raise AssertionError("Function could not be applied to a simple " "cube. The error was: {0}".format(ex)) data = function(u.Quantity(self._get_filled_data(fill=self._fill_value), self.unit, copy=False), *args) return self._new_cube_with(data=data, unit=data.unit) @warn_slow def _cube_on_cube_operation(self, function, cube, equivalencies=[], **kwargs): """ Apply an operation between two cubes. Inherits the metadata of the left cube. Parameters ---------- function : function A function to apply to the cubes cube : SpectralCube Another cube to put into the function equivalencies : list A list of astropy equivalencies kwargs : dict Passed to np.testing.assert_almost_equal """ assert cube.shape == self.shape if not self.unit.is_equivalent(cube.unit, equivalencies=equivalencies): raise u.UnitsError("{0} is not equivalent to {1}" .format(self.unit, cube.unit)) if not wcs_utils.check_equality(self.wcs, cube.wcs, warn_missing=True, **kwargs): warnings.warn("Cube WCSs do not match, but their shapes do", WCSMismatchWarning) try: test_result = function(np.ones([1,1,1])*self.unit, np.ones([1,1,1])*self.unit) # First, check that function returns same # of dims? assert test_result.shape == (1,1,1) except Exception as ex: raise AssertionError("Function {1} could not be applied to a " "pair of simple " "cube. The error was: {0}".format(ex, function)) cube = cube.to(self.unit) data = function(self._data, cube._data) try: # multiplication, division, etc. are valid inter-unit operations unit = function(self.unit, cube.unit) except TypeError: # addition, subtraction are not unit = self.unit return self._new_cube_with(data=data, unit=unit) def apply_function(self, function, axis=None, weights=None, unit=None, projection=False, progressbar=False, update_function=None, keep_shape=False, **kwargs): """ Apply a function to valid data along the specified axis or to the whole cube, optionally using a weight array that is the same shape (or at least can be sliced in the same way) Parameters ---------- function : function A function that can be applied to a numpy array. Does not need to be nan-aware axis : 1, 2, 3, or None The axis to operate along. If None, the return is scalar. weights : (optional) np.ndarray An array with the same shape (or slicing abilities/results) as the data cube unit : (optional) `~astropy.units.Unit` The unit of the output projection or value. Not all functions should return quantities with units. projection : bool Return a projection if the resulting array is 2D? progressbar : bool Show a progressbar while iterating over the slices/rays through the cube? keep_shape : bool If `True`, the returned object will be the same dimensionality as the cube. update_function : function An alternative tracker for the progress of applying the function to the cube data. If ``progressbar`` is ``True``, this argument is ignored. Returns ------- result : :class:`~spectral_cube.lower_dimensional_structures.Projection` or `~astropy.units.Quantity` or float The result depends on the value of ``axis``, ``projection``, and ``unit``. If ``axis`` is None, the return will be a scalar with or without units. If axis is an integer, the return will be a :class:`~spectral_cube.lower_dimensional_structures.Projection` if ``projection`` is set """ if axis is None: out = function(self.flattened(), **kwargs) if unit is not None: return u.Quantity(out, unit=unit) else: return out if hasattr(axis, '__len__'): raise NotImplementedError("`apply_function` does not support " "function application across multiple " "axes. Try `apply_numpy_function`.") # determine the output array shape nx, ny = self._get_flat_shape(axis) nz = self.shape[axis] if keep_shape else 1 # allocate memory for output array out = np.empty([nz, nx, ny]) * np.nan if progressbar: progressbar = ProgressBar(nx*ny) pbu = progressbar.update elif update_function is not None: pbu = update_function else: pbu = lambda: True # iterate over "lines of sight" through the cube for y, x, slc in self._iter_rays(axis): # acquire the flattened, valid data for the slice data = self.flattened(slc, weights=weights) if len(data) != 0: result = function(data, **kwargs) if hasattr(result, 'value'): # store result in array out[:, y, x] = result.value else: out[:, y, x] = result pbu() if not keep_shape: out = out[0, :, :] if projection and axis in (0, 1, 2): new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis]) meta = {'collapse_axis': axis} meta.update(self._meta) return Projection(out, copy=False, wcs=new_wcs, meta=meta, unit=unit, header=self._nowcs_header) else: return out def _iter_rays(self, axis=None): """ Iterate over view corresponding to lines-of-sight through a cube along the specified axis """ ny, nx = self._get_flat_shape(axis) for y in xrange(ny): for x in xrange(nx): # create length-1 view for each position slc = [slice(y, y + 1), slice(x, x + 1), ] # create a length-N slice (all-inclusive) along the selected axis slc.insert(axis, slice(None)) yield y, x, slc def _iter_slices(self, axis, fill=np.nan, check_endian=False): """ Iterate over the cube one slice at a time, replacing masked elements with fill """ view = [slice(None)] * 3 for x in range(self.shape[axis]): view[axis] = x yield self._get_filled_data(view=view, fill=fill, check_endian=check_endian) def _iter_mask_slices(self, axis): """ Iterate over the cube one slice at a time, replacing masked elements with fill """ view = [slice(None)] * 3 for x in range(self.shape[axis]): view[axis] = x yield self._mask.include(data=self._data, view=view, wcs=self._wcs, wcs_tolerance=self._wcs_tolerance, ) def flattened(self, slice=(), weights=None): """ Return a slice of the cube giving only the valid data (i.e., removing bad values) Parameters ---------- slice: 3-tuple A length-3 tuple of view (or any equivalent valid slice of a cube) weights: (optional) np.ndarray An array with the same shape (or slicing abilities/results) as the data cube """ data = self._mask._flattened(data=self._data, wcs=self._wcs, view=slice) if weights is not None: weights = self._mask._flattened(data=weights, wcs=self._wcs, view=slice) return u.Quantity(data * weights, self.unit, copy=False) else: return u.Quantity(data, self.unit, copy=False) def flattened_world(self, view=()): """ Retrieve the world coordinates corresponding to the extracted flattened version of the cube """ # NOTE: this should be moved to SpatialCoordMixinClass once masks # are implemented for lower dim objects - EK lon,lat,spec = self.world[view] spec = self._mask._flattened(data=spec, wcs=self._wcs, view=slice) lon = self._mask._flattened(data=lon, wcs=self._wcs, view=slice) lat = self._mask._flattened(data=lat, wcs=self._wcs, view=slice) return lat,lon,spec def median(self, axis=None, iterate_rays=False, **kwargs): """ Compute the median of an array, optionally along an axis. Ignores excluded mask elements. Parameters ---------- axis : int (optional) The axis to collapse iterate_rays : bool Iterate over individual rays? This mode is slower but can save RAM costs, which may be extreme for large cubes Returns ------- med : ndarray The median """ try: from bottleneck import nanmedian bnok = True except ImportError: bnok = False # slicewise median is nonsense, must force how = 'cube' # bottleneck.nanmedian does not allow axis to be a list or tuple if bnok and not iterate_rays and not isinstance(axis, (list, tuple)): log.debug("Using bottleneck nanmedian") result = self.apply_numpy_function(nanmedian, axis=axis, projection=True, unit=self.unit, how='cube', check_endian=True, **kwargs) elif hasattr(np, 'nanmedian') and not iterate_rays: log.debug("Using numpy nanmedian") result = self.apply_numpy_function(np.nanmedian, axis=axis, projection=True, unit=self.unit, how='cube',**kwargs) else: log.debug("Using numpy median iterating over rays") result = self.apply_function(np.median, projection=True, axis=axis, unit=self.unit, **kwargs) return result def percentile(self, q, axis=None, iterate_rays=False, **kwargs): """ Return percentiles of the data. Parameters ---------- q : float The percentile to compute axis : int, or None Which axis to compute percentiles over iterate_rays : bool Iterate over individual rays? This mode is slower but can save RAM costs, which may be extreme for large cubes """ if hasattr(np, 'nanpercentile') and not iterate_rays: result = self.apply_numpy_function(np.nanpercentile, q=q, axis=axis, projection=True, unit=self.unit, how='cube', **kwargs) else: result = self.apply_function(np.percentile, q=q, axis=axis, projection=True, unit=self.unit, **kwargs) return result def with_mask(self, mask, inherit_mask=True, wcs_tolerance=None): """ Return a new SpectralCube instance that contains a composite mask of the current SpectralCube and the new ``mask``. Values of the mask that are ``True`` will be *included* (masks are analogous to numpy boolean index arrays, they are the inverse of the ``.mask`` attribute of a numpy masked array). Parameters ---------- mask : :class:`MaskBase` instance, or boolean numpy array The mask to apply. If a boolean array is supplied, it will be converted into a mask, assuming that `True` values indicate included elements. inherit_mask : bool (optional, default=True) If True, combines the provided mask with the mask currently attached to the cube wcs_tolerance : None or float The tolerance of difference in WCS parameters between the cube and the mask. Defaults to `self._wcs_tolerance` (which itself defaults to 0.0) if unspecified Returns ------- new_cube : :class:`SpectralCube` A cube with the new mask applied. Notes ----- This operation returns a view into the data, and not a copy. """ if isinstance(mask, np.ndarray): if not is_broadcastable_and_smaller(mask.shape, self._data.shape): raise ValueError("Mask shape is not broadcastable to data shape: " "%s vs %s" % (mask.shape, self._data.shape)) mask = BooleanArrayMask(mask, self._wcs, shape=self._data.shape) if self._mask is not None and inherit_mask: new_mask = self._mask & mask else: new_mask = mask new_mask._validate_wcs(new_data=self._data, new_wcs=self._wcs, wcs_tolerance=wcs_tolerance or self._wcs_tolerance) return self._new_cube_with(mask=new_mask, wcs_tolerance=wcs_tolerance) def __getitem__(self, view): # Need to allow self[:], self[:,:] if isinstance(view, (slice,int,np.int64)): view = (view, slice(None), slice(None)) elif len(view) == 2: view = view + (slice(None),) elif len(view) > 3: raise IndexError("Too many indices") meta = {} meta.update(self._meta) slice_data = [(s.start, s.stop, s.step) if hasattr(s,'start') else s for s in view] if 'slice' in meta: meta['slice'].append(slice_data) else: meta['slice'] = [slice_data] intslices = [2-ii for ii,s in enumerate(view) if not hasattr(s,'start')] if intslices: if len(intslices) > 1: if 2 in intslices: raise NotImplementedError("1D slices along non-spectral " "axes are not yet implemented.") newwcs = self._wcs.sub([a for a in (1,2,3) if a not in [x+1 for x in intslices]]) return self._oned_spectrum(value=self._data[view], wcs=newwcs, copy=False, unit=self.unit, spectral_unit=self._spectral_unit, mask=self.mask[view], meta=meta, ) # only one element, so drop an axis newwcs = wcs_utils.drop_axis(self._wcs, intslices[0]) header = self._nowcs_header if intslices[0] == 0: # celestial: can report the wavelength/frequency of the axis header['CRVAL3'] = self.spectral_axis[intslices[0]].value header['CDELT3'] = self.wcs.sub([wcs.WCSSUB_SPECTRAL]).wcs.cdelt[0] header['CUNIT3'] = self._spectral_unit.to_string(format='FITS') return Slice(value=self.filled_data[view], wcs=newwcs, copy=False, unit=self.unit, header=header, meta=meta) newmask = self._mask[view] if self._mask is not None else None newwcs = wcs_utils.slice_wcs(self._wcs, view, shape=self.shape) return self._new_cube_with(data=self._data[view], wcs=newwcs, mask=newmask, meta=meta) @property def unitless(self): """Return a copy of self with unit set to None""" newcube = self._new_cube_with() newcube._unit = None return newcube def with_fill_value(self, fill_value): """ Create a new :class:`SpectralCube` with a different `fill_value`. Notes ----- This method is fast (it does not copy any data) """ return self._new_cube_with(fill_value=fill_value) def with_spectral_unit(self, unit, velocity_convention=None, rest_value=None): """ Returns a new Cube with a different Spectral Axis unit Parameters ---------- unit : :class:`~astropy.units.Unit` Any valid spectral unit: velocity, (wave)length, or frequency. Only vacuum units are supported. velocity_convention : 'relativistic', 'radio', or 'optical' The velocity convention to use for the output velocity axis. Required if the output type is velocity. This can be either one of the above strings, or an `astropy.units` equivalency. rest_value : :class:`~astropy.units.Quantity` A rest wavelength or frequency with appropriate units. Required if output type is velocity. The cube's WCS should include this already if the *input* type is velocity, but the WCS's rest wavelength/frequency can be overridden with this parameter. .. note: This must be the rest frequency/wavelength *in vacuum*, even if your cube has air wavelength units """ newwcs,newmeta = self._new_spectral_wcs(unit=unit, velocity_convention=velocity_convention, rest_value=rest_value) if self._mask is not None: newmask = self._mask.with_spectral_unit(unit, velocity_convention=velocity_convention, rest_value=rest_value) newmask._wcs = newwcs else: newmask = None cube = self._new_cube_with(wcs=newwcs, mask=newmask, meta=newmeta, spectral_unit=unit) return cube @cube_utils.slice_syntax def unmasked_data(self, view): """ Return a view of the subset of the underlying data, ignoring the mask. Returns ------- data : Quantity instance The unmasked data """ return u.Quantity(self._data[view], self.unit, copy=False) def unmasked_copy(self): """ Return a copy of the cube with no mask (i.e., all data included) """ newcube = self._new_cube_with() newcube._mask = None return newcube @cached def _pix_cen(self): """ Offset of every pixel from the origin, along each direction Returns ------- tuple of spectral_offset, y_offset, x_offset, each 3D arrays describing the distance from the origin Notes ----- These arrays are broadcast, and are not memory intensive Each array is in the units of the corresponding wcs.cunit, but this is implicit (e.g., they are not astropy Quantity arrays) """ # Start off by extracting the world coordinates of the pixels _, lat, lon = self.world[0, :, :] spectral, _, _ = self.world[:, 0, 0] spectral -= spectral[0] # offset from first pixel # Convert to radians lon = np.radians(lon) lat = np.radians(lat) # Find the dx and dy arrays from astropy.coordinates.angle_utilities import angular_separation dx = angular_separation(lon[:, :-1], lat[:, :-1], lon[:, 1:], lat[:, :-1]) dy = angular_separation(lon[:-1, :], lat[:-1, :], lon[1:, :], lat[1:, :]) # Find the cumulative offset - need to add a zero at the start x = np.zeros(self._data.shape[1:]) y = np.zeros(self._data.shape[1:]) x[:, 1:] = np.cumsum(np.degrees(dx), axis=1) y[1:, :] = np.cumsum(np.degrees(dy), axis=0) x, y, spectral = np.broadcast_arrays(x[None,:,:], y[None,:,:], spectral[:,None,None]) return spectral, y, x @cached def _pix_size_slice(self, axis): """ Return the size of each pixel along any given direction. Assumes pixels have equal size. Also assumes that the spectral and spatial directions are separable, which is enforced throughout this code. Parameters ---------- axis : 0, 1, or 2 The axis along which to compute the pixel size Returns ------- Pixel size in units of either degrees or the appropriate spectral unit """ if axis == 0: # note that self._spectral_scale is required here because wcs # forces into units of m, m/s, or Hz return np.abs(self.wcs.pixel_scale_matrix[2,2]) * self._spectral_scale elif axis in (1,2): # the pixel size is a projection. I think the pixel_scale_matrix # must be symmetric, such that psm[axis,:]**2 == psm[:,axis]**2 return np.sum(self.wcs.pixel_scale_matrix[2-axis,:]**2)**0.5 else: raise ValueError("Cubes have 3 axes.") @cached def _pix_size(self): """ Return the size of each pixel along each direction, in world units Returns ------- dv, dy, dx : tuple of 3D arrays The extent of each pixel along each direction Notes ----- These arrays are broadcast, and are not memory intensive Each array is in the units of the corresponding wcs.cunit, but this is implicit (e.g., they are not astropy Quantity arrays) """ # First, scale along x direction xpix = np.linspace(-0.5, self._data.shape[2] - 0.5, self._data.shape[2] + 1) ypix = np.linspace(0., self._data.shape[1] - 1, self._data.shape[1]) xpix, ypix = np.meshgrid(xpix, ypix) zpix = np.zeros(xpix.shape) lon, lat, _ = self._wcs.all_pix2world(xpix, ypix, zpix, 0) # Convert to radians lon = np.radians(lon) lat = np.radians(lat) # Find the dx and dy arrays from astropy.coordinates.angle_utilities import angular_separation dx = angular_separation(lon[:, :-1], lat[:, :-1], lon[:, 1:], lat[:, :-1]) # Next, scale along y direction xpix = np.linspace(0., self._data.shape[2] - 1, self._data.shape[2]) ypix = np.linspace(-0.5, self._data.shape[1] - 0.5, self._data.shape[1] + 1) xpix, ypix = np.meshgrid(xpix, ypix) zpix = np.zeros(xpix.shape) lon, lat, _ = self._wcs.all_pix2world(xpix, ypix, zpix, 0) # Convert to radians lon = np.radians(lon) lat = np.radians(lat) # Find the dx and dy arrays from astropy.coordinates.angle_utilities import angular_separation dy = angular_separation(lon[:-1, :], lat[:-1, :], lon[1:, :], lat[1:, :]) # Next, spectral coordinates zpix = np.linspace(-0.5, self._data.shape[0] - 0.5, self._data.shape[0] + 1) xpix = np.zeros(zpix.shape) ypix = np.zeros(zpix.shape) _, _, spectral = self._wcs.all_pix2world(xpix, ypix, zpix, 0) # Take spectral units into account # order of operations here is crucial! If this is done after # broadcasting, the full array size is allocated, which is bad! dspectral = np.diff(spectral) * self._spectral_scale dx = np.abs(np.degrees(dx.reshape(1, dx.shape[0], dx.shape[1]))) dy = np.abs(np.degrees(dy.reshape(1, dy.shape[0], dy.shape[1]))) dspectral = np.abs(dspectral.reshape(-1, 1, 1)) dx, dy, dspectral = np.broadcast_arrays(dx, dy, dspectral) return dspectral, dy, dx def moment(self, order=0, axis=0, how='auto'): """ Compute moments along the spectral axis. Moments are defined as follows: Moment 0: .. math:: M_0 \\int I dl Moment 1: .. math:: M_1 = \\frac{\\int I l dl}{M_0} Moment N: .. math:: M_N = \\frac{\\int I (l - M_1)^N dl}{M_0} .. warning:: Note that these follow the mathematical definitions of moments, and therefore the second moment will return a variance map. To get linewidth maps, you can instead use the :meth:`~SpectralCube.linewidth_fwhm` or :meth:`~SpectralCube.linewidth_sigma` methods. Parameters ---------- order : int The order of the moment to take. Default=0 axis : int The axis along which to compute the moment. Default=0 how : cube | slice | ray | auto How to compute the moment. All strategies give the same result, but certain strategies are more efficient depending on data size and layout. Cube/slice/ray iterate over decreasing subsets of the data, to conserve memory. Default='auto' Returns ------- map [, wcs] The moment map (numpy array) and, if wcs=True, the WCS object describing the map Notes ----- Generally, how='cube' is fastest for small cubes that easily fit into memory. how='slice' is best for most larger datasets. how='ray' is probably only a good idea for very large cubes whose data are contiguous over the axis of the moment map. For the first moment, the result for axis=1, 2 is the angular offset *relative to the cube face*. For axis=0, it is the *absolute* velocity/frequency of the first moment. """ if axis == 0 and order == 2: warnings.warn("Note that the second moment returned will be a " "variance map. To get a linewidth map, use the " "SpectralCube.linewidth_fwhm() or " "SpectralCube.linewidth_sigma() methods instead.", VarianceWarning) from ._moments import (moment_slicewise, moment_cubewise, moment_raywise, moment_auto) dispatch = dict(slice=moment_slicewise, cube=moment_cubewise, ray=moment_raywise, auto=moment_auto) if how not in dispatch: return ValueError("Invalid how. Must be in %s" % sorted(list(dispatch.keys()))) out = dispatch[how](self, order, axis) # apply units if order == 0: if axis == 0 and self._spectral_unit is not None: axunit = unit = self._spectral_unit else: axunit = unit = u.Unit(self._wcs.wcs.cunit[np2wcs[axis]]) out = u.Quantity(out, self.unit * axunit, copy=False) else: if axis == 0 and self._spectral_unit is not None: unit = self._spectral_unit ** max(order, 1) else: unit = u.Unit(self._wcs.wcs.cunit[np2wcs[axis]]) ** max(order, 1) out = u.Quantity(out, unit, copy=False) # special case: for order=1, axis=0, you usually want # the absolute velocity and not the offset if order == 1 and axis == 0: out += self.world[0, :, :][0] new_wcs = wcs_utils.drop_axis(self._wcs, np2wcs[axis]) meta = {'moment_order': order, 'moment_axis': axis, 'moment_method': how} meta.update(self._meta) return Projection(out, copy=False, wcs=new_wcs, meta=meta, header=self._nowcs_header) def moment0(self, axis=0, how='auto'): """ Compute the zeroth moment along an axis. See :meth:`moment`. """ return self.moment(axis=axis, order=0, how=how) def moment1(self, axis=0, how='auto'): """ Compute the 1st moment along an axis. For an explanation of the ``axis`` and ``how`` parameters, see :meth:`moment`. """ return self.moment(axis=axis, order=1, how=how) def moment2(self, axis=0, how='auto'): """ Compute the 2nd moment along an axis. For an explanation of the ``axis`` and ``how`` parameters, see :meth:`moment`. """ return self.moment(axis=axis, order=2, how=how) def linewidth_sigma(self, how='auto'): """ Compute a (sigma) linewidth map along the spectral axis. For an explanation of the ``how`` parameter, see :meth:`moment`. """ with np.errstate(invalid='ignore'): with warnings.catch_warnings(): warnings.simplefilter("ignore", VarianceWarning) return np.sqrt(self.moment2(how=how)) def linewidth_fwhm(self, how='auto'): """ Compute a (FWHM) linewidth map along the spectral axis. For an explanation of the ``how`` parameter, see :meth:`moment`. """ return self.linewidth_sigma() * SIGMA2FWHM @property def spectral_axis(self): """ A `~astropy.units.Quantity` array containing the central values of each channel along the spectral axis. """ return self.world[:, 0, 0][0].ravel() @property def velocity_convention(self): """ The `~astropy.units.equivalencies` that describes the spectral axis """ return spectral_axis.determine_vconv_from_ctype(self.wcs.wcs.ctype[self.wcs.wcs.spec]) def closest_spectral_channel(self, value): """ Find the index of the closest spectral channel to the specified spectral coordinate. Parameters ---------- value : :class:`~astropy.units.Quantity` The value of the spectral coordinate to search for. """ # TODO: we have to not compute this every time spectral_axis = self.spectral_axis try: value = value.to(spectral_axis.unit, equivalencies=u.spectral()) except u.UnitsError: if value.unit.is_equivalent(u.Hz, equivalencies=u.spectral()): if spectral_axis.unit.is_equivalent(u.m / u.s): raise u.UnitsError("Spectral axis is in velocity units and " "'value' is in frequency-equivalent units " "- use SpectralCube.with_spectral_unit " "first to convert the cube to frequency-" "equivalent units, or search for a " "velocity instead") else: raise u.UnitsError("Unexpected spectral axis units: {0}".format(spectral_axis.unit)) elif value.unit.is_equivalent(u.m / u.s): if spectral_axis.unit.is_equivalent(u.Hz, equivalencies=u.spectral()): raise u.UnitsError("Spectral axis is in frequency-equivalent " "units and 'value' is in velocity units " "- use SpectralCube.with_spectral_unit " "first to convert the cube to frequency-" "equivalent units, or search for a " "velocity instead") else: raise u.UnitsError("Unexpected spectral axis units: {0}".format(spectral_axis.unit)) else: raise u.UnitsError("'value' should be in frequency equivalent or velocity units (got {0})".format(value.unit)) # TODO: optimize the next line - just brute force for now return np.argmin(np.abs(spectral_axis - value)) def spectral_slab(self, lo, hi): """ Extract a new cube between two spectral coordinates Parameters ---------- lo, hi : :class:`~astropy.units.Quantity` The lower and upper spectral coordinate for the slab range. The units should be compatible with the units of the spectral axis. If the spectral axis is in frequency-equivalent units and you want to select a range in velocity, or vice-versa, you should first use :meth:`~spectral_cube.SpectralCube.with_spectral_unit` to convert the units of the spectral axis. """ # Find range of values for spectral axis ilo = self.closest_spectral_channel(lo) ihi = self.closest_spectral_channel(hi) if ilo > ihi: ilo, ihi = ihi, ilo ihi += 1 # Create WCS slab wcs_slab = self._wcs.deepcopy() wcs_slab.wcs.crpix[2] -= ilo # Create mask slab if self._mask is None: mask_slab = None else: try: mask_slab = self._mask[ilo:ihi, :, :] except NotImplementedError: warnings.warn("Mask slicing not implemented for " "{0} - dropping mask". format(self._mask.__class__.__name__), NotImplementedWarning ) mask_slab = None # Create new spectral cube slab = self._new_cube_with(data=self._data[ilo:ihi], wcs=wcs_slab, mask=mask_slab) # TODO: we could change the WCS to give a spectral axis in the # correct units as requested - so if the initial cube is in Hz and we # request a range in km/s, we could adjust the WCS to be in km/s # instead return slab def minimal_subcube(self, spatial_only=False): """ Return the minimum enclosing subcube where the mask is valid Parameters ---------- spatial_only: bool Only compute the minimal subcube in the spatial dimensions """ return self[self.subcube_slices_from_mask(self._mask, spatial_only=spatial_only)] def subcube_from_mask(self, region_mask): """ Given a mask, return the minimal subcube that encloses the mask Parameters ---------- region_mask: `masks.MaskBase` or boolean `numpy.ndarray` The mask with appropraite WCS or an ndarray with matched coordinates """ return self[self.subcube_slices_from_mask(region_mask)] def subcube_slices_from_mask(self, region_mask, spatial_only=False): """ Given a mask, return the slices corresponding to the minimum subcube that encloses the mask Parameters ---------- region_mask: `masks.MaskBase` or boolean `numpy.ndarray` The mask with appropriate WCS or an ndarray with matched coordinates spatial_only: bool Return only slices that affect the spatial dimensions; the spectral dimension will be left unchanged """ if not scipyOK: raise ImportError("Scipy could not be imported: this function won't work.") if isinstance(region_mask, np.ndarray): if is_broadcastable_and_smaller(region_mask.shape, self.shape): region_mask = BooleanArrayMask(region_mask, self._wcs) else: raise ValueError("Mask shape does not match cube shape.") include = region_mask.include(self._data, self._wcs, wcs_tolerance=self._wcs_tolerance) if not include.any(): return (slice(0),)*3 slices = ndimage.find_objects(np.broadcast_arrays(include, self._data)[0])[0] if spatial_only: slices = (slice(None), slices[1], slices[2]) return slices def subcube(self, xlo='min', xhi='max', ylo='min', yhi='max', zlo='min', zhi='max', rest_value=None): """ Extract a sub-cube spatially and spectrally. Parameters ---------- [xyz]lo/[xyz]hi : int or :class:`~astropy.units.Quantity` or ``min``/``max`` The endpoints to extract. If given as a quantity, will be interpreted as World coordinates. If given as a string or int, will be interpreted as pixel coordinates. """ limit_dict = {'xlo':0 if xlo == 'min' else xlo, 'ylo':0 if ylo == 'min' else ylo, 'zlo':0 if zlo == 'min' else zlo, 'xhi':self.shape[2] if xhi=='max' else xhi, 'yhi':self.shape[1] if yhi=='max' else yhi, 'zhi':self.shape[0] if zhi=='max' else zhi} dims = {'x': 2, 'y': 1, 'z': 0} # Specific warning for slicing a frequency axis with a velocity or # vice/versa if ((hasattr(zlo, 'unit') and not zlo.unit.is_equivalent(self.spectral_axis.unit)) or (hasattr(zhi, 'unit') and not zhi.unit.is_equivalent(self.spectral_axis.unit))): raise u.UnitsError("Spectral units are not equivalent to the " "spectral slice. Use `.with_spectral_unit` " "to convert to equivalent units first") for val in (xlo,ylo,xhi,yhi): if hasattr(val, 'unit') and not val.unit.is_equivalent(u.degree): raise u.UnitsError("The X and Y slices must be specified in " "degree-equivalent units.") # list to track which entries had units united = [] for lim in limit_dict: limval = limit_dict[lim] if hasattr(limval, 'unit'): united.append(lim) dim = dims[lim[0]] sl = [slice(0,1)]*2 sl.insert(dim, slice(None)) spine = self.world[sl][dim] val = np.argmin(np.abs(limval-spine)) if limval > spine.max() or limval < spine.min(): log.warning("The limit {0} is out of bounds." " Using min/max instead.".format(lim)) limit_dict[lim] = val for xx in 'zyx': hi,lo = limit_dict[xx+'hi'], limit_dict[xx+'lo'] if hi < lo: # must have high > low limit_dict[xx+'hi'], limit_dict[xx+'lo'] = lo, hi if xx+'lo' in united: # End-inclusive indexing: need to add one for the high slice # Only do this for converted values, not for pixel values # (i.e., if the xlo/ylo/zlo value had units) limit_dict[xx+'hi'] += 1 for xx in 'zyx': if limit_dict[xx+'hi'] == limit_dict[xx+'lo']: # I think this should be unreachable now raise ValueError("The slice in the {0} direction will remove " "all elements. If you want a single-channel " "slice, you need a different approach." .format(xx)) slices = [slice(limit_dict[xx+'lo'], limit_dict[xx+'hi']) for xx in 'zyx'] log.debug('slices: {0}'.format(slices)) return self[slices] def subcube_from_ds9region(self, ds9region, allow_empty=False): """ Extract a masked subcube from a ds9 region or a pyregion Region object (only functions on celestial dimensions) Parameters ---------- ds9region: str or `pyregion.Shape` The region to extract allow_empty: bool If this is False, an exception will be raised if the region contains no overlap with the cube """ import pyregion if isinstance(ds9region, six.string_types): shapelist = pyregion.parse(ds9region) else: shapelist = ds9region if shapelist[0].coord_format not in ('physical','image'): # Requires astropy >0.4... # pixel_regions = shapelist.as_imagecoord(self.wcs.celestial.to_header()) # convert the regions to image (pixel) coordinates celhdr = self.wcs.sub([wcs.WCSSUB_CELESTIAL]).to_header() celhdr['NAXIS1'] = self.shape[2] celhdr['NAXIS2'] = self.shape[1] pixel_regions = shapelist.as_imagecoord(celhdr) recompute_shifted_mask = False else: pixel_regions = copy.deepcopy(shapelist) # we need to change the reference pixel after cropping recompute_shifted_mask = True # This is a hack to use mpl to determine the outer bounds of the regions # (but it's a legit hack - pyregion needs a major internal refactor # before we can approach this any other way, I think -AG) mpl_objs = pixel_regions.get_mpl_patches_texts(origin=0)[0] # Find the minimal enclosing box containing all of the regions # (this will speed up the mask creation below) extent = mpl_objs[0].get_extents() xlo, ylo = extent.min xhi, yhi = extent.max all_extents = [obj.get_extents() for obj in mpl_objs] for ext in all_extents: xlo = int(np.floor(xlo if xlo < ext.min[0] else ext.min[0])) ylo = int(np.floor(ylo if ylo < ext.min[1] else ext.min[1])) xhi = int(np.ceil(xhi if xhi > ext.max[0] else ext.max[0])) yhi = int(np.ceil(yhi if yhi > ext.max[1] else ext.max[1])) # Negative indices will do bad things, like wrap around the cube # If xhi/yhi are negative, there is not overlap if (xhi < 0) or (yhi < 0): raise ValueError("Region is outside of cube.") # if xlo/ylo are negative, we need to crop if xlo < 0: xlo = 0 if ylo < 0: ylo = 0 log.debug("Region boundaries: ") log.debug("xlo={xlo}, ylo={ylo}, xhi={xhi}, yhi={yhi}".format(xlo=xlo, ylo=ylo, xhi=xhi, yhi=yhi)) subcube = self.subcube(xlo=xlo, ylo=ylo, xhi=xhi, yhi=yhi) if any(dim == 0 for dim in subcube.shape): if allow_empty: warnings.warn("The derived subset is empty: the region does not" " overlap with the cube (but allow_empty=True).") else: raise ValueError("The derived subset is empty: the region does not" " overlap with the cube.") if recompute_shifted_mask: # for pixel-based regions (which we use in tests), we need to shift # the coordinates for mask computation because we're cropping the # cube for reg in pixel_regions: reg.params[0].v -= xlo reg.params[1].v -= ylo reg.params[0].text = str(reg.params[0].v) reg.params[1].text = str(reg.params[1].v) reg.coord_list[0] -= xlo reg.coord_list[1] -= ylo # use the pixel-based, shifted mask mask = pixel_regions.get_mask(header=subcube.wcs.celestial.to_header(), shape=subcube.shape[1:]) else: # use the original, coordinate-based mask since the pixel mask has # *not* been shfited to match the original coordinate system celhdr = subcube.wcs.celestial.to_header() celhdr['NAXIS1'] = self.shape[2] celhdr['NAXIS2'] = self.shape[1] mask = shapelist.get_mask(header=celhdr, shape=subcube.shape[1:]) if not allow_empty and mask.sum() == 0: raise ValueError("The derived subset is empty: the region does not" " overlap with the cube. However, this is likely " "to be a bug, since at an earlier stage there was " "overlap.") masked_subcube = subcube.with_mask(BooleanArrayMask(mask, subcube.wcs, shape=subcube.shape)) # by using ceil / floor above, we potentially introduced a NaN buffer # that we can now crop out return masked_subcube.minimal_subcube(spatial_only=True) def _val_to_own_unit(self, value, operation='compare', tofrom='to', keepunit=False): """ Given a value, check if it has a unit. If it does, convert to the cube's unit. If it doesn't, raise an exception. """ if isinstance(value, SpectralCube): if self.unit.is_equivalent(value.unit): return value else: return value.to(self.unit) elif hasattr(value, 'unit'): if keepunit: return value.to(self.unit) else: return value.to(self.unit).value else: raise ValueError("Can only {operation} cube objects {tofrom}" " SpectralCubes or Quantities with " "a unit attribute." .format(operation=operation, tofrom=tofrom)) def __gt__(self, value): """ Return a LazyMask representing the inequality Parameters ---------- value : number The threshold """ value = self._val_to_own_unit(value) return LazyComparisonMask(operator.gt, value, data=self._data, wcs=self._wcs) def __ge__(self, value): value = self._val_to_own_unit(value) return LazyComparisonMask(operator.ge, value, data=self._data, wcs=self._wcs) def __le__(self, value): value = self._val_to_own_unit(value) return LazyComparisonMask(operator.le, value, data=self._data, wcs=self._wcs) def __lt__(self, value): value = self._val_to_own_unit(value) return LazyComparisonMask(operator.lt, value, data=self._data, wcs=self._wcs) def __eq__(self, value): value = self._val_to_own_unit(value) return LazyComparisonMask(operator.eq, value, data=self._data, wcs=self._wcs) def __hash__(self): return id(self) def __ne__(self, value): value = self._val_to_own_unit(value) return LazyComparisonMask(operator.ne, value, data=self._data, wcs=self._wcs) def __add__(self, value): if isinstance(value, SpectralCube): return self._cube_on_cube_operation(operator.add, value) else: value = self._val_to_own_unit(value, operation='add', tofrom='from', keepunit=True) return self._apply_everywhere(operator.add, value) def __sub__(self, value): if isinstance(value, SpectralCube): return self._cube_on_cube_operation(operator.sub, value) else: value = self._val_to_own_unit(value, operation='subtract', tofrom='from', keepunit=True) return self._apply_everywhere(operator.sub, value) def __mul__(self, value): if isinstance(value, SpectralCube): return self._cube_on_cube_operation(operator.mul, value) else: return self._apply_everywhere(operator.mul, value) def __truediv__(self, value): return self.__div__(value) def __div__(self, value): if isinstance(value, SpectralCube): return self._cube_on_cube_operation(operator.truediv, value) else: return self._apply_everywhere(operator.truediv, value) def __pow__(self, value): if isinstance(value, SpectralCube): return self._cube_on_cube_operation(operator.pow, value) else: return self._apply_everywhere(operator.pow, value) @classmethod def read(cls, filename, format=None, hdu=None, **kwargs): """ Read a spectral cube from a file. If the file contains Stokes axes, they will automatically be dropped. If you want to read in all Stokes informtion, use :meth:`~spectral_cube.StokesSpectralCube.read` instead. Parameters ---------- filename : str The file to read the cube from format : str The format of the file to read. (Currently limited to 'fits' and 'casa_image') hdu : int or str For FITS files, the HDU to read in (can be the ID or name of an HDU). kwargs : dict If the format is 'fits', the kwargs are passed to :func:`~astropy.io.fits.open`. """ from .io.core import read from .stokes_spectral_cube import StokesSpectralCube cube = read(filename, format=format, hdu=hdu, **kwargs) if isinstance(cube, StokesSpectralCube): if hasattr(cube, 'I'): warnings.warn("Cube is a Stokes cube, " "returning spectral cube for I component") return cube.I else: raise ValueError("Spectral cube is a Stokes cube that " "does not have an I component") else: return cube def write(self, filename, overwrite=False, format=None): """ Write the spectral cube to a file. Parameters ---------- filename : str The path to write the file to format : str The format of the file to write. (Currently limited to 'fits') overwrite : bool If True, overwrite ``filename`` if it exists """ from .io.core import write write(filename, self, overwrite=overwrite, format=format) def to_yt(self, spectral_factor=1.0, nprocs=None, **kwargs): """ Convert a spectral cube to a yt object that can be further analyzed in yt. Parameters ---------- spectral_factor : float, optional Factor by which to stretch the spectral axis. If set to 1, one pixel in spectral coordinates is equivalent to one pixel in spatial coordinates. If using yt 3.0 or later, additional keyword arguments will be passed onto yt's ``FITSDataset`` constructor. See the yt documentation (http://yt-project.org/docs/3.0/examining/loading_data.html?#fits-data) for details on options for reading FITS data. """ import yt if (('dev' in yt.__version__) or (LooseVersion(yt.__version__) >= LooseVersion('3.0'))): from yt.frontends.fits.api import FITSDataset from yt.units.unit_object import UnitParseError hdu = PrimaryHDU(self._get_filled_data(fill=0.), header=self.wcs.to_header()) units = str(self.unit.to_string()) hdu.header["BUNIT"] = units hdu.header["BTYPE"] = "flux" ds = FITSDataset(hdu, nprocs=nprocs, spectral_factor=spectral_factor, **kwargs) # Check to make sure the units are legit try: ds.quan(1.0,units) except UnitParseError: raise RuntimeError("The unit %s was not parsed by yt. " % units+ "Check to make sure it is correct.") else: from yt.mods import load_uniform_grid data = {'flux': self._get_filled_data(fill=0.).transpose()} nz, ny, nx = self.shape if nprocs is None: nprocs = 1 bbox = np.array([[0.5,float(nx)+0.5], [0.5,float(ny)+0.5], [0.5,spectral_factor*float(nz)+0.5]]) ds = load_uniform_grid(data, [nx,ny,nz], 1., bbox=bbox, nprocs=nprocs, periodicity=(False, False, False)) return ytCube(self, ds, spectral_factor=spectral_factor) def to_glue(self, name=None, glue_app=None, dataset=None, start_gui=True): """ Send data to a new or existing Glue application Parameters ---------- name : str or None The name of the dataset within Glue. If None, defaults to 'SpectralCube'. If a dataset with the given name already exists, a new dataset with "_" appended will be added instead. glue_app : GlueApplication or None A glue application to send the data to. If this is not specified, a new glue application will be started if one does not already exist for this cube. Otherwise, the data will be sent to the existing glue application, `self._glue_app`. dataset : glue.core.Data or None An existing Data object to add the cube to. This is a good way to compare cubes with the same dimensions. Supercedes ``glue_app`` start_gui : bool Start the GUI when this is run. Set to `False` for testing. """ if name is None: name = 'SpectralCube' from glue.app.qt import GlueApplication from glue.core import DataCollection, Data from glue.core.coordinates import coordinates_from_header from glue.viewers.image.qt.viewer_widget import ImageWidget if dataset is not None: if name in [d.label for d in dataset.components]: name = name+"_" dataset[name] = self else: result = Data(label=name) result.coords = coordinates_from_header(self.header) result.add_component(self, name) if glue_app is None: if hasattr(self,'_glue_app'): glue_app = self._glue_app else: # Start a new glue session. This will quit when done. # I don't think the return statement is ever reached, based on # past attempts [@ChrisBeaumont - chime in here if you'd like] dc = DataCollection([result]) #start Glue ga = self._glue_app = GlueApplication(dc) self._glue_viewer = ga.new_data_viewer(ImageWidget, data=result) if start_gui: self._glue_app.start() return self._glue_app glue_app.add_datasets(self._glue_app.data_collection, result) def to_pvextractor(self): """ Open the cube in a quick viewer written in matplotlib that allows you to create PV extractions within the GUI """ from pvextractor.gui import PVSlicer return PVSlicer(self) def to_ds9(self, ds9id=None, newframe=False): """ Send the data to ds9 (this will create a copy in memory) Parameters ---------- ds9id: None or string The DS9 session ID. If 'None', a new one will be created. To find your ds9 session ID, open the ds9 menu option File:XPA:Information and look for the XPA_METHOD string, e.g. ``XPA_METHOD: 86ab2314:60063``. You would then calll this function as ``cube.to_ds9('86ab2314:60063')`` newframe: bool Send the cube to a new frame or to the current frame? """ try: import ds9 except ImportError: import pyds9 as ds9 if ds9id is None: dd = ds9.DS9(start=True) else: dd = ds9.DS9(target=ds9id, start=False) if newframe: dd.set('frame new') dd.set_pyfits(self.hdulist) return dd @property def header(self): log.debug("Creating header") # Preserve non-WCS information from previous header iteration header = self._nowcs_header header.update(self.wcs.to_header()) if self.unit == u.dimensionless_unscaled and 'BUNIT' in self._meta: # preserve the BUNIT even though it's not technically valid # (Jy/Beam) header['BUNIT'] = self._meta['BUNIT'] else: header['BUNIT'] = self.unit.to_string(format='FITS') header.insert(2, Card(keyword='NAXIS', value=self._data.ndim)) header.insert(3, Card(keyword='NAXIS1', value=self.shape[2])) header.insert(4, Card(keyword='NAXIS2', value=self.shape[1])) header.insert(5, Card(keyword='NAXIS3', value=self.shape[0])) # Preserve the cube's spectral units # (if CUNIT3 is not in the header, it is whatever that type's default unit is) if 'CUNIT3' in header and self._spectral_unit != u.Unit(header['CUNIT3']): header['CDELT3'] *= self._spectral_scale header['CRVAL3'] *= self._spectral_scale header['CUNIT3'] = self._spectral_unit.to_string(format='FITS') if 'beam' in self._meta: header = self._meta['beam'].attach_to_header(header) # TODO: incorporate other relevant metadata here return header @property def hdu(self): """ HDU version of self """ log.debug("Creating HDU") hdu = PrimaryHDU(self.filled_data[:].value, header=self.header) return hdu @property def hdulist(self): return HDUList(self.hdu) @warn_slow def to(self, unit, equivalencies=()): """ Return the cube converted to the given unit (assuming it is equivalent). If conversion was required, this will be a copy, otherwise it will """ if not isinstance(unit, u.Unit): unit = u.Unit(unit) if unit == self.unit: # No copying return self if self.unit.is_equivalent(u.Jy/u.beam): # replace "beam" with the actual beam if not hasattr(self, 'beam'): raise ValueError("To convert cubes with Jy/beam units, " "the cube needs to have a beam defined.") brightness_unit = self.unit * u.beam # create a beam equivalency for brightness temperature bmequiv = self.beam.jtok_equiv(self.with_spectral_unit(u.Hz).spectral_axis) factor = brightness_unit.to(unit, equivalencies=bmequiv+list(equivalencies)) else: # scaling factor factor = self.unit.to(unit, equivalencies=equivalencies) # special case: array in equivalencies # (I don't think this should have to be special cased, but I don't know # how to manipulate broadcasting rules any other way) if hasattr(factor, '__len__') and len(factor) == len(self): return self._new_cube_with(data=self._data*factor[:,None,None], unit=unit) else: return self._new_cube_with(data=self._data*factor, unit=unit) def find_lines(self, velocity_offset=None, velocity_convention=None, rest_value=None, **kwargs): """ Using astroquery's splatalogue interface, search for lines within the spectral band. See `astroquery.splatalogue.Splatalogue` for information on keyword arguments Parameters ---------- velocity_offset : u.km/u.s equivalent An offset by which the spectral axis should be shifted before searching splatalogue. This value will be *added* to the velocity, so if you want to redshift a spectrum, make this value positive, and if you want to un-redshift it, make this value negative. velocity_convention : 'radio', 'optical', 'relativistic' The doppler convention to pass to `with_spectral_unit` rest_value : u.GHz equivalent The rest frequency (or wavelength or energy) to be passed to `with_spectral_unit` """ warnings.warn("The line-finding routine is experimental. Please " "report bugs on the Issues page: " "https://github.com/radio-astro-tools/spectral-cube/issues") from astroquery.splatalogue import Splatalogue if velocity_convention in DOPPLER_CONVENTIONS: velocity_convention = DOPPLER_CONVENTIONS[velocity_convention] if velocity_offset is not None: newspecaxis = self.with_spectral_unit(u.km/u.s, velocity_convention=velocity_convention, rest_value=rest_value).spectral_axis spectral_axis = (newspecaxis + velocity_offset).to(u.GHz, velocity_convention(rest_value)) else: spectral_axis = self.spectral_axis.to(u.GHz) numin,numax = spectral_axis.min(), spectral_axis.max() log.log(19, "Min/max frequency: {0},{1}".format(numin, numax)) result = Splatalogue.query_lines(numin, numax, **kwargs) return result def reproject(self, header, order='bilinear'): """ Reproject the cube into a new header. Fills the data with the cube's ``fill_value`` to replace bad values before reprojection. Parameters ---------- header : `astropy.io.fits.Header` A header specifying a cube in valid WCS order : int or str, optional The order of the interpolation (if ``mode`` is set to ``'interpolation'``). This can be either one of the following strings: * 'nearest-neighbor' * 'bilinear' * 'biquadratic' * 'bicubic' or an integer. A value of ``0`` indicates nearest neighbor interpolation. """ try: from reproject.version import version except ImportError: raise ImportError("Requires the reproject package to be" " installed.") # Need version > 0.2 to work with cubes from distutils.version import LooseVersion if LooseVersion(version) < "0.3": raise Warning("Requires version >=0.3 of reproject. The current " "version is: {}".format(version)) from reproject import reproject_interp # TODO: Find the minimal subcube that contains the header and only reproject that # (see FITS_tools.regrid_cube for a guide on how to do this) newwcs = wcs.WCS(header) shape_out = [header['NAXIS{0}'.format(i + 1)] for i in range(header['NAXIS'])][::-1] newcube, newcube_valid = reproject_interp((self.filled_data[:], self.header), newwcs, shape_out=shape_out, order=order, independent_celestial_slices=True) return self._new_cube_with(data=newcube, wcs=newwcs, mask=BooleanArrayMask(newcube_valid.astype('bool'), newwcs), meta=self.meta, ) class SpectralCube(BaseSpectralCube): __name__ = "SpectralCube" _oned_spectrum = OneDSpectrum def __init__(self, data, wcs, mask=None, meta=None, fill_value=np.nan, header=None, allow_huge_operations=False, beam=None, wcs_tolerance=0.0, **kwargs): super(SpectralCube, self).__init__(data=data, wcs=wcs, mask=mask, meta=meta, fill_value=fill_value, header=header, allow_huge_operations=allow_huge_operations, wcs_tolerance=wcs_tolerance, **kwargs) # Beam loading must happen *after* WCS is read if beam is None: beam = cube_utils.try_load_beam(self.header) else: if not isinstance(beam, Beam): raise TypeError("beam must be a radio_beam.Beam object.") if beam is not None: self.beam = beam self._meta['beam'] = beam self._header.update(beam.to_header_keywords()) self.pixels_per_beam = (self.beam.sr / (astropy.wcs.utils.proj_plane_pixel_area(self.wcs) * u.deg**2)).to(u.dimensionless_unscaled).value def _new_cube_with(self, **kwargs): beam = kwargs.pop('beam', None) if 'beam' in self._meta and beam is None: beam = self.beam newcube = super(SpectralCube, self)._new_cube_with(beam=beam, **kwargs) return newcube _new_cube_with.__doc__ = BaseSpectralCube._new_cube_with.__doc__ def with_beam(self, beam): ''' Attach a beam object to the `~SpectralCube`. Parameters ---------- beam : `~radio_beam.Beam` `Beam` object defining the resolution element of the `~SpectralCube`. ''' if not isinstance(beam, Beam): raise TypeError("beam must be a radio_beam.Beam object.") meta = self._meta.copy() meta['beam'] = beam header = self._header.copy() header.update(beam.to_header_keywords()) newcube = self._new_cube_with(meta=self.meta, beam=beam) return newcube def spatial_smooth_median(self, ksize, update_function=None, **kwargs): """ Smooth the image in each spatial-spatial plane of the cube using a median filter. Parameters ---------- ksize : int Size of the median filter (scipy.ndimage.filters.median_filter) update_function : method Method that is called to update an external progressbar If provided, it disables the default `astropy.utils.console.ProgressBar` kwargs : dict Passed to the convolve function """ if not scipyOK: raise ImportError("Scipy could not be imported: this function won't work.") shape = self.shape # "imagelist" is a generator # the boolean check will skip smoothing for bad spectra # TODO: should spatial good/bad be cached? imagelist = ((self.filled_data[ii], self.mask.include(view=(ii, slice(None), slice(None)))) for ii in range(self.shape[0])) if update_function is None: pb = ProgressBar(shape[0]) update_function = pb.update def _gsmooth_image(args): """ Helper function to smooth a spectrum """ (im, includemask),kwargs = args update_function() if includemask.any(): return ndimage.filters.median_filter(im, size=ksize) else: return im # could be numcores, except _gsmooth_spectrum is unpicklable with cube_utils._map_context(1) as map: smoothcube_ = np.array([x for x in map(_gsmooth_image, zip(imagelist, itertools.cycle([kwargs]), ) ) ]) # TODO: do something about the mask? newcube = self._new_cube_with(data=smoothcube_, wcs=self.wcs, mask=self.mask, meta=self.meta, fill_value=self.fill_value) return newcube def spatial_smooth(self, kernel, #numcores=None, convolve=convolution.convolve, update_function=None, **kwargs): """ Smooth the image in each spatial-spatial plane of the cube. Parameters ---------- kernel : `~astropy.convolution.Kernel2D` A 2D kernel from astropy convolve : function The astropy convolution function to use, either `astropy.convolution.convolve` or `astropy.convolution.convolve_fft` update_function : method Method that is called to update an external progressbar If provided, it disables the default `astropy.utils.console.ProgressBar` kwargs : dict Passed to the convolve function """ shape = self.shape # "imagelist" is a generator # the boolean check will skip smoothing for bad spectra # TODO: should spatial good/bad be cached? imagelist = ((self.filled_data[ii], self.mask.include(view=(ii, slice(None), slice(None)))) for ii in range(self.shape[0])) if update_function is None: pb = ProgressBar(shape[0]) update_function = pb.update def _gsmooth_image(args): """ Helper function to smooth an image """ (im, includemask),kernel,kwargs = args update_function() if includemask.any(): return convolve(im, kernel, normalize_kernel=True, **kwargs) else: return im # could be numcores, except _gsmooth_spectrum is unpicklable with cube_utils._map_context(1) as map: smoothcube_ = np.array([x for x in map(_gsmooth_image, zip(imagelist, itertools.cycle([kernel]), itertools.cycle([kwargs]), ) ) ]) # TODO: do something about the mask? newcube = self._new_cube_with(data=smoothcube_, wcs=self.wcs, mask=self.mask, meta=self.meta, fill_value=self.fill_value) return newcube def spectral_smooth_median(self, ksize, use_memmap=True, verbose=0, num_cores=None, **kwargs): """ Smooth the cube along the spectral dimension Parameters ---------- ksize : int Size of the median filter (scipy.ndimage.filters.median_filter) verbose : int Verbosity level to pass to joblib use_memmap : bool If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory. num_cores : int or None The number of cores to use if running in parallel kwargs : dict Not used at the moment. """ if not scipyOK: raise ImportError("Scipy could not be imported: this function won't work.") return self._apply_function_parallel_spectral(ndimage.filters.median_filter, data=self.filled_data, size=ksize, num_cores=num_cores, use_memmap=use_memmap, **kwargs) def _apply_function_parallel_spectral(self, function, data, num_cores=None, verbose=0, use_memmap=True, parallel=True, **kwargs ): """ Apply a function in parallel along the spectral dimension. The function will be performed on data with masked values replaced with the cube's fill value. Parameters ---------- function : function The function to apply in the spectral dimension. It must take two arguments: an array representing a spectrum and a boolean array representing the mask. It may also accept **kwargs. The function must return an object with the same shape as the input spectrum. verbose : int Verbosity level to pass to joblib use_memmap : bool If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory. num_cores : int or None The number of cores to use if running in parallel parallel : bool If set to ``False``, will force the use of a single core without using ``joblib``. kwargs : dict Passed to ``function`` """ shape = self.shape # 'spectra' is a generator # the boolean check will skip the function for bad spectra # TODO: should spatial good/bad be cached? spectra = ((data[:,jj,ii], self.mask.include(view=(slice(None), jj, ii)), ii, jj, ) for jj in range(shape[1]) for ii in range(shape[2])) if use_memmap: ntf = tempfile.NamedTemporaryFile() outcube = np.memmap(ntf, mode='w+', shape=shape, dtype=np.float) else: if self._is_huge and not self.allow_huge_operations: raise ValueError("Applying a function without ``use_memmap`` " "requires loading the whole array into " "memory *twice*, which can overload the " "machine's memory for large cubes. Either " "set ``use_memmap=True`` or set " "``cube.allow_huge_operations=True`` to " "override this restriction.") outcube = np.empty(shape=shape, dtype=np.float) if parallel and use_memmap: # it is not possible to run joblib parallelization without memmap try: from joblib import Parallel, delayed Parallel(n_jobs=num_cores, verbose=verbose, max_nbytes=None)(delayed(_apply_function)(arg, outcube, function, **kwargs) for arg in spectra) except ImportError: if num_cores is not None and num_cores > 1: warnings.warn("Could not import joblib. Will run in serial.", ImportError) parallel = False # this isn't an else statement because we want to catch the case where # the above clause fails on ImportError if not parallel or not use_memmap: if verbose > 0: progressbar = ProgressBar(self.shape[1]*self.shape[2]) pbu = progressbar.update else: pbu = object for arg in spectra: _apply_function(arg, outcube, function, **kwargs) pbu() # TODO: do something about the mask? newcube = self._new_cube_with(data=outcube, wcs=self.wcs, mask=self.mask, meta=self.meta, fill_value=self.fill_value) return newcube def spectral_smooth(self, kernel, convolve=convolution.convolve, verbose=0, use_memmap=True, num_cores=None, **kwargs): """ Smooth the cube along the spectral dimension Note that the mask is left unchanged in this operation. Parameters ---------- kernel : `~astropy.convolution.Kernel1D` A 1D kernel from astropy convolve : function The astropy convolution function to use, either `astropy.convolution.convolve` or `astropy.convolution.convolve_fft` verbose : int Verbosity level to pass to joblib use_memmap : bool If specified, a memory mapped temporary file on disk will be written to rather than storing the intermediate spectra in memory. num_cores : int or None The number of cores to use if running in parallel kwargs : dict Passed to the convolve function """ return self._apply_function_parallel_spectral(convolve, data=self.filled_data, kernel=kernel, normalize_kernel=True, num_cores=num_cores, use_memmap=use_memmap, verbose=verbose, **kwargs) def spectral_interpolate(self, spectral_grid, suppress_smooth_warning=False, fill_value=None, update_function=None): """Resample the cube spectrally onto a specific grid Parameters ---------- spectral_grid : array An array of the spectral positions to regrid onto suppress_smooth_warning : bool If disabled, a warning will be raised when interpolating onto a grid that does not nyquist sample the existing grid. Disable this if you have already appropriately smoothed the data. fill_value : float Value for extrapolated spectral values that lie outside of the spectral range defined in the original data. The default is to use the nearest spectral channel in the cube. update_function : method Method that is called to update an external progressbar If provided, it disables the default `astropy.utils.console.ProgressBar` Returns ------- cube : SpectralCube """ inaxis = self.spectral_axis.to(spectral_grid.unit) indiff = np.mean(np.diff(inaxis)) outdiff = np.mean(np.diff(spectral_grid)) # account for reversed axes if outdiff < 0: spectral_grid = spectral_grid[::-1] outdiff = np.mean(np.diff(spectral_grid)) outslice = slice(None, None, -1) else: outslice = slice(None, None, 1) cubedata = self.filled_data specslice = slice(None) if indiff >= 0 else slice(None, None, -1) inaxis = inaxis[specslice] indiff = np.mean(np.diff(inaxis)) # insanity checks if indiff < 0 or outdiff < 0: raise ValueError("impossible.") assert np.all(np.diff(spectral_grid) > 0) assert np.all(np.diff(inaxis) > 0) np.testing.assert_allclose(np.diff(spectral_grid), outdiff, err_msg="Output grid must be linear") if outdiff > 2 * indiff and not suppress_smooth_warning: warnings.warn("Input grid has too small a spacing. The data should " "be smoothed prior to resampling.") newcube = np.empty([spectral_grid.size, self.shape[1], self.shape[2]], dtype=cubedata[:1, 0, 0].dtype) newmask = np.empty([spectral_grid.size, self.shape[1], self.shape[2]], dtype='bool') yy,xx = np.indices(self.shape[1:]) if update_function is None: pb = ProgressBar(xx.size) update_function = pb.update for ix, iy in (zip(xx.flat, yy.flat)): mask = self.mask.include(view=(specslice, iy, ix)) if any(mask): newcube[outslice,iy,ix] = \ np.interp(spectral_grid.value, inaxis.value, cubedata[specslice,iy,ix].value, left=fill_value, right=fill_value) if all(mask): newmask[:,iy,ix] = True else: interped = np.interp(spectral_grid.value, inaxis.value, mask) > 0 newmask[outslice,iy,ix] = interped else: newmask[:, iy, ix] = False newcube[:, iy, ix] = np.NaN update_function() newwcs = self.wcs.deepcopy() newwcs.wcs.crpix[2] = 1 newwcs.wcs.crval[2] = spectral_grid[0].value if outslice.step > 0 \ else spectral_grid[-1].value newwcs.wcs.cunit[2] = spectral_grid.unit.to_string('FITS') newwcs.wcs.cdelt[2] = outdiff.value if outslice.step > 0 \ else -outdiff.value newwcs.wcs.set() newbmask = BooleanArrayMask(newmask, wcs=newwcs) newcube = self._new_cube_with(data=newcube, wcs=newwcs, mask=newbmask, meta=self.meta, fill_value=self.fill_value) return newcube def convolve_to(self, beam, convolve=convolution.convolve_fft, update_function=None): """ Convolve each channel in the cube to a specified beam Parameters ---------- beam : `radio_beam.Beam` The beam to convolve to convolve : function The astropy convolution function to use, either `astropy.convolution.convolve` or `astropy.convolution.convolve_fft` update_function : method Method that is called to update an external progressbar If provided, it disables the default `astropy.utils.console.ProgressBar` Returns ------- cube : `SpectralCube` A SpectralCube with a single ``beam`` """ pixscale = wcs.utils.proj_plane_pixel_area(self.wcs.celestial)**0.5*u.deg convolution_kernel = beam.deconvolve(self.beam).as_kernel(pixscale) if update_function is None: pb = ProgressBar(self.shape[0]) update_function = pb.update newdata = np.empty(self.shape) for ii,img in enumerate(self.filled_data[:]): newdata[ii,:,:] = convolve(img, convolution_kernel, normalize_kernel=True) update_function() newcube = self._new_cube_with(data=newdata, beam=beam) return newcube def mask_channels(self, goodchannels): """ Helper function to mask out channels. This function is equivalent to adding a mask with ``cube[view]`` where ``view`` is broadcastable to the cube shape, but it accepts 1D arrays that are not normally broadcastable. Parameters ---------- goodchannels : array A 1D boolean array declaring which channels should be kept. Returns ------- cube : `SpectralCube` A cube with the specified channels masked """ goodchannels = np.asarray(goodchannels, dtype='bool') if goodchannels.ndim != 1: raise ValueError("goodchannels mask must be one-dimensional") if goodchannels.size != self.shape[0]: raise ValueError("goodchannels must have a length equal to the " "cube's spectral dimension.") return self.with_mask(goodchannels[:,None,None]) class VaryingResolutionSpectralCube(BaseSpectralCube, MultiBeamMixinClass): """ A variant of the SpectralCube class that has PSF (beam) information on a per-channel basis. """ __name__ = "VaryingResolutionSpectralCube" _oned_spectrum = VaryingResolutionOneDSpectrum def __init__(self, *args, **kwargs): """ Create a SpectralCube with an associated beam table. The new VaryingResolutionSpectralCube will have a ``beams`` attribute and a ``beam_threshold`` attribute as described below. It will perform some additional checks when trying to perform analysis across image frames. Three new keyword arguments are accepted: Other Parameters ---------------- beam_table : `numpy.recarray` A table of beam major and minor axes in arcseconds and position angles, with labels BMAJ, BMIN, BPA beams : list A list of `radio_beam.Beam` objects beam_threshold : float or dict The fractional threshold above which beams are considered different. A dictionary may be used with entries 'area', 'major', 'minor', 'pa' so that you can specify a different fractional threshold for each of these. For example, if you want to check only that the areas are the same, and not worry about the shape (which might be a bad idea...), you could set ``beam_threshold={'area':0.01, 'major':1.5, 'minor':1.5, 'pa':5.0}`` """ # these types of cube are undefined without the radio_beam package beam_table = kwargs.pop('beam_table', None) beams = kwargs.pop('beams', None) beam_threshold = kwargs.pop('beam_threshold', 0.01) if (beam_table is None and beams is None): raise ValueError( "Must give either a beam table or a list of beams to " "initialize a VaryingResolutionSpectralCube") super(VaryingResolutionSpectralCube, self).__init__(*args, **kwargs) if isinstance(beam_table, BinTableHDU): beam_data_table = beam_table.data else: beam_data_table = beam_table if beam_table is not None: # CASA beam tables are in arcsec, and that's what we support beams = Beams(major=u.Quantity(beam_data_table['BMAJ'], u.arcsec), minor=u.Quantity(beam_data_table['BMIN'], u.arcsec), pa=u.Quantity(beam_data_table['BPA'], u.deg), meta=[{key: row[key] for key in beam_table.names if key not in ('BMAJ','BPA', 'BMIN')} for row in beam_data_table], ) goodbeams = beams.isfinite # track which, if any, beams are masked for later use self._goodbeams_mask = goodbeams if not all(goodbeams): warnings.warn("There were {0} non-finite beams; layers with " "non-finite beams will be masked out.".format( np.count_nonzero(~goodbeams))) beam_mask = BooleanArrayMask(goodbeams[:,None,None], wcs=self._wcs, shape=self.shape, ) if not is_broadcastable_and_smaller(beam_mask.shape, self._data.shape): # this should never be allowed to happen raise ValueError("Beam mask shape is not broadcastable to data shape: " "%s vs %s" % (beam_mask.shape, self._data.shape)) assert beam_mask.shape == self.shape new_mask = self._mask & beam_mask new_mask._validate_wcs(new_data=self._data, new_wcs=self._wcs) self._mask = new_mask if (len(beams) != self.shape[0]): raise ValueError("Beam list must have same size as spectral " "dimension") self._beams = beams self.beam_threshold = beam_threshold @property def beams(self): return self._beams def __getitem__(self, view): # Need to allow self[:], self[:,:] if isinstance(view, (slice,int,np.int64)): view = (view, slice(None), slice(None)) elif len(view) == 2: view = view + (slice(None),) elif len(view) > 3: raise IndexError("Too many indices") meta = {} meta.update(self._meta) slice_data = [(s.start, s.stop, s.step) if hasattr(s,'start') else s for s in view] if 'slice' in meta: meta['slice'].append(slice_data) else: meta['slice'] = [slice_data] # intslices identifies the slices that are given by integers, i.e. # indices. Other slices are slice objects, e.g. obj[5:10], and have # 'start' attributes. intslices = [2-ii for ii,s in enumerate(view) if not hasattr(s,'start')] # for beams, we care only about the first slice, independent of its # type specslice = view[0] if intslices: if len(intslices) > 1: if 2 in intslices: raise NotImplementedError("1D slices along non-spectral " "axes are not yet implemented.") newwcs = self._wcs.sub([a for a in (1,2,3) if a not in [x+1 for x in intslices]]) return self._oned_spectrum(value=self._data[view], wcs=newwcs, copy=False, unit=self.unit, spectral_unit=self._spectral_unit, mask=self.mask[view], beams=self.beams[specslice], meta=meta) # only one element, so drop an axis newwcs = wcs_utils.drop_axis(self._wcs, intslices[0]) header = self._nowcs_header # Slice objects know how to parse Beam objects stored in the # metadata # A 2D slice with a VRSC should not be allowed along a # position-spectral axis if not isinstance(self.beams[specslice], Beam): raise AttributeError("2D slices along a spectral axis are not " "allowed for " "VaryingResolutionSpectralCubes. Convolve" " to a common resolution with " "`convolve_to` before attempting " "position-spectral slicing.") meta['beam'] = self.beams[specslice] return Slice(value=self.filled_data[view], wcs=newwcs, copy=False, unit=self.unit, header=header, meta=meta) newmask = self._mask[view] if self._mask is not None else None newwcs = wcs_utils.slice_wcs(self._wcs, view, shape=self.shape) newwcs._naxis = list(self.shape) # this is an assertion to ensure that the WCS produced is valid # (this is basically a regression test for #442) assert newwcs[:, slice(None), slice(None)] return self._new_cube_with(data=self._data[view], wcs=newwcs, mask=newmask, beams=self.beams[specslice], meta=meta) def spectral_slab(self, lo, hi): """ Extract a new cube between two spectral coordinates Parameters ---------- lo, hi : :class:`~astropy.units.Quantity` The lower and upper spectral coordinate for the slab range. The units should be compatible with the units of the spectral axis. If the spectral axis is in frequency-equivalent units and you want to select a range in velocity, or vice-versa, you should first use :meth:`~spectral_cube.SpectralCube.with_spectral_unit` to convert the units of the spectral axis. """ # Find range of values for spectral axis ilo = self.closest_spectral_channel(lo) ihi = self.closest_spectral_channel(hi) if ilo > ihi: ilo, ihi = ihi, ilo ihi += 1 # Create WCS slab wcs_slab = self._wcs.deepcopy() wcs_slab.wcs.crpix[2] -= ilo # Create mask slab if self._mask is None: mask_slab = None else: try: mask_slab = self._mask[ilo:ihi, :, :] except NotImplementedError: warnings.warn("Mask slicing not implemented for " "{0} - dropping mask". format(self._mask.__class__.__name__)) mask_slab = None # Create new spectral cube slab = self._new_cube_with(data=self._data[ilo:ihi], wcs=wcs_slab, beams=self.beams[ilo:ihi], mask=mask_slab) return slab def _new_cube_with(self, goodbeams_mask=None, **kwargs): beams = kwargs.pop('beams', self.beams) beam_threshold = kwargs.pop('beam_threshold', self.beam_threshold) VRSC = VaryingResolutionSpectralCube newcube = super(VRSC, self)._new_cube_with(beams=beams, beam_threshold=beam_threshold, **kwargs) if goodbeams_mask is not None: newcube._goodbeams_mask = goodbeams_mask else: newcube._goodbeams_mask = newcube.beams.isfinite return newcube _new_cube_with.__doc__ = BaseSpectralCube._new_cube_with.__doc__ def _check_beam_areas(self, threshold, mean_beam, mask=None): """ Check that the beam areas are the same to within some threshold """ if mask is not None: assert len(mask) == len(self.beams) mask = np.array(mask, dtype='bool') else: mask = np.ones(len(self.beams), dtype='bool') qtys = dict(sr=self.beams.sr, major=self.beams.major.to(u.deg), minor=self.beams.minor.to(u.deg), # position angles are not really comparable #pa=u.Quantity([bm.pa for bm in self.beams], u.deg), ) errormessage = "" for (qtyname, qty) in (qtys.items()): minv = qty[mask].min() maxv = qty[mask].max() mn = getattr(mean_beam, qtyname) maxdiff = (np.max(np.abs(u.Quantity((maxv-mn, minv-mn))))/mn).decompose() if isinstance(threshold, dict): th = threshold[qtyname] else: th = threshold if maxdiff > th: errormessage += ("Beam {2}s differ by up to {0}x, which is greater" " than the threshold {1}\n".format(maxdiff, threshold, qtyname )) if errormessage != "": raise ValueError(errormessage) def identify_bad_beams(self, threshold, reference_beam=None, criteria=['sr','major','minor'], mid_value=np.nanmedian): """ Mask out any layers in the cube that have beams that differ from the central value of the beam by more than the specified threshold. An acceptable beam area can also be specified directly. Parameters ---------- threshold : float Fractional threshold reference_beam : Beam A beam to use as the reference. If unspecified, ``mid_value`` will be used to select a middle beam criteria : list A list of criteria to compare. Can include 'sr','major','minor','pa' or any subset of those. mid_value : function The function used to determine the 'mid' value to compare to. This will identify the middle-valued beam area. Returns ------- includemask : np.array A boolean array where ``True`` indicates the good beams """ includemask = np.ones(self.beams.size, dtype='bool') all_criteria = ['sr','major','minor','pa'] if not set.issubset(set(criteria), set(all_criteria)): raise ValueError("Criteria must be one of the allowed options: " "{0}".format(all_criteria)) props = {prop: u.Quantity([getattr(beam, prop) for beam in self.beams]) for prop in all_criteria} if reference_beam is None: reference_beam = Beam(major=mid_value(props['major']), minor=mid_value(props['minor']), pa=mid_value(props['pa']) ) for prop in criteria: val = props[prop] mid = getattr(reference_beam, prop) diff = np.abs((val-mid)/mid) assert diff.shape == includemask.shape includemask[diff > threshold] = False return includemask def mask_out_bad_beams(self, threshold, reference_beam=None, criteria=['sr','major','minor'], mid_value=np.nanmedian): """ See `identify_bad_beams`. This function returns a masked cube Returns ------- newcube : VaryingResolutionSpectralCube The cube with bad beams masked out """ goodbeams = self.identify_bad_beams(threshold=threshold, reference_beam=reference_beam, criteria=criteria, mid_value=mid_value) includemask = BooleanArrayMask(goodbeams[:,None,None], self._wcs, shape=self._data.shape) return self._new_cube_with(mask=self.mask & includemask, beam_threshold=threshold, goodbeams_mask=self._goodbeams_mask & goodbeams, ) def average_beams(self, threshold, mask='compute', warn=False): """ Average the beams. Note that this operation only makes sense in limited contexts! Generally one would want to convolve all the beams to a common shape, but this method is meant to handle the "simple" case when all your beams are the same to within some small factor and can therefore be arithmetically averaged. Parameters ---------- threshold : float The fractional difference between beam major, minor, and pa to permit mask : 'compute', None, or boolean array The mask to apply to the beams. Useful for excluding bad channels and edge beams. warn : bool Warn if successful? Returns ------- new_beam : radio_beam.Beam A new radio beam object that is the average of the unmasked beams """ if mask == 'compute': beam_mask = np.any(self.mask.include() & self._goodbeams_mask[:,None,None], axis=(1,2)) else: if mask.ndim > 1: beam_mask = mask & self._goodbeams_mask[:,None,None] else: beam_mask = mask & self._goodbeams_mask new_beam = self.beams.average_beam(includemask=beam_mask) if np.isnan(new_beam): raise ValueError("Beam was not finite after averaging. " "This either indicates that there was a problem " "with the include mask, one of the beam's values, " "or a bug.") self._check_beam_areas(threshold, mean_beam=new_beam, mask=beam_mask) if warn: warnings.warn("Arithmetic beam averaging is being performed. This is " "not a mathematically robust operation, but is being " "permitted because the beams differ by " "<{0}".format(threshold), BeamAverageWarning ) return new_beam def _handle_beam_areas_wrapper(self, function, beam_threshold=None): """ Wrapper: if the function takes "axis" and is operating over axis 0 (the spectral axis), check that the beam threshold is not exceeded before performing the operation Also, if the operation *is* valid, average the beam appropriately to get the output """ if beam_threshold is None: beam_threshold = self.beam_threshold def newfunc(*args, **kwargs): """ Wrapper function around the standard operations to handle beams when creating projections """ # check that the spectral axis is being operated over. If it is, # we need to average beams # moments are a special case b/c they default to axis=0 need_to_handle_beams = (('axis' in kwargs and ((kwargs['axis']==0) or (hasattr(kwargs['axis'], '__len__') and 0 in kwargs['axis']))) or ('axis' not in kwargs and 'moment' in function.__name__)) if need_to_handle_beams: # do this check *first* so we don't do an expensive operation # and crash afterward avg_beam = self.average_beams(beam_threshold, warn=True) result = function(*args, **kwargs) if not isinstance(result, LowerDimensionalObject): # numpy arrays are sometimes returned; these have no metadata return result elif need_to_handle_beams: result.meta['beam'] = avg_beam result._beam = avg_beam return result return newfunc def __getattribute__(self, attrname): """ For any functions that operate over the spectral axis, perform beam sameness checks before performing the operation to avoid unexpected results """ # short name to avoid long lines below VRSC = VaryingResolutionSpectralCube # what about apply_numpy_function, apply_function? since they're # called by some of these, maybe *only* those should be wrapped to # avoid redundant calls if attrname in ('moment', 'apply_numpy_function', 'apply_function'): origfunc = super(VRSC, self).__getattribute__(attrname) return self._handle_beam_areas_wrapper(origfunc) else: return super(VRSC, self).__getattribute__(attrname) @property def hdu(self): raise ValueError("For VaryingResolutionSpectralCube's, use hdulist " "instead of hdu.") @property def hdulist(self): """ HDUList version of self """ hdu = PrimaryHDU(self.filled_data[:].value, header=self.header) from .cube_utils import beams_to_bintable bmhdu = beams_to_bintable(self.beams) return HDUList([hdu, bmhdu]) def convolve_to(self, beam, allow_smaller=False, convolve=convolution.convolve_fft, update_function=None): """ Convolve each channel in the cube to a specified beam .. warning:: Note that if there is any misaligment between the cube's spatial pixel axes and the WCS's spatial axes *and* the beams are not round, the convolution kernels used here may be incorrect. Be wary in such cases! Parameters ---------- beam : `radio_beam.Beam` The beam to convolve to allow_smaller : bool If the specified target beam is smaller than the beam in a channel in any dimension and this is ``False``, it will raise an exception. convolve : function The astropy convolution function to use, either `astropy.convolution.convolve` or `astropy.convolution.convolve_fft` update_function : method Method that is called to update an external progressbar If provided, it disables the default `astropy.utils.console.ProgressBar` Returns ------- cube : `SpectralCube` A SpectralCube with a single ``beam`` """ if ((self.wcs.celestial.wcs.get_pc()[0,1] != 0 or self.wcs.celestial.wcs.get_pc()[1,0] != 0)): warnings.warn("The beams will produce convolution kernels " "that are not aware of any misaligment " "between pixel and world coordinates, " "and there are off-diagonal elements of the " "WCS spatial transformation matrix. " "Unexpected results are likely.") pixscale = wcs.utils.proj_plane_pixel_area(self.wcs.celestial)**0.5*u.deg convolution_kernels = [] for bm,valid in zip(self.beams, self._goodbeams_mask): if not valid: # just skip masked-out beams convolution_kernels.append(None) continue elif beam == bm: # Point response when beams are equal, don't convolve. convolution_kernels.append(None) continue try: cb = beam.deconvolve(bm) ck = cb.as_kernel(pixscale) convolution_kernels.append(ck) except ValueError: if allow_smaller: convolution_kernels.append(None) else: raise if update_function is None: pb = ProgressBar(self.shape[0]) update_function = pb.update newdata = np.empty(self.shape) for ii,(img,kernel) in enumerate(zip(self.filled_data[:], convolution_kernels)): # Kernel can only be None when `allow_smaller` is True, # or if the beams are equal. Only the latter is really valid. if kernel is None: newdata[ii, :, :] = img else: newdata[ii, :, :] = convolve(img, kernel, normalize_kernel=True) update_function() newcube = SpectralCube(data=newdata, wcs=self.wcs, mask=self.mask, meta=self.meta, fill_value=self.fill_value, header=self.header, allow_huge_operations=self.allow_huge_operations, beam=beam, wcs_tolerance=self._wcs_tolerance) return newcube def spectral_interpolate(self, *args, **kwargs): raise AttributeError("VaryingResolutionSpectralCubes can't be " "spectrally interpolated. Convolve to a " "common resolution with `convolve_to` before " "attempting spectral interpolation.") def spectral_smooth(self, *args, **kwargs): raise AttributeError("VaryingResolutionSpectralCubes can't be " "spectrally smoothed. Convolve to a " "common resolution with `convolve_to` before " "attempting spectral smoothed.") @warn_slow def to(self, unit, equivalencies=()): """ Return the cube converted to the given unit (assuming it is equivalent). If conversion was required, this will be a copy, otherwise it will """ if not isinstance(unit, u.Unit): unit = u.Unit(unit) if unit == self.unit: # No copying return self if self.unit.is_equivalent(u.Jy/u.beam): # replace "beam" with the actual beam if not hasattr(self, 'beams'): raise ValueError("To convert cubes with Jy/beam units, " "the cube needs to have beams defined.") factor = self.jtok_factors(equivalencies=equivalencies) * (self.unit*u.beam).to(u.Jy) else: # scaling factor factor = self.unit.to(unit, equivalencies=equivalencies) # special case: array in equivalencies # (I don't think this should have to be special cased, but I don't know # how to manipulate broadcasting rules any other way) if hasattr(factor, '__len__') and len(factor) == len(self): return self._new_cube_with(data=self._data*factor[:,None,None], unit=unit) else: return self._new_cube_with(data=self._data*factor, unit=unit) def mask_channels(self, goodchannels): """ Helper function to mask out channels. This function is equivalent to adding a mask with ``cube[view]`` where ``view`` is broadcastable to the cube shape, but it accepts 1D arrays that are not normally broadcastable. Additionally, for `VaryingResolutionSpectralCube` s, the beams in the bad channels will not be checked when averaging, convolving, and doing other operations that are multibeam-aware. Parameters ---------- goodchannels : array A 1D boolean array declaring which channels should be kept. Returns ------- cube : `SpectralCube` A cube with the specified channels masked """ goodchannels = np.asarray(goodchannels, dtype='bool') if goodchannels.ndim != 1: raise ValueError("goodchannels mask must be one-dimensional") if goodchannels.size != self.shape[0]: raise ValueError("goodchannels must have a length equal to the " "cube's spectral dimension.") mask = BooleanArrayMask(goodchannels[:,None,None], self._wcs, shape=self._data.shape) return self._new_cube_with(mask=mask, goodbeams_mask=goodchannels & self._goodbeams_mask) spectral-cube-0.4.3/spectral_cube/stokes_spectral_cube.py0000644000077000000240000001506013161003310023646 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import numpy as np from astropy.extern import six from .spectral_cube import SpectralCube, BaseSpectralCube from . import wcs_utils from .masks import BooleanArrayMask, is_broadcastable_and_smaller __all__ = ['StokesSpectalCube'] VALID_STOKES = ['I', 'Q', 'U', 'V', 'RR', 'LL', 'RL', 'LR'] class StokesSpectralCube(object): """ A class to store a spectral cube with multiple Stokes parameters. The individual Stokes cubes can share a common mask in addition to having component-specific masks. """ def __init__(self, stokes_data, mask=None, meta=None, fill_value=None): self._stokes_data = stokes_data self._meta = meta or {} self._fill_value = fill_value reference = tuple(stokes_data.keys())[0] for component in stokes_data: if not isinstance(stokes_data[component], BaseSpectralCube): raise TypeError("stokes_data should be a dictionary of " "SpectralCube objects") if not wcs_utils.check_equality(stokes_data[component].wcs, stokes_data[reference].wcs): raise ValueError("All spectral cubes in stokes_data " "should have the same WCS") if component not in VALID_STOKES: raise ValueError("Invalid Stokes component: {0} - should be " "one of I, Q, U, V, RR, LL, RL, LR".format(component)) if stokes_data[component].shape != stokes_data[reference].shape: raise ValueError("All spectral cubes should have the same shape") self._wcs = stokes_data[reference].wcs self._shape = stokes_data[reference].shape if isinstance(mask, BooleanArrayMask): if not is_broadcastable_and_smaller(mask.shape, self._shape): raise ValueError("Mask shape is not broadcastable to data shape:" " {0} vs {1}".format(mask.shape, self._shape)) self._mask = mask @property def shape(self): return self._shape @property def mask(self): """ The underlying mask """ return self._mask @property def wcs(self): return self._wcs def __dir__(self): if six.PY2: return self.components + dir(type(self)) + list(self.__dict__) else: return self.components + super(StokesSpectralCube, self).__dir__() @property def components(self): return list(self._stokes_data.keys()) def __getattr__(self, attribute): """ Descriptor to return the Stokes cubes """ if attribute in self._stokes_data: if self.mask is not None: return self._stokes_data[attribute].with_mask(self.mask) else: return self._stokes_data[attribute] else: raise AttributeError("StokesSpectralCube has no attribute {0}".format(attribute)) def with_mask(self, mask, inherit_mask=True): """ Return a new StokesSpectralCube instance that contains a composite mask of the current StokesSpectralCube and the new ``mask``. Parameters ---------- mask : :class:`MaskBase` instance, or boolean numpy array The mask to apply. If a boolean array is supplied, it will be converted into a mask, assuming that `True` values indicate included elements. inherit_mask : bool (optional, default=True) If True, combines the provided mask with the mask currently attached to the cube Returns ------- new_cube : :class:`StokesSpectralCube` A cube with the new mask applied. Notes ----- This operation returns a view into the data, and not a copy. """ if isinstance(mask, np.ndarray): if not is_broadcastable_and_smaller(mask.shape, self.shape): raise ValueError("Mask shape is not broadcastable to data shape: " "%s vs %s" % (mask.shape, self.shape)) mask = BooleanArrayMask(mask, self.wcs) if self._mask is not None: return self._new_cube_with(mask=self.mask & mask if inherit_mask else mask) else: return self._new_cube_with(mask=mask) def _new_cube_with(self, stokes_data=None, mask=None, meta=None, fill_value=None): data = self._stokes_data if stokes_data is None else stokes_data mask = self._mask if mask is None else mask if meta is None: meta = {} meta.update(self._meta) fill_value = self._fill_value if fill_value is None else fill_value cube = StokesSpectralCube(stokes_data=data, mask=mask, meta=meta, fill_value=fill_value) return cube def with_spectral_unit(self, unit, **kwargs): stokes_data = {k: self._stokes_data[k].with_spectral_unit(unit, **kwargs) for k in self._stokes_data} return self._new_cube_with(stokes_data=stokes_data) @classmethod def read(cls, filename, format=None, hdu=None, **kwargs): """ Read a spectral cube from a file. If the file contains Stokes axes, they will be read in. If you are only interested in the unpolarized emission (I), you can use :meth:`~spectral_cube.SpectralCube.read` instead. Parameters ---------- filename : str The file to read the cube from format : str The format of the file to read. (Currently limited to 'fits' and 'casa_image') hdu : int or str For FITS files, the HDU to read in (can be the ID or name of an HDU). Returns ------- cube : :class:`SpectralCube` """ from .io.core import read cube = read(filename, format=format, hdu=hdu) if isinstance(cube, BaseSpectralCube): cube = StokesSpectralCube({'I': cube}) return cube def write(self, filename, overwrite=False, format=None): """ Write the spectral cube to a file. Parameters ---------- filename : str The path to write the file to format : str The format of the file to write. (Currently limited to 'fits') overwrite : bool If True, overwrite ``filename`` if it exists """ raise NotImplementedError("") spectral-cube-0.4.3/spectral_cube/tests/0000755000077000000240000000000013261442571020252 5ustar adamstaff00000000000000spectral-cube-0.4.3/spectral_cube/tests/__init__.py0000644000077000000240000000024712643464660022374 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import os def path(filename): return os.path.join(os.path.dirname(__file__), 'data', filename) spectral-cube-0.4.3/spectral_cube/tests/coveragerc0000644000077000000240000000140012551776560022321 0ustar adamstaff00000000000000[run] source = {packagename} omit = {packagename}/_astropy_init* {packagename}/conftest* {packagename}/cython_version* {packagename}/setup_package* {packagename}/*/setup_package* {packagename}/*/*/setup_package* {packagename}/tests/* {packagename}/*/tests/* {packagename}/*/*/tests/* {packagename}/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}spectral-cube-0.4.3/spectral_cube/tests/data/0000755000077000000240000000000013261442571021163 5ustar adamstaff00000000000000spectral-cube-0.4.3/spectral_cube/tests/data/255-fk5.reg0000644000077000000240000000035113161003310022636 0ustar adamstaff00000000000000# Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 fk5 circle(1:36:14.969,+29:56:07.68,2.6509") spectral-cube-0.4.3/spectral_cube/tests/data/255-pixel.reg0000644000077000000240000000035013161003310023271 0ustar adamstaff00000000000000# Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 image circle(2.5282832,3.4612342,1.3254484) spectral-cube-0.4.3/spectral_cube/tests/data/255.fits0000644000077000000240000002070013243374306022364 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = 64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 5 NAXIS2 = 5 NAXIS3 = 2 TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / CDELT3 = 1.28821496879E+00 / CRPIX3 = 1.00000000000E+00 / CRVAL3 = -3.21214698632E+02 / CTYPE3 = 'VOPT' / CUNIT3 = 'km/s' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END   !"#$%&'()*+,-./01spectral-cube-0.4.3/spectral_cube/tests/data/255_delta.fits0000644000077000000240000002070013243374306023535 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 5 NAXIS2 = 5 NAXIS3 = 2 TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / CDELT3 = 1.28821496879E+00 / CRPIX3 = 1.00000000000E+00 / CRVAL3 = -3.21214698632E+02 / CTYPE3 = 'VOPT' / CUNIT3 = 'km/s' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?ðspectral-cube-0.4.3/spectral_cube/tests/data/455_delta_beams.fits0000644000077000000240000003410013243374306024705 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 5 NAXIS2 = 5 NAXIS3 = 4 EXTEND = T TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / CDELT3 = 1.28821496879E+00 / CRPIX3 = 1.00000000000E+00 / CRVAL3 = -3.21214698632E+02 / CTYPE3 = 'VOPT' / CUNIT3 = 'km/s' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?ð?ð?ð?ðXTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 20 / length of dimension 1 NAXIS2 = 4 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 5 / number of table fields TTYPE1 = 'BMAJ ' TFORM1 = 'E ' TTYPE2 = 'BMIN ' TFORM2 = 'E ' TTYPE3 = 'BPA ' TFORM3 = 'E ' TTYPE4 = 'CHAN ' TFORM4 = 'J ' TTYPE5 = 'POL ' TFORM5 = 'J ' END =ÌÌÍ>ÌÌÍ>LÌÍ>™™šB4>™™š>LÌÍBp>ÌÌÍ=ÌÌÍAðspectral-cube-0.4.3/spectral_cube/tests/data/522_delta.fits0000644000077000000240000002070013243374306023532 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 2 NAXIS2 = 2 NAXIS3 = 5 TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / CDELT3 = 1.28821496879E+00 / CRPIX3 = 1.00000000000E+00 / CRVAL3 = -3.21214698632E+02 / CTYPE3 = 'VOPT' / CUNIT3 = 'km/s' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?ð?ð?ð?ðspectral-cube-0.4.3/spectral_cube/tests/data/522_delta_beams.fits0000644000077000000240000003410013243374306024700 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 2 NAXIS2 = 2 NAXIS3 = 5 EXTEND = T TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / CDELT3 = 1.28821496879E+00 / CRPIX3 = 1.00000000000E+00 / CRVAL3 = -3.21214698632E+02 / CTYPE3 = 'VOPT' / CUNIT3 = 'km/s' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?ð?ð?ð?ðXTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 20 / length of dimension 1 NAXIS2 = 5 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 5 / number of table fields TTYPE1 = 'BMAJ ' TFORM1 = 'E ' TTYPE2 = 'BMIN ' TFORM2 = 'E ' TTYPE3 = 'BPA ' TFORM3 = 'E ' TTYPE4 = 'CHAN ' TFORM4 = 'J ' TTYPE5 = 'POL ' TFORM5 = 'J ' END =ÌÌÍ?>LÌÍ>ÌÌÍB4>™™š>™™šBp>ÌÌÍ>LÌÍAð?=ÌÌÍspectral-cube-0.4.3/spectral_cube/tests/data/55.fits0000644000077000000240000001320013243374306022277 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = 64 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 5 NAXIS2 = 5 TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END  spectral-cube-0.4.3/spectral_cube/tests/data/55_delta.fits0000644000077000000240000001320013243374306023450 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 5 NAXIS2 = 5 TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?ðspectral-cube-0.4.3/spectral_cube/tests/data/5_spectral.fits0000644000077000000240000001320013243374306024107 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 1 / number of array dimensions NAXIS1 = 5 WCSAXES = 1 / Number of coordinate axes CRPIX1 = 1.0 / Pixel coordinate of reference point CDELT1 = 1288.21496879 / [m/s] Coordinate increment at reference point CUNIT1 = 'm/s' / Units of coordinate increment and value CTYPE1 = 'VOPT' / Optical velocity (linear) CRVAL1 = -321214.698632 / [m/s] Coordinate value at reference point LONPOLE = 180.0 / [deg] Native longitude of celestial pole LATPOLE = 30.5765277962 / [deg] Native latitude of celestial pole RESTFRQ = 1420405718.41 / [Hz] Line rest frequency SPECSYS = 'BARYCENT' / Reference frame of spectral coordinates MJD-OBS = 50982.687793981 / [d] MJD of observation matching DATE-OBS DATE-OBS= '1998-06-18T16:30:25.4' / ISO-8601 observation date matching MJD-OBS END ?ð@@@spectral-cube-0.4.3/spectral_cube/tests/data/5_spectral_beams.fits0000644000077000000240000002640013243374306025264 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 1 / number of array dimensions NAXIS1 = 5 EXTEND = T WCSAXES = 1 / Number of coordinate axes CRPIX1 = 1.0 / Pixel coordinate of reference point CDELT1 = 1288.21496879 / [m/s] Coordinate increment at reference point CUNIT1 = 'm/s' / Units of coordinate increment and value CTYPE1 = 'VOPT' / Optical velocity (linear) CRVAL1 = -321214.698632 / [m/s] Coordinate value at reference point LONPOLE = 180.0 / [deg] Native longitude of celestial pole LATPOLE = 30.5765277962 / [deg] Native latitude of celestial pole RESTFRQ = 1420405718.41 / [Hz] Line rest frequency SPECSYS = 'BARYCENT' / Reference frame of spectral coordinates MJD-OBS = 50982.687793981 / [d] MJD of observation matching DATE-OBS DATE-OBS= '1998-06-18T16:30:25.4' / ISO-8601 observation date matching MJD-OBS END ?ð@@@XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 20 / length of dimension 1 NAXIS2 = 5 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 5 / number of table fields TTYPE1 = 'BMAJ ' TFORM1 = 'E ' TTYPE2 = 'BMIN ' TFORM2 = 'E ' TTYPE3 = 'BPA ' TFORM3 = 'E ' TTYPE4 = 'CHAN ' TFORM4 = 'J ' TTYPE5 = 'POL ' TFORM5 = 'J ' END =ÌÌÍ?>LÌÍ>ÌÌÍB4>™™š>™™šBp>ÌÌÍ>LÌÍAð?=ÌÌÍspectral-cube-0.4.3/spectral_cube/tests/data/__init__.py0000644000077000000240000000000013161003310023241 0ustar adamstaff00000000000000spectral-cube-0.4.3/spectral_cube/tests/data/adv.fits0000644000077000000240000002070013243374306022623 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 2 NAXIS2 = 3 NAXIS3 = 4 TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / CDELT3 = 1.28821496879E+00 / CRPIX3 = 1.00000000000E+00 / CRVAL3 = -3.21214698632E+02 / CTYPE3 = 'VOPT' / CUNIT3 = 'km/s' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?á~¦ŽESÌÌÍ>LÌÍ>™™šB4>™™š>LÌÍBp>ÌÌÍ=ÌÌÍAðspectral-cube-0.4.3/spectral_cube/tests/data/adv_Jybeam_lower.fits0000644000077000000240000002070013243374306025322 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 2 NAXIS2 = 3 NAXIS3 = 4 TELESCOP= 'VLA ' / CDELT1 = -5.55555561268E-04 / CRPIX1 = 1.37300000000E+03 / CRVAL1 = 2.31837500515E+01 / CUNIT1 = 'deg' CTYPE1 = 'RA---SIN' / CDELT2 = 5.55555561268E-04 / CRPIX2 = 1.15200000000E+03 / CRVAL2 = 3.05765277962E+01 / CUNIT2 = 'deg' CTYPE2 = 'DEC--SIN' / CDELT3 = 1.28821496879E+00 / CRPIX3 = 1.00000000000E+00 / CRVAL3 = -3.21214698632E+02 / CTYPE3 = 'VOPT' / CUNIT3 = 'km/s' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'Jy/beam ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?á~¦ŽES3hS@@ ÝŠº¿ð?ð?ð?Ø+x( Ñf¼_¨ž<HJy/beam RA DEC VELOCITY EQUATORIAL 0IRAS2A ‰i·í+í?lÛ%"sá?C@ÃxT׿úD$ëöÕ=ï+í?lÛ%"sá?0HDO ´?*\ÂE“ AéVÔ½à@ eË6¡Ì 6®Å>™§<X3ETd0ETd¯'4Py‡»' •»õ†½»KЇ»Íj˜»€Ñ»vÕ¡»vˆ¼»Wí¼4³»ëÚ»wü¼_¨ž<Žé›<Ìæ<›•ž<*çš< øŠ<€£‹<»†<˜šj<¤cjf4'), ('BMIN', '>f4'), ('BPA', '>f4'), ('CHAN', '>i4'), ('POL', '>i4')]) beams['BMAJ'] = [0.1,0.2,0.3,0.4] # arcseconds beams['BMIN'] = [0.4,0.3,0.2,0.1] beams['BPA'] = [0,45,60,30] # degrees beams['CHAN'] = [0,0,0,0] beams['POL'] = [0,0,0,0] beams = fits.BinTableHDU(beams) # Single Stokes h = fits.header.Header.fromtextfile(HEADER_FILENAME) h['BUNIT'] = 'K' # Kelvins are a valid unit, JY/BEAM are not: they should be tested separately h['NAXIS1'] = 2 h['NAXIS2'] = 3 h['NAXIS3'] = 4 h['NAXIS4'] = 1 d = np.random.random((1, 2, 3, 4)) fits.writeto('advs.fits', d, h, overwrite=True) d, h = transpose(d, h, [1, 2, 3, 0]) fits.writeto('dvsa.fits', d, h, overwrite=True) d, h = transpose(d, h, [1, 2, 3, 0]) fits.writeto('vsad.fits', d, h, overwrite=True) d, h = transpose(d, h, [1, 2, 3, 0]) fits.writeto('sadv.fits', d, h, overwrite=True) d, h = transpose(d, h, [0, 2, 1, 3]) fits.writeto('sdav.fits', d, h, overwrite=True) del h['BMAJ'], h['BMIN'], h['BPA'] # want 4 spectral channels d = np.random.random((4, 3, 2, 1)) hdul = fits.HDUList([fits.PrimaryHDU(data=d, header=h), beams]) hdul.writeto('sdav_beams.fits', overwrite=True) # 3D files h = fits.header.Header.fromtextfile(HEADER_FILENAME) h['BUNIT'] = 'K' # Kelvins are a valid unit, JY/BEAM are not: they should be tested separately h['NAXIS1'] = 2 h['NAXIS2'] = 3 h['NAXIS3'] = 4 h['NAXIS'] = 3 for k in list(h.keys()): if k.endswith('4'): del h[k] d = np.random.random((4, 3, 2)) fits.writeto('adv.fits', d, h, overwrite=True) h['BUNIT'] = 'JY/BEAM' fits.writeto('adv_JYBEAM_upper.fits', d, h, overwrite=True) h['BUNIT'] = 'Jy/beam' fits.writeto('adv_Jybeam_lower.fits', d, h, overwrite=True) h['BUNIT'] = ' Jy / beam ' fits.writeto('adv_Jybeam_whitespace.fits', d, h, overwrite=True) bmaj, bmin, bpa = h['BMAJ'], h['BMIN'], h['BPA'] del h['BMAJ'], h['BMIN'], h['BPA'] hdul = fits.HDUList([fits.PrimaryHDU(data=d, header=h), beams]) hdul.writeto('adv_beams.fits', overwrite=True) h['BUNIT'] = 'K' h['BMAJ'] = bmaj h['BMIN'] = bmin h['BPA'] = bpa d, h = transpose(d, h, [2, 0, 1]) fits.writeto('vad.fits', d, h, overwrite=True) d, h = transpose(d, h, [2, 1, 0]) fits.writeto('vda.fits', d, h, overwrite=True) h['BUNIT'] = 'JY/BEAM' fits.writeto('vda_JYBEAM_upper.fits', d, h, overwrite=True) h['BUNIT'] = 'Jy/beam' fits.writeto('vda_Jybeam_lower.fits', d, h, overwrite=True) h['BUNIT'] = ' Jy / beam ' fits.writeto('vda_Jybeam_whitespace.fits', d, h, overwrite=True) del h['BMAJ'], h['BMIN'], h['BPA'] hdul = fits.HDUList([fits.PrimaryHDU(data=d, header=h), beams]) hdul.writeto('vda_beams.fits', overwrite=True) # make a version with spatial pixels h = fits.header.Header.fromtextfile(HEADER_FILENAME) for k in list(h.keys()): if k.endswith('4'): del h[k] h['BUNIT'] = 'K' # Kelvins are a valid unit, JY/BEAM are not: they should be tested separately d = np.arange(2*5*5).reshape((2,5,5)) fits.writeto('255.fits', d, h, overwrite=True) # test cube for convolution, regridding d = np.zeros([2,5,5], dtype='float') d[0,2,2] = 1.0 fits.writeto('255_delta.fits', d, h, overwrite=True) d = np.zeros([4,5,5], dtype='float') d[:,2,2] = 1.0 hdul = fits.HDUList([fits.PrimaryHDU(data=d, header=h), beams]) hdul.writeto('455_delta_beams.fits', overwrite=True) d = np.zeros([5,2,2], dtype='float') d[2,:,:] = 1.0 fits.writeto('522_delta.fits', d, h, overwrite=True) beams = np.recarray(5, dtype=[('BMAJ', '>f4'), ('BMIN', '>f4'), ('BPA', '>f4'), ('CHAN', '>i4'), ('POL', '>i4')]) beams['BMAJ'] = [0.1,0.2,0.3,0.4,0.5] # arcseconds beams['BMIN'] = [0.5,0.4,0.3,0.2,0.1] beams['BPA'] = [0,45,60,30,0] # degrees beams['CHAN'] = [0,0,0,0,0] beams['POL'] = [0,0,0,0,0] beams = fits.BinTableHDU(beams) hdul = fits.HDUList([fits.PrimaryHDU(data=d, header=h), beams]) hdul.writeto('522_delta_beams.fits', overwrite=True) # Make a 2D spatial version h = fits.header.Header.fromtextfile(HEADER_FILENAME) for k in list(h.keys()): if k.endswith('4') or k.endswith('3'): del h[k] h['BUNIT'] = 'K' d = np.arange(5 * 5).reshape((5, 5)) fits.writeto('55.fits', d, h, overwrite=True) # test cube for convolution, regridding d = np.zeros([5, 5], dtype='float') d[2, 2] = 1.0 fits.writeto('55_delta.fits', d, h, overwrite=True) # oneD spectra d = np.arange(5, dtype='float') h = wcs.WCS(fits.Header.fromtextfile(HEADER_FILENAME)).sub([wcs.WCSSUB_SPECTRAL]).to_header() fits.writeto('5_spectral.fits', d, h, overwrite=True) hdul = fits.HDUList([fits.PrimaryHDU(data=d, header=h), beams]) hdul.writeto('5_spectral_beams.fits', overwrite=True) spectral-cube-0.4.3/spectral_cube/tests/data/no_overlap_fk5.reg0000644000077000000240000000035212551776560024604 0ustar adamstaff00000000000000# Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 fk5 box(24.061421,29.934662,4",2",0.43894166) spectral-cube-0.4.3/spectral_cube/tests/data/no_overlap_image.reg0000644000077000000240000000033112551776560025176 0ustar adamstaff00000000000000# Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 image box(4.5528417,2,2,1,0) spectral-cube-0.4.3/spectral_cube/tests/data/partial_overlap_fk5.reg0000644000077000000240000000035212551776560025624 0ustar adamstaff00000000000000# Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 fk5 box(24.062383,29.934656,4",2",0.43894166) spectral-cube-0.4.3/spectral_cube/tests/data/partial_overlap_image.reg0000644000077000000240000000032312551776560026217 0ustar adamstaff00000000000000# Region file format: DS9 version 4.1 global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 image box(2.5,2,2,1,0) spectral-cube-0.4.3/spectral_cube/tests/data/sadv.fits0000644000077000000240000002070013243374306023006 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 4 / number of array dimensions NAXIS1 = 1 NAXIS2 = 4 NAXIS3 = 3 NAXIS4 = 2 TELESCOP= 'VLA ' / CDELT1 = 1.0 CRPIX1 = 1.0 CRVAL1 = 1.0 CUNIT1 = 'km/s ' CTYPE1 = 'STOKES ' CDELT2 = -0.000555555561268 CRPIX2 = 1373.0 CRVAL2 = 23.1837500515 CUNIT2 = 'deg ' CTYPE2 = 'RA---SIN' CDELT3 = 0.000555555561268 CRPIX3 = 1152.0 CRVAL3 = 30.5765277962 CTYPE3 = 'DEC--SIN' CUNIT3 = 'deg ' CDELT4 = 1.28821496879 CRPIX4 = 1.0 CRVAL4 = -321.214698632 CTYPE4 = 'VOPT ' CUNIT4 = 'km/s ' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.0002777777777777778 BMIN = 0.0002777777777777778 BPA = 0.0 END ?×øw_Qì?îl@h»ÖT?çl~gQ?ã(5Öc,°?Ãøk7"!„?Ã÷ á™L?­½"dUp?ë·· U·µ?ã´?°§8 lª ?î]E3ÄЉ?îæu&@Π?éÞdŠªŸ?Ó~ÊÀ ²ê?¹ 'c{p?åå<¨öÏa?Ü+u]wRÜ?¿=åÓ…°?ß°ú~ªb?¡›_Kh?í'r1?ÐÙéu80?å3aï}v?Óóf¸?à¤e°Ôî¨XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 20 / length of dimension 1 NAXIS2 = 4 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 5 / number of table fields TTYPE1 = 'BMAJ ' TFORM1 = 'E ' TTYPE2 = 'BMIN ' TFORM2 = 'E ' TTYPE3 = 'BPA ' TFORM3 = 'E ' TTYPE4 = 'CHAN ' TFORM4 = 'J ' TTYPE5 = 'POL ' TFORM5 = 'J ' END =ÌÌÍ>ÌÌÍ>LÌÍ>™™šB4>™™š>LÌÍBp>ÌÌÍ=ÌÌÍAðspectral-cube-0.4.3/spectral_cube/tests/data/vad.fits0000644000077000000240000002070013243374306022623 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 4 NAXIS2 = 2 NAXIS3 = 3 TELESCOP= 'VLA ' / CDELT1 = 1.28821496879 CRPIX1 = 1.0 CRVAL1 = -321.214698632 CUNIT1 = 'km/s ' CTYPE1 = 'VOPT ' CDELT2 = -0.000555555561268 CRPIX2 = 1373.0 CRVAL2 = 23.1837500515 CUNIT2 = 'deg ' CTYPE2 = 'RA---SIN' CDELT3 = 0.000555555561268 CRPIX3 = 1152.0 CRVAL3 = 30.5765277962 CTYPE3 = 'DEC--SIN' CUNIT3 = 'deg ' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'K ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.000277777777777777 BMIN = 0.000277777777777777 BPA = 0.0 END ?á~¦ŽESÌÌÍ>LÌÍ>™™šB4>™™š>LÌÍBp>ÌÌÍ=ÌÌÍAðspectral-cube-0.4.3/spectral_cube/tests/data/vda_Jybeam_lower.fits0000644000077000000240000002070013243374306025322 0ustar adamstaff00000000000000SIMPLE = T / conforms to FITS standard BITPIX = -64 / array data type NAXIS = 3 / number of array dimensions NAXIS1 = 3 NAXIS2 = 2 NAXIS3 = 4 TELESCOP= 'VLA ' / CDELT1 = 0.000555555561268 CRPIX1 = 1152.0 CRVAL1 = 30.5765277962 CUNIT1 = 'deg ' CTYPE1 = 'DEC--SIN' CDELT2 = -0.000555555561268 CRPIX2 = 1373.0 CRVAL2 = 23.1837500515 CUNIT2 = 'deg ' CTYPE2 = 'RA---SIN' CDELT3 = 1.28821496879 CRPIX3 = 1.0 CRVAL3 = -321.214698632 CTYPE3 = 'VOPT ' CUNIT3 = 'km/s ' SPECSYS = 'BARYCENT' DATE-OBS= '1998-06-18T16:30:25.4' / RESTFREQ= 1.42040571841E+09 / CELLSCAL= 'CONSTANT' / BUNIT = 'Jy/beam ' EPOCH = 2.00000000000E+03 / OBJECT = 'M33 ' / OBSERVER= 'AT206 ' / VOBS = -2.57256763070E+01 / LTYPE = 'channel ' / LSTART = 2.15000000000E+02 / LWIDTH = 1.00000000000E+00 / LSTEP = 1.00000000000E+00 / BTYPE = 'intensity' / DATAMIN = -6.57081836835E-03 / DATAMAX = 1.52362231165E-02 / BMAJ = 0.000277777777777777 BMIN = 0.000277777777777777 BPA = 0.0 END ?á~¦ŽES 2, data=data, wcs=wcs) assert_allclose(m.include(data, wcs), [[[0, 0, 0, 1, 1]]]) assert_allclose(m.exclude(data, wcs), [[[1, 1, 1, 0, 0]]]) assert_allclose(m._filled(data, wcs), [[[np.nan, np.nan, np.nan, 3, 4]]]) assert_allclose(m._flattened(data, wcs), [3, 4]) assert_allclose(m.include(data, wcs, view=(0, 0, slice(1, 4))), [0, 0, 1]) assert_allclose(m.exclude(data, wcs, view=(0, 0, slice(1, 4))), [1, 1, 0]) assert_allclose(m._filled(data, wcs, view=(0, 0, slice(1, 4))), [np.nan, np.nan, 3]) assert_allclose(m._flattened(data, wcs, view=(0, 0, slice(1, 4))), [3]) # Now if we call with different data, the results for include and exclude # should *not* change. data = (3 - np.arange(5)).reshape((1, 1, 5)) assert_allclose(m.include(data, wcs), [[[0, 0, 0, 1, 1]]]) assert_allclose(m.exclude(data, wcs), [[[1, 1, 1, 0, 0]]]) assert_allclose(m._filled(data, wcs), [[[np.nan, np.nan, np.nan, 0, -1]]]) assert_allclose(m._flattened(data, wcs), [0, -1]) assert_allclose(m.include(data, wcs, view=(0, 0, slice(1, 4))), [0, 0, 1]) assert_allclose(m.exclude(data, wcs, view=(0, 0, slice(1, 4))), [1, 1, 0]) assert_allclose(m._filled(data, wcs, view=(0, 0, slice(1, 4))), [np.nan, np.nan, 0]) assert_allclose(m._flattened(data, wcs, view=(0, 0, slice(1, 4))), [0]) def test_lazy_comparison_mask(): data = np.arange(5).reshape((1, 1, 5)) wcs = WCS() m = LazyComparisonMask(operator.gt, 2, data=data, wcs=wcs) assert_allclose(m.include(data, wcs), [[[0, 0, 0, 1, 1]]]) assert_allclose(m.exclude(data, wcs), [[[1, 1, 1, 0, 0]]]) assert_allclose(m._filled(data, wcs), [[[np.nan, np.nan, np.nan, 3, 4]]]) assert_allclose(m._flattened(data, wcs), [3, 4]) assert_allclose(m.include(data, wcs, view=(0, 0, slice(1, 4))), [0, 0, 1]) assert_allclose(m.exclude(data, wcs, view=(0, 0, slice(1, 4))), [1, 1, 0]) assert_allclose(m._filled(data, wcs, view=(0, 0, slice(1, 4))), [np.nan, np.nan, 3]) assert_allclose(m._flattened(data, wcs, view=(0, 0, slice(1, 4))), [3]) # Now if we call with different data, the results for include and exclude # should *not* change. data = (3 - np.arange(5)).reshape((1, 1, 5)) assert_allclose(m.include(data, wcs), [[[0, 0, 0, 1, 1]]]) assert_allclose(m.exclude(data, wcs), [[[1, 1, 1, 0, 0]]]) assert_allclose(m._filled(data, wcs), [[[np.nan, np.nan, np.nan, 0, -1]]]) assert_allclose(m._flattened(data, wcs), [0, -1]) assert_allclose(m.include(data, wcs, view=(0, 0, slice(1, 4))), [0, 0, 1]) assert_allclose(m.exclude(data, wcs, view=(0, 0, slice(1, 4))), [1, 1, 0]) assert_allclose(m._filled(data, wcs, view=(0, 0, slice(1, 4))), [np.nan, np.nan, 0]) assert_allclose(m._flattened(data, wcs, view=(0, 0, slice(1, 4))), [0]) def test_function_mask_incorrect_shape(): # The following function will return the incorrect shape because it does # not apply the view def threshold(data, wcs, view=()): return data > 2 m = FunctionMask(threshold) data = np.arange(5).reshape((1, 1, 5)) wcs = WCS() with pytest.raises(ValueError) as exc: m.include(data, wcs, view=(0, 0, slice(1, 4))) assert exc.value.args[0] == "Function did not return mask with correct shape - expected (3,), got (1, 1, 5)" def test_function_mask(): def threshold(data, wcs, view=()): return data[view] > 2 m = FunctionMask(threshold) data = np.arange(5).reshape((1, 1, 5)) wcs = WCS() assert_allclose(m.include(data, wcs), [[[0, 0, 0, 1, 1]]]) assert_allclose(m.exclude(data, wcs), [[[1, 1, 1, 0, 0]]]) assert_allclose(m._filled(data, wcs), [[[np.nan, np.nan, np.nan, 3, 4]]]) assert_allclose(m._flattened(data, wcs), [3, 4]) assert_allclose(m.include(data, wcs, view=(0, 0, slice(1, 4))), [0, 0, 1]) assert_allclose(m.exclude(data, wcs, view=(0, 0, slice(1, 4))), [1, 1, 0]) assert_allclose(m._filled(data, wcs, view=(0, 0, slice(1, 4))), [np.nan, np.nan, 3]) assert_allclose(m._flattened(data, wcs, view=(0, 0, slice(1, 4))), [3]) # Now if we call with different data, the results for include and exclude # *should* change. data = (3 - np.arange(5)).reshape((1, 1, 5)) assert_allclose(m.include(data, wcs), [[[1, 0, 0, 0, 0]]]) assert_allclose(m.exclude(data, wcs), [[[0, 1, 1, 1, 1]]]) assert_allclose(m._filled(data, wcs), [[[3, np.nan, np.nan, np.nan, np.nan]]]) assert_allclose(m._flattened(data, wcs), [3]) assert_allclose(m.include(data, wcs, view=(0, 0, slice(0, 3))), [1, 0, 0]) assert_allclose(m.exclude(data, wcs, view=(0, 0, slice(0, 3))), [0, 1, 1]) assert_allclose(m._filled(data, wcs, view=(0, 0, slice(0, 3))), [3, np.nan, np.nan]) assert_allclose(m._flattened(data, wcs, view=(0, 0, slice(0, 3))), [3]) def test_composite_mask(): def lower_threshold(data, wcs, view=()): return data[view] > 0 def upper_threshold(data, wcs, view=()): return data[view] < 3 m1 = FunctionMask(lower_threshold) m2 = FunctionMask(upper_threshold) m = m1 & m2 data = np.arange(5).reshape((1, 1, 5)) wcs = WCS() assert_allclose(m.include(data, wcs), [[[0, 1, 1, 0, 0]]]) assert_allclose(m.exclude(data, wcs), [[[1, 0, 0, 1, 1]]]) assert_allclose(m._filled(data, wcs), [[[np.nan, 1, 2, np.nan, np.nan]]]) assert_allclose(m._flattened(data, wcs), [1, 2]) assert_allclose(m.include(data, wcs, view=(0, 0, slice(1, 4))), [1, 1, 0]) assert_allclose(m.exclude(data, wcs, view=(0, 0, slice(1, 4))), [0, 0, 1]) assert_allclose(m._filled(data, wcs, view=(0, 0, slice(1, 4))), [1, 2, np.nan]) assert_allclose(m._flattened(data, wcs, view=(0, 0, slice(1, 4))), [1, 2]) def test_mask_logic(): data = np.arange(5).reshape((1, 1, 5)) wcs = WCS() def threshold_1(data, wcs, view=()): return data[view] > 0 def threshold_2(data, wcs, view=()): return data[view] < 4 def threshold_3(data, wcs, view=()): return data[view] != 2 m1 = FunctionMask(threshold_1) m2 = FunctionMask(threshold_2) m3 = FunctionMask(threshold_3) m = m1 & m2 assert_allclose(m.include(data, wcs), [[[0, 1, 1, 1, 0]]]) m = m1 | m2 assert_allclose(m.include(data, wcs), [[[1, 1, 1, 1, 1]]]) m = m1 | ~m2 assert_allclose(m.include(data, wcs), [[[0, 1, 1, 1, 1]]]) m = m1 & m2 & m3 assert_allclose(m.include(data, wcs), [[[0, 1, 0, 1, 0]]]) m = (m1 | m3) & m2 assert_allclose(m.include(data, wcs), [[[1, 1, 1, 1, 0]]]) m = m1 ^ m2 assert_allclose(m.include(data, wcs), [[[1, 0, 0, 0, 1]]]) m = m1 ^ m3 assert_allclose(m.include(data, wcs), [[[1, 0, 1, 0, 0]]]) @pytest.mark.parametrize(('name'), (('advs'), ('dvsa'), ('sdav'), ('sadv'), ('vsad'), ('vad'), ('adv'), )) def test_mask_spectral_unit(name): cube, data = cube_and_raw(name + '.fits') mask = BooleanArrayMask(data, cube._wcs) mask_freq = mask.with_spectral_unit(u.Hz) assert mask_freq._wcs.wcs.ctype[mask_freq._wcs.wcs.spec] == 'FREQ-W2F' # values taken from header rest = 1.42040571841E+09*u.Hz crval = -3.21214698632E+05*u.m/u.s outcv = crval.to(u.m, u.doppler_optical(rest)).to(u.Hz, u.spectral()) assert_allclose(mask_freq._wcs.wcs.crval[mask_freq._wcs.wcs.spec], outcv.to(u.Hz).value) def test_wcs_validity_check(): cube, data = cube_and_raw('adv.fits') mask = BooleanArrayMask(data>0, cube._wcs) cube = cube.with_mask(mask) s2 = cube.spectral_slab(-2 * u.km / u.s, 2 * u.km / u.s) s3 = s2.with_spectral_unit(u.km / u.s, velocity_convention=u.doppler_radio) # just checking that this works, not that it does anything in particular moment_map = s3.moment(order=1) def test_wcs_validity_check_failure(): cube, data = cube_and_raw('adv.fits') wcs2 = cube.wcs.copy() # add some difference in the 4th decimal place wcs2.wcs.crval[2] += 0.00001 # can make a mask mask = BooleanArrayMask(data>0, cube._wcs) # but if it's not exactly equal, an error should be raised at this step with pytest.raises(ValueError) as exc: cube = cube.with_mask(mask) assert exc.value.args[0] == "WCS does not match mask WCS" # this one should work though cube = cube.with_mask(mask, wcs_tolerance=1e-4) assert cube._wcs_tolerance == 1e-4 # then the rest of this should be OK s2 = cube.spectral_slab(-2 * u.km / u.s, 2 * u.km / u.s) s3 = s2.with_spectral_unit(u.km / u.s, velocity_convention=u.doppler_radio) # just checking that this works, not that it does anything in particular moment_map = s3.moment(order=1) def test_mask_spectral_unit_functions(): cube, data = cube_and_raw('adv.fits') # function mask should do nothing mask1 = FunctionMask(lambda x: x>0) mask_freq1 = mask1.with_spectral_unit(u.Hz) # lazy mask behaves like booleanarraymask mask2 = LazyMask(lambda x: x>0, cube=cube) mask_freq2 = mask2.with_spectral_unit(u.Hz) assert mask_freq2._wcs.wcs.ctype[mask_freq2._wcs.wcs.spec] == 'FREQ-W2F' # values taken from header rest = 1.42040571841E+09*u.Hz crval = -3.21214698632E+05*u.m/u.s outcv = crval.to(u.m, u.doppler_optical(rest)).to(u.Hz, u.spectral()) assert_allclose(mask_freq2._wcs.wcs.crval[mask_freq2._wcs.wcs.spec], outcv.to(u.Hz).value) # again, test that it works mask3 = CompositeMask(mask1,mask2) mask_freq3 = mask3.with_spectral_unit(u.Hz) mask_freq3 = CompositeMask(mask_freq1,mask_freq2) mask_freq_freq3 = mask_freq3.with_spectral_unit(u.Hz) # this one should fail #failedmask = CompositeMask(mask_freq1,mask2) def is_broadcastable_try(shp1, shp2): """ Test whether an array shape can be broadcast to another (this is the try/fail approach, which is guaranteed right.... right?) http://stackoverflow.com/questions/24743753/test-if-an-array-is-broadcastable-to-a-shape/24745359#24745359 """ #This variant does not work as of np 1.10: the strided arrays aren't #writable and therefore apparently cannot be broadcast # x = np.array([1]) # a = as_strided(x, shape=shp1, strides=[0] * len(shp1)) # b = as_strided(x, shape=shp2, strides=[0] * len(shp2)) a = np.ones(shp1) b = np.ones(shp2) try: c = np.broadcast_arrays(a, b) # reverse order: compare last dim first (as broadcasting does) if any(bi med mcube = cube.with_mask(mask) assert all(mask[:,1,1].include() == mask.include()[:,1,1]) spec = mcube[:,1,1] assert spec.ndim == 1 assert all(spec.mask.include() == mask.include()[:,1,1]) assert spec[:-1].mask.include().shape == (3,) assert all(spec[:-1].mask.include() == mask.include()[:-1,1,1]) assert isinstance(spec[0], u.Quantity) spec = mcube[:-1,1,1] assert spec.ndim == 1 assert hasattr(spec, '_fill_value') assert all(spec.mask.include() == mask.include()[:-1,1,1]) assert spec[:-1].mask.include().shape == (2,) assert all(spec[:-1].mask.include() == mask.include()[:-2,1,1]) assert isinstance(spec[0], u.Quantity) def test_1dcomparison_mask_1d_index(): cube, data = cube_and_raw('adv.fits') med = cube.median() mask = cube > med mcube = cube.with_mask(mask) assert all(mask[:,1,1].include() == mask.include()[:,1,1]) spec = mcube[:,1,1] assert spec.ndim == 1 assert all(spec.mask.include() == [True,False,False,True]) assert spec[:-1].mask.include().shape == (3,) assert all(spec[:-1].mask.include() == [True,False,False]) assert isinstance(spec[0], u.Quantity) def test_1dmask_indexing(): cube, data = cube_and_raw('adv.fits') med = cube.median() mask = cube > med mcube = cube.with_mask(mask) assert all(mask[:,1,1].include() == mask.include()[:,1,1]) spec = mcube[:,1,1] badvals = np.array([False,True,True,False], dtype='bool') assert np.all(np.isnan(spec[badvals])) assert not np.any(np.isnan(spec[~badvals])) def test_numpy_ma_tools(): """ check that np.ma.core.is_masked works """ cube, data = cube_and_raw('adv.fits') med = cube.median() mask = cube > med mcube = cube.with_mask(mask) assert np.ma.core.is_masked(mcube) assert np.ma.core.getmask(mcube) is not None assert np.ma.core.is_masked(mcube[:,0,0]) assert np.ma.core.getmask(mcube[:,0,0]) is not None @pytest.mark.xfail def test_numpy_ma_tools_2d(): """ This depends on 2D objects keeping masks, which depends on #395. so, TODO: un-xfail this """ cube, data = cube_and_raw('adv.fits') med = cube.median() mask = cube > med mcube = cube.with_mask(mask) assert np.ma.core.is_masked(mcube[0,:,:]) assert np.ma.core.getmask(mcube[0,:,:]) is not None def test_filled(): """ test that 'filled' works """ cube, data = cube_and_raw('adv.fits') med = cube.median() mask = cube > med mcube = cube.with_mask(mask) assert np.isnan(mcube._fill_value) filled = mcube.filled(np.nan) filled_ = mcube.filled() assert_allclose(filled, filled_) assert (np.isnan(filled) == mcube.mask.exclude()).all() def test_boolean_array_composite_mask(): cube, data = cube_and_raw('adv.fits') med = cube.median() mask = cube > med arrmask = cube.max(axis=0) > med # we're just testing that this doesn't fail combined_mask = mask & arrmask mcube = cube.with_mask(combined_mask) # not doing assert_almost_equal because I don't want to worry about precision assert (mcube.sum() > 9.5 * u.K) & (mcube.sum() < 9.6*u.K) spectral-cube-0.4.3/spectral_cube/tests/test_moments.py0000644000077000000240000001554113161003310023332 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import warnings from distutils.version import LooseVersion import pytest import numpy as np import astropy from astropy.wcs import WCS from astropy import units as u from astropy.io import fits from ..spectral_cube import SpectralCube, VarianceWarning from .helpers import assert_allclose # the back of the book dv = 3e-2 * u.Unit('m/s') dy = 2e-5 * u.Unit('deg') dx = 1e-5 * u.Unit('deg') data_unit = u.K m0v = np.array([[27, 30, 33], [36, 39, 42], [45, 48, 51]]) * data_unit * dv m0y = np.array([[9, 12, 15], [36, 39, 42], [63, 66, 69]]) * data_unit * dy m0x = np.array([[3, 12, 21], [30, 39, 48], [57, 66, 75]]) * data_unit * dx # M1V is a special case, where we return the actual coordinate m1v = np.array([[1.66666667, 1.6, 1.54545455], [1.5, 1.46153846, 1.42857143], [1.4, 1.375, 1.35294118]]) * dv + 2 * u.Unit('m/s') m1y = np.array([[1.66666667, 1.5, 1.4], [1.16666667, 1.15384615, 1.14285714], [1.0952381, 1.09090909, 1.08695652]]) * dy m1x = np.array([[1.66666667, 1.16666667, 1.0952381], [1.06666667, 1.05128205, 1.04166667], [1.03508772, 1.03030303, 1.02666667]]) * dx m2v = np.array([[0.22222222, 0.30666667, 0.36914601], [0.41666667, 0.45364892, 0.4829932], [0.50666667, 0.52604167, 0.54209919]]) * dv ** 2 m2y = np.array([[0.22222222, 0.41666667, 0.50666667], [0.63888889, 0.64299803, 0.6462585], [0.65759637, 0.6584022, 0.65910523]]) * dy ** 2 m2x = np.array([[0.22222222, 0.63888889, 0.65759637], [0.66222222, 0.66403682, 0.66493056], [0.66543552, 0.66574839, 0.66595556]]) * dx ** 2 MOMENTS = [[m0v, m0y, m0x], [m1v, m1y, m1x], [m2v, m2y, m2x]] # In issue 184, the cubes were corrected such that they all have valid units # Therefore, no separate tests are needed for moments-with-units and those # without MOMENTSu = MOMENTS def moment_cube(): cube = np.arange(27).reshape([3, 3, 3]).astype(np.float) wcs = WCS(naxis=3) wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN', 'VELO'] # choose values to minimize spherical distortions wcs.wcs.cdelt = np.array([-1, 2, 3], dtype='float32') / 1e5 wcs.wcs.crpix = np.array([1, 1, 1], dtype='float32') wcs.wcs.crval = np.array([0, 1e-3, 2e-3], dtype='float32') wcs.wcs.cunit = ['deg', 'deg', 'km/s'] header = wcs.to_header() header['BUNIT'] = 'K' hdu = fits.PrimaryHDU(data=cube, header=header) return hdu axis_order = pytest.mark.parametrize(('axis', 'order'), ((0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2))) if LooseVersion(astropy.__version__[:3]) >= LooseVersion('1.0'): # The relative error is slightly larger on astropy-dev # There is no obvious reason for this. rtol = 2e-7 atol = 1e-30 else: rtol = 1e-7 atol = 0.0 @axis_order def test_strategies_consistent(axis, order): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) cwise = sc.moment(axis=axis, order=order, how='cube') swise = sc.moment(axis=axis, order=order, how='slice') rwise = sc.moment(axis=axis, order=order, how='ray') assert_allclose(cwise, swise, rtol=rtol, atol=atol) assert_allclose(cwise, rwise, rtol=rtol, atol=atol) @pytest.mark.parametrize(('order', 'axis', 'how'), [(o, a, h) for o in [0, 1, 2] for a in [0, 1, 2] for h in ['cube', 'slice', 'auto', 'ray']]) def test_reference(order, axis, how): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) mom_sc = sc.moment(order=order, axis=axis, how=how) assert_allclose(mom_sc, MOMENTS[order][axis]) @axis_order def test_consistent_mask_handling(axis, order): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) sc._mask = sc > 4*u.K cwise = sc.moment(axis=axis, order=order, how='cube') swise = sc.moment(axis=axis, order=order, how='slice') rwise = sc.moment(axis=axis, order=order, how='ray') assert_allclose(cwise, swise, rtol=rtol, atol=atol) assert_allclose(cwise, rwise, rtol=rtol, atol=atol) def test_convenience_methods(): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) assert_allclose(sc.moment0(axis=0), MOMENTS[0][0]) assert_allclose(sc.moment1(axis=2), MOMENTS[1][2]) assert_allclose(sc.moment2(axis=1), MOMENTS[2][1]) def test_linewidth(): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) with warnings.catch_warnings(record=True) as w: assert_allclose(sc.moment2(), MOMENTS[2][0]) assert len(w) == 1 assert w[0].category == VarianceWarning assert str(w[0].message) == ("Note that the second moment returned will be a " "variance map. To get a linewidth map, use the " "SpectralCube.linewidth_fwhm() or " "SpectralCube.linewidth_sigma() methods instead.") with warnings.catch_warnings(record=True) as w: assert_allclose(sc.linewidth_sigma(), MOMENTS[2][0] ** 0.5) assert_allclose(sc.linewidth_fwhm(), MOMENTS[2][0] ** 0.5 * 2.3548200450309493) assert len(w) == 0 def test_preserve_unit(): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) sc_kms = sc.with_spectral_unit(u.km/u.s) m0 = sc_kms.moment0(axis=0) m1 = sc_kms.moment1(axis=0) assert_allclose(m0, MOMENTS[0][0].to(u.K*u.km/u.s)) assert_allclose(m1, MOMENTS[1][0].to(u.km/u.s)) def test_with_flux_unit(): """ As of Issue 184, redundant with test_reference """ mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) sc._unit = u.K sc_kms = sc.with_spectral_unit(u.km/u.s) m0 = sc_kms.moment0(axis=0) m1 = sc_kms.moment1(axis=0) assert sc.unit == u.K assert sc.filled_data[:].unit == u.K assert_allclose(m0, MOMENTS[0][0].to(u.K*u.km/u.s)) assert_allclose(m1, MOMENTS[1][0].to(u.km/u.s)) @pytest.mark.parametrize(('order', 'axis', 'how'), [(o, a, h) for o in [0, 1, 2] for a in [0, 1, 2] for h in ['cube', 'slice', 'auto', 'ray']]) def test_how_withfluxunit(order, axis, how): """ Regression test for issue 180 As of issue 184, this is mostly redundant with test_reference except that it (kind of) checks that units are set """ mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) sc._unit = u.K mom_sc = sc.moment(order=order, axis=axis, how=how) assert sc.unit == u.K assert sc.filled_data[:].unit == u.K assert_allclose(mom_sc, MOMENTSu[order][axis]) spectral-cube-0.4.3/spectral_cube/tests/test_performance.py0000644000077000000240000000705713261015477024176 0ustar adamstaff00000000000000""" Performance-related tests to make sure we don't use more memory than we should """ from __future__ import print_function, absolute_import, division import pytest from .test_moments import moment_cube from .helpers import assert_allclose from ..spectral_cube import SpectralCube from . import utilities from astropy import convolution def find_base_nbytes(obj): # from http://stackoverflow.com/questions/34637875/size-of-numpy-strided-array-broadcast-array-in-memory if obj.base is not None: return find_base_nbytes(obj.base) return obj.nbytes def test_pix_size(): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) s,y,x = sc._pix_size() # float64 by default bytes_per_pix = 8 assert find_base_nbytes(s) == sc.shape[0]*bytes_per_pix assert find_base_nbytes(y) == sc.shape[1]*sc.shape[2]*bytes_per_pix assert find_base_nbytes(x) == sc.shape[1]*sc.shape[2]*bytes_per_pix def test_compare_pix_size_approaches(): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) sa,ya,xa = sc._pix_size() s,y,x = (sc._pix_size_slice(ii) for ii in range(3)) assert_allclose(sa, s) assert_allclose(ya, y) assert_allclose(xa, x) def test_pix_cen(): mc_hdu = moment_cube() sc = SpectralCube.read(mc_hdu) s,y,x = sc._pix_cen() # float64 by default bytes_per_pix = 8 assert find_base_nbytes(s) == sc.shape[0]*bytes_per_pix assert find_base_nbytes(y) == sc.shape[1]*sc.shape[2]*bytes_per_pix assert find_base_nbytes(x) == sc.shape[1]*sc.shape[2]*bytes_per_pix @pytest.mark.skipif('True') def test_parallel_performance_smoothing(): import timeit setup = 'cube,_ = utilities.generate_gaussian_cube(shape=(300,64,64))' stmt = 'result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(20.0), num_cores={0}, use_memmap=False)' rslt = {} for ncores in (1,2,3,4): time = timeit.timeit(stmt=stmt.format(ncores), setup=setup, number=5, globals=globals()) rslt[ncores] = time print() print("memmap=False") print(rslt) setup = 'cube,_ = utilities.generate_gaussian_cube(shape=(300,64,64))' stmt = 'result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(20.0), num_cores={0}, use_memmap=True)' rslt = {} for ncores in (1,2,3,4): time = timeit.timeit(stmt=stmt.format(ncores), setup=setup, number=5, globals=globals()) rslt[ncores] = time stmt = 'result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(20.0), num_cores={0}, use_memmap=True, parallel=False)' rslt[0] = timeit.timeit(stmt=stmt.format(1), setup=setup, number=5, globals=globals()) print() print("memmap=True") print(rslt) if False: for shape in [(300,64,64), (600,64,64), (900,64,64), (300,128,128), (300,256,256), (900,256,256)]: setup = 'cube,_ = utilities.generate_gaussian_cube(shape={0})'.format(shape) stmt = 'result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(20.0), num_cores={0}, use_memmap=True)' rslt = {} for ncores in (1,2,3,4): time = timeit.timeit(stmt=stmt.format(ncores), setup=setup, number=5, globals=globals()) rslt[ncores] = time stmt = 'result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(20.0), num_cores={0}, use_memmap=True, parallel=False)' rslt[0] = timeit.timeit(stmt=stmt.format(1), setup=setup, number=5, globals=globals()) print() print("memmap=True shape={0}".format(shape)) print(rslt) spectral-cube-0.4.3/spectral_cube/tests/test_projection.py0000644000077000000240000003576413242700604024047 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import warnings import pytest import numpy as np from astropy import units as u from astropy.wcs import WCS from astropy.io import fits from radio_beam import Beam from .helpers import assert_allclose from .test_spectral_cube import cube_and_raw from ..spectral_cube import SpectralCube from ..masks import BooleanArrayMask from ..lower_dimensional_structures import Projection, Slice, OneDSpectrum from ..utils import SliceWarning, WCSCelestialError from . import path # set up for parametrization LDOs = (Projection, Slice, OneDSpectrum) LDOs_2d = (Projection, Slice,) two_qty_2d = np.ones((2,2)) * u.Jy twelve_qty_2d = np.ones((12,12)) * u.Jy two_qty_1d = np.ones((2,)) * u.Jy twelve_qty_1d = np.ones((12,)) * u.Jy data_two = (two_qty_2d, two_qty_2d, two_qty_1d) data_twelve = (twelve_qty_2d, twelve_qty_2d, twelve_qty_1d) data_two_2d = (two_qty_2d, two_qty_2d,) data_twelve_2d = (twelve_qty_2d, twelve_qty_2d,) def load_projection(filename): hdu = fits.open(path(filename))[0] proj = Projection.from_hdu(hdu) return proj, hdu @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs_2d, data_two_2d)) def test_slices_of_projections_not_projections(LDO, data): # slices of projections that have <2 dimensions should not be projections p = LDO(data, copy=False) assert not isinstance(p[0,0], LDO) assert not isinstance(p[0], LDO) @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs_2d, data_twelve_2d)) def test_copy_false(LDO, data): # copy the data so we can manipulate inplace without affecting other tests image = data.copy() p = LDO(image, copy=False) image[3,4] = 2 * u.Jy assert_allclose(p[3,4], 2 * u.Jy) @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs, data_twelve)) def test_write(LDO, data, tmpdir): p = LDO(data) p.write(tmpdir.join('test.fits').strpath) @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs_2d, data_twelve_2d)) def test_preserve_wcs_to(LDO, data): # regression for #256 image = data.copy() p = LDO(image, copy=False) image[3,4] = 2 * u.Jy p2 = p.to(u.mJy) assert_allclose(p[3,4], 2 * u.Jy) assert_allclose(p[3,4], 2000 * u.mJy) assert p2.wcs == p.wcs @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs, data_twelve)) def test_multiplication(LDO, data): # regression: 265 p = LDO(data, copy=False) p2 = p * 5 assert p2.unit == u.Jy assert hasattr(p2, '_wcs') assert p2.wcs == p.wcs assert np.all(p2.value == 5) @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs, data_twelve)) def test_unit_division(LDO, data): # regression: 265 image = data p = LDO(image, copy=False) p2 = p / u.beam assert p2.unit == u.Jy/u.beam assert hasattr(p2, '_wcs') assert p2.wcs == p.wcs @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs_2d, data_twelve_2d)) def test_isnan(LDO, data): # Check that np.isnan strips units image = data.copy() image[5,6] = np.nan p = LDO(image, copy=False) mask = np.isnan(p) assert mask.sum() == 1 assert not hasattr(mask, 'unit') @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs, data_twelve)) def test_self_arith(LDO, data): image = data p = LDO(image, copy=False) p2 = p + p assert hasattr(p2, '_wcs') assert p2.wcs == p.wcs assert np.all(p2.value==2) p2 = p - p assert hasattr(p2, '_wcs') assert p2.wcs == p.wcs assert np.all(p2.value==0) def test_onedspectrum_specaxis_units(): test_wcs = WCS(naxis=1) test_wcs.wcs.cunit = ["m/s"] test_wcs.wcs.ctype = ["VELO-LSR"] p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs) assert p.spectral_axis.unit == u.Unit("m/s") def test_onedspectrum_with_spectral_unit(): test_wcs = WCS(naxis=1) test_wcs.wcs.cunit = ["m/s"] test_wcs.wcs.ctype = ["VELO-LSR"] p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs) p_new = p.with_spectral_unit(u.km/u.s) assert p_new.spectral_axis.unit == u.Unit("km/s") np.testing.assert_equal(p_new.spectral_axis.value, 1e-3*p.spectral_axis.value) def test_onedspectrum_input_mask_type(): test_wcs = WCS(naxis=1) test_wcs.wcs.cunit = ["m/s"] test_wcs.wcs.ctype = ["VELO-LSR"] np_mask = np.ones(twelve_qty_1d.shape, dtype=bool) np_mask[1] = False bool_mask = BooleanArrayMask(np_mask, wcs=test_wcs, shape=np_mask.shape) # numpy array p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs, mask=np_mask) assert (p.mask.include() == bool_mask.include()).all() # MaskBase p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs, mask=bool_mask) assert (p.mask.include() == bool_mask.include()).all() # No mask ones_mask = BooleanArrayMask(np.ones(twelve_qty_1d.shape, dtype=bool), wcs=test_wcs, shape=np_mask.shape) p = OneDSpectrum(twelve_qty_1d, wcs=test_wcs, mask=None) assert (p.mask.include() == ones_mask.include()).all() def test_slice_tricks(): test_wcs_1 = WCS(naxis=1) test_wcs_2 = WCS(naxis=2) spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1) im = Slice(twelve_qty_2d, wcs=test_wcs_2) with warnings.catch_warnings(record=True) as w: new = spec[:,None,None] * im[None,:,:] assert new.ndim == 3 # two warnings because we're doing BOTH slices! assert len(w) == 2 assert w[0].category == SliceWarning with warnings.catch_warnings(record=True) as w: new = spec.array[:,None,None] * im.array[None,:,:] assert new.ndim == 3 assert len(w) == 0 def test_array_property(): test_wcs_1 = WCS(naxis=1) spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1) arr = spec.array # these are supposed to be the same object, but the 'is' tests fails! assert spec.array.data == spec.data assert isinstance(arr, np.ndarray) assert not isinstance(arr, u.Quantity) def test_quantity_property(): test_wcs_1 = WCS(naxis=1) spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1) arr = spec.quantity # these are supposed to be the same object, but the 'is' tests fails! assert spec.array.data == spec.data assert isinstance(arr, u.Quantity) assert not isinstance(arr, OneDSpectrum) def test_projection_with_beam(): exp_beam = Beam(1.0 * u.arcsec) proj, hdu = load_projection("55.fits") # uses from_hdu, which passes beam as kwarg assert proj.beam == exp_beam assert proj.meta['beam'] == exp_beam # load beam from meta exp_beam = Beam(1.5 * u.arcsec) meta = {"beam": exp_beam} new_proj = Projection(hdu.data, wcs=proj.wcs, meta=meta) assert new_proj.beam == exp_beam assert new_proj.meta['beam'] == exp_beam # load beam from given header exp_beam = Beam(2.0 * u.arcsec) header = hdu.header.copy() header = exp_beam.attach_to_header(header) new_proj = Projection(hdu.data, wcs=proj.wcs, header=header, read_beam=True) assert new_proj.beam == exp_beam assert new_proj.meta['beam'] == exp_beam # load beam from beam object exp_beam = Beam(3.0 * u.arcsec) header = hdu.header.copy() del header["BMAJ"], header["BMIN"], header["BPA"] new_proj = Projection(hdu.data, wcs=proj.wcs, header=header, beam=exp_beam) assert new_proj.beam == exp_beam assert new_proj.meta['beam'] == exp_beam def test_projection_attach_beam(): exp_beam = Beam(1.0 * u.arcsec) newbeam = Beam(2.0 * u.arcsec) proj, hdu = load_projection("55.fits") new_proj = proj.with_beam(newbeam) assert proj.beam == exp_beam assert proj.meta['beam'] == exp_beam assert new_proj.beam == newbeam assert new_proj.meta['beam'] == newbeam @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs_2d, data_two_2d)) def test_projection_from_hdu(LDO, data): p = LDO(data, copy=False) hdu = p.hdu p_new = LDO.from_hdu(hdu) assert (p == p_new).all() @pytest.mark.parametrize(('LDO', 'data'), zip(LDOs_2d, data_two_2d)) def test_projection_from_hdu_with_beam(LDO, data): p = LDO(data, copy=False) hdu = p.hdu beam = Beam(1 * u.arcsec) hdu.header = beam.attach_to_header(hdu.header) p_new = LDO.from_hdu(hdu) assert (p == p_new).all() assert beam == p_new.meta['beam'] assert beam == p_new.beam def test_projection_subimage(): proj, hdu = load_projection("55.fits") proj1 = proj.subimage(xlo=1, xhi=3) proj2 = proj.subimage(xlo=24.06269 * u.deg, xhi=24.06206 * u.deg) assert proj1.shape == (5, 2) assert proj2.shape == (5, 2) assert proj1.wcs.wcs.compare(proj2.wcs.wcs) proj3 = proj.subimage(ylo=1, yhi=3) proj4 = proj.subimage(ylo=29.93464 * u.deg, yhi=29.93522 * u.deg) assert proj3.shape == (2, 5) assert proj4.shape == (2, 5) assert proj3.wcs.wcs.compare(proj4.wcs.wcs) proj5 = proj.subimage() assert proj5.shape == proj.shape assert proj5.wcs.wcs.compare(proj.wcs.wcs) assert np.all(proj5.value == proj.value) def test_projection_subimage_nocelestial_fail(): cube, data = cube_and_raw('255_delta.fits') proj = cube.moment0(axis=1) with pytest.raises(WCSCelestialError) as exc: proj.subimage(xlo=1, xhi=3) assert exc.value.args[0] == ("WCS does not contain two spatial axes.") @pytest.mark.xfail def test_mask_convolve(): # Numpy is fundamentally incompatible with the objects we have created. # np.ma.is_masked(array) checks specifically for the array's _mask # attribute. We would have to refactor deeply to correct this, and I # really don't want to do that because 'None' is a much more reasonable # and less dangerous default for a mask. test_wcs_1 = WCS(naxis=1) spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1) assert spec.mask is False from astropy.convolution import convolve,Box1DKernel convolve(spec, Box1DKernel(3)) def test_convolve(): test_wcs_1 = WCS(naxis=1) spec = OneDSpectrum(twelve_qty_1d, wcs=test_wcs_1) from astropy.convolution import Box1DKernel specsmooth = spec.spectral_smooth(Box1DKernel(1)) np.testing.assert_allclose(spec, specsmooth) def test_spectral_interpolate(): test_wcs_1 = WCS(naxis=1) test_wcs_1.wcs.cunit[0] = 'GHz' spec = OneDSpectrum(np.arange(12)*u.Jy, wcs=test_wcs_1) new_xaxis = test_wcs_1.wcs_pix2world(np.linspace(0,11,23), 0)[0] * u.Unit(test_wcs_1.wcs.cunit[0]) new_spec = spec.spectral_interpolate(new_xaxis) np.testing.assert_allclose(new_spec, np.linspace(0,11,23)*u.Jy) def test_spectral_interpolate_with_mask(): hdu = fits.open(path("522_delta.fits"))[0] # Swap the velocity axis so indiff < 0 in spectral_interpolate hdu.header["CDELT3"] = - hdu.header["CDELT3"] cube = SpectralCube.read(hdu) mask = np.ones(cube.shape, dtype=bool) mask[:2] = False masked_cube = cube.with_mask(mask) spec = masked_cube[:, 0, 0] # midpoint between each position sg = (spec.spectral_axis[1:] + spec.spectral_axis[:-1])/2. result = spec.spectral_interpolate(spectral_grid=sg[::-1]) # The output makes CDELT3 > 0 (reversed spectral axis) so the masked # portion are the final 2 channels. np.testing.assert_almost_equal(result.filled_data[:].value, [0.0, 0.5, np.NaN, np.NaN]) def test_spectral_interpolate_reversed(): cube, data = cube_and_raw('522_delta.fits') # Reverse spectral axis sg = cube.spectral_axis[::-1] spec = cube[:, 0, 0] result = spec.spectral_interpolate(spectral_grid=sg) np.testing.assert_almost_equal(sg.value, result.spectral_axis.value) def test_spectral_interpolate_with_fillvalue(): cube, data = cube_and_raw('522_delta.fits') # Step one channel out of bounds. sg = ((cube.spectral_axis[0]) - (cube.spectral_axis[1] - cube.spectral_axis[0]) * np.linspace(1,4,4)) spec = cube[:, 0, 0] result = spec.spectral_interpolate(spectral_grid=sg, fill_value=42) np.testing.assert_almost_equal(result.value, np.ones(4)*42) def test_spectral_units(): # regression test for issue 391 cube, data = cube_and_raw('255_delta.fits') sp = cube[:,0,0] assert sp.spectral_axis.unit == u.km/u.s assert sp.header['CUNIT1'] == 'km s-1' sp = cube.with_spectral_unit(u.m/u.s)[:,0,0] assert sp.spectral_axis.unit == u.m/u.s assert sp.header['CUNIT1'] in ('m s-1', 'm/s') def test_repr_1d(): cube, data = cube_and_raw('255_delta.fits') sp = cube[:,0,0] print(sp) print(sp[1:-1]) assert 'OneDSpectrum' in sp.__repr__() assert 'OneDSpectrum' in sp[1:-1].__repr__() def test_1d_slices(): cube, data = cube_and_raw('255_delta.fits') sp = cube[:,0,0] assert sp.max() == cube.max(axis=0)[0,0] assert not isinstance(sp.max(), OneDSpectrum) sp = cube[:-1,0,0] assert sp.max() == cube[:-1,:,:].max(axis=0)[0,0] assert not isinstance(sp.max(), OneDSpectrum) @pytest.mark.parametrize('method', ('min', 'max', 'std', 'mean', 'sum', 'cumsum', 'nansum', 'ptp', 'var'), ) def test_1d_slice_reductions(method): cube, data = cube_and_raw('255_delta.fits') sp = cube[:,0,0] if hasattr(cube, method): assert getattr(sp, method)() == getattr(cube, method)(axis=0)[0,0] else: getattr(sp, method)() assert hasattr(sp, '_fill_value') assert 'OneDSpectrum' in sp.__repr__() assert 'OneDSpectrum' in sp[1:-1].__repr__() def test_1d_slice_round(): cube, data = cube_and_raw('255_delta.fits') sp = cube[:,0,0] assert all(sp.value.round() == sp.round().value) assert hasattr(sp, '_fill_value') assert hasattr(sp.round(), '_fill_value') assert 'OneDSpectrum' in sp.round().__repr__() assert 'OneDSpectrum' in sp[1:-1].round().__repr__() def test_LDO_arithmetic(): cube, data = cube_and_raw('vda.fits') sp = cube[:,0,0] spx2 = sp * 2 assert np.all(spx2.value == sp.value*2) assert np.all(spx2.filled_data[:].value == sp.value*2) def test_beam_jtok_2D(): cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'Jy / beam' cube._unit = u.Jy / u.beam plane = cube[0] freq = cube.with_spectral_unit(u.GHz).spectral_axis[0] equiv = plane.beam.jtok_equiv(freq) jtok = plane.beam.jtok(freq) Kplane = plane.to(u.K, equivalencies=equiv, freq=freq) np.testing.assert_almost_equal(Kplane.value, (plane.value * jtok).value) # test that the beam equivalencies are correctly automatically defined Kplane = plane.to(u.K, freq=freq) np.testing.assert_almost_equal(Kplane.value, (plane.value * jtok).value) spectral-cube-0.4.3/spectral_cube/tests/test_regrid.py0000644000077000000240000002464313261015477023151 0ustar adamstaff00000000000000import pytest import numpy as np from astropy import units as u from astropy import convolution from astropy.wcs import WCS from astropy import wcs from astropy.io import fits from radio_beam import beam, Beam from .. import SpectralCube from ..utils import WCSCelestialError from .test_spectral_cube import cube_and_raw from .test_projection import load_projection from . import path try: import reproject REPROJECT_INSTALLED = True except ImportError: REPROJECT_INSTALLED = False try: import joblib JOBLIB_INSTALLED = True except ImportError: JOBLIB_INSTALLED = False def test_convolution(): cube, data = cube_and_raw('255_delta.fits') # 1" convolved with 1.5" -> 1.8027.... target_beam = Beam(1.802775637731995*u.arcsec, 1.802775637731995*u.arcsec, 0*u.deg) conv_cube = cube.convolve_to(target_beam) expected = convolution.Gaussian2DKernel((1.5*u.arcsec / beam.SIGMA_TO_FWHM / (5.555555555555e-4*u.deg)).decompose().value, x_size=5, y_size=5, ) expected.normalize() np.testing.assert_almost_equal(expected.array, conv_cube.filled_data[0,:,:].value) # 2nd layer is all zeros assert np.all(conv_cube.filled_data[1,:,:] == 0.0) def test_beams_convolution(): cube, data = cube_and_raw('455_delta_beams.fits') # 1" convolved with 1.5" -> 1.8027.... target_beam = Beam(1.802775637731995*u.arcsec, 1.802775637731995*u.arcsec, 0*u.deg) conv_cube = cube.convolve_to(target_beam) pixscale = wcs.utils.proj_plane_pixel_area(cube.wcs.celestial)**0.5*u.deg for ii,bm in enumerate(cube.beams): expected = target_beam.deconvolve(bm).as_kernel(pixscale, x_size=5, y_size=5) expected.normalize() np.testing.assert_almost_equal(expected.array, conv_cube.filled_data[ii,:,:].value) def test_beams_convolution_equal(): cube, data = cube_and_raw('522_delta_beams.fits') # Only checking that the equal beam case is handled correctly. # Fake the beam in the first channel. Then ensure that the first channel # has NOT been convolved. target_beam = Beam(1.0 * u.arcsec, 1.0 * u.arcsec, 0.0 * u.deg) cube.beams.major[0] = target_beam.major cube.beams.minor[0] = target_beam.minor cube.beams.pa[0] = target_beam.pa conv_cube = cube.convolve_to(target_beam) np.testing.assert_almost_equal(cube.filled_data[0].value, conv_cube.filled_data[0].value) @pytest.mark.skipif('not REPROJECT_INSTALLED') def test_reproject(): cube, data = cube_and_raw('adv.fits') wcs_in = WCS(cube.header) wcs_out = wcs_in.deepcopy() wcs_out.wcs.ctype = ['GLON-SIN', 'GLAT-SIN', wcs_in.wcs.ctype[2]] wcs_out.wcs.crval = [134.37608, -31.939241, wcs_in.wcs.crval[2]] wcs_out.wcs.crpix = [2., 2., wcs_in.wcs.crpix[2]] header_out = cube.header header_out['NAXIS1'] = 4 header_out['NAXIS2'] = 5 header_out['NAXIS3'] = cube.shape[0] header_out.update(wcs_out.to_header()) result = cube.reproject(header_out) assert result.shape == (cube.shape[0], 5, 4) def test_spectral_smooth(): cube, data = cube_and_raw('522_delta.fits') result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), use_memmap=False) np.testing.assert_almost_equal(result[:,0,0].value, convolution.Gaussian1DKernel(1.0, x_size=5).array, 4) result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), use_memmap=True) np.testing.assert_almost_equal(result[:,0,0].value, convolution.Gaussian1DKernel(1.0, x_size=5).array, 4) # TODO: uncomment this when we figure out how to make it work @pytest.mark.skipif('not JOBLIB_INSTALLED') def test_spectral_smooth_4cores(): cube, data = cube_and_raw('522_delta.fits') result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), num_cores=4, use_memmap=True) np.testing.assert_almost_equal(result[:,0,0].value, convolution.Gaussian1DKernel(1.0, x_size=5).array, 4) # this is one way to test non-parallel mode result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), num_cores=4, use_memmap=False) np.testing.assert_almost_equal(result[:,0,0].value, convolution.Gaussian1DKernel(1.0, x_size=5).array, 4) # num_cores = 4 is a contradiction with parallel=False, but we want to make # sure it does the same thing result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), num_cores=4, parallel=False) np.testing.assert_almost_equal(result[:,0,0].value, convolution.Gaussian1DKernel(1.0, x_size=5).array, 4) def test_spectral_smooth_fail(): cube, data = cube_and_raw('522_delta_beams.fits') with pytest.raises(AttributeError) as exc: cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0)) assert exc.value.args[0] == ("VaryingResolutionSpectralCubes can't be " "spectrally smoothed. Convolve to a " "common resolution with `convolve_to` before " "attempting spectral smoothed.") def test_spectral_interpolate(): cube, data = cube_and_raw('522_delta.fits') orig_wcs = cube.wcs.deepcopy() # midpoint between each position sg = (cube.spectral_axis[1:] + cube.spectral_axis[:-1])/2. result = cube.spectral_interpolate(spectral_grid=sg) np.testing.assert_almost_equal(result[:,0,0].value, [0.0, 0.5, 0.5, 0.0]) assert cube.wcs.wcs.compare(orig_wcs.wcs) def test_spectral_interpolate_with_fillvalue(): cube, data = cube_and_raw('522_delta.fits') # Step one channel out of bounds. sg = ((cube.spectral_axis[0]) - (cube.spectral_axis[1] - cube.spectral_axis[0]) * np.linspace(1,4,4)) result = cube.spectral_interpolate(spectral_grid=sg, fill_value=42) np.testing.assert_almost_equal(result[:,0,0].value, np.ones(4)*42) def test_spectral_interpolate_fail(): cube, data = cube_and_raw('522_delta_beams.fits') with pytest.raises(AttributeError) as exc: cube.spectral_interpolate(5) assert exc.value.args[0] == ("VaryingResolutionSpectralCubes can't be " "spectrally interpolated. Convolve to a " "common resolution with `convolve_to` before " "attempting spectral interpolation.") def test_spectral_interpolate_with_mask(): hdu = fits.open(path("522_delta.fits"))[0] # Swap the velocity axis so indiff < 0 in spectral_interpolate hdu.header["CDELT3"] = - hdu.header["CDELT3"] cube = SpectralCube.read(hdu) mask = np.ones(cube.shape, dtype=bool) mask[:2] = False masked_cube = cube.with_mask(mask) orig_wcs = cube.wcs.deepcopy() # midpoint between each position sg = (cube.spectral_axis[1:] + cube.spectral_axis[:-1])/2. result = masked_cube.spectral_interpolate(spectral_grid=sg[::-1]) # The output makes CDELT3 > 0 (reversed spectral axis) so the masked # portion are the final 2 channels. np.testing.assert_almost_equal(result[:,0, 0].value, [0.0, 0.5, np.NaN, np.NaN]) assert cube.wcs.wcs.compare(orig_wcs.wcs) def test_spectral_interpolate_reversed(): cube, data = cube_and_raw('522_delta.fits') orig_wcs = cube.wcs.deepcopy() # Reverse spectral axis sg = cube.spectral_axis[::-1] result = cube.spectral_interpolate(spectral_grid=sg) np.testing.assert_almost_equal(sg.value, result.spectral_axis.value) def test_convolution_2D(): proj, hdu = load_projection("55_delta.fits") # 1" convolved with 1.5" -> 1.8027.... target_beam = Beam(1.802775637731995*u.arcsec, 1.802775637731995*u.arcsec, 0*u.deg) conv_proj = proj.convolve_to(target_beam) expected = convolution.Gaussian2DKernel((1.5*u.arcsec / beam.SIGMA_TO_FWHM / (5.555555555555e-4*u.deg)).decompose().value, x_size=5, y_size=5, ) expected.normalize() np.testing.assert_almost_equal(expected.array, conv_proj.value) assert conv_proj.beam == target_beam def test_nocelestial_convolution_2D_fail(): cube, data = cube_and_raw('255_delta.fits') proj = cube.moment0(axis=1) test_beam = Beam(1.0 * u.arcsec) with pytest.raises(WCSCelestialError) as exc: proj.convolve_to(test_beam) assert exc.value.args[0] == ("WCS does not contain two spatial axes.") @pytest.mark.skipif('not REPROJECT_INSTALLED') def test_reproject_2D(): proj, hdu = load_projection("55.fits") wcs_in = WCS(proj.header) wcs_out = wcs_in.deepcopy() wcs_out.wcs.ctype = ['GLON-SIN', 'GLAT-SIN'] wcs_out.wcs.crval = [134.37608, -31.939241] wcs_out.wcs.crpix = [2., 2.] header_out = proj.header header_out['NAXIS1'] = 4 header_out['NAXIS2'] = 5 header_out.update(wcs_out.to_header()) result = proj.reproject(header_out) assert result.shape == (5, 4) assert result.beam == proj.beam @pytest.mark.skipif('not REPROJECT_INSTALLED') def test_nocelestial_reproject_2D_fail(): cube, data = cube_and_raw('255_delta.fits') proj = cube.moment0(axis=1) with pytest.raises(WCSCelestialError) as exc: proj.reproject(cube.header) assert exc.value.args[0] == ("WCS does not contain two spatial axes.") spectral-cube-0.4.3/spectral_cube/tests/test_spectral_axis.py0000644000077000000240000006016113256751340024531 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division from astropy import wcs from astropy.io import fits from astropy import units as u from astropy import constants from astropy.tests.helper import pytest import warnings import os import numpy as np from .helpers import assert_allclose from . import path as data_path from ..spectral_axis import (convert_spectral_axis, determine_ctype_from_vconv, cdelt_derivative, determine_vconv_from_ctype, get_rest_value_from_wcs, air_to_vac, air_to_vac_deriv, vac_to_air) def test_cube_wcs_freqtovel(): header = fits.Header.fromtextfile(data_path('cubewcs1.hdr')) w1 = wcs.WCS(header) # CTYPE3 = 'FREQ' newwcs = convert_spectral_axis(w1, 'km/s', 'VRAD', rest_value=w1.wcs.restfrq*u.Hz) assert newwcs.wcs.ctype[2] == 'VRAD' assert newwcs.wcs.crval[2] == 305.2461585938794 assert newwcs.wcs.cunit[2] == u.Unit('km/s') newwcs = convert_spectral_axis(w1, 'km/s', 'VRAD') assert newwcs.wcs.ctype[2] == 'VRAD' assert newwcs.wcs.crval[2] == 305.2461585938794 assert newwcs.wcs.cunit[2] == u.Unit('km/s') def test_cube_wcs_freqtovopt(): header = fits.Header.fromtextfile(data_path('cubewcs1.hdr')) w1 = wcs.WCS(header) w2 = convert_spectral_axis(w1, 'km/s', 'VOPT') # TODO: what should w2's values be? test them # these need to be set to zero to test the failure w1.wcs.restfrq = 0.0 w1.wcs.restwav = 0.0 with pytest.raises(ValueError) as exc: convert_spectral_axis(w1, 'km/s', 'VOPT') assert exc.value.args[0] == 'If converting from wavelength/frequency to speed, a reference wavelength/frequency is required.' @pytest.mark.parametrize('wcstype',('Z','W','R','V')) def test_greisen2006(wcstype): # This is the header extracted from Greisen 2006, including many examples # of valid transforms. It should be the gold standard (in principle) hdr = fits.Header.fromtextfile(data_path('greisen2006.hdr')) # We have not implemented frame conversions, so we can only convert bary # <-> bary in this case wcs0 = wcs.WCS(hdr, key='F') wcs1 = wcs.WCS(hdr, key=wcstype) if wcstype in ('R','V','Z'): if wcs1.wcs.restfrq: rest = wcs1.wcs.restfrq*u.Hz elif wcs1.wcs.restwav: rest = wcs1.wcs.restwav*u.m else: rest = None outunit = u.Unit(wcs1.wcs.cunit[wcs1.wcs.spec]) out_ctype = wcs1.wcs.ctype[wcs1.wcs.spec] wcs2 = convert_spectral_axis(wcs0, outunit, out_ctype, rest_value=rest) assert_allclose(wcs2.wcs.cdelt[wcs2.wcs.spec], wcs1.wcs.cdelt[wcs1.wcs.spec], rtol=1.e-3) assert_allclose(wcs2.wcs.crval[wcs2.wcs.spec], wcs1.wcs.crval[wcs1.wcs.spec], rtol=1.e-3) assert wcs2.wcs.ctype[wcs2.wcs.spec] == wcs1.wcs.ctype[wcs1.wcs.spec] assert wcs2.wcs.cunit[wcs2.wcs.spec] == wcs1.wcs.cunit[wcs1.wcs.spec] # round trip test: inunit = u.Unit(wcs0.wcs.cunit[wcs0.wcs.spec]) in_ctype = wcs0.wcs.ctype[wcs0.wcs.spec] wcs3 = convert_spectral_axis(wcs2, inunit, in_ctype, rest_value=rest) assert_allclose(wcs3.wcs.crval[wcs3.wcs.spec], wcs0.wcs.crval[wcs0.wcs.spec], rtol=1.e-3) assert_allclose(wcs3.wcs.cdelt[wcs3.wcs.spec], wcs0.wcs.cdelt[wcs0.wcs.spec], rtol=1.e-3) assert wcs3.wcs.ctype[wcs3.wcs.spec] == wcs0.wcs.ctype[wcs0.wcs.spec] assert wcs3.wcs.cunit[wcs3.wcs.spec] == wcs0.wcs.cunit[wcs0.wcs.spec] def test_byhand_f2v(): # VELO-F2V CRVAL3F = 1.37847121643E+09 CDELT3F = 9.764775E+04 RESTFRQV= 1.420405752E+09 CRVAL3V = 8.98134229811E+06 CDELT3V = -2.1217551E+04 CUNIT3V = 'm/s' CUNIT3F = 'Hz' crvalf = CRVAL3F * u.Unit(CUNIT3F) crvalv = CRVAL3V * u.Unit(CUNIT3V) restfreq = RESTFRQV * u.Unit(CUNIT3F) cdeltf = CDELT3F * u.Unit(CUNIT3F) cdeltv = CDELT3V * u.Unit(CUNIT3V) # (Pdb) crval_in,crval_lin1,crval_lin2,crval_out # (, , , ) (Pdb) # cdelt_in, cdelt_lin1, cdelt_lin2, cdelt_out # (, , , ) crvalv_computed = crvalf.to(CUNIT3V, u.doppler_relativistic(restfreq)) cdeltv_computed = -4*constants.c*cdeltf*crvalf*restfreq**2 / (crvalf**2+restfreq**2)**2 cdeltv_computed_byfunction = cdelt_derivative(crvalf, cdeltf, intype='frequency', outtype='speed', rest=restfreq) # this should be EXACT assert cdeltv_computed == cdeltv_computed_byfunction assert_allclose(crvalv_computed, crvalv, rtol=1.e-3) assert_allclose(cdeltv_computed, cdeltv, rtol=1.e-3) # round trip # (Pdb) crval_in,crval_lin1,crval_lin2,crval_out # (, , # , ) # (Pdb) cdelt_in, cdelt_lin1, cdelt_lin2, cdelt_out # (, , , ) crvalf_computed = crvalv_computed.to(CUNIT3F, u.doppler_relativistic(restfreq)) cdeltf_computed = -(cdeltv_computed * constants.c * restfreq / ((constants.c+crvalv_computed)*(constants.c**2 - crvalv_computed**2)**0.5)) assert_allclose(crvalf_computed, crvalf, rtol=1.e-2) assert_allclose(cdeltf_computed, cdeltf, rtol=1.e-2) cdeltf_computed_byfunction = cdelt_derivative(crvalv_computed, cdeltv_computed, intype='speed', outtype='frequency', rest=restfreq) # this should be EXACT assert cdeltf_computed == cdeltf_computed_byfunction def test_byhand_vrad(): # VRAD CRVAL3F = 1.37847121643E+09 CDELT3F = 9.764775E+04 RESTFRQR= 1.420405752E+09 CRVAL3R = 8.85075090419E+06 CDELT3R = -2.0609645E+04 CUNIT3R = 'm/s' CUNIT3F = 'Hz' crvalf = CRVAL3F * u.Unit(CUNIT3F) crvalv = CRVAL3R * u.Unit(CUNIT3R) restfreq = RESTFRQR * u.Unit(CUNIT3F) cdeltf = CDELT3F * u.Unit(CUNIT3F) cdeltv = CDELT3R * u.Unit(CUNIT3R) # (Pdb) crval_in,crval_lin1,crval_lin2,crval_out # (, , , ) # (Pdb) cdelt_in, cdelt_lin1, cdelt_lin2, cdelt_out # (, , , ) crvalv_computed = crvalf.to(CUNIT3R, u.doppler_radio(restfreq)) cdeltv_computed = -(cdeltf / restfreq)*constants.c assert_allclose(crvalv_computed, crvalv, rtol=1.e-3) assert_allclose(cdeltv_computed, cdeltv, rtol=1.e-3) crvalf_computed = crvalv_computed.to(CUNIT3F, u.doppler_radio(restfreq)) cdeltf_computed = -(cdeltv_computed/constants.c) * restfreq assert_allclose(crvalf_computed, crvalf, rtol=1.e-3) assert_allclose(cdeltf_computed, cdeltf, rtol=1.e-3) # round trip: # (Pdb) crval_in,crval_lin1,crval_lin2,crval_out # (, , , ) # (Pdb) cdelt_in, cdelt_lin1, cdelt_lin2, cdelt_out # (, , , ) # (Pdb) myunit,lin_cunit,out_lin_cunit,outunit # WRONG (Unit("m / s"), Unit("m / s"), Unit("Hz"), Unit("Hz")) def test_byhand_vopt(): # VOPT: case "Z" CRVAL3F = 1.37847121643E+09 CDELT3F = 9.764775E+04 CUNIT3F = 'Hz' RESTWAVZ= 0.211061139 #CTYPE3Z = 'VOPT-F2W' # This comes from Greisen 2006, but appears to be wrong: CRVAL3Z = 9.120000E+06 CRVAL3Z = 9.120002206E+06 CDELT3Z = -2.1882651E+04 CUNIT3Z = 'm/s' crvalf = CRVAL3F * u.Unit(CUNIT3F) crvalv = CRVAL3Z * u.Unit(CUNIT3Z) restwav = RESTWAVZ * u.m cdeltf = CDELT3F * u.Unit(CUNIT3F) cdeltv = CDELT3Z * u.Unit(CUNIT3Z) # Forward: freq -> vopt # crval: (, , , ) # cdelt: (, , , ) #crvalv_computed = crvalf.to(CUNIT3R, u.doppler_radio(restwav)) crvalw_computed = crvalf.to(u.m, u.spectral()) crvalw_computed32 = crvalf.astype('float32').to(u.m, u.spectral()) cdeltw_computed = -(cdeltf / crvalf**2)*constants.c cdeltw_computed_byfunction = cdelt_derivative(crvalf, cdeltf, intype='frequency', outtype='length', rest=None) # this should be EXACT assert cdeltw_computed == cdeltw_computed_byfunction crvalv_computed = crvalw_computed.to(CUNIT3Z, u.doppler_optical(restwav)) crvalv_computed32 = crvalw_computed32.astype('float32').to(CUNIT3Z, u.doppler_optical(restwav)) #cdeltv_computed = (cdeltw_computed * # 4*constants.c*crvalw_computed*restwav**2 / # (restwav**2+crvalw_computed**2)**2) cdeltv_computed = (cdeltw_computed / restwav)*constants.c cdeltv_computed_byfunction = cdelt_derivative(crvalw_computed, cdeltw_computed, intype='length', outtype='speed', rest=restwav, linear=True) # Disagreement is 2.5e-7: good, but not really great... #assert np.abs((crvalv_computed-crvalv)/crvalv) < 1e-6 assert_allclose(crvalv_computed, crvalv, rtol=1.e-2) assert_allclose(cdeltv_computed, cdeltv, rtol=1.e-2) # Round=trip test: # from velo_opt -> freq # (, , , ) # (, , , ) crvalw_computed = crvalv_computed.to(u.m, u.doppler_optical(restwav)) cdeltw_computed = (cdeltv_computed/constants.c) * restwav cdeltw_computed_byfunction = cdelt_derivative(crvalv_computed, cdeltv_computed, intype='speed', outtype='length', rest=restwav, linear=True) assert cdeltw_computed == cdeltw_computed_byfunction crvalf_computed = crvalw_computed.to(CUNIT3F, u.spectral()) cdeltf_computed = -cdeltw_computed * constants.c / crvalw_computed**2 assert_allclose(crvalf_computed, crvalf, rtol=1.e-3) assert_allclose(cdeltf_computed, cdeltf, rtol=1.e-3) cdeltf_computed_byfunction = cdelt_derivative(crvalw_computed, cdeltw_computed, intype='length', outtype='frequency', rest=None) assert cdeltf_computed == cdeltf_computed_byfunction # Fails intentionally (but not really worth testing) #crvalf_computed = crvalv_computed.to(CUNIT3F, u.spectral()+u.doppler_optical(restwav)) #cdeltf_computed = -(cdeltv_computed / constants.c) * restwav.to(u.Hz, u.spectral()) #assert_allclose(crvalf_computed, crvalf, rtol=1.e-3) #assert_allclose(cdeltf_computed, cdeltf, rtol=1.e-3) def test_byhand_f2w(): CRVAL3F = 1.37847121643E+09 CDELT3F = 9.764775E+04 CUNIT3F = 'Hz' #CTYPE3W = 'WAVE-F2W' CRVAL3W = 0.217481841062 CDELT3W = -1.5405916E-05 CUNIT3W = 'm' crvalf = CRVAL3F * u.Unit(CUNIT3F) crvalw = CRVAL3W * u.Unit(CUNIT3W) cdeltf = CDELT3F * u.Unit(CUNIT3F) cdeltw = CDELT3W * u.Unit(CUNIT3W) crvalf_computed = crvalw.to(CUNIT3F, u.spectral()) cdeltf_computed = -constants.c * cdeltw / crvalw**2 assert_allclose(crvalf_computed, crvalf, rtol=0.1) assert_allclose(cdeltf_computed, cdeltf, rtol=0.1) @pytest.mark.parametrize(('ctype','unit','velocity_convention','result'), (('VELO-F2V', "Hz", None, 'FREQ'), ('VELO-F2V', "m", None, 'WAVE-F2W'), ('VOPT', "m", None, 'WAVE'), ('VOPT', "Hz", None, 'FREQ-W2F'), ('VELO', "Hz", None, 'FREQ-V2F'), ('WAVE', "Hz", None, 'FREQ-W2F'), ('FREQ', 'm/s', None, ValueError('A velocity convention must be specified')), ('FREQ', 'm/s', u.doppler_radio, 'VRAD'), ('FREQ', 'm/s', u.doppler_optical, 'VOPT-F2W'), ('FREQ', 'm/s', u.doppler_relativistic, 'VELO-F2V'), ('WAVE', 'm/s', u.doppler_radio, 'VRAD-W2F'))) def test_ctype_determinator(ctype,unit,velocity_convention,result): if isinstance(result, Exception): with pytest.raises(Exception) as exc: determine_ctype_from_vconv(ctype, unit, velocity_convention=velocity_convention) assert exc.value.args[0] == result.args[0] assert type(exc.value) == type(result) else: outctype = determine_ctype_from_vconv(ctype, unit, velocity_convention=velocity_convention) assert outctype == result @pytest.mark.parametrize(('ctype','vconv'), (('VELO-F2W', u.doppler_optical), ('VELO-F2V', u.doppler_relativistic), ('VRAD', u.doppler_radio), ('VOPT', u.doppler_optical), ('VELO', u.doppler_relativistic), ('WAVE', u.doppler_optical), ('WAVE-F2W', u.doppler_optical), ('WAVE-V2W', u.doppler_optical), ('FREQ', u.doppler_radio), ('FREQ-V2F', u.doppler_radio), ('FREQ-W2F', u.doppler_radio),)) def test_vconv_determinator(ctype, vconv): assert determine_vconv_from_ctype(ctype) == vconv @pytest.mark.parametrize(('name'), (('advs'), ('dvsa'), ('sdav'), ('sadv'), ('vsad'), ('vad'), ('adv'), )) def test_vopt_to_freq(name): h = fits.getheader(data_path(name+".fits")) wcs0 = wcs.WCS(h) # check to make sure astropy.wcs's "fix" changes VELO-HEL to VOPT assert wcs0.wcs.ctype[wcs0.wcs.spec] == 'VOPT' out_ctype = determine_ctype_from_vconv('VOPT', u.Hz) wcs1 = convert_spectral_axis(wcs0, u.Hz, out_ctype) assert wcs1.wcs.ctype[wcs1.wcs.spec] == 'FREQ-W2F' @pytest.mark.parametrize('wcstype',('Z','W','R','V','F')) def test_change_rest_frequency(wcstype): # This is the header extracted from Greisen 2006, including many examples # of valid transforms. It should be the gold standard (in principle) hdr = fits.Header.fromtextfile(data_path('greisen2006.hdr')) wcs0 = wcs.WCS(hdr, key=wcstype) old_rest = get_rest_value_from_wcs(wcs0) if old_rest is None: # This test doesn't matter if there was no rest frequency in the first # place but I prefer to keep the option open in case we want to try # forcing a rest frequency on some of the non-velocity frames at some # point return vconv1 = determine_vconv_from_ctype(hdr['CTYPE3'+wcstype]) new_rest = (100*u.km/u.s).to(u.Hz, vconv1(old_rest)) wcs1 = wcs.WCS(hdr, key='V') vconv2 = determine_vconv_from_ctype(hdr['CTYPE3V']) inunit = u.Unit(wcs0.wcs.cunit[wcs0.wcs.spec]) outunit = u.Unit(wcs1.wcs.cunit[wcs1.wcs.spec]) # VELO-F2V out_ctype = wcs1.wcs.ctype[wcs1.wcs.spec] wcs2 = convert_spectral_axis(wcs0, outunit, out_ctype, rest_value=new_rest) sp1 = wcs1.sub([wcs.WCSSUB_SPECTRAL]) sp2 = wcs2.sub([wcs.WCSSUB_SPECTRAL]) p_old = sp1.wcs_world2pix([old_rest.to(inunit, vconv1(old_rest)).value, new_rest.to(inunit, vconv1(old_rest)).value],0) p_new = sp2.wcs_world2pix([old_rest.to(outunit, vconv2(new_rest)).value, new_rest.to(outunit, vconv2(new_rest)).value],0) assert_allclose(p_old, p_new, rtol=1e-3) assert_allclose(p_old, p_new, rtol=1e-3) # from http://classic.sdss.org/dr5/products/spectra/vacwavelength.html # these aren't accurate enough for my liking, but I can't find a better one readily air_vac = { 'H-beta':(4861.363, 4862.721)*u.AA, '[O III]':(4958.911, 4960.295)*u.AA, '[O III]':(5006.843, 5008.239)*u.AA, '[N II]':(6548.05, 6549.86)*u.AA, 'H-alpha':(6562.801, 6564.614)*u.AA, '[N II]':(6583.45, 6585.27)*u.AA, '[S II]':(6716.44, 6718.29)*u.AA, '[S II]':(6730.82, 6732.68)*u.AA, } @pytest.mark.parametrize(('air','vac'), air_vac.values()) def test_air_to_vac(air, vac): # This is the accuracy provided by the line list we have. # I'm not sure if the formula are incorrect or if the reference wavelengths # are, but this is an accuracy of only 6 km/s, which is *very bad* for # astrophysical applications. assert np.abs((air_to_vac(air)- vac)) < 0.15*u.AA assert np.abs((vac_to_air(vac)- air)) < 0.15*u.AA assert np.abs((air_to_vac(air)- vac)/vac) < 2e-5 assert np.abs((vac_to_air(vac)- air)/air) < 2e-5 # round tripping assert np.abs((vac_to_air(air_to_vac(air))-air))/air < 1e-8 assert np.abs((air_to_vac(vac_to_air(vac))-vac))/vac < 1e-8 def test_byhand_awav2vel(): # AWAV CRVAL3A = (6560*u.AA).to(u.m).value CDELT3A = (1.0*u.AA).to(u.m).value CUNIT3A = 'm' CRPIX3A = 1.0 # restwav MUST be vacuum restwl = air_to_vac(6562.81*u.AA) RESTWAV = restwl.to(u.m).value CRVAL3V = (CRVAL3A*u.m).to(u.m/u.s, u.doppler_optical(restwl)).value CDELT3V = (CDELT3A*u.m*air_to_vac_deriv(CRVAL3A*u.m)/restwl) * constants.c CUNIT3V = 'm/s' mywcs = wcs.WCS(naxis=1) mywcs.wcs.ctype[0] = 'AWAV' mywcs.wcs.crval[0] = CRVAL3A mywcs.wcs.crpix[0] = CRPIX3A mywcs.wcs.cunit[0] = CUNIT3A mywcs.wcs.cdelt[0] = CDELT3A mywcs.wcs.restwav = RESTWAV mywcs.wcs.set() newwcs = convert_spectral_axis(mywcs, u.km/u.s, determine_ctype_from_vconv(mywcs.wcs.ctype[0], u.km/u.s, 'optical')) newwcs.wcs.set() assert newwcs.wcs.cunit[0] == 'm / s' np.testing.assert_almost_equal(newwcs.wcs.crval, air_to_vac(CRVAL3A*u.m).to(u.m/u.s, u.doppler_optical(restwl)).value) # Check that the cdelts match the expected cdelt, 1 angstrom / rest # wavelength (vac) np.testing.assert_almost_equal(newwcs.wcs.cdelt, CDELT3V.to(u.m/u.s).value) # Check that the reference wavelength is 2.81 angstroms up np.testing.assert_almost_equal(newwcs.wcs_pix2world((2.81,), 0), 0.0, decimal=3) # Go through a full-on sanity check: vline = 100*u.km/u.s wave_line_vac = vline.to(u.AA, u.doppler_optical(restwl)) wave_line_air = vac_to_air(wave_line_vac) pix_line_input = mywcs.wcs_world2pix((wave_line_air.to(u.m).value,), 0) pix_line_output = newwcs.wcs_world2pix((vline.to(u.m/u.s).value,), 0) np.testing.assert_almost_equal(pix_line_output, pix_line_input, decimal=4) def test_byhand_awav2wav(): # AWAV CRVAL3A = (6560*u.AA).to(u.m).value CDELT3A = (1.0*u.AA).to(u.m).value CUNIT3A = 'm' CRPIX3A = 1.0 mywcs = wcs.WCS(naxis=1) mywcs.wcs.ctype[0] = 'AWAV' mywcs.wcs.crval[0] = CRVAL3A mywcs.wcs.crpix[0] = CRPIX3A mywcs.wcs.cunit[0] = CUNIT3A mywcs.wcs.cdelt[0] = CDELT3A mywcs.wcs.set() newwcs = convert_spectral_axis(mywcs, u.AA, 'WAVE') newwcs.wcs.set() np.testing.assert_almost_equal(newwcs.wcs_pix2world((0,),0), air_to_vac(mywcs.wcs_pix2world((0,),0)*u.m).value) np.testing.assert_almost_equal(newwcs.wcs_pix2world((10,),0), air_to_vac(mywcs.wcs_pix2world((10,),0)*u.m).value) # At least one of the components MUST change assert not (mywcs.wcs.crval[0] == newwcs.wcs.crval[0] and mywcs.wcs.crpix[0] == newwcs.wcs.crpix[0]) class test_nir_sinfoni_base(object): def setup_method(self, method): CD3_3 = 0.000245000002905726 # CD rotation matrix CTYPE3 = 'WAVE ' # wavelength axis in microns CRPIX3 = 1109. # Reference pixel in z CRVAL3 = 2.20000004768372 # central wavelength CDELT3 = 0.000245000002905726 # microns per pixel CUNIT3 = 'um ' # spectral unit SPECSYS = 'TOPOCENT' # Coordinate reference frame self.rest_wavelength = 2.1218*u.um self.mywcs = wcs.WCS(naxis=1) self.mywcs.wcs.ctype[0] = CTYPE3 self.mywcs.wcs.crval[0] = CRVAL3 self.mywcs.wcs.crpix[0] = CRPIX3 self.mywcs.wcs.cunit[0] = CUNIT3 self.mywcs.wcs.cdelt[0] = CDELT3 self.mywcs.wcs.cd = [[CD3_3]] self.mywcs.wcs.specsys = SPECSYS self.mywcs.wcs.set() self.wavelengths = np.array([[2.12160005e-06, 2.12184505e-06, 2.12209005e-06]]) np.testing.assert_almost_equal(self.mywcs.wcs_pix2world([788,789,790], 0), self.wavelengths) def test_nir_sinfoni_example_optical(self): mywcs = self.mywcs.copy() velocities_opt = ((self.wavelengths*u.m-self.rest_wavelength)/(self.wavelengths*u.m) * constants.c).to(u.km/u.s) newwcs_opt = convert_spectral_axis(mywcs, u.km/u.s, 'VOPT', rest_value=self.rest_wavelength) assert newwcs_opt.wcs.cunit[0] == u.km/u.s newwcs_opt.wcs.set() worldpix_opt = newwcs_opt.wcs_pix2world([788,789,790], 0) assert newwcs_opt.wcs.cunit[0] == u.m/u.s np.testing.assert_almost_equal(worldpix_opt, velocities_opt.to(newwcs_opt.wcs.cunit[0]).value) def test_nir_sinfoni_example_radio(self): mywcs = self.mywcs.copy() velocities_rad = ((self.wavelengths*u.m-self.rest_wavelength)/(self.rest_wavelength) * constants.c).to(u.km/u.s) newwcs_rad = convert_spectral_axis(mywcs, u.km/u.s, 'VRAD', rest_value=self.rest_wavelength) assert newwcs_rad.wcs.cunit[0] == u.km/u.s newwcs_rad.wcs.set() worldpix_rad = newwcs_rad.wcs_pix2world([788,789,790], 0) assert newwcs_rad.wcs.cunit[0] == u.m/u.s np.testing.assert_almost_equal(worldpix_rad, velocities_rad.to(newwcs_rad.wcs.cunit[0]).value) spectral-cube-0.4.3/spectral_cube/tests/test_spectral_cube.py0000644000077000000240000017074013261015477024510 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import operator import itertools import warnings import mmap from distutils.version import LooseVersion import pytest import astropy from astropy.io import fits from astropy import units as u from astropy.wcs import WCS from astropy.wcs import _wcs from astropy.tests.helper import assert_quantity_allclose from astropy.extern import six from astropy.convolution import Gaussian2DKernel, Tophat2DKernel import numpy as np from .. import (SpectralCube, VaryingResolutionSpectralCube, BooleanArrayMask, FunctionMask, LazyMask, CompositeMask) from ..spectral_cube import OneDSpectrum, Projection, VaryingResolutionOneDSpectrum from ..np_compat import allbadtonan from .. import spectral_axis from .. import base_class from .. import utils from . import path from .helpers import assert_allclose, assert_array_equal # needed to test for warnings later warnings.simplefilter('always', UserWarning) warnings.simplefilter('error', utils.UnsupportedIterationStrategyWarning) warnings.simplefilter('error', utils.NotImplementedWarning) warnings.simplefilter('error', utils.WCSMismatchWarning) try: import joblib JOBLIB_INSTALLED = True except ImportError: JOBLIB_INSTALLED = False try: import scipy.ndimage SCIPYOK = True except ImportError: SCIPYOK = False try: import yt YT_INSTALLED = True YT_LT_301 = LooseVersion(yt.__version__) < LooseVersion('3.0.1') except ImportError: YT_INSTALLED = False YT_LT_301 = False try: import bottleneck BOTTLENECK_INSTALLED = True except ImportError: BOTTLENECK_INSTALLED = False from radio_beam import Beam NUMPY_LT_19 = LooseVersion(np.__version__) < LooseVersion('1.9.0') def cube_and_raw(filename): p = path(filename) d = fits.getdata(p) c = SpectralCube.read(p, format='fits', mode='readonly') return c, d def test_arithmetic_warning(recwarn): cube, data = cube_and_raw('vda_Jybeam_lower.fits') assert not cube._is_huge # make sure the small cube raises a warning about loading into memory cube + 5*cube.unit w = recwarn.list[-1] assert 'requires loading the entire cube into' in str(w.message) def test_huge_disallowed(): cube, data = cube_and_raw('vda_Jybeam_lower.fits') cube = SpectralCube(data=data, wcs=cube.wcs) assert not cube._is_huge # We need to reduce the memory threshold rather than use a large cube to # make sure we don't use too much memory during testing. from .. import cube_utils OLD_MEMORY_THRESHOLD = cube_utils.MEMORY_THRESHOLD try: cube_utils.MEMORY_THRESHOLD = 10 assert cube._is_huge with pytest.raises(ValueError) as exc: cube + 5*cube.unit assert 'entire cube into memory' in exc.value.args[0] with pytest.raises(ValueError) as exc: cube.max(how='cube') assert 'entire cube into memory' in exc.value.args[0] cube.allow_huge_operations = True # just make sure it doesn't fail cube + 5*cube.unit finally: cube_utils.MEMORY_THRESHOLD = OLD_MEMORY_THRESHOLD class BaseTest(object): def setup_method(self, method): c, d = cube_and_raw('adv.fits') mask = BooleanArrayMask(d > 0.5, c._wcs) c._mask = mask self.c = c self.mask = mask self.d = d class BaseTestMultiBeams(object): def setup_method(self, method): c, d = cube_and_raw('adv_beams.fits') mask = BooleanArrayMask(d > 0.5, c._wcs) c._mask = mask self.c = c self.mask = mask self.d = d translist = [('advs', [0, 1, 2, 3]), ('dvsa', [2, 3, 0, 1]), ('sdav', [0, 2, 1, 3]), ('sadv', [0, 1, 2, 3]), ('vsad', [3, 0, 1, 2]), ('vad', [2, 0, 1]), ('vda', [0, 2, 1]), ('adv', [0, 1, 2]), ] translist_vrsc = [('vda_beams', [0, 2, 1])] class TestSpectralCube(object): @pytest.mark.parametrize(('name', 'trans'), translist + translist_vrsc) def test_consistent_transposition(self, name, trans): """data() should return velocity axis first, then world 1, then world 0""" c, d = cube_and_raw(name + '.fits') expected = np.squeeze(d.transpose(trans)) assert_allclose(c._get_filled_data(), expected) @pytest.mark.parametrize(('file', 'view'), ( ('adv.fits', np.s_[:, :,:]), ('adv.fits', np.s_[::2, :, :2]), ('adv.fits', np.s_[0]), )) def test_world(self, file, view): p = path(file) d = fits.getdata(p) wcs = WCS(p) c = SpectralCube(d, wcs) shp = d.shape inds = np.indices(d.shape) pix = np.column_stack([i.ravel() for i in inds[::-1]]) world = wcs.all_pix2world(pix, 0).T world = [w.reshape(shp) for w in world] world = [w[view] * u.Unit(wcs.wcs.cunit[i]) for i, w in enumerate(world)][::-1] w2 = c.world[view] for result, expected in zip(w2, world): assert_allclose(result, expected) @pytest.mark.parametrize('view', (np.s_[:, :,:], np.s_[:2, :3, ::2])) def test_world_transposes_3d(self, view): c1, d1 = cube_and_raw('adv.fits') c2, d2 = cube_and_raw('vad.fits') for w1, w2 in zip(c1.world[view], c2.world[view]): assert_allclose(w1, w2) @pytest.mark.parametrize('view', (np.s_[:, :,:], np.s_[:2, :3, ::2], np.s_[::3, ::2, :1], np.s_[:], )) def test_world_transposes_4d(self, view): c1, d1 = cube_and_raw('advs.fits') c2, d2 = cube_and_raw('sadv.fits') for w1, w2 in zip(c1.world[view], c2.world[view]): assert_allclose(w1, w2) @pytest.mark.parametrize(('name','masktype','unit'), itertools.product(('advs', 'dvsa', 'sdav', 'sadv', 'vsad', 'vad', 'adv',), (BooleanArrayMask, LazyMask, FunctionMask, CompositeMask), ('Hz', u.Hz), ) ) def test_with_spectral_unit(self, name, masktype, unit): cube, data = cube_and_raw(name + '.fits') cube_freq = cube.with_spectral_unit(unit) if masktype == BooleanArrayMask: # don't use data here: # data haven't necessarily been rearranged to the correct shape by # cube_utils.orient mask = BooleanArrayMask(cube.filled_data[:].value>0, wcs=cube._wcs) elif masktype == LazyMask: mask = LazyMask(lambda x: x>0, cube=cube) elif masktype == FunctionMask: mask = FunctionMask(lambda x: x>0) elif masktype == CompositeMask: mask1 = FunctionMask(lambda x: x>0) mask2 = LazyMask(lambda x: x>0, cube) mask = CompositeMask(mask1, mask2) cube2 = cube.with_mask(mask) cube_masked_freq = cube2.with_spectral_unit(unit) assert cube_freq._wcs.wcs.ctype[cube_freq._wcs.wcs.spec] == 'FREQ-W2F' assert cube_masked_freq._wcs.wcs.ctype[cube_masked_freq._wcs.wcs.spec] == 'FREQ-W2F' assert cube_masked_freq._mask._wcs.wcs.ctype[cube_masked_freq._mask._wcs.wcs.spec] == 'FREQ-W2F' # values taken from header rest = 1.42040571841E+09*u.Hz crval = -3.21214698632E+05*u.m/u.s outcv = crval.to(u.m, u.doppler_optical(rest)).to(u.Hz, u.spectral()) assert_allclose(cube_freq._wcs.wcs.crval[cube_freq._wcs.wcs.spec], outcv.to(u.Hz).value) assert_allclose(cube_masked_freq._wcs.wcs.crval[cube_masked_freq._wcs.wcs.spec], outcv.to(u.Hz).value) assert_allclose(cube_masked_freq._mask._wcs.wcs.crval[cube_masked_freq._mask._wcs.wcs.spec], outcv.to(u.Hz).value) @pytest.mark.parametrize(('operation', 'value'), ((operator.add, 0.5*u.K), (operator.sub, 0.5*u.K), (operator.mul, 0.5*u.K), (operator.truediv, 0.5*u.K), (operator.div if hasattr(operator,'div') else operator.floordiv, 0.5*u.K), )) def test_apply_everywhere(self, operation, value): c1, d1 = cube_and_raw('advs.fits') # append 'o' to indicate that it has been operated on c1o = c1._apply_everywhere(operation, value) d1o = operation(u.Quantity(d1, u.K), value) assert np.all(d1o == c1o.filled_data[:]) # allclose fails on identical data? #assert_allclose(d1o, c1o.filled_data[:]) @pytest.mark.parametrize(('name', 'trans'), translist) def test_getitem(self, name, trans): c, d = cube_and_raw(name + '.fits') expected = np.squeeze(d.transpose(trans)) assert_allclose(c[0,:,:].value, expected[0,:,:]) assert_allclose(c[:,:,0].value, expected[:,:,0]) assert_allclose(c[:,0,:].value, expected[:,0,:]) # Not implemented: #assert_allclose(c[0,0,:].value, expected[0,0,:]) #assert_allclose(c[0,:,0].value, expected[0,:,0]) assert_allclose(c[:,0,0].value, expected[:,0,0]) assert_allclose(c[1,:,:].value, expected[1,:,:]) assert_allclose(c[:,:,1].value, expected[:,:,1]) assert_allclose(c[:,1,:].value, expected[:,1,:]) # Not implemented: #assert_allclose(c[1,1,:].value, expected[1,1,:]) #assert_allclose(c[1,:,1].value, expected[1,:,1]) assert_allclose(c[:,1,1].value, expected[:,1,1]) c2 = c.with_spectral_unit(u.km/u.s, velocity_convention='radio') assert_allclose(c2[0,:,:].value, expected[0,:,:]) assert_allclose(c2[:,:,0].value, expected[:,:,0]) assert_allclose(c2[:,0,:].value, expected[:,0,:]) # Not implemented: #assert_allclose(c2[0,0,:].value, expected[0,0,:]) #assert_allclose(c2[0,:,0].value, expected[0,:,0]) assert_allclose(c2[:,0,0].value, expected[:,0,0]) assert_allclose(c2[1,:,:].value, expected[1,:,:]) assert_allclose(c2[:,:,1].value, expected[:,:,1]) assert_allclose(c2[:,1,:].value, expected[:,1,:]) # Not implemented: #assert_allclose(c2[1,1,:].value, expected[1,1,:]) #assert_allclose(c2[1,:,1].value, expected[1,:,1]) assert_allclose(c2[:,1,1].value, expected[:,1,1]) @pytest.mark.parametrize(('name', 'trans'), translist_vrsc) def test_getitem_vrsc(self, name, trans): c, d = cube_and_raw(name + '.fits') expected = np.squeeze(d.transpose(trans)) # No pv slices for VRSC. assert_allclose(c[0,:,:].value, expected[0,:,:]) # Not implemented: #assert_allclose(c[0,0,:].value, expected[0,0,:]) #assert_allclose(c[0,:,0].value, expected[0,:,0]) assert_allclose(c[:,0,0].value, expected[:,0,0]) assert_allclose(c[1,:,:].value, expected[1,:,:]) # Not implemented: #assert_allclose(c[1,1,:].value, expected[1,1,:]) #assert_allclose(c[1,:,1].value, expected[1,:,1]) assert_allclose(c[:,1,1].value, expected[:,1,1]) c2 = c.with_spectral_unit(u.km/u.s, velocity_convention='radio') assert_allclose(c2[0,:,:].value, expected[0,:,:]) # Not implemented: #assert_allclose(c2[0,0,:].value, expected[0,0,:]) #assert_allclose(c2[0,:,0].value, expected[0,:,0]) assert_allclose(c2[:,0,0].value, expected[:,0,0]) assert_allclose(c2[1,:,:].value, expected[1,:,:]) # Not implemented: #assert_allclose(c2[1,1,:].value, expected[1,1,:]) #assert_allclose(c2[1,:,1].value, expected[1,:,1]) assert_allclose(c2[:,1,1].value, expected[:,1,1]) # @pytest.mark.xfail(raises=AttributeError) @pytest.mark.parametrize(('name', 'trans'), translist_vrsc) def test_getitem_vrsc(self, name, trans): c, d = cube_and_raw(name + '.fits') expected = np.squeeze(d.transpose(trans)) assert_allclose(c[:,:,0].value, expected[:,:,0]) class TestArithmetic(object): def setup_method(self, method): self.c1, self.d1 = cube_and_raw('adv.fits') # make nice easy-to-test numbers self.d1.flat[:] = np.arange(self.d1.size) self.c1._data.flat[:] = np.arange(self.d1.size) @pytest.mark.parametrize(('value'),(1,1.0,2,2.0)) def test_add(self,value): d2 = self.d1 + value c2 = self.c1 + value*u.K assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K def test_add_cubes(self): d2 = self.d1 + self.d1 c2 = self.c1 + self.c1 assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K @pytest.mark.parametrize(('value'),(1,1.0,2,2.0)) def test_subtract(self, value): d2 = self.d1 - value c2 = self.c1 - value*u.K assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K # regression test #251: the _data attribute must not be a quantity assert not hasattr(c2._data, 'unit') def test_subtract_cubes(self): d2 = self.d1 - self.d1 c2 = self.c1 - self.c1 assert np.all(d2 == c2.filled_data[:].value) assert np.all(c2.filled_data[:].value == 0) assert c2.unit == u.K # regression test #251: the _data attribute must not be a quantity assert not hasattr(c2._data, 'unit') @pytest.mark.parametrize(('value'),(1,1.0,2,2.0)) def test_mul(self, value): d2 = self.d1 * value c2 = self.c1 * value assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K def test_mul_cubes(self): d2 = self.d1 * self.d1 c2 = self.c1 * self.c1 assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K**2 @pytest.mark.parametrize(('value'),(1,1.0,2,2.0)) def test_div(self, value): d2 = self.d1 / value c2 = self.c1 / value assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K def test_div_cubes(self): d2 = self.d1 / self.d1 c2 = self.c1 / self.c1 assert np.all((d2 == c2.filled_data[:].value) | (np.isnan(c2.filled_data[:]))) assert np.all((c2.filled_data[:] == 1) | (np.isnan(c2.filled_data[:]))) assert c2.unit == u.dimensionless_unscaled @pytest.mark.parametrize(('value'), (1,1.0,2,2.0)) def test_pow(self, value): d2 = self.d1 ** value c2 = self.c1 ** value assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K**value def test_cube_add(self): c2 = self.c1 + self.c1 d2 = self.d1 + self.d1 assert np.all(d2 == c2.filled_data[:].value) assert c2.unit == u.K class TestFilters(BaseTest): def test_mask_data(self): c, d = self.c, self.d expected = np.where(d > .5, d, np.nan) assert_allclose(c._get_filled_data(), expected) expected = np.where(d > .5, d, 0) assert_allclose(c._get_filled_data(fill=0), expected) @pytest.mark.parametrize('operation', (operator.lt, operator.gt, operator.le, operator.ge)) def test_mask_comparison(self, operation): c, d = self.c, self.d dmask = operation(d, 0.6) & self.c.mask.include() cmask = operation(c, 0.6*u.K) assert (self.c.mask.include() & cmask.include()).sum() == dmask.sum() np.testing.assert_almost_equal(c.with_mask(cmask).sum().value, d[dmask].sum()) def test_flatten(self): c, d = self.c, self.d expected = d[d > 0.5] assert_allclose(c.flattened(), expected) def test_flatten_weights(self): c, d = self.c, self.d expected = d[d > 0.5] ** 2 assert_allclose(c.flattened(weights=d), expected) def test_slice(self): c, d = self.c, self.d expected = d[:3, :2, ::2] expected = expected[expected > 0.5] assert_allclose(c[0:3, 0:2, 0::2].flattened(), expected) class TestNumpyMethods(BaseTest): def _check_numpy(self, cubemethod, array, func): for axis in [None, 0, 1, 2]: for how in ['auto', 'slice', 'cube', 'ray']: expected = func(array, axis=axis) actual = cubemethod(axis=axis) assert_allclose(actual, expected) def test_sum(self): d = np.where(self.d > 0.5, self.d, np.nan) self._check_numpy(self.c.sum, d, allbadtonan(np.nansum)) # Need a secondary check to make sure it works with no # axis keyword being passed (regression test for issue introduced in # 150) assert np.all(self.c.sum().value == np.nansum(d)) def test_max(self): d = np.where(self.d > 0.5, self.d, np.nan) self._check_numpy(self.c.max, d, np.nanmax) def test_min(self): d = np.where(self.d > 0.5, self.d, np.nan) self._check_numpy(self.c.min, d, np.nanmin) def test_argmax(self): d = np.where(self.d > 0.5, self.d, -10) self._check_numpy(self.c.argmax, d, np.nanargmax) def test_argmin(self): d = np.where(self.d > 0.5, self.d, 10) self._check_numpy(self.c.argmin, d, np.nanargmin) @pytest.mark.parametrize('iterate_rays', (True,False)) def test_median(self, iterate_rays): # Make sure that medians ignore empty/bad/NaN values m = np.empty(self.d.shape[1:]) for y in range(m.shape[0]): for x in range(m.shape[1]): ray = self.d[:, y, x] # the cube mask is for values >0.5 ray = ray[ray > 0.5] m[y, x] = np.median(ray) scmed = self.c.median(axis=0, iterate_rays=iterate_rays) assert_allclose(scmed, m) assert not np.any(np.isnan(scmed.value)) assert scmed.unit == self.c.unit @pytest.mark.skipif('NUMPY_LT_19') def test_bad_median_apply(self): # this is a test for manually-applied numpy medians, which are different # from the cube.median method that does "the right thing" # # for regular median, we expect a failure, which is why we don't use # regular median. scmed = self.c.apply_numpy_function(np.median, axis=0) # this checks whether numpy <=1.9.3 has a bug? # as far as I can tell, np==1.9.3 no longer has this bug/feature #if LooseVersion(np.__version__) <= LooseVersion('1.9.3'): # # print statements added so we get more info in the travis builds # print("Numpy version is: {0}".format(LooseVersion(np.__version__))) # assert np.count_nonzero(np.isnan(scmed)) == 5 #else: # print("Numpy version is: {0}".format(LooseVersion(np.__version__))) assert np.count_nonzero(np.isnan(scmed)) == 6 scmed = self.c.apply_numpy_function(np.nanmedian, axis=0) assert np.count_nonzero(np.isnan(scmed)) == 0 # use a more aggressive mask to force there to be some all-nan axes m2 = self.c>0.65*self.c.unit scmed = self.c.with_mask(m2).apply_numpy_function(np.nanmedian, axis=0) assert np.count_nonzero(np.isnan(scmed)) == 1 @pytest.mark.parametrize('iterate_rays', (True,False)) def test_bad_median(self, iterate_rays): # This should have the same result as np.nanmedian, though it might be # faster if bottleneck loads scmed = self.c.median(axis=0, iterate_rays=iterate_rays) assert np.count_nonzero(np.isnan(scmed)) == 0 m2 = self.c>0.65*self.c.unit scmed = self.c.with_mask(m2).median(axis=0, iterate_rays=iterate_rays) assert np.count_nonzero(np.isnan(scmed)) == 1 @pytest.mark.parametrize(('pct', 'iterate_rays'), (zip((3,25,50,75,97)*2,(True,)*5 + (False,)*5))) def test_percentile(self, pct, iterate_rays): m = np.empty(self.d.sum(axis=0).shape) for y in range(m.shape[0]): for x in range(m.shape[1]): ray = self.d[:, y, x] ray = ray[ray > 0.5] m[y, x] = np.percentile(ray, pct) scpct = self.c.percentile(pct, axis=0, iterate_rays=iterate_rays) assert_allclose(scpct, m) assert not np.any(np.isnan(scpct.value)) assert scpct.unit == self.c.unit @pytest.mark.parametrize('method', ('sum', 'min', 'max', 'std', 'mad_std', 'median', 'argmin', 'argmax')) def test_transpose(self, method): c1, d1 = cube_and_raw('adv.fits') c2, d2 = cube_and_raw('vad.fits') for axis in [None, 0, 1, 2]: assert_allclose(getattr(c1, method)(axis=axis), getattr(c2, method)(axis=axis)) # check that all these accept progressbar kwargs assert_allclose(getattr(c1, method)(axis=axis, progressbar=True), getattr(c2, method)(axis=axis, progressbar=True)) class TestSlab(BaseTest): def test_closest_spectral_channel(self): c = self.c ms = u.m / u.s assert c.closest_spectral_channel(-321214.698632 * ms) == 0 assert c.closest_spectral_channel(-319926.48366321 * ms) == 1 assert c.closest_spectral_channel(-318638.26869442 * ms) == 2 assert c.closest_spectral_channel(-320000 * ms) == 1 assert c.closest_spectral_channel(-340000 * ms) == 0 assert c.closest_spectral_channel(0 * ms) == 3 def test_spectral_channel_bad_units(self): with pytest.raises(u.UnitsError) as exc: self.c.closest_spectral_channel(1 * u.s) assert exc.value.args[0] == "'value' should be in frequency equivalent or velocity units (got s)" with pytest.raises(u.UnitsError) as exc: self.c.closest_spectral_channel(1. * u.Hz) assert exc.value.args[0] == "Spectral axis is in velocity units and 'value' is in frequency-equivalent units - use SpectralCube.with_spectral_unit first to convert the cube to frequency-equivalent units, or search for a velocity instead" def test_slab(self): ms = u.m / u.s c2 = self.c.spectral_slab(-320000 * ms, -318600 * ms) assert_allclose(c2._data, self.d[1:3]) assert c2._mask is not None def test_slab_reverse_limits(self): ms = u.m / u.s c2 = self.c.spectral_slab(-318600 * ms, -320000 * ms) assert_allclose(c2._data, self.d[1:3]) assert c2._mask is not None def test_slab_preserves_wcs(self): # regression test ms = u.m / u.s crpix = list(self.c._wcs.wcs.crpix) self.c.spectral_slab(-318600 * ms, -320000 * ms) assert list(self.c._wcs.wcs.crpix) == crpix class TestSlabMultiBeams(BaseTestMultiBeams, TestSlab): """ same tests with multibeams """ pass class TestRepr(BaseTest): def test_repr(self): assert repr(self.c) == """ SpectralCube with shape=(4, 3, 2) and unit=K: n_x: 2 type_x: RA---SIN unit_x: deg range: 24.062698 deg: 24.063349 deg n_y: 3 type_y: DEC--SIN unit_y: deg range: 29.934094 deg: 29.935209 deg n_s: 4 type_s: VOPT unit_s: km / s range: -321.215 km / s: -317.350 km / s """.strip() def test_repr_withunit(self): self.c._unit = u.Jy assert repr(self.c) == """ SpectralCube with shape=(4, 3, 2) and unit=Jy: n_x: 2 type_x: RA---SIN unit_x: deg range: 24.062698 deg: 24.063349 deg n_y: 3 type_y: DEC--SIN unit_y: deg range: 29.934094 deg: 29.935209 deg n_s: 4 type_s: VOPT unit_s: km / s range: -321.215 km / s: -317.350 km / s """.strip() @pytest.mark.xfail @pytest.mark.skipif('not YT_INSTALLED') class TestYt(): def setup_method(self, method): self.cube = SpectralCube.read(path('adv.fits')) # Without any special arguments self.ytc1 = self.cube.to_yt() # With spectral factor = 0.5 self.spectral_factor = 0.5 self.ytc2 = self.cube.to_yt(spectral_factor=self.spectral_factor) # With nprocs = 4 self.nprocs = 4 self.ytc3 = self.cube.to_yt(nprocs=self.nprocs) def test_yt(self): # The following assertions just make sure everything is # kosher with the datasets generated in different ways ytc1,ytc2,ytc3 = self.ytc1,self.ytc2,self.ytc3 ds1,ds2,ds3 = ytc1.dataset, ytc2.dataset, ytc3.dataset assert_array_equal(ds1.domain_dimensions, ds2.domain_dimensions) assert_array_equal(ds2.domain_dimensions, ds3.domain_dimensions) assert_allclose(ds1.domain_left_edge.value, ds2.domain_left_edge.value) assert_allclose(ds2.domain_left_edge.value, ds3.domain_left_edge.value) assert_allclose(ds1.domain_width.value, ds2.domain_width.value*np.array([1,1,1.0/self.spectral_factor])) assert_allclose(ds1.domain_width.value, ds3.domain_width.value) assert self.nprocs == len(ds3.index.grids) assert ds1.spec_cube assert ds2.spec_cube assert ds3.spec_cube ds1.index ds2.index ds3.index unit1 = ds1.field_info["fits","flux"].units unit2 = ds2.field_info["fits","flux"].units unit3 = ds3.field_info["fits","flux"].units ds1.quan(1.0,unit1) ds2.quan(1.0,unit2) ds3.quan(1.0,unit3) @pytest.mark.skipif('YT_LT_301', reason='yt 3.0 has a FITS-related bug') def test_yt_fluxcompare(self): # Now check that we can compute quantities of the flux # and that they are equal ytc1,ytc2,ytc3 = self.ytc1,self.ytc2,self.ytc3 ds1,ds2,ds3 = ytc1.dataset, ytc2.dataset, ytc3.dataset dd1 = ds1.all_data() dd2 = ds2.all_data() dd3 = ds3.all_data() flux1_tot = dd1.quantities.total_quantity("flux") flux2_tot = dd2.quantities.total_quantity("flux") flux3_tot = dd3.quantities.total_quantity("flux") flux1_min, flux1_max = dd1.quantities.extrema("flux") flux2_min, flux2_max = dd2.quantities.extrema("flux") flux3_min, flux3_max = dd3.quantities.extrema("flux") assert flux1_tot == flux2_tot assert flux1_tot == flux3_tot assert flux1_min == flux2_min assert flux1_min == flux3_min assert flux1_max == flux2_max assert flux1_max == flux3_max def test_yt_roundtrip_wcs(self): # Now test round-trip conversions between yt and world coordinates ytc1,ytc2,ytc3 = self.ytc1,self.ytc2,self.ytc3 ds1,ds2,ds3 = ytc1.dataset, ytc2.dataset, ytc3.dataset yt_coord1 = ds1.domain_left_edge + np.random.random(size=3)*ds1.domain_width world_coord1 = ytc1.yt2world(yt_coord1) assert_allclose(ytc1.world2yt(world_coord1), yt_coord1.value) yt_coord2 = ds2.domain_left_edge + np.random.random(size=3)*ds2.domain_width world_coord2 = ytc2.yt2world(yt_coord2) assert_allclose(ytc2.world2yt(world_coord2), yt_coord2.value) yt_coord3 = ds3.domain_left_edge + np.random.random(size=3)*ds3.domain_width world_coord3 = ytc3.yt2world(yt_coord3) assert_allclose(ytc3.world2yt(world_coord3), yt_coord3.value) def test_read_write_rountrip(tmpdir): cube = SpectralCube.read(path('adv.fits')) tmp_file = str(tmpdir.join('test.fits')) cube.write(tmp_file) cube2 = SpectralCube.read(tmp_file) assert cube.shape == cube.shape assert_allclose(cube._data, cube2._data) if (((hasattr(_wcs, '__version__') and LooseVersion(_wcs.__version__) < LooseVersion('5.9')) or not hasattr(_wcs, '__version__'))): # see https://github.com/astropy/astropy/pull/3992 for reasons: # we should upgrade this for 5.10 when the absolute accuracy is # maximized assert cube._wcs.to_header_string() == cube2._wcs.to_header_string() # in 5.11 and maybe even 5.12, the round trip fails. Maybe # https://github.com/astropy/astropy/issues/4292 will solve it? @pytest.mark.parametrize(('memmap', 'base'), ((True, mmap.mmap), (False, None))) def test_read_memmap(memmap, base): cube = SpectralCube.read(path('adv.fits'), memmap=memmap) bb = cube.base while hasattr(bb, 'base'): bb = bb.base if base is None: assert bb is None else: assert isinstance(bb, base) def _dummy_cube(): data = np.array([[[0, 1, 2, 3, 4]]]) wcs = WCS(naxis=3) wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN', 'VELO-HEL'] def lower_threshold(data, wcs, view=()): return data[view] > 0 m1 = FunctionMask(lower_threshold) cube = SpectralCube(data, wcs=wcs, mask=m1) return cube def test_with_mask(): def upper_threshold(data, wcs, view=()): return data[view] < 3 m2 = FunctionMask(upper_threshold) cube = _dummy_cube() cube2 = cube.with_mask(m2) assert_allclose(cube._get_filled_data(), [[[np.nan, 1, 2, 3, 4]]]) assert_allclose(cube2._get_filled_data(), [[[np.nan, 1, 2, np.nan, np.nan]]]) def test_with_mask_with_boolean_array(): cube = _dummy_cube() mask = cube._data > 2 cube2 = cube.with_mask(mask, inherit_mask=False) assert isinstance(cube2._mask, BooleanArrayMask) assert cube2._mask._wcs is cube._wcs assert cube2._mask._mask is mask def test_with_mask_with_good_array_shape(): cube = _dummy_cube() mask = np.zeros((1, 5), dtype=np.bool) cube2 = cube.with_mask(mask, inherit_mask=False) assert isinstance(cube2._mask, BooleanArrayMask) np.testing.assert_equal(cube2._mask._mask, mask.reshape((1, 1, 5))) def test_with_mask_with_bad_array_shape(): cube = _dummy_cube() mask = np.zeros((5, 5), dtype=np.bool) with pytest.raises(ValueError) as exc: cube.with_mask(mask) assert exc.value.args[0] == ("Mask shape is not broadcastable to data shape: " "(5, 5) vs (1, 1, 5)") class TestMasks(BaseTest): @pytest.mark.parametrize('op', (operator.gt, operator.lt, operator.le, operator.ge)) def test_operator_threshold(self, op): # choose thresh to exercise proper equality tests thresh = self.d.ravel()[0] m = op(self.c, thresh*u.K) self.c._mask = m expected = self.d[op(self.d, thresh)] actual = self.c.flattened() assert_allclose(actual, expected) def test_preserve_spectral_unit(): # astropy.wcs has a tendancy to change spectral units from e.g. km/s to # m/s, so we have a workaround - check that it works. cube, data = cube_and_raw('advs.fits') cube_freq = cube.with_spectral_unit(u.GHz) assert cube_freq.wcs.wcs.cunit[2] == 'Hz' # check internal assert cube_freq.spectral_axis.unit is u.GHz # Check that this preferred unit is propagated new_cube = cube_freq.with_fill_value(fill_value=3.4) assert new_cube.spectral_axis.unit is u.GHz @pytest.mark.skipif('not BOTTLENECK_INSTALLED') def test_endians(): """ Test that the endianness checking returns something in Native form (this is only needed for non-numpy functions that worry about the endianness of their data) WARNING: Because the endianness is machine-dependent, this may fail on different architectures! This is because numpy automatically converts little-endian to native in the dtype parameter; I need a workaround for this. """ big = np.array([[[1],[2]]], dtype='>f4') lil = np.array([[[1],[2]]], dtype='' assert xlil.dtype.byteorder == '=' def test_header_naxis(): cube, data = cube_and_raw('advs.fits') assert cube.header['NAXIS'] == 3 # NOT data.ndim == 4 assert cube.header['NAXIS1'] == data.shape[3] assert cube.header['NAXIS2'] == data.shape[2] assert cube.header['NAXIS3'] == data.shape[1] assert 'NAXIS4' not in cube.header def test_slicing(): cube, data = cube_and_raw('advs.fits') # just to check that we're starting in the right place assert cube.shape == (2,3,4) sl = cube[:,1,:] assert sl.shape == (2,4) v = cube[1:2,:,:] assert v.shape == (1,3,4) # make sure this works. Not sure what keys to test for... v.header assert cube[:,:,:].shape == (2,3,4) assert cube[:,:].shape == (2,3,4) assert cube[:].shape == (2,3,4) assert cube[:1,:1,:1].shape == (1,1,1) @pytest.mark.parametrize(('view','naxis'), [((slice(None), 1, slice(None)), 2), ((1, slice(None), slice(None)), 2), ((slice(None), slice(None), 1), 2), ((slice(None), slice(None), slice(1)), 3), ((slice(1), slice(1), slice(1)), 3), ((slice(None, None, -1), slice(None), slice(None)), 3), ]) def test_slice_wcs(view, naxis): cube, data = cube_and_raw('advs.fits') sl = cube[view] assert sl.wcs.naxis == naxis def test_slice_wcs_reversal(): cube, data = cube_and_raw('advs.fits') view = (slice(None,None,-1), slice(None), slice(None)) rcube = cube[view] rrcube = rcube[view] np.testing.assert_array_equal(np.diff(cube.spectral_axis), -np.diff(rcube.spectral_axis)) np.testing.assert_array_equal(rrcube.spectral_axis.value, cube.spectral_axis.value) np.testing.assert_array_equal(rcube.spectral_axis.value, cube.spectral_axis.value[::-1]) np.testing.assert_array_equal(rrcube.world_extrema.value, cube.world_extrema.value) # check that the lon, lat arrays are *entirely* unchanged np.testing.assert_array_equal(rrcube.spatial_coordinate_map[0].value, cube.spatial_coordinate_map[0].value) np.testing.assert_array_equal(rrcube.spatial_coordinate_map[1].value, cube.spatial_coordinate_map[1].value) def test_spectral_slice_preserve_units(): cube, data = cube_and_raw('advs.fits') cube = cube.with_spectral_unit(u.km/u.s) sl = cube[:,0,0] assert cube._spectral_unit == u.km/u.s assert sl._spectral_unit == u.km/u.s assert cube.spectral_axis.unit == u.km/u.s assert sl.spectral_axis.unit == u.km/u.s def test_header_units_consistent(): cube, data = cube_and_raw('advs.fits') cube_ms = cube.with_spectral_unit(u.m/u.s) cube_kms = cube.with_spectral_unit(u.km/u.s) cube_Mms = cube.with_spectral_unit(u.Mm/u.s) assert cube.header['CUNIT3'] == 'km s-1' assert cube_ms.header['CUNIT3'] == 'm s-1' assert cube_kms.header['CUNIT3'] == 'km s-1' assert cube_Mms.header['CUNIT3'] == 'Mm s-1' # Wow, the tolerance here is really terrible... assert_allclose(cube_Mms.header['CDELT3'], cube.header['CDELT3']/1e3,rtol=1e-3,atol=1e-5) assert_allclose(cube.header['CDELT3'], cube_kms.header['CDELT3'],rtol=1e-2,atol=1e-5) assert_allclose(cube.header['CDELT3']*1e3, cube_ms.header['CDELT3'],rtol=1e-2,atol=1e-5) cube_freq = cube.with_spectral_unit(u.Hz) assert cube_freq.header['CUNIT3'] == 'Hz' cube_freq_GHz = cube.with_spectral_unit(u.GHz) assert cube_freq_GHz.header['CUNIT3'] == 'GHz' def test_spectral_unit_conventions(): cube, data = cube_and_raw('advs.fits') cube_frq = cube.with_spectral_unit(u.Hz) cube_opt = cube.with_spectral_unit(u.km/u.s, rest_value=cube_frq.spectral_axis[0], velocity_convention='optical') cube_rad = cube.with_spectral_unit(u.km/u.s, rest_value=cube_frq.spectral_axis[0], velocity_convention='radio') cube_rel = cube.with_spectral_unit(u.km/u.s, rest_value=cube_frq.spectral_axis[0], velocity_convention='relativistic') # should all be exactly 0 km/s for x in (cube_rel.spectral_axis[0], cube_rad.spectral_axis[0], cube_opt.spectral_axis[0]): np.testing.assert_almost_equal(0,x.value) assert cube_rel.spectral_axis[1] != cube_rad.spectral_axis[1] assert cube_opt.spectral_axis[1] != cube_rad.spectral_axis[1] assert cube_rel.spectral_axis[1] != cube_opt.spectral_axis[1] assert cube_rel.velocity_convention == u.doppler_relativistic assert cube_rad.velocity_convention == u.doppler_radio assert cube_opt.velocity_convention == u.doppler_optical def test_invalid_spectral_unit_conventions(): cube, data = cube_and_raw('advs.fits') with pytest.raises(ValueError) as exc: cube.with_spectral_unit(u.km/u.s, velocity_convention='invalid velocity convention') assert exc.value.args[0] == ("Velocity convention must be radio, optical, " "or relativistic.") @pytest.mark.parametrize('rest', (50, 50*u.K)) def test_invalid_rest(rest): cube, data = cube_and_raw('advs.fits') with pytest.raises(ValueError) as exc: cube.with_spectral_unit(u.km/u.s, velocity_convention='radio', rest_value=rest) assert exc.value.args[0] == ("Rest value must be specified as an astropy " "quantity with spectral equivalence.") def test_airwave_to_wave(): cube, data = cube_and_raw('advs.fits') cube._wcs.wcs.ctype[2] = 'AWAV' cube._wcs.wcs.cunit[2] = 'm' cube._spectral_unit = u.m cube._wcs.wcs.cdelt[2] = 1e-7 cube._wcs.wcs.crval[2] = 5e-7 ax1 = cube.spectral_axis ax2 = cube.with_spectral_unit(u.m).spectral_axis np.testing.assert_almost_equal(spectral_axis.air_to_vac(ax1).value, ax2.value) @pytest.mark.parametrize(('func','how','axis'), itertools.product(('sum','std','max','min','mean'), ('slice','cube','auto'), (0,1,2) )) def test_twod_numpy(func, how, axis): # Check that a numpy function returns the correct result when applied along # one axis # This is partly a regression test for #211 cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K proj = getattr(cube,func)(axis=axis, how=how) # data has a redundant 1st axis dproj = getattr(data,func)(axis=(0,axis+1)).squeeze() assert isinstance(proj, Projection) np.testing.assert_equal(proj.value, dproj) assert cube.unit == proj.unit @pytest.mark.parametrize(('func','how','axis'), itertools.product(('sum','std','max','min','mean'), ('slice','cube','auto'), ((0,1),(1,2),(0,2)) )) def test_twod_numpy_twoaxes(func, how, axis): # Check that a numpy function returns the correct result when applied along # one axis # This is partly a regression test for #211 cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K if func == 'mean' and axis != (1,2): with warnings.catch_warnings(record=True) as wrn: spec = getattr(cube,func)(axis=axis, how=how) assert 'Averaging over a spatial and a spectral' in str(wrn[-1].message) spec = getattr(cube,func)(axis=axis, how=how) # data has a redundant 1st axis dspec = getattr(data.squeeze(),func)(axis=axis) if axis == (1,2): assert isinstance(spec, OneDSpectrum) assert cube.unit == spec.unit np.testing.assert_almost_equal(spec.value, dspec) else: np.testing.assert_almost_equal(spec, dspec) def test_preserves_header_values(): # Check that the non-WCS header parameters are preserved during projection cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K cube._header['OBJECT'] = 'TestName' proj = cube.sum(axis=0, how='auto') assert isinstance(proj, Projection) assert proj.header['OBJECT'] == 'TestName' assert proj.hdu.header['OBJECT'] == 'TestName' @pytest.mark.parametrize('func',('sum','std','max','min','mean')) def test_oned_numpy(func): # Check that a numpy function returns an appropriate spectrum cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K spec = getattr(cube,func)(axis=(1,2)) dspec = getattr(data,func)(axis=(2,3)).squeeze() assert isinstance(spec, OneDSpectrum) # data has a redundant 1st axis np.testing.assert_equal(spec.value, dspec) assert cube.unit == spec.unit def test_oned_slice(): # Check that a slice returns an appropriate spectrum cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K spec = cube[:,0,0] assert isinstance(spec, OneDSpectrum) # data has a redundant 1st axis np.testing.assert_equal(spec.value, data[0,:,0,0]) assert cube.unit == spec.unit assert spec.header['BUNIT'] == cube.header['BUNIT'] def test_oned_slice_beams(): # Check that a slice returns an appropriate spectrum cube, data = cube_and_raw('sdav_beams.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K spec = cube[:,0,0] assert isinstance(spec, OneDSpectrum) # data has a redundant 1st axis np.testing.assert_equal(spec.value, data[:,0,0,0]) assert cube.unit == spec.unit assert spec.header['BUNIT'] == cube.header['BUNIT'] assert hasattr(spec, 'beams') assert 'BMAJ' in spec.hdulist[1].data.names def test_subcube_slab_beams(): cube, data = cube_and_raw('sdav_beams.fits') slcube = cube[1:] assert all(slcube.hdulist[1].data['CHAN'] == np.arange(slcube.shape[0])) # collapsing to one dimension raywise doesn't make sense and is therefore # not supported. @pytest.mark.parametrize('how', ('auto', 'cube', 'slice')) def test_oned_collapse(how): # Check that an operation along the spatial dims returns an appropriate # spectrum cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K spec = cube.mean(axis=(1,2), how=how) assert isinstance(spec, OneDSpectrum) # data has a redundant 1st axis np.testing.assert_equal(spec.value, data.mean(axis=(0,2,3))) assert cube.unit == spec.unit assert spec.header['BUNIT'] == cube.header['BUNIT'] def test_oned_collapse_beams(): # Check that an operation along the spatial dims returns an appropriate # spectrum cube, data = cube_and_raw('sdav_beams.fits') cube._meta['BUNIT'] = 'K' cube._unit = u.K spec = cube.mean(axis=(1,2)) assert isinstance(spec, OneDSpectrum) # data has a redundant 1st axis np.testing.assert_equal(spec.value, data.mean(axis=(1,2,3))) assert cube.unit == spec.unit assert spec.header['BUNIT'] == cube.header['BUNIT'] assert hasattr(spec, 'beams') assert 'BMAJ' in spec.hdulist[1].data.names def test_preserve_bunit(): cube, data = cube_and_raw('advs.fits') assert cube.header['BUNIT'] == 'K' hdu = fits.open(path('advs.fits'))[0] hdu.header['BUNIT'] = 'Jy' cube = SpectralCube.read(hdu) assert cube.unit == u.Jy assert cube.header['BUNIT'] == 'Jy' def test_preserve_beam(): cube, data = cube_and_raw('advs.fits') beam = Beam.from_fits_header(path("advs.fits")) assert cube.beam == beam def test_beam_attach_to_header(): cube, data = cube_and_raw('adv.fits') header = cube._header.copy() del header["BMAJ"], header["BMIN"], header["BPA"] newcube = SpectralCube(data=data, wcs=cube.wcs, header=header, beam=cube.beam) assert cube.header["BMAJ"] == newcube.header["BMAJ"] assert cube.header["BMIN"] == newcube.header["BMIN"] assert cube.header["BPA"] == newcube.header["BPA"] # Should be in meta too assert newcube.meta['beam'] == cube.beam def test_beam_custom(): cube, data = cube_and_raw('adv.fits') header = cube._header.copy() beam = Beam.from_fits_header(header) del header["BMAJ"], header["BMIN"], header["BPA"] newcube = SpectralCube(data=data, wcs=cube.wcs, header=header) # newcube should now not have a beam assert not hasattr(newcube, "beam") # Attach the beam newcube = newcube.with_beam(beam=beam) assert newcube.beam == cube.beam # Header should be updated assert cube.header["BMAJ"] == newcube.header["BMAJ"] assert cube.header["BMIN"] == newcube.header["BMIN"] assert cube.header["BPA"] == newcube.header["BPA"] # Should be in meta too assert newcube.meta['beam'] == cube.beam # Try changing the beam properties newbeam = Beam(beam.major * 2) newcube2 = newcube.with_beam(beam=newbeam) assert newcube2.beam == newbeam # Header should be updated assert newcube2.header["BMAJ"] == newbeam.major.value assert newcube2.header["BMIN"] == newbeam.minor.value assert newcube2.header["BPA"] == newbeam.pa.value # Should be in meta too assert newcube2.meta['beam'] == newbeam def test_multibeam_slice(): cube, data = cube_and_raw('vda_beams.fits') assert isinstance(cube, VaryingResolutionSpectralCube) np.testing.assert_almost_equal(cube.beams[0].major.value, 0.1) np.testing.assert_almost_equal(cube.beams[3].major.value, 0.4) scube = cube[:2,:,:] np.testing.assert_almost_equal(scube.beams[0].major.value, 0.1) np.testing.assert_almost_equal(scube.beams[1].major.value, 0.2) flatslice = cube[0,:,:] np.testing.assert_almost_equal(flatslice.header['BMAJ'], (0.1/3600.)) def test_basic_unit_conversion(): cube, data = cube_and_raw('advs.fits') assert cube.unit == u.K mKcube = cube.to(u.mK) np.testing.assert_almost_equal(mKcube.filled_data[:].value, (cube.filled_data[:].value * 1e3)) def test_basic_unit_conversion_beams(): cube, data = cube_and_raw('vda_beams.fits') cube._unit = u.K # want beams, but we want to force the unit to be something non-beamy cube._meta['BUNIT'] = 'K' assert cube.unit == u.K mKcube = cube.to(u.mK) np.testing.assert_almost_equal(mKcube.filled_data[:].value, (cube.filled_data[:].value * 1e3)) def test_beam_jtok_array(): cube, data = cube_and_raw('advs.fits') cube._meta['BUNIT'] = 'Jy / beam' cube._unit = u.Jy/u.beam equiv = cube.beam.jtok_equiv(cube.with_spectral_unit(u.GHz).spectral_axis) jtok = cube.beam.jtok(cube.with_spectral_unit(u.GHz).spectral_axis) Kcube = cube.to(u.K, equivalencies=equiv) np.testing.assert_almost_equal(Kcube.filled_data[:].value, (cube.filled_data[:].value * jtok[:,None,None]).value) # test that the beam equivalencies are correctly automatically defined Kcube = cube.to(u.K) np.testing.assert_almost_equal(Kcube.filled_data[:].value, (cube.filled_data[:].value * jtok[:,None,None]).value) def test_multibeam_jtok_array(): cube, data = cube_and_raw('vda_beams.fits') assert cube.meta['BUNIT'].strip() == 'Jy / beam' assert cube.unit.is_equivalent(u.Jy/u.beam) #equiv = [bm.jtok_equiv(frq) for bm, frq in zip(cube.beams, cube.with_spectral_unit(u.GHz).spectral_axis)] jtok = u.Quantity([bm.jtok(frq) for bm, frq in zip(cube.beams, cube.with_spectral_unit(u.GHz).spectral_axis)]) # don't try this, it's nonsense for the multibeam case # Kcube = cube.to(u.K, equivalencies=equiv) # np.testing.assert_almost_equal(Kcube.filled_data[:].value, # (cube.filled_data[:].value * # jtok[:,None,None]).value) # test that the beam equivalencies are correctly automatically defined Kcube = cube.to(u.K) np.testing.assert_almost_equal(Kcube.filled_data[:].value, (cube.filled_data[:].value * jtok[:,None,None]).value) def test_beam_jtok(): # regression test for an error introduced when the previous test was solved # (the "is this an array?" test used len(x) where x could be scalar) cube, data = cube_and_raw('advs.fits') # technically this should be jy/beam, but astropy's equivalency doesn't # handle this yet cube._meta['BUNIT'] = 'Jy' cube._unit = u.Jy equiv = cube.beam.jtok_equiv(np.median(cube.with_spectral_unit(u.GHz).spectral_axis)) jtok = cube.beam.jtok(np.median(cube.with_spectral_unit(u.GHz).spectral_axis)) Kcube = cube.to(u.K, equivalencies=equiv) np.testing.assert_almost_equal(Kcube.filled_data[:].value, (cube.filled_data[:].value * jtok).value) def test_varyres_moment(): cube, data = cube_and_raw('vda_beams.fits') assert isinstance(cube, VaryingResolutionSpectralCube) # the beams are very different, but for this test we don't care cube.beam_threshold = 1.0 with warnings.catch_warnings(record=True) as wrn: warnings.simplefilter('default') m0 = cube.moment0() assert "Arithmetic beam averaging is being performed" in str(wrn[-1].message) assert_quantity_allclose(m0.meta['beam'].major, 0.25*u.arcsec) def test_append_beam_to_hdr(): cube, data = cube_and_raw('advs.fits') orig_hdr = fits.getheader(path('advs.fits')) assert cube.header['BMAJ'] == orig_hdr['BMAJ'] assert cube.header['BMIN'] == orig_hdr['BMIN'] assert cube.header['BPA'] == orig_hdr['BPA'] def test_cube_with_swapped_axes(): """ Regression test for #208 """ cube, data = cube_and_raw('vda.fits') # Check that masking works (this should apply a lazy mask) cube.filled_data[:] def test_jybeam_upper(): cube, data = cube_and_raw('vda_JYBEAM_upper.fits') assert cube.unit == u.Jy/u.beam assert hasattr(cube, 'beam') np.testing.assert_almost_equal(cube.beam.sr.value, (((1*u.arcsec/np.sqrt(8*np.log(2)))**2).to(u.sr)*2*np.pi).value) def test_jybeam_lower(): cube, data = cube_and_raw('vda_Jybeam_lower.fits') assert cube.unit == u.Jy/u.beam assert hasattr(cube, 'beam') np.testing.assert_almost_equal(cube.beam.sr.value, (((1*u.arcsec/np.sqrt(8*np.log(2)))**2).to(u.sr)*2*np.pi).value) # Regression test for #257 (https://github.com/radio-astro-tools/spectral-cube/pull/257) def test_jybeam_whitespace(): cube, data = cube_and_raw('vda_Jybeam_whitespace.fits') assert cube.unit == u.Jy/u.beam assert hasattr(cube, 'beam') np.testing.assert_almost_equal(cube.beam.sr.value, (((1*u.arcsec/np.sqrt(8*np.log(2)))**2).to(u.sr)*2*np.pi).value) def test_beam_proj_meta(): cube, data = cube_and_raw('advs.fits') moment = cube.moment0(axis=0) # regression test for #250 assert 'beam' in moment.meta assert 'BMAJ' in moment.hdu.header slc = cube[0,:,:] assert 'beam' in slc.meta proj = cube.max(axis=0) assert 'beam' in proj.meta def test_proj_meta(): cube, data = cube_and_raw('advs.fits') moment = cube.moment0(axis=0) assert 'BUNIT' in moment.meta assert moment.meta['BUNIT'] == 'K' slc = cube[0,:,:] assert 'BUNIT' in slc.meta assert slc.meta['BUNIT'] == 'K' proj = cube.max(axis=0) assert 'BUNIT' in proj.meta assert proj.meta['BUNIT'] == 'K' def test_pix_sign(): cube, data = cube_and_raw('advs.fits') s,y,x = (cube._pix_size_slice(ii) for ii in range(3)) assert s>0 assert y>0 assert x>0 cube.wcs.wcs.cdelt *= -1 s,y,x = (cube._pix_size_slice(ii) for ii in range(3)) assert s>0 assert y>0 assert x>0 cube.wcs.wcs.pc *= -1 s,y,x = (cube._pix_size_slice(ii) for ii in range(3)) assert s>0 assert y>0 assert x>0 def test_varyres_moment_logic_issue364(): """ regression test for issue364 """ cube, data = cube_and_raw('vda_beams.fits') assert isinstance(cube, VaryingResolutionSpectralCube) # the beams are very different, but for this test we don't care cube.beam_threshold = 1.0 with warnings.catch_warnings(record=True) as wrn: warnings.simplefilter('default') # note that cube.moment(order=0) is different from cube.moment0() # because cube.moment0() calls cube.moment(order=0, axis=(whatever)), # but cube.moment doesn't necessarily have to receive the axis kwarg m0 = cube.moment(order=0) if six.PY2: # sad face, tests do not work pass else: assert "Arithmetic beam averaging is being performed" in str(wrn[-1].message) assert_quantity_allclose(m0.meta['beam'].major, 0.25*u.arcsec) def test_mask_bad_beams(): cube, data = cube_and_raw('vda_beams.fits') # make sure all of the beams are initially good (finite) assert np.all(cube._goodbeams_mask) # make sure cropping the cube maintains the mask assert np.all(cube[:3]._goodbeams_mask) # middle two beams have same area masked_cube = cube.mask_out_bad_beams(0.01, reference_beam=Beam(0.3*u.arcsec, 0.2*u.arcsec, 60*u.deg)) assert np.all(masked_cube.mask.include()[:,0,0] == [False,False,True,False]) assert np.all(masked_cube._goodbeams_mask == [False,False,True,False]) mean = masked_cube.mean(axis=0) assert np.all(mean == cube[2,:,:]) masked_cube2 = cube.mask_out_bad_beams(0.5,) mean2 = masked_cube2.mean(axis=0) assert np.all(mean2 == (cube[2,:,:]+cube[1,:,:])/2) assert np.all(masked_cube2._goodbeams_mask == [False,True,True,False]) def test_convolve_to_with_bad_beams(): cube, data = cube_and_raw('vda_beams.fits') convolved = cube.convolve_to(Beam(0.5*u.arcsec)) with pytest.raises(ValueError) as exc: # should not work: biggest beam is 0.4" convolved = cube.convolve_to(Beam(0.35*u.arcsec)) assert exc.value.args[0] == "Beam could not be deconvolved" # middle two beams are smaller than 0.4 masked_cube = cube.mask_channels([False, True, True, False]) # should work: biggest beam is 0.3 arcsec (major) convolved = masked_cube.convolve_to(Beam(0.35*u.arcsec)) # this is a copout test; should really check for correctness... assert np.all(np.isfinite(convolved.filled_data[1:3])) def test_jybeam_factors(): cube, data = cube_and_raw('vda_beams.fits') assert_allclose(cube.jtok_factors(), [15111171.12641629, 10074201.06746361, 10074287.73828087, 15111561.14508185]) def test_channelmask_singlebeam(): cube, data = cube_and_raw('adv.fits') masked_cube = cube.mask_channels([False, True, True, False]) assert np.all(masked_cube.mask.include()[:,0,0] == [False, True, True, False]) def test_mad_std(): cube, data = cube_and_raw('adv.fits') if int(astropy.__version__[0]) < 2: with pytest.raises(NotImplementedError) as exc: cube.mad_std() else: # mad_std run manually on data result = np.array([[0.15509701, 0.45763670], [0.55907956, 0.42932451], [0.48819454, 0.25499305]]) np.testing.assert_almost_equal(cube.mad_std(axis=0).value, result) mcube = cube.with_mask(cube < 0.98*u.K) result2 = np.array([[0.15509701, 0.45763670], [0.55907956, 0.23835865], [0.48819454, 0.25499305]]) np.testing.assert_almost_equal(mcube.mad_std(axis=0).value, result2) def test_caching(): cube, data = cube_and_raw('adv.fits') assert len(cube._cache) == 0 worldextrema = cube.world_extrema assert len(cube._cache) == 1 # see https://stackoverflow.com/questions/46181936/access-a-parent-class-property-getter-from-the-child-class world_extrema_function = base_class.SpatialCoordMixinClass.world_extrema.fget.wrapped_function assert cube.world_extrema is cube._cache[(world_extrema_function, ())] np.testing.assert_almost_equal(worldextrema.value, cube.world_extrema.value) def test_spatial_smooth_g2d(): cube, data = cube_and_raw('adv.fits') # # Guassian 2D smoothing test # g2d = Gaussian2DKernel(3) cube_g2d = cube.spatial_smooth(g2d) # Check first slice result0 = np.array([[ 0.06653894, 0.06598313], [ 0.07206352, 0.07151016], [ 0.0702898 , 0.0697944 ]]) np.testing.assert_almost_equal(cube_g2d[0].value, result0) # Check third slice result2 = np.array([[ 0.04217102, 0.04183251], [ 0.04470876, 0.04438826], [ 0.04269588, 0.04242956]]) np.testing.assert_almost_equal(cube_g2d[2].value, result2) def test_spatial_smooth_t2d(): cube, data = cube_and_raw('adv.fits') # # Tophat 2D smoothing test # t2d = Tophat2DKernel(3) cube_t2d = cube.spatial_smooth(t2d) # Check first slice result0 = np.array([[ 0.14864167, 0.14864167], [ 0.14864167, 0.14864167], [ 0.14864167, 0.14864167]]) np.testing.assert_almost_equal(cube_t2d[0].value, result0) # Check third slice result2 = np.array([[ 0.09203958, 0.09203958], [ 0.09203958, 0.09203958], [ 0.09203958, 0.09203958]]) np.testing.assert_almost_equal(cube_t2d[2].value, result2) @pytest.mark.skipif('not SCIPYOK') def test_spatial_smooth_median(): cube, data = cube_and_raw('adv.fits') cube_median = cube.spatial_smooth_median(3) # Check first slice result0 = np.array([[ 0.54671028, 0.54671028], [ 0.89482735, 0.77513282], [ 0.93949894, 0.89482735]]) np.testing.assert_almost_equal(cube_median[0].value, result0) # Check third slice result2 = np.array([[ 0.38867729, 0.35675333], [ 0.38867729, 0.35675333], [ 0.35675333, 0.54269608]]) np.testing.assert_almost_equal(cube_median[2].value, result2) @pytest.mark.skipif('not SCIPYOK') def test_spectral_smooth_median(): cube, data = cube_and_raw('adv.fits') cube_spectral_median = cube.spectral_smooth_median(3) # Check first slice result = np.array([0.77513282, 0.35675333, 0.35675333, 0.98688694]) np.testing.assert_almost_equal(cube_spectral_median[:,1,1].value, result) @pytest.mark.skipif('not SCIPYOK') @pytest.mark.skipif('not JOBLIB_INSTALLED') def test_spectral_smooth_median_4cores(): cube, data = cube_and_raw('adv.fits') cube_spectral_median = cube.spectral_smooth_median(3, num_cores=4) # Check first slice result = np.array([0.77513282, 0.35675333, 0.35675333, 0.98688694]) np.testing.assert_almost_equal(cube_spectral_median[:,1,1].value, result) def test_initialization_from_units(): """ Regression test for issue 447 """ cube, data = cube_and_raw('adv.fits') newcube = SpectralCube(data=cube.filled_data[:], wcs=cube.wcs) assert newcube.unit == cube.unit def test_varyres_spectra(): cube, data = cube_and_raw('vda_beams.fits') assert isinstance(cube, VaryingResolutionSpectralCube) sp = cube[:,0,0] assert isinstance(sp, VaryingResolutionOneDSpectrum) assert hasattr(sp, 'beams') sp = cube.mean(axis=(1,2)) assert isinstance(sp, VaryingResolutionOneDSpectrum) assert hasattr(sp, 'beams') def test_median_2axis(): """ As of this writing the bottleneck.nanmedian did not accept an axis that is a tuple/list so this test is to make sure that is properly taken into account. :return: """ cube, data = cube_and_raw('adv.fits') cube_median = cube.median(axis=(1, 2)) # Check first slice result0 = np.array([0.83498009, 0.2606566 , 0.37271531, 0.48548023]) np.testing.assert_almost_equal(cube_median.value, result0) spectral-cube-0.4.3/spectral_cube/tests/test_stokes_spectral_cube.py0000644000077000000240000001576613161003310026064 0ustar adamstaff00000000000000from collections import OrderedDict import numpy as np from numpy.testing import assert_allclose, assert_equal from astropy.wcs import WCS from astropy.tests.helper import pytest from astropy.utils import NumpyRNGContext from ..spectral_cube import SpectralCube from ..stokes_spectral_cube import StokesSpectralCube from ..masks import BooleanArrayMask class TestStokesSpectralCube(): def setup_class(self): self.wcs = WCS(naxis=3) self.wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN', 'FREQ'] self.data = np.arange(4)[:, None, None, None] * np.ones((5, 20, 30)) def test_direct_init(self): stokes_data = dict(I=SpectralCube(self.data[0], wcs=self.wcs), Q=SpectralCube(self.data[1], wcs=self.wcs), U=SpectralCube(self.data[2], wcs=self.wcs), V=SpectralCube(self.data[3], wcs=self.wcs)) cube = StokesSpectralCube(stokes_data) def test_direct_init_invalid_type(self): stokes_data = dict(I=self.data[0], Q=self.data[1], U=self.data[2], V=self.data[3]) with pytest.raises(TypeError) as exc: cube = StokesSpectralCube(stokes_data) assert exc.value.args[0] == "stokes_data should be a dictionary of SpectralCube objects" def test_direct_init_invalid_shape(self): stokes_data = dict(I=SpectralCube(np.ones((6, 2, 30)), wcs=self.wcs), Q=SpectralCube(self.data[1], wcs=self.wcs), U=SpectralCube(self.data[2], wcs=self.wcs), V=SpectralCube(self.data[3], wcs=self.wcs)) with pytest.raises(ValueError) as exc: cube = StokesSpectralCube(stokes_data) assert exc.value.args[0] == "All spectral cubes should have the same shape" @pytest.mark.parametrize('component', ('I', 'Q', 'U', 'V', 'RR', 'RL', 'LR', 'LL')) def test_valid_component_name(self, component): stokes_data = {component: SpectralCube(self.data[0], wcs=self.wcs)} cube = StokesSpectralCube(stokes_data) assert cube.components == [component] @pytest.mark.parametrize('component', ('A', 'B', 'IQUV')) def test_invalid_component_name(self, component): stokes_data = {component: SpectralCube(self.data[0], wcs=self.wcs)} with pytest.raises(ValueError) as exc: cube = StokesSpectralCube(stokes_data) assert exc.value.args[0] == "Invalid Stokes component: {0} - should be one of I, Q, U, V, RR, LL, RL, LR".format(component) def test_invalid_wcs(self): wcs2 = WCS(naxis=3) wcs2.wcs.ctype = ['GLON-CAR', 'GLAT-CAR', 'FREQ'] stokes_data = dict(I=SpectralCube(self.data[0], wcs=self.wcs), Q=SpectralCube(self.data[1], wcs2)) with pytest.raises(ValueError) as exc: cube = StokesSpectralCube(stokes_data) assert exc.value.args[0] == "All spectral cubes in stokes_data should have the same WCS" def test_attributes(self): stokes_data = OrderedDict() stokes_data['I'] = SpectralCube(self.data[0], wcs=self.wcs) stokes_data['Q'] = SpectralCube(self.data[1], wcs=self.wcs) stokes_data['U'] = SpectralCube(self.data[2], wcs=self.wcs) stokes_data['V'] = SpectralCube(self.data[3], wcs=self.wcs) cube = StokesSpectralCube(stokes_data) assert_allclose(cube.I.unmasked_data[...], 0) assert_allclose(cube.Q.unmasked_data[...], 1) assert_allclose(cube.U.unmasked_data[...], 2) assert_allclose(cube.V.unmasked_data[...], 3) assert cube.components == ['I', 'Q', 'U', 'V'] def test_dir(self): stokes_data = dict(I=SpectralCube(self.data[0], wcs=self.wcs), Q=SpectralCube(self.data[1], wcs=self.wcs), U=SpectralCube(self.data[2], wcs=self.wcs)) cube = StokesSpectralCube(stokes_data) attributes = dir(cube) for stokes in 'IQU': assert stokes in attributes assert 'V' not in attributes assert 'mask' in attributes assert 'wcs' in attributes assert 'shape' in attributes def test_mask(self): with NumpyRNGContext(12345): mask1 = BooleanArrayMask(np.random.random((5, 20, 30)) > 0.2, wcs=self.wcs) # Deliberately don't use a BooleanArrayMask to check auto-conversion mask2 = np.random.random((5, 20, 30)) > 0.4 stokes_data = dict(I=SpectralCube(self.data[0], wcs=self.wcs), Q=SpectralCube(self.data[1], wcs=self.wcs), U=SpectralCube(self.data[2], wcs=self.wcs), V=SpectralCube(self.data[3], wcs=self.wcs)) cube1 = StokesSpectralCube(stokes_data, mask=mask1) cube2 = cube1.with_mask(mask2) assert_equal(cube2.mask.include(), (mask1).include() & mask2) def test_mask_invalid_component_name(self): stokes_data = {'BANANA': SpectralCube(self.data[0], wcs=self.wcs)} with pytest.raises(ValueError) as exc: cube = StokesSpectralCube(stokes_data) assert exc.value.args[0] == "Invalid Stokes component: BANANA - should be one of I, Q, U, V, RR, LL, RL, LR" def test_mask_invalid_shape(self): stokes_data = dict(I=SpectralCube(self.data[0], wcs=self.wcs), Q=SpectralCube(self.data[1], wcs=self.wcs), U=SpectralCube(self.data[2], wcs=self.wcs), V=SpectralCube(self.data[3], wcs=self.wcs)) mask1 = BooleanArrayMask(np.random.random((5, 20, 15)) > 0.2, wcs=self.wcs) with pytest.raises(ValueError) as exc: cube1 = StokesSpectralCube(stokes_data, mask=mask1) assert exc.value.args[0] == "Mask shape is not broadcastable to data shape: (5, 20, 15) vs (5, 20, 30)" def test_separate_mask(self): with NumpyRNGContext(12345): mask1 = BooleanArrayMask(np.random.random((5, 20, 30)) > 0.2, wcs=self.wcs) mask2 = [BooleanArrayMask(np.random.random((5, 20, 30)) > 0.4, wcs=self.wcs) for i in range(4)] mask3 = BooleanArrayMask(np.random.random((5, 20, 30)) > 0.2, wcs=self.wcs) stokes_data = dict(I=SpectralCube(self.data[0], wcs=self.wcs, mask=mask2[0]), Q=SpectralCube(self.data[1], wcs=self.wcs, mask=mask2[1]), U=SpectralCube(self.data[2], wcs=self.wcs, mask=mask2[2]), V=SpectralCube(self.data[3], wcs=self.wcs, mask=mask2[3])) cube1 = StokesSpectralCube(stokes_data, mask=mask1) assert_equal(cube1.I.mask.include(), (mask1 & mask2[0]).include()) assert_equal(cube1.Q.mask.include(), (mask1 & mask2[1]).include()) assert_equal(cube1.U.mask.include(), (mask1 & mask2[2]).include()) assert_equal(cube1.V.mask.include(), (mask1 & mask2[3]).include()) cube2 = cube1.I.with_mask(mask3) assert_equal(cube2.mask.include(), (mask1 & mask2[0] & mask3).include()) spectral-cube-0.4.3/spectral_cube/tests/test_subcubes.py0000644000077000000240000000633213242700604023473 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import pytest from astropy import units as u from astropy import wcs import numpy as np from . import path from .helpers import assert_allclose, assert_array_equal from .test_spectral_cube import cube_and_raw try: import pyregion pyregionOK = True except ImportError: pyregionOK = False def test_subcube(): cube, data = cube_and_raw('advs.fits') sc1 = cube.subcube(xlo=1, xhi=3) sc2 = cube.subcube(xlo=24.06269*u.deg, xhi=24.06206*u.deg) sc2b = cube.subcube(xlo=24.06206*u.deg, xhi=24.06269*u.deg) assert sc1.shape == (2,3,2) assert sc2.shape == (2,3,2) assert sc2b.shape == (2,3,2) assert sc1.wcs.wcs.compare(sc2.wcs.wcs) assert sc1.wcs.wcs.compare(sc2b.wcs.wcs) sc3 = cube.subcube(ylo=1, yhi=3) sc4 = cube.subcube(ylo=29.93464 * u.deg, yhi=29.93522 * u.deg) assert sc3.shape == (2, 2, 4) assert sc4.shape == (2, 2, 4) assert sc3.wcs.wcs.compare(sc4.wcs.wcs) sc5 = cube.subcube() assert sc5.shape == cube.shape assert sc5.wcs.wcs.compare(cube.wcs.wcs) assert np.all(sc5._data == cube._data) @pytest.mark.skipif('not pyregionOK', reason='Could not import pyregion') @pytest.mark.parametrize(('regfile','result'), (('fk5.reg', [slice(None),1,slice(None)]), ('image.reg', [slice(None),1,slice(None)]), ('partial_overlap_image.reg', [slice(None),1,1]), ('no_overlap_image.reg', ValueError), ('partial_overlap_fk5.reg', [slice(None),1,1]), ('no_overlap_fk5.reg', ValueError), )) def test_ds9region(regfile, result): cube, data = cube_and_raw('adv.fits') regions = pyregion.open(path(regfile)) if isinstance(result, type) and issubclass(result, Exception): with pytest.raises(result) as exc: sc = cube.subcube_from_ds9region(regions) # this assertion is redundant, I think... assert exc.errisinstance(result) else: sc = cube.subcube_from_ds9region(regions) scsum = sc.sum() dsum = data[result].sum() assert_allclose(scsum, dsum) @pytest.mark.skipif('not pyregionOK', reason='Could not import pyregion') @pytest.mark.parametrize('regfile', ('255-fk5.reg', '255-pixel.reg'), ) def test_ds9region_255(regfile): # specific test for correctness cube, data = cube_and_raw('255.fits') regions = pyregion.open(path(regfile)) subhdr = cube.wcs.sub([wcs.WCSSUB_CELESTIAL]).to_header() mask = regions.get_mask(header=subhdr, shape=cube.shape[1:]) assert_array_equal(cube[0,:,:][mask].value, [11,12,16,17]) subcube = cube.subcube_from_ds9region(regions) assert_array_equal(subcube[0,:,:].value, np.array([11,12,16,17]).reshape((2,2))) #region = 'fk5\ncircle(29.9346557, 24.0623827, 0.11111)' #subcube = cube.subcube_from_ds9region(region) # THIS TEST FAILS! # I think the coordinate transformation in ds9 is wrong; # it uses kapteyn? #region = 'circle(2,2,2)' #subcube = cube.subcube_from_ds9region(region) spectral-cube-0.4.3/spectral_cube/tests/test_visualization.py0000644000077000000240000000314513242700604024560 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import pytest try: import pvextractor PVEXTRACTOR_INSTALLED = True except ImportError: PVEXTRACTOR_INSTALLED = False try: import matplotlib.pyplot as plt MATPLOTLIB_INSTALLED = True except ImportError: MATPLOTLIB_INSTALLED = False try: import aplpy APLPY_INSTALLED = True except ImportError: APLPY_INSTALLED = False from .. import (SpectralCube, BooleanArrayMask, FunctionMask, LazyMask, CompositeMask) from ..spectral_cube import OneDSpectrum, Projection from ..np_compat import allbadtonan from .. import spectral_axis from .test_spectral_cube import cube_and_raw @pytest.mark.skipif("not PVEXTRACTOR_INSTALLED") def test_to_pvextractor(): cube, data = cube_and_raw('vda_Jybeam_lower.fits') pv = cube.to_pvextractor() @pytest.mark.skipif("not MATPLOTLIB_INSTALLED") def test_projvis(): cube, data = cube_and_raw('vda_Jybeam_lower.fits') mom0 = cube.moment0() mom0.quicklook(use_aplpy=False) @pytest.mark.skipif("not MATPLOTLIB_INSTALLED") def test_proj_imshow(): cube, data = cube_and_raw('vda_Jybeam_lower.fits') mom0 = cube.moment0() import pylab as pl pl.imshow(mom0) @pytest.mark.skipif("not APLPY_INSTALLED") def test_projvis_aplpy(): cube, data = cube_and_raw('vda_Jybeam_lower.fits') mom0 = cube.moment0() mom0.quicklook(use_aplpy=True) @pytest.mark.skipif("not APLPY_INSTALLED") def test_mask_quicklook(): cube, data = cube_and_raw('vda_Jybeam_lower.fits') cube.mask.quicklook(view=(0, slice(None), slice(None)), use_aplpy=True) spectral-cube-0.4.3/spectral_cube/tests/test_wcs_utils.py0000644000077000000240000001320113261015477023675 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import pytest from astropy.io import fits from ..wcs_utils import * from . import path def test_wcs_dropping(): wcs = WCS(naxis=4) wcs.wcs.pc = np.zeros([4, 4]) np.fill_diagonal(wcs.wcs.pc, np.arange(1, 5)) pc = wcs.wcs.pc # for later use below dropped = drop_axis(wcs, 0) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([2, 3, 4])) dropped = drop_axis(wcs, 1) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([1, 3, 4])) dropped = drop_axis(wcs, 2) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([1, 2, 4])) dropped = drop_axis(wcs, 3) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([1, 2, 3])) wcs = WCS(naxis=4) wcs.wcs.cd = pc dropped = drop_axis(wcs, 0) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([2, 3, 4])) dropped = drop_axis(wcs, 1) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([1, 3, 4])) dropped = drop_axis(wcs, 2) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([1, 2, 4])) dropped = drop_axis(wcs, 3) assert np.all(dropped.wcs.get_pc().diagonal() == np.array([1, 2, 3])) def test_wcs_swapping(): wcs = WCS(naxis=4) wcs.wcs.pc = np.zeros([4, 4]) np.fill_diagonal(wcs.wcs.pc, np.arange(1, 5)) pc = wcs.wcs.pc # for later use below swapped = wcs_swapaxes(wcs, 0, 1) assert np.all(swapped.wcs.get_pc().diagonal() == np.array([2, 1, 3, 4])) swapped = wcs_swapaxes(wcs, 0, 3) assert np.all(swapped.wcs.get_pc().diagonal() == np.array([4, 2, 3, 1])) swapped = wcs_swapaxes(wcs, 2, 3) assert np.all(swapped.wcs.get_pc().diagonal() == np.array([1, 2, 4, 3])) wcs = WCS(naxis=4) wcs.wcs.cd = pc swapped = wcs_swapaxes(wcs, 0, 1) assert np.all(swapped.wcs.get_pc().diagonal() == np.array([2, 1, 3, 4])) swapped = wcs_swapaxes(wcs, 0, 3) assert np.all(swapped.wcs.get_pc().diagonal() == np.array([4, 2, 3, 1])) swapped = wcs_swapaxes(wcs, 2, 3) assert np.all(swapped.wcs.get_pc().diagonal() == np.array([1, 2, 4, 3])) def test_add_stokes(): wcs = WCS(naxis=3) for ii in range(4): outwcs = add_stokes_axis_to_wcs(wcs, ii) assert outwcs.wcs.naxis == 4 def test_axis_names(): wcs = WCS(path('adv.fits')) assert axis_names(wcs) == ['RA', 'DEC', 'VOPT'] wcs = WCS(path('vad.fits')) assert axis_names(wcs) == ['VOPT', 'RA', 'DEC'] def test_wcs_slice(): wcs = WCS(naxis=3) wcs.wcs.crpix = [50., 45., 30.] wcs_new = slice_wcs(wcs, (slice(10,20), slice(None), slice(20,30))) np.testing.assert_allclose(wcs_new.wcs.crpix, [30., 45., 20.]) def test_wcs_slice_reversal(): wcs = WCS(naxis=3) wcs.wcs.crpix = [50., 45., 30.] wcs.wcs.crval = [0., 0., 0.] wcs.wcs.cdelt = [1., 1., 1.] wcs_new = slice_wcs(wcs, (slice(None, None, -1), slice(None), slice(None)), shape=[100., 150., 200.]) spaxis = wcs.sub([0]).wcs_pix2world(np.arange(100), 0) new_spaxis = wcs_new.sub([0]).wcs_pix2world(np.arange(100), 0) np.testing.assert_allclose(spaxis, new_spaxis[::-1]) def test_reversal_roundtrip(): wcs = WCS(naxis=3) wcs.wcs.crpix = [50., 45., 30.] wcs.wcs.crval = [0., 0., 0.] wcs.wcs.cdelt = [1., 1., 1.] wcs_new = slice_wcs(wcs, (slice(None, None, -1), slice(None), slice(None)), shape=[100., 150., 200.]) spaxis = wcs.sub([0]).wcs_pix2world(np.arange(100), 0) new_spaxis = wcs_new.sub([0]).wcs_pix2world(np.arange(100), 0) np.testing.assert_allclose(spaxis, new_spaxis[::-1]) re_reverse = slice_wcs(wcs_new, (slice(None, None, -1), slice(None), slice(None)), shape=[100., 150., 200.]) new_spaxis = re_reverse.sub([0]).wcs_pix2world(np.arange(100), 0) np.testing.assert_allclose(spaxis, new_spaxis[::-1]) #These are NOT equal, but they are equivalent: CRVAL and CRPIX are shifted #by an acceptable amount # assert check_equality(wcs, re_reverse) re_re_reverse = slice_wcs(re_reverse, (slice(None, None, -1), slice(None), slice(None)), shape=[100., 150., 200.]) re_re_re_reverse = slice_wcs(re_re_reverse, (slice(None, None, -1), slice(None), slice(None)), shape=[100., 150., 200.]) assert check_equality(re_re_re_reverse, re_reverse) def test_wcs_comparison(): wcs1 = WCS(naxis=3) wcs1.wcs.crpix = np.array([50., 45., 30.], dtype='float32') wcs2 = WCS(naxis=3) wcs2.wcs.crpix = np.array([50., 45., 30.], dtype='float64') wcs3 = WCS(naxis=3) wcs3.wcs.crpix = np.array([50., 45., 31.], dtype='float64') wcs4 = WCS(naxis=3) wcs4.wcs.crpix = np.array([50., 45., 30.0001], dtype='float64') assert check_equality(wcs1,wcs2) assert not check_equality(wcs1,wcs3) assert check_equality(wcs1, wcs3, wcs_tolerance=1.0e1) assert not check_equality(wcs1,wcs4) assert check_equality(wcs1, wcs4, wcs_tolerance=1e-3) @pytest.mark.parametrize('fn', ('cubewcs1.hdr', 'cubewcs2.hdr')) def test_strip_wcs(fn): header1 = fits.Header.fromtextfile(path(fn)) header1_stripped = strip_wcs_from_header(header1) with open(path(fn),'r') as fh: hdrlines = fh.readlines() newfn = fn.replace('.hdr', '_blanks.hdr') hdrlines.insert(-20,"\n") hdrlines.insert(-1,"\n") with open(path(newfn),'w') as fh: fh.writelines(hdrlines) header2 = fits.Header.fromtextfile(path(newfn)) header2_stripped = strip_wcs_from_header(header2) assert header1_stripped == header2_stripped spectral-cube-0.4.3/spectral_cube/tests/utilities.py0000644000077000000240000000661413242700604022637 0ustar adamstaff00000000000000 ''' Utilities for tests. ''' import numpy as np import astropy.units as u from astropy.io import fits from astropy.utils import NumpyRNGContext from astropy.extern.six.moves import zip from ..spectral_cube import SpectralCube def generate_header(pixel_scale, spec_scale, beamfwhm, imshape, v0): header = {'CDELT1': -(pixel_scale).to(u.deg).value, 'CDELT2': (pixel_scale).to(u.deg).value, 'BMAJ': beamfwhm.to(u.deg).value, 'BMIN': beamfwhm.to(u.deg).value, 'BPA': 0.0, 'CRPIX1': imshape[0] / 2., 'CRPIX2': imshape[1] / 2., 'CRVAL1': 0.0, 'CRVAL2': 0.0, 'CTYPE1': 'GLON-CAR', 'CTYPE2': 'GLAT-CAR', 'CUNIT1': 'deg', 'CUNIT2': 'deg', 'CRVAL3': v0, 'CUNIT3': spec_scale.unit.to_string(), 'CDELT3': spec_scale.value, 'CRPIX3': 1, 'CTYPE3': 'VRAD', 'BUNIT': 'K', } return fits.Header(header) def generate_hdu(data, pixel_scale, spec_scale, beamfwhm, v0): imshape = data.shape[1:] header = generate_header(pixel_scale, spec_scale, beamfwhm, imshape, v0) return fits.PrimaryHDU(data, header) def gaussian(x, amp, mean, sigma): return amp * np.exp(- (x - mean)**2 / (2 * sigma**2)) def generate_gaussian_cube(shape=(100, 25, 25), sigma=8., amp=1., noise=None, spec_scale=1 * u.km / u.s, pixel_scale=1 * u.arcsec, beamfwhm=3 * u.arcsec, v0=None, vel_surface=None, seed=247825498): ''' Generate a SpectralCube with Gaussian profiles. The peak velocity positions can be given with `vel_surface`. Otherwise, the peaks of the profiles are randomly assigned positions in the cubes. This is primarily to test shuffling and stacking of spectra, rather than trying to be being physically meaningful. Returns ------- spec_cube : SpectralCube The generated cube. mean_positions : array The peak positions in the cube. ''' test_cube = np.empty(shape) mean_positions = np.empty(shape[1:]) spec_middle = int(shape[0] / 2) spec_quarter = int(shape[0] / 4) if v0 is None: v0 = 0 with NumpyRNGContext(seed): spec_inds = np.mgrid[-spec_middle:spec_middle] * spec_scale.value spat_inds = np.indices(shape[1:]) for y, x in zip(spat_inds[0].flatten(), spat_inds[1].flatten()): # Lock the mean to within 25% from the centre if vel_surface is not None: mean_pos = vel_surface[y,x] else: mean_pos = \ np.random.uniform(low=spec_inds[spec_quarter], high=spec_inds[spec_quarter + spec_middle]) test_cube[:, y, x] = gaussian(spec_inds, amp, mean_pos, sigma) mean_positions[y, x] = mean_pos + v0 if noise is not None: test_cube[:, y, x] += np.random.normal(0, noise, shape[0]) test_hdu = generate_hdu(test_cube, pixel_scale, spec_scale, beamfwhm, spec_inds[0] + v0) spec_cube = SpectralCube.read(test_hdu) mean_positions = mean_positions * spec_scale.unit return spec_cube, mean_positions spectral-cube-0.4.3/spectral_cube/utils.py0000644000077000000240000000375113242700604020621 0ustar adamstaff00000000000000import warnings from functools import wraps from astropy.utils.exceptions import AstropyUserWarning def cached(func): """ Decorator to cache function calls """ @wraps(func) def wrapper(self, *args): # The cache lives in the instance so that it gets garbage collected if (func, args) not in self._cache: self._cache[(func, args)] = func(self, *args) return self._cache[(func, args)] wrapper.wrapped_function = func return wrapper def warn_slow(function): @wraps(function) def wrapper(self, *args, **kwargs): # if the function accepts a 'how', the 'cube' approach requires the whole cube in memory warn_how = (kwargs.get('how') == 'cube') or 'how' not in kwargs if self._is_huge and not self.allow_huge_operations and warn_how: raise ValueError("This function ({0}) requires loading the entire " "cube into memory, and the cube is large ({1} " "pixels), so by default we disable this operation. " "To enable the operation, set " "`cube.allow_huge_operations=True` and try again." .format(str(function), self.size)) elif warn_how and not self._is_huge: # TODO: add check for whether cube has been loaded into memory warnings.warn("This function ({0}) requires loading the entire cube into " "memory and may therefore be slow.".format(str(function))) return function(self, *args, **kwargs) return wrapper class UnsupportedIterationStrategyWarning(AstropyUserWarning): pass class VarianceWarning(AstropyUserWarning): pass class SliceWarning(AstropyUserWarning): pass class BeamAverageWarning(AstropyUserWarning): pass class WCSCelestialError(Exception): pass class WCSMismatchWarning(AstropyUserWarning): pass class NotImplementedWarning(AstropyUserWarning): pass spectral-cube-0.4.3/spectral_cube/version.py0000644000077000000240000001622413261442571021154 0ustar adamstaff00000000000000# Autogenerated by Astropy-affiliated package spectral_cube's setup.py on 2018-04-05 15:49:13 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 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 == 128: # git returns 128 if the command is not run from within a git # repository tree. In this case, a warning is produced above but we # return the default dev version of '0'. return '0' elif 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() # This function is tested but it is only ever executed within a subprocess when # creating a fake package, so it doesn't get picked up by coverage metrics. def _get_repo_path(pathname, levels=None): # pragma: no cover """ 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 = "spectral_cube" _last_generated_version = "0.4.3" _last_githash = "aee2d42cd53eaa1be9c72c78342b635457028013" # 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 = 4 bugfix = 3 release = True timestamp = datetime.datetime(2018, 4, 5, 15, 49, 13) debug = False try: from ._compiler import compiler except ImportError: compiler = "unknown" try: from .cython_version import cython_version except ImportError: cython_version = "unknown" spectral-cube-0.4.3/spectral_cube/visualization-tools.py0000644000077000000240000001204313242700604023512 0ustar adamstaff00000000000000import os import numpy as np import aplpy from astropy.utils.console import ProgressBar def check_ffmpeg(ffmpeg_cmd): returncode = os.system('{ffmpeg} > /dev/null 2&> /dev/null'.format(ffmpeg=ffmpeg_cmd)) if returncode not in (0,256): raise OSError("{ffmpeg} not found in the executable path. Return code {code}" .format(ffmpeg=ffmpeg_cmd, code=returncode)) def make_rgb_movie(cube, prefix, v1, v2, vmin, vmax, ffmpeg_cmd='ffmpeg'): """ Make a velocity movie with red, green, and blue corresponding to neighboring velocity channels Parameters ---------- cube : SpectralCube The cube to visualize prefix : str A prefix to prepend to the output png and movie. For example, it could be rgb/sourcename_speciesname v1 : Quantity A value in spectral-axis equivalent units specifying the starting velocity / frequency / wavelength v2 : Quantity A value in spectral-axis equivalent units specifying the ending velocity / frequency / wavelength vmin : float Minimum value to display vmax : float Maximum value to display ffmpeg_cmd : str The system command for ffmpeg. Required to make a movie """ check_ffmpeg(ffmpeg_cmd) # Create the WCS template F = aplpy.FITSFigure(cube[0].hdu) F.show_grayscale() # determine pixel range p1 = cube.closest_spectral_channel(v1) p2 = cube.closest_spectral_channel(v2) for jj,ii in enumerate(ProgressBar(range(p1, p2-1))): rgb = np.array([cube[ii+2], cube[ii+1], cube[ii]]).T.swapaxes(0,1) # in case you manually set min/max rgb[rgb > vmax] = 1 rgb[rgb < vmin] = 0 # this is the unsupported little bit... F._ax1.clear() F._ax1.imshow((rgb-vmin)/(vmax-vmin), extent=F._extent) v1_ = int(np.round(cube.spectral_axis[ii].value)) v2_ = int(np.round(cube.spectral_axis[ii+2].value)) # then write out the files F.save('{2}_v{0}to{1}.png'.format(v1_, v2_, prefix)) # make a sorted version for use with ffmpeg if os.path.exists('{prefix}{0:04d}.png'.format(jj,prefix=prefix)): os.remove('{prefix}{0:04d}.png'.format(jj,prefix=prefix)) os.link('{2}_v{0}to{1}.png'.format(v1_, v2_, prefix), '{prefix}{0:04d}.png'.format(jj, prefix=prefix)) os.system('{ffmpeg} -r 10 -y -i {prefix}%04d.png -c:v libx264 -pix_fmt yuv420p -vf ' '"scale=1024:768" -r 10' # "scale=1024:768,setpts=10*PTS" ' {prefix}_RGB_movie.mp4'.format(prefix=prefix, ffmpeg=ffmpeg_cmd)) def make_multispecies_rgb(cube_r, cube_g, cube_b, prefix, v1, v2, vmin, vmax, ffmpeg_cmd='ffmpeg'): """ Parameters ---------- cube_r, cube_g, cube_b : SpectralCube The three cubes to visualize. They should have identical spatial and spectral dimensions. prefix : str A prefix to prepend to the output png and movie. For example, it could be rgb/sourcename_speciesname v1 : Quantity A value in spectral-axis equivalent units specifying the starting velocity / frequency / wavelength v2 : Quantity A value in spectral-axis equivalent units specifying the ending velocity / frequency / wavelength vmin : float Minimum value to display (constant for all 3 colors) vmax : float Maximum value to display (constant for all 3 colors) ffmpeg_cmd : str The system command for ffmpeg. Required to make a movie """ check_ffmpeg(ffmpeg_cmd) # Create the WCS template F = aplpy.FITSFigure(cube_r[0].hdu) F.show_grayscale() assert cube_r.shape == cube_b.shape assert cube_g.shape == cube_b.shape # determine pixel range p1 = cube_r.closest_spectral_channel(v1) p2 = cube_r.closest_spectral_channel(v2) for jj,ii in enumerate(ProgressBar(range(p1, p2+1))): rgb = np.array([cube_r[ii].value, cube_g[ii].value, cube_b[ii].value ]).T.swapaxes(0,1) # in case you manually set min/max rgb[rgb > vmax] = 1 rgb[rgb < vmin] = 0 # this is the unsupported little bit... F._ax1.clear() F._ax1.imshow((rgb-vmin)/(vmax-vmin), extent=F._extent) v1_ = int(np.round(cube_r.spectral_axis[ii].value)) # then write out the files F.refresh() F.save('{1}_v{0}.png'.format(v1_, prefix)) # make a sorted version for use with ffmpeg if os.path.exists('{prefix}{0:04d}.png'.format(jj,prefix=prefix)): os.remove('{prefix}{0:04d}.png'.format(jj,prefix=prefix)) os.link('{1}_v{0}.png'.format(v1_, prefix), '{prefix}{0:04d}.png'.format(jj, prefix=prefix)) os.system('{ffmpeg} -r 10 -y -i {prefix}%04d.png -c:v libx264 -pix_fmt yuv420p -vf ' '"scale=1024:768" -r 10' # "scale=1024:768,setpts=10*PTS" ' {prefix}_RGB_movie.mp4'.format(prefix=prefix, ffmpeg=ffmpeg_cmd)) spectral-cube-0.4.3/spectral_cube/wcs_utils.py0000644000077000000240000003424013261015477021502 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import numpy as np from astropy.wcs import WCS import warnings from astropy import units as u from astropy import log wcs_parameters_to_preserve = ['cel_offset', 'dateavg', 'dateobs', 'equinox', 'latpole', 'lonpole', 'mjdavg', 'mjdobs', 'name', 'obsgeo', 'phi0', 'radesys', 'restfrq', 'restwav', 'specsys', 'ssysobs', 'ssyssrc', 'theta0', 'velangl', 'velosys', 'zsource'] # not writable: # 'lat', 'lng', 'lattyp', 'lngtyp', bad_spectypes_mapping = {'VELOCITY':'VELO', 'WAVELENG':'WAVE', } def drop_axis(wcs, dropax): """ Drop the ax on axis dropax Remove an axis from the WCS Parameters ---------- wcs: astropy.wcs.WCS The WCS with naxis to be chopped to naxis-1 dropax: int The index of the WCS to drop, counting from 0 (i.e., python convention, not FITS convention) """ inds = list(range(wcs.wcs.naxis)) inds.pop(dropax) inds = np.array(inds) return reindex_wcs(wcs, inds) def add_stokes_axis_to_wcs(wcs, add_before_ind): """ Add a new Stokes axis that is uncorrelated with any other axes Parameters ---------- wcs: astropy.wcs.WCS The WCS to add to add_before_ind: int Index of the WCS to insert the new Stokes axis in front of. To add at the end, do add_before_ind = wcs.wcs.naxis """ naxin = wcs.wcs.naxis naxout = naxin + 1 inds = list(range(naxout)) inds.pop(add_before_ind) inds = np.array(inds) outwcs = WCS(naxis=naxout) for par in wcs_parameters_to_preserve: setattr(outwcs.wcs, par, getattr(wcs.wcs, par)) pc = np.zeros([naxout, naxout]) pc[inds[:, np.newaxis], inds[np.newaxis, :]] = wcs.wcs.get_pc() pc[add_before_ind, add_before_ind] = 1 def append_to_posn(val, posn, lst): """ insert a value at index into a list """ return list(lst)[:posn] + [val] + list(lst)[posn:] outwcs.wcs.crpix = append_to_posn(1, add_before_ind, wcs.wcs.crpix) outwcs.wcs.cdelt = append_to_posn(1, add_before_ind, wcs.wcs.get_cdelt()) outwcs.wcs.crval = append_to_posn(1, add_before_ind, wcs.wcs.crval) outwcs.wcs.cunit = append_to_posn("", add_before_ind, wcs.wcs.cunit) outwcs.wcs.ctype = append_to_posn("STOKES", add_before_ind, wcs.wcs.ctype) outwcs.wcs.cname = append_to_posn("STOKES", add_before_ind, wcs.wcs.cname) outwcs.wcs.pc = pc return outwcs def wcs_swapaxes(wcs, ax0, ax1): """ Swap axes in a WCS Parameters ---------- wcs: astropy.wcs.WCS The WCS to have its axes swapped ax0: int ax1: int The indices of the WCS to be swapped, counting from 0 (i.e., python convention, not FITS convention) """ inds = list(range(wcs.wcs.naxis)) inds[ax0], inds[ax1] = inds[ax1], inds[ax0] inds = np.array(inds) return reindex_wcs(wcs, inds) def reindex_wcs(wcs, inds): """ Re-index a WCS given indices. The number of axes may be reduced. Parameters ---------- wcs: astropy.wcs.WCS The WCS to be manipulated inds: np.array(dtype='int') The indices of the array to keep in the output. e.g. swapaxes: [0,2,1,3] dropaxes: [0,1,3] """ if not isinstance(inds, np.ndarray): raise TypeError("Indices must be an ndarray") if inds.dtype.kind != 'i': raise TypeError('Indices must be integers') outwcs = WCS(naxis=len(inds)) for par in wcs_parameters_to_preserve: setattr(outwcs.wcs, par, getattr(wcs.wcs, par)) cdelt = wcs.wcs.get_cdelt() pc = wcs.wcs.get_pc() outwcs.wcs.crpix = wcs.wcs.crpix[inds] outwcs.wcs.cdelt = cdelt[inds] outwcs.wcs.crval = wcs.wcs.crval[inds] outwcs.wcs.cunit = [wcs.wcs.cunit[i] for i in inds] outwcs.wcs.ctype = [wcs.wcs.ctype[i] for i in inds] outwcs.wcs.cname = [wcs.wcs.cname[i] for i in inds] outwcs.wcs.pc = pc[inds[:, None], inds[None, :]] pv_cards = [] for i, j in enumerate(inds): for k, m, v in wcs.wcs.get_pv(): if k == j: pv_cards.append((i, m, v)) outwcs.wcs.set_pv(pv_cards) ps_cards = [] for i, j in enumerate(inds): for k, m, v in wcs.wcs.get_ps(): if k == j: ps_cards.append((i, m, v)) outwcs.wcs.set_ps(ps_cards) return outwcs def axis_names(wcs): """ Extract world names for each coordinate axis Parameters ---------- wcs : astropy.wcs.WCS The WCS object to extract names from Returns ------- A tuple of names along each axis """ names = list(wcs.wcs.cname) types = wcs.wcs.ctype for i in range(len(names)): if len(names[i]) > 0: continue names[i] = types[i].split('-')[0] return names def slice_wcs(mywcs, view, shape=None, numpy_order=True, drop_degenerate=False): """ Slice a WCS instance using a Numpy slice. The order of the slice should be reversed (as for the data) compared to the natural WCS order. Parameters ---------- view : tuple A tuple containing the same number of slices as the WCS system. The ``step`` method, the third argument to a slice, is not presently supported. numpy_order : bool Use numpy order, i.e. slice the WCS so that an identical slice applied to a numpy array will slice the array and WCS in the same way. If set to `False`, the WCS will be sliced in FITS order, meaning the first slice will be applied to the *last* numpy index but the *first* WCS axis. drop_degenerate : bool Drop axes that are size-1, i.e., any that have an integer index as part of their view? Otherwise, an Exception will be raised. Returns ------- wcs_new : `~astropy.wcs.WCS` A new resampled WCS axis """ if hasattr(view, '__len__') and len(view) > mywcs.wcs.naxis: raise ValueError("Must have # of slices <= # of WCS axes") elif not hasattr(view, '__len__'): # view MUST be an iterable view = [view] if not all([isinstance(x, slice) for x in view]): if drop_degenerate: keeps = [mywcs.naxis-ii for ii,ind in enumerate(view) if isinstance(ind, slice)] mywcs = mywcs.sub(keeps) view = [x for x in view if isinstance(x, slice)] else: raise ValueError("Cannot downsample a WCS with indexing. Use " "wcs.sub or wcs.dropaxis if you want to remove " "axes.") wcs_new = mywcs.deepcopy() for i, iview in enumerate(view): if iview.step is not None and iview.start is None: # Slice from "None" is equivalent to slice from 0 (but one # might want to downsample, so allow slices with # None,None,step or None,stop,step) iview = slice(0, iview.stop, iview.step) if numpy_order: wcs_index = mywcs.wcs.naxis - 1 - i else: wcs_index = i if iview.step is not None and iview.step < 0: if iview.step != -1: raise NotImplementedError("Haven't dealt with resampling & reversing.") # reverse indexing requires the use of shape if shape is None: raise ValueError("Cannot reverse-index a WCS without " "specifying a shape.") if iview.stop is not None: refpix = iview.stop else: refpix = shape[i] # this will raise an inconsistent axis type error if slicing over # celestial axes is attempted # wcs_index+1 is required because sub([0]) = sub([all]) crval = mywcs.sub([wcs_index+1]).wcs_pix2world([refpix-1], 0)[0] crpix = 1 cdelt = mywcs.wcs.cdelt[wcs_index] wcs_new.wcs.crpix[wcs_index] = crpix wcs_new.wcs.crval[wcs_index] = crval wcs_new.wcs.cdelt[wcs_index] = -cdelt elif iview.start is not None: if iview.step not in (None, 1): crpix = mywcs.wcs.crpix[wcs_index] cdelt = mywcs.wcs.cdelt[wcs_index] # equivalently (keep this comment so you can compare eqns): # wcs_new.wcs.crpix[wcs_index] = # (crpix - iview.start)*iview.step + 0.5 - iview.step/2. crp = ((crpix - iview.start - 1.)/iview.step + 0.5 + 1./iview.step/2.) wcs_new.wcs.crpix[wcs_index] = crp wcs_new.wcs.cdelt[wcs_index] = cdelt * iview.step else: wcs_new.wcs.crpix[wcs_index] -= iview.start # Without this, may cause a regression of #234 wcs_new.wcs.set() return wcs_new def check_equality(wcs1, wcs2, warn_missing=False, ignore_keywords=['MJD-OBS', 'VELOSYS'], wcs_tolerance=0.0): """ Check if two WCSs are equal Parameters ---------- wcs1, wcs2: `astropy.wcs.WCS` The WCSs warn_missing: bool Issue warnings if one header is missing a keyword that the other has? ignore_keywords: list of str Keywords that are stored as part of the WCS but do not define part of the coordinate system and therefore can be safely ignored. wcs_tolerance : float The decimal level to check for equality. For example, 1e-2 would have 0.001 and 0.002 as equal, but 1e-3 would have them as inequal """ # TODO: use this to replace the rest of the check_equality code #return wcs1.wcs.compare(wcs2.wcs, cmp=wcs.WCSCOMPARE_ANCILLARY, # tolerance=tolerance) #Until we've switched to the wcs.compare approach, we need to have #np.testing.assert_almost_equal work if wcs_tolerance == 0: exact = True else: exact = False # np.testing.assert_almost_equal wants an integer # e.g., for 0.0001, the integer is 4 decimal = int(np.ceil(-np.log10(wcs_tolerance))) # naive version: # return str(wcs1.to_header()) != str(wcs2.to_header()) h1 = wcs1.to_header() h2 = wcs2.to_header() # Default to headers equal; everything below changes to false if there are # any inequalities OK = True # to keep track of keywords in both matched = [] for c1 in h1.cards: key = c1[0] if key in h2: matched.append(key) c2 = h2.cards[key] # special check for units: "m/s" = "m s-1" if 'UNIT' in key: u1 = u.Unit(c1[1]) u2 = u.Unit(c2[1]) if u1 != u2: if key in ignore_keywords: log.debug("IGNORED Header 1, {0}: {1} != {2}".format(key,u1,u2)) else: OK = False log.debug("Header 1, {0}: {1} != {2}".format(key,u1,u2)) elif isinstance(c1[1], (float, np.float)): try: if exact: assert c1[1] == c2[1] else: np.testing.assert_almost_equal(c1[1], c2[1], decimal=decimal) except AssertionError: if key in ('RESTFRQ','RESTWAV'): warnings.warn("{0} is not equal in WCS; ignoring ".format(key)+ "under the assumption that you want to" " compare velocity cubes.") continue if key in ignore_keywords: log.debug("IGNORED Header 1, {0}: {1} != {2}".format(key,c1[1],c2[1])) else: log.debug("Header 1, {0}: {1} != {2}".format(key,c1[1],c2[1])) OK = False elif c1[1] != c2[1]: if key in ignore_keywords: log.debug("IGNORED Header 1, {0}: {1} != {2}".format(key,c1[1],c2[1])) else: log.debug("Header 1, {0}: {1} != {2}".format(key,c1[1],c2[1])) OK = False else: if warn_missing: warnings.warn("WCS2 is missing card {0}".format(key)) elif key not in ignore_keywords: OK = False # Check that there aren't any cards in header 2 that were missing from # header 1 for c2 in h2.cards: key = c2[0] if key not in matched: if warn_missing: warnings.warn("WCS1 is missing card {0}".format(key)) else: OK = False return OK def strip_wcs_from_header(header): """ Given a header with WCS information, remove ALL WCS information from that header """ hwcs = WCS(header) wcsh = hwcs.to_header() keys_to_keep = [k for k in header if (k and k not in wcsh and 'NAXIS' not in k)] newheader = header.copy() # Strip blanks first. They appear to cause serious problems, like not # deleting things they should! if '' in newheader: del newheader[''] for kw in list(newheader.keys()): if kw not in keys_to_keep: del newheader[kw] for kw in ('CRPIX{ii}', 'CRVAL{ii}', 'CDELT{ii}', 'CUNIT{ii}', 'CTYPE{ii}', 'PC0{ii}_0{jj}', 'CD{ii}_{jj}', 'CROTA{ii}', 'PC{ii}_{jj}', 'PV0{ii}_0{jj}', 'PV{ii}_{jj}'): for ii in range(5): for jj in range(5): k = kw.format(ii=ii,jj=jj) if k in newheader.keys(): del newheader[k] return newheader def diagonal_wcs_to_cdelt(mywcs): """ If a WCS has only diagonal pixel scale matrix elements (which are composed from cdelt*pc), use them to reform the wcs as a CDELT-style wcs with no pc or cd elements """ offdiag = ~np.eye(mywcs.pixel_scale_matrix.shape[0], dtype='bool') if not any(mywcs.pixel_scale_matrix[offdiag]): cdelt = mywcs.pixel_scale_matrix.diagonal() del mywcs.wcs.pc del mywcs.wcs.cd mywcs.wcs.cdelt = cdelt return mywcs spectral-cube-0.4.3/spectral_cube/ytcube.py0000644000077000000240000002566613245574450021000 0ustar adamstaff00000000000000from __future__ import print_function, absolute_import, division import os import subprocess import numpy as np import time from astropy.utils.console import ProgressBar from astropy import log from astropy.extern import six import warnings __all__ = ['ytCube'] class ytCube(object): """ Light wrapper of a yt object with ability to translate yt<->wcs coordinates """ def __init__(self, cube, dataset, spectral_factor=1.0): self.cube = cube self.wcs = cube.wcs self.dataset = dataset self.spectral_factor = spectral_factor def world2yt(self, world_coord, first_index=0): """ Convert a position in world coordinates to the coordinates used by a yt dataset that has been generated using the ``to_yt`` method. Parameters ---------- world_coord: `astropy.wcs.WCS.wcs_world2pix`-valid input The world coordinates first_index: 0 or 1 The first index of the data. In python and yt, this should be zero, but for the FITS coordinates, use 1 """ yt_coord = self.wcs.wcs_world2pix([world_coord], first_index)[0] yt_coord[2] = (yt_coord[2] - 0.5)*self.spectral_factor+0.5 return yt_coord def yt2world(self, yt_coord, first_index=0): """ Convert a position in yt's coordinates to world coordinates from a yt dataset that has been generated using the ``to_yt`` method. Parameters ---------- world_coord: `astropy.wcs.WCS.wcs_pix2world`-valid input The yt pixel coordinates to convert back to world coordinates first_index: 0 or 1 The first index of the data. In python and yt, this should be zero, but for the FITS coordinates, use 1 """ yt_coord = np.array(yt_coord) # stripping off units yt_coord[2] = (yt_coord[2] - 0.5)/self.spectral_factor+0.5 world_coord = self.wcs.wcs_pix2world([yt_coord], first_index)[0] return world_coord def quick_render_movie(self, outdir, size=256, nframes=30, camera_angle=(0,0,1), north_vector=(0,0,1), rot_vector=(1,0,0), colormap='doom', cmap_range='auto', transfer_function='auto', start_index=0, image_prefix="", output_filename='out.mp4', log_scale=False, rescale=True): """ Create a movie rotating the cube 360 degrees from PP -> PV -> PP -> PV -> PP Parameters ---------- outdir: str The output directory in which the individual image frames and the resulting output mp4 file should be stored size: int The size of the individual output frame in pixels (i.e., size=256 will result in a 256x256 image) nframes: int The number of frames in the resulting movie camera_angle: 3-tuple The initial angle of the camera north_vector: 3-tuple The vector of 'north' in the data cube. Default is coincident with the spectral axis rot_vector: 3-tuple The vector around which the camera will be rotated colormap: str A valid colormap. See `yt.show_colormaps` transfer_function: 'auto' or `yt.visualization.volume_rendering.TransferFunction` Either 'auto' to use the colormap specified, or a valid TransferFunction instance log_scale: bool Should the colormap be log scaled? rescale: bool If True, the images will be rescaled to have a common 95th percentile brightness, which can help reduce flickering from having a single bright pixel in some projections start_index : int The number of the first image to save image_prefix : str A string to prepend to the image name for each image that is output output_filename : str The movie file name to output. The suffix may affect the file type created. Defaults to 'out.mp4'. Will be placed in ``outdir`` Returns ------- """ try: import yt except ImportError: raise ImportError("yt could not be imported. Cube renderings are not possible.") scale = np.max(self.cube.shape) if not os.path.exists(outdir): os.makedirs(outdir) elif not os.path.isdir(outdir): raise OSError("Output directory {0} exists and is not a directory.".format(outdir)) if cmap_range == 'auto': upper = self.cube.max().value lower = self.cube.std().value * 3 cmap_range = [lower,upper] if transfer_function == 'auto': tfh = self.auto_transfer_function(cmap_range, log=log_scale) tfh.tf.map_to_colormap(cmap_range[0], cmap_range[1], colormap=colormap) tf = tfh.tf else: tf = transfer_function center = self.dataset.domain_center cam = self.dataset.h.camera(center, camera_angle, scale, size, tf, north_vector=north_vector, fields='flux') im = cam.snapshot() images = [im] pb = ProgressBar(nframes) for ii,im in enumerate(cam.rotation(2 * np.pi, nframes, rot_vector=rot_vector)): images.append(im) im.write_png(os.path.join(outdir,"%s%04i.png" % (image_prefix, ii+start_index)), rescale=False) pb.update(ii+1) log.info("Rendering complete in {0}s".format(time.time() - pb._start_time)) if rescale: _rescale_images(images, os.path.join(outdir, image_prefix)) pipe = _make_movie(outdir, prefix=image_prefix, filename=output_filename) return images def auto_transfer_function(self, cmap_range, log=False, colormap='doom', **kwargs): from yt.visualization.volume_rendering.transfer_function_helper import TransferFunctionHelper tfh = TransferFunctionHelper(self.dataset) tfh.set_field('flux') tfh.set_bounds(bounds=cmap_range) tfh.set_log(log) tfh.build_transfer_function() return tfh def quick_isocontour(self, level='3 sigma', title='', description='', color_map='hot', color_log=False, export_to='sketchfab', filename=None, **kwargs): """ Export isocontours to sketchfab Requires that you have an account on https://sketchfab.com and are logged in Parameters ---------- level: str or float The level of the isocontours to create. Can be specified as n-sigma with strings like '3.3 sigma' or '2 sigma' (there must be a space between the number and the word) title: str A title for the uploaded figure description: str A short description for the uploaded figure color_map: str Any valid colormap. See `yt.show_colormaps` color_log: bool Whether the colormap should be log scaled. With the default parameters, this has no effect. export_to: 'sketchfab', 'obj', 'ply' You can export to sketchfab, to a .obj file (and accompanying .mtl file), or a .ply file. The latter two require ``filename`` specification filename: None or str Optional - prefix for output filenames if ``export_to`` is 'obj', or the full filename when ``export_to`` is 'ply'. Ignored for 'sketchfab' kwargs: dict Keyword arguments are passed to the appropriate yt function Returns ------- The result of the `yt.surface.export_sketchfab` function """ if isinstance(level, six.string_types): sigma = self.cube.std().value level = float(level.split()[0]) * sigma self.dataset.periodicity = (True,True,True) surface = self.dataset.surface(self.dataset.all_data(), "flux", level) if export_to == 'sketchfab': if filename is not None: warnings.warn("sketchfab export does not expect a filename entry") return surface.export_sketchfab(title=title, description=description, color_map=color_map, color_log=color_log, **kwargs) elif export_to == 'obj': if filename is None: raise ValueError("If export_to is not 'sketchfab'," " a filename must be specified") surface.export_obj(filename, color_field='ones', color_map=color_map, color_log=color_log, **kwargs) elif export_to == 'ply': if filename is None: raise ValueError("If export_to is not 'sketchfab'," " a filename must be specified") surface.export_ply(filename, color_field='ones', color_map=color_map, color_log=color_log, **kwargs) else: raise ValueError("export_to must be one of sketchfab,obj,ply") def _rescale_images(images, prefix): """ Save a sequence of images, at a common scaling Reduces flickering """ cmax = max(np.percentile(i[:, :, :3].sum(axis=2), 99.5) for i in images) amax = max(np.percentile(i[:, :, 3], 95) for i in images) for i, image in enumerate(images): image = image.rescale(cmax=cmax, amax=amax).swapaxes(0,1) image.write_png("%s%04i.png" % (prefix, i), rescale=False) def _make_movie(moviepath, prefix="", filename='out.mp4', overwrite=True): """ Use ffmpeg to generate a movie from the image series """ outpath = os.path.join(moviepath, filename) if os.path.exists(outpath) and overwrite: command = ['ffmpeg', '-y', '-r','5','-i', os.path.join(moviepath,prefix+'%04d.png'), '-r','30','-pix_fmt', 'yuv420p', outpath] elif os.path.exists(outpath): log.info("File {0} exists - skipping".format(outpath)) else: command = ['ffmpeg', '-r', '5', '-i', os.path.join(moviepath,prefix+'%04d.png'), '-r','30','-pix_fmt', 'yuv420p', outpath] pipe = subprocess.Popen(command, stdout=subprocess.PIPE, close_fds=True) pipe.wait() return pipe