_id
stringlengths 2
7
| title
stringlengths 1
88
| partition
stringclasses 3
values | text
stringlengths 75
19.8k
| language
stringclasses 1
value | meta_information
dict |
|---|---|---|---|---|---|
q200
|
_nth
|
train
|
def _nth(arr, n):
"""
Return the nth value of array
If it is missing return NaN
"""
try:
return arr.iloc[n]
except (KeyError, IndexError):
return np.nan
|
python
|
{
"resource": ""
}
|
q201
|
make_time
|
train
|
def make_time(h=0, m=0, s=0, ms=0, frames=None, fps=None):
"""
Convert time to milliseconds.
See :func:`pysubs2.time.times_to_ms()`. When both frames and fps are specified,
:func:`pysubs2.time.frames_to_ms()` is called instead.
Raises:
ValueError: Invalid fps, or one of frames/fps is missing.
Example:
>>> make_time(s=1.5)
1500
>>> make_time(frames=50, fps=25)
2000
"""
if frames is None and fps is None:
return times_to_ms(h, m, s, ms)
elif frames is not None and fps is not None:
return frames_to_ms(frames, fps)
else:
raise ValueError("Both fps and frames must be specified")
|
python
|
{
"resource": ""
}
|
q202
|
SSAEvent.shift
|
train
|
def shift(self, h=0, m=0, s=0, ms=0, frames=None, fps=None):
"""
Shift start and end times.
See :meth:`SSAFile.shift()` for full description.
"""
delta = make_time(h=h, m=m, s=s, ms=ms, frames=frames, fps=fps)
self.start += delta
self.end += delta
|
python
|
{
"resource": ""
}
|
q203
|
SSAEvent.equals
|
train
|
def equals(self, other):
"""Field-based equality for SSAEvents."""
if isinstance(other, SSAEvent):
return self.as_dict() == other.as_dict()
else:
raise TypeError("Cannot compare to non-SSAEvent object")
|
python
|
{
"resource": ""
}
|
q204
|
SSAFile.load
|
train
|
def load(cls, path, encoding="utf-8", format_=None, fps=None, **kwargs):
"""
Load subtitle file from given path.
Arguments:
path (str): Path to subtitle file.
encoding (str): Character encoding of input file.
Defaults to UTF-8, you may need to change this.
format_ (str): Optional, forces use of specific parser
(eg. `"srt"`, `"ass"`). Otherwise, format is detected
automatically from file contents. This argument should
be rarely needed.
fps (float): Framerate for frame-based formats (MicroDVD),
for other formats this argument is ignored. Framerate might
be detected from the file, in which case you don't need
to specify it here (when given, this argument overrides
autodetection).
kwargs: Extra options for the parser.
Returns:
SSAFile
Raises:
IOError
UnicodeDecodeError
pysubs2.exceptions.UnknownFPSError
pysubs2.exceptions.UnknownFormatIdentifierError
pysubs2.exceptions.FormatAutodetectionError
Note:
pysubs2 may autodetect subtitle format and/or framerate. These
values are set as :attr:`SSAFile.format` and :attr:`SSAFile.fps`
attributes.
Example:
>>> subs1 = pysubs2.load("subrip-subtitles.srt")
>>> subs2 = pysubs2.load("microdvd-subtitles.sub", fps=23.976)
"""
with open(path, encoding=encoding) as fp:
return cls.from_file(fp, format_, fps=fps, **kwargs)
|
python
|
{
"resource": ""
}
|
q205
|
SSAFile.from_string
|
train
|
def from_string(cls, string, format_=None, fps=None, **kwargs):
"""
Load subtitle file from string.
See :meth:`SSAFile.load()` for full description.
Arguments:
string (str): Subtitle file in a string. Note that the string
must be Unicode (in Python 2).
Returns:
SSAFile
Example:
>>> text = '''
... 1
... 00:00:00,000 --> 00:00:05,000
... An example SubRip file.
... '''
>>> subs = SSAFile.from_string(text)
"""
fp = io.StringIO(string)
return cls.from_file(fp, format_, fps=fps, **kwargs)
|
python
|
{
"resource": ""
}
|
q206
|
SSAFile.from_file
|
train
|
def from_file(cls, fp, format_=None, fps=None, **kwargs):
"""
Read subtitle file from file object.
See :meth:`SSAFile.load()` for full description.
Note:
This is a low-level method. Usually, one of :meth:`SSAFile.load()`
or :meth:`SSAFile.from_string()` is preferable.
Arguments:
fp (file object): A file object, ie. :class:`io.TextIOBase` instance.
Note that the file must be opened in text mode (as opposed to binary).
Returns:
SSAFile
"""
if format_ is None:
# Autodetect subtitle format, then read again using correct parser.
# The file might be a pipe and we need to read it twice,
# so just buffer everything.
text = fp.read()
fragment = text[:10000]
format_ = autodetect_format(fragment)
fp = io.StringIO(text)
impl = get_format_class(format_)
subs = cls() # an empty subtitle file
subs.format = format_
subs.fps = fps
impl.from_file(subs, fp, format_, fps=fps, **kwargs)
return subs
|
python
|
{
"resource": ""
}
|
q207
|
SSAFile.save
|
train
|
def save(self, path, encoding="utf-8", format_=None, fps=None, **kwargs):
"""
Save subtitle file to given path.
Arguments:
path (str): Path to subtitle file.
encoding (str): Character encoding of output file.
Defaults to UTF-8, which should be fine for most purposes.
format_ (str): Optional, specifies desired subtitle format
(eg. `"srt"`, `"ass"`). Otherwise, format is detected
automatically from file extension. Thus, this argument
is rarely needed.
fps (float): Framerate for frame-based formats (MicroDVD),
for other formats this argument is ignored. When omitted,
:attr:`SSAFile.fps` value is used (ie. the framerate used
for loading the file, if any). When the :class:`SSAFile`
wasn't loaded from MicroDVD, or if you wish save it with
different framerate, use this argument. See also
:meth:`SSAFile.transform_framerate()` for fixing bad
frame-based to time-based conversions.
kwargs: Extra options for the writer.
Raises:
IOError
UnicodeEncodeError
pysubs2.exceptions.UnknownFPSError
pysubs2.exceptions.UnknownFormatIdentifierError
pysubs2.exceptions.UnknownFileExtensionError
"""
if format_ is None:
ext = os.path.splitext(path)[1].lower()
format_ = get_format_identifier(ext)
with open(path, "w", encoding=encoding) as fp:
self.to_file(fp, format_, fps=fps, **kwargs)
|
python
|
{
"resource": ""
}
|
q208
|
SSAFile.to_string
|
train
|
def to_string(self, format_, fps=None, **kwargs):
"""
Get subtitle file as a string.
See :meth:`SSAFile.save()` for full description.
Returns:
str
"""
fp = io.StringIO()
self.to_file(fp, format_, fps=fps, **kwargs)
return fp.getvalue()
|
python
|
{
"resource": ""
}
|
q209
|
SSAFile.to_file
|
train
|
def to_file(self, fp, format_, fps=None, **kwargs):
"""
Write subtitle file to file object.
See :meth:`SSAFile.save()` for full description.
Note:
This is a low-level method. Usually, one of :meth:`SSAFile.save()`
or :meth:`SSAFile.to_string()` is preferable.
Arguments:
fp (file object): A file object, ie. :class:`io.TextIOBase` instance.
Note that the file must be opened in text mode (as opposed to binary).
"""
impl = get_format_class(format_)
impl.to_file(self, fp, format_, fps=fps, **kwargs)
|
python
|
{
"resource": ""
}
|
q210
|
SSAFile.rename_style
|
train
|
def rename_style(self, old_name, new_name):
"""
Rename a style, including references to it.
Arguments:
old_name (str): Style to be renamed.
new_name (str): New name for the style (must be unused).
Raises:
KeyError: No style named old_name.
ValueError: new_name is not a legal name (cannot use commas)
or new_name is taken.
"""
if old_name not in self.styles:
raise KeyError("Style %r not found" % old_name)
if new_name in self.styles:
raise ValueError("There is already a style called %r" % new_name)
if not is_valid_field_content(new_name):
raise ValueError("%r is not a valid name" % new_name)
self.styles[new_name] = self.styles[old_name]
del self.styles[old_name]
for line in self:
# XXX also handle \r override tag
if line.style == old_name:
line.style = new_name
|
python
|
{
"resource": ""
}
|
q211
|
SSAFile.import_styles
|
train
|
def import_styles(self, subs, overwrite=True):
"""
Merge in styles from other SSAFile.
Arguments:
subs (SSAFile): Subtitle file imported from.
overwrite (bool): On name conflict, use style from the other file
(default: True).
"""
if not isinstance(subs, SSAFile):
raise TypeError("Must supply an SSAFile.")
for name, style in subs.styles.items():
if name not in self.styles or overwrite:
self.styles[name] = style
|
python
|
{
"resource": ""
}
|
q212
|
SSAFile.equals
|
train
|
def equals(self, other):
"""
Equality of two SSAFiles.
Compares :attr:`SSAFile.info`, :attr:`SSAFile.styles` and :attr:`SSAFile.events`.
Order of entries in OrderedDicts does not matter. "ScriptType" key in info is
considered an implementation detail and thus ignored.
Useful mostly in unit tests. Differences are logged at DEBUG level.
"""
if isinstance(other, SSAFile):
for key in set(chain(self.info.keys(), other.info.keys())) - {"ScriptType"}:
sv, ov = self.info.get(key), other.info.get(key)
if sv is None:
logging.debug("%r missing in self.info", key)
return False
elif ov is None:
logging.debug("%r missing in other.info", key)
return False
elif sv != ov:
logging.debug("info %r differs (self=%r, other=%r)", key, sv, ov)
return False
for key in set(chain(self.styles.keys(), other.styles.keys())):
sv, ov = self.styles.get(key), other.styles.get(key)
if sv is None:
logging.debug("%r missing in self.styles", key)
return False
elif ov is None:
logging.debug("%r missing in other.styles", key)
return False
elif sv != ov:
for k in sv.FIELDS:
if getattr(sv, k) != getattr(ov, k): logging.debug("difference in field %r", k)
logging.debug("style %r differs (self=%r, other=%r)", key, sv.as_dict(), ov.as_dict())
return False
if len(self) != len(other):
logging.debug("different # of subtitles (self=%d, other=%d)", len(self), len(other))
return False
for i, (se, oe) in enumerate(zip(self.events, other.events)):
if not se.equals(oe):
for k in se.FIELDS:
if getattr(se, k) != getattr(oe, k): logging.debug("difference in field %r", k)
logging.debug("event %d differs (self=%r, other=%r)", i, se.as_dict(), oe.as_dict())
return False
return True
else:
raise TypeError("Cannot compare to non-SSAFile object")
|
python
|
{
"resource": ""
}
|
q213
|
get_file_extension
|
train
|
def get_file_extension(format_):
"""Format identifier -> file extension"""
if format_ not in FORMAT_IDENTIFIER_TO_FORMAT_CLASS:
raise UnknownFormatIdentifierError(format_)
for ext, f in FILE_EXTENSION_TO_FORMAT_IDENTIFIER.items():
if f == format_:
return ext
raise RuntimeError("No file extension for format %r" % format_)
|
python
|
{
"resource": ""
}
|
q214
|
autodetect_format
|
train
|
def autodetect_format(content):
"""Return format identifier for given fragment or raise FormatAutodetectionError."""
formats = set()
for impl in FORMAT_IDENTIFIER_TO_FORMAT_CLASS.values():
guess = impl.guess_format(content)
if guess is not None:
formats.add(guess)
if len(formats) == 1:
return formats.pop()
elif not formats:
raise FormatAutodetectionError("No suitable formats")
else:
raise FormatAutodetectionError("Multiple suitable formats (%r)" % formats)
|
python
|
{
"resource": ""
}
|
q215
|
src_reload
|
train
|
async def src_reload(app, path: str = None):
"""
prompt each connected browser to reload by sending websocket message.
:param path: if supplied this must be a path relative to app['static_path'],
eg. reload of a single file is only supported for static resources.
:return: number of sources reloaded
"""
cli_count = len(app[WS])
if cli_count == 0:
return 0
is_html = None
if path:
path = str(Path(app['static_url']) / Path(path).relative_to(app['static_path']))
is_html = mimetypes.guess_type(path)[0] == 'text/html'
reloads = 0
aux_logger.debug('prompting source reload for %d clients', cli_count)
for ws, url in app[WS]:
if path and is_html and path not in {url, url + '.html', url.rstrip('/') + '/index.html'}:
aux_logger.debug('skipping reload for client at %s', url)
continue
aux_logger.debug('reload client at %s', url)
data = {
'command': 'reload',
'path': path or url,
'liveCSS': True,
'liveImg': True,
}
try:
await ws.send_str(json.dumps(data))
except RuntimeError as e:
# eg. "RuntimeError: websocket connection is closing"
aux_logger.error('Error broadcasting change to %s, RuntimeError: %s', path or url, e)
else:
reloads += 1
if reloads:
s = '' if reloads == 1 else 's'
aux_logger.info('prompted reload of %s on %d client%s', path or 'page', reloads, s)
return reloads
|
python
|
{
"resource": ""
}
|
q216
|
Settings.substitute_environ
|
train
|
def substitute_environ(self):
"""
Substitute environment variables into settings.
"""
for attr_name in dir(self):
if attr_name.startswith('_') or attr_name.upper() != attr_name:
continue
orig_value = getattr(self, attr_name)
is_required = isinstance(orig_value, Required)
orig_type = orig_value.v_type if is_required else type(orig_value)
env_var_name = self._ENV_PREFIX + attr_name
env_var = os.getenv(env_var_name, None)
if env_var is not None:
if issubclass(orig_type, bool):
env_var = env_var.upper() in ('1', 'TRUE')
elif issubclass(orig_type, int):
env_var = int(env_var)
elif issubclass(orig_type, Path):
env_var = Path(env_var)
elif issubclass(orig_type, bytes):
env_var = env_var.encode()
# could do floats here and lists etc via json
setattr(self, attr_name, env_var)
elif is_required and attr_name not in self._custom_settings:
raise RuntimeError('The required environment variable "{0}" is currently not set, '
'you\'ll need to run `source activate.settings.sh` '
'or you can set that single environment variable with '
'`export {0}="<value>"`'.format(env_var_name))
|
python
|
{
"resource": ""
}
|
q217
|
serve
|
train
|
def serve(path, livereload, port, verbose):
"""
Serve static files from a directory.
"""
setup_logging(verbose)
run_app(*serve_static(static_path=path, livereload=livereload, port=port))
|
python
|
{
"resource": ""
}
|
q218
|
runserver
|
train
|
def runserver(**config):
"""
Run a development server for an aiohttp apps.
Takes one argument "app-path" which should be a path to either a directory containing a recognized default file
("app.py" or "main.py") or to a specific file. Defaults to the environment variable "AIO_APP_PATH" or ".".
The app path is run directly, see the "--app-factory" option for details on how an app is loaded from a python
module.
"""
active_config = {k: v for k, v in config.items() if v is not None}
setup_logging(config['verbose'])
try:
run_app(*_runserver(**active_config))
except AiohttpDevException as e:
if config['verbose']:
tb = click.style(traceback.format_exc().strip('\n'), fg='white', dim=True)
main_logger.warning('AiohttpDevException traceback:\n%s', tb)
main_logger.error('Error: %s', e)
sys.exit(2)
|
python
|
{
"resource": ""
}
|
q219
|
scenario
|
train
|
def scenario(weight=1, delay=0.0, name=None):
"""Decorator to register a function as a Molotov test.
Options:
- **weight** used by Molotov when the scenarii are randomly picked.
The functions with the highest values are more likely to be picked.
Integer, defaults to 1. This value is ignored when the
*scenario_picker* decorator is used.
- **delay** once the scenario is done, the worker will sleep
*delay* seconds. Float, defaults to 0.
The general --delay argument you can pass to Molotov
will be summed with this delay.
- **name** name of the scenario. If not provided, will use the
function __name___ attribute.
The decorated function receives an :class:`aiohttp.ClientSession` instance.
"""
def _scenario(func, *args, **kw):
_check_coroutine(func)
if weight > 0:
sname = name or func.__name__
data = {'name': sname,
'weight': weight, 'delay': delay,
'func': func, 'args': args, 'kw': kw}
_SCENARIO[sname] = data
@functools.wraps(func)
def __scenario(*args, **kw):
return func(*args, **kw)
return __scenario
return _scenario
|
python
|
{
"resource": ""
}
|
q220
|
request
|
train
|
def request(endpoint, verb='GET', session_options=None, **options):
"""Performs a synchronous request.
Uses a dedicated event loop and aiohttp.ClientSession object.
Options:
- endpoint: the endpoint to call
- verb: the HTTP verb to use (defaults: GET)
- session_options: a dict containing options to initialize the session
(defaults: None)
- options: extra options for the request (defaults: None)
Returns a dict object with the following keys:
- content: the content of the response
- status: the status
- headers: a dict with all the response headers
"""
req = functools.partial(_request, endpoint, verb, session_options,
**options)
return _run_in_fresh_loop(req)
|
python
|
{
"resource": ""
}
|
q221
|
get_var
|
train
|
def get_var(name, factory=None):
"""Gets a global variable given its name.
If factory is not None and the variable is not set, factory
is a callable that will set the variable.
If not set, returns None.
"""
if name not in _VARS and factory is not None:
_VARS[name] = factory()
return _VARS.get(name)
|
python
|
{
"resource": ""
}
|
q222
|
Worker.step
|
train
|
async def step(self, step_id, session, scenario=None):
""" single scenario call.
When it returns 1, it works. -1 the script failed,
0 the test is stopping or needs to stop.
"""
if scenario is None:
scenario = pick_scenario(self.wid, step_id)
try:
await self.send_event('scenario_start', scenario=scenario)
await scenario['func'](session, *scenario['args'],
**scenario['kw'])
await self.send_event('scenario_success', scenario=scenario)
if scenario['delay'] > 0.:
await cancellable_sleep(scenario['delay'])
return 1
except Exception as exc:
await self.send_event('scenario_failure',
scenario=scenario,
exception=exc)
if self.args.verbose > 0:
self.console.print_error(exc)
await self.console.flush()
return -1
|
python
|
{
"resource": ""
}
|
q223
|
main
|
train
|
def main():
"""Moloslave clones a git repo and runs a molotov test
"""
parser = argparse.ArgumentParser(description='Github-based load test')
parser.add_argument('--version', action='store_true', default=False,
help='Displays version and exits.')
parser.add_argument('--virtualenv', type=str, default='virtualenv',
help='Virtualenv executable.')
parser.add_argument('--python', type=str, default=sys.executable,
help='Python executable.')
parser.add_argument('--config', type=str, default='molotov.json',
help='Path of the configuration file.')
parser.add_argument('repo', help='Github repo', type=str, nargs="?")
parser.add_argument('run', help='Test to run', nargs="?")
args = parser.parse_args()
if args.version:
print(__version__)
sys.exit(0)
tempdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tempdir)
print('Working directory is %s' % tempdir)
try:
clone_repo(args.repo)
config_file = os.path.join(tempdir, args.config)
with open(config_file) as f:
config = json.loads(f.read())
# creating the virtualenv
create_virtualenv(args.virtualenv, args.python)
# install deps
if 'requirements' in config['molotov']:
install_reqs(config['molotov']['requirements'])
# load deps into sys.path
pyver = '%d.%d' % (sys.version_info.major, sys.version_info.minor)
site_pkg = os.path.join(tempdir, 'venv', 'lib', 'python' + pyver,
'site-packages')
site.addsitedir(site_pkg)
pkg_resources.working_set.add_entry(site_pkg)
# environment
if 'env' in config['molotov']:
for key, value in config['molotov']['env'].items():
os.environ[key] = value
run_test(**config['molotov']['tests'][args.run])
except Exception:
os.chdir(curdir)
shutil.rmtree(tempdir, ignore_errors=True)
raise
|
python
|
{
"resource": ""
}
|
q224
|
copy_files
|
train
|
def copy_files(source_files, target_directory, source_directory=None):
"""Copies a list of files to the specified directory.
If source_directory is provided, it will be prepended to each source file."""
try:
os.makedirs(target_directory)
except: # TODO: specific exception?
pass
for f in source_files:
source = os.path.join(source_directory, f) if source_directory else f
target = os.path.join(target_directory, f)
shutil.copy2(source, target)
|
python
|
{
"resource": ""
}
|
q225
|
yes_or_no
|
train
|
def yes_or_no(message):
"""Gets user input and returns True for yes and False for no."""
while True:
print message, '(yes/no)',
line = raw_input()
if line is None:
return None
line = line.lower()
if line == 'y' or line == 'ye' or line == 'yes':
return True
if line == 'n' or line == 'no':
return False
|
python
|
{
"resource": ""
}
|
q226
|
add_plugin
|
train
|
def add_plugin(plugin, directory=None):
"""Adds the specified plugin. This returns False if it was already added."""
repo = require_repo(directory)
plugins = get_value(repo, 'plugins', expect_type=dict)
if plugin in plugins:
return False
plugins[plugin] = {}
set_value(repo, 'plugins', plugins)
return True
|
python
|
{
"resource": ""
}
|
q227
|
get_plugin_settings
|
train
|
def get_plugin_settings(plugin, directory=None):
"""Gets the settings for the specified plugin."""
repo = require_repo(directory)
plugins = get_value(repo, 'plugins')
return plugins.get(plugin) if isinstance(plugins, dict) else None
|
python
|
{
"resource": ""
}
|
q228
|
preview
|
train
|
def preview(directory=None, host=None, port=None, watch=True):
"""Runs a local server to preview the working directory of a repository."""
directory = directory or '.'
host = host or '127.0.0.1'
port = port or 5000
# TODO: admin interface
# TODO: use cache_only to keep from modifying output directly
out_directory = build(directory)
# Serve generated site
os.chdir(out_directory)
Handler = SimpleHTTPServer.SimpleHTTPRequestHandler
httpd = SocketServer.TCPServer((host, port), Handler)
print ' * Serving on http://%s:%s/' % (host, port)
httpd.serve_forever()
|
python
|
{
"resource": ""
}
|
q229
|
require_repo
|
train
|
def require_repo(directory=None):
"""Checks for a presentation repository and raises an exception if not found."""
if directory and not os.path.isdir(directory):
raise ValueError('Directory not found: ' + repr(directory))
repo = repo_path(directory)
if not os.path.isdir(repo):
raise RepositoryNotFoundError(directory)
return repo
|
python
|
{
"resource": ""
}
|
q230
|
init
|
train
|
def init(directory=None):
"""Initializes a Gitpress presentation repository at the specified directory."""
repo = repo_path(directory)
if os.path.isdir(repo):
raise RepositoryAlreadyExistsError(directory, repo)
# Initialize repository with default template
shutil.copytree(default_template_path, repo)
message = '"Default presentation content."'
subprocess.call(['git', 'init', '-q', repo])
subprocess.call(['git', 'add', '.'], cwd=repo)
subprocess.call(['git', 'commit', '-q', '-m', message], cwd=repo)
return repo
|
python
|
{
"resource": ""
}
|
q231
|
iterate_presentation_files
|
train
|
def iterate_presentation_files(path=None, excludes=None, includes=None):
"""Iterates the repository presentation files relative to 'path',
not including themes. Note that 'includes' take priority."""
# Defaults
if includes is None:
includes = []
if excludes is None:
excludes = []
# Transform glob patterns to regular expressions
includes_pattern = r'|'.join([fnmatch.translate(x) for x in includes]) or r'$.'
excludes_pattern = r'|'.join([fnmatch.translate(x) for x in excludes]) or r'$.'
includes_re = re.compile(includes_pattern)
excludes_re = re.compile(excludes_pattern)
def included(root, name):
"""Returns True if the specified file is a presentation file."""
full_path = os.path.join(root, name)
# Explicitly included files takes priority
if includes_re.match(full_path):
return True
# Ignore special and excluded files
return (not specials_re.match(name)
and not excludes_re.match(full_path))
# Get a filtered list of paths to be built
for root, dirs, files in os.walk(path):
dirs[:] = [d for d in dirs if included(root, d)]
files = [f for f in files if included(root, f)]
for f in files:
yield os.path.relpath(os.path.join(root, f), path)
|
python
|
{
"resource": ""
}
|
q232
|
read_config_file
|
train
|
def read_config_file(path):
"""Returns the configuration from the specified file."""
try:
with open(path, 'r') as f:
return json.load(f, object_pairs_hook=OrderedDict)
except IOError as ex:
if ex != errno.ENOENT:
raise
return {}
|
python
|
{
"resource": ""
}
|
q233
|
write_config
|
train
|
def write_config(repo_directory, config):
"""Writes the specified configuration to the presentation repository."""
return write_config_file(os.path.join(repo_directory, config_file), config)
|
python
|
{
"resource": ""
}
|
q234
|
write_config_file
|
train
|
def write_config_file(path, config):
"""Writes the specified configuration to the specified file."""
contents = json.dumps(config, indent=4, separators=(',', ': ')) + '\n'
try:
with open(path, 'w') as f:
f.write(contents)
return True
except IOError as ex:
if ex != errno.ENOENT:
raise
return False
|
python
|
{
"resource": ""
}
|
q235
|
get_value
|
train
|
def get_value(repo_directory, key, expect_type=None):
"""Gets the value of the specified key in the config file."""
config = read_config(repo_directory)
value = config.get(key)
if expect_type and value is not None and not isinstance(value, expect_type):
raise ConfigSchemaError('Expected config variable %s to be type %s, got %s'
% (repr(key), repr(expect_type), repr(type(value))))
return value
|
python
|
{
"resource": ""
}
|
q236
|
set_value
|
train
|
def set_value(repo_directory, key, value, strict=True):
"""Sets the value of a particular key in the config file. This has no effect when setting to the same value."""
if value is None:
raise ValueError('Argument "value" must not be None.')
# Read values and do nothing if not making any changes
config = read_config(repo_directory)
old = config.get(key)
if old == value:
return old
# Check schema
if strict and old is not None and not isinstance(old, type(value)):
raise ConfigSchemaError('Expected config variable %s to be type %s, got %s'
% (repr(key), repr(type(value)), repr(type(old))))
# Set new value and save results
config[key] = value
write_config(repo_directory, config)
return old
|
python
|
{
"resource": ""
}
|
q237
|
build
|
train
|
def build(content_directory=None, out_directory=None):
"""Builds the site from its content and presentation repository."""
content_directory = content_directory or '.'
out_directory = os.path.abspath(out_directory or default_out_directory)
repo = require_repo(content_directory)
# Prevent user mistakes
if out_directory == '.':
raise ValueError('Output directory must be different than the source directory: ' + repr(out_directory))
if os.path.basename(os.path.relpath(out_directory, content_directory)) == '..':
raise ValueError('Output directory must not contain the source directory: ' + repr(out_directory))
# TODO: read config
# TODO: use virtualenv
# TODO: init and run plugins
# TODO: process with active theme
# Collect and copy static files
files = presentation_files(repo)
remove_directory(out_directory)
copy_files(files, out_directory, repo)
return out_directory
|
python
|
{
"resource": ""
}
|
q238
|
gpp
|
train
|
def gpp(argv=None):
"""Shortcut function for running the previewing command."""
if argv is None:
argv = sys.argv[1:]
argv.insert(0, 'preview')
return main(argv)
|
python
|
{
"resource": ""
}
|
q239
|
use_theme
|
train
|
def use_theme(theme, directory=None):
"""Switches to the specified theme. This returns False if switching to the already active theme."""
repo = require_repo(directory)
if theme not in list_themes(directory):
raise ThemeNotFoundError(theme)
old_theme = set_value(repo, 'theme', theme)
return old_theme != theme
|
python
|
{
"resource": ""
}
|
q240
|
data_type
|
train
|
def data_type(data, grouped=False, columns=None, key_on='idx', iter_idx=None):
'''Data type check for automatic import'''
if iter_idx:
return Data.from_mult_iters(idx=iter_idx, **data)
if pd:
if isinstance(data, (pd.Series, pd.DataFrame)):
return Data.from_pandas(data, grouped=grouped, columns=columns,
key_on=key_on)
if isinstance(data, (list, tuple, dict)):
return Data.from_iter(data)
else:
raise ValueError('This data type is not supported by Vincent.')
|
python
|
{
"resource": ""
}
|
q241
|
Map.rebind
|
train
|
def rebind(self, column=None, brew='GnBu'):
"""Bind a new column to the data map
Parameters
----------
column: str, default None
Pandas DataFrame column name
brew: str, default None
Color brewer abbreviation. See colors.py
"""
self.data['table'] = Data.keypairs(
self.raw_data, columns=[self.data_key, column])
domain = [Data.serialize(self.raw_data[column].min()),
Data.serialize(self.raw_data[column].quantile(0.95))]
scale = Scale(name='color', type='quantize', domain=domain,
range=brews[brew])
self.scales['color'] = scale
|
python
|
{
"resource": ""
}
|
q242
|
Visualization.axis_titles
|
train
|
def axis_titles(self, x=None, y=None):
"""Apply axis titles to the figure.
This is a convenience method for manually modifying the "Axes" mark.
Parameters
----------
x: string, default 'null'
X-axis title
y: string, default 'null'
Y-axis title
Example
-------
>>>vis.axis_titles(y="Data 1", x="Data 2")
"""
keys = self.axes.get_keys()
if keys:
for key in keys:
if key == 'x':
self.axes[key].title = x
elif key == 'y':
self.axes[key].title = y
else:
self.axes.extend([Axis(type='x', title=x),
Axis(type='y', title=y)])
return self
|
python
|
{
"resource": ""
}
|
q243
|
Visualization._set_axis_properties
|
train
|
def _set_axis_properties(self, axis):
"""Set AxisProperties and PropertySets"""
if not getattr(axis, 'properties'):
axis.properties = AxisProperties()
for prop in ['ticks', 'axis', 'major_ticks', 'minor_ticks',
'title', 'labels']:
setattr(axis.properties, prop, PropertySet())
|
python
|
{
"resource": ""
}
|
q244
|
Visualization._set_all_axis_color
|
train
|
def _set_all_axis_color(self, axis, color):
"""Set axis ticks, title, labels to given color"""
for prop in ['ticks', 'axis', 'major_ticks', 'minor_ticks', 'title',
'labels']:
prop_set = getattr(axis.properties, prop)
if color and prop in ['title', 'labels']:
prop_set.fill = ValueRef(value=color)
elif color and prop in ['axis', 'major_ticks', 'minor_ticks',
'ticks']:
prop_set.stroke = ValueRef(value=color)
|
python
|
{
"resource": ""
}
|
q245
|
Visualization._axis_properties
|
train
|
def _axis_properties(self, axis, title_size, title_offset, label_angle,
label_align, color):
"""Assign axis properties"""
if self.axes:
axis = [a for a in self.axes if a.scale == axis][0]
self._set_axis_properties(axis)
self._set_all_axis_color(axis, color)
if title_size:
axis.properties.title.font_size = ValueRef(value=title_size)
if label_angle:
axis.properties.labels.angle = ValueRef(value=label_angle)
if label_align:
axis.properties.labels.align = ValueRef(value=label_align)
if title_offset:
axis.properties.title.dy = ValueRef(value=title_offset)
else:
raise ValueError('This Visualization has no axes!')
|
python
|
{
"resource": ""
}
|
q246
|
Visualization.common_axis_properties
|
train
|
def common_axis_properties(self, color=None, title_size=None):
"""Set common axis properties such as color
Parameters
----------
color: str, default None
Hex color str, etc
"""
if self.axes:
for axis in self.axes:
self._set_axis_properties(axis)
self._set_all_axis_color(axis, color)
if title_size:
ref = ValueRef(value=title_size)
axis.properties.title.font_size = ref
else:
raise ValueError('This Visualization has no axes!')
return self
|
python
|
{
"resource": ""
}
|
q247
|
Visualization.x_axis_properties
|
train
|
def x_axis_properties(self, title_size=None, title_offset=None,
label_angle=None, label_align=None, color=None):
"""Change x-axis title font size and label angle
Parameters
----------
title_size: int, default None
Title size, in px
title_offset: int, default None
Pixel offset from given axis
label_angle: int, default None
label angle in degrees
label_align: str, default None
Label alignment
color: str, default None
Hex color
"""
self._axis_properties('x', title_size, title_offset, label_angle,
label_align, color)
return self
|
python
|
{
"resource": ""
}
|
q248
|
Visualization.y_axis_properties
|
train
|
def y_axis_properties(self, title_size=None, title_offset=None,
label_angle=None, label_align=None, color=None):
"""Change y-axis title font size and label angle
Parameters
----------
title_size: int, default None
Title size, in px
title_offset: int, default None
Pixel offset from given axis
label_angle: int, default None
label angle in degrees
label_align: str, default None
Label alignment
color: str, default None
Hex color
"""
self._axis_properties('y', title_size, title_offset, label_angle,
label_align, color)
return self
|
python
|
{
"resource": ""
}
|
q249
|
Visualization.legend
|
train
|
def legend(self, title=None, scale='color', text_color=None):
"""Convience method for adding a legend to the figure.
Important: This defaults to the color scale that is generated with
Line, Area, Stacked Line, etc charts. For bar charts, the scale ref is
usually 'y'.
Parameters
----------
title: string, default None
Legend Title
scale: string, default 'color'
Scale reference for legend
text_color: str, default None
Title and label color
"""
self.legends.append(Legend(title=title, fill=scale, offset=0,
properties=LegendProperties()))
if text_color:
color_props = PropertySet(fill=ValueRef(value=text_color))
self.legends[0].properties.labels = color_props
self.legends[0].properties.title = color_props
return self
|
python
|
{
"resource": ""
}
|
q250
|
Visualization.colors
|
train
|
def colors(self, brew=None, range_=None):
"""Convenience method for adding color brewer scales to charts with a
color scale, such as stacked or grouped bars.
See the colors here: http://colorbrewer2.org/
Or here: http://bl.ocks.org/mbostock/5577023
This assumes that a 'color' scale exists on your chart.
Parameters
----------
brew: string, default None
Color brewer scheme (BuGn, YlOrRd, etc)
range: list, default None
List of colors. Ex: ['#ac4142', '#d28445', '#f4bf75']
"""
if brew:
self.scales['color'].range = brews[brew]
elif range_:
self.scales['color'].range = range_
return self
|
python
|
{
"resource": ""
}
|
q251
|
Visualization.validate
|
train
|
def validate(self, require_all=True, scale='colors'):
"""Validate the visualization contents.
Parameters
----------
require_all : boolean, default True
If True (default), then all fields ``data``, ``scales``,
``axes``, and ``marks`` must be defined. The user is allowed to
disable this if the intent is to define the elements
client-side.
If the contents of the visualization are not valid Vega, then a
:class:`ValidationError` is raised.
"""
super(self.__class__, self).validate()
required_attribs = ('data', 'scales', 'axes', 'marks')
for elem in required_attribs:
attr = getattr(self, elem)
if attr:
# Validate each element of the sets of data, etc
for entry in attr:
entry.validate()
names = [a.name for a in attr]
if len(names) != len(set(names)):
raise ValidationError(elem + ' has duplicate names')
elif require_all:
raise ValidationError(
elem + ' must be defined for valid visualization')
|
python
|
{
"resource": ""
}
|
q252
|
Visualization.display
|
train
|
def display(self):
"""Display the visualization inline in the IPython notebook.
This is deprecated, use the following instead::
from IPython.display import display
display(viz)
"""
from IPython.core.display import display, HTML
display(HTML(self._repr_html_()))
|
python
|
{
"resource": ""
}
|
q253
|
Data.validate
|
train
|
def validate(self, *args):
"""Validate contents of class
"""
super(self.__class__, self).validate(*args)
if not self.name:
raise ValidationError('name is required for Data')
|
python
|
{
"resource": ""
}
|
q254
|
Data.serialize
|
train
|
def serialize(obj):
"""Convert an object into a JSON-serializable value
This is used by the ``from_pandas`` and ``from_numpy`` functions to
convert data to JSON-serializable types when loading.
"""
if isinstance(obj, str_types):
return obj
elif hasattr(obj, 'timetuple'):
return int(time.mktime(obj.timetuple())) * 1000
elif hasattr(obj, 'item'):
return obj.item()
elif hasattr(obj, '__float__'):
if isinstance(obj, int):
return int(obj)
else:
return float(obj)
elif hasattr(obj, '__int__'):
return int(obj)
else:
raise LoadError('cannot serialize index of type '
+ type(obj).__name__)
|
python
|
{
"resource": ""
}
|
q255
|
Data.from_pandas
|
train
|
def from_pandas(cls, data, columns=None, key_on='idx', name=None,
series_key='data', grouped=False, records=False, **kwargs):
"""Load values from a pandas ``Series`` or ``DataFrame`` object
Parameters
----------
data : pandas ``Series`` or ``DataFrame``
Pandas object to import data from.
columns: list, default None
DataFrame columns to convert to Data. Keys default to col names.
If columns are given and on_index is False, x-axis data will
default to the first column.
key_on: string, default 'index'
Value to key on for x-axis data. Defaults to index.
name : string, default None
Applies to the ``name`` attribute of the generated class. If
``None`` (default), then the ``name`` attribute of ``pd_obj`` is
used if it exists, or ``'table'`` if it doesn't.
series_key : string, default 'data'
Applies only to ``Series``. If ``None`` (default), then defaults to
data.name. For example, if ``series_key`` is ``'x'``, then the
entries of the ``values`` list
will be ``{'idx': ..., 'col': 'x', 'val': ...}``.
grouped: boolean, default False
Pass true for an extra grouping parameter
records: boolean, defaule False
Requires Pandas 0.12 or greater. Writes the Pandas DataFrame
using the df.to_json(orient='records') formatting.
**kwargs : dict
Additional arguments passed to the :class:`Data` constructor.
"""
# Note: There's an experimental JSON encoder floating around in
# pandas land that hasn't made it into the main branch. This
# function should be revisited if it ever does.
if not pd:
raise LoadError('pandas could not be imported')
if not hasattr(data, 'index'):
raise ValueError('Please load a Pandas object.')
if name:
vega_data = cls(name=name, **kwargs)
else:
vega_data = cls(name='table', **kwargs)
pd_obj = data.copy()
if columns:
pd_obj = data[columns]
if key_on != 'idx':
pd_obj.index = data[key_on]
if records:
# The worst
vega_data.values = json.loads(pd_obj.to_json(orient='records'))
return vega_data
vega_data.values = []
if isinstance(pd_obj, pd.Series):
data_key = data.name or series_key
for i, v in pd_obj.iteritems():
value = {}
value['idx'] = cls.serialize(i)
value['col'] = data_key
value['val'] = cls.serialize(v)
vega_data.values.append(value)
elif isinstance(pd_obj, pd.DataFrame):
# We have to explicitly convert the column names to strings
# because the json serializer doesn't allow for integer keys.
for i, row in pd_obj.iterrows():
for num, (k, v) in enumerate(row.iteritems()):
value = {}
value['idx'] = cls.serialize(i)
value['col'] = cls.serialize(k)
value['val'] = cls.serialize(v)
if grouped:
value['group'] = num
vega_data.values.append(value)
else:
raise ValueError('cannot load from data type '
+ type(pd_obj).__name__)
return vega_data
|
python
|
{
"resource": ""
}
|
q256
|
Data.from_numpy
|
train
|
def from_numpy(cls, np_obj, name, columns, index=None, index_key=None,
**kwargs):
"""Load values from a numpy array
Parameters
----------
np_obj : numpy.ndarray
numpy array to load data from
name : string
``name`` field for the data
columns : iterable
Sequence of column names, from left to right. Must have same
length as the number of columns of ``np_obj``.
index : iterable, default None
Sequence of indices from top to bottom. If ``None`` (default),
then the indices are integers starting at 0. Must have same
length as the number of rows of ``np_obj``.
index_key : string, default None
Key to use for the index. If ``None`` (default), ``idx`` is
used.
**kwargs : dict
Additional arguments passed to the :class:`Data` constructor
Notes
-----
The individual elements of ``np_obj``, ``columns``, and ``index``
must return valid values from :func:`Data.serialize`.
"""
if not np:
raise LoadError('numpy could not be imported')
_assert_is_type('numpy object', np_obj, np.ndarray)
# Integer index if none is provided
index = index or range(np_obj.shape[0])
# Explicitly map dict-keys to strings for JSON serializer.
columns = list(map(str, columns))
index_key = index_key or cls._default_index_key
if len(index) != np_obj.shape[0]:
raise LoadError(
'length of index must be equal to number of rows of array')
elif len(columns) != np_obj.shape[1]:
raise LoadError(
'length of columns must be equal to number of columns of '
'array')
data = cls(name=name, **kwargs)
data.values = [
dict([(index_key, cls.serialize(idx))] +
[(col, x) for col, x in zip(columns, row)])
for idx, row in zip(index, np_obj.tolist())]
return data
|
python
|
{
"resource": ""
}
|
q257
|
Data.from_mult_iters
|
train
|
def from_mult_iters(cls, name=None, idx=None, **kwargs):
"""Load values from multiple iters
Parameters
----------
name : string, default None
Name of the data set. If None (default), the name will be set to
``'table'``.
idx: string, default None
Iterable to use for the data index
**kwargs : dict of iterables
The ``values`` field will contain dictionaries with keys for
each of the iterables provided. For example,
d = Data.from_iters(idx='x', x=[0, 1, 5], y=(10, 20, 30))
would result in ``d`` having a ``values`` field with
[{'idx': 0, 'col': 'y', 'val': 10},
{'idx': 1, 'col': 'y', 'val': 20}
If the iterables are not the same length, then ValueError is
raised.
"""
if not name:
name = 'table'
lengths = [len(v) for v in kwargs.values()]
if len(set(lengths)) != 1:
raise ValueError('Iterables must all be same length')
if not idx:
raise ValueError('Must provide iter name index reference')
index = kwargs.pop(idx)
vega_vals = []
for k, v in sorted(kwargs.items()):
for idx, val in zip(index, v):
value = {}
value['idx'] = idx
value['col'] = k
value['val'] = val
vega_vals.append(value)
return cls(name, values=vega_vals)
|
python
|
{
"resource": ""
}
|
q258
|
Data.from_iter
|
train
|
def from_iter(cls, data, name=None):
"""Convenience method for loading data from an iterable.
Defaults to numerical indexing for x-axis.
Parameters
----------
data: iterable
An iterable of data (list, tuple, dict of key/val pairs)
name: string, default None
Name of the data set. If None (default), the name will be set to
``'table'``.
"""
if not name:
name = 'table'
if isinstance(data, (list, tuple)):
data = {x: y for x, y in enumerate(data)}
values = [{'idx': k, 'col': 'data', 'val': v}
for k, v in sorted(data.items())]
return cls(name, values=values)
|
python
|
{
"resource": ""
}
|
q259
|
Data._numpy_to_values
|
train
|
def _numpy_to_values(data):
'''Convert a NumPy array to values attribute'''
def to_list_no_index(xvals, yvals):
return [{"x": x, "y": np.asscalar(y)}
for x, y in zip(xvals, yvals)]
if len(data.shape) == 1 or data.shape[1] == 1:
xvals = range(data.shape[0] + 1)
values = to_list_no_index(xvals, data)
elif len(data.shape) == 2:
if data.shape[1] == 2:
# NumPy arrays and matrices have different iteration rules.
if isinstance(data, np.matrix):
xidx = (0, 0)
yidx = (0, 1)
else:
xidx = 0
yidx = 1
xvals = [np.asscalar(row[xidx]) for row in data]
yvals = [np.asscalar(row[yidx]) for row in data]
values = [{"x": x, "y": y} for x, y in zip(xvals, yvals)]
else:
raise ValueError('arrays with > 2 columns not supported')
else:
raise ValueError('invalid dimensions for ndarray')
return values
|
python
|
{
"resource": ""
}
|
q260
|
Data.to_json
|
train
|
def to_json(self, validate=False, pretty_print=True, data_path=None):
"""Convert data to JSON
Parameters
----------
data_path : string
If not None, then data is written to a separate file at the
specified path. Note that the ``url`` attribute if the data must
be set independently for the data to load correctly.
Returns
-------
string
Valid Vega JSON.
"""
# TODO: support writing to separate file
return super(self.__class__, self).to_json(validate=validate,
pretty_print=pretty_print)
|
python
|
{
"resource": ""
}
|
q261
|
_assert_is_type
|
train
|
def _assert_is_type(name, value, value_type):
"""Assert that a value must be a given type."""
if not isinstance(value, value_type):
if type(value_type) is tuple:
types = ', '.join(t.__name__ for t in value_type)
raise ValueError('{0} must be one of ({1})'.format(name, types))
else:
raise ValueError('{0} must be {1}'
.format(name, value_type.__name__))
|
python
|
{
"resource": ""
}
|
q262
|
grammar
|
train
|
def grammar(grammar_type=None, grammar_name=None):
"""Decorator to define properties that map to the ``grammar``
dict. This dict is the canonical representation of the Vega grammar
within Vincent.
This decorator is intended for classes that map to some pre-defined JSON
structure, such as axes, data, marks, scales, etc. It is assumed that this
decorates functions with an instance of ``self.grammar``.
Parameters
----------
grammar_type : type or tuple of types, default None
If the argument to the decorated function is not of the given types,
then a ValueError is raised. No type checking is done if the type is
None (default).
grammar_name : string, default None
An optional name to map to the internal ``grammar`` dict. If None
(default), then the key for the dict is the name of the function
being decorated. If not None, then it will be the name specified
here. This is useful if the expected JSON field name is a Python
keyword or has an un-Pythonic name.
This should decorate a "validator" function that should return no value
but raise an exception if the provided value is not valid Vega grammar. If
the validator throws no exception, then the value is assigned to the
``grammar`` dict.
The validator function should take only one argument - the value to be
validated - so that no ``self`` argument is included; the validator
should not modify the class.
If no arguments are given, then no type-checking is done the property
will be mapped to a field with the name of the decorated function.
The doc string for the property is taken from the validator functions's
doc string.
"""
def grammar_creator(validator, name):
def setter(self, value):
if isinstance(grammar_type, (type, tuple)):
_assert_is_type(validator.__name__, value, grammar_type)
validator(value)
self.grammar[name] = value
def getter(self):
return self.grammar.get(name, None)
def deleter(self):
if name in self.grammar:
del self.grammar[name]
return property(getter, setter, deleter, validator.__doc__)
if isinstance(grammar_type, (type, tuple)):
# If grammar_type is a type, return another decorator.
def grammar_dec(validator):
# Make sure to use the grammar name if it's there.
if grammar_name:
return grammar_creator(validator, grammar_name)
else:
return grammar_creator(validator, validator.__name__)
return grammar_dec
elif isinstance(grammar_name, str_types):
# If grammar_name is a string, use that name and return another
# decorator.
def grammar_dec(validator):
return grammar_creator(validator, grammar_name)
return grammar_dec
else:
# Otherwise we assume that grammar_type is actually the function being
# decorated.
return grammar_creator(grammar_type, grammar_type.__name__)
|
python
|
{
"resource": ""
}
|
q263
|
GrammarClass.validate
|
train
|
def validate(self):
"""Validate the contents of the object.
This calls ``setattr`` for each of the class's grammar properties. It
will catch ``ValueError``s raised by the grammar property's setters
and re-raise them as :class:`ValidationError`.
"""
for key, val in self.grammar.items():
try:
setattr(self, key, val)
except ValueError as e:
raise ValidationError('invalid contents: ' + e.args[0])
|
python
|
{
"resource": ""
}
|
q264
|
GrammarClass.to_json
|
train
|
def to_json(self, path=None, html_out=False,
html_path='vega_template.html', validate=False,
pretty_print=True):
"""Convert object to JSON
Parameters
----------
path: string, default None
Path to write JSON out. If there is no path provided, JSON
will be returned as a string to the console.
html_out: boolean, default False
If True, vincent will output an simple HTML scaffold to
visualize the vega json output.
html_path: string, default 'vega_template.html'
Path for the html file (if html_out=True)
validate : boolean
If True, call the object's `validate` method before
serializing. Default is False.
pretty_print : boolean
If True (default), JSON is printed in more-readable form with
indentation and spaces.
Returns
-------
string
JSON serialization of the class's grammar properties.
"""
if validate:
self.validate()
if pretty_print:
dumps_args = {'indent': 2, 'separators': (',', ': ')}
else:
dumps_args = {}
def encoder(obj):
if hasattr(obj, 'grammar'):
return obj.grammar
if html_out:
template = Template(
str(resource_string('vincent', 'vega_template.html')))
with open(html_path, 'w') as f:
f.write(template.substitute(path=path))
if path:
with open(path, 'w') as f:
json.dump(self.grammar, f, default=encoder, sort_keys=True,
**dumps_args)
else:
return json.dumps(self.grammar, default=encoder, sort_keys=True,
**dumps_args)
|
python
|
{
"resource": ""
}
|
q265
|
useful_mimetype
|
train
|
def useful_mimetype(text):
"""Check to see if the given mime type is a MIME type
which is useful in terms of how to treat this file.
"""
if text is None:
return False
mimetype = normalize_mimetype(text)
return mimetype not in [DEFAULT, PLAIN, None]
|
python
|
{
"resource": ""
}
|
q266
|
normalize_extension
|
train
|
def normalize_extension(extension):
"""Normalise a file name extension."""
extension = decode_path(extension)
if extension is None:
return
if extension.startswith('.'):
extension = extension[1:]
if '.' in extension:
_, extension = os.path.splitext(extension)
extension = slugify(extension, sep='')
if extension is None:
return
if len(extension):
return extension
|
python
|
{
"resource": ""
}
|
q267
|
fetch
|
train
|
def fetch(url: str, **kwargs) -> Selector:
"""
Send HTTP request and parse it as a DOM tree.
Args:
url (str): The url of the site.
Returns:
Selector: allows you to select parts of HTML text using CSS or XPath expressions.
"""
kwargs.setdefault('headers', DEFAULT_HEADERS)
try:
res = requests.get(url, **kwargs)
res.raise_for_status()
except requests.RequestException as e:
print(e)
else:
html = res.text
tree = Selector(text=html)
return tree
|
python
|
{
"resource": ""
}
|
q268
|
async_fetch
|
train
|
async def async_fetch(url: str, **kwargs) -> Selector:
"""
Do the fetch in an async style.
Args:
url (str): The url of the site.
Returns:
Selector: allows you to select parts of HTML text using CSS or XPath expressions.
"""
kwargs.setdefault('headers', DEFAULT_HEADERS)
async with aiohttp.ClientSession(**kwargs) as ses:
async with ses.get(url, **kwargs) as res:
html = await res.text()
tree = Selector(text=html)
return tree
|
python
|
{
"resource": ""
}
|
q269
|
links
|
train
|
def links(res: requests.models.Response,
search: str = None,
pattern: str = None) -> list:
"""Get the links of the page.
Args:
res (requests.models.Response): The response of the page.
search (str, optional): Defaults to None. Search the links you want.
pattern (str, optional): Defaults to None. Search the links use a regex pattern.
Returns:
list: All the links of the page.
"""
hrefs = [link.to_text() for link in find_all_links(res.text)]
if search:
hrefs = [href for href in hrefs if search in href]
if pattern:
hrefs = [href for href in hrefs if re.findall(pattern, href)]
return list(set(hrefs))
|
python
|
{
"resource": ""
}
|
q270
|
save_as_json
|
train
|
def save_as_json(total: list,
name='data.json',
sort_by: str = None,
no_duplicate=False,
order='asc'):
"""Save what you crawled as a json file.
Args:
total (list): Total of data you crawled.
name (str, optional): Defaults to 'data.json'. The name of the file.
sort_by (str, optional): Defaults to None. Sort items by a specific key.
no_duplicate (bool, optional): Defaults to False. If True, it will remove duplicated data.
order (str, optional): Defaults to 'asc'. The opposite option is 'desc'.
"""
if sort_by:
reverse = order == 'desc'
total = sorted(total, key=itemgetter(sort_by), reverse=reverse)
if no_duplicate:
total = [key for key, _ in groupby(total)]
data = json.dumps(total, ensure_ascii=False)
Path(name).write_text(data, encoding='utf-8')
|
python
|
{
"resource": ""
}
|
q271
|
IlluminantMixin.set_observer
|
train
|
def set_observer(self, observer):
"""
Validates and sets the color's observer angle.
.. note:: This only changes the observer angle value. It does no conversion
of the color's coordinates.
:param str observer: One of '2' or '10'.
"""
observer = str(observer)
if observer not in color_constants.OBSERVERS:
raise InvalidObserverError(self)
self.observer = observer
|
python
|
{
"resource": ""
}
|
q272
|
IlluminantMixin.set_illuminant
|
train
|
def set_illuminant(self, illuminant):
"""
Validates and sets the color's illuminant.
.. note:: This only changes the illuminant. It does no conversion
of the color's coordinates. For this, you'll want to refer to
:py:meth:`XYZColor.apply_adaptation <colormath.color_objects.XYZColor.apply_adaptation>`.
.. tip:: Call this after setting your observer.
:param str illuminant: One of the various illuminants.
"""
illuminant = illuminant.lower()
if illuminant not in color_constants.ILLUMINANTS[self.observer]:
raise InvalidIlluminantError(illuminant)
self.illuminant = illuminant
|
python
|
{
"resource": ""
}
|
q273
|
SpectralColor.get_numpy_array
|
train
|
def get_numpy_array(self):
"""
Dump this color into NumPy array.
"""
# This holds the obect's spectral data, and will be passed to
# numpy.array() to create a numpy array (matrix) for the matrix math
# that will be done during the conversion to XYZ.
values = []
# Use the required value list to build this dynamically. Default to
# 0.0, since that ultimately won't affect the outcome due to the math
# involved.
for val in self.VALUES:
values.append(getattr(self, val, 0.0))
# Create and the actual numpy array/matrix from the spectral list.
color_array = numpy.array([values])
return color_array
|
python
|
{
"resource": ""
}
|
q274
|
XYZColor.apply_adaptation
|
train
|
def apply_adaptation(self, target_illuminant, adaptation='bradford'):
"""
This applies an adaptation matrix to change the XYZ color's illuminant.
You'll most likely only need this during RGB conversions.
"""
logger.debug(" \- Original illuminant: %s", self.illuminant)
logger.debug(" \- Target illuminant: %s", target_illuminant)
# If the XYZ values were taken with a different reference white than the
# native reference white of the target RGB space, a transformation matrix
# must be applied.
if self.illuminant != target_illuminant:
logger.debug(" \* Applying transformation from %s to %s ",
self.illuminant, target_illuminant)
# Sets the adjusted XYZ values, and the new illuminant.
apply_chromatic_adaptation_on_color(
color=self,
targ_illum=target_illuminant,
adaptation=adaptation)
|
python
|
{
"resource": ""
}
|
q275
|
BaseRGBColor._clamp_rgb_coordinate
|
train
|
def _clamp_rgb_coordinate(self, coord):
"""
Clamps an RGB coordinate, taking into account whether or not the
color is upscaled or not.
:param float coord: The coordinate value.
:rtype: float
:returns: The clamped value.
"""
if not self.is_upscaled:
return min(max(coord, 0.0), 1.0)
else:
return min(max(coord, 1), 255)
|
python
|
{
"resource": ""
}
|
q276
|
BaseRGBColor.get_upscaled_value_tuple
|
train
|
def get_upscaled_value_tuple(self):
"""
Scales an RGB color object from decimal 0.0-1.0 to int 0-255.
"""
# Scale up to 0-255 values.
rgb_r = int(math.floor(0.5 + self.rgb_r * 255))
rgb_g = int(math.floor(0.5 + self.rgb_g * 255))
rgb_b = int(math.floor(0.5 + self.rgb_b * 255))
return rgb_r, rgb_g, rgb_b
|
python
|
{
"resource": ""
}
|
q277
|
auto_density
|
train
|
def auto_density(color):
"""
Given a SpectralColor, automatically choose the correct ANSI T filter.
Returns a tuple with a string representation of the filter the
calculated density.
:param SpectralColor color: The SpectralColor object to calculate
density for.
:rtype: float
:returns: The density value, with the filter selected automatically.
"""
blue_density = ansi_density(color, ANSI_STATUS_T_BLUE)
green_density = ansi_density(color, ANSI_STATUS_T_GREEN)
red_density = ansi_density(color, ANSI_STATUS_T_RED)
densities = [blue_density, green_density, red_density]
min_density = min(densities)
max_density = max(densities)
density_range = max_density - min_density
# See comments in density_standards.py for VISUAL_DENSITY_THRESH to
# understand what this is doing.
if density_range <= VISUAL_DENSITY_THRESH:
return ansi_density(color, ISO_VISUAL)
elif blue_density > green_density and blue_density > red_density:
return blue_density
elif green_density > blue_density and green_density > red_density:
return green_density
else:
return red_density
|
python
|
{
"resource": ""
}
|
q278
|
_get_lab_color1_vector
|
train
|
def _get_lab_color1_vector(color):
"""
Converts an LabColor into a NumPy vector.
:param LabColor color:
:rtype: numpy.ndarray
"""
if not color.__class__.__name__ == 'LabColor':
raise ValueError(
"Delta E functions can only be used with two LabColor objects.")
return numpy.array([color.lab_l, color.lab_a, color.lab_b])
|
python
|
{
"resource": ""
}
|
q279
|
_get_adaptation_matrix
|
train
|
def _get_adaptation_matrix(wp_src, wp_dst, observer, adaptation):
"""
Calculate the correct transformation matrix based on origin and target
illuminants. The observer angle must be the same between illuminants.
See colormath.color_constants.ADAPTATION_MATRICES for a list of possible
adaptations.
Detailed conversion documentation is available at:
http://brucelindbloom.com/Eqn_ChromAdapt.html
"""
# Get the appropriate transformation matrix, [MsubA].
m_sharp = color_constants.ADAPTATION_MATRICES[adaptation]
# In case the white-points are still input as strings
# Get white-points for illuminant
if isinstance(wp_src, str):
orig_illum = wp_src.lower()
wp_src = color_constants.ILLUMINANTS[observer][orig_illum]
elif hasattr(wp_src, '__iter__'):
wp_src = wp_src
if isinstance(wp_dst, str):
targ_illum = wp_dst.lower()
wp_dst = color_constants.ILLUMINANTS[observer][targ_illum]
elif hasattr(wp_dst, '__iter__'):
wp_dst = wp_dst
# Sharpened cone responses ~ rho gamma beta ~ sharpened r g b
rgb_src = numpy.dot(m_sharp, wp_src)
rgb_dst = numpy.dot(m_sharp, wp_dst)
# Ratio of whitepoint sharpened responses
m_rat = numpy.diag(rgb_dst / rgb_src)
# Final transformation matrix
m_xfm = numpy.dot(numpy.dot(pinv(m_sharp), m_rat), m_sharp)
return m_xfm
|
python
|
{
"resource": ""
}
|
q280
|
apply_chromatic_adaptation
|
train
|
def apply_chromatic_adaptation(val_x, val_y, val_z, orig_illum, targ_illum,
observer='2', adaptation='bradford'):
"""
Applies a chromatic adaptation matrix to convert XYZ values between
illuminants. It is important to recognize that color transformation results
in color errors, determined by how far the original illuminant is from the
target illuminant. For example, D65 to A could result in very high maximum
deviance.
An informative article with estimate average Delta E values for each
illuminant conversion may be found at:
http://brucelindbloom.com/ChromAdaptEval.html
"""
# It's silly to have to do this, but some people may want to call this
# function directly, so we'll protect them from messing up upper/lower case.
adaptation = adaptation.lower()
# Get white-points for illuminant
if isinstance(orig_illum, str):
orig_illum = orig_illum.lower()
wp_src = color_constants.ILLUMINANTS[observer][orig_illum]
elif hasattr(orig_illum, '__iter__'):
wp_src = orig_illum
if isinstance(targ_illum, str):
targ_illum = targ_illum.lower()
wp_dst = color_constants.ILLUMINANTS[observer][targ_illum]
elif hasattr(targ_illum, '__iter__'):
wp_dst = targ_illum
logger.debug(" \* Applying adaptation matrix: %s", adaptation)
# Retrieve the appropriate transformation matrix from the constants.
transform_matrix = _get_adaptation_matrix(wp_src, wp_dst,
observer, adaptation)
# Stuff the XYZ values into a NumPy matrix for conversion.
XYZ_matrix = numpy.array((val_x, val_y, val_z))
# Perform the adaptation via matrix multiplication.
result_matrix = numpy.dot(transform_matrix, XYZ_matrix)
# Return individual X, Y, and Z coordinates.
return result_matrix[0], result_matrix[1], result_matrix[2]
|
python
|
{
"resource": ""
}
|
q281
|
apply_chromatic_adaptation_on_color
|
train
|
def apply_chromatic_adaptation_on_color(color, targ_illum, adaptation='bradford'):
"""
Convenience function to apply an adaptation directly to a Color object.
"""
xyz_x = color.xyz_x
xyz_y = color.xyz_y
xyz_z = color.xyz_z
orig_illum = color.illuminant
targ_illum = targ_illum.lower()
observer = color.observer
adaptation = adaptation.lower()
# Return individual X, Y, and Z coordinates.
color.xyz_x, color.xyz_y, color.xyz_z = apply_chromatic_adaptation(
xyz_x, xyz_y, xyz_z, orig_illum, targ_illum,
observer=observer, adaptation=adaptation)
color.set_illuminant(targ_illum)
return color
|
python
|
{
"resource": ""
}
|
q282
|
example_lab_to_xyz
|
train
|
def example_lab_to_xyz():
"""
This function shows a simple conversion of an Lab color to an XYZ color.
"""
print("=== Simple Example: Lab->XYZ ===")
# Instantiate an Lab color object with the given values.
lab = LabColor(0.903, 16.296, -2.22)
# Show a string representation.
print(lab)
# Convert to XYZ.
xyz = convert_color(lab, XYZColor)
print(xyz)
print("=== End Example ===\n")
|
python
|
{
"resource": ""
}
|
q283
|
example_lchab_to_lchuv
|
train
|
def example_lchab_to_lchuv():
"""
This function shows very complex chain of conversions in action.
LCHab to LCHuv involves four different calculations, making this the
conversion requiring the most steps.
"""
print("=== Complex Example: LCHab->LCHuv ===")
# Instantiate an LCHab color object with the given values.
lchab = LCHabColor(0.903, 16.447, 352.252)
# Show a string representation.
print(lchab)
# Convert to LCHuv.
lchuv = convert_color(lchab, LCHuvColor)
print(lchuv)
print("=== End Example ===\n")
|
python
|
{
"resource": ""
}
|
q284
|
example_lab_to_rgb
|
train
|
def example_lab_to_rgb():
"""
Conversions to RGB are a little more complex mathematically. There are also
several kinds of RGB color spaces. When converting from a device-independent
color space to RGB, sRGB is assumed unless otherwise specified with the
target_rgb keyword arg.
"""
print("=== RGB Example: Lab->RGB ===")
# Instantiate an Lab color object with the given values.
lab = LabColor(0.903, 16.296, -2.217)
# Show a string representation.
print(lab)
# Convert to XYZ.
rgb = convert_color(lab, sRGBColor)
print(rgb)
print("=== End Example ===\n")
|
python
|
{
"resource": ""
}
|
q285
|
example_rgb_to_xyz
|
train
|
def example_rgb_to_xyz():
"""
The reverse is similar.
"""
print("=== RGB Example: RGB->XYZ ===")
# Instantiate an Lab color object with the given values.
rgb = sRGBColor(120, 130, 140)
# Show a string representation.
print(rgb)
# Convert RGB to XYZ using a D50 illuminant.
xyz = convert_color(rgb, XYZColor, target_illuminant='D50')
print(xyz)
print("=== End Example ===\n")
|
python
|
{
"resource": ""
}
|
q286
|
example_spectral_to_xyz
|
train
|
def example_spectral_to_xyz():
"""
Instantiate an Lab color object with the given values. Note that the
spectral range can run from 340nm to 830nm. Any omitted values assume a
value of 0.0, which is more or less ignored. For the distribution below,
we are providing an example reading from an X-Rite i1 Pro, which only
measures between 380nm and 730nm.
"""
print("=== Example: Spectral->XYZ ===")
spc = SpectralColor(
observer='2', illuminant='d50',
spec_380nm=0.0600, spec_390nm=0.0600, spec_400nm=0.0641,
spec_410nm=0.0654, spec_420nm=0.0645, spec_430nm=0.0605,
spec_440nm=0.0562, spec_450nm=0.0543, spec_460nm=0.0537,
spec_470nm=0.0541, spec_480nm=0.0559, spec_490nm=0.0603,
spec_500nm=0.0651, spec_510nm=0.0680, spec_520nm=0.0705,
spec_530nm=0.0736, spec_540nm=0.0772, spec_550nm=0.0809,
spec_560nm=0.0870, spec_570nm=0.0990, spec_580nm=0.1128,
spec_590nm=0.1251, spec_600nm=0.1360, spec_610nm=0.1439,
spec_620nm=0.1511, spec_630nm=0.1590, spec_640nm=0.1688,
spec_650nm=0.1828, spec_660nm=0.1996, spec_670nm=0.2187,
spec_680nm=0.2397, spec_690nm=0.2618, spec_700nm=0.2852,
spec_710nm=0.2500, spec_720nm=0.2400, spec_730nm=0.2300)
xyz = convert_color(spc, XYZColor)
print(xyz)
print("=== End Example ===\n")
|
python
|
{
"resource": ""
}
|
q287
|
example_lab_to_ipt
|
train
|
def example_lab_to_ipt():
"""
This function shows a simple conversion of an XYZ color to an IPT color.
"""
print("=== Simple Example: XYZ->IPT ===")
# Instantiate an XYZ color object with the given values.
xyz = XYZColor(0.5, 0.5, 0.5, illuminant='d65')
# Show a string representation.
print(xyz)
# Convert to IPT.
ipt = convert_color(xyz, IPTColor)
print(ipt)
print("=== End Example ===\n")
|
python
|
{
"resource": ""
}
|
q288
|
apply_RGB_matrix
|
train
|
def apply_RGB_matrix(var1, var2, var3, rgb_type, convtype="xyz_to_rgb"):
"""
Applies an RGB working matrix to convert from XYZ to RGB.
The arguments are tersely named var1, var2, and var3 to allow for the
passing of XYZ _or_ RGB values. var1 is X for XYZ, and R for RGB. var2 and
var3 follow suite.
"""
convtype = convtype.lower()
# Retrieve the appropriate transformation matrix from the constants.
rgb_matrix = rgb_type.conversion_matrices[convtype]
logger.debug(" \* Applying RGB conversion matrix: %s->%s",
rgb_type.__class__.__name__, convtype)
# Stuff the RGB/XYZ values into a NumPy matrix for conversion.
var_matrix = numpy.array((
var1, var2, var3
))
# Perform the adaptation via matrix multiplication.
result_matrix = numpy.dot(rgb_matrix, var_matrix)
rgb_r, rgb_g, rgb_b = result_matrix
# Clamp these values to a valid range.
rgb_r = max(rgb_r, 0.0)
rgb_g = max(rgb_g, 0.0)
rgb_b = max(rgb_b, 0.0)
return rgb_r, rgb_g, rgb_b
|
python
|
{
"resource": ""
}
|
q289
|
color_conversion_function
|
train
|
def color_conversion_function(start_type, target_type):
"""
Decorator to indicate a function that performs a conversion from one color
space to another.
This decorator will return the original function unmodified, however it will
be registered in the _conversion_manager so it can be used to perform color
space transformations between color spaces that do not have direct
conversion functions (e.g., Luv to CMYK).
Note: For a conversion to/from RGB supply the BaseRGBColor class.
:param start_type: Starting color space type
:param target_type: Target color space type
"""
def decorator(f):
f.start_type = start_type
f.target_type = target_type
_conversion_manager.add_type_conversion(start_type, target_type, f)
return f
return decorator
|
python
|
{
"resource": ""
}
|
q290
|
Spectral_to_XYZ
|
train
|
def Spectral_to_XYZ(cobj, illuminant_override=None, *args, **kwargs):
"""
Converts spectral readings to XYZ.
"""
# If the user provides an illuminant_override numpy array, use it.
if illuminant_override:
reference_illum = illuminant_override
else:
# Otherwise, look up the illuminant from known standards based
# on the value of 'illuminant' pulled from the SpectralColor object.
try:
reference_illum = spectral_constants.REF_ILLUM_TABLE[cobj.illuminant]
except KeyError:
raise InvalidIlluminantError(cobj.illuminant)
# Get the spectral distribution of the selected standard observer.
if cobj.observer == '10':
std_obs_x = spectral_constants.STDOBSERV_X10
std_obs_y = spectral_constants.STDOBSERV_Y10
std_obs_z = spectral_constants.STDOBSERV_Z10
else:
# Assume 2 degree, since it is theoretically the only other possibility.
std_obs_x = spectral_constants.STDOBSERV_X2
std_obs_y = spectral_constants.STDOBSERV_Y2
std_obs_z = spectral_constants.STDOBSERV_Z2
# This is a NumPy array containing the spectral distribution of the color.
sample = cobj.get_numpy_array()
# The denominator is constant throughout the entire calculation for X,
# Y, and Z coordinates. Calculate it once and re-use.
denom = std_obs_y * reference_illum
# This is also a common element in the calculation whereby the sample
# NumPy array is multiplied by the reference illuminant's power distribution
# (which is also a NumPy array).
sample_by_ref_illum = sample * reference_illum
# Calculate the numerator of the equation to find X.
x_numerator = sample_by_ref_illum * std_obs_x
y_numerator = sample_by_ref_illum * std_obs_y
z_numerator = sample_by_ref_illum * std_obs_z
xyz_x = x_numerator.sum() / denom.sum()
xyz_y = y_numerator.sum() / denom.sum()
xyz_z = z_numerator.sum() / denom.sum()
return XYZColor(
xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)
|
python
|
{
"resource": ""
}
|
q291
|
Lab_to_XYZ
|
train
|
def Lab_to_XYZ(cobj, *args, **kwargs):
"""
Convert from Lab to XYZ
"""
illum = cobj.get_illuminant_xyz()
xyz_y = (cobj.lab_l + 16.0) / 116.0
xyz_x = cobj.lab_a / 500.0 + xyz_y
xyz_z = xyz_y - cobj.lab_b / 200.0
if math.pow(xyz_y, 3) > color_constants.CIE_E:
xyz_y = math.pow(xyz_y, 3)
else:
xyz_y = (xyz_y - 16.0 / 116.0) / 7.787
if math.pow(xyz_x, 3) > color_constants.CIE_E:
xyz_x = math.pow(xyz_x, 3)
else:
xyz_x = (xyz_x - 16.0 / 116.0) / 7.787
if math.pow(xyz_z, 3) > color_constants.CIE_E:
xyz_z = math.pow(xyz_z, 3)
else:
xyz_z = (xyz_z - 16.0 / 116.0) / 7.787
xyz_x = (illum["X"] * xyz_x)
xyz_y = (illum["Y"] * xyz_y)
xyz_z = (illum["Z"] * xyz_z)
return XYZColor(
xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)
|
python
|
{
"resource": ""
}
|
q292
|
Luv_to_XYZ
|
train
|
def Luv_to_XYZ(cobj, *args, **kwargs):
"""
Convert from Luv to XYZ.
"""
illum = cobj.get_illuminant_xyz()
# Without Light, there is no color. Short-circuit this and avoid some
# zero division errors in the var_a_frac calculation.
if cobj.luv_l <= 0.0:
xyz_x = 0.0
xyz_y = 0.0
xyz_z = 0.0
return XYZColor(
xyz_x, xyz_y, xyz_z,
observer=cobj.observer, illuminant=cobj.illuminant)
# Various variables used throughout the conversion.
cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E
u_sub_0 = (4.0 * illum["X"]) / (illum["X"] + 15.0 * illum["Y"] + 3.0 * illum["Z"])
v_sub_0 = (9.0 * illum["Y"]) / (illum["X"] + 15.0 * illum["Y"] + 3.0 * illum["Z"])
var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0
var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0
# Y-coordinate calculations.
if cobj.luv_l > cie_k_times_e:
xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)
else:
xyz_y = cobj.luv_l / color_constants.CIE_K
# X-coordinate calculation.
xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)
# Z-coordinate calculation.
xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)
return XYZColor(
xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)
|
python
|
{
"resource": ""
}
|
q293
|
xyY_to_XYZ
|
train
|
def xyY_to_XYZ(cobj, *args, **kwargs):
"""
Convert from xyY to XYZ.
"""
# avoid division by zero
if cobj.xyy_y == 0.0:
xyz_x = 0.0
xyz_y = 0.0
xyz_z = 0.0
else:
xyz_x = (cobj.xyy_x * cobj.xyy_Y) / cobj.xyy_y
xyz_y = cobj.xyy_Y
xyz_z = ((1.0 - cobj.xyy_x - cobj.xyy_y) * xyz_y) / cobj.xyy_y
return XYZColor(
xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)
|
python
|
{
"resource": ""
}
|
q294
|
XYZ_to_xyY
|
train
|
def XYZ_to_xyY(cobj, *args, **kwargs):
"""
Convert from XYZ to xyY.
"""
xyz_sum = cobj.xyz_x + cobj.xyz_y + cobj.xyz_z
# avoid division by zero
if xyz_sum == 0.0:
xyy_x = 0.0
xyy_y = 0.0
else:
xyy_x = cobj.xyz_x / xyz_sum
xyy_y = cobj.xyz_y / xyz_sum
xyy_Y = cobj.xyz_y
return xyYColor(
xyy_x, xyy_y, xyy_Y, observer=cobj.observer, illuminant=cobj.illuminant)
|
python
|
{
"resource": ""
}
|
q295
|
XYZ_to_Luv
|
train
|
def XYZ_to_Luv(cobj, *args, **kwargs):
"""
Convert from XYZ to Luv
"""
temp_x = cobj.xyz_x
temp_y = cobj.xyz_y
temp_z = cobj.xyz_z
denom = temp_x + (15.0 * temp_y) + (3.0 * temp_z)
# avoid division by zero
if denom == 0.0:
luv_u = 0.0
luv_v = 0.0
else:
luv_u = (4.0 * temp_x) / denom
luv_v = (9.0 * temp_y) / denom
illum = cobj.get_illuminant_xyz()
temp_y = temp_y / illum["Y"]
if temp_y > color_constants.CIE_E:
temp_y = math.pow(temp_y, (1.0 / 3.0))
else:
temp_y = (7.787 * temp_y) + (16.0 / 116.0)
ref_U = (4.0 * illum["X"]) / (illum["X"] + (15.0 * illum["Y"]) + (3.0 * illum["Z"]))
ref_V = (9.0 * illum["Y"]) / (illum["X"] + (15.0 * illum["Y"]) + (3.0 * illum["Z"]))
luv_l = (116.0 * temp_y) - 16.0
luv_u = 13.0 * luv_l * (luv_u - ref_U)
luv_v = 13.0 * luv_l * (luv_v - ref_V)
return LuvColor(
luv_l, luv_u, luv_v, observer=cobj.observer, illuminant=cobj.illuminant)
|
python
|
{
"resource": ""
}
|
q296
|
XYZ_to_Lab
|
train
|
def XYZ_to_Lab(cobj, *args, **kwargs):
"""
Converts XYZ to Lab.
"""
illum = cobj.get_illuminant_xyz()
temp_x = cobj.xyz_x / illum["X"]
temp_y = cobj.xyz_y / illum["Y"]
temp_z = cobj.xyz_z / illum["Z"]
if temp_x > color_constants.CIE_E:
temp_x = math.pow(temp_x, (1.0 / 3.0))
else:
temp_x = (7.787 * temp_x) + (16.0 / 116.0)
if temp_y > color_constants.CIE_E:
temp_y = math.pow(temp_y, (1.0 / 3.0))
else:
temp_y = (7.787 * temp_y) + (16.0 / 116.0)
if temp_z > color_constants.CIE_E:
temp_z = math.pow(temp_z, (1.0 / 3.0))
else:
temp_z = (7.787 * temp_z) + (16.0 / 116.0)
lab_l = (116.0 * temp_y) - 16.0
lab_a = 500.0 * (temp_x - temp_y)
lab_b = 200.0 * (temp_y - temp_z)
return LabColor(
lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)
|
python
|
{
"resource": ""
}
|
q297
|
XYZ_to_RGB
|
train
|
def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):
"""
XYZ to RGB conversion.
"""
temp_X = cobj.xyz_x
temp_Y = cobj.xyz_y
temp_Z = cobj.xyz_z
logger.debug(" \- Target RGB space: %s", target_rgb)
target_illum = target_rgb.native_illuminant
logger.debug(" \- Target native illuminant: %s", target_illum)
logger.debug(" \- XYZ color's illuminant: %s", cobj.illuminant)
# If the XYZ values were taken with a different reference white than the
# native reference white of the target RGB space, a transformation matrix
# must be applied.
if cobj.illuminant != target_illum:
logger.debug(" \* Applying transformation from %s to %s ",
cobj.illuminant, target_illum)
# Get the adjusted XYZ values, adapted for the target illuminant.
temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(
temp_X, temp_Y, temp_Z,
orig_illum=cobj.illuminant, targ_illum=target_illum)
logger.debug(" \* New values: %.3f, %.3f, %.3f",
temp_X, temp_Y, temp_Z)
# Apply an RGB working space matrix to the XYZ values (matrix mul).
rgb_r, rgb_g, rgb_b = apply_RGB_matrix(
temp_X, temp_Y, temp_Z,
rgb_type=target_rgb, convtype="xyz_to_rgb")
# v
linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)
# V
nonlinear_channels = {}
if target_rgb == sRGBColor:
for channel in ['r', 'g', 'b']:
v = linear_channels[channel]
if v <= 0.0031308:
nonlinear_channels[channel] = v * 12.92
else:
nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055
elif target_rgb == BT2020Color:
if kwargs.get('is_12_bits_system'):
a, b = 1.0993, 0.0181
else:
a, b = 1.099, 0.018
for channel in ['r', 'g', 'b']:
v = linear_channels[channel]
if v < b:
nonlinear_channels[channel] = v * 4.5
else:
nonlinear_channels[channel] = a * math.pow(v, 0.45) - (a - 1)
else:
# If it's not sRGB...
for channel in ['r', 'g', 'b']:
v = linear_channels[channel]
nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)
return target_rgb(
nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])
|
python
|
{
"resource": ""
}
|
q298
|
RGB_to_XYZ
|
train
|
def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):
"""
RGB to XYZ conversion. Expects 0-255 RGB values.
Based off of: http://www.brucelindbloom.com/index.html?Eqn_RGB_to_XYZ.html
"""
# Will contain linearized RGB channels (removed the gamma func).
linear_channels = {}
if isinstance(cobj, sRGBColor):
for channel in ['r', 'g', 'b']:
V = getattr(cobj, 'rgb_' + channel)
if V <= 0.04045:
linear_channels[channel] = V / 12.92
else:
linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)
elif isinstance(cobj, BT2020Color):
if kwargs.get('is_12_bits_system'):
a, b, c = 1.0993, 0.0181, 0.081697877417347
else:
a, b, c = 1.099, 0.018, 0.08124794403514049
for channel in ['r', 'g', 'b']:
V = getattr(cobj, 'rgb_' + channel)
if V <= c:
linear_channels[channel] = V / 4.5
else:
linear_channels[channel] = math.pow((V + (a - 1)) / a, 1 / 0.45)
else:
# If it's not sRGB...
gamma = cobj.rgb_gamma
for channel in ['r', 'g', 'b']:
V = getattr(cobj, 'rgb_' + channel)
linear_channels[channel] = math.pow(V, gamma)
# Apply an RGB working space matrix to the XYZ values (matrix mul).
xyz_x, xyz_y, xyz_z = apply_RGB_matrix(
linear_channels['r'], linear_channels['g'], linear_channels['b'],
rgb_type=cobj, convtype="rgb_to_xyz")
if target_illuminant is None:
target_illuminant = cobj.native_illuminant
# The illuminant of the original RGB object. This will always match
# the RGB colorspace's native illuminant.
illuminant = cobj.native_illuminant
xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)
# This will take care of any illuminant changes for us (if source
# illuminant != target illuminant).
xyzcolor.apply_adaptation(target_illuminant)
return xyzcolor
|
python
|
{
"resource": ""
}
|
q299
|
RGB_to_HSV
|
train
|
def RGB_to_HSV(cobj, *args, **kwargs):
"""
Converts from RGB to HSV.
H values are in degrees and are 0 to 360.
S values are a percentage, 0.0 to 1.0.
V values are a percentage, 0.0 to 1.0.
"""
var_R = cobj.rgb_r
var_G = cobj.rgb_g
var_B = cobj.rgb_b
var_max = max(var_R, var_G, var_B)
var_min = min(var_R, var_G, var_B)
var_H = __RGB_to_Hue(var_R, var_G, var_B, var_min, var_max)
if var_max == 0:
var_S = 0
else:
var_S = 1.0 - (var_min / var_max)
var_V = var_max
return HSVColor(
var_H, var_S, var_V)
|
python
|
{
"resource": ""
}
|
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