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26,500 | tensorflow/lucid | lucid/misc/io/serialize_array.py | _normalize_array | def _normalize_array(array, domain=(0, 1)):
"""Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults to (0, 1), if explicitly set to None will use the array's
own range of values and normalize them.
Returns:
normalized PIL.Image
"""
# first copy the input so we're never mutating the user's data
array = np.array(array)
# squeeze helps both with batch=1 and B/W and PIL's mode inference
array = np.squeeze(array)
assert len(array.shape) <= 3
assert np.issubdtype(array.dtype, np.number)
assert not np.isnan(array).any()
low, high = np.min(array), np.max(array)
if domain is None:
message = "No domain specified, normalizing from measured (~%.2f, ~%.2f)"
log.debug(message, low, high)
domain = (low, high)
# clip values if domain was specified and array contains values outside of it
if low < domain[0] or high > domain[1]:
message = "Clipping domain from (~{:.2f}, ~{:.2f}) to (~{:.2f}, ~{:.2f})."
log.info(message.format(low, high, domain[0], domain[1]))
array = array.clip(*domain)
min_value, max_value = np.iinfo(np.uint8).min, np.iinfo(np.uint8).max # 0, 255
# convert signed to unsigned if needed
if np.issubdtype(array.dtype, np.inexact):
offset = domain[0]
if offset != 0:
array -= offset
log.debug("Converting inexact array by subtracting -%.2f.", offset)
scalar = max_value / (domain[1] - domain[0])
if scalar != 1:
array *= scalar
log.debug("Converting inexact array by scaling by %.2f.", scalar)
return array.clip(min_value, max_value).astype(np.uint8) | python | def _normalize_array(array, domain=(0, 1)):
"""Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults to (0, 1), if explicitly set to None will use the array's
own range of values and normalize them.
Returns:
normalized PIL.Image
"""
# first copy the input so we're never mutating the user's data
array = np.array(array)
# squeeze helps both with batch=1 and B/W and PIL's mode inference
array = np.squeeze(array)
assert len(array.shape) <= 3
assert np.issubdtype(array.dtype, np.number)
assert not np.isnan(array).any()
low, high = np.min(array), np.max(array)
if domain is None:
message = "No domain specified, normalizing from measured (~%.2f, ~%.2f)"
log.debug(message, low, high)
domain = (low, high)
# clip values if domain was specified and array contains values outside of it
if low < domain[0] or high > domain[1]:
message = "Clipping domain from (~{:.2f}, ~{:.2f}) to (~{:.2f}, ~{:.2f})."
log.info(message.format(low, high, domain[0], domain[1]))
array = array.clip(*domain)
min_value, max_value = np.iinfo(np.uint8).min, np.iinfo(np.uint8).max # 0, 255
# convert signed to unsigned if needed
if np.issubdtype(array.dtype, np.inexact):
offset = domain[0]
if offset != 0:
array -= offset
log.debug("Converting inexact array by subtracting -%.2f.", offset)
scalar = max_value / (domain[1] - domain[0])
if scalar != 1:
array *= scalar
log.debug("Converting inexact array by scaling by %.2f.", scalar)
return array.clip(min_value, max_value).astype(np.uint8) | [
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domain: expected range of values in array,
defaults to (0, 1), if explicitly set to None will use the array's
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26,501 | tensorflow/lucid | lucid/misc/io/serialize_array.py | _serialize_normalized_array | def _serialize_normalized_array(array, fmt='png', quality=70):
"""Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
dtype = array.dtype
assert np.issubdtype(dtype, np.unsignedinteger)
assert np.max(array) <= np.iinfo(dtype).max
assert array.shape[-1] > 1 # array dims must have been squeezed
image = PIL.Image.fromarray(array)
image_bytes = BytesIO()
image.save(image_bytes, fmt, quality=quality)
# TODO: Python 3 could save a copy here by using `getbuffer()` instead.
image_data = image_bytes.getvalue()
return image_data | python | def _serialize_normalized_array(array, fmt='png', quality=70):
"""Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
dtype = array.dtype
assert np.issubdtype(dtype, np.unsignedinteger)
assert np.max(array) <= np.iinfo(dtype).max
assert array.shape[-1] > 1 # array dims must have been squeezed
image = PIL.Image.fromarray(array)
image_bytes = BytesIO()
image.save(image_bytes, fmt, quality=quality)
# TODO: Python 3 could save a copy here by using `getbuffer()` instead.
image_data = image_bytes.getvalue()
return image_data | [
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26,502 | tensorflow/lucid | lucid/misc/io/serialize_array.py | serialize_array | def serialize_array(array, domain=(0, 1), fmt='png', quality=70):
"""Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
normalized = _normalize_array(array, domain=domain)
return _serialize_normalized_array(normalized, fmt=fmt, quality=quality) | python | def serialize_array(array, domain=(0, 1), fmt='png', quality=70):
"""Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
normalized = _normalize_array(array, domain=domain)
return _serialize_normalized_array(normalized, fmt=fmt, quality=quality) | [
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26,503 | tensorflow/lucid | lucid/misc/channel_reducer.py | ChannelReducer._apply_flat | def _apply_flat(cls, f, acts):
"""Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged.
"""
orig_shape = acts.shape
acts_flat = acts.reshape([-1, acts.shape[-1]])
new_flat = f(acts_flat)
if not isinstance(new_flat, np.ndarray):
return new_flat
shape = list(orig_shape[:-1]) + [-1]
return new_flat.reshape(shape) | python | def _apply_flat(cls, f, acts):
"""Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged.
"""
orig_shape = acts.shape
acts_flat = acts.reshape([-1, acts.shape[-1]])
new_flat = f(acts_flat)
if not isinstance(new_flat, np.ndarray):
return new_flat
shape = list(orig_shape[:-1]) + [-1]
return new_flat.reshape(shape) | [
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26,504 | tensorflow/lucid | lucid/misc/io/showing.py | _image_url | def _image_url(array, fmt='png', mode="data", quality=90, domain=None):
"""Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image
"""
supported_modes = ("data")
if mode not in supported_modes:
message = "Unsupported mode '%s', should be one of '%s'."
raise ValueError(message, mode, supported_modes)
image_data = serialize_array(array, fmt=fmt, quality=quality)
base64_byte_string = base64.b64encode(image_data).decode('ascii')
return "data:image/" + fmt.upper() + ";base64," + base64_byte_string | python | def _image_url(array, fmt='png', mode="data", quality=90, domain=None):
"""Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image
"""
supported_modes = ("data")
if mode not in supported_modes:
message = "Unsupported mode '%s', should be one of '%s'."
raise ValueError(message, mode, supported_modes)
image_data = serialize_array(array, fmt=fmt, quality=quality)
base64_byte_string = base64.b64encode(image_data).decode('ascii')
return "data:image/" + fmt.upper() + ";base64," + base64_byte_string | [
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26,505 | tensorflow/lucid | lucid/misc/io/showing.py | image | def image(array, domain=None, width=None, format='png', **kwargs):
"""Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None
"""
image_data = serialize_array(array, fmt=format, domain=domain)
image = IPython.display.Image(data=image_data, format=format, width=width)
IPython.display.display(image) | python | def image(array, domain=None, width=None, format='png', **kwargs):
"""Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None
"""
image_data = serialize_array(array, fmt=format, domain=domain)
image = IPython.display.Image(data=image_data, format=format, width=width)
IPython.display.display(image) | [
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26,506 | tensorflow/lucid | lucid/misc/io/showing.py | show | def show(thing, domain=(0, 1), **kwargs):
"""Display a nupmy array without having to specify what it represents.
This module will attempt to infer how to display your tensor based on its
rank, shape and dtype. rank 4 tensors will be displayed as image grids, rank
2 and 3 tensors as images.
"""
if isinstance(thing, np.ndarray):
rank = len(thing.shape)
if rank == 4:
log.debug("Show is assuming rank 4 tensor to be a list of images.")
images(thing, domain=domain, **kwargs)
elif rank in (2, 3):
log.debug("Show is assuming rank 2 or 3 tensor to be an image.")
image(thing, domain=domain, **kwargs)
else:
log.warning("Show only supports numpy arrays of rank 2-4. Using repr().")
print(repr(thing))
elif isinstance(thing, (list, tuple)):
log.debug("Show is assuming list or tuple to be a collection of images.")
images(thing, domain=domain, **kwargs)
else:
log.warning("Show only supports numpy arrays so far. Using repr().")
print(repr(thing)) | python | def show(thing, domain=(0, 1), **kwargs):
"""Display a nupmy array without having to specify what it represents.
This module will attempt to infer how to display your tensor based on its
rank, shape and dtype. rank 4 tensors will be displayed as image grids, rank
2 and 3 tensors as images.
"""
if isinstance(thing, np.ndarray):
rank = len(thing.shape)
if rank == 4:
log.debug("Show is assuming rank 4 tensor to be a list of images.")
images(thing, domain=domain, **kwargs)
elif rank in (2, 3):
log.debug("Show is assuming rank 2 or 3 tensor to be an image.")
image(thing, domain=domain, **kwargs)
else:
log.warning("Show only supports numpy arrays of rank 2-4. Using repr().")
print(repr(thing))
elif isinstance(thing, (list, tuple)):
log.debug("Show is assuming list or tuple to be a collection of images.")
images(thing, domain=domain, **kwargs)
else:
log.warning("Show only supports numpy arrays so far. Using repr().")
print(repr(thing)) | [
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26,507 | tensorflow/lucid | lucid/misc/io/showing.py | _strip_consts | def _strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def.
This is mostly a utility function for graph(), and also originates here:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
"""
strip_def = tf.GraphDef()
for n0 in graph_def.node:
n = strip_def.node.add()
n.MergeFrom(n0)
if n.op == 'Const':
tensor = n.attr['value'].tensor
size = len(tensor.tensor_content)
if size > max_const_size:
tensor.tensor_content = tf.compat.as_bytes("<stripped %d bytes>"%size)
return strip_def | python | def _strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def.
This is mostly a utility function for graph(), and also originates here:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
"""
strip_def = tf.GraphDef()
for n0 in graph_def.node:
n = strip_def.node.add()
n.MergeFrom(n0)
if n.op == 'Const':
tensor = n.attr['value'].tensor
size = len(tensor.tensor_content)
if size > max_const_size:
tensor.tensor_content = tf.compat.as_bytes("<stripped %d bytes>"%size)
return strip_def | [
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26,508 | tensorflow/lucid | lucid/misc/ndimage_utils.py | composite | def composite(
background_image,
foreground_image,
foreground_width_ratio=0.25,
foreground_position=(0.0, 0.0),
):
"""Takes two images and composites them."""
if foreground_width_ratio <= 0:
return background_image
composite = background_image.copy()
width = int(foreground_width_ratio * background_image.shape[1])
foreground_resized = resize(foreground_image, width)
size = foreground_resized.shape
x = int(foreground_position[1] * (background_image.shape[1] - size[1]))
y = int(foreground_position[0] * (background_image.shape[0] - size[0]))
# TODO: warn if resulting coordinates are out of bounds?
composite[y : y + size[0], x : x + size[1]] = foreground_resized
return composite | python | def composite(
background_image,
foreground_image,
foreground_width_ratio=0.25,
foreground_position=(0.0, 0.0),
):
"""Takes two images and composites them."""
if foreground_width_ratio <= 0:
return background_image
composite = background_image.copy()
width = int(foreground_width_ratio * background_image.shape[1])
foreground_resized = resize(foreground_image, width)
size = foreground_resized.shape
x = int(foreground_position[1] * (background_image.shape[1] - size[1]))
y = int(foreground_position[0] * (background_image.shape[0] - size[0]))
# TODO: warn if resulting coordinates are out of bounds?
composite[y : y + size[0], x : x + size[1]] = foreground_resized
return composite | [
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26,509 | tensorflow/lucid | lucid/optvis/param/lowres.py | lowres_tensor | def lowres_tensor(shape, underlying_shape, offset=None, sd=None):
"""Produces a tensor paramaterized by a interpolated lower resolution tensor.
This is like what is done in a laplacian pyramid, but a bit more general. It
can be a powerful way to describe images.
Args:
shape: desired shape of resulting tensor
underlying_shape: shape of the tensor being resized into final tensor
offset: Describes how to offset the interpolated vector (like phase in a
Fourier transform). If None, apply no offset. If a scalar, apply the same
offset to each dimension; if a list use each entry for each dimension.
If a int, offset by that much. If False, do not offset. If True, offset by
half the ratio between shape and underlying shape (analagous to 90
degrees).
sd: Standard deviation of initial tensor variable.
Returns:
A tensor paramaterized by a lower resolution tensorflow variable.
"""
sd = sd or 0.01
init_val = sd * np.random.randn(*underlying_shape).astype("float32")
underlying_t = tf.Variable(init_val)
t = resize_bilinear_nd(underlying_t, shape)
if offset is not None:
# Deal with non-list offset
if not isinstance(offset, list):
offset = len(shape) * [offset]
# Deal with the non-int offset entries
for n in range(len(offset)):
if offset[n] is True:
offset[n] = shape[n] / underlying_shape[n] / 2
if offset[n] is False:
offset[n] = 0
offset[n] = int(offset[n])
# Actually apply offset by padding and then croping off the excess.
padding = [(pad, 0) for pad in offset]
t = tf.pad(t, padding, "SYMMETRIC")
begin = len(shape) * [0]
t = tf.slice(t, begin, shape)
return t | python | def lowres_tensor(shape, underlying_shape, offset=None, sd=None):
"""Produces a tensor paramaterized by a interpolated lower resolution tensor.
This is like what is done in a laplacian pyramid, but a bit more general. It
can be a powerful way to describe images.
Args:
shape: desired shape of resulting tensor
underlying_shape: shape of the tensor being resized into final tensor
offset: Describes how to offset the interpolated vector (like phase in a
Fourier transform). If None, apply no offset. If a scalar, apply the same
offset to each dimension; if a list use each entry for each dimension.
If a int, offset by that much. If False, do not offset. If True, offset by
half the ratio between shape and underlying shape (analagous to 90
degrees).
sd: Standard deviation of initial tensor variable.
Returns:
A tensor paramaterized by a lower resolution tensorflow variable.
"""
sd = sd or 0.01
init_val = sd * np.random.randn(*underlying_shape).astype("float32")
underlying_t = tf.Variable(init_val)
t = resize_bilinear_nd(underlying_t, shape)
if offset is not None:
# Deal with non-list offset
if not isinstance(offset, list):
offset = len(shape) * [offset]
# Deal with the non-int offset entries
for n in range(len(offset)):
if offset[n] is True:
offset[n] = shape[n] / underlying_shape[n] / 2
if offset[n] is False:
offset[n] = 0
offset[n] = int(offset[n])
# Actually apply offset by padding and then croping off the excess.
padding = [(pad, 0) for pad in offset]
t = tf.pad(t, padding, "SYMMETRIC")
begin = len(shape) * [0]
t = tf.slice(t, begin, shape)
return t | [
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shape: desired shape of resulting tensor
underlying_shape: shape of the tensor being resized into final tensor
offset: Describes how to offset the interpolated vector (like phase in a
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26,510 | tensorflow/lucid | lucid/misc/tfutil.py | create_session | def create_session(target='', timeout_sec=10):
'''Create an intractive TensorFlow session.
Helper function that creates TF session that uses growing GPU memory
allocation and opration timeout. 'allow_growth' flag prevents TF
from allocating the whole GPU memory an once, which is useful
when having multiple python sessions sharing the same GPU.
'''
graph = tf.Graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.operation_timeout_in_ms = int(timeout_sec*1000)
return tf.InteractiveSession(target=target, graph=graph, config=config) | python | def create_session(target='', timeout_sec=10):
'''Create an intractive TensorFlow session.
Helper function that creates TF session that uses growing GPU memory
allocation and opration timeout. 'allow_growth' flag prevents TF
from allocating the whole GPU memory an once, which is useful
when having multiple python sessions sharing the same GPU.
'''
graph = tf.Graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.operation_timeout_in_ms = int(timeout_sec*1000)
return tf.InteractiveSession(target=target, graph=graph, config=config) | [
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26,511 | tensorflow/lucid | lucid/misc/io/reading.py | read | def read(url, encoding=None, cache=None, mode="rb"):
"""Read from any URL.
Internally differentiates between URLs supported by tf.gfile, such as URLs
with the Google Cloud Storage scheme ('gs://...') or local paths, and HTTP
URLs. This way users don't need to know about the underlying fetch mechanism.
Args:
url: a URL including scheme or a local path
mode: mode in which to open the file. defaults to binary ('rb')
encoding: if specified, encoding that should be used to decode read data
if mode is specified to be text ('r'), this defaults to 'utf-8'.
cache: whether to attempt caching the resource. Defaults to True only if
the given URL specifies a remote resource.
Returns:
All bytes form the specified resource, or a decoded string of those.
"""
with read_handle(url, cache, mode=mode) as handle:
data = handle.read()
if encoding:
data = data.decode(encoding)
return data | python | def read(url, encoding=None, cache=None, mode="rb"):
"""Read from any URL.
Internally differentiates between URLs supported by tf.gfile, such as URLs
with the Google Cloud Storage scheme ('gs://...') or local paths, and HTTP
URLs. This way users don't need to know about the underlying fetch mechanism.
Args:
url: a URL including scheme or a local path
mode: mode in which to open the file. defaults to binary ('rb')
encoding: if specified, encoding that should be used to decode read data
if mode is specified to be text ('r'), this defaults to 'utf-8'.
cache: whether to attempt caching the resource. Defaults to True only if
the given URL specifies a remote resource.
Returns:
All bytes form the specified resource, or a decoded string of those.
"""
with read_handle(url, cache, mode=mode) as handle:
data = handle.read()
if encoding:
data = data.decode(encoding)
return data | [
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26,512 | tensorflow/lucid | lucid/misc/io/reading.py | read_handle | def read_handle(url, cache=None, mode="rb"):
"""Read from any URL with a file handle.
Use this to get a handle to a file rather than eagerly load the data:
```
with read_handle(url) as handle:
result = something.load(handle)
result.do_something()
```
When program execution leaves this `with` block, the handle will be closed
automatically.
Args:
url: a URL including scheme or a local path
Returns:
A file handle to the specified resource if it could be reached.
The handle will be closed automatically once execution leaves this context.
"""
scheme = urlparse(url).scheme
if cache == 'purge':
_purge_cached(url)
cache = None
if _is_remote(scheme) and cache is None:
cache = True
log.debug("Cache not specified, enabling because resource is remote.")
if cache:
handle = _read_and_cache(url, mode=mode)
else:
if scheme in ("http", "https"):
handle = _handle_web_url(url, mode=mode)
elif scheme in ("gs"):
handle = _handle_gfile(url, mode=mode)
else:
handle = open(url, mode=mode)
yield handle
handle.close() | python | def read_handle(url, cache=None, mode="rb"):
"""Read from any URL with a file handle.
Use this to get a handle to a file rather than eagerly load the data:
```
with read_handle(url) as handle:
result = something.load(handle)
result.do_something()
```
When program execution leaves this `with` block, the handle will be closed
automatically.
Args:
url: a URL including scheme or a local path
Returns:
A file handle to the specified resource if it could be reached.
The handle will be closed automatically once execution leaves this context.
"""
scheme = urlparse(url).scheme
if cache == 'purge':
_purge_cached(url)
cache = None
if _is_remote(scheme) and cache is None:
cache = True
log.debug("Cache not specified, enabling because resource is remote.")
if cache:
handle = _read_and_cache(url, mode=mode)
else:
if scheme in ("http", "https"):
handle = _handle_web_url(url, mode=mode)
elif scheme in ("gs"):
handle = _handle_gfile(url, mode=mode)
else:
handle = open(url, mode=mode)
yield handle
handle.close() | [
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26,513 | tensorflow/lucid | lucid/misc/io/reading.py | local_cache_path | def local_cache_path(remote_url):
"""Returns the path that remote_url would be cached at locally."""
local_name = RESERVED_PATH_CHARS.sub("_", remote_url)
return os.path.join(gettempdir(), local_name) | python | def local_cache_path(remote_url):
"""Returns the path that remote_url would be cached at locally."""
local_name = RESERVED_PATH_CHARS.sub("_", remote_url)
return os.path.join(gettempdir(), local_name) | [
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26,514 | tensorflow/lucid | lucid/optvis/param/cppn.py | cppn | def cppn(
width,
batch=1,
num_output_channels=3,
num_hidden_channels=24,
num_layers=8,
activation_func=_composite_activation,
normalize=False,
):
"""Compositional Pattern Producing Network
Args:
width: width of resulting image, equals height
batch: batch dimension of output, note that all params share the same weights!
num_output_channels:
num_hidden_channels:
num_layers:
activation_func:
normalize:
Returns:
The collapsed shape, represented as a list.
"""
r = 3.0 ** 0.5 # std(coord_range) == 1.0
coord_range = tf.linspace(-r, r, width)
y, x = tf.meshgrid(coord_range, coord_range, indexing="ij")
net = tf.stack([tf.stack([x, y], -1)] * batch, 0)
with slim.arg_scope(
[slim.conv2d],
kernel_size=[1, 1],
activation_fn=None,
weights_initializer=tf.initializers.variance_scaling(),
biases_initializer=tf.initializers.random_normal(0.0, 0.1),
):
for i in range(num_layers):
x = slim.conv2d(net, num_hidden_channels)
if normalize:
x = slim.instance_norm(x)
net = activation_func(x)
rgb = slim.conv2d(
net,
num_output_channels,
activation_fn=tf.nn.sigmoid,
weights_initializer=tf.zeros_initializer(),
)
return rgb | python | def cppn(
width,
batch=1,
num_output_channels=3,
num_hidden_channels=24,
num_layers=8,
activation_func=_composite_activation,
normalize=False,
):
"""Compositional Pattern Producing Network
Args:
width: width of resulting image, equals height
batch: batch dimension of output, note that all params share the same weights!
num_output_channels:
num_hidden_channels:
num_layers:
activation_func:
normalize:
Returns:
The collapsed shape, represented as a list.
"""
r = 3.0 ** 0.5 # std(coord_range) == 1.0
coord_range = tf.linspace(-r, r, width)
y, x = tf.meshgrid(coord_range, coord_range, indexing="ij")
net = tf.stack([tf.stack([x, y], -1)] * batch, 0)
with slim.arg_scope(
[slim.conv2d],
kernel_size=[1, 1],
activation_fn=None,
weights_initializer=tf.initializers.variance_scaling(),
biases_initializer=tf.initializers.random_normal(0.0, 0.1),
):
for i in range(num_layers):
x = slim.conv2d(net, num_hidden_channels)
if normalize:
x = slim.instance_norm(x)
net = activation_func(x)
rgb = slim.conv2d(
net,
num_output_channels,
activation_fn=tf.nn.sigmoid,
weights_initializer=tf.zeros_initializer(),
)
return rgb | [
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Args:
width: width of resulting image, equals height
batch: batch dimension of output, note that all params share the same weights!
num_output_channels:
num_hidden_channels:
num_layers:
activation_func:
normalize:
Returns:
The collapsed shape, represented as a list. | [
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26,515 | tensorflow/lucid | lucid/recipes/activation_atlas/main.py | activation_atlas | def activation_atlas(
model,
layer,
grid_size=10,
icon_size=96,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders an Activation Atlas of the given model's layer."""
activations = layer.activations[:number_activations, ...]
layout, = aligned_umap(activations, verbose=verbose)
directions, coordinates, _ = bin_laid_out_activations(
layout, activations, grid_size
)
icons = []
for directions_batch in chunked(directions, icon_batch_size):
icon_batch, losses = render_icons(
directions_batch, model, layer=layer.name, size=icon_size, num_attempts=1
)
icons += icon_batch
canvas = make_canvas(icons, coordinates, grid_size)
return canvas | python | def activation_atlas(
model,
layer,
grid_size=10,
icon_size=96,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders an Activation Atlas of the given model's layer."""
activations = layer.activations[:number_activations, ...]
layout, = aligned_umap(activations, verbose=verbose)
directions, coordinates, _ = bin_laid_out_activations(
layout, activations, grid_size
)
icons = []
for directions_batch in chunked(directions, icon_batch_size):
icon_batch, losses = render_icons(
directions_batch, model, layer=layer.name, size=icon_size, num_attempts=1
)
icons += icon_batch
canvas = make_canvas(icons, coordinates, grid_size)
return canvas | [
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26,516 | tensorflow/lucid | lucid/recipes/activation_atlas/main.py | aligned_activation_atlas | def aligned_activation_atlas(
model1,
layer1,
model2,
layer2,
grid_size=10,
icon_size=80,
num_steps=1024,
whiten_layers=True,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders two aligned Activation Atlases of the given models' layers.
Returns a generator of the two atlasses, and a nested generator for intermediate
atlasses while they're being rendered.
"""
combined_activations = _combine_activations(
layer1, layer2, number_activations=number_activations
)
layouts = aligned_umap(combined_activations, verbose=verbose)
for model, layer, layout in zip((model1, model2), (layer1, layer2), layouts):
directions, coordinates, densities = bin_laid_out_activations(
layout, layer.activations[:number_activations, ...], grid_size, threshold=10
)
def _progressive_canvas_iterator():
icons = []
for directions_batch in chunked(directions, icon_batch_size):
icon_batch, losses = render_icons(
directions_batch,
model,
alpha=False,
layer=layer.name,
size=icon_size,
n_steps=num_steps,
S=layer_inverse_covariance(layer) if whiten_layers else None,
)
icons += icon_batch
yield make_canvas(icons, coordinates, grid_size)
yield _progressive_canvas_iterator() | python | def aligned_activation_atlas(
model1,
layer1,
model2,
layer2,
grid_size=10,
icon_size=80,
num_steps=1024,
whiten_layers=True,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders two aligned Activation Atlases of the given models' layers.
Returns a generator of the two atlasses, and a nested generator for intermediate
atlasses while they're being rendered.
"""
combined_activations = _combine_activations(
layer1, layer2, number_activations=number_activations
)
layouts = aligned_umap(combined_activations, verbose=verbose)
for model, layer, layout in zip((model1, model2), (layer1, layer2), layouts):
directions, coordinates, densities = bin_laid_out_activations(
layout, layer.activations[:number_activations, ...], grid_size, threshold=10
)
def _progressive_canvas_iterator():
icons = []
for directions_batch in chunked(directions, icon_batch_size):
icon_batch, losses = render_icons(
directions_batch,
model,
alpha=False,
layer=layer.name,
size=icon_size,
n_steps=num_steps,
S=layer_inverse_covariance(layer) if whiten_layers else None,
)
icons += icon_batch
yield make_canvas(icons, coordinates, grid_size)
yield _progressive_canvas_iterator() | [
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26,517 | tensorflow/lucid | lucid/recipes/activation_atlas/main.py | _combine_activations | def _combine_activations(
layer1,
layer2,
activations1=None,
activations2=None,
mode=ActivationTranslation.BIDIRECTIONAL,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
):
"""Given two layers, combines their activations according to mode.
ActivationTranslation.ONE_TO_TWO:
Translate activations of layer1 into the space of layer2, and return a tuple of
the translated activations and the original layer2 activations.
ActivationTranslation.BIDIRECTIONAL:
Translate activations of layer1 into the space of layer2, activations of layer2
into the space of layer 1, concatenate them along their channels, and returns a
tuple of the concatenated activations for each layer.
"""
activations1 = activations1 or layer1.activations[:number_activations, ...]
activations2 = activations2 or layer2.activations[:number_activations, ...]
if mode is ActivationTranslation.ONE_TO_TWO:
acts_1_to_2 = push_activations(activations1, layer1, layer2)
return acts_1_to_2, activations2
elif mode is ActivationTranslation.BIDIRECTIONAL:
acts_1_to_2 = push_activations(activations1, layer1, layer2)
acts_2_to_1 = push_activations(activations2, layer2, layer1)
activations_model1 = np.concatenate((activations1, acts_1_to_2), axis=1)
activations_model2 = np.concatenate((acts_2_to_1, activations2), axis=1)
return activations_model1, activations_model2 | python | def _combine_activations(
layer1,
layer2,
activations1=None,
activations2=None,
mode=ActivationTranslation.BIDIRECTIONAL,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
):
"""Given two layers, combines their activations according to mode.
ActivationTranslation.ONE_TO_TWO:
Translate activations of layer1 into the space of layer2, and return a tuple of
the translated activations and the original layer2 activations.
ActivationTranslation.BIDIRECTIONAL:
Translate activations of layer1 into the space of layer2, activations of layer2
into the space of layer 1, concatenate them along their channels, and returns a
tuple of the concatenated activations for each layer.
"""
activations1 = activations1 or layer1.activations[:number_activations, ...]
activations2 = activations2 or layer2.activations[:number_activations, ...]
if mode is ActivationTranslation.ONE_TO_TWO:
acts_1_to_2 = push_activations(activations1, layer1, layer2)
return acts_1_to_2, activations2
elif mode is ActivationTranslation.BIDIRECTIONAL:
acts_1_to_2 = push_activations(activations1, layer1, layer2)
acts_2_to_1 = push_activations(activations2, layer2, layer1)
activations_model1 = np.concatenate((activations1, acts_1_to_2), axis=1)
activations_model2 = np.concatenate((acts_2_to_1, activations2), axis=1)
return activations_model1, activations_model2 | [
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the translated activations and the original layer2 activations.
ActivationTranslation.BIDIRECTIONAL:
Translate activations of layer1 into the space of layer2, activations of layer2
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26,518 | tensorflow/lucid | lucid/recipes/activation_atlas/main.py | bin_laid_out_activations | def bin_laid_out_activations(layout, activations, grid_size, threshold=5):
"""Given a layout and activations, overlays a grid on the layout and returns
averaged activations for each grid cell. If a cell contains less than `threshold`
activations it will be discarded, so the number of returned data is variable."""
assert layout.shape[0] == activations.shape[0]
# calculate which grid cells each activation's layout position falls into
# first bin stays empty because nothing should be < 0, so we add an extra bin
bins = np.linspace(0, 1, num=grid_size + 1)
bins[-1] = np.inf # last bin should include all higher values
indices = np.digitize(layout, bins) - 1 # subtract 1 to account for empty first bin
# because of thresholding we may need to return a variable number of means
means, coordinates, counts = [], [], []
# iterate over all grid cell coordinates to compute their average directions
grid_coordinates = np.indices((grid_size, grid_size)).transpose().reshape(-1, 2)
for xy_coordinates in grid_coordinates:
mask = np.equal(xy_coordinates, indices).all(axis=1)
count = np.count_nonzero(mask)
if count > threshold:
counts.append(count)
coordinates.append(xy_coordinates)
mean = np.average(activations[mask], axis=0)
means.append(mean)
assert len(means) == len(coordinates) == len(counts)
if len(coordinates) == 0:
raise RuntimeError("Binning activations led to 0 cells containing activations!")
return means, coordinates, counts | python | def bin_laid_out_activations(layout, activations, grid_size, threshold=5):
"""Given a layout and activations, overlays a grid on the layout and returns
averaged activations for each grid cell. If a cell contains less than `threshold`
activations it will be discarded, so the number of returned data is variable."""
assert layout.shape[0] == activations.shape[0]
# calculate which grid cells each activation's layout position falls into
# first bin stays empty because nothing should be < 0, so we add an extra bin
bins = np.linspace(0, 1, num=grid_size + 1)
bins[-1] = np.inf # last bin should include all higher values
indices = np.digitize(layout, bins) - 1 # subtract 1 to account for empty first bin
# because of thresholding we may need to return a variable number of means
means, coordinates, counts = [], [], []
# iterate over all grid cell coordinates to compute their average directions
grid_coordinates = np.indices((grid_size, grid_size)).transpose().reshape(-1, 2)
for xy_coordinates in grid_coordinates:
mask = np.equal(xy_coordinates, indices).all(axis=1)
count = np.count_nonzero(mask)
if count > threshold:
counts.append(count)
coordinates.append(xy_coordinates)
mean = np.average(activations[mask], axis=0)
means.append(mean)
assert len(means) == len(coordinates) == len(counts)
if len(coordinates) == 0:
raise RuntimeError("Binning activations led to 0 cells containing activations!")
return means, coordinates, counts | [
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26,519 | tensorflow/lucid | lucid/modelzoo/util.py | frozen_default_graph_def | def frozen_default_graph_def(input_node_names, output_node_names):
"""Return frozen and simplified graph_def of default graph."""
sess = tf.get_default_session()
input_graph_def = tf.get_default_graph().as_graph_def()
pruned_graph = tf.graph_util.remove_training_nodes(
input_graph_def, protected_nodes=(output_node_names + input_node_names)
)
pruned_graph = tf.graph_util.extract_sub_graph(pruned_graph, output_node_names)
# remove explicit device assignments
for node in pruned_graph.node:
node.device = ""
all_variable_names = [v.op.name for v in tf.global_variables()]
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess=sess,
input_graph_def=pruned_graph,
output_node_names=output_node_names,
variable_names_whitelist=all_variable_names,
)
return output_graph_def | python | def frozen_default_graph_def(input_node_names, output_node_names):
"""Return frozen and simplified graph_def of default graph."""
sess = tf.get_default_session()
input_graph_def = tf.get_default_graph().as_graph_def()
pruned_graph = tf.graph_util.remove_training_nodes(
input_graph_def, protected_nodes=(output_node_names + input_node_names)
)
pruned_graph = tf.graph_util.extract_sub_graph(pruned_graph, output_node_names)
# remove explicit device assignments
for node in pruned_graph.node:
node.device = ""
all_variable_names = [v.op.name for v in tf.global_variables()]
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess=sess,
input_graph_def=pruned_graph,
output_node_names=output_node_names,
variable_names_whitelist=all_variable_names,
)
return output_graph_def | [
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26,520 | tensorflow/lucid | lucid/modelzoo/util.py | infuse_metadata | def infuse_metadata(graph_def, info):
"""Embed meta data as a string constant in a TF graph.
This function takes info, converts it into json, and embeds
it in graph_def as a constant op called `__lucid_metadata_json`.
"""
temp_graph = tf.Graph()
with temp_graph.as_default():
tf.constant(json.dumps(info, cls=NumpyJSONEncoder), name=metadata_node_name)
meta_node = temp_graph.as_graph_def().node[0]
graph_def.node.extend([meta_node]) | python | def infuse_metadata(graph_def, info):
"""Embed meta data as a string constant in a TF graph.
This function takes info, converts it into json, and embeds
it in graph_def as a constant op called `__lucid_metadata_json`.
"""
temp_graph = tf.Graph()
with temp_graph.as_default():
tf.constant(json.dumps(info, cls=NumpyJSONEncoder), name=metadata_node_name)
meta_node = temp_graph.as_graph_def().node[0]
graph_def.node.extend([meta_node]) | [
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26,521 | tensorflow/lucid | lucid/modelzoo/util.py | extract_metadata | def extract_metadata(graph_def):
"""Attempt to extract meta data hidden in graph_def.
Looks for a `__lucid_metadata_json` constant string op.
If present, extract it's content and convert it from json to python.
If not, returns None.
"""
meta_matches = [n for n in graph_def.node if n.name==metadata_node_name]
if meta_matches:
assert len(meta_matches) == 1, "found more than 1 lucid metadata node!"
meta_tensor = meta_matches[0].attr['value'].tensor
return json.loads(meta_tensor.string_val[0])
else:
return None | python | def extract_metadata(graph_def):
"""Attempt to extract meta data hidden in graph_def.
Looks for a `__lucid_metadata_json` constant string op.
If present, extract it's content and convert it from json to python.
If not, returns None.
"""
meta_matches = [n for n in graph_def.node if n.name==metadata_node_name]
if meta_matches:
assert len(meta_matches) == 1, "found more than 1 lucid metadata node!"
meta_tensor = meta_matches[0].attr['value'].tensor
return json.loads(meta_tensor.string_val[0])
else:
return None | [
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26,522 | tensorflow/lucid | lucid/modelzoo/util.py | GraphDefHelper.neighborhood | def neighborhood(self, node, degree=4):
"""Am I really handcoding graph traversal please no"""
assert self.by_name[node.name] == node
already_visited = frontier = set([node.name])
for _ in range(degree):
neighbor_names = set()
for node_name in frontier:
outgoing = set(n.name for n in self.by_input[node_name])
incoming = set(self.by_name[node_name].input)
neighbor_names |= incoming | outgoing
frontier = neighbor_names - already_visited
already_visited |= neighbor_names
return [self.by_name[name] for name in already_visited] | python | def neighborhood(self, node, degree=4):
"""Am I really handcoding graph traversal please no"""
assert self.by_name[node.name] == node
already_visited = frontier = set([node.name])
for _ in range(degree):
neighbor_names = set()
for node_name in frontier:
outgoing = set(n.name for n in self.by_input[node_name])
incoming = set(self.by_name[node_name].input)
neighbor_names |= incoming | outgoing
frontier = neighbor_names - already_visited
already_visited |= neighbor_names
return [self.by_name[name] for name in already_visited] | [
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26,523 | Rapptz/discord.py | discord/iterators.py | HistoryIterator._retrieve_messages_before_strategy | async def _retrieve_messages_before_strategy(self, retrieve):
"""Retrieve messages using before parameter."""
before = self.before.id if self.before else None
data = await self.logs_from(self.channel.id, retrieve, before=before)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.before = Object(id=int(data[-1]['id']))
return data | python | async def _retrieve_messages_before_strategy(self, retrieve):
"""Retrieve messages using before parameter."""
before = self.before.id if self.before else None
data = await self.logs_from(self.channel.id, retrieve, before=before)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.before = Object(id=int(data[-1]['id']))
return data | [
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26,524 | Rapptz/discord.py | discord/iterators.py | HistoryIterator._retrieve_messages_after_strategy | async def _retrieve_messages_after_strategy(self, retrieve):
"""Retrieve messages using after parameter."""
after = self.after.id if self.after else None
data = await self.logs_from(self.channel.id, retrieve, after=after)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.after = Object(id=int(data[0]['id']))
return data | python | async def _retrieve_messages_after_strategy(self, retrieve):
"""Retrieve messages using after parameter."""
after = self.after.id if self.after else None
data = await self.logs_from(self.channel.id, retrieve, after=after)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.after = Object(id=int(data[0]['id']))
return data | [
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26,525 | Rapptz/discord.py | discord/iterators.py | HistoryIterator._retrieve_messages_around_strategy | async def _retrieve_messages_around_strategy(self, retrieve):
"""Retrieve messages using around parameter."""
if self.around:
around = self.around.id if self.around else None
data = await self.logs_from(self.channel.id, retrieve, around=around)
self.around = None
return data
return [] | python | async def _retrieve_messages_around_strategy(self, retrieve):
"""Retrieve messages using around parameter."""
if self.around:
around = self.around.id if self.around else None
data = await self.logs_from(self.channel.id, retrieve, around=around)
self.around = None
return data
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26,526 | Rapptz/discord.py | discord/iterators.py | GuildIterator._retrieve_guilds_before_strategy | async def _retrieve_guilds_before_strategy(self, retrieve):
"""Retrieve guilds using before parameter."""
before = self.before.id if self.before else None
data = await self.get_guilds(retrieve, before=before)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.before = Object(id=int(data[-1]['id']))
return data | python | async def _retrieve_guilds_before_strategy(self, retrieve):
"""Retrieve guilds using before parameter."""
before = self.before.id if self.before else None
data = await self.get_guilds(retrieve, before=before)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.before = Object(id=int(data[-1]['id']))
return data | [
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26,527 | Rapptz/discord.py | discord/iterators.py | GuildIterator._retrieve_guilds_after_strategy | async def _retrieve_guilds_after_strategy(self, retrieve):
"""Retrieve guilds using after parameter."""
after = self.after.id if self.after else None
data = await self.get_guilds(retrieve, after=after)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.after = Object(id=int(data[0]['id']))
return data | python | async def _retrieve_guilds_after_strategy(self, retrieve):
"""Retrieve guilds using after parameter."""
after = self.after.id if self.after else None
data = await self.get_guilds(retrieve, after=after)
if len(data):
if self.limit is not None:
self.limit -= retrieve
self.after = Object(id=int(data[0]['id']))
return data | [
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26,528 | Rapptz/discord.py | discord/ext/commands/cog.py | Cog.walk_commands | def walk_commands(self):
"""An iterator that recursively walks through this cog's commands and subcommands."""
from .core import GroupMixin
for command in self.__cog_commands__:
if command.parent is None:
yield command
if isinstance(command, GroupMixin):
yield from command.walk_commands() | python | def walk_commands(self):
"""An iterator that recursively walks through this cog's commands and subcommands."""
from .core import GroupMixin
for command in self.__cog_commands__:
if command.parent is None:
yield command
if isinstance(command, GroupMixin):
yield from command.walk_commands() | [
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26,529 | Rapptz/discord.py | discord/ext/commands/cog.py | Cog.listener | def listener(cls, name=None):
"""A decorator that marks a function as a listener.
This is the cog equivalent of :meth:`.Bot.listen`.
Parameters
------------
name: :class:`str`
The name of the event being listened to. If not provided, it
defaults to the function's name.
Raises
--------
TypeError
The function is not a coroutine function or a string was not passed as
the name.
"""
if name is not None and not isinstance(name, str):
raise TypeError('Cog.listener expected str but received {0.__class__.__name__!r} instead.'.format(name))
def decorator(func):
actual = func
if isinstance(actual, staticmethod):
actual = actual.__func__
if not inspect.iscoroutinefunction(actual):
raise TypeError('Listener function must be a coroutine function.')
actual.__cog_listener__ = True
to_assign = name or actual.__name__
try:
actual.__cog_listener_names__.append(to_assign)
except AttributeError:
actual.__cog_listener_names__ = [to_assign]
# we have to return `func` instead of `actual` because
# we need the type to be `staticmethod` for the metaclass
# to pick it up but the metaclass unfurls the function and
# thus the assignments need to be on the actual function
return func
return decorator | python | def listener(cls, name=None):
"""A decorator that marks a function as a listener.
This is the cog equivalent of :meth:`.Bot.listen`.
Parameters
------------
name: :class:`str`
The name of the event being listened to. If not provided, it
defaults to the function's name.
Raises
--------
TypeError
The function is not a coroutine function or a string was not passed as
the name.
"""
if name is not None and not isinstance(name, str):
raise TypeError('Cog.listener expected str but received {0.__class__.__name__!r} instead.'.format(name))
def decorator(func):
actual = func
if isinstance(actual, staticmethod):
actual = actual.__func__
if not inspect.iscoroutinefunction(actual):
raise TypeError('Listener function must be a coroutine function.')
actual.__cog_listener__ = True
to_assign = name or actual.__name__
try:
actual.__cog_listener_names__.append(to_assign)
except AttributeError:
actual.__cog_listener_names__ = [to_assign]
# we have to return `func` instead of `actual` because
# we need the type to be `staticmethod` for the metaclass
# to pick it up but the metaclass unfurls the function and
# thus the assignments need to be on the actual function
return func
return decorator | [
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26,530 | Rapptz/discord.py | discord/embeds.py | Embed.set_footer | def set_footer(self, *, text=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the footer for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
text: :class:`str`
The footer text.
icon_url: :class:`str`
The URL of the footer icon. Only HTTP(S) is supported.
"""
self._footer = {}
if text is not EmptyEmbed:
self._footer['text'] = str(text)
if icon_url is not EmptyEmbed:
self._footer['icon_url'] = str(icon_url)
return self | python | def set_footer(self, *, text=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the footer for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
text: :class:`str`
The footer text.
icon_url: :class:`str`
The URL of the footer icon. Only HTTP(S) is supported.
"""
self._footer = {}
if text is not EmptyEmbed:
self._footer['text'] = str(text)
if icon_url is not EmptyEmbed:
self._footer['icon_url'] = str(icon_url)
return self | [
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26,531 | Rapptz/discord.py | discord/embeds.py | Embed.set_author | def set_author(self, *, name, url=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the author for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the author.
url: :class:`str`
The URL for the author.
icon_url: :class:`str`
The URL of the author icon. Only HTTP(S) is supported.
"""
self._author = {
'name': str(name)
}
if url is not EmptyEmbed:
self._author['url'] = str(url)
if icon_url is not EmptyEmbed:
self._author['icon_url'] = str(icon_url)
return self | python | def set_author(self, *, name, url=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the author for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the author.
url: :class:`str`
The URL for the author.
icon_url: :class:`str`
The URL of the author icon. Only HTTP(S) is supported.
"""
self._author = {
'name': str(name)
}
if url is not EmptyEmbed:
self._author['url'] = str(url)
if icon_url is not EmptyEmbed:
self._author['icon_url'] = str(icon_url)
return self | [
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26,532 | Rapptz/discord.py | discord/embeds.py | Embed.add_field | def add_field(self, *, name, value, inline=True):
"""Adds a field to the embed object.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the field.
value: :class:`str`
The value of the field.
inline: :class:`bool`
Whether the field should be displayed inline.
"""
field = {
'inline': inline,
'name': str(name),
'value': str(value)
}
try:
self._fields.append(field)
except AttributeError:
self._fields = [field]
return self | python | def add_field(self, *, name, value, inline=True):
"""Adds a field to the embed object.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the field.
value: :class:`str`
The value of the field.
inline: :class:`bool`
Whether the field should be displayed inline.
"""
field = {
'inline': inline,
'name': str(name),
'value': str(value)
}
try:
self._fields.append(field)
except AttributeError:
self._fields = [field]
return self | [
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Parameters
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name: :class:`str`
The name of the field.
value: :class:`str`
The value of the field.
inline: :class:`bool`
Whether the field should be displayed inline. | [
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26,533 | Rapptz/discord.py | discord/embeds.py | Embed.set_field_at | def set_field_at(self, index, *, name, value, inline=True):
"""Modifies a field to the embed object.
The index must point to a valid pre-existing field.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
index: :class:`int`
The index of the field to modify.
name: :class:`str`
The name of the field.
value: :class:`str`
The value of the field.
inline: :class:`bool`
Whether the field should be displayed inline.
Raises
-------
IndexError
An invalid index was provided.
"""
try:
field = self._fields[index]
except (TypeError, IndexError, AttributeError):
raise IndexError('field index out of range')
field['name'] = str(name)
field['value'] = str(value)
field['inline'] = inline
return self | python | def set_field_at(self, index, *, name, value, inline=True):
"""Modifies a field to the embed object.
The index must point to a valid pre-existing field.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
index: :class:`int`
The index of the field to modify.
name: :class:`str`
The name of the field.
value: :class:`str`
The value of the field.
inline: :class:`bool`
Whether the field should be displayed inline.
Raises
-------
IndexError
An invalid index was provided.
"""
try:
field = self._fields[index]
except (TypeError, IndexError, AttributeError):
raise IndexError('field index out of range')
field['name'] = str(name)
field['value'] = str(value)
field['inline'] = inline
return self | [
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26,534 | Rapptz/discord.py | discord/user.py | BaseUser.avatar_url_as | def avatar_url_as(self, *, format=None, static_format='webp', size=1024):
"""Returns a friendly URL version of the avatar the user has.
If the user does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'webp', 'jpeg', 'jpg', 'png' or 'gif', and
'gif' is only valid for animated avatars. The size must be a power of 2
between 16 and 1024.
Parameters
-----------
format: Optional[:class:`str`]
The format to attempt to convert the avatar to.
If the format is ``None``, then it is automatically
detected into either 'gif' or static_format depending on the
avatar being animated or not.
static_format: Optional[:class:`str`]
Format to attempt to convert only non-animated avatars to.
Defaults to 'webp'
size: :class:`int`
The size of the image to display.
Raises
------
InvalidArgument
Bad image format passed to ``format`` or ``static_format``, or
invalid ``size``.
Returns
--------
:class:`Asset`
The resulting CDN asset.
"""
return Asset._from_avatar(self._state, self, format=format, static_format=static_format, size=size) | python | def avatar_url_as(self, *, format=None, static_format='webp', size=1024):
"""Returns a friendly URL version of the avatar the user has.
If the user does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'webp', 'jpeg', 'jpg', 'png' or 'gif', and
'gif' is only valid for animated avatars. The size must be a power of 2
between 16 and 1024.
Parameters
-----------
format: Optional[:class:`str`]
The format to attempt to convert the avatar to.
If the format is ``None``, then it is automatically
detected into either 'gif' or static_format depending on the
avatar being animated or not.
static_format: Optional[:class:`str`]
Format to attempt to convert only non-animated avatars to.
Defaults to 'webp'
size: :class:`int`
The size of the image to display.
Raises
------
InvalidArgument
Bad image format passed to ``format`` or ``static_format``, or
invalid ``size``.
Returns
--------
:class:`Asset`
The resulting CDN asset.
"""
return Asset._from_avatar(self._state, self, format=format, static_format=static_format, size=size) | [
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26,535 | Rapptz/discord.py | discord/user.py | BaseUser.mentioned_in | def mentioned_in(self, message):
"""Checks if the user is mentioned in the specified message.
Parameters
-----------
message: :class:`Message`
The message to check if you're mentioned in.
"""
if message.mention_everyone:
return True
for user in message.mentions:
if user.id == self.id:
return True
return False | python | def mentioned_in(self, message):
"""Checks if the user is mentioned in the specified message.
Parameters
-----------
message: :class:`Message`
The message to check if you're mentioned in.
"""
if message.mention_everyone:
return True
for user in message.mentions:
if user.id == self.id:
return True
return False | [
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26,536 | Rapptz/discord.py | discord/utils.py | time_snowflake | def time_snowflake(datetime_obj, high=False):
"""Returns a numeric snowflake pretending to be created at the given date.
When using as the lower end of a range, use time_snowflake(high=False) - 1 to be inclusive, high=True to be exclusive
When using as the higher end of a range, use time_snowflake(high=True) + 1 to be inclusive, high=False to be exclusive
Parameters
-----------
datetime_obj
A timezone-naive datetime object representing UTC time.
high: :class:`bool`
Whether or not to set the lower 22 bit to high or low.
"""
unix_seconds = (datetime_obj - type(datetime_obj)(1970, 1, 1)).total_seconds()
discord_millis = int(unix_seconds * 1000 - DISCORD_EPOCH)
return (discord_millis << 22) + (2**22-1 if high else 0) | python | def time_snowflake(datetime_obj, high=False):
"""Returns a numeric snowflake pretending to be created at the given date.
When using as the lower end of a range, use time_snowflake(high=False) - 1 to be inclusive, high=True to be exclusive
When using as the higher end of a range, use time_snowflake(high=True) + 1 to be inclusive, high=False to be exclusive
Parameters
-----------
datetime_obj
A timezone-naive datetime object representing UTC time.
high: :class:`bool`
Whether or not to set the lower 22 bit to high or low.
"""
unix_seconds = (datetime_obj - type(datetime_obj)(1970, 1, 1)).total_seconds()
discord_millis = int(unix_seconds * 1000 - DISCORD_EPOCH)
return (discord_millis << 22) + (2**22-1 if high else 0) | [
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A timezone-naive datetime object representing UTC time.
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26,537 | Rapptz/discord.py | discord/utils.py | _string_width | def _string_width(string, *, _IS_ASCII=_IS_ASCII):
"""Returns string's width."""
match = _IS_ASCII.match(string)
if match:
return match.endpos
UNICODE_WIDE_CHAR_TYPE = 'WFA'
width = 0
func = unicodedata.east_asian_width
for char in string:
width += 2 if func(char) in UNICODE_WIDE_CHAR_TYPE else 1
return width | python | def _string_width(string, *, _IS_ASCII=_IS_ASCII):
"""Returns string's width."""
match = _IS_ASCII.match(string)
if match:
return match.endpos
UNICODE_WIDE_CHAR_TYPE = 'WFA'
width = 0
func = unicodedata.east_asian_width
for char in string:
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return width | [
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26,538 | Rapptz/discord.py | discord/utils.py | escape_markdown | def escape_markdown(text, *, as_needed=False, ignore_links=True):
r"""A helper function that escapes Discord's markdown.
Parameters
-----------
text: :class:`str`
The text to escape markdown from.
as_needed: :class:`bool`
Whether to escape the markdown characters as needed. This
means that it does not escape extraneous characters if it's
not necessary, e.g. ``**hello**`` is escaped into ``\*\*hello**``
instead of ``\*\*hello\*\*``. Note however that this can open
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ignore_links: :class:`bool`
Whether to leave links alone when escaping markdown. For example,
if a URL in the text contains characters such as ``_`` then it will
be left alone. This option is not supported with ``as_needed``.
Defaults to ``True``.
Returns
--------
:class:`str`
The text with the markdown special characters escaped with a slash.
"""
if not as_needed:
url_regex = r'(?P<url>(?:https?|steam)://(?:-\.)?(?:[^\s/?\.#-]+\.?)+(?:/[^\s]*)?)'
def replacement(match):
groupdict = match.groupdict()
is_url = groupdict.get('url')
if is_url:
return is_url
return '\\' + groupdict['markdown']
regex = r'(?P<markdown>[_\\~|\*`])'
if ignore_links:
regex = '(?:%s|%s)' % (url_regex, regex)
return re.sub(regex, replacement, text)
else:
text = re.sub(r'\\', r'\\\\', text)
return _MARKDOWN_ESCAPE_REGEX.sub(r'\\\1', text) | python | def escape_markdown(text, *, as_needed=False, ignore_links=True):
r"""A helper function that escapes Discord's markdown.
Parameters
-----------
text: :class:`str`
The text to escape markdown from.
as_needed: :class:`bool`
Whether to escape the markdown characters as needed. This
means that it does not escape extraneous characters if it's
not necessary, e.g. ``**hello**`` is escaped into ``\*\*hello**``
instead of ``\*\*hello\*\*``. Note however that this can open
you up to some clever syntax abuse. Defaults to ``False``.
ignore_links: :class:`bool`
Whether to leave links alone when escaping markdown. For example,
if a URL in the text contains characters such as ``_`` then it will
be left alone. This option is not supported with ``as_needed``.
Defaults to ``True``.
Returns
--------
:class:`str`
The text with the markdown special characters escaped with a slash.
"""
if not as_needed:
url_regex = r'(?P<url>(?:https?|steam)://(?:-\.)?(?:[^\s/?\.#-]+\.?)+(?:/[^\s]*)?)'
def replacement(match):
groupdict = match.groupdict()
is_url = groupdict.get('url')
if is_url:
return is_url
return '\\' + groupdict['markdown']
regex = r'(?P<markdown>[_\\~|\*`])'
if ignore_links:
regex = '(?:%s|%s)' % (url_regex, regex)
return re.sub(regex, replacement, text)
else:
text = re.sub(r'\\', r'\\\\', text)
return _MARKDOWN_ESCAPE_REGEX.sub(r'\\\1', text) | [
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The text to escape markdown from.
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Whether to escape the markdown characters as needed. This
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Whether to leave links alone when escaping markdown. For example,
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26,539 | Rapptz/discord.py | examples/basic_bot.py | add | async def add(ctx, left: int, right: int):
"""Adds two numbers together."""
await ctx.send(left + right) | python | async def add(ctx, left: int, right: int):
"""Adds two numbers together."""
await ctx.send(left + right) | [
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26,540 | Rapptz/discord.py | examples/basic_bot.py | roll | async def roll(ctx, dice: str):
"""Rolls a dice in NdN format."""
try:
rolls, limit = map(int, dice.split('d'))
except Exception:
await ctx.send('Format has to be in NdN!')
return
result = ', '.join(str(random.randint(1, limit)) for r in range(rolls))
await ctx.send(result) | python | async def roll(ctx, dice: str):
"""Rolls a dice in NdN format."""
try:
rolls, limit = map(int, dice.split('d'))
except Exception:
await ctx.send('Format has to be in NdN!')
return
result = ', '.join(str(random.randint(1, limit)) for r in range(rolls))
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26,541 | Rapptz/discord.py | examples/basic_bot.py | repeat | async def repeat(ctx, times: int, content='repeating...'):
"""Repeats a message multiple times."""
for i in range(times):
await ctx.send(content) | python | async def repeat(ctx, times: int, content='repeating...'):
"""Repeats a message multiple times."""
for i in range(times):
await ctx.send(content) | [
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26,542 | Rapptz/discord.py | examples/basic_voice.py | Music.join | async def join(self, ctx, *, channel: discord.VoiceChannel):
"""Joins a voice channel"""
if ctx.voice_client is not None:
return await ctx.voice_client.move_to(channel)
await channel.connect() | python | async def join(self, ctx, *, channel: discord.VoiceChannel):
"""Joins a voice channel"""
if ctx.voice_client is not None:
return await ctx.voice_client.move_to(channel)
await channel.connect() | [
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26,543 | Rapptz/discord.py | examples/basic_voice.py | Music.play | async def play(self, ctx, *, query):
"""Plays a file from the local filesystem"""
source = discord.PCMVolumeTransformer(discord.FFmpegPCMAudio(query))
ctx.voice_client.play(source, after=lambda e: print('Player error: %s' % e) if e else None)
await ctx.send('Now playing: {}'.format(query)) | python | async def play(self, ctx, *, query):
"""Plays a file from the local filesystem"""
source = discord.PCMVolumeTransformer(discord.FFmpegPCMAudio(query))
ctx.voice_client.play(source, after=lambda e: print('Player error: %s' % e) if e else None)
await ctx.send('Now playing: {}'.format(query)) | [
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26,544 | Rapptz/discord.py | examples/basic_voice.py | Music.volume | async def volume(self, ctx, volume: int):
"""Changes the player's volume"""
if ctx.voice_client is None:
return await ctx.send("Not connected to a voice channel.")
ctx.voice_client.source.volume = volume / 100
await ctx.send("Changed volume to {}%".format(volume)) | python | async def volume(self, ctx, volume: int):
"""Changes the player's volume"""
if ctx.voice_client is None:
return await ctx.send("Not connected to a voice channel.")
ctx.voice_client.source.volume = volume / 100
await ctx.send("Changed volume to {}%".format(volume)) | [
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26,545 | Rapptz/discord.py | discord/calls.py | CallMessage.duration | def duration(self):
"""Queries the duration of the call.
If the call has not ended then the current duration will
be returned.
Returns
---------
datetime.timedelta
The timedelta object representing the duration.
"""
if self.ended_timestamp is None:
return datetime.datetime.utcnow() - self.message.created_at
else:
return self.ended_timestamp - self.message.created_at | python | def duration(self):
"""Queries the duration of the call.
If the call has not ended then the current duration will
be returned.
Returns
---------
datetime.timedelta
The timedelta object representing the duration.
"""
if self.ended_timestamp is None:
return datetime.datetime.utcnow() - self.message.created_at
else:
return self.ended_timestamp - self.message.created_at | [
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26,546 | Rapptz/discord.py | discord/webhook.py | Webhook.avatar_url_as | def avatar_url_as(self, *, format=None, size=1024):
"""Returns a friendly URL version of the avatar the webhook has.
If the webhook does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'jpeg', 'jpg', or 'png'.
The size must be a power of 2 between 16 and 1024.
Parameters
-----------
format: Optional[:class:`str`]
The format to attempt to convert the avatar to.
If the format is ``None``, then it is equivalent to png.
size: :class:`int`
The size of the image to display.
Raises
------
InvalidArgument
Bad image format passed to ``format`` or invalid ``size``.
Returns
--------
:class:`Asset`
The resulting CDN asset.
"""
if self.avatar is None:
# Default is always blurple apparently
return Asset(self._state, 'https://cdn.discordapp.com/embed/avatars/0.png')
if not utils.valid_icon_size(size):
raise InvalidArgument("size must be a power of 2 between 16 and 1024")
format = format or 'png'
if format not in ('png', 'jpg', 'jpeg'):
raise InvalidArgument("format must be one of 'png', 'jpg', or 'jpeg'.")
url = 'https://cdn.discordapp.com/avatars/{0.id}/{0.avatar}.{1}?size={2}'.format(self, format, size)
return Asset(self._state, url) | python | def avatar_url_as(self, *, format=None, size=1024):
"""Returns a friendly URL version of the avatar the webhook has.
If the webhook does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'jpeg', 'jpg', or 'png'.
The size must be a power of 2 between 16 and 1024.
Parameters
-----------
format: Optional[:class:`str`]
The format to attempt to convert the avatar to.
If the format is ``None``, then it is equivalent to png.
size: :class:`int`
The size of the image to display.
Raises
------
InvalidArgument
Bad image format passed to ``format`` or invalid ``size``.
Returns
--------
:class:`Asset`
The resulting CDN asset.
"""
if self.avatar is None:
# Default is always blurple apparently
return Asset(self._state, 'https://cdn.discordapp.com/embed/avatars/0.png')
if not utils.valid_icon_size(size):
raise InvalidArgument("size must be a power of 2 between 16 and 1024")
format = format or 'png'
if format not in ('png', 'jpg', 'jpeg'):
raise InvalidArgument("format must be one of 'png', 'jpg', or 'jpeg'.")
url = 'https://cdn.discordapp.com/avatars/{0.id}/{0.avatar}.{1}?size={2}'.format(self, format, size)
return Asset(self._state, url) | [
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The format to attempt to convert the avatar to.
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26,547 | Rapptz/discord.py | discord/abc.py | GuildChannel.overwrites_for | def overwrites_for(self, obj):
"""Returns the channel-specific overwrites for a member or a role.
Parameters
-----------
obj
The :class:`Role` or :class:`abc.User` denoting
whose overwrite to get.
Returns
---------
:class:`PermissionOverwrite`
The permission overwrites for this object.
"""
if isinstance(obj, User):
predicate = lambda p: p.type == 'member'
elif isinstance(obj, Role):
predicate = lambda p: p.type == 'role'
else:
predicate = lambda p: True
for overwrite in filter(predicate, self._overwrites):
if overwrite.id == obj.id:
allow = Permissions(overwrite.allow)
deny = Permissions(overwrite.deny)
return PermissionOverwrite.from_pair(allow, deny)
return PermissionOverwrite() | python | def overwrites_for(self, obj):
"""Returns the channel-specific overwrites for a member or a role.
Parameters
-----------
obj
The :class:`Role` or :class:`abc.User` denoting
whose overwrite to get.
Returns
---------
:class:`PermissionOverwrite`
The permission overwrites for this object.
"""
if isinstance(obj, User):
predicate = lambda p: p.type == 'member'
elif isinstance(obj, Role):
predicate = lambda p: p.type == 'role'
else:
predicate = lambda p: True
for overwrite in filter(predicate, self._overwrites):
if overwrite.id == obj.id:
allow = Permissions(overwrite.allow)
deny = Permissions(overwrite.deny)
return PermissionOverwrite.from_pair(allow, deny)
return PermissionOverwrite() | [
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26,548 | Rapptz/discord.py | discord/abc.py | GuildChannel.overwrites | def overwrites(self):
"""Returns all of the channel's overwrites.
This is returned as a dictionary where the key contains the target which
can be either a :class:`Role` or a :class:`Member` and the key is the
overwrite as a :class:`PermissionOverwrite`.
Returns
--------
Mapping[Union[:class:`Role`, :class:`Member`], :class:`PermissionOverwrite`]:
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"""
ret = {}
for ow in self._overwrites:
allow = Permissions(ow.allow)
deny = Permissions(ow.deny)
overwrite = PermissionOverwrite.from_pair(allow, deny)
if ow.type == 'role':
target = self.guild.get_role(ow.id)
elif ow.type == 'member':
target = self.guild.get_member(ow.id)
# TODO: There is potential data loss here in the non-chunked
# case, i.e. target is None because get_member returned nothing.
# This can be fixed with a slight breaking change to the return type,
# i.e. adding discord.Object to the list of it
# However, for now this is an acceptable compromise.
if target is not None:
ret[target] = overwrite
return ret | python | def overwrites(self):
"""Returns all of the channel's overwrites.
This is returned as a dictionary where the key contains the target which
can be either a :class:`Role` or a :class:`Member` and the key is the
overwrite as a :class:`PermissionOverwrite`.
Returns
--------
Mapping[Union[:class:`Role`, :class:`Member`], :class:`PermissionOverwrite`]:
The channel's permission overwrites.
"""
ret = {}
for ow in self._overwrites:
allow = Permissions(ow.allow)
deny = Permissions(ow.deny)
overwrite = PermissionOverwrite.from_pair(allow, deny)
if ow.type == 'role':
target = self.guild.get_role(ow.id)
elif ow.type == 'member':
target = self.guild.get_member(ow.id)
# TODO: There is potential data loss here in the non-chunked
# case, i.e. target is None because get_member returned nothing.
# This can be fixed with a slight breaking change to the return type,
# i.e. adding discord.Object to the list of it
# However, for now this is an acceptable compromise.
if target is not None:
ret[target] = overwrite
return ret | [
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26,549 | Rapptz/discord.py | discord/ext/tasks/__init__.py | loop | def loop(*, seconds=0, minutes=0, hours=0, count=None, reconnect=True, loop=None):
"""A decorator that schedules a task in the background for you with
optional reconnect logic.
Parameters
------------
seconds: :class:`float`
The number of seconds between every iteration.
minutes: :class:`float`
The number of minutes between every iteration.
hours: :class:`float`
The number of hours between every iteration.
count: Optional[:class:`int`]
The number of loops to do, ``None`` if it should be an
infinite loop.
reconnect: :class:`bool`
Whether to handle errors and restart the task
using an exponential back-off algorithm similar to the
one used in :meth:`discord.Client.connect`.
loop: :class:`asyncio.AbstractEventLoop`
The loop to use to register the task, if not given
defaults to :func:`asyncio.get_event_loop`.
Raises
--------
ValueError
An invalid value was given.
TypeError
The function was not a coroutine.
Returns
---------
:class:`Loop`
The loop helper that handles the background task.
"""
def decorator(func):
return Loop(func, seconds=seconds, minutes=minutes, hours=hours,
count=count, reconnect=reconnect, loop=loop)
return decorator | python | def loop(*, seconds=0, minutes=0, hours=0, count=None, reconnect=True, loop=None):
"""A decorator that schedules a task in the background for you with
optional reconnect logic.
Parameters
------------
seconds: :class:`float`
The number of seconds between every iteration.
minutes: :class:`float`
The number of minutes between every iteration.
hours: :class:`float`
The number of hours between every iteration.
count: Optional[:class:`int`]
The number of loops to do, ``None`` if it should be an
infinite loop.
reconnect: :class:`bool`
Whether to handle errors and restart the task
using an exponential back-off algorithm similar to the
one used in :meth:`discord.Client.connect`.
loop: :class:`asyncio.AbstractEventLoop`
The loop to use to register the task, if not given
defaults to :func:`asyncio.get_event_loop`.
Raises
--------
ValueError
An invalid value was given.
TypeError
The function was not a coroutine.
Returns
---------
:class:`Loop`
The loop helper that handles the background task.
"""
def decorator(func):
return Loop(func, seconds=seconds, minutes=minutes, hours=hours,
count=count, reconnect=reconnect, loop=loop)
return decorator | [
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The number of hours between every iteration.
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The number of loops to do, ``None`` if it should be an
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reconnect: :class:`bool`
Whether to handle errors and restart the task
using an exponential back-off algorithm similar to the
one used in :meth:`discord.Client.connect`.
loop: :class:`asyncio.AbstractEventLoop`
The loop to use to register the task, if not given
defaults to :func:`asyncio.get_event_loop`.
Raises
--------
ValueError
An invalid value was given.
TypeError
The function was not a coroutine.
Returns
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:class:`Loop`
The loop helper that handles the background task. | [
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26,550 | Rapptz/discord.py | discord/ext/tasks/__init__.py | Loop.remove_exception_type | def remove_exception_type(self, exc):
"""Removes an exception type from being handled during the reconnect logic.
Parameters
------------
exc: Type[:class:`BaseException`]
The exception class to handle.
Returns
---------
:class:`bool`
Whether it was successfully removed.
"""
old_length = len(self._valid_exception)
self._valid_exception = tuple(x for x in self._valid_exception if x is not exc)
return len(self._valid_exception) != old_length | python | def remove_exception_type(self, exc):
"""Removes an exception type from being handled during the reconnect logic.
Parameters
------------
exc: Type[:class:`BaseException`]
The exception class to handle.
Returns
---------
:class:`bool`
Whether it was successfully removed.
"""
old_length = len(self._valid_exception)
self._valid_exception = tuple(x for x in self._valid_exception if x is not exc)
return len(self._valid_exception) != old_length | [
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26,551 | Rapptz/discord.py | discord/ext/tasks/__init__.py | Loop.after_loop | def after_loop(self, coro):
"""A function that also acts as a decorator to register a coroutine to be
called after the loop finished running.
Parameters
------------
coro: :term:`py:awaitable`
The coroutine to register after the loop finishes.
Raises
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TypeError
The function was not a coroutine.
"""
if not (inspect.iscoroutinefunction(coro) or inspect.isawaitable(coro)):
raise TypeError('Expected coroutine or awaitable, received {0.__name__!r}.'.format(type(coro)))
self._after_loop = coro | python | def after_loop(self, coro):
"""A function that also acts as a decorator to register a coroutine to be
called after the loop finished running.
Parameters
------------
coro: :term:`py:awaitable`
The coroutine to register after the loop finishes.
Raises
-------
TypeError
The function was not a coroutine.
"""
if not (inspect.iscoroutinefunction(coro) or inspect.isawaitable(coro)):
raise TypeError('Expected coroutine or awaitable, received {0.__name__!r}.'.format(type(coro)))
self._after_loop = coro | [
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26,552 | Rapptz/discord.py | discord/backoff.py | ExponentialBackoff.delay | def delay(self):
"""Compute the next delay
Returns the next delay to wait according to the exponential
backoff algorithm. This is a value between 0 and base * 2^exp
where exponent starts off at 1 and is incremented at every
invocation of this method up to a maximum of 10.
If a period of more than base * 2^11 has passed since the last
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"""
invocation = time.monotonic()
interval = invocation - self._last_invocation
self._last_invocation = invocation
if interval > self._reset_time:
self._exp = 0
self._exp = min(self._exp + 1, self._max)
return self._randfunc(0, self._base * 2 ** self._exp) | python | def delay(self):
"""Compute the next delay
Returns the next delay to wait according to the exponential
backoff algorithm. This is a value between 0 and base * 2^exp
where exponent starts off at 1 and is incremented at every
invocation of this method up to a maximum of 10.
If a period of more than base * 2^11 has passed since the last
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"""
invocation = time.monotonic()
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26,553 | Rapptz/discord.py | discord/permissions.py | Permissions.is_subset | def is_subset(self, other):
"""Returns True if self has the same or fewer permissions as other."""
if isinstance(other, Permissions):
return (self.value & other.value) == self.value
else:
raise TypeError("cannot compare {} with {}".format(self.__class__.__name__, other.__class__.__name__)) | python | def is_subset(self, other):
"""Returns True if self has the same or fewer permissions as other."""
if isinstance(other, Permissions):
return (self.value & other.value) == self.value
else:
raise TypeError("cannot compare {} with {}".format(self.__class__.__name__, other.__class__.__name__)) | [
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26,554 | Rapptz/discord.py | discord/permissions.py | Permissions.update | def update(self, **kwargs):
r"""Bulk updates this permission object.
Allows you to set multiple attributes by using keyword
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listed. Extraneous key/value pairs will be silently ignored.
Parameters
------------
\*\*kwargs
A list of key/value pairs to bulk update permissions with.
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setattr(self, key, value) | python | def update(self, **kwargs):
r"""Bulk updates this permission object.
Allows you to set multiple attributes by using keyword
arguments. The names must be equivalent to the properties
listed. Extraneous key/value pairs will be silently ignored.
Parameters
------------
\*\*kwargs
A list of key/value pairs to bulk update permissions with.
"""
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except AttributeError:
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26,555 | Rapptz/discord.py | discord/ext/commands/core.py | Command.clean_params | def clean_params(self):
"""Retrieves the parameter OrderedDict without the context or self parameters.
Useful for inspecting signature.
"""
result = self.params.copy()
if self.cog is not None:
# first parameter is self
result.popitem(last=False)
try:
# first/second parameter is context
result.popitem(last=False)
except Exception:
raise ValueError('Missing context parameter') from None
return result | python | def clean_params(self):
"""Retrieves the parameter OrderedDict without the context or self parameters.
Useful for inspecting signature.
"""
result = self.params.copy()
if self.cog is not None:
# first parameter is self
result.popitem(last=False)
try:
# first/second parameter is context
result.popitem(last=False)
except Exception:
raise ValueError('Missing context parameter') from None
return result | [
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26,556 | Rapptz/discord.py | discord/ext/commands/core.py | Command.full_parent_name | def full_parent_name(self):
"""Retrieves the fully qualified parent command name.
This the base command name required to execute it. For example,
in ``?one two three`` the parent name would be ``one two``.
"""
entries = []
command = self
while command.parent is not None:
command = command.parent
entries.append(command.name)
return ' '.join(reversed(entries)) | python | def full_parent_name(self):
"""Retrieves the fully qualified parent command name.
This the base command name required to execute it. For example,
in ``?one two three`` the parent name would be ``one two``.
"""
entries = []
command = self
while command.parent is not None:
command = command.parent
entries.append(command.name)
return ' '.join(reversed(entries)) | [
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26,557 | Rapptz/discord.py | discord/ext/commands/core.py | Command.parents | def parents(self):
"""Retrieves the parents of this command.
If the command has no parents then it returns an empty :class:`list`.
For example in commands ``?a b c test``, the parents are ``[c, b, a]``.
.. versionadded:: 1.1.0
"""
entries = []
command = self
while command.parent is not None:
command = command.parent
entries.append(command)
return entries | python | def parents(self):
"""Retrieves the parents of this command.
If the command has no parents then it returns an empty :class:`list`.
For example in commands ``?a b c test``, the parents are ``[c, b, a]``.
.. versionadded:: 1.1.0
"""
entries = []
command = self
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command = command.parent
entries.append(command)
return entries | [
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26,558 | Rapptz/discord.py | discord/ext/commands/core.py | Command.qualified_name | def qualified_name(self):
"""Retrieves the fully qualified command name.
This is the full parent name with the command name as well.
For example, in ``?one two three`` the qualified name would be
``one two three``.
"""
parent = self.full_parent_name
if parent:
return parent + ' ' + self.name
else:
return self.name | python | def qualified_name(self):
"""Retrieves the fully qualified command name.
This is the full parent name with the command name as well.
For example, in ``?one two three`` the qualified name would be
``one two three``.
"""
parent = self.full_parent_name
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return parent + ' ' + self.name
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26,559 | Rapptz/discord.py | discord/ext/commands/core.py | Command.is_on_cooldown | def is_on_cooldown(self, ctx):
"""Checks whether the command is currently on cooldown.
Parameters
-----------
ctx: :class:`.Context.`
The invocation context to use when checking the commands cooldown status.
Returns
--------
:class:`bool`
A boolean indicating if the command is on cooldown.
"""
if not self._buckets.valid:
return False
bucket = self._buckets.get_bucket(ctx.message)
return bucket.get_tokens() == 0 | python | def is_on_cooldown(self, ctx):
"""Checks whether the command is currently on cooldown.
Parameters
-----------
ctx: :class:`.Context.`
The invocation context to use when checking the commands cooldown status.
Returns
--------
:class:`bool`
A boolean indicating if the command is on cooldown.
"""
if not self._buckets.valid:
return False
bucket = self._buckets.get_bucket(ctx.message)
return bucket.get_tokens() == 0 | [
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26,560 | Rapptz/discord.py | discord/ext/commands/core.py | Command.reset_cooldown | def reset_cooldown(self, ctx):
"""Resets the cooldown on this command.
Parameters
-----------
ctx: :class:`.Context`
The invocation context to reset the cooldown under.
"""
if self._buckets.valid:
bucket = self._buckets.get_bucket(ctx.message)
bucket.reset() | python | def reset_cooldown(self, ctx):
"""Resets the cooldown on this command.
Parameters
-----------
ctx: :class:`.Context`
The invocation context to reset the cooldown under.
"""
if self._buckets.valid:
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26,561 | Rapptz/discord.py | discord/ext/commands/core.py | Command.error | def error(self, coro):
"""A decorator that registers a coroutine as a local error handler.
A local error handler is an :func:`.on_command_error` event limited to
a single command. However, the :func:`.on_command_error` is still
invoked afterwards as the catch-all.
Parameters
-----------
coro: :ref:`coroutine <coroutine>`
The coroutine to register as the local error handler.
Raises
-------
TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('The error handler must be a coroutine.')
self.on_error = coro
return coro | python | def error(self, coro):
"""A decorator that registers a coroutine as a local error handler.
A local error handler is an :func:`.on_command_error` event limited to
a single command. However, the :func:`.on_command_error` is still
invoked afterwards as the catch-all.
Parameters
-----------
coro: :ref:`coroutine <coroutine>`
The coroutine to register as the local error handler.
Raises
-------
TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('The error handler must be a coroutine.')
self.on_error = coro
return coro | [
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26,562 | Rapptz/discord.py | discord/ext/commands/core.py | Command.before_invoke | def before_invoke(self, coro):
"""A decorator that registers a coroutine as a pre-invoke hook.
A pre-invoke hook is called directly before the command is
called. This makes it a useful function to set up database
connections or any type of set up required.
This pre-invoke hook takes a sole parameter, a :class:`.Context`.
See :meth:`.Bot.before_invoke` for more info.
Parameters
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coro: :ref:`coroutine <coroutine>`
The coroutine to register as the pre-invoke hook.
Raises
-------
TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('The pre-invoke hook must be a coroutine.')
self._before_invoke = coro
return coro | python | def before_invoke(self, coro):
"""A decorator that registers a coroutine as a pre-invoke hook.
A pre-invoke hook is called directly before the command is
called. This makes it a useful function to set up database
connections or any type of set up required.
This pre-invoke hook takes a sole parameter, a :class:`.Context`.
See :meth:`.Bot.before_invoke` for more info.
Parameters
-----------
coro: :ref:`coroutine <coroutine>`
The coroutine to register as the pre-invoke hook.
Raises
-------
TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('The pre-invoke hook must be a coroutine.')
self._before_invoke = coro
return coro | [
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This pre-invoke hook takes a sole parameter, a :class:`.Context`.
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26,563 | Rapptz/discord.py | discord/ext/commands/core.py | Command.after_invoke | def after_invoke(self, coro):
"""A decorator that registers a coroutine as a post-invoke hook.
A post-invoke hook is called directly after the command is
called. This makes it a useful function to clean-up database
connections or any type of clean up required.
This post-invoke hook takes a sole parameter, a :class:`.Context`.
See :meth:`.Bot.after_invoke` for more info.
Parameters
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coro: :ref:`coroutine <coroutine>`
The coroutine to register as the post-invoke hook.
Raises
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TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('The post-invoke hook must be a coroutine.')
self._after_invoke = coro
return coro | python | def after_invoke(self, coro):
"""A decorator that registers a coroutine as a post-invoke hook.
A post-invoke hook is called directly after the command is
called. This makes it a useful function to clean-up database
connections or any type of clean up required.
This post-invoke hook takes a sole parameter, a :class:`.Context`.
See :meth:`.Bot.after_invoke` for more info.
Parameters
-----------
coro: :ref:`coroutine <coroutine>`
The coroutine to register as the post-invoke hook.
Raises
-------
TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('The post-invoke hook must be a coroutine.')
self._after_invoke = coro
return coro | [
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26,564 | Rapptz/discord.py | discord/ext/commands/core.py | Command.short_doc | def short_doc(self):
"""Gets the "short" documentation of a command.
By default, this is the :attr:`brief` attribute.
If that lookup leads to an empty string then the first line of the
:attr:`help` attribute is used instead.
"""
if self.brief is not None:
return self.brief
if self.help is not None:
return self.help.split('\n', 1)[0]
return '' | python | def short_doc(self):
"""Gets the "short" documentation of a command.
By default, this is the :attr:`brief` attribute.
If that lookup leads to an empty string then the first line of the
:attr:`help` attribute is used instead.
"""
if self.brief is not None:
return self.brief
if self.help is not None:
return self.help.split('\n', 1)[0]
return '' | [
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26,565 | Rapptz/discord.py | discord/ext/commands/core.py | Command.signature | def signature(self):
"""Returns a POSIX-like signature useful for help command output."""
if self.usage is not None:
return self.usage
params = self.clean_params
if not params:
return ''
result = []
for name, param in params.items():
greedy = isinstance(param.annotation, converters._Greedy)
if param.default is not param.empty:
# We don't want None or '' to trigger the [name=value] case and instead it should
# do [name] since [name=None] or [name=] are not exactly useful for the user.
should_print = param.default if isinstance(param.default, str) else param.default is not None
if should_print:
result.append('[%s=%s]' % (name, param.default) if not greedy else
'[%s=%s]...' % (name, param.default))
continue
else:
result.append('[%s]' % name)
elif param.kind == param.VAR_POSITIONAL:
result.append('[%s...]' % name)
elif greedy:
result.append('[%s]...' % name)
elif self._is_typing_optional(param.annotation):
result.append('[%s]' % name)
else:
result.append('<%s>' % name)
return ' '.join(result) | python | def signature(self):
"""Returns a POSIX-like signature useful for help command output."""
if self.usage is not None:
return self.usage
params = self.clean_params
if not params:
return ''
result = []
for name, param in params.items():
greedy = isinstance(param.annotation, converters._Greedy)
if param.default is not param.empty:
# We don't want None or '' to trigger the [name=value] case and instead it should
# do [name] since [name=None] or [name=] are not exactly useful for the user.
should_print = param.default if isinstance(param.default, str) else param.default is not None
if should_print:
result.append('[%s=%s]' % (name, param.default) if not greedy else
'[%s=%s]...' % (name, param.default))
continue
else:
result.append('[%s]' % name)
elif param.kind == param.VAR_POSITIONAL:
result.append('[%s...]' % name)
elif greedy:
result.append('[%s]...' % name)
elif self._is_typing_optional(param.annotation):
result.append('[%s]' % name)
else:
result.append('<%s>' % name)
return ' '.join(result) | [
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26,566 | Rapptz/discord.py | discord/ext/commands/core.py | GroupMixin.walk_commands | def walk_commands(self):
"""An iterator that recursively walks through all commands and subcommands."""
for command in tuple(self.all_commands.values()):
yield command
if isinstance(command, GroupMixin):
yield from command.walk_commands() | python | def walk_commands(self):
"""An iterator that recursively walks through all commands and subcommands."""
for command in tuple(self.all_commands.values()):
yield command
if isinstance(command, GroupMixin):
yield from command.walk_commands() | [
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26,567 | Rapptz/discord.py | discord/gateway.py | DiscordWebSocket.wait_for | def wait_for(self, event, predicate, result=None):
"""Waits for a DISPATCH'd event that meets the predicate.
Parameters
-----------
event: :class:`str`
The event name in all upper case to wait for.
predicate
A function that takes a data parameter to check for event
properties. The data parameter is the 'd' key in the JSON message.
result
A function that takes the same data parameter and executes to send
the result to the future. If None, returns the data.
Returns
--------
asyncio.Future
A future to wait for.
"""
future = self.loop.create_future()
entry = EventListener(event=event, predicate=predicate, result=result, future=future)
self._dispatch_listeners.append(entry)
return future | python | def wait_for(self, event, predicate, result=None):
"""Waits for a DISPATCH'd event that meets the predicate.
Parameters
-----------
event: :class:`str`
The event name in all upper case to wait for.
predicate
A function that takes a data parameter to check for event
properties. The data parameter is the 'd' key in the JSON message.
result
A function that takes the same data parameter and executes to send
the result to the future. If None, returns the data.
Returns
--------
asyncio.Future
A future to wait for.
"""
future = self.loop.create_future()
entry = EventListener(event=event, predicate=predicate, result=result, future=future)
self._dispatch_listeners.append(entry)
return future | [
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26,568 | Rapptz/discord.py | discord/gateway.py | DiscordWebSocket.identify | async def identify(self):
"""Sends the IDENTIFY packet."""
payload = {
'op': self.IDENTIFY,
'd': {
'token': self.token,
'properties': {
'$os': sys.platform,
'$browser': 'discord.py',
'$device': 'discord.py',
'$referrer': '',
'$referring_domain': ''
},
'compress': True,
'large_threshold': 250,
'v': 3
}
}
if not self._connection.is_bot:
payload['d']['synced_guilds'] = []
if self.shard_id is not None and self.shard_count is not None:
payload['d']['shard'] = [self.shard_id, self.shard_count]
state = self._connection
if state._activity is not None or state._status is not None:
payload['d']['presence'] = {
'status': state._status,
'game': state._activity,
'since': 0,
'afk': False
}
await self.send_as_json(payload)
log.info('Shard ID %s has sent the IDENTIFY payload.', self.shard_id) | python | async def identify(self):
"""Sends the IDENTIFY packet."""
payload = {
'op': self.IDENTIFY,
'd': {
'token': self.token,
'properties': {
'$os': sys.platform,
'$browser': 'discord.py',
'$device': 'discord.py',
'$referrer': '',
'$referring_domain': ''
},
'compress': True,
'large_threshold': 250,
'v': 3
}
}
if not self._connection.is_bot:
payload['d']['synced_guilds'] = []
if self.shard_id is not None and self.shard_count is not None:
payload['d']['shard'] = [self.shard_id, self.shard_count]
state = self._connection
if state._activity is not None or state._status is not None:
payload['d']['presence'] = {
'status': state._status,
'game': state._activity,
'since': 0,
'afk': False
}
await self.send_as_json(payload)
log.info('Shard ID %s has sent the IDENTIFY payload.', self.shard_id) | [
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26,569 | Rapptz/discord.py | discord/gateway.py | DiscordWebSocket.resume | async def resume(self):
"""Sends the RESUME packet."""
payload = {
'op': self.RESUME,
'd': {
'seq': self.sequence,
'session_id': self.session_id,
'token': self.token
}
}
await self.send_as_json(payload)
log.info('Shard ID %s has sent the RESUME payload.', self.shard_id) | python | async def resume(self):
"""Sends the RESUME packet."""
payload = {
'op': self.RESUME,
'd': {
'seq': self.sequence,
'session_id': self.session_id,
'token': self.token
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26,570 | Rapptz/discord.py | discord/gateway.py | DiscordWebSocket.poll_event | async def poll_event(self):
"""Polls for a DISPATCH event and handles the general gateway loop.
Raises
------
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"""
try:
msg = await self.recv()
await self.received_message(msg)
except websockets.exceptions.ConnectionClosed as exc:
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log.info('Websocket closed with %s (%s), attempting a reconnect.', exc.code, exc.reason)
raise ResumeWebSocket(self.shard_id) from exc
else:
log.info('Websocket closed with %s (%s), cannot reconnect.', exc.code, exc.reason)
raise ConnectionClosed(exc, shard_id=self.shard_id) from exc | python | async def poll_event(self):
"""Polls for a DISPATCH event and handles the general gateway loop.
Raises
------
ConnectionClosed
The websocket connection was terminated for unhandled reasons.
"""
try:
msg = await self.recv()
await self.received_message(msg)
except websockets.exceptions.ConnectionClosed as exc:
if self._can_handle_close(exc.code):
log.info('Websocket closed with %s (%s), attempting a reconnect.', exc.code, exc.reason)
raise ResumeWebSocket(self.shard_id) from exc
else:
log.info('Websocket closed with %s (%s), cannot reconnect.', exc.code, exc.reason)
raise ConnectionClosed(exc, shard_id=self.shard_id) from exc | [
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26,571 | Rapptz/discord.py | discord/ext/commands/help.py | Paginator.clear | def clear(self):
"""Clears the paginator to have no pages."""
if self.prefix is not None:
self._current_page = [self.prefix]
self._count = len(self.prefix) + 1 # prefix + newline
else:
self._current_page = []
self._count = 0
self._pages = [] | python | def clear(self):
"""Clears the paginator to have no pages."""
if self.prefix is not None:
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self._pages = [] | [
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26,572 | Rapptz/discord.py | discord/ext/commands/help.py | Paginator.add_line | def add_line(self, line='', *, empty=False):
"""Adds a line to the current page.
If the line exceeds the :attr:`max_size` then an exception
is raised.
Parameters
-----------
line: :class:`str`
The line to add.
empty: :class:`bool`
Indicates if another empty line should be added.
Raises
------
RuntimeError
The line was too big for the current :attr:`max_size`.
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max_page_size = self.max_size - self._prefix_len - 2
if len(line) > max_page_size:
raise RuntimeError('Line exceeds maximum page size %s' % (max_page_size))
if self._count + len(line) + 1 > self.max_size:
self.close_page()
self._count += len(line) + 1
self._current_page.append(line)
if empty:
self._current_page.append('')
self._count += 1 | python | def add_line(self, line='', *, empty=False):
"""Adds a line to the current page.
If the line exceeds the :attr:`max_size` then an exception
is raised.
Parameters
-----------
line: :class:`str`
The line to add.
empty: :class:`bool`
Indicates if another empty line should be added.
Raises
------
RuntimeError
The line was too big for the current :attr:`max_size`.
"""
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26,573 | Rapptz/discord.py | discord/ext/commands/help.py | Paginator.close_page | def close_page(self):
"""Prematurely terminate a page."""
if self.suffix is not None:
self._current_page.append(self.suffix)
self._pages.append('\n'.join(self._current_page))
if self.prefix is not None:
self._current_page = [self.prefix]
self._count = len(self.prefix) + 1 # prefix + newline
else:
self._current_page = []
self._count = 0 | python | def close_page(self):
"""Prematurely terminate a page."""
if self.suffix is not None:
self._current_page.append(self.suffix)
self._pages.append('\n'.join(self._current_page))
if self.prefix is not None:
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26,574 | Rapptz/discord.py | discord/ext/commands/help.py | HelpCommand.clean_prefix | def clean_prefix(self):
"""The cleaned up invoke prefix. i.e. mentions are ``@name`` instead of ``<@id>``."""
user = self.context.guild.me if self.context.guild else self.context.bot.user
# this breaks if the prefix mention is not the bot itself but I
# consider this to be an *incredibly* strange use case. I'd rather go
# for this common use case rather than waste performance for the
# odd one.
return self.context.prefix.replace(user.mention, '@' + user.display_name) | python | def clean_prefix(self):
"""The cleaned up invoke prefix. i.e. mentions are ``@name`` instead of ``<@id>``."""
user = self.context.guild.me if self.context.guild else self.context.bot.user
# this breaks if the prefix mention is not the bot itself but I
# consider this to be an *incredibly* strange use case. I'd rather go
# for this common use case rather than waste performance for the
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return self.context.prefix.replace(user.mention, '@' + user.display_name) | [
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26,575 | Rapptz/discord.py | discord/ext/commands/help.py | HelpCommand.get_command_signature | def get_command_signature(self, command):
"""Retrieves the signature portion of the help page.
Parameters
------------
command: :class:`Command`
The command to get the signature of.
Returns
--------
:class:`str`
The signature for the command.
"""
parent = command.full_parent_name
if len(command.aliases) > 0:
aliases = '|'.join(command.aliases)
fmt = '[%s|%s]' % (command.name, aliases)
if parent:
fmt = parent + ' ' + fmt
alias = fmt
else:
alias = command.name if not parent else parent + ' ' + command.name
return '%s%s %s' % (self.clean_prefix, alias, command.signature) | python | def get_command_signature(self, command):
"""Retrieves the signature portion of the help page.
Parameters
------------
command: :class:`Command`
The command to get the signature of.
Returns
--------
:class:`str`
The signature for the command.
"""
parent = command.full_parent_name
if len(command.aliases) > 0:
aliases = '|'.join(command.aliases)
fmt = '[%s|%s]' % (command.name, aliases)
if parent:
fmt = parent + ' ' + fmt
alias = fmt
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alias = command.name if not parent else parent + ' ' + command.name
return '%s%s %s' % (self.clean_prefix, alias, command.signature) | [
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26,576 | Rapptz/discord.py | discord/ext/commands/help.py | HelpCommand.remove_mentions | def remove_mentions(self, string):
"""Removes mentions from the string to prevent abuse.
This includes ``@everyone``, ``@here``, member mentions and role mentions.
"""
def replace(obj, *, transforms=self.MENTION_TRANSFORMS):
return transforms.get(obj.group(0), '@invalid')
return self.MENTION_PATTERN.sub(replace, string) | python | def remove_mentions(self, string):
"""Removes mentions from the string to prevent abuse.
This includes ``@everyone``, ``@here``, member mentions and role mentions.
"""
def replace(obj, *, transforms=self.MENTION_TRANSFORMS):
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26,577 | Rapptz/discord.py | discord/ext/commands/help.py | HelpCommand.get_max_size | def get_max_size(self, commands):
"""Returns the largest name length of the specified command list.
Parameters
------------
commands: Sequence[:class:`Command`]
A sequence of commands to check for the largest size.
Returns
--------
:class:`int`
The maximum width of the commands.
"""
as_lengths = (
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return max(as_lengths, default=0) | python | def get_max_size(self, commands):
"""Returns the largest name length of the specified command list.
Parameters
------------
commands: Sequence[:class:`Command`]
A sequence of commands to check for the largest size.
Returns
--------
:class:`int`
The maximum width of the commands.
"""
as_lengths = (
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26,578 | Rapptz/discord.py | discord/ext/commands/help.py | DefaultHelpCommand.add_indented_commands | def add_indented_commands(self, commands, *, heading, max_size=None):
"""Indents a list of commands after the specified heading.
The formatting is added to the :attr:`paginator`.
The default implementation is the command name indented by
:attr:`indent` spaces, padded to ``max_size`` followed by
the command's :attr:`Command.short_doc` and then shortened
to fit into the :attr:`width`.
Parameters
-----------
commands: Sequence[:class:`Command`]
A list of commands to indent for output.
heading: :class:`str`
The heading to add to the output. This is only added
if the list of commands is greater than 0.
max_size: Optional[:class:`int`]
The max size to use for the gap between indents.
If unspecified, calls :meth:`get_max_size` on the
commands parameter.
"""
if not commands:
return
self.paginator.add_line(heading)
max_size = max_size or self.get_max_size(commands)
get_width = discord.utils._string_width
for command in commands:
name = command.name
width = max_size - (get_width(name) - len(name))
entry = '{0}{1:<{width}} {2}'.format(self.indent * ' ', name, command.short_doc, width=width)
self.paginator.add_line(self.shorten_text(entry)) | python | def add_indented_commands(self, commands, *, heading, max_size=None):
"""Indents a list of commands after the specified heading.
The formatting is added to the :attr:`paginator`.
The default implementation is the command name indented by
:attr:`indent` spaces, padded to ``max_size`` followed by
the command's :attr:`Command.short_doc` and then shortened
to fit into the :attr:`width`.
Parameters
-----------
commands: Sequence[:class:`Command`]
A list of commands to indent for output.
heading: :class:`str`
The heading to add to the output. This is only added
if the list of commands is greater than 0.
max_size: Optional[:class:`int`]
The max size to use for the gap between indents.
If unspecified, calls :meth:`get_max_size` on the
commands parameter.
"""
if not commands:
return
self.paginator.add_line(heading)
max_size = max_size or self.get_max_size(commands)
get_width = discord.utils._string_width
for command in commands:
name = command.name
width = max_size - (get_width(name) - len(name))
entry = '{0}{1:<{width}} {2}'.format(self.indent * ' ', name, command.short_doc, width=width)
self.paginator.add_line(self.shorten_text(entry)) | [
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Parameters
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commands: Sequence[:class:`Command`]
A list of commands to indent for output.
heading: :class:`str`
The heading to add to the output. This is only added
if the list of commands is greater than 0.
max_size: Optional[:class:`int`]
The max size to use for the gap between indents.
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26,579 | Rapptz/discord.py | discord/ext/commands/help.py | MinimalHelpCommand.add_bot_commands_formatting | def add_bot_commands_formatting(self, commands, heading):
"""Adds the minified bot heading with commands to the output.
The formatting should be added to the :attr:`paginator`.
The default implementation is a bold underline heading followed
by commands separated by an EN SPACE (U+2002) in the next line.
Parameters
-----------
commands: Sequence[:class:`Command`]
A list of commands that belong to the heading.
heading: :class:`str`
The heading to add to the line.
"""
if commands:
# U+2002 Middle Dot
joined = '\u2002'.join(c.name for c in commands)
self.paginator.add_line('__**%s**__' % heading)
self.paginator.add_line(joined) | python | def add_bot_commands_formatting(self, commands, heading):
"""Adds the minified bot heading with commands to the output.
The formatting should be added to the :attr:`paginator`.
The default implementation is a bold underline heading followed
by commands separated by an EN SPACE (U+2002) in the next line.
Parameters
-----------
commands: Sequence[:class:`Command`]
A list of commands that belong to the heading.
heading: :class:`str`
The heading to add to the line.
"""
if commands:
# U+2002 Middle Dot
joined = '\u2002'.join(c.name for c in commands)
self.paginator.add_line('__**%s**__' % heading)
self.paginator.add_line(joined) | [
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A list of commands that belong to the heading.
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26,580 | Rapptz/discord.py | discord/ext/commands/help.py | MinimalHelpCommand.add_subcommand_formatting | def add_subcommand_formatting(self, command):
"""Adds formatting information on a subcommand.
The formatting should be added to the :attr:`paginator`.
The default implementation is the prefix and the :attr:`Command.qualified_name`
optionally followed by an En dash and the command's :attr:`Command.short_doc`.
Parameters
-----------
command: :class:`Command`
The command to show information of.
"""
fmt = '{0}{1} \N{EN DASH} {2}' if command.short_doc else '{0}{1}'
self.paginator.add_line(fmt.format(self.clean_prefix, command.qualified_name, command.short_doc)) | python | def add_subcommand_formatting(self, command):
"""Adds formatting information on a subcommand.
The formatting should be added to the :attr:`paginator`.
The default implementation is the prefix and the :attr:`Command.qualified_name`
optionally followed by an En dash and the command's :attr:`Command.short_doc`.
Parameters
-----------
command: :class:`Command`
The command to show information of.
"""
fmt = '{0}{1} \N{EN DASH} {2}' if command.short_doc else '{0}{1}'
self.paginator.add_line(fmt.format(self.clean_prefix, command.qualified_name, command.short_doc)) | [
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The command to show information of. | [
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26,581 | Rapptz/discord.py | discord/ext/commands/help.py | MinimalHelpCommand.add_aliases_formatting | def add_aliases_formatting(self, aliases):
"""Adds the formatting information on a command's aliases.
The formatting should be added to the :attr:`paginator`.
The default implementation is the :attr:`aliases_heading` bolded
followed by a comma separated list of aliases.
This is not called if there are no aliases to format.
Parameters
-----------
aliases: Sequence[:class:`str`]
A list of aliases to format.
"""
self.paginator.add_line('**%s** %s' % (self.aliases_heading, ', '.join(aliases)), empty=True) | python | def add_aliases_formatting(self, aliases):
"""Adds the formatting information on a command's aliases.
The formatting should be added to the :attr:`paginator`.
The default implementation is the :attr:`aliases_heading` bolded
followed by a comma separated list of aliases.
This is not called if there are no aliases to format.
Parameters
-----------
aliases: Sequence[:class:`str`]
A list of aliases to format.
"""
self.paginator.add_line('**%s** %s' % (self.aliases_heading, ', '.join(aliases)), empty=True) | [
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26,582 | Rapptz/discord.py | discord/ext/commands/help.py | MinimalHelpCommand.add_command_formatting | def add_command_formatting(self, command):
"""A utility function to format commands and groups.
Parameters
------------
command: :class:`Command`
The command to format.
"""
if command.description:
self.paginator.add_line(command.description, empty=True)
signature = self.get_command_signature(command)
if command.aliases:
self.paginator.add_line(signature)
self.add_aliases_formatting(command.aliases)
else:
self.paginator.add_line(signature, empty=True)
if command.help:
try:
self.paginator.add_line(command.help, empty=True)
except RuntimeError:
for line in command.help.splitlines():
self.paginator.add_line(line)
self.paginator.add_line() | python | def add_command_formatting(self, command):
"""A utility function to format commands and groups.
Parameters
------------
command: :class:`Command`
The command to format.
"""
if command.description:
self.paginator.add_line(command.description, empty=True)
signature = self.get_command_signature(command)
if command.aliases:
self.paginator.add_line(signature)
self.add_aliases_formatting(command.aliases)
else:
self.paginator.add_line(signature, empty=True)
if command.help:
try:
self.paginator.add_line(command.help, empty=True)
except RuntimeError:
for line in command.help.splitlines():
self.paginator.add_line(line)
self.paginator.add_line() | [
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26,583 | Rapptz/discord.py | discord/voice_client.py | VoiceClient.send_audio_packet | def send_audio_packet(self, data, *, encode=True):
"""Sends an audio packet composed of the data.
You must be connected to play audio.
Parameters
----------
data: bytes
The :term:`py:bytes-like object` denoting PCM or Opus voice data.
encode: bool
Indicates if ``data`` should be encoded into Opus.
Raises
-------
ClientException
You are not connected.
OpusError
Encoding the data failed.
"""
self.checked_add('sequence', 1, 65535)
if encode:
encoded_data = self.encoder.encode(data, self.encoder.SAMPLES_PER_FRAME)
else:
encoded_data = data
packet = self._get_voice_packet(encoded_data)
try:
self.socket.sendto(packet, (self.endpoint_ip, self.voice_port))
except BlockingIOError:
log.warning('A packet has been dropped (seq: %s, timestamp: %s)', self.sequence, self.timestamp)
self.checked_add('timestamp', self.encoder.SAMPLES_PER_FRAME, 4294967295) | python | def send_audio_packet(self, data, *, encode=True):
"""Sends an audio packet composed of the data.
You must be connected to play audio.
Parameters
----------
data: bytes
The :term:`py:bytes-like object` denoting PCM or Opus voice data.
encode: bool
Indicates if ``data`` should be encoded into Opus.
Raises
-------
ClientException
You are not connected.
OpusError
Encoding the data failed.
"""
self.checked_add('sequence', 1, 65535)
if encode:
encoded_data = self.encoder.encode(data, self.encoder.SAMPLES_PER_FRAME)
else:
encoded_data = data
packet = self._get_voice_packet(encoded_data)
try:
self.socket.sendto(packet, (self.endpoint_ip, self.voice_port))
except BlockingIOError:
log.warning('A packet has been dropped (seq: %s, timestamp: %s)', self.sequence, self.timestamp)
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26,584 | Rapptz/discord.py | discord/client.py | Client.clear | def clear(self):
"""Clears the internal state of the bot.
After this, the bot can be considered "re-opened", i.e. :meth:`.is_closed`
and :meth:`.is_ready` both return ``False`` along with the bot's internal
cache cleared.
"""
self._closed = False
self._ready.clear()
self._connection.clear()
self.http.recreate() | python | def clear(self):
"""Clears the internal state of the bot.
After this, the bot can be considered "re-opened", i.e. :meth:`.is_closed`
and :meth:`.is_ready` both return ``False`` along with the bot's internal
cache cleared.
"""
self._closed = False
self._ready.clear()
self._connection.clear()
self.http.recreate() | [
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26,585 | Rapptz/discord.py | discord/client.py | Client.run | def run(self, *args, **kwargs):
"""A blocking call that abstracts away the event loop
initialisation from you.
If you want more control over the event loop then this
function should not be used. Use :meth:`start` coroutine
or :meth:`connect` + :meth:`login`.
Roughly Equivalent to: ::
try:
loop.run_until_complete(start(*args, **kwargs))
except KeyboardInterrupt:
loop.run_until_complete(logout())
# cancel all tasks lingering
finally:
loop.close()
.. warning::
This function must be the last function to call due to the fact that it
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"""
async def runner():
try:
await self.start(*args, **kwargs)
finally:
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try:
self.loop.run_until_complete(runner())
except KeyboardInterrupt:
log.info('Received signal to terminate bot and event loop.')
finally:
log.info('Cleaning up tasks.')
_cleanup_loop(self.loop) | python | def run(self, *args, **kwargs):
"""A blocking call that abstracts away the event loop
initialisation from you.
If you want more control over the event loop then this
function should not be used. Use :meth:`start` coroutine
or :meth:`connect` + :meth:`login`.
Roughly Equivalent to: ::
try:
loop.run_until_complete(start(*args, **kwargs))
except KeyboardInterrupt:
loop.run_until_complete(logout())
# cancel all tasks lingering
finally:
loop.close()
.. warning::
This function must be the last function to call due to the fact that it
is blocking. That means that registration of events or anything being
called after this function call will not execute until it returns.
"""
async def runner():
try:
await self.start(*args, **kwargs)
finally:
await self.close()
try:
self.loop.run_until_complete(runner())
except KeyboardInterrupt:
log.info('Received signal to terminate bot and event loop.')
finally:
log.info('Cleaning up tasks.')
_cleanup_loop(self.loop) | [
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initialisation from you.
If you want more control over the event loop then this
function should not be used. Use :meth:`start` coroutine
or :meth:`connect` + :meth:`login`.
Roughly Equivalent to: ::
try:
loop.run_until_complete(start(*args, **kwargs))
except KeyboardInterrupt:
loop.run_until_complete(logout())
# cancel all tasks lingering
finally:
loop.close()
.. warning::
This function must be the last function to call due to the fact that it
is blocking. That means that registration of events or anything being
called after this function call will not execute until it returns. | [
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26,586 | Rapptz/discord.py | discord/client.py | Client.event | def event(self, coro):
"""A decorator that registers an event to listen to.
You can find more info about the events on the :ref:`documentation below <discord-api-events>`.
The events must be a |corourl|_, if not, :exc:`TypeError` is raised.
Example
---------
.. code-block:: python3
@client.event
async def on_ready():
print('Ready!')
Raises
--------
TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('event registered must be a coroutine function')
setattr(self, coro.__name__, coro)
log.debug('%s has successfully been registered as an event', coro.__name__)
return coro | python | def event(self, coro):
"""A decorator that registers an event to listen to.
You can find more info about the events on the :ref:`documentation below <discord-api-events>`.
The events must be a |corourl|_, if not, :exc:`TypeError` is raised.
Example
---------
.. code-block:: python3
@client.event
async def on_ready():
print('Ready!')
Raises
--------
TypeError
The coroutine passed is not actually a coroutine.
"""
if not asyncio.iscoroutinefunction(coro):
raise TypeError('event registered must be a coroutine function')
setattr(self, coro.__name__, coro)
log.debug('%s has successfully been registered as an event', coro.__name__)
return coro | [
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Example
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.. code-block:: python3
@client.event
async def on_ready():
print('Ready!')
Raises
--------
TypeError
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26,587 | Rapptz/discord.py | discord/message.py | Message.raw_role_mentions | def raw_role_mentions(self):
"""A property that returns an array of role IDs matched with
the syntax of <@&role_id> in the message content.
"""
return [int(x) for x in re.findall(r'<@&([0-9]+)>', self.content)] | python | def raw_role_mentions(self):
"""A property that returns an array of role IDs matched with
the syntax of <@&role_id> in the message content.
"""
return [int(x) for x in re.findall(r'<@&([0-9]+)>', self.content)] | [
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26,588 | Rapptz/discord.py | discord/ext/commands/bot.py | when_mentioned_or | def when_mentioned_or(*prefixes):
"""A callable that implements when mentioned or other prefixes provided.
These are meant to be passed into the :attr:`.Bot.command_prefix` attribute.
Example
--------
.. code-block:: python3
bot = commands.Bot(command_prefix=commands.when_mentioned_or('!'))
.. note::
This callable returns another callable, so if this is done inside a custom
callable, you must call the returned callable, for example:
.. code-block:: python3
async def get_prefix(bot, message):
extras = await prefixes_for(message.guild) # returns a list
return commands.when_mentioned_or(*extras)(bot, message)
See Also
----------
:func:`.when_mentioned`
"""
def inner(bot, msg):
r = list(prefixes)
r = when_mentioned(bot, msg) + r
return r
return inner | python | def when_mentioned_or(*prefixes):
"""A callable that implements when mentioned or other prefixes provided.
These are meant to be passed into the :attr:`.Bot.command_prefix` attribute.
Example
--------
.. code-block:: python3
bot = commands.Bot(command_prefix=commands.when_mentioned_or('!'))
.. note::
This callable returns another callable, so if this is done inside a custom
callable, you must call the returned callable, for example:
.. code-block:: python3
async def get_prefix(bot, message):
extras = await prefixes_for(message.guild) # returns a list
return commands.when_mentioned_or(*extras)(bot, message)
See Also
----------
:func:`.when_mentioned`
"""
def inner(bot, msg):
r = list(prefixes)
r = when_mentioned(bot, msg) + r
return r
return inner | [
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Example
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.. code-block:: python3
bot = commands.Bot(command_prefix=commands.when_mentioned_or('!'))
.. note::
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.. code-block:: python3
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26,589 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.add_check | def add_check(self, func, *, call_once=False):
"""Adds a global check to the bot.
This is the non-decorator interface to :meth:`.check`
and :meth:`.check_once`.
Parameters
-----------
func
The function that was used as a global check.
call_once: :class:`bool`
If the function should only be called once per
:meth:`.Command.invoke` call.
"""
if call_once:
self._check_once.append(func)
else:
self._checks.append(func) | python | def add_check(self, func, *, call_once=False):
"""Adds a global check to the bot.
This is the non-decorator interface to :meth:`.check`
and :meth:`.check_once`.
Parameters
-----------
func
The function that was used as a global check.
call_once: :class:`bool`
If the function should only be called once per
:meth:`.Command.invoke` call.
"""
if call_once:
self._check_once.append(func)
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The function that was used as a global check.
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26,590 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.remove_check | def remove_check(self, func, *, call_once=False):
"""Removes a global check from the bot.
This function is idempotent and will not raise an exception
if the function is not in the global checks.
Parameters
-----------
func
The function to remove from the global checks.
call_once: :class:`bool`
If the function was added with ``call_once=True`` in
the :meth:`.Bot.add_check` call or using :meth:`.check_once`.
"""
l = self._check_once if call_once else self._checks
try:
l.remove(func)
except ValueError:
pass | python | def remove_check(self, func, *, call_once=False):
"""Removes a global check from the bot.
This function is idempotent and will not raise an exception
if the function is not in the global checks.
Parameters
-----------
func
The function to remove from the global checks.
call_once: :class:`bool`
If the function was added with ``call_once=True`` in
the :meth:`.Bot.add_check` call or using :meth:`.check_once`.
"""
l = self._check_once if call_once else self._checks
try:
l.remove(func)
except ValueError:
pass | [
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26,591 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.remove_listener | def remove_listener(self, func, name=None):
"""Removes a listener from the pool of listeners.
Parameters
-----------
func
The function that was used as a listener to remove.
name: :class:`str`
The name of the event we want to remove. Defaults to
``func.__name__``.
"""
name = func.__name__ if name is None else name
if name in self.extra_events:
try:
self.extra_events[name].remove(func)
except ValueError:
pass | python | def remove_listener(self, func, name=None):
"""Removes a listener from the pool of listeners.
Parameters
-----------
func
The function that was used as a listener to remove.
name: :class:`str`
The name of the event we want to remove. Defaults to
``func.__name__``.
"""
name = func.__name__ if name is None else name
if name in self.extra_events:
try:
self.extra_events[name].remove(func)
except ValueError:
pass | [
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26,592 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.add_cog | def add_cog(self, cog):
"""Adds a "cog" to the bot.
A cog is a class that has its own event listeners and commands.
Parameters
-----------
cog: :class:`.Cog`
The cog to register to the bot.
Raises
-------
TypeError
The cog does not inherit from :class:`.Cog`.
CommandError
An error happened during loading.
"""
if not isinstance(cog, Cog):
raise TypeError('cogs must derive from Cog')
cog = cog._inject(self)
self.__cogs[cog.__cog_name__] = cog | python | def add_cog(self, cog):
"""Adds a "cog" to the bot.
A cog is a class that has its own event listeners and commands.
Parameters
-----------
cog: :class:`.Cog`
The cog to register to the bot.
Raises
-------
TypeError
The cog does not inherit from :class:`.Cog`.
CommandError
An error happened during loading.
"""
if not isinstance(cog, Cog):
raise TypeError('cogs must derive from Cog')
cog = cog._inject(self)
self.__cogs[cog.__cog_name__] = cog | [
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The cog to register to the bot.
Raises
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The cog does not inherit from :class:`.Cog`.
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26,593 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.remove_cog | def remove_cog(self, name):
"""Removes a cog from the bot.
All registered commands and event listeners that the
cog has registered will be removed as well.
If no cog is found then this method has no effect.
Parameters
-----------
name: :class:`str`
The name of the cog to remove.
"""
cog = self.__cogs.pop(name, None)
if cog is None:
return
help_command = self._help_command
if help_command and help_command.cog is cog:
help_command.cog = None
cog._eject(self) | python | def remove_cog(self, name):
"""Removes a cog from the bot.
All registered commands and event listeners that the
cog has registered will be removed as well.
If no cog is found then this method has no effect.
Parameters
-----------
name: :class:`str`
The name of the cog to remove.
"""
cog = self.__cogs.pop(name, None)
if cog is None:
return
help_command = self._help_command
if help_command and help_command.cog is cog:
help_command.cog = None
cog._eject(self) | [
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26,594 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.load_extension | def load_extension(self, name):
"""Loads an extension.
An extension is a python module that contains commands, cogs, or
listeners.
An extension must have a global function, ``setup`` defined as
the entry point on what to do when the extension is loaded. This entry
point must have a single argument, the ``bot``.
Parameters
------------
name: :class:`str`
The extension name to load. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
``foo.test`` if you want to import ``foo/test.py``.
Raises
--------
ExtensionNotFound
The extension could not be imported.
ExtensionAlreadyLoaded
The extension is already loaded.
NoEntryPointError
The extension does not have a setup function.
ExtensionFailed
The extension setup function had an execution error.
"""
if name in self.__extensions:
raise errors.ExtensionAlreadyLoaded(name)
try:
lib = importlib.import_module(name)
except ImportError as e:
raise errors.ExtensionNotFound(name, e) from e
else:
self._load_from_module_spec(lib, name) | python | def load_extension(self, name):
"""Loads an extension.
An extension is a python module that contains commands, cogs, or
listeners.
An extension must have a global function, ``setup`` defined as
the entry point on what to do when the extension is loaded. This entry
point must have a single argument, the ``bot``.
Parameters
------------
name: :class:`str`
The extension name to load. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
``foo.test`` if you want to import ``foo/test.py``.
Raises
--------
ExtensionNotFound
The extension could not be imported.
ExtensionAlreadyLoaded
The extension is already loaded.
NoEntryPointError
The extension does not have a setup function.
ExtensionFailed
The extension setup function had an execution error.
"""
if name in self.__extensions:
raise errors.ExtensionAlreadyLoaded(name)
try:
lib = importlib.import_module(name)
except ImportError as e:
raise errors.ExtensionNotFound(name, e) from e
else:
self._load_from_module_spec(lib, name) | [
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Raises
--------
ExtensionNotFound
The extension could not be imported.
ExtensionAlreadyLoaded
The extension is already loaded.
NoEntryPointError
The extension does not have a setup function.
ExtensionFailed
The extension setup function had an execution error. | [
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] | 05d4f7f9620ef33635d6ac965b26528e09cdaf5b | https://github.com/Rapptz/discord.py/blob/05d4f7f9620ef33635d6ac965b26528e09cdaf5b/discord/ext/commands/bot.py#L584-L621 |
26,595 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.unload_extension | def unload_extension(self, name):
"""Unloads an extension.
When the extension is unloaded, all commands, listeners, and cogs are
removed from the bot and the module is un-imported.
The extension can provide an optional global function, ``teardown``,
to do miscellaneous clean-up if necessary. This function takes a single
parameter, the ``bot``, similar to ``setup`` from
:meth:`~.Bot.load_extension`.
Parameters
------------
name: :class:`str`
The extension name to unload. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
``foo.test`` if you want to import ``foo/test.py``.
Raises
-------
ExtensionNotLoaded
The extension was not loaded.
"""
lib = self.__extensions.get(name)
if lib is None:
raise errors.ExtensionNotLoaded(name)
self._remove_module_references(lib.__name__)
self._call_module_finalizers(lib, name) | python | def unload_extension(self, name):
"""Unloads an extension.
When the extension is unloaded, all commands, listeners, and cogs are
removed from the bot and the module is un-imported.
The extension can provide an optional global function, ``teardown``,
to do miscellaneous clean-up if necessary. This function takes a single
parameter, the ``bot``, similar to ``setup`` from
:meth:`~.Bot.load_extension`.
Parameters
------------
name: :class:`str`
The extension name to unload. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
``foo.test`` if you want to import ``foo/test.py``.
Raises
-------
ExtensionNotLoaded
The extension was not loaded.
"""
lib = self.__extensions.get(name)
if lib is None:
raise errors.ExtensionNotLoaded(name)
self._remove_module_references(lib.__name__)
self._call_module_finalizers(lib, name) | [
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------------
name: :class:`str`
The extension name to unload. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
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Raises
-------
ExtensionNotLoaded
The extension was not loaded. | [
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] | 05d4f7f9620ef33635d6ac965b26528e09cdaf5b | https://github.com/Rapptz/discord.py/blob/05d4f7f9620ef33635d6ac965b26528e09cdaf5b/discord/ext/commands/bot.py#L623-L652 |
26,596 | Rapptz/discord.py | discord/ext/commands/bot.py | BotBase.reload_extension | def reload_extension(self, name):
"""Atomically reloads an extension.
This replaces the extension with the same extension, only refreshed. This is
equivalent to a :meth:`unload_extension` followed by a :meth:`load_extension`
except done in an atomic way. That is, if an operation fails mid-reload then
the bot will roll-back to the prior working state.
Parameters
------------
name: :class:`str`
The extension name to reload. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
``foo.test`` if you want to import ``foo/test.py``.
Raises
-------
ExtensionNotLoaded
The extension was not loaded.
ExtensionNotFound
The extension could not be imported.
NoEntryPointError
The extension does not have a setup function.
ExtensionFailed
The extension setup function had an execution error.
"""
lib = self.__extensions.get(name)
if lib is None:
raise errors.ExtensionNotLoaded(name)
# get the previous module states from sys modules
modules = {
name: module
for name, module in sys.modules.items()
if _is_submodule(lib.__name__, name)
}
try:
# Unload and then load the module...
self._remove_module_references(lib.__name__)
self._call_module_finalizers(lib, name)
self.load_extension(name)
except Exception as e:
# if the load failed, the remnants should have been
# cleaned from the load_extension function call
# so let's load it from our old compiled library.
self._load_from_module_spec(lib, name)
# revert sys.modules back to normal and raise back to caller
sys.modules.update(modules)
raise | python | def reload_extension(self, name):
"""Atomically reloads an extension.
This replaces the extension with the same extension, only refreshed. This is
equivalent to a :meth:`unload_extension` followed by a :meth:`load_extension`
except done in an atomic way. That is, if an operation fails mid-reload then
the bot will roll-back to the prior working state.
Parameters
------------
name: :class:`str`
The extension name to reload. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
``foo.test`` if you want to import ``foo/test.py``.
Raises
-------
ExtensionNotLoaded
The extension was not loaded.
ExtensionNotFound
The extension could not be imported.
NoEntryPointError
The extension does not have a setup function.
ExtensionFailed
The extension setup function had an execution error.
"""
lib = self.__extensions.get(name)
if lib is None:
raise errors.ExtensionNotLoaded(name)
# get the previous module states from sys modules
modules = {
name: module
for name, module in sys.modules.items()
if _is_submodule(lib.__name__, name)
}
try:
# Unload and then load the module...
self._remove_module_references(lib.__name__)
self._call_module_finalizers(lib, name)
self.load_extension(name)
except Exception as e:
# if the load failed, the remnants should have been
# cleaned from the load_extension function call
# so let's load it from our old compiled library.
self._load_from_module_spec(lib, name)
# revert sys.modules back to normal and raise back to caller
sys.modules.update(modules)
raise | [
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equivalent to a :meth:`unload_extension` followed by a :meth:`load_extension`
except done in an atomic way. That is, if an operation fails mid-reload then
the bot will roll-back to the prior working state.
Parameters
------------
name: :class:`str`
The extension name to reload. It must be dot separated like
regular Python imports if accessing a sub-module. e.g.
``foo.test`` if you want to import ``foo/test.py``.
Raises
-------
ExtensionNotLoaded
The extension was not loaded.
ExtensionNotFound
The extension could not be imported.
NoEntryPointError
The extension does not have a setup function.
ExtensionFailed
The extension setup function had an execution error. | [
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"."
] | 05d4f7f9620ef33635d6ac965b26528e09cdaf5b | https://github.com/Rapptz/discord.py/blob/05d4f7f9620ef33635d6ac965b26528e09cdaf5b/discord/ext/commands/bot.py#L654-L705 |
26,597 | Rapptz/discord.py | discord/guild.py | Guild.chunked | def chunked(self):
"""Returns a boolean indicating if the guild is "chunked".
A chunked guild means that :attr:`member_count` is equal to the
number of members stored in the internal :attr:`members` cache.
If this value returns ``False``, then you should request for
offline members.
"""
count = getattr(self, '_member_count', None)
if count is None:
return False
return count == len(self._members) | python | def chunked(self):
"""Returns a boolean indicating if the guild is "chunked".
A chunked guild means that :attr:`member_count` is equal to the
number of members stored in the internal :attr:`members` cache.
If this value returns ``False``, then you should request for
offline members.
"""
count = getattr(self, '_member_count', None)
if count is None:
return False
return count == len(self._members) | [
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If this value returns ``False``, then you should request for
offline members. | [
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26,598 | Rapptz/discord.py | discord/guild.py | Guild.shard_id | def shard_id(self):
"""Returns the shard ID for this guild if applicable."""
count = self._state.shard_count
if count is None:
return None
return (self.id >> 22) % count | python | def shard_id(self):
"""Returns the shard ID for this guild if applicable."""
count = self._state.shard_count
if count is None:
return None
return (self.id >> 22) % count | [
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26,599 | Rapptz/discord.py | discord/guild.py | Guild.get_member_named | def get_member_named(self, name):
"""Returns the first member found that matches the name provided.
The name can have an optional discriminator argument, e.g. "Jake#0001"
or "Jake" will both do the lookup. However the former will give a more
precise result. Note that the discriminator must have all 4 digits
for this to work.
If a nickname is passed, then it is looked up via the nickname. Note
however, that a nickname + discriminator combo will not lookup the nickname
but rather the username + discriminator combo due to nickname + discriminator
not being unique.
If no member is found, ``None`` is returned.
Parameters
-----------
name: :class:`str`
The name of the member to lookup with an optional discriminator.
Returns
--------
:class:`Member`
The member in this guild with the associated name. If not found
then ``None`` is returned.
"""
result = None
members = self.members
if len(name) > 5 and name[-5] == '#':
# The 5 length is checking to see if #0000 is in the string,
# as a#0000 has a length of 6, the minimum for a potential
# discriminator lookup.
potential_discriminator = name[-4:]
# do the actual lookup and return if found
# if it isn't found then we'll do a full name lookup below.
result = utils.get(members, name=name[:-5], discriminator=potential_discriminator)
if result is not None:
return result
def pred(m):
return m.nick == name or m.name == name
return utils.find(pred, members) | python | def get_member_named(self, name):
"""Returns the first member found that matches the name provided.
The name can have an optional discriminator argument, e.g. "Jake#0001"
or "Jake" will both do the lookup. However the former will give a more
precise result. Note that the discriminator must have all 4 digits
for this to work.
If a nickname is passed, then it is looked up via the nickname. Note
however, that a nickname + discriminator combo will not lookup the nickname
but rather the username + discriminator combo due to nickname + discriminator
not being unique.
If no member is found, ``None`` is returned.
Parameters
-----------
name: :class:`str`
The name of the member to lookup with an optional discriminator.
Returns
--------
:class:`Member`
The member in this guild with the associated name. If not found
then ``None`` is returned.
"""
result = None
members = self.members
if len(name) > 5 and name[-5] == '#':
# The 5 length is checking to see if #0000 is in the string,
# as a#0000 has a length of 6, the minimum for a potential
# discriminator lookup.
potential_discriminator = name[-4:]
# do the actual lookup and return if found
# if it isn't found then we'll do a full name lookup below.
result = utils.get(members, name=name[:-5], discriminator=potential_discriminator)
if result is not None:
return result
def pred(m):
return m.nick == name or m.name == name
return utils.find(pred, members) | [
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or "Jake" will both do the lookup. However the former will give a more
precise result. Note that the discriminator must have all 4 digits
for this to work.
If a nickname is passed, then it is looked up via the nickname. Note
however, that a nickname + discriminator combo will not lookup the nickname
but rather the username + discriminator combo due to nickname + discriminator
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If no member is found, ``None`` is returned.
Parameters
-----------
name: :class:`str`
The name of the member to lookup with an optional discriminator.
Returns
--------
:class:`Member`
The member in this guild with the associated name. If not found
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