id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
238,300 | IDSIA/sacred | sacred/utils.py | iterate_flattened_separately | def iterate_flattened_separately(dictionary, manually_sorted_keys=None):
"""
Recursively iterate over the items of a dictionary in a special order.
First iterate over manually sorted keys and then over all items that are
non-dictionary values (sorted by keys), then over the rest
(sorted by keys), providing full dotted paths for every leaf.
"""
if manually_sorted_keys is None:
manually_sorted_keys = []
for key in manually_sorted_keys:
if key in dictionary:
yield key, dictionary[key]
single_line_keys = [key for key in dictionary.keys() if
key not in manually_sorted_keys and
(not dictionary[key] or
not isinstance(dictionary[key], dict))]
for key in sorted(single_line_keys):
yield key, dictionary[key]
multi_line_keys = [key for key in dictionary.keys() if
key not in manually_sorted_keys and
(dictionary[key] and
isinstance(dictionary[key], dict))]
for key in sorted(multi_line_keys):
yield key, PATHCHANGE
for k, val in iterate_flattened_separately(dictionary[key],
manually_sorted_keys):
yield join_paths(key, k), val | python | def iterate_flattened_separately(dictionary, manually_sorted_keys=None):
if manually_sorted_keys is None:
manually_sorted_keys = []
for key in manually_sorted_keys:
if key in dictionary:
yield key, dictionary[key]
single_line_keys = [key for key in dictionary.keys() if
key not in manually_sorted_keys and
(not dictionary[key] or
not isinstance(dictionary[key], dict))]
for key in sorted(single_line_keys):
yield key, dictionary[key]
multi_line_keys = [key for key in dictionary.keys() if
key not in manually_sorted_keys and
(dictionary[key] and
isinstance(dictionary[key], dict))]
for key in sorted(multi_line_keys):
yield key, PATHCHANGE
for k, val in iterate_flattened_separately(dictionary[key],
manually_sorted_keys):
yield join_paths(key, k), val | [
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238,301 | IDSIA/sacred | sacred/utils.py | iterate_flattened | def iterate_flattened(d):
"""
Recursively iterate over the items of a dictionary.
Provides a full dotted paths for every leaf.
"""
for key in sorted(d.keys()):
value = d[key]
if isinstance(value, dict) and value:
for k, v in iterate_flattened(d[key]):
yield join_paths(key, k), v
else:
yield key, value | python | def iterate_flattened(d):
for key in sorted(d.keys()):
value = d[key]
if isinstance(value, dict) and value:
for k, v in iterate_flattened(d[key]):
yield join_paths(key, k), v
else:
yield key, value | [
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Provides a full dotted paths for every leaf. | [
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238,302 | IDSIA/sacred | sacred/utils.py | set_by_dotted_path | def set_by_dotted_path(d, path, value):
"""
Set an entry in a nested dict using a dotted path.
Will create dictionaries as needed.
Examples
--------
>>> d = {'foo': {'bar': 7}}
>>> set_by_dotted_path(d, 'foo.bar', 10)
>>> d
{'foo': {'bar': 10}}
>>> set_by_dotted_path(d, 'foo.d.baz', 3)
>>> d
{'foo': {'bar': 10, 'd': {'baz': 3}}}
"""
split_path = path.split('.')
current_option = d
for p in split_path[:-1]:
if p not in current_option:
current_option[p] = dict()
current_option = current_option[p]
current_option[split_path[-1]] = value | python | def set_by_dotted_path(d, path, value):
split_path = path.split('.')
current_option = d
for p in split_path[:-1]:
if p not in current_option:
current_option[p] = dict()
current_option = current_option[p]
current_option[split_path[-1]] = value | [
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Will create dictionaries as needed.
Examples
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>>> d = {'foo': {'bar': 7}}
>>> set_by_dotted_path(d, 'foo.bar', 10)
>>> d
{'foo': {'bar': 10}}
>>> set_by_dotted_path(d, 'foo.d.baz', 3)
>>> d
{'foo': {'bar': 10, 'd': {'baz': 3}}} | [
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238,303 | IDSIA/sacred | sacred/utils.py | get_by_dotted_path | def get_by_dotted_path(d, path, default=None):
"""
Get an entry from nested dictionaries using a dotted path.
Example:
>>> get_by_dotted_path({'foo': {'a': 12}}, 'foo.a')
12
"""
if not path:
return d
split_path = path.split('.')
current_option = d
for p in split_path:
if p not in current_option:
return default
current_option = current_option[p]
return current_option | python | def get_by_dotted_path(d, path, default=None):
if not path:
return d
split_path = path.split('.')
current_option = d
for p in split_path:
if p not in current_option:
return default
current_option = current_option[p]
return current_option | [
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238,304 | IDSIA/sacred | sacred/utils.py | iter_path_splits | def iter_path_splits(path):
"""
Iterate over possible splits of a dotted path.
The first part can be empty the second should not be.
Example:
>>> list(iter_path_splits('foo.bar.baz'))
[('', 'foo.bar.baz'),
('foo', 'bar.baz'),
('foo.bar', 'baz')]
"""
split_path = path.split('.')
for i in range(len(split_path)):
p1 = join_paths(*split_path[:i])
p2 = join_paths(*split_path[i:])
yield p1, p2 | python | def iter_path_splits(path):
split_path = path.split('.')
for i in range(len(split_path)):
p1 = join_paths(*split_path[:i])
p2 = join_paths(*split_path[i:])
yield p1, p2 | [
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238,305 | IDSIA/sacred | sacred/utils.py | is_prefix | def is_prefix(pre_path, path):
"""Return True if pre_path is a path-prefix of path."""
pre_path = pre_path.strip('.')
path = path.strip('.')
return not pre_path or path.startswith(pre_path + '.') | python | def is_prefix(pre_path, path):
pre_path = pre_path.strip('.')
path = path.strip('.')
return not pre_path or path.startswith(pre_path + '.') | [
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238,306 | IDSIA/sacred | sacred/utils.py | rel_path | def rel_path(base, path):
"""Return path relative to base."""
if base == path:
return ''
assert is_prefix(base, path), "{} not a prefix of {}".format(base, path)
return path[len(base):].strip('.') | python | def rel_path(base, path):
if base == path:
return ''
assert is_prefix(base, path), "{} not a prefix of {}".format(base, path)
return path[len(base):].strip('.') | [
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238,307 | IDSIA/sacred | sacred/utils.py | convert_to_nested_dict | def convert_to_nested_dict(dotted_dict):
"""Convert a dict with dotted path keys to corresponding nested dict."""
nested_dict = {}
for k, v in iterate_flattened(dotted_dict):
set_by_dotted_path(nested_dict, k, v)
return nested_dict | python | def convert_to_nested_dict(dotted_dict):
nested_dict = {}
for k, v in iterate_flattened(dotted_dict):
set_by_dotted_path(nested_dict, k, v)
return nested_dict | [
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238,308 | IDSIA/sacred | sacred/utils.py | format_filtered_stacktrace | def format_filtered_stacktrace(filter_traceback='default'):
"""
Returns the traceback as `string`.
`filter_traceback` can be one of:
- 'always': always filter out sacred internals
- 'default': Default behaviour: filter out sacred internals
if the exception did not originate from within sacred, and
print just the internal stack trace otherwise
- 'never': don't filter, always print full traceback
- All other values will fall back to 'never'.
"""
exc_type, exc_value, exc_traceback = sys.exc_info()
# determine if last exception is from sacred
current_tb = exc_traceback
while current_tb.tb_next is not None:
current_tb = current_tb.tb_next
if filter_traceback == 'default' \
and _is_sacred_frame(current_tb.tb_frame):
# just print sacred internal trace
header = ["Exception originated from within Sacred.\n"
"Traceback (most recent calls):\n"]
texts = tb.format_exception(exc_type, exc_value, current_tb)
return ''.join(header + texts[1:]).strip()
elif filter_traceback in ('default', 'always'):
# print filtered stacktrace
if sys.version_info >= (3, 5):
tb_exception = \
tb.TracebackException(exc_type, exc_value, exc_traceback,
limit=None)
return ''.join(filtered_traceback_format(tb_exception))
else:
s = "Traceback (most recent calls WITHOUT Sacred internals):"
current_tb = exc_traceback
while current_tb is not None:
if not _is_sacred_frame(current_tb.tb_frame):
tb.print_tb(current_tb, 1)
current_tb = current_tb.tb_next
s += "\n".join(tb.format_exception_only(exc_type,
exc_value)).strip()
return s
elif filter_traceback == 'never':
# print full stacktrace
return '\n'.join(
tb.format_exception(exc_type, exc_value, exc_traceback))
else:
raise ValueError('Unknown value for filter_traceback: ' +
filter_traceback) | python | def format_filtered_stacktrace(filter_traceback='default'):
exc_type, exc_value, exc_traceback = sys.exc_info()
# determine if last exception is from sacred
current_tb = exc_traceback
while current_tb.tb_next is not None:
current_tb = current_tb.tb_next
if filter_traceback == 'default' \
and _is_sacred_frame(current_tb.tb_frame):
# just print sacred internal trace
header = ["Exception originated from within Sacred.\n"
"Traceback (most recent calls):\n"]
texts = tb.format_exception(exc_type, exc_value, current_tb)
return ''.join(header + texts[1:]).strip()
elif filter_traceback in ('default', 'always'):
# print filtered stacktrace
if sys.version_info >= (3, 5):
tb_exception = \
tb.TracebackException(exc_type, exc_value, exc_traceback,
limit=None)
return ''.join(filtered_traceback_format(tb_exception))
else:
s = "Traceback (most recent calls WITHOUT Sacred internals):"
current_tb = exc_traceback
while current_tb is not None:
if not _is_sacred_frame(current_tb.tb_frame):
tb.print_tb(current_tb, 1)
current_tb = current_tb.tb_next
s += "\n".join(tb.format_exception_only(exc_type,
exc_value)).strip()
return s
elif filter_traceback == 'never':
# print full stacktrace
return '\n'.join(
tb.format_exception(exc_type, exc_value, exc_traceback))
else:
raise ValueError('Unknown value for filter_traceback: ' +
filter_traceback) | [
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238,309 | IDSIA/sacred | sacred/utils.py | get_inheritors | def get_inheritors(cls):
"""Get a set of all classes that inherit from the given class."""
subclasses = set()
work = [cls]
while work:
parent = work.pop()
for child in parent.__subclasses__():
if child not in subclasses:
subclasses.add(child)
work.append(child)
return subclasses | python | def get_inheritors(cls):
subclasses = set()
work = [cls]
while work:
parent = work.pop()
for child in parent.__subclasses__():
if child not in subclasses:
subclasses.add(child)
work.append(child)
return subclasses | [
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238,310 | IDSIA/sacred | sacred/utils.py | apply_backspaces_and_linefeeds | def apply_backspaces_and_linefeeds(text):
"""
Interpret backspaces and linefeeds in text like a terminal would.
Interpret text like a terminal by removing backspace and linefeed
characters and applying them line by line.
If final line ends with a carriage it keeps it to be concatenable with next
output chunk.
"""
orig_lines = text.split('\n')
orig_lines_len = len(orig_lines)
new_lines = []
for orig_line_idx, orig_line in enumerate(orig_lines):
chars, cursor = [], 0
orig_line_len = len(orig_line)
for orig_char_idx, orig_char in enumerate(orig_line):
if orig_char == '\r' and (orig_char_idx != orig_line_len - 1 or
orig_line_idx != orig_lines_len - 1):
cursor = 0
elif orig_char == '\b':
cursor = max(0, cursor - 1)
else:
if (orig_char == '\r' and
orig_char_idx == orig_line_len - 1 and
orig_line_idx == orig_lines_len - 1):
cursor = len(chars)
if cursor == len(chars):
chars.append(orig_char)
else:
chars[cursor] = orig_char
cursor += 1
new_lines.append(''.join(chars))
return '\n'.join(new_lines) | python | def apply_backspaces_and_linefeeds(text):
orig_lines = text.split('\n')
orig_lines_len = len(orig_lines)
new_lines = []
for orig_line_idx, orig_line in enumerate(orig_lines):
chars, cursor = [], 0
orig_line_len = len(orig_line)
for orig_char_idx, orig_char in enumerate(orig_line):
if orig_char == '\r' and (orig_char_idx != orig_line_len - 1 or
orig_line_idx != orig_lines_len - 1):
cursor = 0
elif orig_char == '\b':
cursor = max(0, cursor - 1)
else:
if (orig_char == '\r' and
orig_char_idx == orig_line_len - 1 and
orig_line_idx == orig_lines_len - 1):
cursor = len(chars)
if cursor == len(chars):
chars.append(orig_char)
else:
chars[cursor] = orig_char
cursor += 1
new_lines.append(''.join(chars))
return '\n'.join(new_lines) | [
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238,311 | IDSIA/sacred | sacred/utils.py | module_is_imported | def module_is_imported(modname, scope=None):
"""Checks if a module is imported within the current namespace."""
# return early if modname is not even cached
if not module_is_in_cache(modname):
return False
if scope is None: # use globals() of the caller by default
scope = inspect.stack()[1][0].f_globals
for m in scope.values():
if isinstance(m, type(sys)) and m.__name__ == modname:
return True
return False | python | def module_is_imported(modname, scope=None):
# return early if modname is not even cached
if not module_is_in_cache(modname):
return False
if scope is None: # use globals() of the caller by default
scope = inspect.stack()[1][0].f_globals
for m in scope.values():
if isinstance(m, type(sys)) and m.__name__ == modname:
return True
return False | [
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238,312 | IDSIA/sacred | sacred/observers/telegram_obs.py | TelegramObserver.from_config | def from_config(cls, filename):
"""
Create a TelegramObserver from a given configuration file.
The file can be in any format supported by Sacred
(.json, .pickle, [.yaml]).
It has to specify a ``token`` and a ``chat_id`` and can optionally set
``silent_completion``,``completed_text``, ``interrupted_text``, and
``failed_text``.
"""
import telegram
d = load_config_file(filename)
request = cls.get_proxy_request(d) if 'proxy_url' in d else None
if 'token' in d and 'chat_id' in d:
bot = telegram.Bot(d['token'], request=request)
obs = cls(bot, **d)
else:
raise ValueError("Telegram configuration file must contain "
"entries for 'token' and 'chat_id'!")
for k in ['completed_text', 'interrupted_text', 'failed_text']:
if k in d:
setattr(obs, k, d[k])
return obs | python | def from_config(cls, filename):
import telegram
d = load_config_file(filename)
request = cls.get_proxy_request(d) if 'proxy_url' in d else None
if 'token' in d and 'chat_id' in d:
bot = telegram.Bot(d['token'], request=request)
obs = cls(bot, **d)
else:
raise ValueError("Telegram configuration file must contain "
"entries for 'token' and 'chat_id'!")
for k in ['completed_text', 'interrupted_text', 'failed_text']:
if k in d:
setattr(obs, k, d[k])
return obs | [
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238,313 | IDSIA/sacred | sacred/observers/mongo.py | MongoObserver.log_metrics | def log_metrics(self, metrics_by_name, info):
"""Store new measurements to the database.
Take measurements and store them into
the metrics collection in the database.
Additionally, reference the metrics
in the info["metrics"] dictionary.
"""
if self.metrics is None:
# If, for whatever reason, the metrics collection has not been set
# do not try to save anything there.
return
for key in metrics_by_name:
query = {"run_id": self.run_entry['_id'],
"name": key}
push = {"steps": {"$each": metrics_by_name[key]["steps"]},
"values": {"$each": metrics_by_name[key]["values"]},
"timestamps": {"$each": metrics_by_name[key]["timestamps"]}
}
update = {"$push": push}
result = self.metrics.update_one(query, update, upsert=True)
if result.upserted_id is not None:
# This is the first time we are storing this metric
info.setdefault("metrics", []) \
.append({"name": key, "id": str(result.upserted_id)}) | python | def log_metrics(self, metrics_by_name, info):
if self.metrics is None:
# If, for whatever reason, the metrics collection has not been set
# do not try to save anything there.
return
for key in metrics_by_name:
query = {"run_id": self.run_entry['_id'],
"name": key}
push = {"steps": {"$each": metrics_by_name[key]["steps"]},
"values": {"$each": metrics_by_name[key]["values"]},
"timestamps": {"$each": metrics_by_name[key]["timestamps"]}
}
update = {"$push": push}
result = self.metrics.update_one(query, update, upsert=True)
if result.upserted_id is not None:
# This is the first time we are storing this metric
info.setdefault("metrics", []) \
.append({"name": key, "id": str(result.upserted_id)}) | [
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238,314 | IDSIA/sacred | sacred/dependencies.py | get_digest | def get_digest(filename):
"""Compute the MD5 hash for a given file."""
h = hashlib.md5()
with open(filename, 'rb') as f:
data = f.read(1 * MB)
while data:
h.update(data)
data = f.read(1 * MB)
return h.hexdigest() | python | def get_digest(filename):
h = hashlib.md5()
with open(filename, 'rb') as f:
data = f.read(1 * MB)
while data:
h.update(data)
data = f.read(1 * MB)
return h.hexdigest() | [
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238,315 | IDSIA/sacred | sacred/dependencies.py | get_commit_if_possible | def get_commit_if_possible(filename):
"""Try to retrieve VCS information for a given file.
Currently only supports git using the gitpython package.
Parameters
----------
filename : str
Returns
-------
path: str
The base path of the repository
commit: str
The commit hash
is_dirty: bool
True if there are uncommitted changes in the repository
"""
# git
if opt.has_gitpython:
from git import Repo, InvalidGitRepositoryError
try:
directory = os.path.dirname(filename)
repo = Repo(directory, search_parent_directories=True)
try:
path = repo.remote().url
except ValueError:
path = 'git:/' + repo.working_dir
is_dirty = repo.is_dirty()
commit = repo.head.commit.hexsha
return path, commit, is_dirty
except (InvalidGitRepositoryError, ValueError):
pass
return None, None, None | python | def get_commit_if_possible(filename):
# git
if opt.has_gitpython:
from git import Repo, InvalidGitRepositoryError
try:
directory = os.path.dirname(filename)
repo = Repo(directory, search_parent_directories=True)
try:
path = repo.remote().url
except ValueError:
path = 'git:/' + repo.working_dir
is_dirty = repo.is_dirty()
commit = repo.head.commit.hexsha
return path, commit, is_dirty
except (InvalidGitRepositoryError, ValueError):
pass
return None, None, None | [
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path: str
The base path of the repository
commit: str
The commit hash
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238,316 | IDSIA/sacred | sacred/dependencies.py | convert_path_to_module_parts | def convert_path_to_module_parts(path):
"""Convert path to a python file into list of module names."""
module_parts = splitall(path)
if module_parts[-1] in ['__init__.py', '__init__.pyc']:
# remove trailing __init__.py
module_parts = module_parts[:-1]
else:
# remove file extension
module_parts[-1], _ = os.path.splitext(module_parts[-1])
return module_parts | python | def convert_path_to_module_parts(path):
module_parts = splitall(path)
if module_parts[-1] in ['__init__.py', '__init__.pyc']:
# remove trailing __init__.py
module_parts = module_parts[:-1]
else:
# remove file extension
module_parts[-1], _ = os.path.splitext(module_parts[-1])
return module_parts | [
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238,317 | IDSIA/sacred | sacred/dependencies.py | is_local_source | def is_local_source(filename, modname, experiment_path):
"""Check if a module comes from the given experiment path.
Check if a module, given by name and filename, is from (a subdirectory of )
the given experiment path.
This is used to determine if the module is a local source file, or rather
a package dependency.
Parameters
----------
filename: str
The absolute filename of the module in question.
(Usually module.__file__)
modname: str
The full name of the module including parent namespaces.
experiment_path: str
The base path of the experiment.
Returns
-------
bool:
True if the module was imported locally from (a subdir of) the
experiment_path, and False otherwise.
"""
if not is_subdir(filename, experiment_path):
return False
rel_path = os.path.relpath(filename, experiment_path)
path_parts = convert_path_to_module_parts(rel_path)
mod_parts = modname.split('.')
if path_parts == mod_parts:
return True
if len(path_parts) > len(mod_parts):
return False
abs_path_parts = convert_path_to_module_parts(os.path.abspath(filename))
return all([p == m for p, m in zip(reversed(abs_path_parts),
reversed(mod_parts))]) | python | def is_local_source(filename, modname, experiment_path):
if not is_subdir(filename, experiment_path):
return False
rel_path = os.path.relpath(filename, experiment_path)
path_parts = convert_path_to_module_parts(rel_path)
mod_parts = modname.split('.')
if path_parts == mod_parts:
return True
if len(path_parts) > len(mod_parts):
return False
abs_path_parts = convert_path_to_module_parts(os.path.abspath(filename))
return all([p == m for p, m in zip(reversed(abs_path_parts),
reversed(mod_parts))]) | [
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The absolute filename of the module in question.
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The base path of the experiment.
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238,318 | IDSIA/sacred | sacred/dependencies.py | gather_sources_and_dependencies | def gather_sources_and_dependencies(globs, base_dir=None):
"""Scan the given globals for modules and return them as dependencies."""
experiment_path, main = get_main_file(globs)
base_dir = base_dir or experiment_path
gather_sources = source_discovery_strategies[SETTINGS['DISCOVER_SOURCES']]
sources = gather_sources(globs, base_dir)
if main is not None:
sources.add(main)
gather_dependencies = dependency_discovery_strategies[
SETTINGS['DISCOVER_DEPENDENCIES']]
dependencies = gather_dependencies(globs, base_dir)
if opt.has_numpy:
# Add numpy as a dependency because it might be used for randomness
dependencies.add(PackageDependency.create(opt.np))
return main, sources, dependencies | python | def gather_sources_and_dependencies(globs, base_dir=None):
experiment_path, main = get_main_file(globs)
base_dir = base_dir or experiment_path
gather_sources = source_discovery_strategies[SETTINGS['DISCOVER_SOURCES']]
sources = gather_sources(globs, base_dir)
if main is not None:
sources.add(main)
gather_dependencies = dependency_discovery_strategies[
SETTINGS['DISCOVER_DEPENDENCIES']]
dependencies = gather_dependencies(globs, base_dir)
if opt.has_numpy:
# Add numpy as a dependency because it might be used for randomness
dependencies.add(PackageDependency.create(opt.np))
return main, sources, dependencies | [
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238,319 | IDSIA/sacred | sacred/config/utils.py | assert_is_valid_key | def assert_is_valid_key(key):
"""
Raise KeyError if a given config key violates any requirements.
The requirements are the following and can be individually deactivated
in ``sacred.SETTINGS.CONFIG_KEYS``:
* ENFORCE_MONGO_COMPATIBLE (default: True):
make sure the keys don't contain a '.' or start with a '$'
* ENFORCE_JSONPICKLE_COMPATIBLE (default: True):
make sure the keys do not contain any reserved jsonpickle tags
This is very important. Only deactivate if you know what you are doing.
* ENFORCE_STRING (default: False):
make sure all keys are string.
* ENFORCE_VALID_PYTHON_IDENTIFIER (default: False):
make sure all keys are valid python identifiers.
Parameters
----------
key:
The key that should be checked
Raises
------
KeyError:
if the key violates any requirements
"""
if SETTINGS.CONFIG.ENFORCE_KEYS_MONGO_COMPATIBLE and (
isinstance(key, basestring) and ('.' in key or key[0] == '$')):
raise KeyError('Invalid key "{}". Config-keys cannot '
'contain "." or start with "$"'.format(key))
if SETTINGS.CONFIG.ENFORCE_KEYS_JSONPICKLE_COMPATIBLE and \
isinstance(key, basestring) and (
key in jsonpickle.tags.RESERVED or key.startswith('json://')):
raise KeyError('Invalid key "{}". Config-keys cannot be one of the'
'reserved jsonpickle tags: {}'
.format(key, jsonpickle.tags.RESERVED))
if SETTINGS.CONFIG.ENFORCE_STRING_KEYS and (
not isinstance(key, basestring)):
raise KeyError('Invalid key "{}". Config-keys have to be strings, '
'but was {}'.format(key, type(key)))
if SETTINGS.CONFIG.ENFORCE_VALID_PYTHON_IDENTIFIER_KEYS and (
isinstance(key, basestring) and not PYTHON_IDENTIFIER.match(key)):
raise KeyError('Key "{}" is not a valid python identifier'
.format(key))
if SETTINGS.CONFIG.ENFORCE_KEYS_NO_EQUALS and (
isinstance(key, basestring) and '=' in key):
raise KeyError('Invalid key "{}". Config keys may not contain an'
'equals sign ("=").'.format('=')) | python | def assert_is_valid_key(key):
if SETTINGS.CONFIG.ENFORCE_KEYS_MONGO_COMPATIBLE and (
isinstance(key, basestring) and ('.' in key or key[0] == '$')):
raise KeyError('Invalid key "{}". Config-keys cannot '
'contain "." or start with "$"'.format(key))
if SETTINGS.CONFIG.ENFORCE_KEYS_JSONPICKLE_COMPATIBLE and \
isinstance(key, basestring) and (
key in jsonpickle.tags.RESERVED or key.startswith('json://')):
raise KeyError('Invalid key "{}". Config-keys cannot be one of the'
'reserved jsonpickle tags: {}'
.format(key, jsonpickle.tags.RESERVED))
if SETTINGS.CONFIG.ENFORCE_STRING_KEYS and (
not isinstance(key, basestring)):
raise KeyError('Invalid key "{}". Config-keys have to be strings, '
'but was {}'.format(key, type(key)))
if SETTINGS.CONFIG.ENFORCE_VALID_PYTHON_IDENTIFIER_KEYS and (
isinstance(key, basestring) and not PYTHON_IDENTIFIER.match(key)):
raise KeyError('Key "{}" is not a valid python identifier'
.format(key))
if SETTINGS.CONFIG.ENFORCE_KEYS_NO_EQUALS and (
isinstance(key, basestring) and '=' in key):
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238,320 | IDSIA/sacred | sacred/observers/slack.py | SlackObserver.from_config | def from_config(cls, filename):
"""
Create a SlackObserver from a given configuration file.
The file can be in any format supported by Sacred
(.json, .pickle, [.yaml]).
It has to specify a ``webhook_url`` and can optionally set
``bot_name``, ``icon``, ``completed_text``, ``interrupted_text``, and
``failed_text``.
"""
d = load_config_file(filename)
obs = None
if 'webhook_url' in d:
obs = cls(d['webhook_url'])
else:
raise ValueError("Slack configuration file must contain "
"an entry for 'webhook_url'!")
for k in ['completed_text', 'interrupted_text', 'failed_text',
'bot_name', 'icon']:
if k in d:
setattr(obs, k, d[k])
return obs | python | def from_config(cls, filename):
d = load_config_file(filename)
obs = None
if 'webhook_url' in d:
obs = cls(d['webhook_url'])
else:
raise ValueError("Slack configuration file must contain "
"an entry for 'webhook_url'!")
for k in ['completed_text', 'interrupted_text', 'failed_text',
'bot_name', 'icon']:
if k in d:
setattr(obs, k, d[k])
return obs | [
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The file can be in any format supported by Sacred
(.json, .pickle, [.yaml]).
It has to specify a ``webhook_url`` and can optionally set
``bot_name``, ``icon``, ``completed_text``, ``interrupted_text``, and
``failed_text``. | [
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238,321 | IDSIA/sacred | sacred/host_info.py | get_host_info | def get_host_info():
"""Collect some information about the machine this experiment runs on.
Returns
-------
dict
A dictionary with information about the CPU, the OS and the
Python version of this machine.
"""
host_info = {}
for k, v in host_info_gatherers.items():
try:
host_info[k] = v()
except IgnoreHostInfo:
pass
return host_info | python | def get_host_info():
host_info = {}
for k, v in host_info_gatherers.items():
try:
host_info[k] = v()
except IgnoreHostInfo:
pass
return host_info | [
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Returns
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238,322 | IDSIA/sacred | sacred/host_info.py | host_info_getter | def host_info_getter(func, name=None):
"""
The decorated function is added to the process of collecting the host_info.
This just adds the decorated function to the global
``sacred.host_info.host_info_gatherers`` dictionary.
The functions from that dictionary are used when collecting the host info
using :py:func:`~sacred.host_info.get_host_info`.
Parameters
----------
func : callable
A function that can be called without arguments and returns some
json-serializable information.
name : str, optional
The name of the corresponding entry in host_info.
Defaults to the name of the function.
Returns
-------
The function itself.
"""
name = name or func.__name__
host_info_gatherers[name] = func
return func | python | def host_info_getter(func, name=None):
name = name or func.__name__
host_info_gatherers[name] = func
return func | [
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The name of the corresponding entry in host_info.
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238,323 | IDSIA/sacred | sacred/metrics_logger.py | linearize_metrics | def linearize_metrics(logged_metrics):
"""
Group metrics by name.
Takes a list of individual measurements, possibly belonging
to different metrics and groups them by name.
:param logged_metrics: A list of ScalarMetricLogEntries
:return: Measured values grouped by the metric name:
{"metric_name1": {"steps": [0,1,2], "values": [4, 5, 6],
"timestamps": [datetime, datetime, datetime]},
"metric_name2": {...}}
"""
metrics_by_name = {}
for metric_entry in logged_metrics:
if metric_entry.name not in metrics_by_name:
metrics_by_name[metric_entry.name] = {
"steps": [],
"values": [],
"timestamps": [],
"name": metric_entry.name
}
metrics_by_name[metric_entry.name]["steps"] \
.append(metric_entry.step)
metrics_by_name[metric_entry.name]["values"] \
.append(metric_entry.value)
metrics_by_name[metric_entry.name]["timestamps"] \
.append(metric_entry.timestamp)
return metrics_by_name | python | def linearize_metrics(logged_metrics):
metrics_by_name = {}
for metric_entry in logged_metrics:
if metric_entry.name not in metrics_by_name:
metrics_by_name[metric_entry.name] = {
"steps": [],
"values": [],
"timestamps": [],
"name": metric_entry.name
}
metrics_by_name[metric_entry.name]["steps"] \
.append(metric_entry.step)
metrics_by_name[metric_entry.name]["values"] \
.append(metric_entry.value)
metrics_by_name[metric_entry.name]["timestamps"] \
.append(metric_entry.timestamp)
return metrics_by_name | [
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Takes a list of individual measurements, possibly belonging
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:return: Measured values grouped by the metric name:
{"metric_name1": {"steps": [0,1,2], "values": [4, 5, 6],
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238,324 | IDSIA/sacred | sacred/metrics_logger.py | MetricsLogger.get_last_metrics | def get_last_metrics(self):
"""Read all measurement events since last call of the method.
:return List[ScalarMetricLogEntry]
"""
read_up_to = self._logged_metrics.qsize()
messages = []
for i in range(read_up_to):
try:
messages.append(self._logged_metrics.get_nowait())
except Empty:
pass
return messages | python | def get_last_metrics(self):
read_up_to = self._logged_metrics.qsize()
messages = []
for i in range(read_up_to):
try:
messages.append(self._logged_metrics.get_nowait())
except Empty:
pass
return messages | [
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238,325 | IDSIA/sacred | sacred/commandline_options.py | gather_command_line_options | def gather_command_line_options(filter_disabled=None):
"""Get a sorted list of all CommandLineOption subclasses."""
if filter_disabled is None:
filter_disabled = not SETTINGS.COMMAND_LINE.SHOW_DISABLED_OPTIONS
options = [opt for opt in get_inheritors(CommandLineOption)
if not filter_disabled or opt._enabled]
return sorted(options, key=lambda opt: opt.__name__) | python | def gather_command_line_options(filter_disabled=None):
if filter_disabled is None:
filter_disabled = not SETTINGS.COMMAND_LINE.SHOW_DISABLED_OPTIONS
options = [opt for opt in get_inheritors(CommandLineOption)
if not filter_disabled or opt._enabled]
return sorted(options, key=lambda opt: opt.__name__) | [
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238,326 | IDSIA/sacred | sacred/commandline_options.py | LoglevelOption.apply | def apply(cls, args, run):
"""Adjust the loglevel of the root-logger of this run."""
# TODO: sacred.initialize.create_run already takes care of this
try:
lvl = int(args)
except ValueError:
lvl = args
run.root_logger.setLevel(lvl) | python | def apply(cls, args, run):
# TODO: sacred.initialize.create_run already takes care of this
try:
lvl = int(args)
except ValueError:
lvl = args
run.root_logger.setLevel(lvl) | [
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238,327 | IDSIA/sacred | sacred/commandline_options.py | PriorityOption.apply | def apply(cls, args, run):
"""Add priority info for this run."""
try:
priority = float(args)
except ValueError:
raise ValueError("The PRIORITY argument must be a number! "
"(but was '{}')".format(args))
run.meta_info['priority'] = priority | python | def apply(cls, args, run):
try:
priority = float(args)
except ValueError:
raise ValueError("The PRIORITY argument must be a number! "
"(but was '{}')".format(args))
run.meta_info['priority'] = priority | [
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238,328 | IDSIA/sacred | sacred/ingredient.py | Ingredient.capture | def capture(self, function=None, prefix=None):
"""
Decorator to turn a function into a captured function.
The missing arguments of captured functions are automatically filled
from the configuration if possible.
See :ref:`captured_functions` for more information.
If a ``prefix`` is specified, the search for suitable
entries is performed in the corresponding subtree of the configuration.
"""
if function in self.captured_functions:
return function
captured_function = create_captured_function(function, prefix=prefix)
self.captured_functions.append(captured_function)
return captured_function | python | def capture(self, function=None, prefix=None):
if function in self.captured_functions:
return function
captured_function = create_captured_function(function, prefix=prefix)
self.captured_functions.append(captured_function)
return captured_function | [
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The missing arguments of captured functions are automatically filled
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See :ref:`captured_functions` for more information.
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238,329 | IDSIA/sacred | sacred/ingredient.py | Ingredient.pre_run_hook | def pre_run_hook(self, func, prefix=None):
"""
Decorator to add a pre-run hook to this ingredient.
Pre-run hooks are captured functions that are run, just before the
main function is executed.
"""
cf = self.capture(func, prefix=prefix)
self.pre_run_hooks.append(cf)
return cf | python | def pre_run_hook(self, func, prefix=None):
cf = self.capture(func, prefix=prefix)
self.pre_run_hooks.append(cf)
return cf | [
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238,330 | IDSIA/sacred | sacred/ingredient.py | Ingredient.post_run_hook | def post_run_hook(self, func, prefix=None):
"""
Decorator to add a post-run hook to this ingredient.
Post-run hooks are captured functions that are run, just after the
main function is executed.
"""
cf = self.capture(func, prefix=prefix)
self.post_run_hooks.append(cf)
return cf | python | def post_run_hook(self, func, prefix=None):
cf = self.capture(func, prefix=prefix)
self.post_run_hooks.append(cf)
return cf | [
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238,331 | IDSIA/sacred | sacred/ingredient.py | Ingredient.command | def command(self, function=None, prefix=None, unobserved=False):
"""
Decorator to define a new command for this Ingredient or Experiment.
The name of the command will be the name of the function. It can be
called from the command-line or by using the run_command function.
Commands are automatically also captured functions.
The command can be given a prefix, to restrict its configuration space
to a subtree. (see ``capture`` for more information)
A command can be made unobserved (i.e. ignoring all observers) by
passing the unobserved=True keyword argument.
"""
captured_f = self.capture(function, prefix=prefix)
captured_f.unobserved = unobserved
self.commands[function.__name__] = captured_f
return captured_f | python | def command(self, function=None, prefix=None, unobserved=False):
captured_f = self.capture(function, prefix=prefix)
captured_f.unobserved = unobserved
self.commands[function.__name__] = captured_f
return captured_f | [
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Commands are automatically also captured functions.
The command can be given a prefix, to restrict its configuration space
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238,332 | IDSIA/sacred | sacred/ingredient.py | Ingredient.config | def config(self, function):
"""
Decorator to add a function to the configuration of the Experiment.
The decorated function is turned into a
:class:`~sacred.config_scope.ConfigScope` and added to the
Ingredient/Experiment.
When the experiment is run, this function will also be executed and
all json-serializable local variables inside it will end up as entries
in the configuration of the experiment.
"""
self.configurations.append(ConfigScope(function))
return self.configurations[-1] | python | def config(self, function):
self.configurations.append(ConfigScope(function))
return self.configurations[-1] | [
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238,333 | IDSIA/sacred | sacred/ingredient.py | Ingredient.named_config | def named_config(self, func):
"""
Decorator to turn a function into a named configuration.
See :ref:`named_configurations`.
"""
config_scope = ConfigScope(func)
self._add_named_config(func.__name__, config_scope)
return config_scope | python | def named_config(self, func):
config_scope = ConfigScope(func)
self._add_named_config(func.__name__, config_scope)
return config_scope | [
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238,334 | IDSIA/sacred | sacred/ingredient.py | Ingredient.config_hook | def config_hook(self, func):
"""
Decorator to add a config hook to this ingredient.
Config hooks need to be a function that takes 3 parameters and returns
a dictionary:
(config, command_name, logger) --> dict
Config hooks are run after the configuration of this Ingredient, but
before any further ingredient-configurations are run.
The dictionary returned by a config hook is used to update the
config updates.
Note that they are not restricted to the local namespace of the
ingredient.
"""
argspec = inspect.getargspec(func)
args = ['config', 'command_name', 'logger']
if not (argspec.args == args and argspec.varargs is None and
argspec.keywords is None and argspec.defaults is None):
raise ValueError('Wrong signature for config_hook. Expected: '
'(config, command_name, logger)')
self.config_hooks.append(func)
return self.config_hooks[-1] | python | def config_hook(self, func):
argspec = inspect.getargspec(func)
args = ['config', 'command_name', 'logger']
if not (argspec.args == args and argspec.varargs is None and
argspec.keywords is None and argspec.defaults is None):
raise ValueError('Wrong signature for config_hook. Expected: '
'(config, command_name, logger)')
self.config_hooks.append(func)
return self.config_hooks[-1] | [
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Config hooks need to be a function that takes 3 parameters and returns
a dictionary:
(config, command_name, logger) --> dict
Config hooks are run after the configuration of this Ingredient, but
before any further ingredient-configurations are run.
The dictionary returned by a config hook is used to update the
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238,335 | IDSIA/sacred | sacred/ingredient.py | Ingredient.add_package_dependency | def add_package_dependency(self, package_name, version):
"""
Add a package to the list of dependencies.
:param package_name: The name of the package dependency
:type package_name: str
:param version: The (minimum) version of the package
:type version: str
"""
if not PEP440_VERSION_PATTERN.match(version):
raise ValueError('Invalid Version: "{}"'.format(version))
self.dependencies.add(PackageDependency(package_name, version)) | python | def add_package_dependency(self, package_name, version):
if not PEP440_VERSION_PATTERN.match(version):
raise ValueError('Invalid Version: "{}"'.format(version))
self.dependencies.add(PackageDependency(package_name, version)) | [
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:param package_name: The name of the package dependency
:type package_name: str
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238,336 | IDSIA/sacred | sacred/ingredient.py | Ingredient._gather | def _gather(self, func):
"""
Function needed and used by gathering functions through the decorator
`gather_from_ingredients` in `Ingredient`. Don't use this function by
itself outside of the decorator!
By overwriting this function you can filter what is visible when
gathering something (e.g. commands). See `Experiment._gather` for an
example.
"""
for ingredient, _ in self.traverse_ingredients():
for item in func(ingredient):
yield item | python | def _gather(self, func):
for ingredient, _ in self.traverse_ingredients():
for item in func(ingredient):
yield item | [
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By overwriting this function you can filter what is visible when
gathering something (e.g. commands). See `Experiment._gather` for an
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238,337 | IDSIA/sacred | sacred/ingredient.py | Ingredient.gather_commands | def gather_commands(self, ingredient):
"""Collect all commands from this ingredient and its sub-ingredients.
Yields
------
cmd_name: str
The full (dotted) name of the command.
cmd: function
The corresponding captured function.
"""
for command_name, command in ingredient.commands.items():
yield join_paths(ingredient.path, command_name), command | python | def gather_commands(self, ingredient):
for command_name, command in ingredient.commands.items():
yield join_paths(ingredient.path, command_name), command | [
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Yields
------
cmd_name: str
The full (dotted) name of the command.
cmd: function
The corresponding captured function. | [
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238,338 | IDSIA/sacred | sacred/ingredient.py | Ingredient.gather_named_configs | def gather_named_configs(self, ingredient):
"""Collect all named configs from this ingredient and its
sub-ingredients.
Yields
------
config_name: str
The full (dotted) name of the named config.
config: ConfigScope or ConfigDict or basestring
The corresponding named config.
"""
for config_name, config in ingredient.named_configs.items():
yield join_paths(ingredient.path, config_name), config | python | def gather_named_configs(self, ingredient):
for config_name, config in ingredient.named_configs.items():
yield join_paths(ingredient.path, config_name), config | [
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Yields
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config_name: str
The full (dotted) name of the named config.
config: ConfigScope or ConfigDict or basestring
The corresponding named config. | [
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238,339 | IDSIA/sacred | sacred/ingredient.py | Ingredient.get_experiment_info | def get_experiment_info(self):
"""Get a dictionary with information about this experiment.
Contains:
* *name*: the name
* *sources*: a list of sources (filename, md5)
* *dependencies*: a list of package dependencies (name, version)
:return: experiment information
:rtype: dict
"""
dependencies = set()
sources = set()
for ing, _ in self.traverse_ingredients():
dependencies |= ing.dependencies
sources |= ing.sources
for dep in dependencies:
dep.fill_missing_version()
mainfile = (self.mainfile.to_json(self.base_dir)[0]
if self.mainfile else None)
def name_lower(d):
return d.name.lower()
return dict(
name=self.path,
base_dir=self.base_dir,
sources=[s.to_json(self.base_dir) for s in sorted(sources)],
dependencies=[d.to_json()
for d in sorted(dependencies, key=name_lower)],
repositories=collect_repositories(sources),
mainfile=mainfile
) | python | def get_experiment_info(self):
dependencies = set()
sources = set()
for ing, _ in self.traverse_ingredients():
dependencies |= ing.dependencies
sources |= ing.sources
for dep in dependencies:
dep.fill_missing_version()
mainfile = (self.mainfile.to_json(self.base_dir)[0]
if self.mainfile else None)
def name_lower(d):
return d.name.lower()
return dict(
name=self.path,
base_dir=self.base_dir,
sources=[s.to_json(self.base_dir) for s in sorted(sources)],
dependencies=[d.to_json()
for d in sorted(dependencies, key=name_lower)],
repositories=collect_repositories(sources),
mainfile=mainfile
) | [
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* *dependencies*: a list of package dependencies (name, version)
:return: experiment information
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238,340 | IDSIA/sacred | sacred/ingredient.py | Ingredient.traverse_ingredients | def traverse_ingredients(self):
"""Recursively traverse this ingredient and its sub-ingredients.
Yields
------
ingredient: sacred.Ingredient
The ingredient as traversed in preorder.
depth: int
The depth of the ingredient starting from 0.
Raises
------
CircularDependencyError:
If a circular structure among ingredients was detected.
"""
if self._is_traversing:
raise CircularDependencyError(ingredients=[self])
else:
self._is_traversing = True
yield self, 0
with CircularDependencyError.track(self):
for ingredient in self.ingredients:
for ingred, depth in ingredient.traverse_ingredients():
yield ingred, depth + 1
self._is_traversing = False | python | def traverse_ingredients(self):
if self._is_traversing:
raise CircularDependencyError(ingredients=[self])
else:
self._is_traversing = True
yield self, 0
with CircularDependencyError.track(self):
for ingredient in self.ingredients:
for ingred, depth in ingredient.traverse_ingredients():
yield ingred, depth + 1
self._is_traversing = False | [
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Yields
------
ingredient: sacred.Ingredient
The ingredient as traversed in preorder.
depth: int
The depth of the ingredient starting from 0.
Raises
------
CircularDependencyError:
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238,341 | simonw/datasette | datasette/utils.py | path_from_row_pks | def path_from_row_pks(row, pks, use_rowid, quote=True):
""" Generate an optionally URL-quoted unique identifier
for a row from its primary keys."""
if use_rowid:
bits = [row['rowid']]
else:
bits = [
row[pk]["value"] if isinstance(row[pk], dict) else row[pk]
for pk in pks
]
if quote:
bits = [urllib.parse.quote_plus(str(bit)) for bit in bits]
else:
bits = [str(bit) for bit in bits]
return ','.join(bits) | python | def path_from_row_pks(row, pks, use_rowid, quote=True):
if use_rowid:
bits = [row['rowid']]
else:
bits = [
row[pk]["value"] if isinstance(row[pk], dict) else row[pk]
for pk in pks
]
if quote:
bits = [urllib.parse.quote_plus(str(bit)) for bit in bits]
else:
bits = [str(bit) for bit in bits]
return ','.join(bits) | [
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238,342 | simonw/datasette | datasette/utils.py | detect_primary_keys | def detect_primary_keys(conn, table):
" Figure out primary keys for a table. "
table_info_rows = [
row
for row in conn.execute(
'PRAGMA table_info("{}")'.format(table)
).fetchall()
if row[-1]
]
table_info_rows.sort(key=lambda row: row[-1])
return [str(r[1]) for r in table_info_rows] | python | def detect_primary_keys(conn, table):
" Figure out primary keys for a table. "
table_info_rows = [
row
for row in conn.execute(
'PRAGMA table_info("{}")'.format(table)
).fetchall()
if row[-1]
]
table_info_rows.sort(key=lambda row: row[-1])
return [str(r[1]) for r in table_info_rows] | [
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238,343 | simonw/datasette | datasette/utils.py | detect_fts | def detect_fts(conn, table):
"Detect if table has a corresponding FTS virtual table and return it"
rows = conn.execute(detect_fts_sql(table)).fetchall()
if len(rows) == 0:
return None
else:
return rows[0][0] | python | def detect_fts(conn, table):
"Detect if table has a corresponding FTS virtual table and return it"
rows = conn.execute(detect_fts_sql(table)).fetchall()
if len(rows) == 0:
return None
else:
return rows[0][0] | [
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238,344 | simonw/datasette | datasette/app.py | Datasette.metadata | def metadata(self, key=None, database=None, table=None, fallback=True):
"""
Looks up metadata, cascading backwards from specified level.
Returns None if metadata value is not found.
"""
assert not (database is None and table is not None), \
"Cannot call metadata() with table= specified but not database="
databases = self._metadata.get("databases") or {}
search_list = []
if database is not None:
search_list.append(databases.get(database) or {})
if table is not None:
table_metadata = (
(databases.get(database) or {}).get("tables") or {}
).get(table) or {}
search_list.insert(0, table_metadata)
search_list.append(self._metadata)
if not fallback:
# No fallback allowed, so just use the first one in the list
search_list = search_list[:1]
if key is not None:
for item in search_list:
if key in item:
return item[key]
return None
else:
# Return the merged list
m = {}
for item in search_list:
m.update(item)
return m | python | def metadata(self, key=None, database=None, table=None, fallback=True):
assert not (database is None and table is not None), \
"Cannot call metadata() with table= specified but not database="
databases = self._metadata.get("databases") or {}
search_list = []
if database is not None:
search_list.append(databases.get(database) or {})
if table is not None:
table_metadata = (
(databases.get(database) or {}).get("tables") or {}
).get(table) or {}
search_list.insert(0, table_metadata)
search_list.append(self._metadata)
if not fallback:
# No fallback allowed, so just use the first one in the list
search_list = search_list[:1]
if key is not None:
for item in search_list:
if key in item:
return item[key]
return None
else:
# Return the merged list
m = {}
for item in search_list:
m.update(item)
return m | [
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238,345 | simonw/datasette | datasette/app.py | Datasette.inspect | def inspect(self):
" Inspect the database and return a dictionary of table metadata "
if self._inspect:
return self._inspect
self._inspect = {}
for filename in self.files:
if filename is MEMORY:
self._inspect[":memory:"] = {
"hash": "000",
"file": ":memory:",
"size": 0,
"views": {},
"tables": {},
}
else:
path = Path(filename)
name = path.stem
if name in self._inspect:
raise Exception("Multiple files with same stem %s" % name)
try:
with sqlite3.connect(
"file:{}?mode=ro".format(path), uri=True
) as conn:
self.prepare_connection(conn)
self._inspect[name] = {
"hash": inspect_hash(path),
"file": str(path),
"size": path.stat().st_size,
"views": inspect_views(conn),
"tables": inspect_tables(conn, (self.metadata("databases") or {}).get(name, {}))
}
except sqlite3.OperationalError as e:
if (e.args[0] == 'no such module: VirtualSpatialIndex'):
raise click.UsageError(
"It looks like you're trying to load a SpatiaLite"
" database without first loading the SpatiaLite module."
"\n\nRead more: https://datasette.readthedocs.io/en/latest/spatialite.html"
)
else:
raise
return self._inspect | python | def inspect(self):
" Inspect the database and return a dictionary of table metadata "
if self._inspect:
return self._inspect
self._inspect = {}
for filename in self.files:
if filename is MEMORY:
self._inspect[":memory:"] = {
"hash": "000",
"file": ":memory:",
"size": 0,
"views": {},
"tables": {},
}
else:
path = Path(filename)
name = path.stem
if name in self._inspect:
raise Exception("Multiple files with same stem %s" % name)
try:
with sqlite3.connect(
"file:{}?mode=ro".format(path), uri=True
) as conn:
self.prepare_connection(conn)
self._inspect[name] = {
"hash": inspect_hash(path),
"file": str(path),
"size": path.stat().st_size,
"views": inspect_views(conn),
"tables": inspect_tables(conn, (self.metadata("databases") or {}).get(name, {}))
}
except sqlite3.OperationalError as e:
if (e.args[0] == 'no such module: VirtualSpatialIndex'):
raise click.UsageError(
"It looks like you're trying to load a SpatiaLite"
" database without first loading the SpatiaLite module."
"\n\nRead more: https://datasette.readthedocs.io/en/latest/spatialite.html"
)
else:
raise
return self._inspect | [
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] | 11b352b4d52fd02a422776edebb14f12e4994d3b | https://github.com/simonw/datasette/blob/11b352b4d52fd02a422776edebb14f12e4994d3b/datasette/app.py#L414-L455 |
238,346 | simonw/datasette | datasette/app.py | Datasette.table_metadata | def table_metadata(self, database, table):
"Fetch table-specific metadata."
return (self.metadata("databases") or {}).get(database, {}).get(
"tables", {}
).get(
table, {}
) | python | def table_metadata(self, database, table):
"Fetch table-specific metadata."
return (self.metadata("databases") or {}).get(database, {}).get(
"tables", {}
).get(
table, {}
) | [
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238,347 | simonw/datasette | datasette/app.py | Datasette.execute | async def execute(
self,
db_name,
sql,
params=None,
truncate=False,
custom_time_limit=None,
page_size=None,
):
"""Executes sql against db_name in a thread"""
page_size = page_size or self.page_size
def sql_operation_in_thread(conn):
time_limit_ms = self.sql_time_limit_ms
if custom_time_limit and custom_time_limit < time_limit_ms:
time_limit_ms = custom_time_limit
with sqlite_timelimit(conn, time_limit_ms):
try:
cursor = conn.cursor()
cursor.execute(sql, params or {})
max_returned_rows = self.max_returned_rows
if max_returned_rows == page_size:
max_returned_rows += 1
if max_returned_rows and truncate:
rows = cursor.fetchmany(max_returned_rows + 1)
truncated = len(rows) > max_returned_rows
rows = rows[:max_returned_rows]
else:
rows = cursor.fetchall()
truncated = False
except sqlite3.OperationalError as e:
if e.args == ('interrupted',):
raise InterruptedError(e)
print(
"ERROR: conn={}, sql = {}, params = {}: {}".format(
conn, repr(sql), params, e
)
)
raise
if truncate:
return Results(rows, truncated, cursor.description)
else:
return Results(rows, False, cursor.description)
with trace("sql", (db_name, sql.strip(), params)):
results = await self.execute_against_connection_in_thread(
db_name, sql_operation_in_thread
)
return results | python | async def execute(
self,
db_name,
sql,
params=None,
truncate=False,
custom_time_limit=None,
page_size=None,
):
page_size = page_size or self.page_size
def sql_operation_in_thread(conn):
time_limit_ms = self.sql_time_limit_ms
if custom_time_limit and custom_time_limit < time_limit_ms:
time_limit_ms = custom_time_limit
with sqlite_timelimit(conn, time_limit_ms):
try:
cursor = conn.cursor()
cursor.execute(sql, params or {})
max_returned_rows = self.max_returned_rows
if max_returned_rows == page_size:
max_returned_rows += 1
if max_returned_rows and truncate:
rows = cursor.fetchmany(max_returned_rows + 1)
truncated = len(rows) > max_returned_rows
rows = rows[:max_returned_rows]
else:
rows = cursor.fetchall()
truncated = False
except sqlite3.OperationalError as e:
if e.args == ('interrupted',):
raise InterruptedError(e)
print(
"ERROR: conn={}, sql = {}, params = {}: {}".format(
conn, repr(sql), params, e
)
)
raise
if truncate:
return Results(rows, truncated, cursor.description)
else:
return Results(rows, False, cursor.description)
with trace("sql", (db_name, sql.strip(), params)):
results = await self.execute_against_connection_in_thread(
db_name, sql_operation_in_thread
)
return results | [
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238,348 | simonw/datasette | datasette/inspect.py | inspect_hash | def inspect_hash(path):
" Calculate the hash of a database, efficiently. "
m = hashlib.sha256()
with path.open("rb") as fp:
while True:
data = fp.read(HASH_BLOCK_SIZE)
if not data:
break
m.update(data)
return m.hexdigest() | python | def inspect_hash(path):
" Calculate the hash of a database, efficiently. "
m = hashlib.sha256()
with path.open("rb") as fp:
while True:
data = fp.read(HASH_BLOCK_SIZE)
if not data:
break
m.update(data)
return m.hexdigest() | [
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238,349 | simonw/datasette | datasette/inspect.py | inspect_tables | def inspect_tables(conn, database_metadata):
" List tables and their row counts, excluding uninteresting tables. "
tables = {}
table_names = [
r["name"]
for r in conn.execute(
'select * from sqlite_master where type="table"'
)
]
for table in table_names:
table_metadata = database_metadata.get("tables", {}).get(
table, {}
)
try:
count = conn.execute(
"select count(*) from {}".format(escape_sqlite(table))
).fetchone()[0]
except sqlite3.OperationalError:
# This can happen when running against a FTS virtual table
# e.g. "select count(*) from some_fts;"
count = 0
column_names = table_columns(conn, table)
tables[table] = {
"name": table,
"columns": column_names,
"primary_keys": detect_primary_keys(conn, table),
"count": count,
"hidden": table_metadata.get("hidden") or False,
"fts_table": detect_fts(conn, table),
}
foreign_keys = get_all_foreign_keys(conn)
for table, info in foreign_keys.items():
tables[table]["foreign_keys"] = info
# Mark tables 'hidden' if they relate to FTS virtual tables
hidden_tables = [
r["name"]
for r in conn.execute(
"""
select name from sqlite_master
where rootpage = 0
and sql like '%VIRTUAL TABLE%USING FTS%'
"""
)
]
if detect_spatialite(conn):
# Also hide Spatialite internal tables
hidden_tables += [
"ElementaryGeometries",
"SpatialIndex",
"geometry_columns",
"spatial_ref_sys",
"spatialite_history",
"sql_statements_log",
"sqlite_sequence",
"views_geometry_columns",
"virts_geometry_columns",
] + [
r["name"]
for r in conn.execute(
"""
select name from sqlite_master
where name like "idx_%"
and type = "table"
"""
)
]
for t in tables.keys():
for hidden_table in hidden_tables:
if t == hidden_table or t.startswith(hidden_table):
tables[t]["hidden"] = True
continue
return tables | python | def inspect_tables(conn, database_metadata):
" List tables and their row counts, excluding uninteresting tables. "
tables = {}
table_names = [
r["name"]
for r in conn.execute(
'select * from sqlite_master where type="table"'
)
]
for table in table_names:
table_metadata = database_metadata.get("tables", {}).get(
table, {}
)
try:
count = conn.execute(
"select count(*) from {}".format(escape_sqlite(table))
).fetchone()[0]
except sqlite3.OperationalError:
# This can happen when running against a FTS virtual table
# e.g. "select count(*) from some_fts;"
count = 0
column_names = table_columns(conn, table)
tables[table] = {
"name": table,
"columns": column_names,
"primary_keys": detect_primary_keys(conn, table),
"count": count,
"hidden": table_metadata.get("hidden") or False,
"fts_table": detect_fts(conn, table),
}
foreign_keys = get_all_foreign_keys(conn)
for table, info in foreign_keys.items():
tables[table]["foreign_keys"] = info
# Mark tables 'hidden' if they relate to FTS virtual tables
hidden_tables = [
r["name"]
for r in conn.execute(
"""
select name from sqlite_master
where rootpage = 0
and sql like '%VIRTUAL TABLE%USING FTS%'
"""
)
]
if detect_spatialite(conn):
# Also hide Spatialite internal tables
hidden_tables += [
"ElementaryGeometries",
"SpatialIndex",
"geometry_columns",
"spatial_ref_sys",
"spatialite_history",
"sql_statements_log",
"sqlite_sequence",
"views_geometry_columns",
"virts_geometry_columns",
] + [
r["name"]
for r in conn.execute(
"""
select name from sqlite_master
where name like "idx_%"
and type = "table"
"""
)
]
for t in tables.keys():
for hidden_table in hidden_tables:
if t == hidden_table or t.startswith(hidden_table):
tables[t]["hidden"] = True
continue
return tables | [
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] | 11b352b4d52fd02a422776edebb14f12e4994d3b | https://github.com/simonw/datasette/blob/11b352b4d52fd02a422776edebb14f12e4994d3b/datasette/inspect.py#L35-L115 |
238,350 | simonw/datasette | datasette/filters.py | Filters.convert_unit | def convert_unit(self, column, value):
"If the user has provided a unit in the query, convert it into the column unit, if present."
if column not in self.units:
return value
# Try to interpret the value as a unit
value = self.ureg(value)
if isinstance(value, numbers.Number):
# It's just a bare number, assume it's the column unit
return value
column_unit = self.ureg(self.units[column])
return value.to(column_unit).magnitude | python | def convert_unit(self, column, value):
"If the user has provided a unit in the query, convert it into the column unit, if present."
if column not in self.units:
return value
# Try to interpret the value as a unit
value = self.ureg(value)
if isinstance(value, numbers.Number):
# It's just a bare number, assume it's the column unit
return value
column_unit = self.ureg(self.units[column])
return value.to(column_unit).magnitude | [
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238,351 | simonw/datasette | datasette/cli.py | skeleton | def skeleton(files, metadata, sqlite_extensions):
"Generate a skeleton metadata.json file for specified SQLite databases"
if os.path.exists(metadata):
click.secho(
"File {} already exists, will not over-write".format(metadata),
bg="red",
fg="white",
bold=True,
err=True,
)
sys.exit(1)
app = Datasette(files, sqlite_extensions=sqlite_extensions)
databases = {}
for database_name, info in app.inspect().items():
databases[database_name] = {
"title": None,
"description": None,
"description_html": None,
"license": None,
"license_url": None,
"source": None,
"source_url": None,
"queries": {},
"tables": {
table_name: {
"title": None,
"description": None,
"description_html": None,
"license": None,
"license_url": None,
"source": None,
"source_url": None,
"units": {},
}
for table_name in (info.get("tables") or {})
},
}
open(metadata, "w").write(
json.dumps(
{
"title": None,
"description": None,
"description_html": None,
"license": None,
"license_url": None,
"source": None,
"source_url": None,
"databases": databases,
},
indent=4,
)
)
click.echo("Wrote skeleton to {}".format(metadata)) | python | def skeleton(files, metadata, sqlite_extensions):
"Generate a skeleton metadata.json file for specified SQLite databases"
if os.path.exists(metadata):
click.secho(
"File {} already exists, will not over-write".format(metadata),
bg="red",
fg="white",
bold=True,
err=True,
)
sys.exit(1)
app = Datasette(files, sqlite_extensions=sqlite_extensions)
databases = {}
for database_name, info in app.inspect().items():
databases[database_name] = {
"title": None,
"description": None,
"description_html": None,
"license": None,
"license_url": None,
"source": None,
"source_url": None,
"queries": {},
"tables": {
table_name: {
"title": None,
"description": None,
"description_html": None,
"license": None,
"license_url": None,
"source": None,
"source_url": None,
"units": {},
}
for table_name in (info.get("tables") or {})
},
}
open(metadata, "w").write(
json.dumps(
{
"title": None,
"description": None,
"description_html": None,
"license": None,
"license_url": None,
"source": None,
"source_url": None,
"databases": databases,
},
indent=4,
)
)
click.echo("Wrote skeleton to {}".format(metadata)) | [
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] | 11b352b4d52fd02a422776edebb14f12e4994d3b | https://github.com/simonw/datasette/blob/11b352b4d52fd02a422776edebb14f12e4994d3b/datasette/cli.py#L107-L159 |
238,352 | simonw/datasette | datasette/cli.py | plugins | def plugins(all, plugins_dir):
"List currently available plugins"
app = Datasette([], plugins_dir=plugins_dir)
click.echo(json.dumps(app.plugins(all), indent=4)) | python | def plugins(all, plugins_dir):
"List currently available plugins"
app = Datasette([], plugins_dir=plugins_dir)
click.echo(json.dumps(app.plugins(all), indent=4)) | [
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"ind... | List currently available plugins | [
"List",
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"available",
"plugins"
] | 11b352b4d52fd02a422776edebb14f12e4994d3b | https://github.com/simonw/datasette/blob/11b352b4d52fd02a422776edebb14f12e4994d3b/datasette/cli.py#L169-L172 |
238,353 | simonw/datasette | datasette/cli.py | package | def package(
files,
tag,
metadata,
extra_options,
branch,
template_dir,
plugins_dir,
static,
install,
spatialite,
version_note,
**extra_metadata
):
"Package specified SQLite files into a new datasette Docker container"
if not shutil.which("docker"):
click.secho(
' The package command requires "docker" to be installed and configured ',
bg="red",
fg="white",
bold=True,
err=True,
)
sys.exit(1)
with temporary_docker_directory(
files,
"datasette",
metadata,
extra_options,
branch,
template_dir,
plugins_dir,
static,
install,
spatialite,
version_note,
extra_metadata,
):
args = ["docker", "build"]
if tag:
args.append("-t")
args.append(tag)
args.append(".")
call(args) | python | def package(
files,
tag,
metadata,
extra_options,
branch,
template_dir,
plugins_dir,
static,
install,
spatialite,
version_note,
**extra_metadata
):
"Package specified SQLite files into a new datasette Docker container"
if not shutil.which("docker"):
click.secho(
' The package command requires "docker" to be installed and configured ',
bg="red",
fg="white",
bold=True,
err=True,
)
sys.exit(1)
with temporary_docker_directory(
files,
"datasette",
metadata,
extra_options,
branch,
template_dir,
plugins_dir,
static,
install,
spatialite,
version_note,
extra_metadata,
):
args = ["docker", "build"]
if tag:
args.append("-t")
args.append(tag)
args.append(".")
call(args) | [
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] | 11b352b4d52fd02a422776edebb14f12e4994d3b | https://github.com/simonw/datasette/blob/11b352b4d52fd02a422776edebb14f12e4994d3b/datasette/cli.py#L222-L265 |
238,354 | simonw/datasette | datasette/cli.py | serve | def serve(
files,
immutable,
host,
port,
debug,
reload,
cors,
sqlite_extensions,
inspect_file,
metadata,
template_dir,
plugins_dir,
static,
memory,
config,
version_note,
help_config,
):
"""Serve up specified SQLite database files with a web UI"""
if help_config:
formatter = formatting.HelpFormatter()
with formatter.section("Config options"):
formatter.write_dl([
(option.name, '{} (default={})'.format(
option.help, option.default
))
for option in CONFIG_OPTIONS
])
click.echo(formatter.getvalue())
sys.exit(0)
if reload:
import hupper
reloader = hupper.start_reloader("datasette.cli.serve")
reloader.watch_files(files)
if metadata:
reloader.watch_files([metadata.name])
inspect_data = None
if inspect_file:
inspect_data = json.load(open(inspect_file))
metadata_data = None
if metadata:
metadata_data = json.loads(metadata.read())
click.echo("Serve! files={} (immutables={}) on port {}".format(files, immutable, port))
ds = Datasette(
files,
immutables=immutable,
cache_headers=not debug and not reload,
cors=cors,
inspect_data=inspect_data,
metadata=metadata_data,
sqlite_extensions=sqlite_extensions,
template_dir=template_dir,
plugins_dir=plugins_dir,
static_mounts=static,
config=dict(config),
memory=memory,
version_note=version_note,
)
# Force initial hashing/table counting
ds.inspect()
ds.app().run(host=host, port=port, debug=debug) | python | def serve(
files,
immutable,
host,
port,
debug,
reload,
cors,
sqlite_extensions,
inspect_file,
metadata,
template_dir,
plugins_dir,
static,
memory,
config,
version_note,
help_config,
):
if help_config:
formatter = formatting.HelpFormatter()
with formatter.section("Config options"):
formatter.write_dl([
(option.name, '{} (default={})'.format(
option.help, option.default
))
for option in CONFIG_OPTIONS
])
click.echo(formatter.getvalue())
sys.exit(0)
if reload:
import hupper
reloader = hupper.start_reloader("datasette.cli.serve")
reloader.watch_files(files)
if metadata:
reloader.watch_files([metadata.name])
inspect_data = None
if inspect_file:
inspect_data = json.load(open(inspect_file))
metadata_data = None
if metadata:
metadata_data = json.loads(metadata.read())
click.echo("Serve! files={} (immutables={}) on port {}".format(files, immutable, port))
ds = Datasette(
files,
immutables=immutable,
cache_headers=not debug and not reload,
cors=cors,
inspect_data=inspect_data,
metadata=metadata_data,
sqlite_extensions=sqlite_extensions,
template_dir=template_dir,
plugins_dir=plugins_dir,
static_mounts=static,
config=dict(config),
memory=memory,
version_note=version_note,
)
# Force initial hashing/table counting
ds.inspect()
ds.app().run(host=host, port=port, debug=debug) | [
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... | Serve up specified SQLite database files with a web UI | [
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] | 11b352b4d52fd02a422776edebb14f12e4994d3b | https://github.com/simonw/datasette/blob/11b352b4d52fd02a422776edebb14f12e4994d3b/datasette/cli.py#L340-L405 |
238,355 | ResidentMario/missingno | missingno/missingno.py | bar | def bar(df, figsize=(24, 10), fontsize=16, labels=None, log=False, color='dimgray', inline=False,
filter=None, n=0, p=0, sort=None):
"""
A bar chart visualization of the nullity of the given DataFrame.
:param df: The input DataFrame.
:param log: Whether or not to display a logorithmic plot. Defaults to False (linear).
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param sort: The sort to apply to the heatmap. Should be one of "ascending", "descending", or None (default).
:param figsize: The size of the figure to display.
:param fontsize: The figure's font size. This default to 16.
:param labels: Whether or not to display the column names. Would need to be turned off on particularly large
displays. Defaults to True.
:param color: The color of the filled columns. Default to the RGB multiple `(0.25, 0.25, 0.25)`.
:return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing.
"""
nullity_counts = len(df) - df.isnull().sum()
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort)
plt.figure(figsize=figsize)
(nullity_counts / len(df)).plot(kind='bar', figsize=figsize, fontsize=fontsize, log=log, color=color)
ax1 = plt.gca()
axes = [ax1]
# Start appending elements, starting with a modified bottom x axis.
if labels or (labels is None and len(df.columns) <= 50):
ax1.set_xticklabels(ax1.get_xticklabels(), rotation=45, ha='right', fontsize=fontsize)
# Create the numerical ticks.
ax2 = ax1.twinx()
axes.append(ax2)
if not log:
ax1.set_ylim([0, 1])
ax2.set_yticks(ax1.get_yticks())
ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
else:
# For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
# appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
# is used, we have to make it match the `ax1` layout ourselves.
ax2.set_yscale('log')
ax2.set_ylim(ax1.get_ylim())
ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
else:
ax1.set_xticks([])
# Create the third axis, which displays columnar totals above the rest of the plot.
ax3 = ax1.twiny()
axes.append(ax3)
ax3.set_xticks(ax1.get_xticks())
ax3.set_xlim(ax1.get_xlim())
ax3.set_xticklabels(nullity_counts.values, fontsize=fontsize, rotation=45, ha='left')
ax3.grid(False)
for ax in axes:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
if inline:
plt.show()
else:
return ax1 | python | def bar(df, figsize=(24, 10), fontsize=16, labels=None, log=False, color='dimgray', inline=False,
filter=None, n=0, p=0, sort=None):
nullity_counts = len(df) - df.isnull().sum()
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort)
plt.figure(figsize=figsize)
(nullity_counts / len(df)).plot(kind='bar', figsize=figsize, fontsize=fontsize, log=log, color=color)
ax1 = plt.gca()
axes = [ax1]
# Start appending elements, starting with a modified bottom x axis.
if labels or (labels is None and len(df.columns) <= 50):
ax1.set_xticklabels(ax1.get_xticklabels(), rotation=45, ha='right', fontsize=fontsize)
# Create the numerical ticks.
ax2 = ax1.twinx()
axes.append(ax2)
if not log:
ax1.set_ylim([0, 1])
ax2.set_yticks(ax1.get_yticks())
ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
else:
# For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
# appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
# is used, we have to make it match the `ax1` layout ourselves.
ax2.set_yscale('log')
ax2.set_ylim(ax1.get_ylim())
ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
else:
ax1.set_xticks([])
# Create the third axis, which displays columnar totals above the rest of the plot.
ax3 = ax1.twiny()
axes.append(ax3)
ax3.set_xticks(ax1.get_xticks())
ax3.set_xlim(ax1.get_xlim())
ax3.set_xticklabels(nullity_counts.values, fontsize=fontsize, rotation=45, ha='left')
ax3.grid(False)
for ax in axes:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
if inline:
plt.show()
else:
return ax1 | [
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:param df: The input DataFrame.
:param log: Whether or not to display a logorithmic plot. Defaults to False (linear).
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param sort: The sort to apply to the heatmap. Should be one of "ascending", "descending", or None (default).
:param figsize: The size of the figure to display.
:param fontsize: The figure's font size. This default to 16.
:param labels: Whether or not to display the column names. Would need to be turned off on particularly large
displays. Defaults to True.
:param color: The color of the filled columns. Default to the RGB multiple `(0.25, 0.25, 0.25)`.
:return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing. | [
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238,356 | ResidentMario/missingno | missingno/missingno.py | heatmap | def heatmap(df, inline=False,
filter=None, n=0, p=0, sort=None,
figsize=(20, 12), fontsize=16, labels=True,
cmap='RdBu', vmin=-1, vmax=1, cbar=True
):
"""
Presents a `seaborn` heatmap visualization of nullity correlation in the given DataFrame.
Note that this visualization has no special support for large datasets. For those, try the dendrogram instead.
:param df: The DataFrame whose completeness is being heatmapped.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default). See
`nullity_filter()` for more information.
:param n: The cap on the number of columns to include in the filtered DataFrame. See `nullity_filter()` for
more information.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame. See `nullity_filter()` for
more information.
:param sort: The sort to apply to the heatmap. Should be one of "ascending", "descending", or None. See
`nullity_sort()` for more information.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to (20, 12).
:param fontsize: The figure's font size.
:param labels: Whether or not to label each matrix entry with its correlation (default is True).
:param cmap: What `matplotlib` colormap to use. Defaults to `RdBu`.
:param vmin: The normalized colormap threshold. Defaults to -1, e.g. the bottom of the color scale.
:param vmax: The normalized colormap threshold. Defaults to 1, e.g. the bottom of the color scale.
:param inline: Whether or not the figure is inline. If it's not then instead of getting plotted, this method will
return its figure.
:return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing.
"""
# Apply filters and sorts, set up the figure.
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort)
plt.figure(figsize=figsize)
gs = gridspec.GridSpec(1, 1)
ax0 = plt.subplot(gs[0])
# Remove completely filled or completely empty variables.
df = df.iloc[:,[i for i, n in enumerate(np.var(df.isnull(), axis='rows')) if n > 0]]
# Create and mask the correlation matrix. Construct the base heatmap.
corr_mat = df.isnull().corr()
mask = np.zeros_like(corr_mat)
mask[np.triu_indices_from(mask)] = True
if labels:
sns.heatmap(corr_mat, mask=mask, cmap=cmap, ax=ax0, cbar=cbar,
annot=True, annot_kws={'size': fontsize - 2},
vmin=vmin, vmax=vmax)
else:
sns.heatmap(corr_mat, mask=mask, cmap=cmap, ax=ax0, cbar=cbar,
vmin=vmin, vmax=vmax)
# Apply visual corrections and modifications.
ax0.xaxis.tick_bottom()
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=45, ha='right', fontsize=fontsize)
ax0.set_yticklabels(ax0.yaxis.get_majorticklabels(), fontsize=fontsize, rotation=0)
ax0.set_yticklabels(ax0.yaxis.get_majorticklabels(), rotation=0, fontsize=fontsize)
ax0.xaxis.set_ticks_position('none')
ax0.yaxis.set_ticks_position('none')
ax0.patch.set_visible(False)
for text in ax0.texts:
t = float(text.get_text())
if 0.95 <= t < 1:
text.set_text('<1')
elif -1 < t <= -0.95:
text.set_text('>-1')
elif t == 1:
text.set_text('1')
elif t == -1:
text.set_text('-1')
elif -0.05 < t < 0.05:
text.set_text('')
else:
text.set_text(round(t, 1))
if inline:
plt.show()
else:
return ax0 | python | def heatmap(df, inline=False,
filter=None, n=0, p=0, sort=None,
figsize=(20, 12), fontsize=16, labels=True,
cmap='RdBu', vmin=-1, vmax=1, cbar=True
):
# Apply filters and sorts, set up the figure.
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort)
plt.figure(figsize=figsize)
gs = gridspec.GridSpec(1, 1)
ax0 = plt.subplot(gs[0])
# Remove completely filled or completely empty variables.
df = df.iloc[:,[i for i, n in enumerate(np.var(df.isnull(), axis='rows')) if n > 0]]
# Create and mask the correlation matrix. Construct the base heatmap.
corr_mat = df.isnull().corr()
mask = np.zeros_like(corr_mat)
mask[np.triu_indices_from(mask)] = True
if labels:
sns.heatmap(corr_mat, mask=mask, cmap=cmap, ax=ax0, cbar=cbar,
annot=True, annot_kws={'size': fontsize - 2},
vmin=vmin, vmax=vmax)
else:
sns.heatmap(corr_mat, mask=mask, cmap=cmap, ax=ax0, cbar=cbar,
vmin=vmin, vmax=vmax)
# Apply visual corrections and modifications.
ax0.xaxis.tick_bottom()
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=45, ha='right', fontsize=fontsize)
ax0.set_yticklabels(ax0.yaxis.get_majorticklabels(), fontsize=fontsize, rotation=0)
ax0.set_yticklabels(ax0.yaxis.get_majorticklabels(), rotation=0, fontsize=fontsize)
ax0.xaxis.set_ticks_position('none')
ax0.yaxis.set_ticks_position('none')
ax0.patch.set_visible(False)
for text in ax0.texts:
t = float(text.get_text())
if 0.95 <= t < 1:
text.set_text('<1')
elif -1 < t <= -0.95:
text.set_text('>-1')
elif t == 1:
text.set_text('1')
elif t == -1:
text.set_text('-1')
elif -0.05 < t < 0.05:
text.set_text('')
else:
text.set_text(round(t, 1))
if inline:
plt.show()
else:
return ax0 | [
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:param df: The DataFrame whose completeness is being heatmapped.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default). See
`nullity_filter()` for more information.
:param n: The cap on the number of columns to include in the filtered DataFrame. See `nullity_filter()` for
more information.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame. See `nullity_filter()` for
more information.
:param sort: The sort to apply to the heatmap. Should be one of "ascending", "descending", or None. See
`nullity_sort()` for more information.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to (20, 12).
:param fontsize: The figure's font size.
:param labels: Whether or not to label each matrix entry with its correlation (default is True).
:param cmap: What `matplotlib` colormap to use. Defaults to `RdBu`.
:param vmin: The normalized colormap threshold. Defaults to -1, e.g. the bottom of the color scale.
:param vmax: The normalized colormap threshold. Defaults to 1, e.g. the bottom of the color scale.
:param inline: Whether or not the figure is inline. If it's not then instead of getting plotted, this method will
return its figure.
:return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing. | [
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238,357 | ResidentMario/missingno | missingno/missingno.py | dendrogram | def dendrogram(df, method='average',
filter=None, n=0, p=0, sort=None,
orientation=None, figsize=None,
fontsize=16, inline=False
):
"""
Fits a `scipy` hierarchical clustering algorithm to the given DataFrame's variables and visualizes the results as
a `scipy` dendrogram.
The default vertical display will fit up to 50 columns. If more than 50 columns are specified and orientation is
left unspecified the dendrogram will automatically swap to a horizontal display to fit the additional variables.
:param df: The DataFrame whose completeness is being dendrogrammed.
:param method: The distance measure being used for clustering. This is a parameter that is passed to
`scipy.hierarchy`.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param sort: The sort to apply to the heatmap. Should be one of "ascending", "descending", or None.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to `(25, 10)`.
:param fontsize: The figure's font size.
:param orientation: The way the dendrogram is oriented. Defaults to top-down if there are less than or equal to 50
columns and left-right if there are more.
:param inline: Whether or not the figure is inline. If it's not then instead of getting plotted, this method will
return its figure.
:return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing.
"""
if not figsize:
if len(df.columns) <= 50 or orientation == 'top' or orientation == 'bottom':
figsize = (25, 10)
else:
figsize = (25, (25 + len(df.columns) - 50)*0.5)
plt.figure(figsize=figsize)
gs = gridspec.GridSpec(1, 1)
ax0 = plt.subplot(gs[0])
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort)
# Link the hierarchical output matrix, figure out orientation, construct base dendrogram.
x = np.transpose(df.isnull().astype(int).values)
z = hierarchy.linkage(x, method)
if not orientation:
if len(df.columns) > 50:
orientation = 'left'
else:
orientation = 'bottom'
hierarchy.dendrogram(z,
orientation=orientation,
labels=df.columns.tolist(),
distance_sort='descending',
link_color_func=lambda c: 'black',
leaf_font_size=fontsize,
ax=ax0
)
# Remove extraneous default visual elements.
ax0.set_aspect('auto')
ax0.grid(b=False)
if orientation == 'bottom':
ax0.xaxis.tick_top()
ax0.xaxis.set_ticks_position('none')
ax0.yaxis.set_ticks_position('none')
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax0.spines['bottom'].set_visible(False)
ax0.spines['left'].set_visible(False)
ax0.patch.set_visible(False)
# Set up the categorical axis labels and draw.
if orientation == 'bottom':
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=45, ha='left')
elif orientation == 'top':
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=45, ha='right')
if orientation == 'bottom' or orientation == 'top':
ax0.tick_params(axis='y', labelsize=int(fontsize / 16 * 20))
else:
ax0.tick_params(axis='x', labelsize=int(fontsize / 16 * 20))
if inline:
plt.show()
else:
return ax0 | python | def dendrogram(df, method='average',
filter=None, n=0, p=0, sort=None,
orientation=None, figsize=None,
fontsize=16, inline=False
):
if not figsize:
if len(df.columns) <= 50 or orientation == 'top' or orientation == 'bottom':
figsize = (25, 10)
else:
figsize = (25, (25 + len(df.columns) - 50)*0.5)
plt.figure(figsize=figsize)
gs = gridspec.GridSpec(1, 1)
ax0 = plt.subplot(gs[0])
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort)
# Link the hierarchical output matrix, figure out orientation, construct base dendrogram.
x = np.transpose(df.isnull().astype(int).values)
z = hierarchy.linkage(x, method)
if not orientation:
if len(df.columns) > 50:
orientation = 'left'
else:
orientation = 'bottom'
hierarchy.dendrogram(z,
orientation=orientation,
labels=df.columns.tolist(),
distance_sort='descending',
link_color_func=lambda c: 'black',
leaf_font_size=fontsize,
ax=ax0
)
# Remove extraneous default visual elements.
ax0.set_aspect('auto')
ax0.grid(b=False)
if orientation == 'bottom':
ax0.xaxis.tick_top()
ax0.xaxis.set_ticks_position('none')
ax0.yaxis.set_ticks_position('none')
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax0.spines['bottom'].set_visible(False)
ax0.spines['left'].set_visible(False)
ax0.patch.set_visible(False)
# Set up the categorical axis labels and draw.
if orientation == 'bottom':
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=45, ha='left')
elif orientation == 'top':
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=45, ha='right')
if orientation == 'bottom' or orientation == 'top':
ax0.tick_params(axis='y', labelsize=int(fontsize / 16 * 20))
else:
ax0.tick_params(axis='x', labelsize=int(fontsize / 16 * 20))
if inline:
plt.show()
else:
return ax0 | [
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The default vertical display will fit up to 50 columns. If more than 50 columns are specified and orientation is
left unspecified the dendrogram will automatically swap to a horizontal display to fit the additional variables.
:param df: The DataFrame whose completeness is being dendrogrammed.
:param method: The distance measure being used for clustering. This is a parameter that is passed to
`scipy.hierarchy`.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param sort: The sort to apply to the heatmap. Should be one of "ascending", "descending", or None.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to `(25, 10)`.
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:param inline: Whether or not the figure is inline. If it's not then instead of getting plotted, this method will
return its figure.
:return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing. | [
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238,358 | ResidentMario/missingno | missingno/utils.py | nullity_sort | def nullity_sort(df, sort=None):
"""
Sorts a DataFrame according to its nullity, in either ascending or descending order.
:param df: The DataFrame object being sorted.
:param sort: The sorting method: either "ascending", "descending", or None (default).
:return: The nullity-sorted DataFrame.
"""
if sort == 'ascending':
return df.iloc[np.argsort(df.count(axis='columns').values), :]
elif sort == 'descending':
return df.iloc[np.flipud(np.argsort(df.count(axis='columns').values)), :]
else:
return df | python | def nullity_sort(df, sort=None):
if sort == 'ascending':
return df.iloc[np.argsort(df.count(axis='columns').values), :]
elif sort == 'descending':
return df.iloc[np.flipud(np.argsort(df.count(axis='columns').values)), :]
else:
return df | [
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:param df: The DataFrame object being sorted.
:param sort: The sorting method: either "ascending", "descending", or None (default).
:return: The nullity-sorted DataFrame. | [
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238,359 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/session_entity_types_client.py | SessionEntityTypesClient.from_service_account_file | def from_service_account_file(cls, filename, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
dialogflow_v2.SessionEntityTypesClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_file(
filename)
kwargs['credentials'] = credentials
return cls(*args, **kwargs) | python | def from_service_account_file(cls, filename, *args, **kwargs):
credentials = service_account.Credentials.from_service_account_file(
filename)
kwargs['credentials'] = credentials
return cls(*args, **kwargs) | [
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Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
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238,360 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/session_entity_types_client.py | SessionEntityTypesClient.session_entity_type_path | def session_entity_type_path(cls, project, session, entity_type):
"""Return a fully-qualified session_entity_type string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/sessions/{session}/entityTypes/{entity_type}',
project=project,
session=session,
entity_type=entity_type,
) | python | def session_entity_type_path(cls, project, session, entity_type):
return google.api_core.path_template.expand(
'projects/{project}/agent/sessions/{session}/entityTypes/{entity_type}',
project=project,
session=session,
entity_type=entity_type,
) | [
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238,361 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/session_entity_types_client.py | SessionEntityTypesClient.create_session_entity_type | def create_session_entity_type(
self,
parent,
session_entity_type,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Creates a session entity type.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> parent = client.session_path('[PROJECT]', '[SESSION]')
>>>
>>> # TODO: Initialize ``session_entity_type``:
>>> session_entity_type = {}
>>>
>>> response = client.create_session_entity_type(parent, session_entity_type)
Args:
parent (str): Required. The session to create a session entity type for.
Format: ``projects/<Project ID>/agent/sessions/<Session ID>``.
session_entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.SessionEntityType]): Required. The session entity type to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.SessionEntityType`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.SessionEntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'create_session_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'create_session_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_session_entity_type,
default_retry=self._method_configs[
'CreateSessionEntityType'].retry,
default_timeout=self._method_configs[
'CreateSessionEntityType'].timeout,
client_info=self._client_info,
)
request = session_entity_type_pb2.CreateSessionEntityTypeRequest(
parent=parent,
session_entity_type=session_entity_type,
)
return self._inner_api_calls['create_session_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def create_session_entity_type(
self,
parent,
session_entity_type,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'create_session_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'create_session_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_session_entity_type,
default_retry=self._method_configs[
'CreateSessionEntityType'].retry,
default_timeout=self._method_configs[
'CreateSessionEntityType'].timeout,
client_info=self._client_info,
)
request = session_entity_type_pb2.CreateSessionEntityTypeRequest(
parent=parent,
session_entity_type=session_entity_type,
)
return self._inner_api_calls['create_session_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> parent = client.session_path('[PROJECT]', '[SESSION]')
>>>
>>> # TODO: Initialize ``session_entity_type``:
>>> session_entity_type = {}
>>>
>>> response = client.create_session_entity_type(parent, session_entity_type)
Args:
parent (str): Required. The session to create a session entity type for.
Format: ``projects/<Project ID>/agent/sessions/<Session ID>``.
session_entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.SessionEntityType]): Required. The session entity type to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.SessionEntityType`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.SessionEntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/session_entity_types_client.py#L351-L415 |
238,362 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/session_entity_types_client.py | SessionEntityTypesClient.update_session_entity_type | def update_session_entity_type(
self,
session_entity_type,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Updates the specified session entity type.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> # TODO: Initialize ``session_entity_type``:
>>> session_entity_type = {}
>>>
>>> response = client.update_session_entity_type(session_entity_type)
Args:
session_entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.SessionEntityType]): Required. The entity type to update. Format:
``projects/<Project ID>/agent/sessions/<Session ID>/entityTypes/<Entity Type
Display Name>``.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.SessionEntityType`
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.SessionEntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'update_session_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'update_session_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_session_entity_type,
default_retry=self._method_configs[
'UpdateSessionEntityType'].retry,
default_timeout=self._method_configs[
'UpdateSessionEntityType'].timeout,
client_info=self._client_info,
)
request = session_entity_type_pb2.UpdateSessionEntityTypeRequest(
session_entity_type=session_entity_type,
update_mask=update_mask,
)
return self._inner_api_calls['update_session_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def update_session_entity_type(
self,
session_entity_type,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'update_session_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'update_session_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_session_entity_type,
default_retry=self._method_configs[
'UpdateSessionEntityType'].retry,
default_timeout=self._method_configs[
'UpdateSessionEntityType'].timeout,
client_info=self._client_info,
)
request = session_entity_type_pb2.UpdateSessionEntityTypeRequest(
session_entity_type=session_entity_type,
update_mask=update_mask,
)
return self._inner_api_calls['update_session_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> # TODO: Initialize ``session_entity_type``:
>>> session_entity_type = {}
>>>
>>> response = client.update_session_entity_type(session_entity_type)
Args:
session_entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.SessionEntityType]): Required. The entity type to update. Format:
``projects/<Project ID>/agent/sessions/<Session ID>/entityTypes/<Entity Type
Display Name>``.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.SessionEntityType`
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.SessionEntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/session_entity_types_client.py#L417-L482 |
238,363 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/session_entity_types_client.py | SessionEntityTypesClient.delete_session_entity_type | def delete_session_entity_type(
self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified session entity type.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> name = client.session_entity_type_path('[PROJECT]', '[SESSION]', '[ENTITY_TYPE]')
>>>
>>> client.delete_session_entity_type(name)
Args:
name (str): Required. The name of the entity type to delete. Format:
``projects/<Project ID>/agent/sessions/<Session ID>/entityTypes/<Entity Type
Display Name>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'delete_session_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'delete_session_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.delete_session_entity_type,
default_retry=self._method_configs[
'DeleteSessionEntityType'].retry,
default_timeout=self._method_configs[
'DeleteSessionEntityType'].timeout,
client_info=self._client_info,
)
request = session_entity_type_pb2.DeleteSessionEntityTypeRequest(
name=name, )
self._inner_api_calls['delete_session_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def delete_session_entity_type(
self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'delete_session_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'delete_session_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.delete_session_entity_type,
default_retry=self._method_configs[
'DeleteSessionEntityType'].retry,
default_timeout=self._method_configs[
'DeleteSessionEntityType'].timeout,
client_info=self._client_info,
)
request = session_entity_type_pb2.DeleteSessionEntityTypeRequest(
name=name, )
self._inner_api_calls['delete_session_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> name = client.session_entity_type_path('[PROJECT]', '[SESSION]', '[ENTITY_TYPE]')
>>>
>>> client.delete_session_entity_type(name)
Args:
name (str): Required. The name of the entity type to delete. Format:
``projects/<Project ID>/agent/sessions/<Session ID>/entityTypes/<Entity Type
Display Name>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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"type",
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/session_entity_types_client.py#L484-L537 |
238,364 | googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | list_knowledge_bases | def list_knowledge_bases(project_id):
"""Lists the Knowledge bases belonging to a project.
Args:
project_id: The GCP project linked with the agent."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
print('Knowledge Bases for: {}'.format(project_id))
for knowledge_base in client.list_knowledge_bases(project_path):
print(' - Display Name: {}'.format(knowledge_base.display_name))
print(' - Knowledge ID: {}\n'.format(knowledge_base.name)) | python | def list_knowledge_bases(project_id):
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
print('Knowledge Bases for: {}'.format(project_id))
for knowledge_base in client.list_knowledge_bases(project_path):
print(' - Display Name: {}'.format(knowledge_base.display_name))
print(' - Knowledge ID: {}\n'.format(knowledge_base.name)) | [
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238,365 | googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | create_knowledge_base | def create_knowledge_base(project_id, display_name):
"""Creates a Knowledge base.
Args:
project_id: The GCP project linked with the agent.
display_name: The display name of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
knowledge_base = dialogflow.types.KnowledgeBase(
display_name=display_name)
response = client.create_knowledge_base(project_path, knowledge_base)
print('Knowledge Base created:\n')
print('Display Name: {}\n'.format(response.display_name))
print('Knowledge ID: {}\n'.format(response.name)) | python | def create_knowledge_base(project_id, display_name):
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
knowledge_base = dialogflow.types.KnowledgeBase(
display_name=display_name)
response = client.create_knowledge_base(project_path, knowledge_base)
print('Knowledge Base created:\n')
print('Display Name: {}\n'.format(response.display_name))
print('Knowledge ID: {}\n'.format(response.name)) | [
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Args:
project_id: The GCP project linked with the agent.
display_name: The display name of the Knowledge base. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/samples/knowledge_base_management.py#L52-L69 |
238,366 | googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | get_knowledge_base | def get_knowledge_base(project_id, knowledge_base_id):
"""Gets a specific Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
knowledge_base_path = client.knowledge_base_path(
project_id, knowledge_base_id)
response = client.get_knowledge_base(knowledge_base_path)
print('Got Knowledge Base:')
print(' - Display Name: {}'.format(response.display_name))
print(' - Knowledge ID: {}'.format(response.name)) | python | def get_knowledge_base(project_id, knowledge_base_id):
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
knowledge_base_path = client.knowledge_base_path(
project_id, knowledge_base_id)
response = client.get_knowledge_base(knowledge_base_path)
print('Got Knowledge Base:')
print(' - Display Name: {}'.format(response.display_name))
print(' - Knowledge ID: {}'.format(response.name)) | [
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Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base. | [
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238,367 | googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | delete_knowledge_base | def delete_knowledge_base(project_id, knowledge_base_id):
"""Deletes a specific Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
knowledge_base_path = client.knowledge_base_path(
project_id, knowledge_base_id)
response = client.delete_knowledge_base(knowledge_base_path)
print('Knowledge Base deleted.'.format(response)) | python | def delete_knowledge_base(project_id, knowledge_base_id):
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
knowledge_base_path = client.knowledge_base_path(
project_id, knowledge_base_id)
response = client.delete_knowledge_base(knowledge_base_path)
print('Knowledge Base deleted.'.format(response)) | [
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Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base. | [
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238,368 | googleapis/dialogflow-python-client-v2 | samples/detect_intent_with_texttospeech_response.py | detect_intent_with_texttospeech_response | def detect_intent_with_texttospeech_response(project_id, session_id, texts,
language_code):
"""Returns the result of detect intent with texts as inputs and includes
the response in an audio format.
Using the same `session_id` between requests allows continuation
of the conversaion."""
import dialogflow_v2beta1 as dialogflow
session_client = dialogflow.SessionsClient()
session_path = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session_path))
for text in texts:
text_input = dialogflow.types.TextInput(
text=text, language_code=language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
# Set the query parameters with sentiment analysis
output_audio_config = dialogflow.types.OutputAudioConfig(
audio_encoding=dialogflow.enums.OutputAudioEncoding
.OUTPUT_AUDIO_ENCODING_LINEAR_16)
response = session_client.detect_intent(
session=session_path, query_input=query_input,
output_audio_config=output_audio_config)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text))
# The response's audio_content is binary.
with open('output.wav', 'wb') as out:
out.write(response.output_audio)
print('Audio content written to file "output.wav"') | python | def detect_intent_with_texttospeech_response(project_id, session_id, texts,
language_code):
import dialogflow_v2beta1 as dialogflow
session_client = dialogflow.SessionsClient()
session_path = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session_path))
for text in texts:
text_input = dialogflow.types.TextInput(
text=text, language_code=language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
# Set the query parameters with sentiment analysis
output_audio_config = dialogflow.types.OutputAudioConfig(
audio_encoding=dialogflow.enums.OutputAudioEncoding
.OUTPUT_AUDIO_ENCODING_LINEAR_16)
response = session_client.detect_intent(
session=session_path, query_input=query_input,
output_audio_config=output_audio_config)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text))
# The response's audio_content is binary.
with open('output.wav', 'wb') as out:
out.write(response.output_audio)
print('Audio content written to file "output.wav"') | [
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Using the same `session_id` between requests allows continuation
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238,369 | googleapis/dialogflow-python-client-v2 | samples/detect_intent_with_model_selection.py | detect_intent_with_model_selection | def detect_intent_with_model_selection(project_id, session_id, audio_file_path,
language_code):
"""Returns the result of detect intent with model selection on an audio file
as input
Using the same `session_id` between requests allows continuation
of the conversaion."""
import dialogflow_v2beta1 as dialogflow
session_client = dialogflow.SessionsClient()
# Note: hard coding audio_encoding and sample_rate_hertz for simplicity.
audio_encoding = dialogflow.enums.AudioEncoding.AUDIO_ENCODING_LINEAR_16
sample_rate_hertz = 16000
session_path = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session_path))
with open(audio_file_path, 'rb') as audio_file:
input_audio = audio_file.read()
# Which Speech model to select for the given request.
# Possible models: video, phone_call, command_and_search, default
model = 'phone_call'
audio_config = dialogflow.types.InputAudioConfig(
audio_encoding=audio_encoding, language_code=language_code,
sample_rate_hertz=sample_rate_hertz,
model=model)
query_input = dialogflow.types.QueryInput(audio_config=audio_config)
response = session_client.detect_intent(
session=session_path, query_input=query_input,
input_audio=input_audio)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text)) | python | def detect_intent_with_model_selection(project_id, session_id, audio_file_path,
language_code):
import dialogflow_v2beta1 as dialogflow
session_client = dialogflow.SessionsClient()
# Note: hard coding audio_encoding and sample_rate_hertz for simplicity.
audio_encoding = dialogflow.enums.AudioEncoding.AUDIO_ENCODING_LINEAR_16
sample_rate_hertz = 16000
session_path = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session_path))
with open(audio_file_path, 'rb') as audio_file:
input_audio = audio_file.read()
# Which Speech model to select for the given request.
# Possible models: video, phone_call, command_and_search, default
model = 'phone_call'
audio_config = dialogflow.types.InputAudioConfig(
audio_encoding=audio_encoding, language_code=language_code,
sample_rate_hertz=sample_rate_hertz,
model=model)
query_input = dialogflow.types.QueryInput(audio_config=audio_config)
response = session_client.detect_intent(
session=session_path, query_input=query_input,
input_audio=input_audio)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text)) | [
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as input
Using the same `session_id` between requests allows continuation
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238,370 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/knowledge_bases_client.py | KnowledgeBasesClient.knowledge_base_path | def knowledge_base_path(cls, project, knowledge_base):
"""Return a fully-qualified knowledge_base string."""
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}',
project=project,
knowledge_base=knowledge_base,
) | python | def knowledge_base_path(cls, project, knowledge_base):
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}',
project=project,
knowledge_base=knowledge_base,
) | [
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238,371 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/knowledge_bases_client.py | KnowledgeBasesClient.get_knowledge_base | def get_knowledge_base(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified knowledge base.
Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.KnowledgeBasesClient()
>>>
>>> name = client.knowledge_base_path('[PROJECT]', '[KNOWLEDGE_BASE]')
>>>
>>> response = client.get_knowledge_base(name)
Args:
name (str): Required. The name of the knowledge base to retrieve.
Format ``projects/<Project ID>/knowledgeBases/<Knowledge Base ID>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2beta1.types.KnowledgeBase` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'get_knowledge_base' not in self._inner_api_calls:
self._inner_api_calls[
'get_knowledge_base'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_knowledge_base,
default_retry=self._method_configs[
'GetKnowledgeBase'].retry,
default_timeout=self._method_configs['GetKnowledgeBase']
.timeout,
client_info=self._client_info,
)
request = knowledge_base_pb2.GetKnowledgeBaseRequest(name=name, )
return self._inner_api_calls['get_knowledge_base'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def get_knowledge_base(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'get_knowledge_base' not in self._inner_api_calls:
self._inner_api_calls[
'get_knowledge_base'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_knowledge_base,
default_retry=self._method_configs[
'GetKnowledgeBase'].retry,
default_timeout=self._method_configs['GetKnowledgeBase']
.timeout,
client_info=self._client_info,
)
request = knowledge_base_pb2.GetKnowledgeBaseRequest(name=name, )
return self._inner_api_calls['get_knowledge_base'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.KnowledgeBasesClient()
>>>
>>> name = client.knowledge_base_path('[PROJECT]', '[KNOWLEDGE_BASE]')
>>>
>>> response = client.get_knowledge_base(name)
Args:
name (str): Required. The name of the knowledge base to retrieve.
Format ``projects/<Project ID>/knowledgeBases/<Knowledge Base ID>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2beta1.types.KnowledgeBase` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2beta1/gapic/knowledge_bases_client.py#L283-L336 |
238,372 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/knowledge_bases_client.py | KnowledgeBasesClient.create_knowledge_base | def create_knowledge_base(self,
parent,
knowledge_base,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Creates a knowledge base.
Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.KnowledgeBasesClient()
>>>
>>> parent = client.project_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``knowledge_base``:
>>> knowledge_base = {}
>>>
>>> response = client.create_knowledge_base(parent, knowledge_base)
Args:
parent (str): Required. The agent to create a knowledge base for.
Format: ``projects/<Project ID>/agent``.
knowledge_base (Union[dict, ~google.cloud.dialogflow_v2beta1.types.KnowledgeBase]): Required. The knowledge base to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2beta1.types.KnowledgeBase`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2beta1.types.KnowledgeBase` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'create_knowledge_base' not in self._inner_api_calls:
self._inner_api_calls[
'create_knowledge_base'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_knowledge_base,
default_retry=self._method_configs[
'CreateKnowledgeBase'].retry,
default_timeout=self._method_configs['CreateKnowledgeBase']
.timeout,
client_info=self._client_info,
)
request = knowledge_base_pb2.CreateKnowledgeBaseRequest(
parent=parent,
knowledge_base=knowledge_base,
)
return self._inner_api_calls['create_knowledge_base'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def create_knowledge_base(self,
parent,
knowledge_base,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'create_knowledge_base' not in self._inner_api_calls:
self._inner_api_calls[
'create_knowledge_base'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_knowledge_base,
default_retry=self._method_configs[
'CreateKnowledgeBase'].retry,
default_timeout=self._method_configs['CreateKnowledgeBase']
.timeout,
client_info=self._client_info,
)
request = knowledge_base_pb2.CreateKnowledgeBaseRequest(
parent=parent,
knowledge_base=knowledge_base,
)
return self._inner_api_calls['create_knowledge_base'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.KnowledgeBasesClient()
>>>
>>> parent = client.project_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``knowledge_base``:
>>> knowledge_base = {}
>>>
>>> response = client.create_knowledge_base(parent, knowledge_base)
Args:
parent (str): Required. The agent to create a knowledge base for.
Format: ``projects/<Project ID>/agent``.
knowledge_base (Union[dict, ~google.cloud.dialogflow_v2beta1.types.KnowledgeBase]): Required. The knowledge base to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2beta1.types.KnowledgeBase`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2beta1.types.KnowledgeBase` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
"Creates",
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2beta1/gapic/knowledge_bases_client.py#L338-L401 |
238,373 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/session_entity_types_client.py | SessionEntityTypesClient.environment_session_path | def environment_session_path(cls, project, environment, user, session):
"""Return a fully-qualified environment_session string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}',
project=project,
environment=environment,
user=user,
session=session,
) | python | def environment_session_path(cls, project, environment, user, session):
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}',
project=project,
environment=environment,
user=user,
session=session,
) | [
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238,374 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/session_entity_types_client.py | SessionEntityTypesClient.environment_session_entity_type_path | def environment_session_entity_type_path(cls, project, environment, user,
session, entity_type):
"""Return a fully-qualified environment_session_entity_type string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}/entityTypes/{entity_type}',
project=project,
environment=environment,
user=user,
session=session,
entity_type=entity_type,
) | python | def environment_session_entity_type_path(cls, project, environment, user,
session, entity_type):
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}/entityTypes/{entity_type}',
project=project,
environment=environment,
user=user,
session=session,
entity_type=entity_type,
) | [
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238,375 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/documents_client.py | DocumentsClient.document_path | def document_path(cls, project, knowledge_base, document):
"""Return a fully-qualified document string."""
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}/documents/{document}',
project=project,
knowledge_base=knowledge_base,
document=document,
) | python | def document_path(cls, project, knowledge_base, document):
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}/documents/{document}',
project=project,
knowledge_base=knowledge_base,
document=document,
) | [
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238,376 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/documents_client.py | DocumentsClient.list_documents | def list_documents(self,
parent,
page_size=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Returns the list of all documents of the knowledge base.
Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.DocumentsClient()
>>>
>>> parent = client.knowledge_base_path('[PROJECT]', '[KNOWLEDGE_BASE]')
>>>
>>> # Iterate over all results
>>> for element in client.list_documents(parent):
... # process element
... pass
>>>
>>>
>>> # Alternatively:
>>>
>>> # Iterate over results one page at a time
>>> for page in client.list_documents(parent, options=CallOptions(page_token=INITIAL_PAGE)):
... for element in page:
... # process element
... pass
Args:
parent (str): Required. The knowledge base to list all documents for.
Format: ``projects/<Project ID>/knowledgeBases/<Knowledge Base ID>``.
page_size (int): The maximum number of resources contained in the
underlying API response. If page streaming is performed per-
resource, this parameter does not affect the return value. If page
streaming is performed per-page, this determines the maximum number
of resources in a page.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.gax.PageIterator` instance. By default, this
is an iterable of :class:`~google.cloud.dialogflow_v2beta1.types.Document` instances.
This object can also be configured to iterate over the pages
of the response through the `options` parameter.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'list_documents' not in self._inner_api_calls:
self._inner_api_calls[
'list_documents'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.list_documents,
default_retry=self._method_configs['ListDocuments'].retry,
default_timeout=self._method_configs['ListDocuments']
.timeout,
client_info=self._client_info,
)
request = document_pb2.ListDocumentsRequest(
parent=parent,
page_size=page_size,
)
iterator = google.api_core.page_iterator.GRPCIterator(
client=None,
method=functools.partial(
self._inner_api_calls['list_documents'],
retry=retry,
timeout=timeout,
metadata=metadata),
request=request,
items_field='documents',
request_token_field='page_token',
response_token_field='next_page_token',
)
return iterator | python | def list_documents(self,
parent,
page_size=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'list_documents' not in self._inner_api_calls:
self._inner_api_calls[
'list_documents'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.list_documents,
default_retry=self._method_configs['ListDocuments'].retry,
default_timeout=self._method_configs['ListDocuments']
.timeout,
client_info=self._client_info,
)
request = document_pb2.ListDocumentsRequest(
parent=parent,
page_size=page_size,
)
iterator = google.api_core.page_iterator.GRPCIterator(
client=None,
method=functools.partial(
self._inner_api_calls['list_documents'],
retry=retry,
timeout=timeout,
metadata=metadata),
request=request,
items_field='documents',
request_token_field='page_token',
response_token_field='next_page_token',
)
return iterator | [
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Example:
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>>>
>>> client = dialogflow_v2beta1.DocumentsClient()
>>>
>>> parent = client.knowledge_base_path('[PROJECT]', '[KNOWLEDGE_BASE]')
>>>
>>> # Iterate over all results
>>> for element in client.list_documents(parent):
... # process element
... pass
>>>
>>>
>>> # Alternatively:
>>>
>>> # Iterate over results one page at a time
>>> for page in client.list_documents(parent, options=CallOptions(page_token=INITIAL_PAGE)):
... for element in page:
... # process element
... pass
Args:
parent (str): Required. The knowledge base to list all documents for.
Format: ``projects/<Project ID>/knowledgeBases/<Knowledge Base ID>``.
page_size (int): The maximum number of resources contained in the
underlying API response. If page streaming is performed per-
resource, this parameter does not affect the return value. If page
streaming is performed per-page, this determines the maximum number
of resources in a page.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.gax.PageIterator` instance. By default, this
is an iterable of :class:`~google.cloud.dialogflow_v2beta1.types.Document` instances.
This object can also be configured to iterate over the pages
of the response through the `options` parameter.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2beta1/gapic/documents_client.py#L187-L274 |
238,377 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/documents_client.py | DocumentsClient.delete_document | def delete_document(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified document.
Operation <response: ``google.protobuf.Empty``,
metadata: [KnowledgeOperationMetadata][google.cloud.dialogflow.v2beta1.KnowledgeOperationMetadata]>
Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.DocumentsClient()
>>>
>>> name = client.document_path('[PROJECT]', '[KNOWLEDGE_BASE]', '[DOCUMENT]')
>>>
>>> response = client.delete_document(name)
>>>
>>> def callback(operation_future):
... # Handle result.
... result = operation_future.result()
>>>
>>> response.add_done_callback(callback)
>>>
>>> # Handle metadata.
>>> metadata = response.metadata()
Args:
name (str): The name of the document to delete.
Format: ``projects/<Project ID>/knowledgeBases/<Knowledge Base
ID>/documents/<Document ID>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2beta1.types._OperationFuture` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'delete_document' not in self._inner_api_calls:
self._inner_api_calls[
'delete_document'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.delete_document,
default_retry=self._method_configs['DeleteDocument'].retry,
default_timeout=self._method_configs['DeleteDocument']
.timeout,
client_info=self._client_info,
)
request = document_pb2.DeleteDocumentRequest(name=name, )
operation = self._inner_api_calls['delete_document'](
request, retry=retry, timeout=timeout, metadata=metadata)
return google.api_core.operation.from_gapic(
operation,
self.transport._operations_client,
empty_pb2.Empty,
metadata_type=document_pb2.KnowledgeOperationMetadata,
) | python | def delete_document(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'delete_document' not in self._inner_api_calls:
self._inner_api_calls[
'delete_document'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.delete_document,
default_retry=self._method_configs['DeleteDocument'].retry,
default_timeout=self._method_configs['DeleteDocument']
.timeout,
client_info=self._client_info,
)
request = document_pb2.DeleteDocumentRequest(name=name, )
operation = self._inner_api_calls['delete_document'](
request, retry=retry, timeout=timeout, metadata=metadata)
return google.api_core.operation.from_gapic(
operation,
self.transport._operations_client,
empty_pb2.Empty,
metadata_type=document_pb2.KnowledgeOperationMetadata,
) | [
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Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.DocumentsClient()
>>>
>>> name = client.document_path('[PROJECT]', '[KNOWLEDGE_BASE]', '[DOCUMENT]')
>>>
>>> response = client.delete_document(name)
>>>
>>> def callback(operation_future):
... # Handle result.
... result = operation_future.result()
>>>
>>> response.add_done_callback(callback)
>>>
>>> # Handle metadata.
>>> metadata = response.metadata()
Args:
name (str): The name of the document to delete.
Format: ``projects/<Project ID>/knowledgeBases/<Knowledge Base
ID>/documents/<Document ID>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2beta1.types._OperationFuture` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2beta1/gapic/documents_client.py#L413-L484 |
238,378 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/contexts_client.py | ContextsClient.context_path | def context_path(cls, project, session, context):
"""Return a fully-qualified context string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/sessions/{session}/contexts/{context}',
project=project,
session=session,
context=context,
) | python | def context_path(cls, project, session, context):
return google.api_core.path_template.expand(
'projects/{project}/agent/sessions/{session}/contexts/{context}',
project=project,
session=session,
context=context,
) | [
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238,379 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/contexts_client.py | ContextsClient.get_context | def get_context(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified context.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.ContextsClient()
>>>
>>> name = client.context_path('[PROJECT]', '[SESSION]', '[CONTEXT]')
>>>
>>> response = client.get_context(name)
Args:
name (str): Required. The name of the context. Format:
``projects/<Project ID>/agent/sessions/<Session ID>/contexts/<Context ID>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Context` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'get_context' not in self._inner_api_calls:
self._inner_api_calls[
'get_context'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_context,
default_retry=self._method_configs['GetContext'].retry,
default_timeout=self._method_configs['GetContext'].timeout,
client_info=self._client_info,
)
request = context_pb2.GetContextRequest(name=name, )
return self._inner_api_calls['get_context'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def get_context(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'get_context' not in self._inner_api_calls:
self._inner_api_calls[
'get_context'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_context,
default_retry=self._method_configs['GetContext'].retry,
default_timeout=self._method_configs['GetContext'].timeout,
client_info=self._client_info,
)
request = context_pb2.GetContextRequest(name=name, )
return self._inner_api_calls['get_context'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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>>>
>>> client = dialogflow_v2.ContextsClient()
>>>
>>> name = client.context_path('[PROJECT]', '[SESSION]', '[CONTEXT]')
>>>
>>> response = client.get_context(name)
Args:
name (str): Required. The name of the context. Format:
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retry (Optional[google.api_core.retry.Retry]): A retry object used
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timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Context` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/contexts_client.py#L290-L341 |
238,380 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/contexts_client.py | ContextsClient.update_context | def update_context(self,
context,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Updates the specified context.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.ContextsClient()
>>>
>>> # TODO: Initialize ``context``:
>>> context = {}
>>>
>>> response = client.update_context(context)
Args:
context (Union[dict, ~google.cloud.dialogflow_v2.types.Context]): Required. The context to update.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Context`
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Context` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'update_context' not in self._inner_api_calls:
self._inner_api_calls[
'update_context'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_context,
default_retry=self._method_configs['UpdateContext'].retry,
default_timeout=self._method_configs['UpdateContext']
.timeout,
client_info=self._client_info,
)
request = context_pb2.UpdateContextRequest(
context=context,
update_mask=update_mask,
)
return self._inner_api_calls['update_context'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def update_context(self,
context,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'update_context' not in self._inner_api_calls:
self._inner_api_calls[
'update_context'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_context,
default_retry=self._method_configs['UpdateContext'].retry,
default_timeout=self._method_configs['UpdateContext']
.timeout,
client_info=self._client_info,
)
request = context_pb2.UpdateContextRequest(
context=context,
update_mask=update_mask,
)
return self._inner_api_calls['update_context'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.ContextsClient()
>>>
>>> # TODO: Initialize ``context``:
>>> context = {}
>>>
>>> response = client.update_context(context)
Args:
context (Union[dict, ~google.cloud.dialogflow_v2.types.Context]): Required. The context to update.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Context`
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Context` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
"Updates",
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"context",
"."
] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/contexts_client.py#L407-L468 |
238,381 | googleapis/dialogflow-python-client-v2 | samples/session_entity_type_management.py | create_session_entity_type | def create_session_entity_type(project_id, session_id, entity_values,
entity_type_display_name, entity_override_mode):
"""Create a session entity type with the given display name."""
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypesClient()
session_path = session_entity_types_client.session_path(
project_id, session_id)
session_entity_type_name = (
session_entity_types_client.session_entity_type_path(
project_id, session_id, entity_type_display_name))
# Here we use the entity value as the only synonym.
entities = [
dialogflow.types.EntityType.Entity(value=value, synonyms=[value])
for value in entity_values]
session_entity_type = dialogflow.types.SessionEntityType(
name=session_entity_type_name,
entity_override_mode=entity_override_mode,
entities=entities)
response = session_entity_types_client.create_session_entity_type(
session_path, session_entity_type)
print('SessionEntityType created: \n\n{}'.format(response)) | python | def create_session_entity_type(project_id, session_id, entity_values,
entity_type_display_name, entity_override_mode):
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypesClient()
session_path = session_entity_types_client.session_path(
project_id, session_id)
session_entity_type_name = (
session_entity_types_client.session_entity_type_path(
project_id, session_id, entity_type_display_name))
# Here we use the entity value as the only synonym.
entities = [
dialogflow.types.EntityType.Entity(value=value, synonyms=[value])
for value in entity_values]
session_entity_type = dialogflow.types.SessionEntityType(
name=session_entity_type_name,
entity_override_mode=entity_override_mode,
entities=entities)
response = session_entity_types_client.create_session_entity_type(
session_path, session_entity_type)
print('SessionEntityType created: \n\n{}'.format(response)) | [
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238,382 | googleapis/dialogflow-python-client-v2 | samples/session_entity_type_management.py | delete_session_entity_type | def delete_session_entity_type(project_id, session_id,
entity_type_display_name):
"""Delete session entity type with the given entity type display name."""
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypesClient()
session_entity_type_name = (
session_entity_types_client.session_entity_type_path(
project_id, session_id, entity_type_display_name))
session_entity_types_client.delete_session_entity_type(
session_entity_type_name) | python | def delete_session_entity_type(project_id, session_id,
entity_type_display_name):
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypesClient()
session_entity_type_name = (
session_entity_types_client.session_entity_type_path(
project_id, session_id, entity_type_display_name))
session_entity_types_client.delete_session_entity_type(
session_entity_type_name) | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/samples/session_entity_type_management.py#L83-L94 |
238,383 | googleapis/dialogflow-python-client-v2 | samples/entity_type_management.py | create_entity_type | def create_entity_type(project_id, display_name, kind):
"""Create an entity type with the given display name."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
parent = entity_types_client.project_agent_path(project_id)
entity_type = dialogflow.types.EntityType(
display_name=display_name, kind=kind)
response = entity_types_client.create_entity_type(parent, entity_type)
print('Entity type created: \n{}'.format(response)) | python | def create_entity_type(project_id, display_name, kind):
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
parent = entity_types_client.project_agent_path(project_id)
entity_type = dialogflow.types.EntityType(
display_name=display_name, kind=kind)
response = entity_types_client.create_entity_type(parent, entity_type)
print('Entity type created: \n{}'.format(response)) | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/samples/entity_type_management.py#L45-L56 |
238,384 | googleapis/dialogflow-python-client-v2 | samples/entity_type_management.py | delete_entity_type | def delete_entity_type(project_id, entity_type_id):
"""Delete entity type with the given entity type name."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
entity_type_path = entity_types_client.entity_type_path(
project_id, entity_type_id)
entity_types_client.delete_entity_type(entity_type_path) | python | def delete_entity_type(project_id, entity_type_id):
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
entity_type_path = entity_types_client.entity_type_path(
project_id, entity_type_id)
entity_types_client.delete_entity_type(entity_type_path) | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/samples/entity_type_management.py#L61-L69 |
238,385 | googleapis/dialogflow-python-client-v2 | samples/detect_intent_texts.py | detect_intent_texts | def detect_intent_texts(project_id, session_id, texts, language_code):
"""Returns the result of detect intent with texts as inputs.
Using the same `session_id` between requests allows continuation
of the conversation."""
import dialogflow_v2 as dialogflow
session_client = dialogflow.SessionsClient()
session = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session))
for text in texts:
text_input = dialogflow.types.TextInput(
text=text, language_code=language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
response = session_client.detect_intent(
session=session, query_input=query_input)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text)) | python | def detect_intent_texts(project_id, session_id, texts, language_code):
import dialogflow_v2 as dialogflow
session_client = dialogflow.SessionsClient()
session = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session))
for text in texts:
text_input = dialogflow.types.TextInput(
text=text, language_code=language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
response = session_client.detect_intent(
session=session, query_input=query_input)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text)) | [
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Using the same `session_id` between requests allows continuation
of the conversation. | [
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238,386 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.entity_type_path | def entity_type_path(cls, project, entity_type):
"""Return a fully-qualified entity_type string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/entityTypes/{entity_type}',
project=project,
entity_type=entity_type,
) | python | def entity_type_path(cls, project, entity_type):
return google.api_core.path_template.expand(
'projects/{project}/agent/entityTypes/{entity_type}',
project=project,
entity_type=entity_type,
) | [
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"-",
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/entity_types_client.py#L118-L124 |
238,387 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.get_entity_type | def get_entity_type(self,
name,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified entity type.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> name = client.entity_type_path('[PROJECT]', '[ENTITY_TYPE]')
>>>
>>> response = client.get_entity_type(name)
Args:
name (str): Required. The name of the entity type.
Format: ``projects/<Project ID>/agent/entityTypes/<EntityType ID>``.
language_code (str): Optional. The language to retrieve entity synonyms for. If not specified,
the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.EntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'get_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'get_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_entity_type,
default_retry=self._method_configs['GetEntityType'].retry,
default_timeout=self._method_configs['GetEntityType']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.GetEntityTypeRequest(
name=name,
language_code=language_code,
)
return self._inner_api_calls['get_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def get_entity_type(self,
name,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'get_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'get_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_entity_type,
default_retry=self._method_configs['GetEntityType'].retry,
default_timeout=self._method_configs['GetEntityType']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.GetEntityTypeRequest(
name=name,
language_code=language_code,
)
return self._inner_api_calls['get_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> name = client.entity_type_path('[PROJECT]', '[ENTITY_TYPE]')
>>>
>>> response = client.get_entity_type(name)
Args:
name (str): Required. The name of the entity type.
Format: ``projects/<Project ID>/agent/entityTypes/<EntityType ID>``.
language_code (str): Optional. The language to retrieve entity synonyms for. If not specified,
the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.EntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
"Retrieves",
"the",
"specified",
"entity",
"type",
"."
] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/entity_types_client.py#L311-L372 |
238,388 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.create_entity_type | def create_entity_type(self,
parent,
entity_type,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Creates an entity type in the specified agent.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> parent = client.project_agent_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``entity_type``:
>>> entity_type = {}
>>>
>>> response = client.create_entity_type(parent, entity_type)
Args:
parent (str): Required. The agent to create a entity type for.
Format: ``projects/<Project ID>/agent``.
entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.EntityType]): Required. The entity type to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.EntityType`
language_code (str): Optional. The language of entity synonyms defined in ``entity_type``. If not
specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.EntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'create_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'create_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_entity_type,
default_retry=self._method_configs[
'CreateEntityType'].retry,
default_timeout=self._method_configs['CreateEntityType']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.CreateEntityTypeRequest(
parent=parent,
entity_type=entity_type,
language_code=language_code,
)
return self._inner_api_calls['create_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def create_entity_type(self,
parent,
entity_type,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'create_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'create_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_entity_type,
default_retry=self._method_configs[
'CreateEntityType'].retry,
default_timeout=self._method_configs['CreateEntityType']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.CreateEntityTypeRequest(
parent=parent,
entity_type=entity_type,
language_code=language_code,
)
return self._inner_api_calls['create_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> parent = client.project_agent_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``entity_type``:
>>> entity_type = {}
>>>
>>> response = client.create_entity_type(parent, entity_type)
Args:
parent (str): Required. The agent to create a entity type for.
Format: ``projects/<Project ID>/agent``.
entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.EntityType]): Required. The entity type to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.EntityType`
language_code (str): Optional. The language of entity synonyms defined in ``entity_type``. If not
specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.EntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/entity_types_client.py#L374-L444 |
238,389 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.update_entity_type | def update_entity_type(self,
entity_type,
language_code=None,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Updates the specified entity type.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> # TODO: Initialize ``entity_type``:
>>> entity_type = {}
>>>
>>> response = client.update_entity_type(entity_type)
Args:
entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.EntityType]): Required. The entity type to update.
Format: ``projects/<Project ID>/agent/entityTypes/<EntityType ID>``.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.EntityType`
language_code (str): Optional. The language of entity synonyms defined in ``entity_type``. If not
specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.EntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'update_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'update_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_entity_type,
default_retry=self._method_configs[
'UpdateEntityType'].retry,
default_timeout=self._method_configs['UpdateEntityType']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.UpdateEntityTypeRequest(
entity_type=entity_type,
language_code=language_code,
update_mask=update_mask,
)
return self._inner_api_calls['update_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def update_entity_type(self,
entity_type,
language_code=None,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'update_entity_type' not in self._inner_api_calls:
self._inner_api_calls[
'update_entity_type'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_entity_type,
default_retry=self._method_configs[
'UpdateEntityType'].retry,
default_timeout=self._method_configs['UpdateEntityType']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.UpdateEntityTypeRequest(
entity_type=entity_type,
language_code=language_code,
update_mask=update_mask,
)
return self._inner_api_calls['update_entity_type'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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"... | Updates the specified entity type.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> # TODO: Initialize ``entity_type``:
>>> entity_type = {}
>>>
>>> response = client.update_entity_type(entity_type)
Args:
entity_type (Union[dict, ~google.cloud.dialogflow_v2.types.EntityType]): Required. The entity type to update.
Format: ``projects/<Project ID>/agent/entityTypes/<EntityType ID>``.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.EntityType`
language_code (str): Optional. The language of entity synonyms defined in ``entity_type``. If not
specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.EntityType` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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"."
] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/entity_types_client.py#L446-L516 |
238,390 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.batch_delete_entities | def batch_delete_entities(self,
parent,
entity_values,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes entities in the specified entity type.
Operation <response: ``google.protobuf.Empty``,
metadata: [google.protobuf.Struct][google.protobuf.Struct]>
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> parent = client.entity_type_path('[PROJECT]', '[ENTITY_TYPE]')
>>>
>>> # TODO: Initialize ``entity_values``:
>>> entity_values = []
>>>
>>> response = client.batch_delete_entities(parent, entity_values)
>>>
>>> def callback(operation_future):
... # Handle result.
... result = operation_future.result()
>>>
>>> response.add_done_callback(callback)
>>>
>>> # Handle metadata.
>>> metadata = response.metadata()
Args:
parent (str): Required. The name of the entity type to delete entries for. Format:
``projects/<Project ID>/agent/entityTypes/<Entity Type ID>``.
entity_values (list[str]): Required. The canonical ``values`` of the entities to delete. Note that
these are not fully-qualified names, i.e. they don't start with
``projects/<Project ID>``.
language_code (str): Optional. The language of entity synonyms defined in ``entities``. If not
specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types._OperationFuture` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'batch_delete_entities' not in self._inner_api_calls:
self._inner_api_calls[
'batch_delete_entities'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.batch_delete_entities,
default_retry=self._method_configs[
'BatchDeleteEntities'].retry,
default_timeout=self._method_configs['BatchDeleteEntities']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.BatchDeleteEntitiesRequest(
parent=parent,
entity_values=entity_values,
language_code=language_code,
)
operation = self._inner_api_calls['batch_delete_entities'](
request, retry=retry, timeout=timeout, metadata=metadata)
return google.api_core.operation.from_gapic(
operation,
self.transport._operations_client,
empty_pb2.Empty,
metadata_type=struct_pb2.Struct,
) | python | def batch_delete_entities(self,
parent,
entity_values,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'batch_delete_entities' not in self._inner_api_calls:
self._inner_api_calls[
'batch_delete_entities'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.batch_delete_entities,
default_retry=self._method_configs[
'BatchDeleteEntities'].retry,
default_timeout=self._method_configs['BatchDeleteEntities']
.timeout,
client_info=self._client_info,
)
request = entity_type_pb2.BatchDeleteEntitiesRequest(
parent=parent,
entity_values=entity_values,
language_code=language_code,
)
operation = self._inner_api_calls['batch_delete_entities'](
request, retry=retry, timeout=timeout, metadata=metadata)
return google.api_core.operation.from_gapic(
operation,
self.transport._operations_client,
empty_pb2.Empty,
metadata_type=struct_pb2.Struct,
) | [
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Operation <response: ``google.protobuf.Empty``,
metadata: [google.protobuf.Struct][google.protobuf.Struct]>
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.EntityTypesClient()
>>>
>>> parent = client.entity_type_path('[PROJECT]', '[ENTITY_TYPE]')
>>>
>>> # TODO: Initialize ``entity_values``:
>>> entity_values = []
>>>
>>> response = client.batch_delete_entities(parent, entity_values)
>>>
>>> def callback(operation_future):
... # Handle result.
... result = operation_future.result()
>>>
>>> response.add_done_callback(callback)
>>>
>>> # Handle metadata.
>>> metadata = response.metadata()
Args:
parent (str): Required. The name of the entity type to delete entries for. Format:
``projects/<Project ID>/agent/entityTypes/<Entity Type ID>``.
entity_values (list[str]): Required. The canonical ``values`` of the entities to delete. Note that
these are not fully-qualified names, i.e. they don't start with
``projects/<Project ID>``.
language_code (str): Optional. The language of entity synonyms defined in ``entities``. If not
specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types._OperationFuture` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/entity_types_client.py#L945-L1033 |
238,391 | googleapis/dialogflow-python-client-v2 | samples/detect_intent_knowledge.py | detect_intent_knowledge | def detect_intent_knowledge(project_id, session_id, language_code,
knowledge_base_id, texts):
"""Returns the result of detect intent with querying Knowledge Connector.
Args:
project_id: The GCP project linked with the agent you are going to query.
session_id: Id of the session, using the same `session_id` between requests
allows continuation of the conversation.
language_code: Language of the queries.
knowledge_base_id: The Knowledge base's id to query against.
texts: A list of text queries to send.
"""
import dialogflow_v2beta1 as dialogflow
session_client = dialogflow.SessionsClient()
session_path = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session_path))
for text in texts:
text_input = dialogflow.types.TextInput(
text=text, language_code=language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
knowledge_base_path = dialogflow.knowledge_bases_client \
.KnowledgeBasesClient \
.knowledge_base_path(project_id, knowledge_base_id)
query_params = dialogflow.types.QueryParameters(
knowledge_base_names=[knowledge_base_path])
response = session_client.detect_intent(
session=session_path, query_input=query_input,
query_params=query_params)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text))
print('Knowledge results:')
knowledge_answers = response.query_result.knowledge_answers
for answers in knowledge_answers.answers:
print(' - Answer: {}'.format(answers.answer))
print(' - Confidence: {}'.format(
answers.match_confidence)) | python | def detect_intent_knowledge(project_id, session_id, language_code,
knowledge_base_id, texts):
import dialogflow_v2beta1 as dialogflow
session_client = dialogflow.SessionsClient()
session_path = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session_path))
for text in texts:
text_input = dialogflow.types.TextInput(
text=text, language_code=language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
knowledge_base_path = dialogflow.knowledge_bases_client \
.KnowledgeBasesClient \
.knowledge_base_path(project_id, knowledge_base_id)
query_params = dialogflow.types.QueryParameters(
knowledge_base_names=[knowledge_base_path])
response = session_client.detect_intent(
session=session_path, query_input=query_input,
query_params=query_params)
print('=' * 20)
print('Query text: {}'.format(response.query_result.query_text))
print('Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
print('Fulfillment text: {}\n'.format(
response.query_result.fulfillment_text))
print('Knowledge results:')
knowledge_answers = response.query_result.knowledge_answers
for answers in knowledge_answers.answers:
print(' - Answer: {}'.format(answers.answer))
print(' - Confidence: {}'.format(
answers.match_confidence)) | [
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session_id: Id of the session, using the same `session_id` between requests
allows continuation of the conversation.
language_code: Language of the queries.
knowledge_base_id: The Knowledge base's id to query against.
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238,392 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.intent_path | def intent_path(cls, project, intent):
"""Return a fully-qualified intent string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/intents/{intent}',
project=project,
intent=intent,
) | python | def intent_path(cls, project, intent):
return google.api_core.path_template.expand(
'projects/{project}/agent/intents/{intent}',
project=project,
intent=intent,
) | [
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238,393 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.agent_path | def agent_path(cls, project, agent):
"""Return a fully-qualified agent string."""
return google.api_core.path_template.expand(
'projects/{project}/agents/{agent}',
project=project,
agent=agent,
) | python | def agent_path(cls, project, agent):
return google.api_core.path_template.expand(
'projects/{project}/agents/{agent}',
project=project,
agent=agent,
) | [
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")... | Return a fully-qualified agent string. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/intents_client.py#L134-L140 |
238,394 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.get_intent | def get_intent(self,
name,
language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified intent.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> name = client.intent_path('[PROJECT]', '[INTENT]')
>>>
>>> response = client.get_intent(name)
Args:
name (str): Required. The name of the intent.
Format: ``projects/<Project ID>/agent/intents/<Intent ID>``.
language_code (str): Optional. The language to retrieve training phrases, parameters and rich
messages for. If not specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
intent_view (~google.cloud.dialogflow_v2.types.IntentView): Optional. The resource view to apply to the returned intent.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Intent` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'get_intent' not in self._inner_api_calls:
self._inner_api_calls[
'get_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_intent,
default_retry=self._method_configs['GetIntent'].retry,
default_timeout=self._method_configs['GetIntent'].timeout,
client_info=self._client_info,
)
request = intent_pb2.GetIntentRequest(
name=name,
language_code=language_code,
intent_view=intent_view,
)
return self._inner_api_calls['get_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def get_intent(self,
name,
language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'get_intent' not in self._inner_api_calls:
self._inner_api_calls[
'get_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.get_intent,
default_retry=self._method_configs['GetIntent'].retry,
default_timeout=self._method_configs['GetIntent'].timeout,
client_info=self._client_info,
)
request = intent_pb2.GetIntentRequest(
name=name,
language_code=language_code,
intent_view=intent_view,
)
return self._inner_api_calls['get_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> name = client.intent_path('[PROJECT]', '[INTENT]')
>>>
>>> response = client.get_intent(name)
Args:
name (str): Required. The name of the intent.
Format: ``projects/<Project ID>/agent/intents/<Intent ID>``.
language_code (str): Optional. The language to retrieve training phrases, parameters and rich
messages for. If not specified, the agent's default language is used.
[More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
intent_view (~google.cloud.dialogflow_v2.types.IntentView): Optional. The resource view to apply to the returned intent.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Intent` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
"Retrieves",
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"."
] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/intents_client.py#L328-L391 |
238,395 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.create_intent | def create_intent(self,
parent,
intent,
language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Creates an intent in the specified agent.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> parent = client.project_agent_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``intent``:
>>> intent = {}
>>>
>>> response = client.create_intent(parent, intent)
Args:
parent (str): Required. The agent to create a intent for.
Format: ``projects/<Project ID>/agent``.
intent (Union[dict, ~google.cloud.dialogflow_v2.types.Intent]): Required. The intent to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Intent`
language_code (str): Optional. The language of training phrases, parameters and rich messages
defined in ``intent``. If not specified, the agent's default language is
used. [More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
intent_view (~google.cloud.dialogflow_v2.types.IntentView): Optional. The resource view to apply to the returned intent.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Intent` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'create_intent' not in self._inner_api_calls:
self._inner_api_calls[
'create_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_intent,
default_retry=self._method_configs['CreateIntent'].retry,
default_timeout=self._method_configs['CreateIntent']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.CreateIntentRequest(
parent=parent,
intent=intent,
language_code=language_code,
intent_view=intent_view,
)
return self._inner_api_calls['create_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def create_intent(self,
parent,
intent,
language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'create_intent' not in self._inner_api_calls:
self._inner_api_calls[
'create_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.create_intent,
default_retry=self._method_configs['CreateIntent'].retry,
default_timeout=self._method_configs['CreateIntent']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.CreateIntentRequest(
parent=parent,
intent=intent,
language_code=language_code,
intent_view=intent_view,
)
return self._inner_api_calls['create_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> parent = client.project_agent_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``intent``:
>>> intent = {}
>>>
>>> response = client.create_intent(parent, intent)
Args:
parent (str): Required. The agent to create a intent for.
Format: ``projects/<Project ID>/agent``.
intent (Union[dict, ~google.cloud.dialogflow_v2.types.Intent]): Required. The intent to create.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Intent`
language_code (str): Optional. The language of training phrases, parameters and rich messages
defined in ``intent``. If not specified, the agent's default language is
used. [More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
intent_view (~google.cloud.dialogflow_v2.types.IntentView): Optional. The resource view to apply to the returned intent.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Intent` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/intents_client.py#L393-L465 |
238,396 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.update_intent | def update_intent(self,
intent,
language_code,
update_mask=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Updates the specified intent.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> # TODO: Initialize ``intent``:
>>> intent = {}
>>>
>>> # TODO: Initialize ``language_code``:
>>> language_code = ''
>>>
>>> response = client.update_intent(intent, language_code)
Args:
intent (Union[dict, ~google.cloud.dialogflow_v2.types.Intent]): Required. The intent to update.
Format: ``projects/<Project ID>/agent/intents/<Intent ID>``.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Intent`
language_code (str): Optional. The language of training phrases, parameters and rich messages
defined in ``intent``. If not specified, the agent's default language is
used. [More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
intent_view (~google.cloud.dialogflow_v2.types.IntentView): Optional. The resource view to apply to the returned intent.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Intent` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'update_intent' not in self._inner_api_calls:
self._inner_api_calls[
'update_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_intent,
default_retry=self._method_configs['UpdateIntent'].retry,
default_timeout=self._method_configs['UpdateIntent']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.UpdateIntentRequest(
intent=intent,
language_code=language_code,
update_mask=update_mask,
intent_view=intent_view,
)
return self._inner_api_calls['update_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def update_intent(self,
intent,
language_code,
update_mask=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'update_intent' not in self._inner_api_calls:
self._inner_api_calls[
'update_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.update_intent,
default_retry=self._method_configs['UpdateIntent'].retry,
default_timeout=self._method_configs['UpdateIntent']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.UpdateIntentRequest(
intent=intent,
language_code=language_code,
update_mask=update_mask,
intent_view=intent_view,
)
return self._inner_api_calls['update_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> # TODO: Initialize ``intent``:
>>> intent = {}
>>>
>>> # TODO: Initialize ``language_code``:
>>> language_code = ''
>>>
>>> response = client.update_intent(intent, language_code)
Args:
intent (Union[dict, ~google.cloud.dialogflow_v2.types.Intent]): Required. The intent to update.
Format: ``projects/<Project ID>/agent/intents/<Intent ID>``.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Intent`
language_code (str): Optional. The language of training phrases, parameters and rich messages
defined in ``intent``. If not specified, the agent's default language is
used. [More than a dozen
languages](https://dialogflow.com/docs/reference/language) are supported.
Note: languages must be enabled in the agent, before they can be used.
update_mask (Union[dict, ~google.cloud.dialogflow_v2.types.FieldMask]): Optional. The mask to control which fields get updated.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.FieldMask`
intent_view (~google.cloud.dialogflow_v2.types.IntentView): Optional. The resource view to apply to the returned intent.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types.Intent` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
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] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/intents_client.py#L467-L542 |
238,397 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.delete_intent | def delete_intent(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified intent.
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> name = client.intent_path('[PROJECT]', '[INTENT]')
>>>
>>> client.delete_intent(name)
Args:
name (str): Required. The name of the intent to delete.
Format: ``projects/<Project ID>/agent/intents/<Intent ID>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'delete_intent' not in self._inner_api_calls:
self._inner_api_calls[
'delete_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.delete_intent,
default_retry=self._method_configs['DeleteIntent'].retry,
default_timeout=self._method_configs['DeleteIntent']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.DeleteIntentRequest(name=name, )
self._inner_api_calls['delete_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | python | def delete_intent(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'delete_intent' not in self._inner_api_calls:
self._inner_api_calls[
'delete_intent'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.delete_intent,
default_retry=self._method_configs['DeleteIntent'].retry,
default_timeout=self._method_configs['DeleteIntent']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.DeleteIntentRequest(name=name, )
self._inner_api_calls['delete_intent'](
request, retry=retry, timeout=timeout, metadata=metadata) | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> name = client.intent_path('[PROJECT]', '[INTENT]')
>>>
>>> client.delete_intent(name)
Args:
name (str): Required. The name of the intent to delete.
Format: ``projects/<Project ID>/agent/intents/<Intent ID>``.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
"Deletes",
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"."
] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/intents_client.py#L544-L593 |
238,398 | googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.batch_delete_intents | def batch_delete_intents(self,
parent,
intents,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes intents in the specified agent.
Operation <response: ``google.protobuf.Empty``>
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> parent = client.project_agent_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``intents``:
>>> intents = []
>>>
>>> response = client.batch_delete_intents(parent, intents)
>>>
>>> def callback(operation_future):
... # Handle result.
... result = operation_future.result()
>>>
>>> response.add_done_callback(callback)
>>>
>>> # Handle metadata.
>>> metadata = response.metadata()
Args:
parent (str): Required. The name of the agent to delete all entities types for. Format:
``projects/<Project ID>/agent``.
intents (list[Union[dict, ~google.cloud.dialogflow_v2.types.Intent]]): Required. The collection of intents to delete. Only intent ``name`` must be
filled in.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Intent`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types._OperationFuture` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if 'batch_delete_intents' not in self._inner_api_calls:
self._inner_api_calls[
'batch_delete_intents'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.batch_delete_intents,
default_retry=self._method_configs[
'BatchDeleteIntents'].retry,
default_timeout=self._method_configs['BatchDeleteIntents']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.BatchDeleteIntentsRequest(
parent=parent,
intents=intents,
)
operation = self._inner_api_calls['batch_delete_intents'](
request, retry=retry, timeout=timeout, metadata=metadata)
return google.api_core.operation.from_gapic(
operation,
self.transport._operations_client,
empty_pb2.Empty,
metadata_type=struct_pb2.Struct,
) | python | def batch_delete_intents(self,
parent,
intents,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
# Wrap the transport method to add retry and timeout logic.
if 'batch_delete_intents' not in self._inner_api_calls:
self._inner_api_calls[
'batch_delete_intents'] = google.api_core.gapic_v1.method.wrap_method(
self.transport.batch_delete_intents,
default_retry=self._method_configs[
'BatchDeleteIntents'].retry,
default_timeout=self._method_configs['BatchDeleteIntents']
.timeout,
client_info=self._client_info,
)
request = intent_pb2.BatchDeleteIntentsRequest(
parent=parent,
intents=intents,
)
operation = self._inner_api_calls['batch_delete_intents'](
request, retry=retry, timeout=timeout, metadata=metadata)
return google.api_core.operation.from_gapic(
operation,
self.transport._operations_client,
empty_pb2.Empty,
metadata_type=struct_pb2.Struct,
) | [
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... | Deletes intents in the specified agent.
Operation <response: ``google.protobuf.Empty``>
Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.IntentsClient()
>>>
>>> parent = client.project_agent_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``intents``:
>>> intents = []
>>>
>>> response = client.batch_delete_intents(parent, intents)
>>>
>>> def callback(operation_future):
... # Handle result.
... result = operation_future.result()
>>>
>>> response.add_done_callback(callback)
>>>
>>> # Handle metadata.
>>> metadata = response.metadata()
Args:
parent (str): Required. The name of the agent to delete all entities types for. Format:
``projects/<Project ID>/agent``.
intents (list[Union[dict, ~google.cloud.dialogflow_v2.types.Intent]]): Required. The collection of intents to delete. Only intent ``name`` must be
filled in.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.dialogflow_v2.types.Intent`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.dialogflow_v2.types._OperationFuture` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
"Deletes",
"intents",
"in",
"the",
"specified",
"agent",
"."
] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2/gapic/intents_client.py#L704-L785 |
238,399 | googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/contexts_client.py | ContextsClient.environment_context_path | def environment_context_path(cls, project, environment, user, session,
context):
"""Return a fully-qualified environment_context string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}/contexts/{context}',
project=project,
environment=environment,
user=user,
session=session,
context=context,
) | python | def environment_context_path(cls, project, environment, user, session,
context):
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}/contexts/{context}',
project=project,
environment=environment,
user=user,
session=session,
context=context,
) | [
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"'projects/{project}/agent/environments/{environm... | Return a fully-qualified environment_context string. | [
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"-",
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"environment_context",
"string",
"."
] | 8c9c8709222efe427b76c9c8fcc04a0c4a0760b5 | https://github.com/googleapis/dialogflow-python-client-v2/blob/8c9c8709222efe427b76c9c8fcc04a0c4a0760b5/dialogflow_v2beta1/gapic/contexts_client.py#L125-L135 |
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