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1,400 | def test_poisson_vs_mse():
rng = np.random.RandomState(42)
n_train, n_test, n_features = 500, 500, 10
X = datasets.make_low_rank_matrix(
n_samples=n_train + n_test, n_features=n_features, random_state=rng
)
# We create a log-linear Poisson model and downscale coef as it will get
# exponentiated.
coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
y = rng.poisson(lam=np.exp(X @ coef))
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=n_test, random_state=rng
)
# We prevent some overfitting by setting min_samples_split=10.
forest_poi = RandomForestRegressor(
criterion="poisson", min_samples_leaf=10, max_features="sqrt", random_state=rng
)
forest_mse = RandomForestRegressor(
criterion="squared_error",
min_samples_leaf=10,
max_features="sqrt",
random_state=rng,
)
forest_poi.fit(X_train, y_train)
forest_mse.fit(X_train, y_train)
dummy = DummyRegressor(strategy="mean").fit(X_train, y_train)
for X, y, val in [(X_train, y_train, "train"), (X_test, y_test, "test")]:
metric_poi = mean_poisson_deviance(y, forest_poi.predict(X))
# squared_error forest might produce non-positive predictions => clip
# If y = 0 for those, the poisson deviance gets too good.
# If we drew more samples, we would eventually get y > 0 and the
# poisson deviance would explode, i.e. be undefined. Therefore, we do
# not clip to a tiny value like 1e-15, but to 1e-6. This acts like a
# small penalty to the non-positive predictions.
metric_mse = mean_poisson_deviance(
y, np.clip(forest_mse.predict(X), 1e-6, None)
)
metric_dummy = mean_poisson_deviance(y, dummy.predict(X))
# As squared_error might correctly predict 0 in train set, its train
# score can be better than Poisson. This is no longer the case for the
# test set. But keep the above comment for clipping in mind.
if val == "test":
assert metric_poi < metric_mse
assert metric_poi < 0.5 * metric_dummy
@pytest.mark.parametrize("criterion", ("poisson", "squared_error")) | Test that random forest with poisson criterion performs better than
mse for a poisson target.
There is a similar test for DecisionTreeRegressor.
| 22 | 247 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_poisson_vs_mse():
rng = np.random.RandomState(42)
n_train, n_test, n_features = 500, 500, 10
X = datasets.make_low_rank_matrix(
n_samples=n_train + n_test, n_features=n_features, random_state=rng
)
# We create a log-linear Poisson model and downscale coef as it will get
# exponentiated.
coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
y = rng.poisson(lam=np.exp(X @ coef))
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=n_test, random_state=rng
)
# We prevent some overfitting by setting min_samples_split=10.
forest_poi = RandomForestRegressor(
criterion="poisson", min_samples_leaf=10, max_features="sqrt", random_state=rng
)
forest_mse = RandomForestRegressor(
criterion="squared_error",
min_samples_leaf=10,
max_features="sqrt",
random_state=rng,
)
forest_poi.fit(X_train, y_train)
forest_mse.fit(X_train, y_train)
dummy = DummyRegressor(strategy="mean").fit(X_train, y_train)
for X, y, val in [(X_train, y_train, "train"), (X_test, y_test, "test")]:
metric_poi = mean_poisson_deviance(y, forest_poi.predict(X))
# squared_error forest might produce non-positive predictions => clip
# If y = 0 for those, the poisson deviance gets too good.
# If we drew more samples, we would eventually get y > 0 and the
# poisson deviance would explode, i.e. be undefined. Therefore, we do
# not clip to a tiny value like 1e-15, but to 1e-6. This acts like a
# small penalty to the non-positive predictions.
metric_mse = mean_poisson_deviance(
y, np.clip(forest_mse.predict(X), 1e-6, None)
)
metric_dummy = mean_poisson_deviance(y, dummy.predict(X))
# As squared_error might correctly predict 0 in train set, its train
# score can be better than Poisson. This is no longer the case for the
# test set. But keep the above comment for clipping in mind.
if val == "test":
assert metric_poi < metric_mse
assert metric_poi < 0.5 * metric_dummy
@pytest.mark.parametrize("criterion", ("poisson", "squared_error"))
```
###Assistant : Test that random forest with poisson criterion performs better than
mse for a poisson target.
There is a similar test for DecisionTreeRegressor.
|
1,401 | def _external_caller_info():
frame = inspect.currentframe()
caller = frame
levels = 0
while caller.f_code.co_filename == __file__:
caller = caller.f_back
levels += 1
return {
"lineno": caller.f_lineno,
"filename": os.path.basename(caller.f_code.co_filename),
}
| Get the info from the caller frame.
Used to override the logging function and line number with the correct
ones. See the comment on _patched_makeRecord for more info.
| 28 | 28 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _external_caller_info():
frame = inspect.currentframe()
caller = frame
levels = 0
while caller.f_code.co_filename == __file__:
caller = caller.f_back
levels += 1
return {
"lineno": caller.f_lineno,
"filename": os.path.basename(caller.f_code.co_filename),
}
```
###Assistant : Get the info from the caller frame.
Used to override the logging function and line number with the correct
ones. See the comment on _patched_makeRecord for more info.
|
1,402 | def _try_restart_fedora(self) -> None:
try:
util.run_script(['systemctl', 'restart', 'httpd'])
except errors.SubprocessError as err:
raise errors.MisconfigurationError(str(err))
# Finish with actual config check to see if systemctl restart helped
super().config_test()
|
Tries to restart httpd using systemctl to generate the self signed key pair.
| 13 | 27 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _try_restart_fedora(self) -> None:
try:
util.run_script(['systemctl', 'restart', 'httpd'])
except errors.SubprocessError as err:
raise errors.MisconfigurationError(str(err))
# Finish with actual config check to see if systemctl restart helped
super().config_test()
```
###Assistant :
Tries to restart httpd using systemctl to generate the self signed key pair.
|
1,403 | async def sock_accept(self, sock):
base_events._check_ssl_socket(sock)
if self._debug and sock.gettimeout() != 0:
raise ValueError("the socket must be non-blocking")
fut = self.create_future()
self._sock_accept(fut, sock)
return await fut
| Accept a connection.
The socket must be bound to an address and listening for connections.
The return value is a pair (conn, address) where conn is a new socket
object usable to send and receive data on the connection, and address
is the address bound to the socket on the other end of the connection.
| 55 | 25 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
async def sock_accept(self, sock):
base_events._check_ssl_socket(sock)
if self._debug and sock.gettimeout() != 0:
raise ValueError("the socket must be non-blocking")
fut = self.create_future()
self._sock_accept(fut, sock)
return await fut
```
###Assistant : Accept a connection.
The socket must be bound to an address and listening for connections.
The return value is a pair (conn, address) where conn is a new socket
object usable to send and receive data on the connection, and address
is the address bound to the socket on the other end of the connection.
|
1,404 | def test_state(self, api, state):
stream = AdsInsights(
api=api,
start_date=datetime(2010, 1, 1),
end_date=datetime(2011, 1, 1),
)
assert stream.state == {}
stream.state = state
actual_state = stream.state
actual_state["slices"] = sorted(actual_state.get("slices", []))
state["slices"] = sorted(state.get("slices", []))
assert actual_state == state
| State setter/getter should work with all combinations | 7 | 37 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_state(self, api, state):
stream = AdsInsights(
api=api,
start_date=datetime(2010, 1, 1),
end_date=datetime(2011, 1, 1),
)
assert stream.state == {}
stream.state = state
actual_state = stream.state
actual_state["slices"] = sorted(actual_state.get("slices", []))
state["slices"] = sorted(state.get("slices", []))
assert actual_state == state
```
###Assistant : State setter/getter should work with all combinations
|
1,405 | def get_snql_query(self) -> None:
raise NotImplementedError("get_snql_query cannot be implemented for MetricsQueryBuilder")
| Because metrics table queries need to make multiple requests per metric type this function cannot be
inmplemented see run_query | 19 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_snql_query(self) -> None:
raise NotImplementedError("get_snql_query cannot be implemented for MetricsQueryBuilder")
```
###Assistant : Because metrics table queries need to make multiple requests per metric type this function cannot be
inmplemented see run_query
|
1,406 | def _validate(self, params):
remote_worker_stats = [w.validate.remote(**params) for w in self.remote_workers]
return remote_worker_stats
| Runs validation for each worker. Returns results as promises. | 9 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _validate(self, params):
remote_worker_stats = [w.validate.remote(**params) for w in self.remote_workers]
return remote_worker_stats
```
###Assistant : Runs validation for each worker. Returns results as promises.
|
1,407 | def set_aspect(self, aspect, adjustable=None, anchor=None, share=False):
_api.check_in_list(('auto', 'equal', 'equalxy', 'equalyz', 'equalxz'),
aspect=aspect)
super().set_aspect(
aspect='auto', adjustable=adjustable, anchor=anchor, share=share)
if aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'):
if aspect == 'equal':
axis_indices = [0, 1, 2]
elif aspect == 'equalxy':
axis_indices = [0, 1]
elif aspect == 'equalxz':
axis_indices = [0, 2]
elif aspect == 'equalyz':
axis_indices = [1, 2]
view_intervals = np.array([self.xaxis.get_view_interval(),
self.yaxis.get_view_interval(),
self.zaxis.get_view_interval()])
mean = np.mean(view_intervals, axis=1)
delta = np.max(np.ptp(view_intervals, axis=1))
deltas = delta * self._box_aspect / min(self._box_aspect)
for i, set_lim in enumerate((self.set_xlim3d,
self.set_ylim3d,
self.set_zlim3d)):
if i in axis_indices:
set_lim(mean[i] - deltas[i]/2., mean[i] + deltas[i]/2.)
|
Set the aspect ratios.
Parameters
----------
aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'}
Possible values:
========= ==================================================
value description
========= ==================================================
'auto' automatic; fill the position rectangle with data.
'equal' adapt all the axes to have equal aspect ratios.
'equalxy' adapt the x and y axes to have equal aspect ratios.
'equalxz' adapt the x and z axes to have equal aspect ratios.
'equalyz' adapt the y and z axes to have equal aspect ratios.
========= ==================================================
adjustable : None
Currently ignored by Axes3D
If not *None*, this defines which parameter will be adjusted to
meet the required aspect. See `.set_adjustable` for further
details.
anchor : None or str or 2-tuple of float, optional
If not *None*, this defines where the Axes will be drawn if there
is extra space due to aspect constraints. The most common way to
to specify the anchor are abbreviations of cardinal directions:
===== =====================
value description
===== =====================
'C' centered
'SW' lower left corner
'S' middle of bottom edge
'SE' lower right corner
etc.
===== =====================
See `~.Axes.set_anchor` for further details.
share : bool, default: False
If ``True``, apply the settings to all shared Axes.
See Also
--------
mpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect
| 195 | 94 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def set_aspect(self, aspect, adjustable=None, anchor=None, share=False):
_api.check_in_list(('auto', 'equal', 'equalxy', 'equalyz', 'equalxz'),
aspect=aspect)
super().set_aspect(
aspect='auto', adjustable=adjustable, anchor=anchor, share=share)
if aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'):
if aspect == 'equal':
axis_indices = [0, 1, 2]
elif aspect == 'equalxy':
axis_indices = [0, 1]
elif aspect == 'equalxz':
axis_indices = [0, 2]
elif aspect == 'equalyz':
axis_indices = [1, 2]
view_intervals = np.array([self.xaxis.get_view_interval(),
self.yaxis.get_view_interval(),
self.zaxis.get_view_interval()])
mean = np.mean(view_intervals, axis=1)
delta = np.max(np.ptp(view_intervals, axis=1))
deltas = delta * self._box_aspect / min(self._box_aspect)
for i, set_lim in enumerate((self.set_xlim3d,
self.set_ylim3d,
self.set_zlim3d)):
if i in axis_indices:
set_lim(mean[i] - deltas[i]/2., mean[i] + deltas[i]/2.)
```
###Assistant :
Set the aspect ratios.
Parameters
----------
aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'}
Possible values:
========= ==================================================
value description
========= ==================================================
'auto' automatic; fill the position rectangle with data.
'equal' adapt all the axes to have equal aspect ratios.
'equalxy' adapt the x and y axes to have equal aspect ratios.
'equalxz' adapt the x and z axes to have equal aspect ratios.
'equalyz' adapt the y and z axes to have equal aspect ratios.
========= ==================================================
adjustable : None
Currently ignored by Axes3D
If not *None*, this defines which parameter will be adjusted to
meet the required aspect. See `.set_adjustable` for further
details.
anchor : None or str or 2-tuple of float, optional
If not *None*, this defines where the Axes will be drawn if there
is extra space due to aspect constraints. The most common way to
to specify the anchor are abbreviations of cardinal directions:
===== =====================
value description
===== =====================
'C' centered
'SW' lower left corner
'S' middle of bottom edge
'SE' lower right corner
etc.
===== =====================
See `~.Axes.set_anchor` for further details.
share : bool, default: False
If ``True``, apply the settings to all shared Axes.
See Also
--------
mpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect
|
1,408 | def __getattr__(cls, name):
if _is_dunder(name):
raise AttributeError(name)
try:
return cls._member_map_[name]
except KeyError:
raise AttributeError(name) from None
|
Return the enum member matching `name`
We use __getattr__ instead of descriptors or inserting into the enum
class' __dict__ in order to support `name` and `value` being both
properties for enum members (which live in the class' __dict__) and
enum members themselves.
| 42 | 16 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def __getattr__(cls, name):
if _is_dunder(name):
raise AttributeError(name)
try:
return cls._member_map_[name]
except KeyError:
raise AttributeError(name) from None
```
###Assistant :
Return the enum member matching `name`
We use __getattr__ instead of descriptors or inserting into the enum
class' __dict__ in order to support `name` and `value` being both
properties for enum members (which live in the class' __dict__) and
enum members themselves.
|
1,409 | def test_n_clusters(n_clusters):
rng = np.random.RandomState(0)
X = rng.rand(10, 2)
bisect_means = BisectingKMeans(n_clusters=n_clusters, random_state=0)
bisect_means.fit(X)
assert_array_equal(np.unique(bisect_means.labels_), np.arange(n_clusters))
| Test if resulting labels are in range [0, n_clusters - 1]. | 11 | 16 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_n_clusters(n_clusters):
rng = np.random.RandomState(0)
X = rng.rand(10, 2)
bisect_means = BisectingKMeans(n_clusters=n_clusters, random_state=0)
bisect_means.fit(X)
assert_array_equal(np.unique(bisect_means.labels_), np.arange(n_clusters))
```
###Assistant : Test if resulting labels are in range [0, n_clusters - 1].
|
1,410 | def test_nested_ungrouped_nav(self):
nav_cfg = [
{'Home': 'index.md'},
{'Contact': 'about/contact.md'},
{'License Title': 'about/sub/license.md'},
]
expected = dedent(
)
cfg = load_config(nav=nav_cfg, site_url='http://example.com/')
fs = [
File(list(item.values())[0], cfg['docs_dir'], cfg['site_dir'], cfg['use_directory_urls'])
for item in nav_cfg
]
files = Files(fs)
site_navigation = get_navigation(files, cfg)
self.assertEqual(str(site_navigation).strip(), expected)
self.assertEqual(len(site_navigation.items), 3)
self.assertEqual(len(site_navigation.pages), 3)
|
Page(title='Home', url='/')
Page(title='Contact', url='/about/contact/')
Page(title='License Title', url='/about/sub/license/')
| 7 | 46 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_nested_ungrouped_nav(self):
nav_cfg = [
{'Home': 'index.md'},
{'Contact': 'about/contact.md'},
{'License Title': 'about/sub/license.md'},
]
expected = dedent(
)
cfg = load_config(nav=nav_cfg, site_url='http://example.com/')
fs = [
File(list(item.values())[0], cfg['docs_dir'], cfg['site_dir'], cfg['use_directory_urls'])
for item in nav_cfg
]
files = Files(fs)
site_navigation = get_navigation(files, cfg)
self.assertEqual(str(site_navigation).strip(), expected)
self.assertEqual(len(site_navigation.items), 3)
self.assertEqual(len(site_navigation.pages), 3)
```
###Assistant :
Page(title='Home', url='/')
Page(title='Contact', url='/about/contact/')
Page(title='License Title', url='/about/sub/license/')
|
1,411 | def _get_log_file_names(self, name, unique=False):
if unique:
log_stdout = self._make_inc_temp(
suffix=".out", prefix=name, directory_name=self._logs_dir
)
log_stderr = self._make_inc_temp(
suffix=".err", prefix=name, directory_name=self._logs_dir
)
else:
log_stdout = os.path.join(self._logs_dir, f"{name}.out")
log_stderr = os.path.join(self._logs_dir, f"{name}.err")
return log_stdout, log_stderr
| Generate partially randomized filenames for log files.
Args:
name (str): descriptive string for this log file.
unique (bool): if true, a counter will be attached to `name` to
ensure the returned filename is not already used.
Returns:
A tuple of two file names for redirecting (stdout, stderr).
| 47 | 32 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _get_log_file_names(self, name, unique=False):
if unique:
log_stdout = self._make_inc_temp(
suffix=".out", prefix=name, directory_name=self._logs_dir
)
log_stderr = self._make_inc_temp(
suffix=".err", prefix=name, directory_name=self._logs_dir
)
else:
log_stdout = os.path.join(self._logs_dir, f"{name}.out")
log_stderr = os.path.join(self._logs_dir, f"{name}.err")
return log_stdout, log_stderr
```
###Assistant : Generate partially randomized filenames for log files.
Args:
name (str): descriptive string for this log file.
unique (bool): if true, a counter will be attached to `name` to
ensure the returned filename is not already used.
Returns:
A tuple of two file names for redirecting (stdout, stderr).
|
1,412 | def yeardayscalendar(self, year, width=3):
months = [
self.monthdayscalendar(year, i)
for i in range(January, January+12)
]
return [months[i:i+width] for i in range(0, len(months), width) ]
|
Return the data for the specified year ready for formatting (similar to
yeardatescalendar()). Entries in the week lists are day numbers.
Day numbers outside this month are zero.
| 28 | 24 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def yeardayscalendar(self, year, width=3):
months = [
self.monthdayscalendar(year, i)
for i in range(January, January+12)
]
return [months[i:i+width] for i in range(0, len(months), width) ]
```
###Assistant :
Return the data for the specified year ready for formatting (similar to
yeardatescalendar()). Entries in the week lists are day numbers.
Day numbers outside this month are zero.
|
1,413 | def test_simplelistfilter_without_parameter(self):
modeladmin = DecadeFilterBookAdminWithoutParameter(Book, site)
request = self.request_factory.get("/", {})
request.user = self.alfred
msg = "The list filter 'DecadeListFilterWithoutParameter' does not specify a 'parameter_name'."
with self.assertRaisesMessage(ImproperlyConfigured, msg):
modeladmin.get_changelist_instance(request)
|
Any SimpleListFilter must define a parameter_name.
| 6 | 28 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_simplelistfilter_without_parameter(self):
modeladmin = DecadeFilterBookAdminWithoutParameter(Book, site)
request = self.request_factory.get("/", {})
request.user = self.alfred
msg = "The list filter 'DecadeListFilterWithoutParameter' does not specify a 'parameter_name'."
with self.assertRaisesMessage(ImproperlyConfigured, msg):
modeladmin.get_changelist_instance(request)
```
###Assistant :
Any SimpleListFilter must define a parameter_name.
|
1,414 | def from_session_or_email(cls, request, organization, email, instance=None, logger=None):
invite_token, invite_member_id = get_invite_details(request)
try:
if invite_token and invite_member_id:
om = OrganizationMember.objects.get(token=invite_token, id=invite_member_id)
else:
om = OrganizationMember.objects.get(
email=email, organization=organization, user=None
)
except OrganizationMember.DoesNotExist:
# Unable to locate the pending organization member. Cannot setup
# the invite helper.
return None
return cls(
request=request, member_id=om.id, token=om.token, instance=instance, logger=logger
)
|
Initializes the ApiInviteHelper by locating the pending organization
member via the currently set pending invite details in the session, or
via the passed email if no cookie is currently set.
| 30 | 54 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def from_session_or_email(cls, request, organization, email, instance=None, logger=None):
invite_token, invite_member_id = get_invite_details(request)
try:
if invite_token and invite_member_id:
om = OrganizationMember.objects.get(token=invite_token, id=invite_member_id)
else:
om = OrganizationMember.objects.get(
email=email, organization=organization, user=None
)
except OrganizationMember.DoesNotExist:
# Unable to locate the pending organization member. Cannot setup
# the invite helper.
return None
return cls(
request=request, member_id=om.id, token=om.token, instance=instance, logger=logger
)
```
###Assistant :
Initializes the ApiInviteHelper by locating the pending organization
member via the currently set pending invite details in the session, or
via the passed email if no cookie is currently set.
|
1,415 | async def test_multiple_bleak_scanner_instances(hass):
install_multiple_bleak_catcher()
instance = bleak.BleakScanner()
assert isinstance(instance, HaBleakScannerWrapper)
uninstall_multiple_bleak_catcher()
with patch("bleak.get_platform_scanner_backend_type"):
instance = bleak.BleakScanner()
assert not isinstance(instance, HaBleakScannerWrapper)
| Test creating multiple BleakScanners without an integration. | 7 | 20 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
async def test_multiple_bleak_scanner_instances(hass):
install_multiple_bleak_catcher()
instance = bleak.BleakScanner()
assert isinstance(instance, HaBleakScannerWrapper)
uninstall_multiple_bleak_catcher()
with patch("bleak.get_platform_scanner_backend_type"):
instance = bleak.BleakScanner()
assert not isinstance(instance, HaBleakScannerWrapper)
```
###Assistant : Test creating multiple BleakScanners without an integration.
|
1,416 | def highlight_string(value, highlight, trim_pre=None, trim_post=None, trim_placeholder='...'):
# Split value on highlight string
try:
pre, match, post = re.split(fr'({highlight})', value, maxsplit=1, flags=re.IGNORECASE)
except ValueError:
# Match not found
return escape(value)
# Trim pre/post sections to length
if trim_pre and len(pre) > trim_pre:
pre = trim_placeholder + pre[-trim_pre:]
if trim_post and len(post) > trim_post:
post = post[:trim_post] + trim_placeholder
return f'{escape(pre)}<mark>{escape(match)}</mark>{escape(post)}'
|
Highlight a string within a string and optionally trim the pre/post portions of the original string.
| 16 | 59 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def highlight_string(value, highlight, trim_pre=None, trim_post=None, trim_placeholder='...'):
# Split value on highlight string
try:
pre, match, post = re.split(fr'({highlight})', value, maxsplit=1, flags=re.IGNORECASE)
except ValueError:
# Match not found
return escape(value)
# Trim pre/post sections to length
if trim_pre and len(pre) > trim_pre:
pre = trim_placeholder + pre[-trim_pre:]
if trim_post and len(post) > trim_post:
post = post[:trim_post] + trim_placeholder
return f'{escape(pre)}<mark>{escape(match)}</mark>{escape(post)}'
```
###Assistant :
Highlight a string within a string and optionally trim the pre/post portions of the original string.
|
1,417 | def node_table(self):
self._check_connected()
node_table = self.global_state_accessor.get_node_table()
results = []
for node_info_item in node_table:
item = gcs_utils.GcsNodeInfo.FromString(node_info_item)
node_info = {
"NodeID": ray._private.utils.binary_to_hex(item.node_id),
"Alive": item.state
== gcs_utils.GcsNodeInfo.GcsNodeState.Value("ALIVE"),
"NodeManagerAddress": item.node_manager_address,
"NodeManagerHostname": item.node_manager_hostname,
"NodeManagerPort": item.node_manager_port,
"ObjectManagerPort": item.object_manager_port,
"ObjectStoreSocketName": item.object_store_socket_name,
"RayletSocketName": item.raylet_socket_name,
"MetricsExportPort": item.metrics_export_port,
"NodeName": item.node_name,
}
node_info["alive"] = node_info["Alive"]
node_info["Resources"] = (
{key: value for key, value in item.resources_total.items()}
if node_info["Alive"]
else {}
)
results.append(node_info)
return results
| Fetch and parse the Gcs node info table.
Returns:
Information about the node in the cluster.
| 16 | 63 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def node_table(self):
self._check_connected()
node_table = self.global_state_accessor.get_node_table()
results = []
for node_info_item in node_table:
item = gcs_utils.GcsNodeInfo.FromString(node_info_item)
node_info = {
"NodeID": ray._private.utils.binary_to_hex(item.node_id),
"Alive": item.state
== gcs_utils.GcsNodeInfo.GcsNodeState.Value("ALIVE"),
"NodeManagerAddress": item.node_manager_address,
"NodeManagerHostname": item.node_manager_hostname,
"NodeManagerPort": item.node_manager_port,
"ObjectManagerPort": item.object_manager_port,
"ObjectStoreSocketName": item.object_store_socket_name,
"RayletSocketName": item.raylet_socket_name,
"MetricsExportPort": item.metrics_export_port,
"NodeName": item.node_name,
}
node_info["alive"] = node_info["Alive"]
node_info["Resources"] = (
{key: value for key, value in item.resources_total.items()}
if node_info["Alive"]
else {}
)
results.append(node_info)
return results
```
###Assistant : Fetch and parse the Gcs node info table.
Returns:
Information about the node in the cluster.
|
1,418 | def vocabulary_size(self):
if tf.executing_eagerly():
return (
int(self.lookup_table.size().numpy())
+ self._token_start_index()
)
else:
return self.lookup_table.size() + self._token_start_index()
| Gets the current size of the layer's vocabulary.
Returns:
The integer size of the vocabulary, including optional mask and oov indices.
| 21 | 15 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def vocabulary_size(self):
if tf.executing_eagerly():
return (
int(self.lookup_table.size().numpy())
+ self._token_start_index()
)
else:
return self.lookup_table.size() + self._token_start_index()
```
###Assistant : Gets the current size of the layer's vocabulary.
Returns:
The integer size of the vocabulary, including optional mask and oov indices.
|
1,419 | def test_version_with_invalid_names():
lslpp_mydog_out =
ver_chk = MagicMock(return_value={"retcode": 1, "stdout": lslpp_mydog_out})
with patch.dict(aixpkg.__grains__, {"osarch": "PowerPC_POWER8"}), patch.dict(
aixpkg.__salt__,
{"cmd.run_all": ver_chk},
):
versions_checked = aixpkg.version(
"mydog", versions_as_list=True, use_context=False
)
assert ver_chk.call_count == 1
ver_chk.assert_called_with("lslpp -Lq mydog", python_shell=False)
assert versions_checked == ""
|
test version of packages
lslpp: Fileset mydog not installed.
State codes:
A -- Applied.
B -- Broken.
C -- Committed.
E -- EFIX Locked.
O -- Obsolete. (partially migrated to newer version)
? -- Inconsistent State...Run lppchk -v.
Type codes:
F -- Installp Fileset
P -- Product
C -- Component
T -- Feature
R -- RPM Package
E -- Interim Fix
| 61 | 38 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_version_with_invalid_names():
lslpp_mydog_out =
ver_chk = MagicMock(return_value={"retcode": 1, "stdout": lslpp_mydog_out})
with patch.dict(aixpkg.__grains__, {"osarch": "PowerPC_POWER8"}), patch.dict(
aixpkg.__salt__,
{"cmd.run_all": ver_chk},
):
versions_checked = aixpkg.version(
"mydog", versions_as_list=True, use_context=False
)
assert ver_chk.call_count == 1
ver_chk.assert_called_with("lslpp -Lq mydog", python_shell=False)
assert versions_checked == ""
```
###Assistant :
test version of packages
lslpp: Fileset mydog not installed.
State codes:
A -- Applied.
B -- Broken.
C -- Committed.
E -- EFIX Locked.
O -- Obsolete. (partially migrated to newer version)
? -- Inconsistent State...Run lppchk -v.
Type codes:
F -- Installp Fileset
P -- Product
C -- Component
T -- Feature
R -- RPM Package
E -- Interim Fix
|
1,420 | def test_mapped_literal_length_increase_adds_additional_ti(dag_maker, session):
with dag_maker(session=session) as dag:
| Test that when the length of mapped literal increases, additional ti is added | 13 | 7 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_mapped_literal_length_increase_adds_additional_ti(dag_maker, session):
with dag_maker(session=session) as dag:
```
###Assistant : Test that when the length of mapped literal increases, additional ti is added
|
1,421 | def test_installed_without_username(self):
# Remove username to simulate privacy mode
del self.user_data_from_bitbucket["principal"]["username"]
response = self.client.post(self.path, data=self.user_data_from_bitbucket)
assert response.status_code == 200
integration = Integration.objects.get(provider=self.provider, external_id=self.client_key)
assert integration.name == self.user_display_name
assert integration.metadata == self.user_metadata
| Test a user (not team) installation where the user has hidden their username from public view | 16 | 31 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_installed_without_username(self):
# Remove username to simulate privacy mode
del self.user_data_from_bitbucket["principal"]["username"]
response = self.client.post(self.path, data=self.user_data_from_bitbucket)
assert response.status_code == 200
integration = Integration.objects.get(provider=self.provider, external_id=self.client_key)
assert integration.name == self.user_display_name
assert integration.metadata == self.user_metadata
```
###Assistant : Test a user (not team) installation where the user has hidden their username from public view
|
1,422 | def _command_display(self, command):
build_tabs = getattr(self, f"_{command}_tabs")
build_tabs()
| Build the relevant command specific tabs based on the incoming Faceswap command.
Parameters
----------
command: str
The Faceswap command that is being executed
| 23 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _command_display(self, command):
build_tabs = getattr(self, f"_{command}_tabs")
build_tabs()
```
###Assistant : Build the relevant command specific tabs based on the incoming Faceswap command.
Parameters
----------
command: str
The Faceswap command that is being executed
|
1,423 | def itermerged(self):
for key in self:
val = self._container[key.lower()]
yield val[0], ", ".join(val[1:])
| Iterate over all headers, merging duplicate ones together. | 8 | 13 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def itermerged(self):
for key in self:
val = self._container[key.lower()]
yield val[0], ", ".join(val[1:])
```
###Assistant : Iterate over all headers, merging duplicate ones together.
|
1,424 | def get_file_from_path(self, path):
return self.src_paths.get(os.path.normpath(path))
| Return a File instance with File.src_path equal to path. | 9 | 5 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_file_from_path(self, path):
return self.src_paths.get(os.path.normpath(path))
```
###Assistant : Return a File instance with File.src_path equal to path.
|
1,425 | def sleepdeprived(request):
module = request.node.get_closest_marker(
"sleepdeprived_patched_module").args[0]
old_sleep, module.sleep = module.sleep, noop
try:
yield
finally:
module.sleep = old_sleep
# Taken from
# http://bitbucket.org/runeh/snippets/src/tip/missing_modules.py
@pytest.fixture | Mock sleep method in patched module to do nothing.
Example:
>>> import time
>>> @pytest.mark.sleepdeprived_patched_module(time)
>>> def test_foo(self, sleepdeprived):
>>> pass
| 21 | 23 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def sleepdeprived(request):
module = request.node.get_closest_marker(
"sleepdeprived_patched_module").args[0]
old_sleep, module.sleep = module.sleep, noop
try:
yield
finally:
module.sleep = old_sleep
# Taken from
# http://bitbucket.org/runeh/snippets/src/tip/missing_modules.py
@pytest.fixture
```
###Assistant : Mock sleep method in patched module to do nothing.
Example:
>>> import time
>>> @pytest.mark.sleepdeprived_patched_module(time)
>>> def test_foo(self, sleepdeprived):
>>> pass
|
1,426 | def _set_mouse_bindings(self) -> None:
logger.debug("Binding mouse events")
if system() == "Linux":
self._canvas.tag_bind(self._canvas.image_id, "<Button-4>", self._on_bound_zoom)
self._canvas.tag_bind(self._canvas.image_id, "<Button-5>", self._on_bound_zoom)
else:
self._canvas.tag_bind(self._canvas.image_id, "<MouseWheel>", self._on_bound_zoom)
self._canvas.tag_bind(self._canvas.image_id, "<Button-1>", self._on_mouse_click)
self._canvas.tag_bind(self._canvas.image_id, "<B1-Motion>", self._on_mouse_drag)
logger.debug("Bound mouse events")
| Set the mouse bindings for interacting with the preview image
Mousewheel: Zoom in and out
Mouse click: Move image
| 19 | 30 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _set_mouse_bindings(self) -> None:
logger.debug("Binding mouse events")
if system() == "Linux":
self._canvas.tag_bind(self._canvas.image_id, "<Button-4>", self._on_bound_zoom)
self._canvas.tag_bind(self._canvas.image_id, "<Button-5>", self._on_bound_zoom)
else:
self._canvas.tag_bind(self._canvas.image_id, "<MouseWheel>", self._on_bound_zoom)
self._canvas.tag_bind(self._canvas.image_id, "<Button-1>", self._on_mouse_click)
self._canvas.tag_bind(self._canvas.image_id, "<B1-Motion>", self._on_mouse_drag)
logger.debug("Bound mouse events")
```
###Assistant : Set the mouse bindings for interacting with the preview image
Mousewheel: Zoom in and out
Mouse click: Move image
|
1,427 | async def test_create_area_with_id_already_in_use(registry):
area1 = registry.async_create("mock")
updated_area1 = registry.async_update(area1.id, name="New Name")
assert updated_area1.id == area1.id
area2 = registry.async_create("mock")
assert area2.id == "mock_2"
| Make sure that we can't create an area with a name already in use. | 14 | 22 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
async def test_create_area_with_id_already_in_use(registry):
area1 = registry.async_create("mock")
updated_area1 = registry.async_update(area1.id, name="New Name")
assert updated_area1.id == area1.id
area2 = registry.async_create("mock")
assert area2.id == "mock_2"
```
###Assistant : Make sure that we can't create an area with a name already in use.
|
1,428 | def forward(self, *args, **kwargs):
labels = kwargs.pop('labels', None)
pooled, encoded = super(ErnieModelForSequenceClassification, self).forward(*args, **kwargs)
hidden = self.dropout(pooled)
logits = self.classifier(hidden)
if labels is not None:
if len(labels.shape) != 1:
labels = labels.squeeze()
loss = F.cross_entropy(logits, labels)
else:
loss = None
return loss, logits
|
Args:
labels (optional, `Variable` of shape [batch_size]):
ground truth label id for each sentence
Returns:
loss (`Variable` of shape []):
Cross entropy loss mean over batch
if labels not set, returns None
logits (`Variable` of shape [batch_size, hidden_size]):
output logits of classifier
| 42 | 43 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def forward(self, *args, **kwargs):
labels = kwargs.pop('labels', None)
pooled, encoded = super(ErnieModelForSequenceClassification, self).forward(*args, **kwargs)
hidden = self.dropout(pooled)
logits = self.classifier(hidden)
if labels is not None:
if len(labels.shape) != 1:
labels = labels.squeeze()
loss = F.cross_entropy(logits, labels)
else:
loss = None
return loss, logits
```
###Assistant :
Args:
labels (optional, `Variable` of shape [batch_size]):
ground truth label id for each sentence
Returns:
loss (`Variable` of shape []):
Cross entropy loss mean over batch
if labels not set, returns None
logits (`Variable` of shape [batch_size, hidden_size]):
output logits of classifier
|
1,429 | def _get_project(self, name):
raise NotImplementedError('Please implement in the subclass')
|
For a given project, get a dictionary mapping available versions to Distribution
instances.
This should be implemented in subclasses.
If called from a locate() request, self.matcher will be set to a
matcher for the requirement to satisfy, otherwise it will be None.
| 42 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _get_project(self, name):
raise NotImplementedError('Please implement in the subclass')
```
###Assistant :
For a given project, get a dictionary mapping available versions to Distribution
instances.
This should be implemented in subclasses.
If called from a locate() request, self.matcher will be set to a
matcher for the requirement to satisfy, otherwise it will be None.
|
1,430 | def test_pickle_empty(self):
arr = np.array([]).reshape(999999, 0)
pk_dmp = pickle.dumps(arr)
pk_load = pickle.loads(pk_dmp)
assert pk_load.size == 0
| Checking if an empty array pickled and un-pickled will not cause a
segmentation fault | 14 | 16 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_pickle_empty(self):
arr = np.array([]).reshape(999999, 0)
pk_dmp = pickle.dumps(arr)
pk_load = pickle.loads(pk_dmp)
assert pk_load.size == 0
```
###Assistant : Checking if an empty array pickled and un-pickled will not cause a
segmentation fault
|
1,431 | def get_semantics(cls, kwargs, semantics=None):
# TODO this should be get_variables since we have included x and y
if semantics is None:
semantics = cls.semantics
variables = {}
for key, val in kwargs.items():
if key in semantics and val is not None:
variables[key] = val
return variables
| Subset a dictionary arguments with known semantic variables. | 8 | 46 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_semantics(cls, kwargs, semantics=None):
# TODO this should be get_variables since we have included x and y
if semantics is None:
semantics = cls.semantics
variables = {}
for key, val in kwargs.items():
if key in semantics and val is not None:
variables[key] = val
return variables
```
###Assistant : Subset a dictionary arguments with known semantic variables.
|
1,432 | def convert_deps_to_pip(deps, project=None, r=True, include_index=True):
from pipenv.vendor.requirementslib.models.requirements import Requirement
dependencies = []
for dep_name, dep in deps.items():
if project:
project.clear_pipfile_cache()
indexes = getattr(project, "pipfile_sources", []) if project is not None else []
new_dep = Requirement.from_pipfile(dep_name, dep)
if new_dep.index:
include_index = True
req = new_dep.as_line(sources=indexes if include_index else None).strip()
dependencies.append(req)
if not r:
return dependencies
# Write requirements.txt to tmp directory.
from pipenv.vendor.vistir.path import create_tracked_tempfile
f = create_tracked_tempfile(suffix="-requirements.txt", delete=False)
f.write("\n".join(dependencies).encode("utf-8"))
f.close()
return f.name
| "Converts a Pipfile-formatted dependency to a pip-formatted one. | 8 | 72 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def convert_deps_to_pip(deps, project=None, r=True, include_index=True):
from pipenv.vendor.requirementslib.models.requirements import Requirement
dependencies = []
for dep_name, dep in deps.items():
if project:
project.clear_pipfile_cache()
indexes = getattr(project, "pipfile_sources", []) if project is not None else []
new_dep = Requirement.from_pipfile(dep_name, dep)
if new_dep.index:
include_index = True
req = new_dep.as_line(sources=indexes if include_index else None).strip()
dependencies.append(req)
if not r:
return dependencies
# Write requirements.txt to tmp directory.
from pipenv.vendor.vistir.path import create_tracked_tempfile
f = create_tracked_tempfile(suffix="-requirements.txt", delete=False)
f.write("\n".join(dependencies).encode("utf-8"))
f.close()
return f.name
```
###Assistant : "Converts a Pipfile-formatted dependency to a pip-formatted one.
|
1,433 | def handle_trial_end(self, data):
hyper_params = nni.load(data['hyper_params'])
if self.is_created_in_previous_exp(hyper_params['parameter_id']):
# The end of the recovered trial is ignored
return
self._handle_trial_end(hyper_params['parameter_id'])
if data['trial_job_id'] in self.job_id_para_id_map:
del self.job_id_para_id_map[data['trial_job_id']]
|
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
event: the job's state
hyper_params: the hyperparameters (a string) generated and returned by tuner
| 32 | 25 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def handle_trial_end(self, data):
hyper_params = nni.load(data['hyper_params'])
if self.is_created_in_previous_exp(hyper_params['parameter_id']):
# The end of the recovered trial is ignored
return
self._handle_trial_end(hyper_params['parameter_id'])
if data['trial_job_id'] in self.job_id_para_id_map:
del self.job_id_para_id_map[data['trial_job_id']]
```
###Assistant :
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
event: the job's state
hyper_params: the hyperparameters (a string) generated and returned by tuner
|
1,434 | def _copy_future_state(source, dest):
assert source.done()
if dest.cancelled():
return
assert not dest.done()
if source.cancelled():
dest.cancel()
else:
exception = source.exception()
if exception is not None:
dest.set_exception(_convert_future_exc(exception))
else:
result = source.result()
dest.set_result(result)
| Internal helper to copy state from another Future.
The other Future may be a concurrent.futures.Future.
| 15 | 29 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _copy_future_state(source, dest):
assert source.done()
if dest.cancelled():
return
assert not dest.done()
if source.cancelled():
dest.cancel()
else:
exception = source.exception()
if exception is not None:
dest.set_exception(_convert_future_exc(exception))
else:
result = source.result()
dest.set_result(result)
```
###Assistant : Internal helper to copy state from another Future.
The other Future may be a concurrent.futures.Future.
|
1,435 | def test_get_feature_names_invalid_dtypes(names, dtypes):
pd = pytest.importorskip("pandas")
X = pd.DataFrame([[1, 2], [4, 5], [5, 6]], columns=names)
msg = re.escape(
"Feature names only support names that are all strings. "
f"Got feature names with dtypes: {dtypes}."
)
with pytest.raises(TypeError, match=msg):
names = _get_feature_names(X)
| Get feature names errors when the feature names have mixed dtypes | 11 | 41 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_get_feature_names_invalid_dtypes(names, dtypes):
pd = pytest.importorskip("pandas")
X = pd.DataFrame([[1, 2], [4, 5], [5, 6]], columns=names)
msg = re.escape(
"Feature names only support names that are all strings. "
f"Got feature names with dtypes: {dtypes}."
)
with pytest.raises(TypeError, match=msg):
names = _get_feature_names(X)
```
###Assistant : Get feature names errors when the feature names have mixed dtypes
|
1,436 | def lookup(address, port, s):
# We may get an ipv4-mapped ipv6 address here, e.g. ::ffff:127.0.0.1.
# Those still appear as "127.0.0.1" in the table, so we need to strip the prefix.
address = re.sub(r"^::ffff:(?=\d+.\d+.\d+.\d+$)", "", address)
s = s.decode()
# ALL tcp 192.168.1.13:57474 -> 23.205.82.58:443 ESTABLISHED:ESTABLISHED
specv4 = f"{address}:{port}"
# ALL tcp 2a01:e35:8bae:50f0:9d9b:ef0d:2de3:b733[58505] -> 2606:4700:30::681f:4ad0[443] ESTABLISHED:ESTABLISHED
specv6 = f"{address}[{port}]"
for i in s.split("\n"):
if "ESTABLISHED:ESTABLISHED" in i and specv4 in i:
s = i.split()
if len(s) > 4:
if sys.platform.startswith("freebsd"):
# strip parentheses for FreeBSD pfctl
s = s[3][1:-1].split(":")
else:
s = s[4].split(":")
if len(s) == 2:
return s[0], int(s[1])
elif "ESTABLISHED:ESTABLISHED" in i and specv6 in i:
s = i.split()
if len(s) > 4:
s = s[4].split("[")
port = s[1].split("]")
port = port[0]
return s[0], int(port)
raise RuntimeError("Could not resolve original destination.")
|
Parse the pfctl state output s, to look up the destination host
matching the client (address, port).
Returns an (address, port) tuple, or None.
| 24 | 133 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def lookup(address, port, s):
# We may get an ipv4-mapped ipv6 address here, e.g. ::ffff:127.0.0.1.
# Those still appear as "127.0.0.1" in the table, so we need to strip the prefix.
address = re.sub(r"^::ffff:(?=\d+.\d+.\d+.\d+$)", "", address)
s = s.decode()
# ALL tcp 192.168.1.13:57474 -> 23.205.82.58:443 ESTABLISHED:ESTABLISHED
specv4 = f"{address}:{port}"
# ALL tcp 2a01:e35:8bae:50f0:9d9b:ef0d:2de3:b733[58505] -> 2606:4700:30::681f:4ad0[443] ESTABLISHED:ESTABLISHED
specv6 = f"{address}[{port}]"
for i in s.split("\n"):
if "ESTABLISHED:ESTABLISHED" in i and specv4 in i:
s = i.split()
if len(s) > 4:
if sys.platform.startswith("freebsd"):
# strip parentheses for FreeBSD pfctl
s = s[3][1:-1].split(":")
else:
s = s[4].split(":")
if len(s) == 2:
return s[0], int(s[1])
elif "ESTABLISHED:ESTABLISHED" in i and specv6 in i:
s = i.split()
if len(s) > 4:
s = s[4].split("[")
port = s[1].split("]")
port = port[0]
return s[0], int(port)
raise RuntimeError("Could not resolve original destination.")
```
###Assistant :
Parse the pfctl state output s, to look up the destination host
matching the client (address, port).
Returns an (address, port) tuple, or None.
|
1,437 | def _hydrate_rules(cls, project_id, rules, type=OwnerRuleType.OWNERSHIP_RULE.value):
owners = [owner for rule in rules for owner in rule.owners]
actors = {
key: val
for key, val in resolve_actors({owner for owner in owners}, project_id).items()
if val
}
result = [
(rule, ActorTuple.resolve_many([actors[owner] for owner in rule.owners]), type)
for rule in rules
]
return result
|
Get the last matching rule to take the most precedence.
| 10 | 51 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _hydrate_rules(cls, project_id, rules, type=OwnerRuleType.OWNERSHIP_RULE.value):
owners = [owner for rule in rules for owner in rule.owners]
actors = {
key: val
for key, val in resolve_actors({owner for owner in owners}, project_id).items()
if val
}
result = [
(rule, ActorTuple.resolve_many([actors[owner] for owner in rule.owners]), type)
for rule in rules
]
return result
```
###Assistant :
Get the last matching rule to take the most precedence.
|
1,438 | def activate(self) -> str:
load_kube_config_from_dict(
config_dict=self.config,
context=self.context,
)
return self.current_context()
|
Convenience method for activating the k8s config stored in an instance of this block
Returns current_context for sanity check
| 19 | 10 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def activate(self) -> str:
load_kube_config_from_dict(
config_dict=self.config,
context=self.context,
)
return self.current_context()
```
###Assistant :
Convenience method for activating the k8s config stored in an instance of this block
Returns current_context for sanity check
|
1,439 | def as_coeff_add(self, *deps) -> tuple[Expr, tuple[Expr, ...]]:
if deps:
if not self.has_free(*deps):
return self, tuple()
return S.Zero, (self,)
| Return the tuple (c, args) where self is written as an Add, ``a``.
c should be a Rational added to any terms of the Add that are
independent of deps.
args should be a tuple of all other terms of ``a``; args is empty
if self is a Number or if self is independent of deps (when given).
This should be used when you do not know if self is an Add or not but
you want to treat self as an Add or if you want to process the
individual arguments of the tail of self as an Add.
- if you know self is an Add and want only the head, use self.args[0];
- if you do not want to process the arguments of the tail but need the
tail then use self.as_two_terms() which gives the head and tail.
- if you want to split self into an independent and dependent parts
use ``self.as_independent(*deps)``
>>> from sympy import S
>>> from sympy.abc import x, y
>>> (S(3)).as_coeff_add()
(3, ())
>>> (3 + x).as_coeff_add()
(3, (x,))
>>> (3 + x + y).as_coeff_add(x)
(y + 3, (x,))
>>> (3 + y).as_coeff_add(x)
(y + 3, ())
| 195 | 18 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def as_coeff_add(self, *deps) -> tuple[Expr, tuple[Expr, ...]]:
if deps:
if not self.has_free(*deps):
return self, tuple()
return S.Zero, (self,)
```
###Assistant : Return the tuple (c, args) where self is written as an Add, ``a``.
c should be a Rational added to any terms of the Add that are
independent of deps.
args should be a tuple of all other terms of ``a``; args is empty
if self is a Number or if self is independent of deps (when given).
This should be used when you do not know if self is an Add or not but
you want to treat self as an Add or if you want to process the
individual arguments of the tail of self as an Add.
- if you know self is an Add and want only the head, use self.args[0];
- if you do not want to process the arguments of the tail but need the
tail then use self.as_two_terms() which gives the head and tail.
- if you want to split self into an independent and dependent parts
use ``self.as_independent(*deps)``
>>> from sympy import S
>>> from sympy.abc import x, y
>>> (S(3)).as_coeff_add()
(3, ())
>>> (3 + x).as_coeff_add()
(3, (x,))
>>> (3 + x + y).as_coeff_add(x)
(y + 3, (x,))
>>> (3 + y).as_coeff_add(x)
(y + 3, ())
|
1,440 | def create_github_url(url):
repo_only_url = re.compile(
r"https:\/\/github\.com\/[a-z\d](?:[a-z\d]|-(?=[a-z\d])){0,38}\/[a-zA-Z0-9]+$"
)
re_branch = re.compile("/(tree|blob)/(.+?)/")
# Check if the given url is a url to a GitHub repo. If it is, tell the
# user to use 'git clone' to download it
if re.match(repo_only_url, url):
print(
"✘ The given url is a complete repository. Use 'git clone' to download the repository",
"red",
)
sys.exit()
# extract the branch name from the given url (e.g master)
branch = re_branch.search(url)
if branch:
download_dirs = url[branch.end() :]
api_url = (
url[: branch.start()].replace("github.com", "api.github.com/repos", 1)
+ "/contents/"
+ download_dirs
+ "?ref="
+ branch.group(2)
)
return api_url, download_dirs
raise ValueError("The given url is not a valid GitHub url")
# Modification of https://github.com/sdushantha/gitdir/blob/master/gitdir/gitdir.py |
From the given url, produce a URL that is compatible with Github's REST API. Can handle blob or tree paths.
| 20 | 112 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def create_github_url(url):
repo_only_url = re.compile(
r"https:\/\/github\.com\/[a-z\d](?:[a-z\d]|-(?=[a-z\d])){0,38}\/[a-zA-Z0-9]+$"
)
re_branch = re.compile("/(tree|blob)/(.+?)/")
# Check if the given url is a url to a GitHub repo. If it is, tell the
# user to use 'git clone' to download it
if re.match(repo_only_url, url):
print(
"✘ The given url is a complete repository. Use 'git clone' to download the repository",
"red",
)
sys.exit()
# extract the branch name from the given url (e.g master)
branch = re_branch.search(url)
if branch:
download_dirs = url[branch.end() :]
api_url = (
url[: branch.start()].replace("github.com", "api.github.com/repos", 1)
+ "/contents/"
+ download_dirs
+ "?ref="
+ branch.group(2)
)
return api_url, download_dirs
raise ValueError("The given url is not a valid GitHub url")
# Modification of https://github.com/sdushantha/gitdir/blob/master/gitdir/gitdir.py
```
###Assistant :
From the given url, produce a URL that is compatible with Github's REST API. Can handle blob or tree paths.
|
1,441 | def homogeneity_score(labels_true, labels_pred):
return homogeneity_completeness_v_measure(labels_true, labels_pred)[0]
| Homogeneity metric of a cluster labeling given a ground truth.
A clustering result satisfies homogeneity if all of its clusters
contain only data points which are members of a single class.
This metric is independent of the absolute values of the labels:
a permutation of the class or cluster label values won't change the
score value in any way.
This metric is not symmetric: switching ``label_true`` with ``label_pred``
will return the :func:`completeness_score` which will be different in
general.
Read more in the :ref:`User Guide <homogeneity_completeness>`.
Parameters
----------
labels_true : int array, shape = [n_samples]
Ground truth class labels to be used as a reference.
labels_pred : array-like of shape (n_samples,)
Cluster labels to evaluate.
Returns
-------
homogeneity : float
Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling.
See Also
--------
completeness_score : Completeness metric of cluster labeling.
v_measure_score : V-Measure (NMI with arithmetic mean option).
References
----------
.. [1] `Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A
conditional entropy-based external cluster evaluation measure
<https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
Examples
--------
Perfect labelings are homogeneous::
>>> from sklearn.metrics.cluster import homogeneity_score
>>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])
1.0
Non-perfect labelings that further split classes into more clusters can be
perfectly homogeneous::
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
1.000000
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
1.000000
Clusters that include samples from different classes do not make for an
homogeneous labeling::
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
0.0...
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
0.0...
| 263 | 6 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def homogeneity_score(labels_true, labels_pred):
return homogeneity_completeness_v_measure(labels_true, labels_pred)[0]
```
###Assistant : Homogeneity metric of a cluster labeling given a ground truth.
A clustering result satisfies homogeneity if all of its clusters
contain only data points which are members of a single class.
This metric is independent of the absolute values of the labels:
a permutation of the class or cluster label values won't change the
score value in any way.
This metric is not symmetric: switching ``label_true`` with ``label_pred``
will return the :func:`completeness_score` which will be different in
general.
Read more in the :ref:`User Guide <homogeneity_completeness>`.
Parameters
----------
labels_true : int array, shape = [n_samples]
Ground truth class labels to be used as a reference.
labels_pred : array-like of shape (n_samples,)
Cluster labels to evaluate.
Returns
-------
homogeneity : float
Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling.
See Also
--------
completeness_score : Completeness metric of cluster labeling.
v_measure_score : V-Measure (NMI with arithmetic mean option).
References
----------
.. [1] `Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A
conditional entropy-based external cluster evaluation measure
<https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
Examples
--------
Perfect labelings are homogeneous::
>>> from sklearn.metrics.cluster import homogeneity_score
>>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])
1.0
Non-perfect labelings that further split classes into more clusters can be
perfectly homogeneous::
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
1.000000
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
1.000000
Clusters that include samples from different classes do not make for an
homogeneous labeling::
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
0.0...
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
0.0...
|
1,442 | def dup_chebyshevt(n, K):
if n < 1:
return [K.one]
m2, m1 = [K.one], [K.one, K.zero]
for i in range(2, n+1):
m2, m1 = m1, dup_sub(dup_mul_ground(dup_lshift(m1, 1, K), K(2), K), m2, K)
return m1
| Low-level implementation of Chebyshev polynomials of the first kind. | 9 | 33 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def dup_chebyshevt(n, K):
if n < 1:
return [K.one]
m2, m1 = [K.one], [K.one, K.zero]
for i in range(2, n+1):
m2, m1 = m1, dup_sub(dup_mul_ground(dup_lshift(m1, 1, K), K(2), K), m2, K)
return m1
```
###Assistant : Low-level implementation of Chebyshev polynomials of the first kind.
|
1,443 | def get_markdown_toc(markdown_source):
md = markdown.Markdown(extensions=['toc'])
md.convert(markdown_source)
return md.toc_tokens
| Return TOC generated by Markdown parser from Markdown source text. | 10 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_markdown_toc(markdown_source):
md = markdown.Markdown(extensions=['toc'])
md.convert(markdown_source)
return md.toc_tokens
```
###Assistant : Return TOC generated by Markdown parser from Markdown source text.
|
1,444 | def get_value_data_from_instance(self, instance):
return {
"id": instance.pk,
"edit_url": AdminURLFinder().get_edit_url(instance),
}
|
Given a model instance, return a value that we can pass to both the server-side template
and the client-side rendering code (via telepath) that contains all the information needed
for display. Typically this is a dict of id, title etc; it must be JSON-serialisable.
| 44 | 10 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_value_data_from_instance(self, instance):
return {
"id": instance.pk,
"edit_url": AdminURLFinder().get_edit_url(instance),
}
```
###Assistant :
Given a model instance, return a value that we can pass to both the server-side template
and the client-side rendering code (via telepath) that contains all the information needed
for display. Typically this is a dict of id, title etc; it must be JSON-serialisable.
|
1,445 | def set_options(icon=None, button_color=None, element_size=(None, None), button_element_size=(None, None),
margins=(None, None),
element_padding=(None, None), auto_size_text=None, auto_size_buttons=None, font=None, border_width=None,
slider_border_width=None, slider_relief=None, slider_orientation=None,
autoclose_time=None, message_box_line_width=None,
progress_meter_border_depth=None, progress_meter_style=None,
progress_meter_relief=None, progress_meter_color=None, progress_meter_size=None,
text_justification=None, background_color=None, element_background_color=None,
text_element_background_color=None, input_elements_background_color=None, input_text_color=None,
scrollbar_color=None, text_color=None, element_text_color=None, debug_win_size=(None, None),
window_location=(None, None), error_button_color=(None, None), tooltip_time=None, tooltip_font=None, use_ttk_buttons=None, ttk_theme=None,
suppress_error_popups=None, suppress_raise_key_errors=None, suppress_key_guessing=None,warn_button_key_duplicates=False, enable_treeview_869_patch=None,
enable_mac_notitlebar_patch=None, use_custom_titlebar=None, titlebar_background_color=None, titlebar_text_color=None, titlebar_font=None,
titlebar_icon=None, user_settings_path=None, pysimplegui_settings_path=None, pysimplegui_settings_filename=None, keep_on_top=None, dpi_awareness=None, scaling=None, disable_modal_windows=None, tooltip_offset=(None, None)):
global DEFAULT_ELEMENT_SIZE
global DEFAULT_BUTTON_ELEMENT_SIZE
global DEFAULT_MARGINS # Margins for each LEFT/RIGHT margin is first term
global DEFAULT_ELEMENT_PADDING # Padding between elements (row, col) in pixels
global DEFAULT_AUTOSIZE_TEXT
global DEFAULT_AUTOSIZE_BUTTONS
global DEFAULT_FONT
global DEFAULT_BORDER_WIDTH
global DEFAULT_AUTOCLOSE_TIME
global DEFAULT_BUTTON_COLOR
global MESSAGE_BOX_LINE_WIDTH
global DEFAULT_PROGRESS_BAR_BORDER_WIDTH
global DEFAULT_PROGRESS_BAR_STYLE
global DEFAULT_PROGRESS_BAR_RELIEF
global DEFAULT_PROGRESS_BAR_COLOR
global DEFAULT_PROGRESS_BAR_SIZE
global DEFAULT_TEXT_JUSTIFICATION
global DEFAULT_DEBUG_WINDOW_SIZE
global DEFAULT_SLIDER_BORDER_WIDTH
global DEFAULT_SLIDER_RELIEF
global DEFAULT_SLIDER_ORIENTATION
global DEFAULT_BACKGROUND_COLOR
global DEFAULT_INPUT_ELEMENTS_COLOR
global DEFAULT_ELEMENT_BACKGROUND_COLOR
global DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR
global DEFAULT_SCROLLBAR_COLOR
global DEFAULT_TEXT_COLOR
global DEFAULT_WINDOW_LOCATION
global DEFAULT_ELEMENT_TEXT_COLOR
global DEFAULT_INPUT_TEXT_COLOR
global DEFAULT_TOOLTIP_TIME
global DEFAULT_ERROR_BUTTON_COLOR
global DEFAULT_TTK_THEME
global USE_TTK_BUTTONS
global TOOLTIP_FONT
global SUPPRESS_ERROR_POPUPS
global SUPPRESS_RAISE_KEY_ERRORS
global SUPPRESS_KEY_GUESSING
global WARN_DUPLICATE_BUTTON_KEY_ERRORS
global ENABLE_TREEVIEW_869_PATCH
global ENABLE_MAC_NOTITLEBAR_PATCH
global USE_CUSTOM_TITLEBAR
global CUSTOM_TITLEBAR_BACKGROUND_COLOR
global CUSTOM_TITLEBAR_TEXT_COLOR
global CUSTOM_TITLEBAR_ICON
global CUSTOM_TITLEBAR_FONT
global DEFAULT_USER_SETTINGS_PATH
global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH
global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME
global DEFAULT_KEEP_ON_TOP
global DEFAULT_SCALING
global DEFAULT_MODAL_WINDOWS_ENABLED
global DEFAULT_TOOLTIP_OFFSET
global _pysimplegui_user_settings
# global _my_windows
if icon:
Window._user_defined_icon = icon
# _my_windows._user_defined_icon = icon
if button_color != None:
if button_color == COLOR_SYSTEM_DEFAULT:
DEFAULT_BUTTON_COLOR = (COLOR_SYSTEM_DEFAULT, COLOR_SYSTEM_DEFAULT)
else:
DEFAULT_BUTTON_COLOR = button_color
if element_size != (None, None):
DEFAULT_ELEMENT_SIZE = element_size
if button_element_size != (None, None):
DEFAULT_BUTTON_ELEMENT_SIZE = button_element_size
if margins != (None, None):
DEFAULT_MARGINS = margins
if element_padding != (None, None):
DEFAULT_ELEMENT_PADDING = element_padding
if auto_size_text != None:
DEFAULT_AUTOSIZE_TEXT = auto_size_text
if auto_size_buttons != None:
DEFAULT_AUTOSIZE_BUTTONS = auto_size_buttons
if font != None:
DEFAULT_FONT = font
if border_width != None:
DEFAULT_BORDER_WIDTH = border_width
if autoclose_time != None:
DEFAULT_AUTOCLOSE_TIME = autoclose_time
if message_box_line_width != None:
MESSAGE_BOX_LINE_WIDTH = message_box_line_width
if progress_meter_border_depth != None:
DEFAULT_PROGRESS_BAR_BORDER_WIDTH = progress_meter_border_depth
if progress_meter_style != None:
warnings.warn('You can no longer set a progress bar style. All ttk styles must be the same for the window', UserWarning)
# DEFAULT_PROGRESS_BAR_STYLE = progress_meter_style
if progress_meter_relief != None:
DEFAULT_PROGRESS_BAR_RELIEF = progress_meter_relief
if progress_meter_color != None:
DEFAULT_PROGRESS_BAR_COLOR = progress_meter_color
if progress_meter_size != None:
DEFAULT_PROGRESS_BAR_SIZE = progress_meter_size
if slider_border_width != None:
DEFAULT_SLIDER_BORDER_WIDTH = slider_border_width
if slider_orientation != None:
DEFAULT_SLIDER_ORIENTATION = slider_orientation
if slider_relief != None:
DEFAULT_SLIDER_RELIEF = slider_relief
if text_justification != None:
DEFAULT_TEXT_JUSTIFICATION = text_justification
if background_color != None:
DEFAULT_BACKGROUND_COLOR = background_color
if text_element_background_color != None:
DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR = text_element_background_color
if input_elements_background_color != None:
DEFAULT_INPUT_ELEMENTS_COLOR = input_elements_background_color
if element_background_color != None:
DEFAULT_ELEMENT_BACKGROUND_COLOR = element_background_color
if window_location != (None, None):
DEFAULT_WINDOW_LOCATION = window_location
if debug_win_size != (None, None):
DEFAULT_DEBUG_WINDOW_SIZE = debug_win_size
if text_color != None:
DEFAULT_TEXT_COLOR = text_color
if scrollbar_color != None:
DEFAULT_SCROLLBAR_COLOR = scrollbar_color
if element_text_color != None:
DEFAULT_ELEMENT_TEXT_COLOR = element_text_color
if input_text_color is not None:
DEFAULT_INPUT_TEXT_COLOR = input_text_color
if tooltip_time is not None:
DEFAULT_TOOLTIP_TIME = tooltip_time
if error_button_color != (None, None):
DEFAULT_ERROR_BUTTON_COLOR = error_button_color
if ttk_theme is not None:
DEFAULT_TTK_THEME = ttk_theme
if use_ttk_buttons is not None:
USE_TTK_BUTTONS = use_ttk_buttons
if tooltip_font is not None:
TOOLTIP_FONT = tooltip_font
if suppress_error_popups is not None:
SUPPRESS_ERROR_POPUPS = suppress_error_popups
if suppress_raise_key_errors is not None:
SUPPRESS_RAISE_KEY_ERRORS = suppress_raise_key_errors
if suppress_key_guessing is not None:
SUPPRESS_KEY_GUESSING = suppress_key_guessing
if warn_button_key_duplicates is not None:
WARN_DUPLICATE_BUTTON_KEY_ERRORS = warn_button_key_duplicates
if enable_treeview_869_patch is not None:
ENABLE_TREEVIEW_869_PATCH = enable_treeview_869_patch
if enable_mac_notitlebar_patch is not None:
ENABLE_MAC_NOTITLEBAR_PATCH = enable_mac_notitlebar_patch
if use_custom_titlebar is not None:
USE_CUSTOM_TITLEBAR = use_custom_titlebar
if titlebar_background_color is not None:
CUSTOM_TITLEBAR_BACKGROUND_COLOR = titlebar_background_color
if titlebar_text_color is not None:
CUSTOM_TITLEBAR_TEXT_COLOR = titlebar_text_color
if titlebar_font is not None:
CUSTOM_TITLEBAR_FONT = titlebar_font
if titlebar_icon is not None:
CUSTOM_TITLEBAR_ICON = titlebar_icon
if user_settings_path is not None:
DEFAULT_USER_SETTINGS_PATH = user_settings_path
if pysimplegui_settings_path is not None:
DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH = pysimplegui_settings_path
if pysimplegui_settings_filename is not None:
DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME = pysimplegui_settings_filename
if pysimplegui_settings_filename is not None or pysimplegui_settings_filename is not None:
_pysimplegui_user_settings = UserSettings(filename=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME,
path=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH)
if keep_on_top is not None:
DEFAULT_KEEP_ON_TOP = keep_on_top
if dpi_awareness is True:
if running_windows():
if platform.release() == "7":
ctypes.windll.user32.SetProcessDPIAware()
elif platform.release() == "8" or platform.release() == "10":
ctypes.windll.shcore.SetProcessDpiAwareness(1)
if scaling is not None:
DEFAULT_SCALING = scaling
if disable_modal_windows is not None:
DEFAULT_MODAL_WINDOWS_ENABLED = not disable_modal_windows
if tooltip_offset != (None, None):
DEFAULT_TOOLTIP_OFFSET = tooltip_offset
return True
# ----------------------------------------------------------------- #
# .########.##.....##.########.##.....##.########..######.
# ....##....##.....##.##.......###...###.##.......##....##
# ....##....##.....##.##.......####.####.##.......##......
# ....##....#########.######...##.###.##.######....######.
# ....##....##.....##.##.......##.....##.##.............##
# ....##....##.....##.##.......##.....##.##.......##....##
# ....##....##.....##.########.##.....##.########..######.
# ----------------------------------------------------------------- #
# The official Theme code
#################### ChangeLookAndFeel #######################
# Predefined settings that will change the colors and styles #
# of the elements. #
##############################################################
LOOK_AND_FEEL_TABLE = {
"SystemDefault": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT, "TEXT_INPUT": COLOR_SYSTEM_DEFAULT,
"SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, "PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1,
"SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
"SystemDefaultForReal": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT,
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"COLOR_LIST": ["#343434", "#8e8b82", "#e9dcbe", "#f3f3f3"], "DESCRIPTION": ["Grey", "Brown"], },
"LightBrown12": {"BACKGROUND": "#8e8b82", "TEXT": "#f3f3f3", "INPUT": "#e9dcbe", "TEXT_INPUT": "#000000", "SCROLL": "#e9dcbe",
"BUTTON": ("#f3f3f3", "#8e8b82"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#343434", "#8e8b82", "#e9dcbe", "#f3f3f3"], "DESCRIPTION": ["Grey", "Brown"], },
"DarkTeal9": {"BACKGROUND": "#13445a", "TEXT": "#fef4e8", "INPUT": "#446878", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#446878",
"BUTTON": ("#fef4e8", "#446878"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#13445a", "#970747", "#446878", "#fef4e8"], "DESCRIPTION": ["Red", "Grey", "Blue", "Wedding", "Retro"], },
"DarkBlue14": {"BACKGROUND": "#21273d", "TEXT": "#f1f6f8", "INPUT": "#b9d4f1", "TEXT_INPUT": "#000000", "SCROLL": "#b9d4f1",
"BUTTON": ("#FFFFFF", "#6a759b"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#21273d", "#6a759b", "#b9d4f1", "#f1f6f8"], "DESCRIPTION": ["Blue", "#000000", "Grey", "Cold", "Winter"], },
"LightBlue6": {"BACKGROUND": "#f1f6f8", "TEXT": "#21273d", "INPUT": "#6a759b", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#21273d",
"BUTTON": ("#f1f6f8", "#6a759b"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#21273d", "#6a759b", "#b9d4f1", "#f1f6f8"], "DESCRIPTION": ["Blue", "#000000", "Grey", "Cold", "Winter"], },
"DarkGreen4": {"BACKGROUND": "#044343", "TEXT": "#e4e4e4", "INPUT": "#045757", "TEXT_INPUT": "#e4e4e4", "SCROLL": "#045757",
"BUTTON": ("#e4e4e4", "#045757"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#222222", "#044343", "#045757", "#e4e4e4"], "DESCRIPTION": ["#000000", "Turquoise", "Grey", "Dark"], },
"DarkGreen5": {"BACKGROUND": "#1b4b36", "TEXT": "#e0e7f1", "INPUT": "#aebd77", "TEXT_INPUT": "#000000", "SCROLL": "#aebd77",
"BUTTON": ("#FFFFFF", "#538f6a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#1b4b36", "#538f6a", "#aebd77", "#e0e7f1"], "DESCRIPTION": ["Green", "Grey"], },
"DarkTeal10": {"BACKGROUND": "#0d3446", "TEXT": "#d8dfe2", "INPUT": "#71adb5", "TEXT_INPUT": "#000000", "SCROLL": "#71adb5",
"BUTTON": ("#FFFFFF", "#176d81"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#0d3446", "#176d81", "#71adb5", "#d8dfe2"], "DESCRIPTION": ["Grey", "Turquoise", "Winter", "Cold"], },
"DarkGrey6": {"BACKGROUND": "#3e3e3e", "TEXT": "#ededed", "INPUT": "#68868c", "TEXT_INPUT": "#ededed", "SCROLL": "#68868c",
"BUTTON": ("#FFFFFF", "#405559"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#3e3e3e", "#405559", "#68868c", "#ededed"], "DESCRIPTION": ["Grey", "Turquoise", "Winter"], },
"DarkTeal11": {"BACKGROUND": "#405559", "TEXT": "#ededed", "INPUT": "#68868c", "TEXT_INPUT": "#ededed", "SCROLL": "#68868c",
"BUTTON": ("#ededed", "#68868c"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#3e3e3e", "#405559", "#68868c", "#ededed"], "DESCRIPTION": ["Grey", "Turquoise", "Winter"], },
"LightBlue7": {"BACKGROUND": "#9ed0e0", "TEXT": "#19483f", "INPUT": "#5c868e", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#19483f",
"BUTTON": ("#FFFFFF", "#19483f"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#19483f", "#5c868e", "#ff6a38", "#9ed0e0"], "DESCRIPTION": ["Orange", "Blue", "Turquoise"], },
"LightGreen10": {"BACKGROUND": "#d8ebb5", "TEXT": "#205d67", "INPUT": "#639a67", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#205d67",
"BUTTON": ("#d8ebb5", "#205d67"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#205d67", "#639a67", "#d9bf77", "#d8ebb5"], "DESCRIPTION": ["Blue", "Green", "Brown", "Vintage"], },
"DarkBlue15": {"BACKGROUND": "#151680", "TEXT": "#f1fea4", "INPUT": "#375fc0", "TEXT_INPUT": "#f1fea4", "SCROLL": "#375fc0",
"BUTTON": ("#f1fea4", "#1c44ac"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#151680", "#1c44ac", "#375fc0", "#f1fea4"], "DESCRIPTION": ["Blue", "Yellow", "Cold"], },
"DarkBlue16": {"BACKGROUND": "#1c44ac", "TEXT": "#f1fea4", "INPUT": "#375fc0", "TEXT_INPUT": "#f1fea4", "SCROLL": "#375fc0",
"BUTTON": ("#f1fea4", "#151680"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#151680", "#1c44ac", "#375fc0", "#f1fea4"], "DESCRIPTION": ["Blue", "Yellow", "Cold"], },
"DarkTeal12": {"BACKGROUND": "#004a7c", "TEXT": "#fafafa", "INPUT": "#e8f1f5", "TEXT_INPUT": "#000000", "SCROLL": "#e8f1f5",
"BUTTON": ("#fafafa", "#005691"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#004a7c", "#005691", "#e8f1f5", "#fafafa"], "DESCRIPTION": ["Grey", "Blue", "Cold", "Winter"], },
"LightBrown13": {"BACKGROUND": "#ebf5ee", "TEXT": "#921224", "INPUT": "#bdc6b8", "TEXT_INPUT": "#921224", "SCROLL": "#921224",
"BUTTON": ("#FFFFFF", "#921224"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#921224", "#bdc6b8", "#bce0da", "#ebf5ee"], "DESCRIPTION": ["Red", "Blue", "Grey", "Vintage", "Wedding"], },
"DarkBlue17": {"BACKGROUND": "#21294c", "TEXT": "#f9f2d7", "INPUT": "#f2dea8", "TEXT_INPUT": "#000000", "SCROLL": "#f2dea8",
"BUTTON": ("#f9f2d7", "#141829"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#141829", "#21294c", "#f2dea8", "#f9f2d7"], "DESCRIPTION": ["#000000", "Blue", "Yellow"], },
"DarkBrown6": {"BACKGROUND": "#785e4d", "TEXT": "#f2eee3", "INPUT": "#baaf92", "TEXT_INPUT": "#000000", "SCROLL": "#baaf92",
"BUTTON": ("#FFFFFF", "#785e4d"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#785e4d", "#ff8426", "#baaf92", "#f2eee3"], "DESCRIPTION": ["Grey", "Brown", "Orange", "Autumn"], },
"DarkGreen6": {"BACKGROUND": "#5c715e", "TEXT": "#f2f9f1", "INPUT": "#ddeedf", "TEXT_INPUT": "#000000", "SCROLL": "#ddeedf",
"BUTTON": ("#f2f9f1", "#5c715e"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#5c715e", "#b6cdbd", "#ddeedf", "#f2f9f1"], "DESCRIPTION": ["Grey", "Green", "Vintage"], },
"DarkGreen7": {"BACKGROUND": "#0C231E", "TEXT": "#efbe1c", "INPUT": "#153C33", "TEXT_INPUT": "#efbe1c", "SCROLL": "#153C33",
"BUTTON": ("#efbe1c", "#153C33"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey7": {"BACKGROUND": "#4b586e", "TEXT": "#dddddd", "INPUT": "#574e6d", "TEXT_INPUT": "#dddddd", "SCROLL": "#574e6d",
"BUTTON": ("#dddddd", "#43405d"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#43405d", "#4b586e", "#574e6d", "#dddddd"], "DESCRIPTION": ["Grey", "Winter", "Cold"], },
"DarkRed2": {"BACKGROUND": "#ab1212", "TEXT": "#f6e4b5", "INPUT": "#cd3131", "TEXT_INPUT": "#f6e4b5", "SCROLL": "#cd3131", "BUTTON": ("#f6e4b5", "#ab1212"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#ab1212", "#1fad9f", "#cd3131", "#f6e4b5"], "DESCRIPTION": ["Turquoise", "Red", "Yellow"], },
"LightGrey6": {"BACKGROUND": "#e3e3e3", "TEXT": "#233142", "INPUT": "#455d7a", "TEXT_INPUT": "#e3e3e3", "SCROLL": "#233142",
"BUTTON": ("#e3e3e3", "#455d7a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#233142", "#455d7a", "#f95959", "#e3e3e3"], "DESCRIPTION": ["#000000", "Blue", "Red", "Grey"], },
"HotDogStand": {"BACKGROUND": "red", "TEXT": "yellow", "INPUT": "yellow", "TEXT_INPUT": "#000000", "SCROLL": "yellow", "BUTTON": ("red", "yellow"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey8": {"BACKGROUND": "#19232D", "TEXT": "#ffffff", "INPUT": "#32414B", "TEXT_INPUT": "#ffffff", "SCROLL": "#505F69",
"BUTTON": ("#ffffff", "#32414B"), "PROGRESS": ("#505F69", "#32414B"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey9": {"BACKGROUND": "#36393F", "TEXT": "#DCDDDE", "INPUT": "#40444B", "TEXT_INPUT": "#ffffff", "SCROLL": "#202225",
"BUTTON": ("#202225", "#B9BBBE"), "PROGRESS": ("#202225", "#40444B"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey10": {"BACKGROUND": "#1c1e23", "TEXT": "#cccdcf", "INPUT": "#272a31", "TEXT_INPUT": "#8b9fde", "SCROLL": "#313641",
"BUTTON": ("#f5f5f6", "#2e3d5a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey11": {"BACKGROUND": "#1c1e23", "TEXT": "#cccdcf", "INPUT": "#313641", "TEXT_INPUT": "#cccdcf", "SCROLL": "#313641",
"BUTTON": ("#f5f5f6", "#313641"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey12": {"BACKGROUND": "#1c1e23", "TEXT": "#8b9fde", "INPUT": "#313641", "TEXT_INPUT": "#8b9fde", "SCROLL": "#313641",
"BUTTON": ("#cccdcf", "#2e3d5a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey13": {"BACKGROUND": "#1c1e23", "TEXT": "#cccdcf", "INPUT": "#272a31", "TEXT_INPUT": "#cccdcf", "SCROLL": "#313641",
"BUTTON": ("#8b9fde", "#313641"), "PROGRESS": ("#cccdcf", "#272a31"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey14": {"BACKGROUND": "#24292e", "TEXT": "#fafbfc", "INPUT": "#1d2125", "TEXT_INPUT": "#fafbfc", "SCROLL": "#1d2125",
"BUTTON": ("#fafbfc", "#155398"), "PROGRESS": ("#155398", "#1d2125"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBrown7": {"BACKGROUND": "#2c2417", "TEXT": "#baa379", "INPUT": "#baa379", "TEXT_INPUT": "#000000", "SCROLL": "#392e1c",
"BUTTON": ("#000000", "#baa379"), "PROGRESS": ("#baa379", "#453923"), "BORDER": 1, "SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
"Python": {"BACKGROUND": "#3d7aab", "TEXT": "#ffde56", "INPUT": "#295273", "TEXT_INPUT": "#ffde56", "SCROLL": "#295273", "BUTTON": ("#ffde56", "#295273"),
"PROGRESS": ("#ffde56", "#295273"), "BORDER": 1, "SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
}
|
:param icon: Can be either a filename or Base64 value. For Windows if filename, it MUST be ICO format. For Linux, must NOT be ICO. Most portable is to use a Base64 of a PNG file. This works universally across all OS's
:type icon: bytes | str
:param button_color: Color of the button (text, background)
:type button_color: (str, str) or str
:param element_size: element size (width, height) in characters
:type element_size: (int, int)
:param button_element_size: Size of button
:type button_element_size: (int, int)
:param margins: (left/right, top/bottom) tkinter margins around outsize. Amount of pixels to leave inside the window's frame around the edges before your elements are shown.
:type margins: (int, int)
:param element_padding: Default amount of padding to put around elements in window (left/right, top/bottom) or ((left, right), (top, bottom))
:type element_padding: (int, int) or ((int, int),(int,int))
:param auto_size_text: True if the Widget should be shrunk to exactly fit the number of chars to show
:type auto_size_text: bool
:param auto_size_buttons: True if Buttons in this Window should be sized to exactly fit the text on this.
:type auto_size_buttons: (bool)
:param font: specifies the font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike
:type font: (str or (str, int[, str]) or None)
:param border_width: width of border around element
:type border_width: (int)
:param slider_border_width: Width of the border around sliders
:type slider_border_width: (int)
:param slider_relief: Type of relief to use for sliders
:type slider_relief: (str)
:param slider_orientation: ???
:type slider_orientation: ???
:param autoclose_time: ???
:type autoclose_time: ???
:param message_box_line_width: ???
:type message_box_line_width: ???
:param progress_meter_border_depth: ???
:type progress_meter_border_depth: ???
:param progress_meter_style: You can no longer set a progress bar style. All ttk styles must be the same for the window
:type progress_meter_style: ???
:param progress_meter_relief:
:type progress_meter_relief: ???
:param progress_meter_color: ???
:type progress_meter_color: ???
:param progress_meter_size: ???
:type progress_meter_size: ???
:param text_justification: Default text justification for all Text Elements in window
:type text_justification: 'left' | 'right' | 'center'
:param background_color: color of background
:type background_color: (str)
:param element_background_color: element background color
:type element_background_color: (str)
:param text_element_background_color: text element background color
:type text_element_background_color: (str)
:param input_elements_background_color: Default color to use for the background of input elements
:type input_elements_background_color: (str)
:param input_text_color: Default color to use for the text for Input elements
:type input_text_color: (str)
:param scrollbar_color: Default color to use for the slider trough
:type scrollbar_color: (str)
:param text_color: color of the text
:type text_color: (str)
:param element_text_color: Default color to use for Text elements
:type element_text_color: (str)
:param debug_win_size: window size
:type debug_win_size: (int, int)
:param window_location: Default location to place windows. Not setting will center windows on the display
:type window_location: (int, int) | None
:param error_button_color: (Default = (None))
:type error_button_color: ???
:param tooltip_time: time in milliseconds to wait before showing a tooltip. Default is 400ms
:type tooltip_time: (int)
:param tooltip_font: font to use for all tooltips
:type tooltip_font: str or Tuple[str, int] or Tuple[str, int, str]
:param use_ttk_buttons: if True will cause all buttons to be ttk buttons
:type use_ttk_buttons: (bool)
:param ttk_theme: Theme to use with ttk widgets. Choices (on Windows) include - 'default', 'winnative', 'clam', 'alt', 'classic', 'vista', 'xpnative'
:type ttk_theme: (str)
:param suppress_error_popups: If True then error popups will not be shown if generated internally to PySimpleGUI
:type suppress_error_popups: (bool)
:param suppress_raise_key_errors: If True then key errors won't be raised (you'll still get popup error)
:type suppress_raise_key_errors: (bool)
:param suppress_key_guessing: If True then key errors won't try and find closest matches for you
:type suppress_key_guessing: (bool)
:param warn_button_key_duplicates: If True then duplicate Button Keys generate warnings (not recommended as they're expected)
:type warn_button_key_duplicates: (bool)
:param enable_treeview_869_patch: If True, then will use the treeview color patch for tk 8.6.9
:type enable_treeview_869_patch: (bool)
:param enable_mac_notitlebar_patch: If True then Windows with no titlebar use an alternative technique when tkinter version < 8.6.10
:type enable_mac_notitlebar_patch: (bool)
:param use_custom_titlebar: If True then a custom titlebar is used instead of the normal system titlebar
:type use_custom_titlebar: (bool)
:param titlebar_background_color: If custom titlebar indicated by use_custom_titlebar, then use this as background color
:type titlebar_background_color: str | None
:param titlebar_text_color: If custom titlebar indicated by use_custom_titlebar, then use this as text color
:type titlebar_text_color: str | None
:param titlebar_font: If custom titlebar indicated by use_custom_titlebar, then use this as title font
:type titlebar_font: (str or (str, int[, str]) or None) | None
:param titlebar_icon: If custom titlebar indicated by use_custom_titlebar, then use this as the icon (file or base64 bytes)
:type titlebar_icon: bytes | str
:param user_settings_path: default path for user_settings API calls. Expanded with os.path.expanduser so can contain ~ to represent user
:type user_settings_path: (str)
:param pysimplegui_settings_path: default path for the global PySimpleGUI user_settings
:type pysimplegui_settings_path: (str)
:param pysimplegui_settings_filename: default filename for the global PySimpleGUI user_settings
:type pysimplegui_settings_filename: (str)
:param keep_on_top: If True then all windows will automatically be set to keep_on_top=True
:type keep_on_top: (bool)
:param dpi_awareness: If True then will turn on DPI awareness (Windows only at the moment)
:type dpi_awareness: (bool)
:param scaling: Sets the default scaling for all windows including popups, etc.
:type scaling: (float)
:param disable_modal_windows: If True then all windows, including popups, will not be modal windows
:type disable_modal_windows: (bool)
:param tooltip_offset: Offset to use for tooltips as a tuple. These values will be added to the mouse location when the widget was entered.
:type tooltip_offset: ((None, None) | (int, int))
:return: None
:rtype: None
| 889 | 4,824 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def set_options(icon=None, button_color=None, element_size=(None, None), button_element_size=(None, None),
margins=(None, None),
element_padding=(None, None), auto_size_text=None, auto_size_buttons=None, font=None, border_width=None,
slider_border_width=None, slider_relief=None, slider_orientation=None,
autoclose_time=None, message_box_line_width=None,
progress_meter_border_depth=None, progress_meter_style=None,
progress_meter_relief=None, progress_meter_color=None, progress_meter_size=None,
text_justification=None, background_color=None, element_background_color=None,
text_element_background_color=None, input_elements_background_color=None, input_text_color=None,
scrollbar_color=None, text_color=None, element_text_color=None, debug_win_size=(None, None),
window_location=(None, None), error_button_color=(None, None), tooltip_time=None, tooltip_font=None, use_ttk_buttons=None, ttk_theme=None,
suppress_error_popups=None, suppress_raise_key_errors=None, suppress_key_guessing=None,warn_button_key_duplicates=False, enable_treeview_869_patch=None,
enable_mac_notitlebar_patch=None, use_custom_titlebar=None, titlebar_background_color=None, titlebar_text_color=None, titlebar_font=None,
titlebar_icon=None, user_settings_path=None, pysimplegui_settings_path=None, pysimplegui_settings_filename=None, keep_on_top=None, dpi_awareness=None, scaling=None, disable_modal_windows=None, tooltip_offset=(None, None)):
global DEFAULT_ELEMENT_SIZE
global DEFAULT_BUTTON_ELEMENT_SIZE
global DEFAULT_MARGINS # Margins for each LEFT/RIGHT margin is first term
global DEFAULT_ELEMENT_PADDING # Padding between elements (row, col) in pixels
global DEFAULT_AUTOSIZE_TEXT
global DEFAULT_AUTOSIZE_BUTTONS
global DEFAULT_FONT
global DEFAULT_BORDER_WIDTH
global DEFAULT_AUTOCLOSE_TIME
global DEFAULT_BUTTON_COLOR
global MESSAGE_BOX_LINE_WIDTH
global DEFAULT_PROGRESS_BAR_BORDER_WIDTH
global DEFAULT_PROGRESS_BAR_STYLE
global DEFAULT_PROGRESS_BAR_RELIEF
global DEFAULT_PROGRESS_BAR_COLOR
global DEFAULT_PROGRESS_BAR_SIZE
global DEFAULT_TEXT_JUSTIFICATION
global DEFAULT_DEBUG_WINDOW_SIZE
global DEFAULT_SLIDER_BORDER_WIDTH
global DEFAULT_SLIDER_RELIEF
global DEFAULT_SLIDER_ORIENTATION
global DEFAULT_BACKGROUND_COLOR
global DEFAULT_INPUT_ELEMENTS_COLOR
global DEFAULT_ELEMENT_BACKGROUND_COLOR
global DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR
global DEFAULT_SCROLLBAR_COLOR
global DEFAULT_TEXT_COLOR
global DEFAULT_WINDOW_LOCATION
global DEFAULT_ELEMENT_TEXT_COLOR
global DEFAULT_INPUT_TEXT_COLOR
global DEFAULT_TOOLTIP_TIME
global DEFAULT_ERROR_BUTTON_COLOR
global DEFAULT_TTK_THEME
global USE_TTK_BUTTONS
global TOOLTIP_FONT
global SUPPRESS_ERROR_POPUPS
global SUPPRESS_RAISE_KEY_ERRORS
global SUPPRESS_KEY_GUESSING
global WARN_DUPLICATE_BUTTON_KEY_ERRORS
global ENABLE_TREEVIEW_869_PATCH
global ENABLE_MAC_NOTITLEBAR_PATCH
global USE_CUSTOM_TITLEBAR
global CUSTOM_TITLEBAR_BACKGROUND_COLOR
global CUSTOM_TITLEBAR_TEXT_COLOR
global CUSTOM_TITLEBAR_ICON
global CUSTOM_TITLEBAR_FONT
global DEFAULT_USER_SETTINGS_PATH
global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH
global DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME
global DEFAULT_KEEP_ON_TOP
global DEFAULT_SCALING
global DEFAULT_MODAL_WINDOWS_ENABLED
global DEFAULT_TOOLTIP_OFFSET
global _pysimplegui_user_settings
# global _my_windows
if icon:
Window._user_defined_icon = icon
# _my_windows._user_defined_icon = icon
if button_color != None:
if button_color == COLOR_SYSTEM_DEFAULT:
DEFAULT_BUTTON_COLOR = (COLOR_SYSTEM_DEFAULT, COLOR_SYSTEM_DEFAULT)
else:
DEFAULT_BUTTON_COLOR = button_color
if element_size != (None, None):
DEFAULT_ELEMENT_SIZE = element_size
if button_element_size != (None, None):
DEFAULT_BUTTON_ELEMENT_SIZE = button_element_size
if margins != (None, None):
DEFAULT_MARGINS = margins
if element_padding != (None, None):
DEFAULT_ELEMENT_PADDING = element_padding
if auto_size_text != None:
DEFAULT_AUTOSIZE_TEXT = auto_size_text
if auto_size_buttons != None:
DEFAULT_AUTOSIZE_BUTTONS = auto_size_buttons
if font != None:
DEFAULT_FONT = font
if border_width != None:
DEFAULT_BORDER_WIDTH = border_width
if autoclose_time != None:
DEFAULT_AUTOCLOSE_TIME = autoclose_time
if message_box_line_width != None:
MESSAGE_BOX_LINE_WIDTH = message_box_line_width
if progress_meter_border_depth != None:
DEFAULT_PROGRESS_BAR_BORDER_WIDTH = progress_meter_border_depth
if progress_meter_style != None:
warnings.warn('You can no longer set a progress bar style. All ttk styles must be the same for the window', UserWarning)
# DEFAULT_PROGRESS_BAR_STYLE = progress_meter_style
if progress_meter_relief != None:
DEFAULT_PROGRESS_BAR_RELIEF = progress_meter_relief
if progress_meter_color != None:
DEFAULT_PROGRESS_BAR_COLOR = progress_meter_color
if progress_meter_size != None:
DEFAULT_PROGRESS_BAR_SIZE = progress_meter_size
if slider_border_width != None:
DEFAULT_SLIDER_BORDER_WIDTH = slider_border_width
if slider_orientation != None:
DEFAULT_SLIDER_ORIENTATION = slider_orientation
if slider_relief != None:
DEFAULT_SLIDER_RELIEF = slider_relief
if text_justification != None:
DEFAULT_TEXT_JUSTIFICATION = text_justification
if background_color != None:
DEFAULT_BACKGROUND_COLOR = background_color
if text_element_background_color != None:
DEFAULT_TEXT_ELEMENT_BACKGROUND_COLOR = text_element_background_color
if input_elements_background_color != None:
DEFAULT_INPUT_ELEMENTS_COLOR = input_elements_background_color
if element_background_color != None:
DEFAULT_ELEMENT_BACKGROUND_COLOR = element_background_color
if window_location != (None, None):
DEFAULT_WINDOW_LOCATION = window_location
if debug_win_size != (None, None):
DEFAULT_DEBUG_WINDOW_SIZE = debug_win_size
if text_color != None:
DEFAULT_TEXT_COLOR = text_color
if scrollbar_color != None:
DEFAULT_SCROLLBAR_COLOR = scrollbar_color
if element_text_color != None:
DEFAULT_ELEMENT_TEXT_COLOR = element_text_color
if input_text_color is not None:
DEFAULT_INPUT_TEXT_COLOR = input_text_color
if tooltip_time is not None:
DEFAULT_TOOLTIP_TIME = tooltip_time
if error_button_color != (None, None):
DEFAULT_ERROR_BUTTON_COLOR = error_button_color
if ttk_theme is not None:
DEFAULT_TTK_THEME = ttk_theme
if use_ttk_buttons is not None:
USE_TTK_BUTTONS = use_ttk_buttons
if tooltip_font is not None:
TOOLTIP_FONT = tooltip_font
if suppress_error_popups is not None:
SUPPRESS_ERROR_POPUPS = suppress_error_popups
if suppress_raise_key_errors is not None:
SUPPRESS_RAISE_KEY_ERRORS = suppress_raise_key_errors
if suppress_key_guessing is not None:
SUPPRESS_KEY_GUESSING = suppress_key_guessing
if warn_button_key_duplicates is not None:
WARN_DUPLICATE_BUTTON_KEY_ERRORS = warn_button_key_duplicates
if enable_treeview_869_patch is not None:
ENABLE_TREEVIEW_869_PATCH = enable_treeview_869_patch
if enable_mac_notitlebar_patch is not None:
ENABLE_MAC_NOTITLEBAR_PATCH = enable_mac_notitlebar_patch
if use_custom_titlebar is not None:
USE_CUSTOM_TITLEBAR = use_custom_titlebar
if titlebar_background_color is not None:
CUSTOM_TITLEBAR_BACKGROUND_COLOR = titlebar_background_color
if titlebar_text_color is not None:
CUSTOM_TITLEBAR_TEXT_COLOR = titlebar_text_color
if titlebar_font is not None:
CUSTOM_TITLEBAR_FONT = titlebar_font
if titlebar_icon is not None:
CUSTOM_TITLEBAR_ICON = titlebar_icon
if user_settings_path is not None:
DEFAULT_USER_SETTINGS_PATH = user_settings_path
if pysimplegui_settings_path is not None:
DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH = pysimplegui_settings_path
if pysimplegui_settings_filename is not None:
DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME = pysimplegui_settings_filename
if pysimplegui_settings_filename is not None or pysimplegui_settings_filename is not None:
_pysimplegui_user_settings = UserSettings(filename=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_FILENAME,
path=DEFAULT_USER_SETTINGS_PYSIMPLEGUI_PATH)
if keep_on_top is not None:
DEFAULT_KEEP_ON_TOP = keep_on_top
if dpi_awareness is True:
if running_windows():
if platform.release() == "7":
ctypes.windll.user32.SetProcessDPIAware()
elif platform.release() == "8" or platform.release() == "10":
ctypes.windll.shcore.SetProcessDpiAwareness(1)
if scaling is not None:
DEFAULT_SCALING = scaling
if disable_modal_windows is not None:
DEFAULT_MODAL_WINDOWS_ENABLED = not disable_modal_windows
if tooltip_offset != (None, None):
DEFAULT_TOOLTIP_OFFSET = tooltip_offset
return True
# ----------------------------------------------------------------- #
# .########.##.....##.########.##.....##.########..######.
# ....##....##.....##.##.......###...###.##.......##....##
# ....##....##.....##.##.......####.####.##.......##......
# ....##....#########.######...##.###.##.######....######.
# ....##....##.....##.##.......##.....##.##.............##
# ....##....##.....##.##.......##.....##.##.......##....##
# ....##....##.....##.########.##.....##.########..######.
# ----------------------------------------------------------------- #
# The official Theme code
#################### ChangeLookAndFeel #######################
# Predefined settings that will change the colors and styles #
# of the elements. #
##############################################################
LOOK_AND_FEEL_TABLE = {
"SystemDefault": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT, "TEXT_INPUT": COLOR_SYSTEM_DEFAULT,
"SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, "PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1,
"SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
"SystemDefaultForReal": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT,
"TEXT_INPUT": COLOR_SYSTEM_DEFAULT, "SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": COLOR_SYSTEM_DEFAULT,
"PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1, "SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
"SystemDefault1": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT, "TEXT_INPUT": COLOR_SYSTEM_DEFAULT,
"SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": COLOR_SYSTEM_DEFAULT, "PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1, "SLIDER_DEPTH": 1,
"PROGRESS_DEPTH": 0, },
"Material1": {"BACKGROUND": "#E3F2FD", "TEXT": "#000000", "INPUT": "#86A8FF", "TEXT_INPUT": "#000000", "SCROLL": "#86A8FF",
"BUTTON": ("#FFFFFF", "#5079D3"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 0, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"ACCENT1": "#FF0266", "ACCENT2": "#FF5C93", "ACCENT3": "#C5003C", },
"Material2": {"BACKGROUND": "#FAFAFA", "TEXT": "#000000", "INPUT": "#004EA1", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#5EA7FF",
"BUTTON": ("#FFFFFF", "#0079D3"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 0, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"ACCENT1": "#FF0266", "ACCENT2": "#FF5C93", "ACCENT3": "#C5003C", },
"Reddit": {"BACKGROUND": "#ffffff", "TEXT": "#1a1a1b", "INPUT": "#dae0e6", "TEXT_INPUT": "#222222", "SCROLL": "#a5a4a4", "BUTTON": ("#FFFFFF", "#0079d3"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, "ACCENT1": "#ff5414", "ACCENT2": "#33a8ff",
"ACCENT3": "#dbf0ff", },
"Topanga": {"BACKGROUND": "#282923", "TEXT": "#E7DB74", "INPUT": "#393a32", "TEXT_INPUT": "#E7C855", "SCROLL": "#E7C855", "BUTTON": ("#E7C855", "#284B5A"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, "ACCENT1": "#c15226", "ACCENT2": "#7a4d5f",
"ACCENT3": "#889743", },
"GreenTan": {"BACKGROUND": "#9FB8AD", "TEXT": '#000000', "INPUT": "#F7F3EC", "TEXT_INPUT": "#000000", "SCROLL": "#F7F3EC", "BUTTON": ("#FFFFFF", "#475841"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Dark": {"BACKGROUND": "#404040", "TEXT": "#FFFFFF", "INPUT": "#4D4D4D", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#707070", "BUTTON": ("#FFFFFF", "#004F00"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"LightGreen": {"BACKGROUND": "#B7CECE", "TEXT": "#000000", "INPUT": "#FDFFF7", "TEXT_INPUT": "#000000", "SCROLL": "#FDFFF7",
"BUTTON": ("#FFFFFF", "#658268"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "ACCENT1": "#76506d",
"ACCENT2": "#5148f1", "ACCENT3": "#0a1c84", "PROGRESS_DEPTH": 0, },
"Dark2": {"BACKGROUND": "#404040", "TEXT": "#FFFFFF", "INPUT": "#FFFFFF", "TEXT_INPUT": "#000000", "SCROLL": "#707070", "BUTTON": ("#FFFFFF", "#004F00"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Black": {"BACKGROUND": "#000000", "TEXT": "#FFFFFF", "INPUT": "#4D4D4D", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#707070", "BUTTON": ("#000000", "#FFFFFF"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Tan": {"BACKGROUND": "#fdf6e3", "TEXT": "#268bd1", "INPUT": "#eee8d5", "TEXT_INPUT": "#6c71c3", "SCROLL": "#eee8d5", "BUTTON": ("#FFFFFF", "#063542"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"TanBlue": {"BACKGROUND": "#e5dece", "TEXT": "#063289", "INPUT": "#f9f8f4", "TEXT_INPUT": "#242834", "SCROLL": "#eee8d5", "BUTTON": ("#FFFFFF", "#063289"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkTanBlue": {"BACKGROUND": "#242834", "TEXT": "#dfe6f8", "INPUT": "#97755c", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#a9afbb",
"BUTTON": ("#FFFFFF", "#063289"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkAmber": {"BACKGROUND": "#2c2825", "TEXT": "#fdcb52", "INPUT": "#705e52", "TEXT_INPUT": "#fdcb52", "SCROLL": "#705e52",
"BUTTON": ("#000000", "#fdcb52"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBlue": {"BACKGROUND": "#1a2835", "TEXT": "#d1ecff", "INPUT": "#335267", "TEXT_INPUT": "#acc2d0", "SCROLL": "#1b6497", "BUTTON": ("#000000", "#fafaf8"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Reds": {"BACKGROUND": "#280001", "TEXT": "#FFFFFF", "INPUT": "#d8d584", "TEXT_INPUT": "#000000", "SCROLL": "#763e00", "BUTTON": ("#000000", "#daad28"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Green": {"BACKGROUND": "#82a459", "TEXT": "#000000", "INPUT": "#d8d584", "TEXT_INPUT": "#000000", "SCROLL": "#e3ecf3", "BUTTON": ("#FFFFFF", "#517239"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"BluePurple": {"BACKGROUND": "#A5CADD", "TEXT": "#6E266E", "INPUT": "#E0F5FF", "TEXT_INPUT": "#000000", "SCROLL": "#E0F5FF",
"BUTTON": ("#FFFFFF", "#303952"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Purple": {"BACKGROUND": "#B0AAC2", "TEXT": "#000000", "INPUT": "#F2EFE8", "SCROLL": "#F2EFE8", "TEXT_INPUT": "#000000", "BUTTON": ("#000000", "#C2D4D8"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"BlueMono": {"BACKGROUND": "#AAB6D3", "TEXT": "#000000", "INPUT": "#F1F4FC", "SCROLL": "#F1F4FC", "TEXT_INPUT": "#000000", "BUTTON": ("#FFFFFF", "#7186C7"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"GreenMono": {"BACKGROUND": "#A8C1B4", "TEXT": "#000000", "INPUT": "#DDE0DE", "SCROLL": "#E3E3E3", "TEXT_INPUT": "#000000",
"BUTTON": ("#FFFFFF", "#6D9F85"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"BrownBlue": {"BACKGROUND": "#64778d", "TEXT": "#FFFFFF", "INPUT": "#f0f3f7", "SCROLL": "#A6B2BE", "TEXT_INPUT": "#000000",
"BUTTON": ("#FFFFFF", "#283b5b"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"BrightColors": {"BACKGROUND": "#b4ffb4", "TEXT": "#000000", "INPUT": "#ffff64", "SCROLL": "#ffb482", "TEXT_INPUT": "#000000",
"BUTTON": ("#000000", "#ffa0dc"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"NeutralBlue": {"BACKGROUND": "#92aa9d", "TEXT": "#000000", "INPUT": "#fcfff6", "SCROLL": "#fcfff6", "TEXT_INPUT": "#000000",
"BUTTON": ("#000000", "#d0dbbd"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Kayak": {"BACKGROUND": "#a7ad7f", "TEXT": "#000000", "INPUT": "#e6d3a8", "SCROLL": "#e6d3a8", "TEXT_INPUT": "#000000", "BUTTON": ("#FFFFFF", "#5d907d"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"SandyBeach": {"BACKGROUND": "#efeccb", "TEXT": "#012f2f", "INPUT": "#e6d3a8", "SCROLL": "#e6d3a8", "TEXT_INPUT": "#012f2f",
"BUTTON": ("#FFFFFF", "#046380"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"TealMono": {"BACKGROUND": "#a8cfdd", "TEXT": "#000000", "INPUT": "#dfedf2", "SCROLL": "#dfedf2", "TEXT_INPUT": "#000000", "BUTTON": ("#FFFFFF", "#183440"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"Default": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT, "TEXT_INPUT": COLOR_SYSTEM_DEFAULT,
"SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR, "PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1, "SLIDER_DEPTH": 1,
"PROGRESS_DEPTH": 0, },
"Default1": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT, "TEXT_INPUT": COLOR_SYSTEM_DEFAULT,
"SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": COLOR_SYSTEM_DEFAULT, "PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1, "SLIDER_DEPTH": 1,
"PROGRESS_DEPTH": 0, },
"DefaultNoMoreNagging": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT,
"TEXT_INPUT": COLOR_SYSTEM_DEFAULT, "SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": OFFICIAL_PYSIMPLEGUI_BUTTON_COLOR,
"PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1, "SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
"GrayGrayGray": {"BACKGROUND": COLOR_SYSTEM_DEFAULT, "TEXT": COLOR_SYSTEM_DEFAULT, "INPUT": COLOR_SYSTEM_DEFAULT, "TEXT_INPUT": COLOR_SYSTEM_DEFAULT,
"SCROLL": COLOR_SYSTEM_DEFAULT, "BUTTON": COLOR_SYSTEM_DEFAULT, "PROGRESS": COLOR_SYSTEM_DEFAULT, "BORDER": 1, "SLIDER_DEPTH": 1,
"PROGRESS_DEPTH": 0, },
"LightBlue": {"BACKGROUND": "#E3F2FD", "TEXT": "#000000", "INPUT": "#86A8FF", "TEXT_INPUT": "#000000", "SCROLL": "#86A8FF",
"BUTTON": ("#FFFFFF", "#5079D3"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 0, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"ACCENT1": "#FF0266", "ACCENT2": "#FF5C93", "ACCENT3": "#C5003C", },
"LightGrey": {"BACKGROUND": "#FAFAFA", "TEXT": "#000000", "INPUT": "#004EA1", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#5EA7FF",
"BUTTON": ("#FFFFFF", "#0079D3"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 0, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"ACCENT1": "#FF0266", "ACCENT2": "#FF5C93", "ACCENT3": "#C5003C", },
"LightGrey1": {"BACKGROUND": "#ffffff", "TEXT": "#1a1a1b", "INPUT": "#dae0e6", "TEXT_INPUT": "#222222", "SCROLL": "#a5a4a4",
"BUTTON": ("#FFFFFF", "#0079d3"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"ACCENT1": "#ff5414", "ACCENT2": "#33a8ff", "ACCENT3": "#dbf0ff", },
"DarkBrown": {"BACKGROUND": "#282923", "TEXT": "#E7DB74", "INPUT": "#393a32", "TEXT_INPUT": "#E7C855", "SCROLL": "#E7C855",
"BUTTON": ("#E7C855", "#284B5A"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"ACCENT1": "#c15226", "ACCENT2": "#7a4d5f", "ACCENT3": "#889743", },
"LightGreen1": {"BACKGROUND": "#9FB8AD", "TEXT": "#000000", "INPUT": "#F7F3EC", "TEXT_INPUT": "#000000", "SCROLL": "#F7F3EC",
"BUTTON": ("#FFFFFF", "#475841"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey": {"BACKGROUND": "#404040", "TEXT": "#FFFFFF", "INPUT": "#4D4D4D", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#707070", "BUTTON": ("#FFFFFF", "#004F00"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"LightGreen2": {"BACKGROUND": "#B7CECE", "TEXT": "#000000", "INPUT": "#FDFFF7", "TEXT_INPUT": "#000000", "SCROLL": "#FDFFF7",
"BUTTON": ("#FFFFFF", "#658268"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "ACCENT1": "#76506d",
"ACCENT2": "#5148f1", "ACCENT3": "#0a1c84", "PROGRESS_DEPTH": 0, },
"DarkGrey1": {"BACKGROUND": "#404040", "TEXT": "#FFFFFF", "INPUT": "#FFFFFF", "TEXT_INPUT": "#000000", "SCROLL": "#707070",
"BUTTON": ("#FFFFFF", "#004F00"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBlack": {"BACKGROUND": "#000000", "TEXT": "#FFFFFF", "INPUT": "#4D4D4D", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#707070",
"BUTTON": ("#000000", "#FFFFFF"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"LightBrown": {"BACKGROUND": "#fdf6e3", "TEXT": "#268bd1", "INPUT": "#eee8d5", "TEXT_INPUT": "#6c71c3", "SCROLL": "#eee8d5",
"BUTTON": ("#FFFFFF", "#063542"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"LightBrown1": {"BACKGROUND": "#e5dece", "TEXT": "#063289", "INPUT": "#f9f8f4", "TEXT_INPUT": "#242834", "SCROLL": "#eee8d5",
"BUTTON": ("#FFFFFF", "#063289"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBlue1": {"BACKGROUND": "#242834", "TEXT": "#dfe6f8", "INPUT": "#97755c", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#a9afbb",
"BUTTON": ("#FFFFFF", "#063289"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBrown1": {"BACKGROUND": "#2c2825", "TEXT": "#fdcb52", "INPUT": "#705e52", "TEXT_INPUT": "#fdcb52", "SCROLL": "#705e52",
"BUTTON": ("#000000", "#fdcb52"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBlue2": {"BACKGROUND": "#1a2835", "TEXT": "#d1ecff", "INPUT": "#335267", "TEXT_INPUT": "#acc2d0", "SCROLL": "#1b6497",
"BUTTON": ("#000000", "#fafaf8"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBrown2": {"BACKGROUND": "#280001", "TEXT": "#FFFFFF", "INPUT": "#d8d584", "TEXT_INPUT": "#000000", "SCROLL": "#763e00",
"BUTTON": ("#000000", "#daad28"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGreen": {"BACKGROUND": "#82a459", "TEXT": "#000000", "INPUT": "#d8d584", "TEXT_INPUT": "#000000", "SCROLL": "#e3ecf3",
"BUTTON": ("#FFFFFF", "#517239"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"LightBlue1": {"BACKGROUND": "#A5CADD", "TEXT": "#6E266E", "INPUT": "#E0F5FF", "TEXT_INPUT": "#000000", "SCROLL": "#E0F5FF",
"BUTTON": ("#FFFFFF", "#303952"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"LightPurple": {"BACKGROUND": "#B0AAC2", "TEXT": "#000000", "INPUT": "#F2EFE8", "SCROLL": "#F2EFE8", "TEXT_INPUT": "#000000",
"BUTTON": ("#000000", "#C2D4D8"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"LightBlue2": {"BACKGROUND": "#AAB6D3", "TEXT": "#000000", "INPUT": "#F1F4FC", "SCROLL": "#F1F4FC", "TEXT_INPUT": "#000000",
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"BUTTON": ("#fff591", "#e41749"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#e41749", "#f5587b", "#ff8a5c", "#fff591"], },
"LightGreen9": {"BACKGROUND": "#f1edb3", "TEXT": "#3b503d", "INPUT": "#4a746e", "TEXT_INPUT": "#f1edb3", "SCROLL": "#3b503d",
"BUTTON": ("#f1edb3", "#3b503d"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#3b503d", "#4a746e", "#c8cf94", "#f1edb3"], "DESCRIPTION": ["Green", "Turquoise", "Yellow"], },
"DarkGreen2": {"BACKGROUND": "#3b503d", "TEXT": "#f1edb3", "INPUT": "#c8cf94", "TEXT_INPUT": "#000000", "SCROLL": "#c8cf94",
"BUTTON": ("#f1edb3", "#3b503d"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#3b503d", "#4a746e", "#c8cf94", "#f1edb3"], "DESCRIPTION": ["Green", "Turquoise", "Yellow"], },
"LightGray1": {"BACKGROUND": "#f2f2f2", "TEXT": "#222831", "INPUT": "#393e46", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#222831",
"BUTTON": ("#f2f2f2", "#222831"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#222831", "#393e46", "#f96d00", "#f2f2f2"], "DESCRIPTION": ["#000000", "Grey", "Orange", "Grey", "Autumn"], },
"DarkGrey4": {"BACKGROUND": "#52524e", "TEXT": "#e9e9e5", "INPUT": "#d4d6c8", "TEXT_INPUT": "#000000", "SCROLL": "#d4d6c8",
"BUTTON": ("#FFFFFF", "#9a9b94"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#52524e", "#9a9b94", "#d4d6c8", "#e9e9e5"], "DESCRIPTION": ["Grey", "Pastel", "Winter"], },
"DarkBlue12": {"BACKGROUND": "#324e7b", "TEXT": "#f8f8f8", "INPUT": "#86a6df", "TEXT_INPUT": "#000000", "SCROLL": "#86a6df",
"BUTTON": ("#FFFFFF", "#5068a9"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#324e7b", "#5068a9", "#86a6df", "#f8f8f8"], "DESCRIPTION": ["Blue", "Grey", "Cold", "Winter"], },
"DarkPurple6": {"BACKGROUND": "#070739", "TEXT": "#e1e099", "INPUT": "#c327ab", "TEXT_INPUT": "#e1e099", "SCROLL": "#c327ab",
"BUTTON": ("#e1e099", "#521477"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#070739", "#521477", "#c327ab", "#e1e099"], "DESCRIPTION": ["#000000", "Purple", "Yellow", "Dark"], },
"DarkPurple7": {"BACKGROUND": "#191930", "TEXT": "#B1B7C5", "INPUT": "#232B5C", "TEXT_INPUT": "#D0E3E7", "SCROLL": "#B1B7C5",
"BUTTON": ("#272D38", "#B1B7C5"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBlue13": {"BACKGROUND": "#203562", "TEXT": "#e3e8f8", "INPUT": "#c0c5cd", "TEXT_INPUT": "#000000", "SCROLL": "#c0c5cd",
"BUTTON": ("#FFFFFF", "#3e588f"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#203562", "#3e588f", "#c0c5cd", "#e3e8f8"], "DESCRIPTION": ["Blue", "Grey", "Wedding", "Cold"], },
"DarkBrown5": {"BACKGROUND": "#3c1b1f", "TEXT": "#f6e1b5", "INPUT": "#e2bf81", "TEXT_INPUT": "#000000", "SCROLL": "#e2bf81",
"BUTTON": ("#3c1b1f", "#f6e1b5"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#3c1b1f", "#b21e4b", "#e2bf81", "#f6e1b5"], "DESCRIPTION": ["Brown", "Red", "Yellow", "Warm"], },
"DarkGreen3": {"BACKGROUND": "#062121", "TEXT": "#eeeeee", "INPUT": "#e4dcad", "TEXT_INPUT": "#000000", "SCROLL": "#e4dcad",
"BUTTON": ("#eeeeee", "#181810"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#062121", "#181810", "#e4dcad", "#eeeeee"], "DESCRIPTION": ["#000000", "#000000", "Brown", "Grey"], },
"DarkBlack1": {"BACKGROUND": "#181810", "TEXT": "#eeeeee", "INPUT": "#e4dcad", "TEXT_INPUT": "#000000", "SCROLL": "#e4dcad",
"BUTTON": ("#FFFFFF", "#062121"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#062121", "#181810", "#e4dcad", "#eeeeee"], "DESCRIPTION": ["#000000", "#000000", "Brown", "Grey"], },
"DarkGrey5": {"BACKGROUND": "#343434", "TEXT": "#f3f3f3", "INPUT": "#e9dcbe", "TEXT_INPUT": "#000000", "SCROLL": "#e9dcbe",
"BUTTON": ("#FFFFFF", "#8e8b82"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#343434", "#8e8b82", "#e9dcbe", "#f3f3f3"], "DESCRIPTION": ["Grey", "Brown"], },
"LightBrown12": {"BACKGROUND": "#8e8b82", "TEXT": "#f3f3f3", "INPUT": "#e9dcbe", "TEXT_INPUT": "#000000", "SCROLL": "#e9dcbe",
"BUTTON": ("#f3f3f3", "#8e8b82"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#343434", "#8e8b82", "#e9dcbe", "#f3f3f3"], "DESCRIPTION": ["Grey", "Brown"], },
"DarkTeal9": {"BACKGROUND": "#13445a", "TEXT": "#fef4e8", "INPUT": "#446878", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#446878",
"BUTTON": ("#fef4e8", "#446878"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#13445a", "#970747", "#446878", "#fef4e8"], "DESCRIPTION": ["Red", "Grey", "Blue", "Wedding", "Retro"], },
"DarkBlue14": {"BACKGROUND": "#21273d", "TEXT": "#f1f6f8", "INPUT": "#b9d4f1", "TEXT_INPUT": "#000000", "SCROLL": "#b9d4f1",
"BUTTON": ("#FFFFFF", "#6a759b"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#21273d", "#6a759b", "#b9d4f1", "#f1f6f8"], "DESCRIPTION": ["Blue", "#000000", "Grey", "Cold", "Winter"], },
"LightBlue6": {"BACKGROUND": "#f1f6f8", "TEXT": "#21273d", "INPUT": "#6a759b", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#21273d",
"BUTTON": ("#f1f6f8", "#6a759b"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#21273d", "#6a759b", "#b9d4f1", "#f1f6f8"], "DESCRIPTION": ["Blue", "#000000", "Grey", "Cold", "Winter"], },
"DarkGreen4": {"BACKGROUND": "#044343", "TEXT": "#e4e4e4", "INPUT": "#045757", "TEXT_INPUT": "#e4e4e4", "SCROLL": "#045757",
"BUTTON": ("#e4e4e4", "#045757"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#222222", "#044343", "#045757", "#e4e4e4"], "DESCRIPTION": ["#000000", "Turquoise", "Grey", "Dark"], },
"DarkGreen5": {"BACKGROUND": "#1b4b36", "TEXT": "#e0e7f1", "INPUT": "#aebd77", "TEXT_INPUT": "#000000", "SCROLL": "#aebd77",
"BUTTON": ("#FFFFFF", "#538f6a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#1b4b36", "#538f6a", "#aebd77", "#e0e7f1"], "DESCRIPTION": ["Green", "Grey"], },
"DarkTeal10": {"BACKGROUND": "#0d3446", "TEXT": "#d8dfe2", "INPUT": "#71adb5", "TEXT_INPUT": "#000000", "SCROLL": "#71adb5",
"BUTTON": ("#FFFFFF", "#176d81"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#0d3446", "#176d81", "#71adb5", "#d8dfe2"], "DESCRIPTION": ["Grey", "Turquoise", "Winter", "Cold"], },
"DarkGrey6": {"BACKGROUND": "#3e3e3e", "TEXT": "#ededed", "INPUT": "#68868c", "TEXT_INPUT": "#ededed", "SCROLL": "#68868c",
"BUTTON": ("#FFFFFF", "#405559"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#3e3e3e", "#405559", "#68868c", "#ededed"], "DESCRIPTION": ["Grey", "Turquoise", "Winter"], },
"DarkTeal11": {"BACKGROUND": "#405559", "TEXT": "#ededed", "INPUT": "#68868c", "TEXT_INPUT": "#ededed", "SCROLL": "#68868c",
"BUTTON": ("#ededed", "#68868c"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#3e3e3e", "#405559", "#68868c", "#ededed"], "DESCRIPTION": ["Grey", "Turquoise", "Winter"], },
"LightBlue7": {"BACKGROUND": "#9ed0e0", "TEXT": "#19483f", "INPUT": "#5c868e", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#19483f",
"BUTTON": ("#FFFFFF", "#19483f"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#19483f", "#5c868e", "#ff6a38", "#9ed0e0"], "DESCRIPTION": ["Orange", "Blue", "Turquoise"], },
"LightGreen10": {"BACKGROUND": "#d8ebb5", "TEXT": "#205d67", "INPUT": "#639a67", "TEXT_INPUT": "#FFFFFF", "SCROLL": "#205d67",
"BUTTON": ("#d8ebb5", "#205d67"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#205d67", "#639a67", "#d9bf77", "#d8ebb5"], "DESCRIPTION": ["Blue", "Green", "Brown", "Vintage"], },
"DarkBlue15": {"BACKGROUND": "#151680", "TEXT": "#f1fea4", "INPUT": "#375fc0", "TEXT_INPUT": "#f1fea4", "SCROLL": "#375fc0",
"BUTTON": ("#f1fea4", "#1c44ac"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#151680", "#1c44ac", "#375fc0", "#f1fea4"], "DESCRIPTION": ["Blue", "Yellow", "Cold"], },
"DarkBlue16": {"BACKGROUND": "#1c44ac", "TEXT": "#f1fea4", "INPUT": "#375fc0", "TEXT_INPUT": "#f1fea4", "SCROLL": "#375fc0",
"BUTTON": ("#f1fea4", "#151680"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#151680", "#1c44ac", "#375fc0", "#f1fea4"], "DESCRIPTION": ["Blue", "Yellow", "Cold"], },
"DarkTeal12": {"BACKGROUND": "#004a7c", "TEXT": "#fafafa", "INPUT": "#e8f1f5", "TEXT_INPUT": "#000000", "SCROLL": "#e8f1f5",
"BUTTON": ("#fafafa", "#005691"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#004a7c", "#005691", "#e8f1f5", "#fafafa"], "DESCRIPTION": ["Grey", "Blue", "Cold", "Winter"], },
"LightBrown13": {"BACKGROUND": "#ebf5ee", "TEXT": "#921224", "INPUT": "#bdc6b8", "TEXT_INPUT": "#921224", "SCROLL": "#921224",
"BUTTON": ("#FFFFFF", "#921224"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#921224", "#bdc6b8", "#bce0da", "#ebf5ee"], "DESCRIPTION": ["Red", "Blue", "Grey", "Vintage", "Wedding"], },
"DarkBlue17": {"BACKGROUND": "#21294c", "TEXT": "#f9f2d7", "INPUT": "#f2dea8", "TEXT_INPUT": "#000000", "SCROLL": "#f2dea8",
"BUTTON": ("#f9f2d7", "#141829"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#141829", "#21294c", "#f2dea8", "#f9f2d7"], "DESCRIPTION": ["#000000", "Blue", "Yellow"], },
"DarkBrown6": {"BACKGROUND": "#785e4d", "TEXT": "#f2eee3", "INPUT": "#baaf92", "TEXT_INPUT": "#000000", "SCROLL": "#baaf92",
"BUTTON": ("#FFFFFF", "#785e4d"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#785e4d", "#ff8426", "#baaf92", "#f2eee3"], "DESCRIPTION": ["Grey", "Brown", "Orange", "Autumn"], },
"DarkGreen6": {"BACKGROUND": "#5c715e", "TEXT": "#f2f9f1", "INPUT": "#ddeedf", "TEXT_INPUT": "#000000", "SCROLL": "#ddeedf",
"BUTTON": ("#f2f9f1", "#5c715e"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#5c715e", "#b6cdbd", "#ddeedf", "#f2f9f1"], "DESCRIPTION": ["Grey", "Green", "Vintage"], },
"DarkGreen7": {"BACKGROUND": "#0C231E", "TEXT": "#efbe1c", "INPUT": "#153C33", "TEXT_INPUT": "#efbe1c", "SCROLL": "#153C33",
"BUTTON": ("#efbe1c", "#153C33"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey7": {"BACKGROUND": "#4b586e", "TEXT": "#dddddd", "INPUT": "#574e6d", "TEXT_INPUT": "#dddddd", "SCROLL": "#574e6d",
"BUTTON": ("#dddddd", "#43405d"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#43405d", "#4b586e", "#574e6d", "#dddddd"], "DESCRIPTION": ["Grey", "Winter", "Cold"], },
"DarkRed2": {"BACKGROUND": "#ab1212", "TEXT": "#f6e4b5", "INPUT": "#cd3131", "TEXT_INPUT": "#f6e4b5", "SCROLL": "#cd3131", "BUTTON": ("#f6e4b5", "#ab1212"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#ab1212", "#1fad9f", "#cd3131", "#f6e4b5"], "DESCRIPTION": ["Turquoise", "Red", "Yellow"], },
"LightGrey6": {"BACKGROUND": "#e3e3e3", "TEXT": "#233142", "INPUT": "#455d7a", "TEXT_INPUT": "#e3e3e3", "SCROLL": "#233142",
"BUTTON": ("#e3e3e3", "#455d7a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0,
"COLOR_LIST": ["#233142", "#455d7a", "#f95959", "#e3e3e3"], "DESCRIPTION": ["#000000", "Blue", "Red", "Grey"], },
"HotDogStand": {"BACKGROUND": "red", "TEXT": "yellow", "INPUT": "yellow", "TEXT_INPUT": "#000000", "SCROLL": "yellow", "BUTTON": ("red", "yellow"),
"PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey8": {"BACKGROUND": "#19232D", "TEXT": "#ffffff", "INPUT": "#32414B", "TEXT_INPUT": "#ffffff", "SCROLL": "#505F69",
"BUTTON": ("#ffffff", "#32414B"), "PROGRESS": ("#505F69", "#32414B"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey9": {"BACKGROUND": "#36393F", "TEXT": "#DCDDDE", "INPUT": "#40444B", "TEXT_INPUT": "#ffffff", "SCROLL": "#202225",
"BUTTON": ("#202225", "#B9BBBE"), "PROGRESS": ("#202225", "#40444B"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey10": {"BACKGROUND": "#1c1e23", "TEXT": "#cccdcf", "INPUT": "#272a31", "TEXT_INPUT": "#8b9fde", "SCROLL": "#313641",
"BUTTON": ("#f5f5f6", "#2e3d5a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey11": {"BACKGROUND": "#1c1e23", "TEXT": "#cccdcf", "INPUT": "#313641", "TEXT_INPUT": "#cccdcf", "SCROLL": "#313641",
"BUTTON": ("#f5f5f6", "#313641"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey12": {"BACKGROUND": "#1c1e23", "TEXT": "#8b9fde", "INPUT": "#313641", "TEXT_INPUT": "#8b9fde", "SCROLL": "#313641",
"BUTTON": ("#cccdcf", "#2e3d5a"), "PROGRESS": DEFAULT_PROGRESS_BAR_COMPUTE, "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey13": {"BACKGROUND": "#1c1e23", "TEXT": "#cccdcf", "INPUT": "#272a31", "TEXT_INPUT": "#cccdcf", "SCROLL": "#313641",
"BUTTON": ("#8b9fde", "#313641"), "PROGRESS": ("#cccdcf", "#272a31"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkGrey14": {"BACKGROUND": "#24292e", "TEXT": "#fafbfc", "INPUT": "#1d2125", "TEXT_INPUT": "#fafbfc", "SCROLL": "#1d2125",
"BUTTON": ("#fafbfc", "#155398"), "PROGRESS": ("#155398", "#1d2125"), "BORDER": 1, "SLIDER_DEPTH": 0, "PROGRESS_DEPTH": 0, },
"DarkBrown7": {"BACKGROUND": "#2c2417", "TEXT": "#baa379", "INPUT": "#baa379", "TEXT_INPUT": "#000000", "SCROLL": "#392e1c",
"BUTTON": ("#000000", "#baa379"), "PROGRESS": ("#baa379", "#453923"), "BORDER": 1, "SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
"Python": {"BACKGROUND": "#3d7aab", "TEXT": "#ffde56", "INPUT": "#295273", "TEXT_INPUT": "#ffde56", "SCROLL": "#295273", "BUTTON": ("#ffde56", "#295273"),
"PROGRESS": ("#ffde56", "#295273"), "BORDER": 1, "SLIDER_DEPTH": 1, "PROGRESS_DEPTH": 0, },
}
```
###Assistant :
:param icon: Can be either a filename or Base64 value. For Windows if filename, it MUST be ICO format. For Linux, must NOT be ICO. Most portable is to use a Base64 of a PNG file. This works universally across all OS's
:type icon: bytes | str
:param button_color: Color of the button (text, background)
:type button_color: (str, str) or str
:param element_size: element size (width, height) in characters
:type element_size: (int, int)
:param button_element_size: Size of button
:type button_element_size: (int, int)
:param margins: (left/right, top/bottom) tkinter margins around outsize. Amount of pixels to leave inside the window's frame around the edges before your elements are shown.
:type margins: (int, int)
:param element_padding: Default amount of padding to put around elements in window (left/right, top/bottom) or ((left, right), (top, bottom))
:type element_padding: (int, int) or ((int, int),(int,int))
:param auto_size_text: True if the Widget should be shrunk to exactly fit the number of chars to show
:type auto_size_text: bool
:param auto_size_buttons: True if Buttons in this Window should be sized to exactly fit the text on this.
:type auto_size_buttons: (bool)
:param font: specifies the font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike
:type font: (str or (str, int[, str]) or None)
:param border_width: width of border around element
:type border_width: (int)
:param slider_border_width: Width of the border around sliders
:type slider_border_width: (int)
:param slider_relief: Type of relief to use for sliders
:type slider_relief: (str)
:param slider_orientation: ???
:type slider_orientation: ???
:param autoclose_time: ???
:type autoclose_time: ???
:param message_box_line_width: ???
:type message_box_line_width: ???
:param progress_meter_border_depth: ???
:type progress_meter_border_depth: ???
:param progress_meter_style: You can no longer set a progress bar style. All ttk styles must be the same for the window
:type progress_meter_style: ???
:param progress_meter_relief:
:type progress_meter_relief: ???
:param progress_meter_color: ???
:type progress_meter_color: ???
:param progress_meter_size: ???
:type progress_meter_size: ???
:param text_justification: Default text justification for all Text Elements in window
:type text_justification: 'left' | 'right' | 'center'
:param background_color: color of background
:type background_color: (str)
:param element_background_color: element background color
:type element_background_color: (str)
:param text_element_background_color: text element background color
:type text_element_background_color: (str)
:param input_elements_background_color: Default color to use for the background of input elements
:type input_elements_background_color: (str)
:param input_text_color: Default color to use for the text for Input elements
:type input_text_color: (str)
:param scrollbar_color: Default color to use for the slider trough
:type scrollbar_color: (str)
:param text_color: color of the text
:type text_color: (str)
:param element_text_color: Default color to use for Text elements
:type element_text_color: (str)
:param debug_win_size: window size
:type debug_win_size: (int, int)
:param window_location: Default location to place windows. Not setting will center windows on the display
:type window_location: (int, int) | None
:param error_button_color: (Default = (None))
:type error_button_color: ???
:param tooltip_time: time in milliseconds to wait before showing a tooltip. Default is 400ms
:type tooltip_time: (int)
:param tooltip_font: font to use for all tooltips
:type tooltip_font: str or Tuple[str, int] or Tuple[str, int, str]
:param use_ttk_buttons: if True will cause all buttons to be ttk buttons
:type use_ttk_buttons: (bool)
:param ttk_theme: Theme to use with ttk widgets. Choices (on Windows) include - 'default', 'winnative', 'clam', 'alt', 'classic', 'vista', 'xpnative'
:type ttk_theme: (str)
:param suppress_error_popups: If True then error popups will not be shown if generated internally to PySimpleGUI
:type suppress_error_popups: (bool)
:param suppress_raise_key_errors: If True then key errors won't be raised (you'll still get popup error)
:type suppress_raise_key_errors: (bool)
:param suppress_key_guessing: If True then key errors won't try and find closest matches for you
:type suppress_key_guessing: (bool)
:param warn_button_key_duplicates: If True then duplicate Button Keys generate warnings (not recommended as they're expected)
:type warn_button_key_duplicates: (bool)
:param enable_treeview_869_patch: If True, then will use the treeview color patch for tk 8.6.9
:type enable_treeview_869_patch: (bool)
:param enable_mac_notitlebar_patch: If True then Windows with no titlebar use an alternative technique when tkinter version < 8.6.10
:type enable_mac_notitlebar_patch: (bool)
:param use_custom_titlebar: If True then a custom titlebar is used instead of the normal system titlebar
:type use_custom_titlebar: (bool)
:param titlebar_background_color: If custom titlebar indicated by use_custom_titlebar, then use this as background color
:type titlebar_background_color: str | None
:param titlebar_text_color: If custom titlebar indicated by use_custom_titlebar, then use this as text color
:type titlebar_text_color: str | None
:param titlebar_font: If custom titlebar indicated by use_custom_titlebar, then use this as title font
:type titlebar_font: (str or (str, int[, str]) or None) | None
:param titlebar_icon: If custom titlebar indicated by use_custom_titlebar, then use this as the icon (file or base64 bytes)
:type titlebar_icon: bytes | str
:param user_settings_path: default path for user_settings API calls. Expanded with os.path.expanduser so can contain ~ to represent user
:type user_settings_path: (str)
:param pysimplegui_settings_path: default path for the global PySimpleGUI user_settings
:type pysimplegui_settings_path: (str)
:param pysimplegui_settings_filename: default filename for the global PySimpleGUI user_settings
:type pysimplegui_settings_filename: (str)
:param keep_on_top: If True then all windows will automatically be set to keep_on_top=True
:type keep_on_top: (bool)
:param dpi_awareness: If True then will turn on DPI awareness (Windows only at the moment)
:type dpi_awareness: (bool)
:param scaling: Sets the default scaling for all windows including popups, etc.
:type scaling: (float)
:param disable_modal_windows: If True then all windows, including popups, will not be modal windows
:type disable_modal_windows: (bool)
:param tooltip_offset: Offset to use for tooltips as a tuple. These values will be added to the mouse location when the widget was entered.
:type tooltip_offset: ((None, None) | (int, int))
:return: None
:rtype: None
|
1,446 | def find_module(module, path=None, imp=None):
if imp is None:
imp = import_module
with cwd_in_path():
try:
return imp(module)
except ImportError:
# Raise a more specific error if the problem is that one of the
# dot-separated segments of the module name is not a package.
if '.' in module:
parts = module.split('.')
for i, part in enumerate(parts[:-1]):
package = '.'.join(parts[:i + 1])
try:
mpart = imp(package)
except ImportError:
# Break out and re-raise the original ImportError
# instead.
break
try:
mpart.__path__
except AttributeError:
raise NotAPackage(package)
raise
| Version of :func:`imp.find_module` supporting dots. | 5 | 84 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def find_module(module, path=None, imp=None):
if imp is None:
imp = import_module
with cwd_in_path():
try:
return imp(module)
except ImportError:
# Raise a more specific error if the problem is that one of the
# dot-separated segments of the module name is not a package.
if '.' in module:
parts = module.split('.')
for i, part in enumerate(parts[:-1]):
package = '.'.join(parts[:i + 1])
try:
mpart = imp(package)
except ImportError:
# Break out and re-raise the original ImportError
# instead.
break
try:
mpart.__path__
except AttributeError:
raise NotAPackage(package)
raise
```
###Assistant : Version of :func:`imp.find_module` supporting dots.
|
1,447 | def get_ffmpeg_path() -> Optional[Path]:
# Check if ffmpeg is installed
global_ffmpeg = shutil.which("ffmpeg")
if global_ffmpeg:
return Path(global_ffmpeg)
# Get local ffmpeg path
return get_local_ffmpeg()
|
Get path to global ffmpeg binary or a local ffmpeg binary.
Or None if not found.
| 16 | 24 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_ffmpeg_path() -> Optional[Path]:
# Check if ffmpeg is installed
global_ffmpeg = shutil.which("ffmpeg")
if global_ffmpeg:
return Path(global_ffmpeg)
# Get local ffmpeg path
return get_local_ffmpeg()
```
###Assistant :
Get path to global ffmpeg binary or a local ffmpeg binary.
Or None if not found.
|
1,448 | def __call__(self, inputs, state, scope=None):
return self._call_wrapped_cell(
inputs, state, cell_call_fn=self.cell.__call__, scope=scope
)
| Runs the RNN cell step computation.
We assume that the wrapped RNNCell is being built within its `__call__`
method. We directly use the wrapped cell's `__call__` in the overridden
wrapper `__call__` method.
This allows to use the wrapped cell and the non-wrapped cell equivalently
when using `__call__`.
Args:
inputs: A tensor with wrapped cell's input.
state: A tensor or tuple of tensors with wrapped cell's state.
scope: VariableScope for the subgraph created in the wrapped cells'
`__call__`.
Returns:
A pair containing:
- Output: A tensor with cell's output.
- New state: A tensor or tuple of tensors with new wrapped cell's state.
| 102 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def __call__(self, inputs, state, scope=None):
return self._call_wrapped_cell(
inputs, state, cell_call_fn=self.cell.__call__, scope=scope
)
```
###Assistant : Runs the RNN cell step computation.
We assume that the wrapped RNNCell is being built within its `__call__`
method. We directly use the wrapped cell's `__call__` in the overridden
wrapper `__call__` method.
This allows to use the wrapped cell and the non-wrapped cell equivalently
when using `__call__`.
Args:
inputs: A tensor with wrapped cell's input.
state: A tensor or tuple of tensors with wrapped cell's state.
scope: VariableScope for the subgraph created in the wrapped cells'
`__call__`.
Returns:
A pair containing:
- Output: A tensor with cell's output.
- New state: A tensor or tuple of tensors with new wrapped cell's state.
|
1,449 | def get_kurtosis(self) -> pd.DataFrame:
vals = list()
for period in portfolio_helper.PERIODS:
vals.append(
[
round(
scipy.stats.kurtosis(
portfolio_helper.filter_df_by_period(self.returns, period)
),
3,
),
round(
scipy.stats.skew(
portfolio_helper.filter_df_by_period(
self.benchmark_returns, period
)
),
3,
),
]
)
return pd.DataFrame(
vals, index=portfolio_helper.PERIODS, columns=["Portfolio", "Benchmark"]
)
| Class method that retrieves kurtosis for portfolio and benchmark selected
Returns
-------
pd.DataFrame
DataFrame with kurtosis for portfolio and benchmark for different periods
| 23 | 38 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_kurtosis(self) -> pd.DataFrame:
vals = list()
for period in portfolio_helper.PERIODS:
vals.append(
[
round(
scipy.stats.kurtosis(
portfolio_helper.filter_df_by_period(self.returns, period)
),
3,
),
round(
scipy.stats.skew(
portfolio_helper.filter_df_by_period(
self.benchmark_returns, period
)
),
3,
),
]
)
return pd.DataFrame(
vals, index=portfolio_helper.PERIODS, columns=["Portfolio", "Benchmark"]
)
```
###Assistant : Class method that retrieves kurtosis for portfolio and benchmark selected
Returns
-------
pd.DataFrame
DataFrame with kurtosis for portfolio and benchmark for different periods
|
1,450 | def _save_model(self, epoch, batch, logs):
logs = logs or {}
if (
isinstance(self.save_freq, int)
or self.epochs_since_last_save >= self.period
):
# Block only when saving interval is reached.
logs = tf_utils.sync_to_numpy_or_python_type(logs)
self.epochs_since_last_save = 0
filepath = self._get_file_path(epoch, batch, logs)
try:
if self.save_best_only:
current = logs.get(self.monitor)
if current is None:
logging.warning(
"Can save best model only with %s available, "
"skipping.",
self.monitor,
)
else:
if self.monitor_op(current, self.best):
if self.verbose > 0:
io_utils.print_msg(
f"\nEpoch {epoch + 1}: {self.monitor} improved "
f"from {self.best:.5f} to {current:.5f}, "
f"saving model to {filepath}"
)
self.best = current
if self.save_weights_only:
self.model.save_weights(
filepath,
overwrite=True,
options=self._options,
)
else:
self.model.save(
filepath,
overwrite=True,
options=self._options,
)
else:
if self.verbose > 0:
io_utils.print_msg(
f"\nEpoch {epoch + 1}: "
f"{self.monitor} did not improve from {self.best:.5f}"
)
else:
if self.verbose > 0:
io_utils.print_msg(
f"\nEpoch {epoch + 1}: saving model to {filepath}"
)
if self.save_weights_only:
self.model.save_weights(
filepath, overwrite=True, options=self._options
)
else:
self.model.save(
filepath, overwrite=True, options=self._options
)
self._maybe_remove_file()
except IsADirectoryError as e: # h5py 3.x
raise IOError(
"Please specify a non-directory filepath for "
"ModelCheckpoint. Filepath used is an existing "
f"directory: {filepath}"
)
except IOError as e: # h5py 2.x
# `e.errno` appears to be `None` so checking the content of `e.args[0]`.
if "is a directory" in str(e.args[0]).lower():
raise IOError(
"Please specify a non-directory filepath for "
"ModelCheckpoint. Filepath used is an existing "
f"directory: f{filepath}"
)
# Re-throw the error for any other causes.
raise e
| Saves the model.
Args:
epoch: the epoch this iteration is in.
batch: the batch this iteration is in. `None` if the `save_freq`
is set to `epoch`.
logs: the `logs` dict passed in to `on_batch_end` or `on_epoch_end`.
| 36 | 230 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _save_model(self, epoch, batch, logs):
logs = logs or {}
if (
isinstance(self.save_freq, int)
or self.epochs_since_last_save >= self.period
):
# Block only when saving interval is reached.
logs = tf_utils.sync_to_numpy_or_python_type(logs)
self.epochs_since_last_save = 0
filepath = self._get_file_path(epoch, batch, logs)
try:
if self.save_best_only:
current = logs.get(self.monitor)
if current is None:
logging.warning(
"Can save best model only with %s available, "
"skipping.",
self.monitor,
)
else:
if self.monitor_op(current, self.best):
if self.verbose > 0:
io_utils.print_msg(
f"\nEpoch {epoch + 1}: {self.monitor} improved "
f"from {self.best:.5f} to {current:.5f}, "
f"saving model to {filepath}"
)
self.best = current
if self.save_weights_only:
self.model.save_weights(
filepath,
overwrite=True,
options=self._options,
)
else:
self.model.save(
filepath,
overwrite=True,
options=self._options,
)
else:
if self.verbose > 0:
io_utils.print_msg(
f"\nEpoch {epoch + 1}: "
f"{self.monitor} did not improve from {self.best:.5f}"
)
else:
if self.verbose > 0:
io_utils.print_msg(
f"\nEpoch {epoch + 1}: saving model to {filepath}"
)
if self.save_weights_only:
self.model.save_weights(
filepath, overwrite=True, options=self._options
)
else:
self.model.save(
filepath, overwrite=True, options=self._options
)
self._maybe_remove_file()
except IsADirectoryError as e: # h5py 3.x
raise IOError(
"Please specify a non-directory filepath for "
"ModelCheckpoint. Filepath used is an existing "
f"directory: {filepath}"
)
except IOError as e: # h5py 2.x
# `e.errno` appears to be `None` so checking the content of `e.args[0]`.
if "is a directory" in str(e.args[0]).lower():
raise IOError(
"Please specify a non-directory filepath for "
"ModelCheckpoint. Filepath used is an existing "
f"directory: f{filepath}"
)
# Re-throw the error for any other causes.
raise e
```
###Assistant : Saves the model.
Args:
epoch: the epoch this iteration is in.
batch: the batch this iteration is in. `None` if the `save_freq`
is set to `epoch`.
logs: the `logs` dict passed in to `on_batch_end` or `on_epoch_end`.
|
1,451 | def steiner_tree(G, terminal_nodes, weight="weight", method=None):
r
if method is None:
import warnings
msg = (
"steiner_tree will change default method from 'kou' to 'mehlhorn'"
"in version 3.2.\nSet the `method` kwarg to remove this warning."
)
warnings.warn(msg, FutureWarning, stacklevel=4)
method = "kou"
try:
algo = ALGORITHMS[method]
except KeyError as e:
msg = f"{method} is not a valid choice for an algorithm."
raise ValueError(msg) from e
edges = algo(G, terminal_nodes, weight)
# For multigraph we should add the minimal weight edge keys
if G.is_multigraph():
edges = (
(u, v, min(G[u][v], key=lambda k: G[u][v][k][weight])) for u, v in edges
)
T = G.edge_subgraph(edges)
return T
| Return an approximation to the minimum Steiner tree of a graph.
The minimum Steiner tree of `G` w.r.t a set of `terminal_nodes` (also *S*)
is a tree within `G` that spans those nodes and has minimum size (sum of
edge weights) among all such trees.
The approximation algorithm is specified with the `method` keyword
argument. All three available algorithms produce a tree whose weight is
within a (2 - (2 / l)) factor of the weight of the optimal Steiner tree,
where *l* is the minimum number of leaf nodes across all possible Steiner
trees.
* `kou` [2]_ (runtime $O(|S| |V|^2)$) computes the minimum spanning tree of
the subgraph of the metric closure of *G* induced by the terminal nodes,
where the metric closure of *G* is the complete graph in which each edge is
weighted by the shortest path distance between the nodes in *G*.
* `mehlhorn` [3]_ (runtime $O(|E|+|V|\log|V|)$) modifies Kou et al.'s
algorithm, beginning by finding the closest terminal node for each
non-terminal. This data is used to create a complete graph containing only
the terminal nodes, in which edge is weighted with the shortest path
distance between them. The algorithm then proceeds in the same way as Kou
et al..
Parameters
----------
G : NetworkX graph
terminal_nodes : list
A list of terminal nodes for which minimum steiner tree is
to be found.
weight : string (default = 'weight')
Use the edge attribute specified by this string as the edge weight.
Any edge attribute not present defaults to 1.
method : string, optional (default = 'kou')
The algorithm to use to approximate the Steiner tree.
Supported options: 'kou', 'mehlhorn'.
Other inputs produce a ValueError.
Returns
-------
NetworkX graph
Approximation to the minimum steiner tree of `G` induced by
`terminal_nodes` .
Notes
-----
For multigraphs, the edge between two nodes with minimum weight is the
edge put into the Steiner tree.
References
----------
.. [1] Steiner_tree_problem on Wikipedia.
https://en.wikipedia.org/wiki/Steiner_tree_problem
.. [2] Kou, L., G. Markowsky, and L. Berman. 1981.
‘A Fast Algorithm for Steiner Trees’.
Acta Informatica 15 (2): 141–45.
https://doi.org/10.1007/BF00288961.
.. [3] Mehlhorn, Kurt. 1988.
‘A Faster Approximation Algorithm for the Steiner Problem in Graphs’.
Information Processing Letters 27 (3): 125–28.
https://doi.org/10.1016/0020-0190(88)90066-X.
| 366 | 102 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def steiner_tree(G, terminal_nodes, weight="weight", method=None):
r
if method is None:
import warnings
msg = (
"steiner_tree will change default method from 'kou' to 'mehlhorn'"
"in version 3.2.\nSet the `method` kwarg to remove this warning."
)
warnings.warn(msg, FutureWarning, stacklevel=4)
method = "kou"
try:
algo = ALGORITHMS[method]
except KeyError as e:
msg = f"{method} is not a valid choice for an algorithm."
raise ValueError(msg) from e
edges = algo(G, terminal_nodes, weight)
# For multigraph we should add the minimal weight edge keys
if G.is_multigraph():
edges = (
(u, v, min(G[u][v], key=lambda k: G[u][v][k][weight])) for u, v in edges
)
T = G.edge_subgraph(edges)
return T
```
###Assistant : Return an approximation to the minimum Steiner tree of a graph.
The minimum Steiner tree of `G` w.r.t a set of `terminal_nodes` (also *S*)
is a tree within `G` that spans those nodes and has minimum size (sum of
edge weights) among all such trees.
The approximation algorithm is specified with the `method` keyword
argument. All three available algorithms produce a tree whose weight is
within a (2 - (2 / l)) factor of the weight of the optimal Steiner tree,
where *l* is the minimum number of leaf nodes across all possible Steiner
trees.
* `kou` [2]_ (runtime $O(|S| |V|^2)$) computes the minimum spanning tree of
the subgraph of the metric closure of *G* induced by the terminal nodes,
where the metric closure of *G* is the complete graph in which each edge is
weighted by the shortest path distance between the nodes in *G*.
* `mehlhorn` [3]_ (runtime $O(|E|+|V|\log|V|)$) modifies Kou et al.'s
algorithm, beginning by finding the closest terminal node for each
non-terminal. This data is used to create a complete graph containing only
the terminal nodes, in which edge is weighted with the shortest path
distance between them. The algorithm then proceeds in the same way as Kou
et al..
Parameters
----------
G : NetworkX graph
terminal_nodes : list
A list of terminal nodes for which minimum steiner tree is
to be found.
weight : string (default = 'weight')
Use the edge attribute specified by this string as the edge weight.
Any edge attribute not present defaults to 1.
method : string, optional (default = 'kou')
The algorithm to use to approximate the Steiner tree.
Supported options: 'kou', 'mehlhorn'.
Other inputs produce a ValueError.
Returns
-------
NetworkX graph
Approximation to the minimum steiner tree of `G` induced by
`terminal_nodes` .
Notes
-----
For multigraphs, the edge between two nodes with minimum weight is the
edge put into the Steiner tree.
References
----------
.. [1] Steiner_tree_problem on Wikipedia.
https://en.wikipedia.org/wiki/Steiner_tree_problem
.. [2] Kou, L., G. Markowsky, and L. Berman. 1981.
‘A Fast Algorithm for Steiner Trees’.
Acta Informatica 15 (2): 141–45.
https://doi.org/10.1007/BF00288961.
.. [3] Mehlhorn, Kurt. 1988.
‘A Faster Approximation Algorithm for the Steiner Problem in Graphs’.
Information Processing Letters 27 (3): 125–28.
https://doi.org/10.1016/0020-0190(88)90066-X.
|
1,452 | def find_object(self, queryset, request):
if "id" in request.GET:
return queryset.get(id=request.GET["id"])
|
Override this to implement more find methods.
| 7 | 10 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def find_object(self, queryset, request):
if "id" in request.GET:
return queryset.get(id=request.GET["id"])
```
###Assistant :
Override this to implement more find methods.
|
1,453 | def _maybe_infer_dtype_type(element):
tipo = None
if hasattr(element, "dtype"):
tipo = element.dtype
elif is_list_like(element):
element = np.asarray(element)
tipo = element.dtype
return tipo
|
Try to infer an object's dtype, for use in arithmetic ops.
Uses `element.dtype` if that's available.
Objects implementing the iterator protocol are cast to a NumPy array,
and from there the array's type is used.
Parameters
----------
element : object
Possibly has a `.dtype` attribute, and possibly the iterator
protocol.
Returns
-------
tipo : type
Examples
--------
>>> from collections import namedtuple
>>> Foo = namedtuple("Foo", "dtype")
>>> _maybe_infer_dtype_type(Foo(np.dtype("i8")))
dtype('int64')
| 70 | 21 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _maybe_infer_dtype_type(element):
tipo = None
if hasattr(element, "dtype"):
tipo = element.dtype
elif is_list_like(element):
element = np.asarray(element)
tipo = element.dtype
return tipo
```
###Assistant :
Try to infer an object's dtype, for use in arithmetic ops.
Uses `element.dtype` if that's available.
Objects implementing the iterator protocol are cast to a NumPy array,
and from there the array's type is used.
Parameters
----------
element : object
Possibly has a `.dtype` attribute, and possibly the iterator
protocol.
Returns
-------
tipo : type
Examples
--------
>>> from collections import namedtuple
>>> Foo = namedtuple("Foo", "dtype")
>>> _maybe_infer_dtype_type(Foo(np.dtype("i8")))
dtype('int64')
|
1,454 | def test_create_api_message_special():
request = get_new_request("Alexa.PowerController", "TurnOn")
directive_header = request["directive"]["header"]
directive_header.pop("correlationToken")
directive = messages.AlexaDirective(request)
msg = directive.response("testName", "testNameSpace")._response
assert "event" in msg
msg = msg["event"]
assert msg["header"]["messageId"] is not None
assert msg["header"]["messageId"] != directive_header["messageId"]
assert "correlationToken" not in msg["header"]
assert msg["header"]["name"] == "testName"
assert msg["header"]["namespace"] == "testNameSpace"
assert msg["header"]["payloadVersion"] == "3"
assert msg["payload"] == {}
assert "endpoint" not in msg
| Create an API message response of a request with non defaults. | 11 | 59 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_create_api_message_special():
request = get_new_request("Alexa.PowerController", "TurnOn")
directive_header = request["directive"]["header"]
directive_header.pop("correlationToken")
directive = messages.AlexaDirective(request)
msg = directive.response("testName", "testNameSpace")._response
assert "event" in msg
msg = msg["event"]
assert msg["header"]["messageId"] is not None
assert msg["header"]["messageId"] != directive_header["messageId"]
assert "correlationToken" not in msg["header"]
assert msg["header"]["name"] == "testName"
assert msg["header"]["namespace"] == "testNameSpace"
assert msg["header"]["payloadVersion"] == "3"
assert msg["payload"] == {}
assert "endpoint" not in msg
```
###Assistant : Create an API message response of a request with non defaults.
|
1,455 | def tearDown(self):
r
del self.model_fp16
del self.model_8bit
gc.collect()
torch.cuda.empty_cache()
|
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
| 27 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def tearDown(self):
r
del self.model_fp16
del self.model_8bit
gc.collect()
torch.cuda.empty_cache()
```
###Assistant :
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
1,456 | def test_pagination_from_sync_and_messages(self):
channel = self._send_relation(RelationTypes.ANNOTATION, "m.reaction", "A")
self.assertEquals(200, channel.code, channel.json_body)
annotation_id = channel.json_body["event_id"]
# Send an event after the relation events.
self.helper.send(self.room, body="Latest event", tok=self.user_token)
# Request /sync, limiting it such that only the latest event is returned
# (and not the relation).
filter = urllib.parse.quote_plus(
'{"room": {"timeline": {"limit": 1}}}'.encode()
)
channel = self.make_request(
"GET", f"/sync?filter={filter}", access_token=self.user_token
)
self.assertEquals(200, channel.code, channel.json_body)
room_timeline = channel.json_body["rooms"]["join"][self.room]["timeline"]
sync_prev_batch = room_timeline["prev_batch"]
self.assertIsNotNone(sync_prev_batch)
# Ensure the relation event is not in the batch returned from /sync.
self.assertNotIn(
annotation_id, [ev["event_id"] for ev in room_timeline["events"]]
)
# Request /messages, limiting it such that only the latest event is
# returned (and not the relation).
channel = self.make_request(
"GET",
f"/rooms/{self.room}/messages?dir=b&limit=1",
access_token=self.user_token,
)
self.assertEquals(200, channel.code, channel.json_body)
messages_end = channel.json_body["end"]
self.assertIsNotNone(messages_end)
# Ensure the relation event is not in the chunk returned from /messages.
self.assertNotIn(
annotation_id, [ev["event_id"] for ev in channel.json_body["chunk"]]
)
# Request /relations with the pagination tokens received from both the
# /sync and /messages responses above, in turn.
#
# This is a tiny bit silly since the client wouldn't know the parent ID
# from the requests above; consider the parent ID to be known from a
# previous /sync.
for from_token in (sync_prev_batch, messages_end):
channel = self.make_request(
"GET",
f"/_matrix/client/unstable/rooms/{self.room}/relations/{self.parent_id}?from={from_token}",
access_token=self.user_token,
)
self.assertEquals(200, channel.code, channel.json_body)
# The relation should be in the returned chunk.
self.assertIn(
annotation_id, [ev["event_id"] for ev in channel.json_body["chunk"]]
)
| Pagination tokens from /sync and /messages can be used to paginate /relations. | 12 | 226 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_pagination_from_sync_and_messages(self):
channel = self._send_relation(RelationTypes.ANNOTATION, "m.reaction", "A")
self.assertEquals(200, channel.code, channel.json_body)
annotation_id = channel.json_body["event_id"]
# Send an event after the relation events.
self.helper.send(self.room, body="Latest event", tok=self.user_token)
# Request /sync, limiting it such that only the latest event is returned
# (and not the relation).
filter = urllib.parse.quote_plus(
'{"room": {"timeline": {"limit": 1}}}'.encode()
)
channel = self.make_request(
"GET", f"/sync?filter={filter}", access_token=self.user_token
)
self.assertEquals(200, channel.code, channel.json_body)
room_timeline = channel.json_body["rooms"]["join"][self.room]["timeline"]
sync_prev_batch = room_timeline["prev_batch"]
self.assertIsNotNone(sync_prev_batch)
# Ensure the relation event is not in the batch returned from /sync.
self.assertNotIn(
annotation_id, [ev["event_id"] for ev in room_timeline["events"]]
)
# Request /messages, limiting it such that only the latest event is
# returned (and not the relation).
channel = self.make_request(
"GET",
f"/rooms/{self.room}/messages?dir=b&limit=1",
access_token=self.user_token,
)
self.assertEquals(200, channel.code, channel.json_body)
messages_end = channel.json_body["end"]
self.assertIsNotNone(messages_end)
# Ensure the relation event is not in the chunk returned from /messages.
self.assertNotIn(
annotation_id, [ev["event_id"] for ev in channel.json_body["chunk"]]
)
# Request /relations with the pagination tokens received from both the
# /sync and /messages responses above, in turn.
#
# This is a tiny bit silly since the client wouldn't know the parent ID
# from the requests above; consider the parent ID to be known from a
# previous /sync.
for from_token in (sync_prev_batch, messages_end):
channel = self.make_request(
"GET",
f"/_matrix/client/unstable/rooms/{self.room}/relations/{self.parent_id}?from={from_token}",
access_token=self.user_token,
)
self.assertEquals(200, channel.code, channel.json_body)
# The relation should be in the returned chunk.
self.assertIn(
annotation_id, [ev["event_id"] for ev in channel.json_body["chunk"]]
)
```
###Assistant : Pagination tokens from /sync and /messages can be used to paginate /relations.
|
1,457 | def download(self, url_or_urls):
url_or_urls = map_nested(self._download, url_or_urls, map_tuple=True)
return url_or_urls
| Download given url(s).
Args:
url_or_urls: url or `list`/`dict` of urls to download and extract. Each
url is a `str`.
Returns:
downloaded_path(s): `str`, The downloaded paths matching the given input
url_or_urls.
Example:
```py
>>> downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
```
| 37 | 10 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def download(self, url_or_urls):
url_or_urls = map_nested(self._download, url_or_urls, map_tuple=True)
return url_or_urls
```
###Assistant : Download given url(s).
Args:
url_or_urls: url or `list`/`dict` of urls to download and extract. Each
url is a `str`.
Returns:
downloaded_path(s): `str`, The downloaded paths matching the given input
url_or_urls.
Example:
```py
>>> downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
```
|
1,458 | def draw_gaussian(image, point, sigma):
# Check if the gaussian is inside
point[0] = round(point[0], 2)
point[1] = round(point[1], 2)
ul = [math.floor(point[0] - 7.5 * sigma), math.floor(point[1] - 7.5 * sigma)]
br = [math.floor(point[0] + 7.5 * sigma), math.floor(point[1] + 7.5 * sigma)]
if (ul[0] > image.shape[1] or ul[1] >
image.shape[0] or br[0] < 1 or br[1] < 1):
return image
size = 15 * sigma + 1
g = _gaussian(size, sigma=0.1)
g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) -
int(max(1, ul[0])) + int(max(1, -ul[0]))]
g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) -
int(max(1, ul[1])) + int(max(1, -ul[1]))]
img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
assert (g_x[0] > 0 and g_y[1] > 0)
image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] = \
image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
image[image > 1] = 1
return image
# Adapted from: https://github.com/1adrianb/face-alignment/blob/master/face_alignment/api.py | Draw gaussian circle at a point in an image.
Args:
image (np.array): An image of shape (H, W)
point (np.array): The center point of the guassian circle
sigma (float): Standard deviation of the gaussian kernel
Returns:
np.array: The image with the drawn gaussian.
| 43 | 148 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def draw_gaussian(image, point, sigma):
# Check if the gaussian is inside
point[0] = round(point[0], 2)
point[1] = round(point[1], 2)
ul = [math.floor(point[0] - 7.5 * sigma), math.floor(point[1] - 7.5 * sigma)]
br = [math.floor(point[0] + 7.5 * sigma), math.floor(point[1] + 7.5 * sigma)]
if (ul[0] > image.shape[1] or ul[1] >
image.shape[0] or br[0] < 1 or br[1] < 1):
return image
size = 15 * sigma + 1
g = _gaussian(size, sigma=0.1)
g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) -
int(max(1, ul[0])) + int(max(1, -ul[0]))]
g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) -
int(max(1, ul[1])) + int(max(1, -ul[1]))]
img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
assert (g_x[0] > 0 and g_y[1] > 0)
image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] = \
image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
image[image > 1] = 1
return image
# Adapted from: https://github.com/1adrianb/face-alignment/blob/master/face_alignment/api.py
```
###Assistant : Draw gaussian circle at a point in an image.
Args:
image (np.array): An image of shape (H, W)
point (np.array): The center point of the guassian circle
sigma (float): Standard deviation of the gaussian kernel
Returns:
np.array: The image with the drawn gaussian.
|
1,459 | def setdefault(self, key, default=None):
if key in self:
return self[key]
self[key] = default
return default
| Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
| 27 | 15 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def setdefault(self, key, default=None):
if key in self:
return self[key]
self[key] = default
return default
```
###Assistant : Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
|
1,460 | def current_year(context):
context["current_year"] = datetime.datetime.now().year
return context
|
Add the current year to the context, so it can be used for the copyright
note, or other places where it is needed.
| 23 | 7 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def current_year(context):
context["current_year"] = datetime.datetime.now().year
return context
```
###Assistant :
Add the current year to the context, so it can be used for the copyright
note, or other places where it is needed.
|
1,461 | def fileformatparser_map(self) -> Mapping[str, type]:
return {
"csv": CsvParser,
"parquet": ParquetParser,
}
# TODO: make these user configurable in spec.json
ab_additional_col = "_ab_additional_properties"
ab_last_mod_col = "_ab_source_file_last_modified"
ab_file_name_col = "_ab_source_file_url"
airbyte_columns = [ab_additional_col, ab_last_mod_col, ab_file_name_col]
datetime_format_string = "%Y-%m-%dT%H:%M:%S%z"
| Mapping where every key is equal 'filetype' and values are corresponding parser classes. | 13 | 37 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def fileformatparser_map(self) -> Mapping[str, type]:
return {
"csv": CsvParser,
"parquet": ParquetParser,
}
# TODO: make these user configurable in spec.json
ab_additional_col = "_ab_additional_properties"
ab_last_mod_col = "_ab_source_file_last_modified"
ab_file_name_col = "_ab_source_file_url"
airbyte_columns = [ab_additional_col, ab_last_mod_col, ab_file_name_col]
datetime_format_string = "%Y-%m-%dT%H:%M:%S%z"
```
###Assistant : Mapping where every key is equal 'filetype' and values are corresponding parser classes.
|
1,462 | def pivot_table(self, index=None, columns=None, values=None, aggfunc="mean"):
from dask.dataframe.reshape import pivot_table
return pivot_table(
self, index=index, columns=columns, values=values, aggfunc=aggfunc
)
|
Create a spreadsheet-style pivot table as a DataFrame. Target ``columns``
must have category dtype to infer result's ``columns``.
``index``, ``columns``, ``values`` and ``aggfunc`` must be all scalar.
Parameters
----------
values : scalar
column to aggregate
index : scalar
column to be index
columns : scalar
column to be columns
aggfunc : {'mean', 'sum', 'count'}, default 'mean'
Returns
-------
table : DataFrame
| 61 | 18 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def pivot_table(self, index=None, columns=None, values=None, aggfunc="mean"):
from dask.dataframe.reshape import pivot_table
return pivot_table(
self, index=index, columns=columns, values=values, aggfunc=aggfunc
)
```
###Assistant :
Create a spreadsheet-style pivot table as a DataFrame. Target ``columns``
must have category dtype to infer result's ``columns``.
``index``, ``columns``, ``values`` and ``aggfunc`` must be all scalar.
Parameters
----------
values : scalar
column to aggregate
index : scalar
column to be index
columns : scalar
column to be columns
aggfunc : {'mean', 'sum', 'count'}, default 'mean'
Returns
-------
table : DataFrame
|
1,463 | def test_multi_trial_reuse_with_failing(ray_start_4_cpus_extra):
os.environ["TUNE_MAX_PENDING_TRIALS_PG"] = "2"
register_trainable("foo2", MyResettableClass)
[trial1, trial2, trial3, trial4] = tune.run(
"foo2",
config={
"fail": tune.grid_search([False, True, False, False]),
"id": -1,
"sleep": 2,
},
reuse_actors=True,
resources_per_trial={"cpu": 2},
raise_on_failed_trial=False,
).trials
assert trial1.last_result["num_resets"] == 0
assert trial3.last_result["num_resets"] == 0
assert trial4.last_result["num_resets"] == 1
| Test that failing trial's actors are not reused.
- 2 trials can run at the same time
- Trial 1 succeeds, trial 2 fails
- Trial 3 will be scheduled after trial 2 failed, so won't reuse actor
- Trial 4 will be scheduled after trial 1 succeeded, so will reuse actor
| 52 | 42 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_multi_trial_reuse_with_failing(ray_start_4_cpus_extra):
os.environ["TUNE_MAX_PENDING_TRIALS_PG"] = "2"
register_trainable("foo2", MyResettableClass)
[trial1, trial2, trial3, trial4] = tune.run(
"foo2",
config={
"fail": tune.grid_search([False, True, False, False]),
"id": -1,
"sleep": 2,
},
reuse_actors=True,
resources_per_trial={"cpu": 2},
raise_on_failed_trial=False,
).trials
assert trial1.last_result["num_resets"] == 0
assert trial3.last_result["num_resets"] == 0
assert trial4.last_result["num_resets"] == 1
```
###Assistant : Test that failing trial's actors are not reused.
- 2 trials can run at the same time
- Trial 1 succeeds, trial 2 fails
- Trial 3 will be scheduled after trial 2 failed, so won't reuse actor
- Trial 4 will be scheduled after trial 1 succeeded, so will reuse actor
|
1,464 | def dispatch(self, request, *args, **kwargs):
page_id = kwargs.get("page_id")
if not get_forms_for_user(self.request.user).filter(id=page_id).exists():
raise PermissionDenied
self.page = get_object_or_404(Page, id=page_id).specific
self.submissions = self.get_queryset()
if self.request.method == "POST":
self.handle_delete(self.submissions)
return redirect(self.get_success_url(), page_id)
return super().dispatch(request, *args, **kwargs)
| Check permissions, set the page and submissions, handle delete | 9 | 32 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def dispatch(self, request, *args, **kwargs):
page_id = kwargs.get("page_id")
if not get_forms_for_user(self.request.user).filter(id=page_id).exists():
raise PermissionDenied
self.page = get_object_or_404(Page, id=page_id).specific
self.submissions = self.get_queryset()
if self.request.method == "POST":
self.handle_delete(self.submissions)
return redirect(self.get_success_url(), page_id)
return super().dispatch(request, *args, **kwargs)
```
###Assistant : Check permissions, set the page and submissions, handle delete
|
1,465 | def _prior_bpd(self, x_start):
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
|
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
| 48 | 30 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _prior_bpd(self, x_start):
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
```
###Assistant :
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
|
1,466 | def test_rolling_non_monotonic(method, expected):
# Based on an example found in computation.rst
use_expanding = [True, False, True, False, True, True, True, True]
df = DataFrame({"values": np.arange(len(use_expanding)) ** 2})
|
Make sure the (rare) branch of non-monotonic indices is covered by a test.
output from 1.1.3 is assumed to be the expected output. Output of sum/mean has
manually been verified.
GH 36933.
| 32 | 27 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_rolling_non_monotonic(method, expected):
# Based on an example found in computation.rst
use_expanding = [True, False, True, False, True, True, True, True]
df = DataFrame({"values": np.arange(len(use_expanding)) ** 2})
```
###Assistant :
Make sure the (rare) branch of non-monotonic indices is covered by a test.
output from 1.1.3 is assumed to be the expected output. Output of sum/mean has
manually been verified.
GH 36933.
|
1,467 | def clip(self, min=None, max=None):
from dask.array.ufunc import clip
return clip(self, min, max)
| Return an array whose values are limited to ``[min, max]``.
One of max or min must be given.
Refer to :func:`dask.array.clip` for full documentation.
See Also
--------
dask.array.clip : equivalent function
| 31 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def clip(self, min=None, max=None):
from dask.array.ufunc import clip
return clip(self, min, max)
```
###Assistant : Return an array whose values are limited to ``[min, max]``.
One of max or min must be given.
Refer to :func:`dask.array.clip` for full documentation.
See Also
--------
dask.array.clip : equivalent function
|
1,468 | def _floor_std(self, std):
r
original_tensor = std.clone().detach()
std = torch.clamp(std, min=self.std_floor)
if torch.any(original_tensor != std):
print(
"[*] Standard deviation was floored! The model is preventing overfitting, nothing serious to worry about"
)
return std
|
It clamps the standard deviation to not to go below some level
This removes the problem when the model tries to cheat for higher likelihoods by converting
one of the gaussians to a point mass.
Args:
std (float Tensor): tensor containing the standard deviation to be
| 46 | 34 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _floor_std(self, std):
r
original_tensor = std.clone().detach()
std = torch.clamp(std, min=self.std_floor)
if torch.any(original_tensor != std):
print(
"[*] Standard deviation was floored! The model is preventing overfitting, nothing serious to worry about"
)
return std
```
###Assistant :
It clamps the standard deviation to not to go below some level
This removes the problem when the model tries to cheat for higher likelihoods by converting
one of the gaussians to a point mass.
Args:
std (float Tensor): tensor containing the standard deviation to be
|
1,469 | def asXML(self, doctag=None, namedItemsOnly=False, indent="", formatted=True):
nl = "\n"
out = []
namedItems = dict((v[1], k) for (k, vlist) in self.__tokdict.items()
for v in vlist)
nextLevelIndent = indent + " "
# collapse out indents if formatting is not desired
if not formatted:
indent = ""
nextLevelIndent = ""
nl = ""
selfTag = None
if doctag is not None:
selfTag = doctag
else:
if self.__name:
selfTag = self.__name
if not selfTag:
if namedItemsOnly:
return ""
else:
selfTag = "ITEM"
out += [nl, indent, "<", selfTag, ">"]
for i, res in enumerate(self.__toklist):
if isinstance(res, ParseResults):
if i in namedItems:
out += [res.asXML(namedItems[i],
namedItemsOnly and doctag is None,
nextLevelIndent,
formatted)]
else:
out += [res.asXML(None,
namedItemsOnly and doctag is None,
nextLevelIndent,
formatted)]
else:
# individual token, see if there is a name for it
resTag = None
if i in namedItems:
resTag = namedItems[i]
if not resTag:
if namedItemsOnly:
continue
else:
resTag = "ITEM"
xmlBodyText = _xml_escape(_ustr(res))
out += [nl, nextLevelIndent, "<", resTag, ">",
xmlBodyText,
"</", resTag, ">"]
out += [nl, indent, "</", selfTag, ">"]
return "".join(out)
|
(Deprecated) Returns the parse results as XML. Tags are created for tokens and lists that have defined results names.
| 19 | 175 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def asXML(self, doctag=None, namedItemsOnly=False, indent="", formatted=True):
nl = "\n"
out = []
namedItems = dict((v[1], k) for (k, vlist) in self.__tokdict.items()
for v in vlist)
nextLevelIndent = indent + " "
# collapse out indents if formatting is not desired
if not formatted:
indent = ""
nextLevelIndent = ""
nl = ""
selfTag = None
if doctag is not None:
selfTag = doctag
else:
if self.__name:
selfTag = self.__name
if not selfTag:
if namedItemsOnly:
return ""
else:
selfTag = "ITEM"
out += [nl, indent, "<", selfTag, ">"]
for i, res in enumerate(self.__toklist):
if isinstance(res, ParseResults):
if i in namedItems:
out += [res.asXML(namedItems[i],
namedItemsOnly and doctag is None,
nextLevelIndent,
formatted)]
else:
out += [res.asXML(None,
namedItemsOnly and doctag is None,
nextLevelIndent,
formatted)]
else:
# individual token, see if there is a name for it
resTag = None
if i in namedItems:
resTag = namedItems[i]
if not resTag:
if namedItemsOnly:
continue
else:
resTag = "ITEM"
xmlBodyText = _xml_escape(_ustr(res))
out += [nl, nextLevelIndent, "<", resTag, ">",
xmlBodyText,
"</", resTag, ">"]
out += [nl, indent, "</", selfTag, ">"]
return "".join(out)
```
###Assistant :
(Deprecated) Returns the parse results as XML. Tags are created for tokens and lists that have defined results names.
|
1,470 | def times_seen_with_pending(self) -> int:
return self.times_seen + self.times_seen_pending
|
Returns `times_seen` with any additional pending updates from `buffers` added on. This value
must be set first.
| 17 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def times_seen_with_pending(self) -> int:
return self.times_seen + self.times_seen_pending
```
###Assistant :
Returns `times_seen` with any additional pending updates from `buffers` added on. This value
must be set first.
|
1,471 | def test_api_create_storage_path(self):
response = self.client.post(
self.ENDPOINT,
json.dumps(
{
"name": "A storage path",
"path": "Somewhere/{asn}",
},
),
content_type="application/json",
)
self.assertEqual(response.status_code, 201)
self.assertEqual(StoragePath.objects.count(), 2)
|
GIVEN:
- API request to create a storage paths
WHEN:
- API is called
THEN:
- Correct HTTP response
- New storage path is created
| 25 | 22 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_api_create_storage_path(self):
response = self.client.post(
self.ENDPOINT,
json.dumps(
{
"name": "A storage path",
"path": "Somewhere/{asn}",
},
),
content_type="application/json",
)
self.assertEqual(response.status_code, 201)
self.assertEqual(StoragePath.objects.count(), 2)
```
###Assistant :
GIVEN:
- API request to create a storage paths
WHEN:
- API is called
THEN:
- Correct HTTP response
- New storage path is created
|
1,472 | def test_set_all_ask_for_prompts_true_from_post(self, post, organization, inventory, org_admin):
r = post(
url=reverse('api:workflow_job_template_list'),
data=dict(
name='workflow that tests ask_for prompts',
organization=organization.id,
inventory=inventory.id,
job_tags='',
skip_tags='',
ask_inventory_on_launch=True,
ask_labels_on_launch=True,
ask_limit_on_launch=True,
ask_scm_branch_on_launch=True,
ask_skip_tags_on_launch=True,
ask_tags_on_launch=True,
ask_variables_on_launch=True,
),
user=org_admin,
expect=201,
)
wfjt = WorkflowJobTemplate.objects.get(id=r.data['id'])
assert wfjt.ask_inventory_on_launch is True
assert wfjt.ask_labels_on_launch is True
assert wfjt.ask_limit_on_launch is True
assert wfjt.ask_scm_branch_on_launch is True
assert wfjt.ask_skip_tags_on_launch is True
assert wfjt.ask_tags_on_launch is True
assert wfjt.ask_variables_on_launch is True
@pytest.mark.django_db |
Tests behaviour and values of ask_for_* fields on WFJT via POST
| 11 | 63 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_set_all_ask_for_prompts_true_from_post(self, post, organization, inventory, org_admin):
r = post(
url=reverse('api:workflow_job_template_list'),
data=dict(
name='workflow that tests ask_for prompts',
organization=organization.id,
inventory=inventory.id,
job_tags='',
skip_tags='',
ask_inventory_on_launch=True,
ask_labels_on_launch=True,
ask_limit_on_launch=True,
ask_scm_branch_on_launch=True,
ask_skip_tags_on_launch=True,
ask_tags_on_launch=True,
ask_variables_on_launch=True,
),
user=org_admin,
expect=201,
)
wfjt = WorkflowJobTemplate.objects.get(id=r.data['id'])
assert wfjt.ask_inventory_on_launch is True
assert wfjt.ask_labels_on_launch is True
assert wfjt.ask_limit_on_launch is True
assert wfjt.ask_scm_branch_on_launch is True
assert wfjt.ask_skip_tags_on_launch is True
assert wfjt.ask_tags_on_launch is True
assert wfjt.ask_variables_on_launch is True
@pytest.mark.django_db
```
###Assistant :
Tests behaviour and values of ask_for_* fields on WFJT via POST
|
1,473 | def get_used_memory():
# Try to accurately figure out the memory usage if we are in a docker
# container.
docker_usage = None
# For cgroups v1:
memory_usage_filename = "/sys/fs/cgroup/memory/memory.stat"
# For cgroups v2:
memory_usage_filename_v2 = "/sys/fs/cgroup/memory.current"
if os.path.exists(memory_usage_filename):
docker_usage = get_cgroupv1_used_memory(memory_usage_filename)
elif os.path.exists(memory_usage_filename_v2):
with open(memory_usage_filename_v2, "r") as f:
docker_usage = int(f.read())
if docker_usage is not None:
return docker_usage
return psutil.virtual_memory().used
| Return the currently used system memory in bytes
Returns:
The total amount of used memory
| 15 | 60 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_used_memory():
# Try to accurately figure out the memory usage if we are in a docker
# container.
docker_usage = None
# For cgroups v1:
memory_usage_filename = "/sys/fs/cgroup/memory/memory.stat"
# For cgroups v2:
memory_usage_filename_v2 = "/sys/fs/cgroup/memory.current"
if os.path.exists(memory_usage_filename):
docker_usage = get_cgroupv1_used_memory(memory_usage_filename)
elif os.path.exists(memory_usage_filename_v2):
with open(memory_usage_filename_v2, "r") as f:
docker_usage = int(f.read())
if docker_usage is not None:
return docker_usage
return psutil.virtual_memory().used
```
###Assistant : Return the currently used system memory in bytes
Returns:
The total amount of used memory
|
1,474 | def pack_x_y_sample_weight(x, y=None, sample_weight=None):
if y is None:
# For single x-input, we do no tuple wrapping since in this case
# there is no ambiguity. This also makes NumPy and Dataset
# consistent in that the user does not have to wrap their Dataset
# data in an unnecessary tuple
if not tf.nest.is_nested(x):
return x
else:
return (x,)
elif sample_weight is None:
return (x, y)
else:
return (x, y, sample_weight)
| Packs user-provided data into a tuple.
This is a convenience utility for packing data into the tuple formats
that `Model.fit` uses.
Standalone usage:
>>> x = tf.ones((10, 1))
>>> data = tf.keras.utils.pack_x_y_sample_weight(x)
>>> isinstance(data, tf.Tensor)
True
>>> y = tf.ones((10, 1))
>>> data = tf.keras.utils.pack_x_y_sample_weight(x, y)
>>> isinstance(data, tuple)
True
>>> x, y = data
Args:
x: Features to pass to `Model`.
y: Ground-truth targets to pass to `Model`.
sample_weight: Sample weight for each element.
Returns:
Tuple in the format used in `Model.fit`.
| 83 | 71 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def pack_x_y_sample_weight(x, y=None, sample_weight=None):
if y is None:
# For single x-input, we do no tuple wrapping since in this case
# there is no ambiguity. This also makes NumPy and Dataset
# consistent in that the user does not have to wrap their Dataset
# data in an unnecessary tuple
if not tf.nest.is_nested(x):
return x
else:
return (x,)
elif sample_weight is None:
return (x, y)
else:
return (x, y, sample_weight)
```
###Assistant : Packs user-provided data into a tuple.
This is a convenience utility for packing data into the tuple formats
that `Model.fit` uses.
Standalone usage:
>>> x = tf.ones((10, 1))
>>> data = tf.keras.utils.pack_x_y_sample_weight(x)
>>> isinstance(data, tf.Tensor)
True
>>> y = tf.ones((10, 1))
>>> data = tf.keras.utils.pack_x_y_sample_weight(x, y)
>>> isinstance(data, tuple)
True
>>> x, y = data
Args:
x: Features to pass to `Model`.
y: Ground-truth targets to pass to `Model`.
sample_weight: Sample weight for each element.
Returns:
Tuple in the format used in `Model.fit`.
|
1,475 | def test_glm_regression_vstacked_X(solver, fit_intercept, glm_dataset):
model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
n_samples, n_features = X.shape
params = dict(
alpha=alpha,
fit_intercept=fit_intercept,
# solver=solver, # only lbfgs available
tol=1e-12,
max_iter=1000,
)
model = clone(model).set_params(**params)
X = X[:, :-1] # remove intercept
X = np.concatenate((X, X), axis=0)
assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)
y = np.r_[y, y]
if fit_intercept:
coef = coef_with_intercept
intercept = coef[-1]
coef = coef[:-1]
else:
coef = coef_without_intercept
intercept = 0
model.fit(X, y)
rtol = 3e-5
assert model.intercept_ == pytest.approx(intercept, rel=rtol)
assert_allclose(model.coef_, coef, rtol=rtol)
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [True, False]) | Test that GLM converges for all solvers to correct solution on vstacked data.
We work with a simple constructed data set with known solution.
Fit on [X] with alpha is the same as fit on [X], [y]
[X], [y] with 1 * alpha.
It is the same alpha as the average loss stays the same.
For wide X, [X', X'] is a singular matrix.
| 64 | 91 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_glm_regression_vstacked_X(solver, fit_intercept, glm_dataset):
model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
n_samples, n_features = X.shape
params = dict(
alpha=alpha,
fit_intercept=fit_intercept,
# solver=solver, # only lbfgs available
tol=1e-12,
max_iter=1000,
)
model = clone(model).set_params(**params)
X = X[:, :-1] # remove intercept
X = np.concatenate((X, X), axis=0)
assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)
y = np.r_[y, y]
if fit_intercept:
coef = coef_with_intercept
intercept = coef[-1]
coef = coef[:-1]
else:
coef = coef_without_intercept
intercept = 0
model.fit(X, y)
rtol = 3e-5
assert model.intercept_ == pytest.approx(intercept, rel=rtol)
assert_allclose(model.coef_, coef, rtol=rtol)
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [True, False])
```
###Assistant : Test that GLM converges for all solvers to correct solution on vstacked data.
We work with a simple constructed data set with known solution.
Fit on [X] with alpha is the same as fit on [X], [y]
[X], [y] with 1 * alpha.
It is the same alpha as the average loss stays the same.
For wide X, [X', X'] is a singular matrix.
|
1,476 | def deprecate_data():
sympy_deprecation_warning(
,
deprecated_since_version="1.4",
active_deprecations_target="deprecated-tensorindextype-attrs",
stacklevel=4,
)
|
The data attribute of TensorIndexType is deprecated. Use The
replace_with_arrays() method instead.
| 12 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def deprecate_data():
sympy_deprecation_warning(
,
deprecated_since_version="1.4",
active_deprecations_target="deprecated-tensorindextype-attrs",
stacklevel=4,
)
```
###Assistant :
The data attribute of TensorIndexType is deprecated. Use The
replace_with_arrays() method instead.
|
1,477 | def fit(self, X, y=None):
self._validate_params()
X = self._check_input(X, reset=True)
if self.check_inverse and not (self.func is None or self.inverse_func is None):
self._check_inverse_transform(X)
return self
| Fit transformer by checking X.
If ``validate`` is ``True``, ``X`` will be checked.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Input array.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self : object
FunctionTransformer class instance.
| 43 | 23 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def fit(self, X, y=None):
self._validate_params()
X = self._check_input(X, reset=True)
if self.check_inverse and not (self.func is None or self.inverse_func is None):
self._check_inverse_transform(X)
return self
```
###Assistant : Fit transformer by checking X.
If ``validate`` is ``True``, ``X`` will be checked.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Input array.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self : object
FunctionTransformer class instance.
|
1,478 | def styleof(expr, styles=default_styles):
style = {}
for typ, sty in styles:
if isinstance(expr, typ):
style.update(sty)
return style
| Merge style dictionaries in order
Examples
========
>>> from sympy import Symbol, Basic, Expr, S
>>> from sympy.printing.dot import styleof
>>> styles = [(Basic, {'color': 'blue', 'shape': 'ellipse'}),
... (Expr, {'color': 'black'})]
>>> styleof(Basic(S(1)), styles)
{'color': 'blue', 'shape': 'ellipse'}
>>> x = Symbol('x')
>>> styleof(x + 1, styles) # this is an Expr
{'color': 'black', 'shape': 'ellipse'}
| 57 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def styleof(expr, styles=default_styles):
style = {}
for typ, sty in styles:
if isinstance(expr, typ):
style.update(sty)
return style
```
###Assistant : Merge style dictionaries in order
Examples
========
>>> from sympy import Symbol, Basic, Expr, S
>>> from sympy.printing.dot import styleof
>>> styles = [(Basic, {'color': 'blue', 'shape': 'ellipse'}),
... (Expr, {'color': 'black'})]
>>> styleof(Basic(S(1)), styles)
{'color': 'blue', 'shape': 'ellipse'}
>>> x = Symbol('x')
>>> styleof(x + 1, styles) # this is an Expr
{'color': 'black', 'shape': 'ellipse'}
|
1,479 | def execute(filters=None):
columns = [
{"fieldname": "creation_date", "label": _("Date"), "fieldtype": "Date", "width": 300},
{
"fieldname": "first_response_time",
"fieldtype": "Duration",
"label": _("First Response Time"),
"width": 300,
},
]
data = frappe.db.sql(
,
(filters.from_date, filters.to_date),
)
return columns, data
|
SELECT
date(creation) as creation_date,
avg(first_response_time) as avg_response_time
FROM tabIssue
WHERE
date(creation) between %s and %s
and first_response_time > 0
GROUP BY creation_date
ORDER BY creation_date desc
| 26 | 36 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def execute(filters=None):
columns = [
{"fieldname": "creation_date", "label": _("Date"), "fieldtype": "Date", "width": 300},
{
"fieldname": "first_response_time",
"fieldtype": "Duration",
"label": _("First Response Time"),
"width": 300,
},
]
data = frappe.db.sql(
,
(filters.from_date, filters.to_date),
)
return columns, data
```
###Assistant :
SELECT
date(creation) as creation_date,
avg(first_response_time) as avg_response_time
FROM tabIssue
WHERE
date(creation) between %s and %s
and first_response_time > 0
GROUP BY creation_date
ORDER BY creation_date desc
|
1,480 | def choose_parent(self, new_node, near_inds):
if not near_inds:
return None
# search nearest cost in near_inds
costs = []
for i in near_inds:
near_node = self.node_list[i]
t_node = self.steer(near_node, new_node)
if t_node and self.check_collision(
t_node, self.obstacle_list, self.robot_radius):
costs.append(self.calc_new_cost(near_node, new_node))
else:
costs.append(float("inf")) # the cost of collision node
min_cost = min(costs)
if min_cost == float("inf"):
print("There is no good path.(min_cost is inf)")
return None
min_ind = near_inds[costs.index(min_cost)]
new_node = self.steer(self.node_list[min_ind], new_node)
new_node.cost = min_cost
return new_node
|
Computes the cheapest point to new_node contained in the list
near_inds and set such a node as the parent of new_node.
Arguments:
--------
new_node, Node
randomly generated node with a path from its neared point
There are not coalitions between this node and th tree.
near_inds: list
Indices of indices of the nodes what are near to new_node
Returns.
------
Node, a copy of new_node
| 65 | 74 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def choose_parent(self, new_node, near_inds):
if not near_inds:
return None
# search nearest cost in near_inds
costs = []
for i in near_inds:
near_node = self.node_list[i]
t_node = self.steer(near_node, new_node)
if t_node and self.check_collision(
t_node, self.obstacle_list, self.robot_radius):
costs.append(self.calc_new_cost(near_node, new_node))
else:
costs.append(float("inf")) # the cost of collision node
min_cost = min(costs)
if min_cost == float("inf"):
print("There is no good path.(min_cost is inf)")
return None
min_ind = near_inds[costs.index(min_cost)]
new_node = self.steer(self.node_list[min_ind], new_node)
new_node.cost = min_cost
return new_node
```
###Assistant :
Computes the cheapest point to new_node contained in the list
near_inds and set such a node as the parent of new_node.
Arguments:
--------
new_node, Node
randomly generated node with a path from its neared point
There are not coalitions between this node and th tree.
near_inds: list
Indices of indices of the nodes what are near to new_node
Returns.
------
Node, a copy of new_node
|
1,481 | def test_a3c_compilation(self):
config = a3c.DEFAULT_CONFIG.copy()
config["num_workers"] = 2
config["num_envs_per_worker"] = 2
num_iterations = 1
# Test against all frameworks.
for _ in framework_iterator(config, with_eager_tracing=True):
for env in ["CartPole-v1", "Pendulum-v1", "PongDeterministic-v0"]:
print("env={}".format(env))
config["model"]["use_lstm"] = env == "CartPole-v1"
trainer = a3c.A3CTrainer(config=config, env=env)
for i in range(num_iterations):
results = trainer.train()
check_train_results(results)
print(results)
check_compute_single_action(
trainer, include_state=config["model"]["use_lstm"]
)
trainer.stop()
| Test whether an A3CTrainer can be built with both frameworks. | 10 | 54 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_a3c_compilation(self):
config = a3c.DEFAULT_CONFIG.copy()
config["num_workers"] = 2
config["num_envs_per_worker"] = 2
num_iterations = 1
# Test against all frameworks.
for _ in framework_iterator(config, with_eager_tracing=True):
for env in ["CartPole-v1", "Pendulum-v1", "PongDeterministic-v0"]:
print("env={}".format(env))
config["model"]["use_lstm"] = env == "CartPole-v1"
trainer = a3c.A3CTrainer(config=config, env=env)
for i in range(num_iterations):
results = trainer.train()
check_train_results(results)
print(results)
check_compute_single_action(
trainer, include_state=config["model"]["use_lstm"]
)
trainer.stop()
```
###Assistant : Test whether an A3CTrainer can be built with both frameworks.
|
1,482 | def extract(self, member, path="", set_attrs=True):
self._check("r")
if isinstance(member, str):
tarinfo = self.getmember(member)
else:
tarinfo = member
# Prepare the link target for makelink().
if tarinfo.islnk():
tarinfo._link_target = os.path.join(path, tarinfo.linkname)
try:
self._extract_member(tarinfo, os.path.join(path, tarinfo.name),
set_attrs=set_attrs)
except EnvironmentError as e:
if self.errorlevel > 0:
raise
else:
if e.filename is None:
self._dbg(1, "tarfile: %s" % e.strerror)
else:
self._dbg(1, "tarfile: %s %r" % (e.strerror, e.filename))
except ExtractError as e:
if self.errorlevel > 1:
raise
else:
self._dbg(1, "tarfile: %s" % e)
| Extract a member from the archive to the current working directory,
using its full name. Its file information is extracted as accurately
as possible. `member' may be a filename or a TarInfo object. You can
specify a different directory using `path'. File attributes (owner,
mtime, mode) are set unless `set_attrs' is False.
| 52 | 76 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def extract(self, member, path="", set_attrs=True):
self._check("r")
if isinstance(member, str):
tarinfo = self.getmember(member)
else:
tarinfo = member
# Prepare the link target for makelink().
if tarinfo.islnk():
tarinfo._link_target = os.path.join(path, tarinfo.linkname)
try:
self._extract_member(tarinfo, os.path.join(path, tarinfo.name),
set_attrs=set_attrs)
except EnvironmentError as e:
if self.errorlevel > 0:
raise
else:
if e.filename is None:
self._dbg(1, "tarfile: %s" % e.strerror)
else:
self._dbg(1, "tarfile: %s %r" % (e.strerror, e.filename))
except ExtractError as e:
if self.errorlevel > 1:
raise
else:
self._dbg(1, "tarfile: %s" % e)
```
###Assistant : Extract a member from the archive to the current working directory,
using its full name. Its file information is extracted as accurately
as possible. `member' may be a filename or a TarInfo object. You can
specify a different directory using `path'. File attributes (owner,
mtime, mode) are set unless `set_attrs' is False.
|
1,483 | def test_artist_from_string():
artist = Artist.from_search_term("artist:gorillaz")
assert artist.name == "Gorillaz"
assert artist.url == "http://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQ"
assert len(artist.urls) > 1
|
Test if Artist class can be initialized from string.
| 9 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_artist_from_string():
artist = Artist.from_search_term("artist:gorillaz")
assert artist.name == "Gorillaz"
assert artist.url == "http://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQ"
assert len(artist.urls) > 1
```
###Assistant :
Test if Artist class can be initialized from string.
|
1,484 | def reset(self):
# custom_info is used for episodic reports and tensorboard logging
self.custom_info["Invalid"] = 0
self.custom_info["Hold"] = 0
self.custom_info["Unknown"] = 0
self.custom_info["pnl_factor"] = 0
self.custom_info["duration_factor"] = 0
self.custom_info["reward_exit"] = 0
self.custom_info["reward_hold"] = 0
for action in self.actions:
self.custom_info[f"{action.name}"] = 0
self._done = False
if self.starting_point is True:
if self.rl_config.get('randomize_starting_position', False):
length_of_data = int(self._end_tick / 4)
start_tick = random.randint(self.window_size + 1, length_of_data)
self._start_tick = start_tick
self._position_history = (self._start_tick * [None]) + [self._position]
else:
self._position_history = (self.window_size * [None]) + [self._position]
self._current_tick = self._start_tick
self._last_trade_tick = None
self._position = Positions.Neutral
self.total_reward = 0.
self._total_profit = 1. # unit
self.history = {}
self.trade_history = []
self.portfolio_log_returns = np.zeros(len(self.prices))
self._profits = [(self._start_tick, 1)]
self.close_trade_profit = []
self._total_unrealized_profit = 1
return self._get_observation()
|
Reset is called at the beginning of every episode
| 9 | 117 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def reset(self):
# custom_info is used for episodic reports and tensorboard logging
self.custom_info["Invalid"] = 0
self.custom_info["Hold"] = 0
self.custom_info["Unknown"] = 0
self.custom_info["pnl_factor"] = 0
self.custom_info["duration_factor"] = 0
self.custom_info["reward_exit"] = 0
self.custom_info["reward_hold"] = 0
for action in self.actions:
self.custom_info[f"{action.name}"] = 0
self._done = False
if self.starting_point is True:
if self.rl_config.get('randomize_starting_position', False):
length_of_data = int(self._end_tick / 4)
start_tick = random.randint(self.window_size + 1, length_of_data)
self._start_tick = start_tick
self._position_history = (self._start_tick * [None]) + [self._position]
else:
self._position_history = (self.window_size * [None]) + [self._position]
self._current_tick = self._start_tick
self._last_trade_tick = None
self._position = Positions.Neutral
self.total_reward = 0.
self._total_profit = 1. # unit
self.history = {}
self.trade_history = []
self.portfolio_log_returns = np.zeros(len(self.prices))
self._profits = [(self._start_tick, 1)]
self.close_trade_profit = []
self._total_unrealized_profit = 1
return self._get_observation()
```
###Assistant :
Reset is called at the beginning of every episode
|
1,485 | def get_ordered_to_be_billed_data(args):
doctype, party = args.get("doctype"), args.get("party")
child_tab = doctype + " Item"
precision = (
get_field_precision(
frappe.get_meta(child_tab).get_field("billed_amt"), currency=get_default_currency()
)
or 2
)
project_field = get_project_field(doctype, party)
return frappe.db.sql(
.format(
parent_tab="tab" + doctype,
child_tab="tab" + child_tab,
precision=precision,
party=party,
date_field=args.get("date"),
project_field=project_field,
order=args.get("order"),
order_by=args.get("order_by"),
)
)
|
Select
`{parent_tab}`.name, `{parent_tab}`.{date_field},
`{parent_tab}`.{party}, `{parent_tab}`.{party}_name,
`{child_tab}`.item_code,
`{child_tab}`.base_amount,
(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)),
(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0)),
(`{child_tab}`.base_amount -
(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)) -
(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))),
`{child_tab}`.item_name, `{child_tab}`.description,
{project_field}, `{parent_tab}`.company
from
`{parent_tab}`, `{child_tab}`
where
`{parent_tab}`.name = `{child_tab}`.parent and `{parent_tab}`.docstatus = 1
and `{parent_tab}`.status not in ('Closed', 'Completed')
and `{child_tab}`.amount > 0
and (`{child_tab}`.base_amount -
round(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1), {precision}) -
(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))) > 0
order by
`{parent_tab}`.{order} {order_by}
| 70 | 44 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_ordered_to_be_billed_data(args):
doctype, party = args.get("doctype"), args.get("party")
child_tab = doctype + " Item"
precision = (
get_field_precision(
frappe.get_meta(child_tab).get_field("billed_amt"), currency=get_default_currency()
)
or 2
)
project_field = get_project_field(doctype, party)
return frappe.db.sql(
.format(
parent_tab="tab" + doctype,
child_tab="tab" + child_tab,
precision=precision,
party=party,
date_field=args.get("date"),
project_field=project_field,
order=args.get("order"),
order_by=args.get("order_by"),
)
)
```
###Assistant :
Select
`{parent_tab}`.name, `{parent_tab}`.{date_field},
`{parent_tab}`.{party}, `{parent_tab}`.{party}_name,
`{child_tab}`.item_code,
`{child_tab}`.base_amount,
(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)),
(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0)),
(`{child_tab}`.base_amount -
(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1)) -
(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))),
`{child_tab}`.item_name, `{child_tab}`.description,
{project_field}, `{parent_tab}`.company
from
`{parent_tab}`, `{child_tab}`
where
`{parent_tab}`.name = `{child_tab}`.parent and `{parent_tab}`.docstatus = 1
and `{parent_tab}`.status not in ('Closed', 'Completed')
and `{child_tab}`.amount > 0
and (`{child_tab}`.base_amount -
round(`{child_tab}`.billed_amt * ifnull(`{parent_tab}`.conversion_rate, 1), {precision}) -
(`{child_tab}`.base_rate * ifnull(`{child_tab}`.returned_qty, 0))) > 0
order by
`{parent_tab}`.{order} {order_by}
|
1,486 | def get_staged_trial(self):
# TODO(xwjiang): This method should consider `self._cached_actor_pg`.
for trial in self._staged_trials:
if self._pg_manager.has_ready(trial):
return trial
return None
| Get a trial whose placement group was successfully staged.
Can also return None if no trial is available.
Returns:
Trial object or None.
| 23 | 19 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_staged_trial(self):
# TODO(xwjiang): This method should consider `self._cached_actor_pg`.
for trial in self._staged_trials:
if self._pg_manager.has_ready(trial):
return trial
return None
```
###Assistant : Get a trial whose placement group was successfully staged.
Can also return None if no trial is available.
Returns:
Trial object or None.
|
1,487 | def register(cls, func, squeeze_self=False, **kwargs):
return super().register(
Resampler.build_resample(func, squeeze_self),
fn_name=func.__name__,
**kwargs
)
|
Build function that do fallback to pandas and aggregate resampled data.
Parameters
----------
func : callable
Aggregation function to execute under resampled frame.
squeeze_self : bool, default: False
Whether or not to squeeze frame before resampling.
**kwargs : kwargs
Additional arguments that will be passed to function builder.
Returns
-------
callable
Function that takes query compiler and does fallback to pandas to resample
time-series data and apply aggregation on it.
| 70 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def register(cls, func, squeeze_self=False, **kwargs):
return super().register(
Resampler.build_resample(func, squeeze_self),
fn_name=func.__name__,
**kwargs
)
```
###Assistant :
Build function that do fallback to pandas and aggregate resampled data.
Parameters
----------
func : callable
Aggregation function to execute under resampled frame.
squeeze_self : bool, default: False
Whether or not to squeeze frame before resampling.
**kwargs : kwargs
Additional arguments that will be passed to function builder.
Returns
-------
callable
Function that takes query compiler and does fallback to pandas to resample
time-series data and apply aggregation on it.
|
1,488 | def aggregate(self, *args, **kwargs):
if self.query.distinct_fields:
raise NotImplementedError("aggregate() + distinct(fields) not implemented.")
self._validate_values_are_expressions(
(*args, *kwargs.values()), method_name="aggregate"
)
for arg in args:
# The default_alias property raises TypeError if default_alias
# can't be set automatically or AttributeError if it isn't an
# attribute.
try:
arg.default_alias
except (AttributeError, TypeError):
raise TypeError("Complex aggregates require an alias")
kwargs[arg.default_alias] = arg
query = self.query.chain()
for (alias, aggregate_expr) in kwargs.items():
query.add_annotation(aggregate_expr, alias, is_summary=True)
annotation = query.annotations[alias]
if not annotation.contains_aggregate:
raise TypeError("%s is not an aggregate expression" % alias)
for expr in annotation.get_source_expressions():
if (
expr.contains_aggregate
and isinstance(expr, Ref)
and expr.refs in kwargs
):
name = expr.refs
raise exceptions.FieldError(
"Cannot compute %s('%s'): '%s' is an aggregate"
% (annotation.name, name, name)
)
return query.get_aggregation(self.db, kwargs)
|
Return a dictionary containing the calculations (aggregation)
over the current queryset.
If args is present the expression is passed as a kwarg using
the Aggregate object's default alias.
| 28 | 117 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def aggregate(self, *args, **kwargs):
if self.query.distinct_fields:
raise NotImplementedError("aggregate() + distinct(fields) not implemented.")
self._validate_values_are_expressions(
(*args, *kwargs.values()), method_name="aggregate"
)
for arg in args:
# The default_alias property raises TypeError if default_alias
# can't be set automatically or AttributeError if it isn't an
# attribute.
try:
arg.default_alias
except (AttributeError, TypeError):
raise TypeError("Complex aggregates require an alias")
kwargs[arg.default_alias] = arg
query = self.query.chain()
for (alias, aggregate_expr) in kwargs.items():
query.add_annotation(aggregate_expr, alias, is_summary=True)
annotation = query.annotations[alias]
if not annotation.contains_aggregate:
raise TypeError("%s is not an aggregate expression" % alias)
for expr in annotation.get_source_expressions():
if (
expr.contains_aggregate
and isinstance(expr, Ref)
and expr.refs in kwargs
):
name = expr.refs
raise exceptions.FieldError(
"Cannot compute %s('%s'): '%s' is an aggregate"
% (annotation.name, name, name)
)
return query.get_aggregation(self.db, kwargs)
```
###Assistant :
Return a dictionary containing the calculations (aggregation)
over the current queryset.
If args is present the expression is passed as a kwarg using
the Aggregate object's default alias.
|
1,489 | def _detect_bytelen_from_str(s):
# type: (str) -> int
assert len(s) >= 2
tmp_len = len(s)
i = 1
while orb(s[i]) & 0x80 > 0:
i += 1
assert i < tmp_len, 'EINVAL: s: out-of-bound read: unfinished AbstractUVarIntField detected' # noqa: E501
ret = i + 1
assert ret >= 0
return ret
| _detect_bytelen_from_str returns the length of the machine
representation of an AbstractUVarIntField starting at the beginning
of s and which is assumed to expand over multiple bytes
(value > _max_prefix_value).
:param str s: the string to parse. It is assumed that it is a multibyte int. # noqa: E501
:return: The bytelength of the AbstractUVarIntField.
:raises: AssertionError
| 56 | 51 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _detect_bytelen_from_str(s):
# type: (str) -> int
assert len(s) >= 2
tmp_len = len(s)
i = 1
while orb(s[i]) & 0x80 > 0:
i += 1
assert i < tmp_len, 'EINVAL: s: out-of-bound read: unfinished AbstractUVarIntField detected' # noqa: E501
ret = i + 1
assert ret >= 0
return ret
```
###Assistant : _detect_bytelen_from_str returns the length of the machine
representation of an AbstractUVarIntField starting at the beginning
of s and which is assumed to expand over multiple bytes
(value > _max_prefix_value).
:param str s: the string to parse. It is assumed that it is a multibyte int. # noqa: E501
:return: The bytelength of the AbstractUVarIntField.
:raises: AssertionError
|
1,490 | def get_gl_entries(voucher_type, voucher_no):
return frappe.db.sql(
,
(voucher_type, voucher_no),
as_dict=1,
)
| select account, debit, credit, cost_center, is_cancelled
from `tabGL Entry` where voucher_type=%s and voucher_no=%s
order by account desc | 17 | 10 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_gl_entries(voucher_type, voucher_no):
return frappe.db.sql(
,
(voucher_type, voucher_no),
as_dict=1,
)
```
###Assistant : select account, debit, credit, cost_center, is_cancelled
from `tabGL Entry` where voucher_type=%s and voucher_no=%s
order by account desc
|
1,491 | def random_normal(mean=0.0, std=1.0, shape=None, dev=None, f=None):
return _cur_framework(f=f).random_normal(mean, std, shape, dev)
|
Draws samples from a normal distribution.
:param mean: The mean of the normal distribution to sample from. Default is 0.
:type mean: float
:param std: The standard deviation of the normal distribution to sample from. Default is 1.
:type std: float
:param shape: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.
If size is None (default), a single value is returned.
:param dev: device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
:type dev: str
:param f: Machine learning framework. Inferred from inputs if None.
:type f: ml_framework, optional
:return: Drawn samples from the parameterized uniform distribution.
| 111 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def random_normal(mean=0.0, std=1.0, shape=None, dev=None, f=None):
return _cur_framework(f=f).random_normal(mean, std, shape, dev)
```
###Assistant :
Draws samples from a normal distribution.
:param mean: The mean of the normal distribution to sample from. Default is 0.
:type mean: float
:param std: The standard deviation of the normal distribution to sample from. Default is 1.
:type std: float
:param shape: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.
If size is None (default), a single value is returned.
:param dev: device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
:type dev: str
:param f: Machine learning framework. Inferred from inputs if None.
:type f: ml_framework, optional
:return: Drawn samples from the parameterized uniform distribution.
|
1,492 | def get_font_preamble(cls):
font_preamble, command = cls._get_font_preamble_and_command()
return font_preamble
|
Return a string containing font configuration for the tex preamble.
| 10 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_font_preamble(cls):
font_preamble, command = cls._get_font_preamble_and_command()
return font_preamble
```
###Assistant :
Return a string containing font configuration for the tex preamble.
|
1,493 | def do_test_explorations(config, dummy_obs, prev_a=None, expected_mean_action=None):
# Test all frameworks.
for _ in framework_iterator(config):
print(f"Algorithm={config.algo_class}")
# Test for both the default Agent's exploration AND the `Random`
# exploration class.
for exploration in [None, "Random"]:
local_config = config.copy()
if exploration == "Random":
local_config.exploration(exploration_config={"type": "Random"})
print("exploration={}".format(exploration or "default"))
algo = local_config.build()
# Make sure all actions drawn are the same, given same
# observations.
actions = []
for _ in range(25):
actions.append(
algo.compute_single_action(
observation=dummy_obs,
explore=False,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None,
)
)
check(actions[-1], actions[0])
# Make sure actions drawn are different
# (around some mean value), given constant observations.
actions = []
for _ in range(500):
actions.append(
algo.compute_single_action(
observation=dummy_obs,
explore=True,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None,
)
)
check(
np.mean(actions),
expected_mean_action if expected_mean_action is not None else 0.5,
atol=0.4,
)
# Check that the stddev is not 0.0 (values differ).
check(np.std(actions), 0.0, false=True)
| Calls an Agent's `compute_actions` with different `explore` options. | 8 | 147 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def do_test_explorations(config, dummy_obs, prev_a=None, expected_mean_action=None):
# Test all frameworks.
for _ in framework_iterator(config):
print(f"Algorithm={config.algo_class}")
# Test for both the default Agent's exploration AND the `Random`
# exploration class.
for exploration in [None, "Random"]:
local_config = config.copy()
if exploration == "Random":
local_config.exploration(exploration_config={"type": "Random"})
print("exploration={}".format(exploration or "default"))
algo = local_config.build()
# Make sure all actions drawn are the same, given same
# observations.
actions = []
for _ in range(25):
actions.append(
algo.compute_single_action(
observation=dummy_obs,
explore=False,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None,
)
)
check(actions[-1], actions[0])
# Make sure actions drawn are different
# (around some mean value), given constant observations.
actions = []
for _ in range(500):
actions.append(
algo.compute_single_action(
observation=dummy_obs,
explore=True,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None,
)
)
check(
np.mean(actions),
expected_mean_action if expected_mean_action is not None else 0.5,
atol=0.4,
)
# Check that the stddev is not 0.0 (values differ).
check(np.std(actions), 0.0, false=True)
```
###Assistant : Calls an Agent's `compute_actions` with different `explore` options.
|
1,494 | def exit_with_success(message, **kwargs):
kwargs.setdefault("style", "green")
app.console.print(message, **kwargs)
raise typer.Exit(0)
|
Utility to print a stylized success message and exit with a zero code
| 13 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def exit_with_success(message, **kwargs):
kwargs.setdefault("style", "green")
app.console.print(message, **kwargs)
raise typer.Exit(0)
```
###Assistant :
Utility to print a stylized success message and exit with a zero code
|
1,495 | def get_keywords() -> Dict[str, str]:
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = "$Format:%d$"
git_full = "$Format:%H$"
git_date = "$Format:%ci$"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
| Get the keywords needed to look up the version information. | 10 | 61 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_keywords() -> Dict[str, str]:
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = "$Format:%d$"
git_full = "$Format:%H$"
git_date = "$Format:%ci$"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
```
###Assistant : Get the keywords needed to look up the version information.
|
1,496 | def take(outname, inname, chunks, index, itemsize, axis=0):
from .core import PerformanceWarning
plan = slicing_plan(chunks[axis], index)
if len(plan) >= len(chunks[axis]) * 10:
factor = math.ceil(len(plan) / len(chunks[axis]))
warnings.warn(
"Slicing with an out-of-order index is generating %d "
"times more chunks" % factor,
PerformanceWarning,
stacklevel=6,
)
if not is_arraylike(index):
index = np.asarray(index)
# Check for chunks from the plan that would violate the user's
# configured chunk size.
nbytes = utils.parse_bytes(config.get("array.chunk-size"))
other_chunks = [chunks[i] for i in range(len(chunks)) if i != axis]
other_numel = np.prod([sum(x) for x in other_chunks])
if math.isnan(other_numel):
warnsize = maxsize = math.inf
else:
maxsize = math.ceil(nbytes / (other_numel * itemsize))
warnsize = maxsize * 5
split = config.get("array.slicing.split-large-chunks", None)
# Warn only when the default is not specified.
warned = split is not None
for _, index_list in plan:
if not warned and len(index_list) > warnsize:
msg = (
"Slicing is producing a large chunk. To accept the large\n"
"chunk and silence this warning, set the option\n"
" >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n"
" ... array[indexer]\n\n"
"To avoid creating the large chunks, set the option\n"
" >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n"
" ... array[indexer]"
)
warnings.warn(msg, PerformanceWarning, stacklevel=6)
warned = True
where_index = []
index_lists = []
for where_idx, index_list in plan:
index_length = len(index_list)
if split and index_length > maxsize:
index_sublist = np.array_split(
index_list, math.ceil(index_length / maxsize)
)
index_lists.extend(index_sublist)
where_index.extend([where_idx] * len(index_sublist))
else:
if not is_arraylike(index_list):
index_list = np.array(index_list)
index_lists.append(index_list)
where_index.append(where_idx)
dims = [range(len(bd)) for bd in chunks]
indims = list(dims)
indims[axis] = list(range(len(where_index)))
keys = list(product([outname], *indims))
outdims = list(dims)
outdims[axis] = where_index
slices = [[colon] * len(bd) for bd in chunks]
slices[axis] = index_lists
slices = list(product(*slices))
inkeys = list(product([inname], *outdims))
values = [(getitem, inkey, slc) for inkey, slc in zip(inkeys, slices)]
chunks2 = list(chunks)
chunks2[axis] = tuple(map(len, index_lists))
dsk = dict(zip(keys, values))
return tuple(chunks2), dsk
| Index array with an iterable of index
Handles a single index by a single list
Mimics ``np.take``
>>> from pprint import pprint
>>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [5, 1, 47, 3], 8, axis=0)
>>> chunks
((2, 1, 1),)
>>> pprint(dsk) # doctest: +ELLIPSIS
{('y', 0): (<function getitem at ...>, ('x', 0), (array([5, 1]),)),
('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),)),
('y', 2): (<function getitem at ...>, ('x', 0), (array([3]),))}
When list is sorted we retain original block structure
>>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [1, 3, 5, 47], 8, axis=0)
>>> chunks
((3, 1),)
>>> pprint(dsk) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
{('y', 0): (<function getitem at ...>,
('x', 0),
(array([1, 3, 5]),)),
('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),))}
When any indexed blocks would otherwise grow larger than
dask.config.array.chunk-size, we might split them,
depending on the value of ``dask.config.slicing.split-large-chunks``.
>>> import dask
>>> with dask.config.set({"array.slicing.split-large-chunks": True}):
... chunks, dsk = take('y', 'x', [(1, 1, 1), (1000, 1000), (1000, 1000)],
... [0] + [1] * 6 + [2], axis=0, itemsize=8)
>>> chunks
((1, 3, 3, 1), (1000, 1000), (1000, 1000))
| 191 | 299 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def take(outname, inname, chunks, index, itemsize, axis=0):
from .core import PerformanceWarning
plan = slicing_plan(chunks[axis], index)
if len(plan) >= len(chunks[axis]) * 10:
factor = math.ceil(len(plan) / len(chunks[axis]))
warnings.warn(
"Slicing with an out-of-order index is generating %d "
"times more chunks" % factor,
PerformanceWarning,
stacklevel=6,
)
if not is_arraylike(index):
index = np.asarray(index)
# Check for chunks from the plan that would violate the user's
# configured chunk size.
nbytes = utils.parse_bytes(config.get("array.chunk-size"))
other_chunks = [chunks[i] for i in range(len(chunks)) if i != axis]
other_numel = np.prod([sum(x) for x in other_chunks])
if math.isnan(other_numel):
warnsize = maxsize = math.inf
else:
maxsize = math.ceil(nbytes / (other_numel * itemsize))
warnsize = maxsize * 5
split = config.get("array.slicing.split-large-chunks", None)
# Warn only when the default is not specified.
warned = split is not None
for _, index_list in plan:
if not warned and len(index_list) > warnsize:
msg = (
"Slicing is producing a large chunk. To accept the large\n"
"chunk and silence this warning, set the option\n"
" >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n"
" ... array[indexer]\n\n"
"To avoid creating the large chunks, set the option\n"
" >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n"
" ... array[indexer]"
)
warnings.warn(msg, PerformanceWarning, stacklevel=6)
warned = True
where_index = []
index_lists = []
for where_idx, index_list in plan:
index_length = len(index_list)
if split and index_length > maxsize:
index_sublist = np.array_split(
index_list, math.ceil(index_length / maxsize)
)
index_lists.extend(index_sublist)
where_index.extend([where_idx] * len(index_sublist))
else:
if not is_arraylike(index_list):
index_list = np.array(index_list)
index_lists.append(index_list)
where_index.append(where_idx)
dims = [range(len(bd)) for bd in chunks]
indims = list(dims)
indims[axis] = list(range(len(where_index)))
keys = list(product([outname], *indims))
outdims = list(dims)
outdims[axis] = where_index
slices = [[colon] * len(bd) for bd in chunks]
slices[axis] = index_lists
slices = list(product(*slices))
inkeys = list(product([inname], *outdims))
values = [(getitem, inkey, slc) for inkey, slc in zip(inkeys, slices)]
chunks2 = list(chunks)
chunks2[axis] = tuple(map(len, index_lists))
dsk = dict(zip(keys, values))
return tuple(chunks2), dsk
```
###Assistant : Index array with an iterable of index
Handles a single index by a single list
Mimics ``np.take``
>>> from pprint import pprint
>>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [5, 1, 47, 3], 8, axis=0)
>>> chunks
((2, 1, 1),)
>>> pprint(dsk) # doctest: +ELLIPSIS
{('y', 0): (<function getitem at ...>, ('x', 0), (array([5, 1]),)),
('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),)),
('y', 2): (<function getitem at ...>, ('x', 0), (array([3]),))}
When list is sorted we retain original block structure
>>> chunks, dsk = take('y', 'x', [(20, 20, 20, 20)], [1, 3, 5, 47], 8, axis=0)
>>> chunks
((3, 1),)
>>> pprint(dsk) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
{('y', 0): (<function getitem at ...>,
('x', 0),
(array([1, 3, 5]),)),
('y', 1): (<function getitem at ...>, ('x', 2), (array([7]),))}
When any indexed blocks would otherwise grow larger than
dask.config.array.chunk-size, we might split them,
depending on the value of ``dask.config.slicing.split-large-chunks``.
>>> import dask
>>> with dask.config.set({"array.slicing.split-large-chunks": True}):
... chunks, dsk = take('y', 'x', [(1, 1, 1), (1000, 1000), (1000, 1000)],
... [0] + [1] * 6 + [2], axis=0, itemsize=8)
>>> chunks
((1, 3, 3, 1), (1000, 1000), (1000, 1000))
|
1,497 | def test_multidb(self):
ContentType.objects.clear_cache()
with self.assertNumQueries(0, using="default"), self.assertNumQueries(
1, using="other"
):
ContentType.objects.get_for_model(Author)
|
When using multiple databases, ContentType.objects.get_for_model() uses
db_for_read().
| 7 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_multidb(self):
ContentType.objects.clear_cache()
with self.assertNumQueries(0, using="default"), self.assertNumQueries(
1, using="other"
):
ContentType.objects.get_for_model(Author)
```
###Assistant :
When using multiple databases, ContentType.objects.get_for_model() uses
db_for_read().
|
1,498 | def get_engle_granger_two_step_cointegration_test(y, x):
warnings.simplefilter(action="ignore", category=FutureWarning)
long_run_ols = sm.OLS(y, sm.add_constant(x))
warnings.simplefilter(action="default", category=FutureWarning)
long_run_ols_fit = long_run_ols.fit()
c, gamma = long_run_ols_fit.params
z = long_run_ols_fit.resid
short_run_ols = sm.OLS(y.diff().iloc[1:], (z.shift().iloc[1:]))
short_run_ols_fit = short_run_ols.fit()
alpha = short_run_ols_fit.params[0]
# NOTE: The p-value returned by the adfuller function assumes we do not estimate z
# first, but test stationarity of an unestimated series directly. This assumption
# should have limited effect for high N, however. Critical values taking this into
# account more accurately are provided in e.g. McKinnon (1990) and Engle & Yoo (1987).
adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None)
return c, gamma, alpha, z, adfstat, pvalue
| Estimates long-run and short-run cointegration relationship for series y and x and apply
the two-step Engle & Granger test for cointegration.
Uses a 2-step process to first estimate coefficients for the long-run relationship
y_t = c + gamma * x_t + z_t
and then the short-term relationship,
y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t,
with z the found residuals of the first equation.
Then tests cointegration by Dickey-Fuller phi=1 vs phi < 1 in
z_t = phi * z_(t-1) + eta_t
If this implies phi < 1, the z series is stationary is concluded to be
stationary, and thus the series y and x are concluded to be cointegrated.
Parameters
----------
y : pd.Series
The first time series of the pair to analyse.
x : pd.Series
The second time series of the pair to analyse.
Returns
-------
c : float
The constant term in the long-run relationship y_t = c + gamma * x_t + z_t. This
describes the static shift of y with respect to gamma * x.
gamma : float
The gamma term in the long-run relationship y_t = c + gamma * x_t + z_t. This
describes the ratio between the const-shifted y and x.
alpha : float
The alpha term in the short-run relationship y_t - y_(t-1) = alpha * z_(t-1) + epsilon. This
gives an indication of the strength of the error correction toward the long-run mean.
z : pd.Series
Series of residuals z_t from the long-run relationship y_t = c + gamma * x_t + z_t, representing
the value of the error correction term.
dfstat : float
The Dickey Fuller test-statistic for phi = 1 vs phi < 1 in the second equation. A more
negative value implies the existence of stronger cointegration.
pvalue : float
The p-value corresponding to the Dickey Fuller test-statistic. A lower value implies
stronger rejection of no-cointegration, thus stronger evidence of cointegration.
| 315 | 103 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_engle_granger_two_step_cointegration_test(y, x):
warnings.simplefilter(action="ignore", category=FutureWarning)
long_run_ols = sm.OLS(y, sm.add_constant(x))
warnings.simplefilter(action="default", category=FutureWarning)
long_run_ols_fit = long_run_ols.fit()
c, gamma = long_run_ols_fit.params
z = long_run_ols_fit.resid
short_run_ols = sm.OLS(y.diff().iloc[1:], (z.shift().iloc[1:]))
short_run_ols_fit = short_run_ols.fit()
alpha = short_run_ols_fit.params[0]
# NOTE: The p-value returned by the adfuller function assumes we do not estimate z
# first, but test stationarity of an unestimated series directly. This assumption
# should have limited effect for high N, however. Critical values taking this into
# account more accurately are provided in e.g. McKinnon (1990) and Engle & Yoo (1987).
adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None)
return c, gamma, alpha, z, adfstat, pvalue
```
###Assistant : Estimates long-run and short-run cointegration relationship for series y and x and apply
the two-step Engle & Granger test for cointegration.
Uses a 2-step process to first estimate coefficients for the long-run relationship
y_t = c + gamma * x_t + z_t
and then the short-term relationship,
y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t,
with z the found residuals of the first equation.
Then tests cointegration by Dickey-Fuller phi=1 vs phi < 1 in
z_t = phi * z_(t-1) + eta_t
If this implies phi < 1, the z series is stationary is concluded to be
stationary, and thus the series y and x are concluded to be cointegrated.
Parameters
----------
y : pd.Series
The first time series of the pair to analyse.
x : pd.Series
The second time series of the pair to analyse.
Returns
-------
c : float
The constant term in the long-run relationship y_t = c + gamma * x_t + z_t. This
describes the static shift of y with respect to gamma * x.
gamma : float
The gamma term in the long-run relationship y_t = c + gamma * x_t + z_t. This
describes the ratio between the const-shifted y and x.
alpha : float
The alpha term in the short-run relationship y_t - y_(t-1) = alpha * z_(t-1) + epsilon. This
gives an indication of the strength of the error correction toward the long-run mean.
z : pd.Series
Series of residuals z_t from the long-run relationship y_t = c + gamma * x_t + z_t, representing
the value of the error correction term.
dfstat : float
The Dickey Fuller test-statistic for phi = 1 vs phi < 1 in the second equation. A more
negative value implies the existence of stronger cointegration.
pvalue : float
The p-value corresponding to the Dickey Fuller test-statistic. A lower value implies
stronger rejection of no-cointegration, thus stronger evidence of cointegration.
|
1,499 | def test_deterministic_order_for_unordered_model(self):
superuser = self._create_superuser("superuser")
for counter in range(1, 51):
UnorderedObject.objects.create(id=counter, bool=True)
|
The primary key is used in the ordering of the changelist's results to
guarantee a deterministic order, even when the model doesn't have any
default ordering defined (#17198).
| 28 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_deterministic_order_for_unordered_model(self):
superuser = self._create_superuser("superuser")
for counter in range(1, 51):
UnorderedObject.objects.create(id=counter, bool=True)
```
###Assistant :
The primary key is used in the ordering of the changelist's results to
guarantee a deterministic order, even when the model doesn't have any
default ordering defined (#17198).
|
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