Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
test__logger_connector__epoch_result_store__train | (tmpdir, monkeypatch) |
Tests that LoggerConnector will properly capture logged information
and reduce them
|
Tests that LoggerConnector will properly capture logged information
and reduce them
| def test__logger_connector__epoch_result_store__train(tmpdir, monkeypatch):
"""
Tests that LoggerConnector will properly capture logged information
and reduce them
"""
monkeypatch.setenv("PL_DEV_DEBUG", "1")
class TestModel(BoringModel):
train_losses = []
@decorator_with_argum... | [
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test__logger_connector__epoch_result_store__train__ttbt | (tmpdir) |
Tests that LoggerConnector will properly capture logged information with ttbt
and reduce them
|
Tests that LoggerConnector will properly capture logged information with ttbt
and reduce them
| def test__logger_connector__epoch_result_store__train__ttbt(tmpdir):
"""
Tests that LoggerConnector will properly capture logged information with ttbt
and reduce them
"""
truncated_bptt_steps = 2
sequence_size = 30
batch_size = 30
x_seq = torch.rand(batch_size, sequence_size, 1)
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test__logger_connector__epoch_result_store__test_multi_dataloaders | (tmpdir, monkeypatch, num_dataloaders) |
Tests that LoggerConnector will properly capture logged information in multi dataloaders scenario
|
Tests that LoggerConnector will properly capture logged information in multi dataloaders scenario
| def test__logger_connector__epoch_result_store__test_multi_dataloaders(tmpdir, monkeypatch, num_dataloaders):
"""
Tests that LoggerConnector will properly capture logged information in multi dataloaders scenario
"""
monkeypatch.setenv("PL_DEV_DEBUG", "1")
class TestModel(BoringModel):
test_... | [
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test_logging_to_progress_bar_with_reserved_key | (tmpdir) | Test that logging a metric with a reserved name to the progress bar raises a warning. | Test that logging a metric with a reserved name to the progress bar raises a warning. | def test_logging_to_progress_bar_with_reserved_key(tmpdir):
""" Test that logging a metric with a reserved name to the progress bar raises a warning. """
class TestModel(BoringModel):
def training_step(self, *args, **kwargs):
output = super().training_step(*args, **kwargs)
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471,
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CometLogger.experiment | (self) | r"""
Actual Comet object. To use Comet features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_comet_function()
| r"""
Actual Comet object. To use Comet features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following. | def experiment(self):
r"""
Actual Comet object. To use Comet features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_comet_function()
"""
if self._experiment is not None:
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226,
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CometLogger.finalize | (self, status: str) | r"""
When calling ``self.experiment.end()``, that experiment won't log any more data to Comet.
That's why, if you need to log any more data, you need to create an ExistingCometExperiment.
For example, to log data when testing your model after training, because when training is
finalized ... | r"""
When calling ``self.experiment.end()``, that experiment won't log any more data to Comet.
That's why, if you need to log any more data, you need to create an ExistingCometExperiment.
For example, to log data when testing your model after training, because when training is
finalized ... | def finalize(self, status: str) -> None:
r"""
When calling ``self.experiment.end()``, that experiment won't log any more data to Comet.
That's why, if you need to log any more data, you need to create an ExistingCometExperiment.
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rename_for_jekyll | (nb_path: Path, warnings: Set[Tuple[str, str]]=None) |
Return a Path's filename string appended with its modified time in YYYY-MM-DD format.
|
Return a Path's filename string appended with its modified time in YYYY-MM-DD format.
| def rename_for_jekyll(nb_path: Path, warnings: Set[Tuple[str, str]]=None) -> str:
"""
Return a Path's filename string appended with its modified time in YYYY-MM-DD format.
"""
assert nb_path.exists(), f'{nb_path} could not be found.'
# Checks if filename is compliant with Jekyll blog posts
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TestSetOps.test_in1d_invert | (self) | Test in1d's invert parameter | Test in1d's invert parameter | def test_in1d_invert(self):
"Test in1d's invert parameter"
# We use two different sizes for the b array here to test the
# two different paths in in1d().
for mult in (1, 10):
a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
b = [2, 3, 4] * mult
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create_button | (p1) |
for creating a new database
|
for creating a new database
| def create_button(p1):
"""
for creating a new database
"""
global cursor
sql_command = """
CREATE TABLE notes (
id INTEGER PRIMARY KEY,
title TEXT,
note TEXT);"""
try:
cursor.execute(sql_command)
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"errorOu... | [
52,
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] | [
68,
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] | python | en | ['en', 'error', 'th'] | False |
clear_button | (p1) |
This function is for the clear button.
This will clear the notice-input field
|
This function is for the clear button.
This will clear the notice-input field
| def clear_button(p1):
"""
This function is for the clear button.
This will clear the notice-input field
"""
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exit_button | (p1) |
function for the exit button.
this will exit the application.
|
function for the exit button.
this will exit the application.
| def exit_button(p1):
"""
function for the exit button.
this will exit the application.
"""
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NativeMixedPrecisionPlugin.backward | (
self,
model: LightningModule,
closure_loss: torch.Tensor,
optimizer: Optimizer,
opt_idx: int,
should_accumulate: bool,
*args,
**kwargs,
) | performs the actual backpropagation
Args:
model: the model to be optimized
closure_loss: the loss value obtained from the closure
optimizer: the optimizer to perform the step lateron
opt_idx: the optimizer's index
should_accumulate: whether to accumul... | performs the actual backpropagation | def backward(
self,
model: LightningModule,
closure_loss: torch.Tensor,
optimizer: Optimizer,
opt_idx: int,
should_accumulate: bool,
*args,
**kwargs,
) -> torch.Tensor:
"""performs the actual backpropagation
Args:
model: th... | [
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NativeMixedPrecisionPlugin.pre_optimizer_step | (
self, pl_module: LightningModule, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs
) | always called before the optimizer step.
Checks that the optimizer is not LBFGS, as this one is not supported by native amp
| always called before the optimizer step.
Checks that the optimizer is not LBFGS, as this one is not supported by native amp
| def pre_optimizer_step(
self, pl_module: LightningModule, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs
) -> bool:
"""always called before the optimizer step.
Checks that the optimizer is not LBFGS, as this one is not supported by native amp
"""
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NativeMixedPrecisionPlugin.post_optimizer_step | (self, optimizer: Optimizer, optimizer_idx: int) | Updates the GradScaler | Updates the GradScaler | def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:
"""Updates the GradScaler"""
self.scaler.step(optimizer)
self.scaler.update() | [
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NativeMixedPrecisionPlugin.train_step_context | (self) | Enable autocast context | Enable autocast context | def train_step_context(self) -> Generator[autocast, None, None]:
"""Enable autocast context"""
with torch.cuda.amp.autocast():
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do_search | () | Extract the posted data; perform the search; return results. | Extract the posted data; perform the search; return results. | def do_search() -> 'html':
"""Extract the posted data; perform the search; return results."""
@copy_current_request_context
def log_request(req: 'flask_request', res: str) -> None:
"""Log details of the web request and the results."""
# raise Exception("Something awful just happened.")
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entry_page | () | Display this webapp's HTML form. | Display this webapp's HTML form. | def entry_page() -> 'html':
"""Display this webapp's HTML form."""
return render_template('entry.html',
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view_the_log | () | Display the contents of the log file as a HTML table. | Display the contents of the log file as a HTML table. | def view_the_log() -> 'html':
"""Display the contents of the log file as a HTML table."""
try:
with UseDatabase(app.config['dbconfig']) as cursor:
_SQL = """select phrase, letters, ip, browser_string, results
from log"""
cursor.execute(_SQL)
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loadFile | (fileName) |
loadFile: This function loads the file and reads its lines.
|
loadFile: This function loads the file and reads its lines.
| def loadFile(fileName):
"""
loadFile: This function loads the file and reads its lines.
"""
global lines
fo = open(fileName)
for line in fo:
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scanner | (string) |
scanner: This function builds the tokens by the content of the file.
The tokens will be saved in list 'tokens'
|
scanner: This function builds the tokens by the content of the file.
The tokens will be saved in list 'tokens'
| def scanner(string):
"""
scanner: This function builds the tokens by the content of the file.
The tokens will be saved in list 'tokens'
"""
global tokens
token = ""
state = 0 # init state
for ch in string:
if state == 0:
if ch == 'm': # catch mov-command
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scan | () |
scan: applys function scanner() to each line of the source code.
|
scan: applys function scanner() to each line of the source code.
| def scan():
"""
scan: applys function scanner() to each line of the source code.
"""
global lines
assert len(lines) > 0, "no lines"
for line in lines:
try:
scanner(line)
except InvalidSyntax:
print("line=", line) | [
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parser | () |
parser: parses the tokens of the list 'tokens'
|
parser: parses the tokens of the list 'tokens'
| def parser():
"""
parser: parses the tokens of the list 'tokens'
"""
global tokens
global eax, ebx, ecx, edx
assert len(tokens) > 0, "no tokens"
pointer = 0 # pointer for tokens
token = Token("", "")
tmpToken = Token("", "")
while pointer < len(tokens):
token = ... | [
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registerLabels | () |
This function search for labels / subprogram-labels and registers this in the 'jumps' list.
|
This function search for labels / subprogram-labels and registers this in the 'jumps' list.
| def registerLabels():
"""
This function search for labels / subprogram-labels and registers this in the 'jumps' list.
"""
for i in range(len(tokens)):
if (tokens[i].t == "label"):
jumps[tokens[i].token] = i
elif tokens[i].t == "subprogram":
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resetInterpreter | () |
resets the interpreter mind.
|
resets the interpreter mind.
| def resetInterpreter():
"""
resets the interpreter mind.
"""
global eax, ebx, ecx, edx, zeroFlag, stack
global variables, jumps, lines, tokens, returnStack
eax = 0
ebx = 0
ecx = 0
edx = 0
zeroFlag = False
stack = []
jumps = {}
variables = {}
lines = []
tok... | [
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main | () |
reads textfiles from the command-line and interprets them.
|
reads textfiles from the command-line and interprets them.
| def main():
"""
reads textfiles from the command-line and interprets them.
"""
# [1:] because the first argument is the program itself.
for arg in sys.argv[1:]:
resetInterpreter() # resets interpreter mind
try:
loadFile(arg)
scan()
regist... | [
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_can_target | (cmd, arch) | Return true if the architecture supports the -arch flag | Return true if the architecture supports the -arch flag | def _can_target(cmd, arch):
"""Return true if the architecture supports the -arch flag"""
newcmd = cmd[:]
fid, filename = tempfile.mkstemp(suffix=".f")
os.close(fid)
try:
d = os.path.dirname(filename)
output = os.path.splitext(filename)[0] + ".o"
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GnuFCompiler.gnu_version_match | (self, version_string) | Handle the different versions of GNU fortran compilers | Handle the different versions of GNU fortran compilers | def gnu_version_match(self, version_string):
"""Handle the different versions of GNU fortran compilers"""
# Strip warning(s) that may be emitted by gfortran
while version_string.startswith('gfortran: warning'):
version_string = version_string[version_string.find('\n') + 1:]
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GnuFCompiler._c_arch_flags | (self) | Return detected arch flags from CFLAGS | Return detected arch flags from CFLAGS | def _c_arch_flags(self):
""" Return detected arch flags from CFLAGS """
from distutils import sysconfig
try:
cflags = sysconfig.get_config_vars()['CFLAGS']
except KeyError:
return []
arch_re = re.compile(r"-arch\s+(\w+)")
arch_flags = []
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Gnu95FCompiler._universal_flags | (self, cmd) | Return a list of -arch flags for every supported architecture. | Return a list of -arch flags for every supported architecture. | def _universal_flags(self, cmd):
"""Return a list of -arch flags for every supported architecture."""
if not sys.platform == 'darwin':
return []
arch_flags = []
# get arches the C compiler gets.
c_archs = self._c_arch_flags()
if "i386" in c_archs:
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Gnu95FCompiler._link_wrapper_lib | (self, objects, output_dir, extra_dll_dir,
chained_dlls, is_archive) | Create a wrapper shared library for the given objects
Return an MSVC-compatible lib
| Create a wrapper shared library for the given objects | def _link_wrapper_lib(self, objects, output_dir, extra_dll_dir,
chained_dlls, is_archive):
"""Create a wrapper shared library for the given objects
Return an MSVC-compatible lib
"""
c_compiler = self.c_compiler
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Gnu95FCompiler.wrap_unlinkable_objects | (self, objects, output_dir, extra_dll_dir) |
Convert a set of object files that are not compatible with the default
linker, to a file that is compatible.
|
Convert a set of object files that are not compatible with the default
linker, to a file that is compatible.
| def wrap_unlinkable_objects(self, objects, output_dir, extra_dll_dir):
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Convert a set of object files that are not compatible with the default
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old_func4 | (self, x) | Summary.
Further info.
| Summary. | def old_func4(self, x):
"""Summary.
Further info.
"""
return x | [
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old_func5 | (self, x) | Summary.
Bizarre indentation.
| Summary. | def old_func5(self, x):
"""Summary.
Bizarre indentation.
"""
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old_func6 | (self, x) |
Also in PEP-257.
|
Also in PEP-257.
| def old_func6(self, x):
"""
Also in PEP-257.
"""
return x | [
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_deprecate_out_named_y | (f) |
Allow the out argument to be passed as the name `y` (deprecated)
In future, this decorator should be removed.
|
Allow the out argument to be passed as the name `y` (deprecated) | def _deprecate_out_named_y(f):
"""
Allow the out argument to be passed as the name `y` (deprecated)
In future, this decorator should be removed.
"""
@functools.wraps(f)
def func(x, out=None, **kwargs):
if 'y' in kwargs:
if 'out' in kwargs:
raise TypeError(
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_fix_out_named_y | (f) |
Allow the out argument to be passed as the name `y` (deprecated)
This decorator should only be used if _deprecate_out_named_y is used on
a corresponding dispatcher function.
|
Allow the out argument to be passed as the name `y` (deprecated) | def _fix_out_named_y(f):
"""
Allow the out argument to be passed as the name `y` (deprecated)
This decorator should only be used if _deprecate_out_named_y is used on
a corresponding dispatcher function.
"""
@functools.wraps(f)
def func(x, out=None, **kwargs):
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_fix_and_maybe_deprecate_out_named_y | (f) |
Use the appropriate decorator, depending upon if dispatching is being used.
|
Use the appropriate decorator, depending upon if dispatching is being used.
| def _fix_and_maybe_deprecate_out_named_y(f):
"""
Use the appropriate decorator, depending upon if dispatching is being used.
"""
if ARRAY_FUNCTION_ENABLED:
return _fix_out_named_y(f)
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fix | (x, out=None) |
Round to nearest integer towards zero.
Round an array of floats element-wise to nearest integer towards zero.
The rounded values are returned as floats.
Parameters
----------
x : array_like
An array of floats to be rounded
y : ndarray, optional
Output array
Returns
... |
Round to nearest integer towards zero. | def fix(x, out=None):
"""
Round to nearest integer towards zero.
Round an array of floats element-wise to nearest integer towards zero.
The rounded values are returned as floats.
Parameters
----------
x : array_like
An array of floats to be rounded
y : ndarray, optional
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isposinf | (x, out=None) |
Test element-wise for positive infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
y : array_like, optional
A boolean array with the same shape as `x` to store the result.
Returns
-------
out : ndarray
A boolean array wi... |
Test element-wise for positive infinity, return result as bool array. | def isposinf(x, out=None):
"""
Test element-wise for positive infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
y : array_like, optional
A boolean array with the same shape as `x` to store the result.
Returns
-------
out : ... | [
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isneginf | (x, out=None) |
Test element-wise for negative infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
out : array_like, optional
A boolean array with the same shape and type as `x` to store the
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Returns
-------
out : ndarray
... |
Test element-wise for negative infinity, return result as bool array. | def isneginf(x, out=None):
"""
Test element-wise for negative infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
out : array_like, optional
A boolean array with the same shape and type as `x` to store the
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Result.get_batch_log_metrics | (self, include_forked_originals=True, add_dataloader_idx=False) |
Gets the metrics to log at the end of the batch step
|
Gets the metrics to log at the end of the batch step | def get_batch_log_metrics(self, include_forked_originals=True, add_dataloader_idx=False) -> dict:
"""
Gets the metrics to log at the end of the batch step
"""
result = {}
meta = self['meta']
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Result.get_epoch_log_metrics | (self, add_dataloader_idx=False) |
Gets the metrics to log at the end of epoch
|
Gets the metrics to log at the end of epoch
| def get_epoch_log_metrics(self, add_dataloader_idx=False) -> dict:
"""
Gets the metrics to log at the end of epoch
"""
result = {}
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Result.get_epoch_pbar_metrics | (self, add_dataloader_idx=False) |
Gets the metrics to log at the end of epoch
|
Gets the metrics to log at the end of epoch
| def get_epoch_pbar_metrics(self, add_dataloader_idx=False):
"""
Gets the metrics to log at the end of epoch
"""
result = {}
meta = self['meta']
for k, options in meta.items():
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if options['forked']:
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Result.get_forked_metrics | (self, add_dataloader_idx=False) |
Gets the metrics to log at the end of epoch
|
Gets the metrics to log at the end of epoch
| def get_forked_metrics(self, add_dataloader_idx=False):
"""
Gets the metrics to log at the end of epoch
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result = {}
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Result.get_batch_pbar_metrics | (self, include_forked_originals=True, add_dataloader_idx=False) |
Gets the metrics to log at the end of the batch step
|
Gets the metrics to log at the end of the batch step
| def get_batch_pbar_metrics(self, include_forked_originals=True, add_dataloader_idx=False):
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Gets the metrics to log at the end of the batch step
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result = {}
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Result.to | (self, *args, **kwargs) | Move all self attributes to the given device. | Move all self attributes to the given device. | def to(self, *args, **kwargs):
"""Move all self attributes to the given device."""
for k, v in self.items():
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Result.cpu | (self) | Move all self attributes to CPU. | Move all self attributes to CPU. | def cpu(self):
"""Move all self attributes to CPU."""
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Result.unpack_batch_size | (sample) |
Recursively unpack sample to find a torch.Tensor.
returns len(tensor) when found, or 1 when it hits an empty or non iterable.
|
Recursively unpack sample to find a torch.Tensor.
returns len(tensor) when found, or 1 when it hits an empty or non iterable.
| def unpack_batch_size(sample):
"""
Recursively unpack sample to find a torch.Tensor.
returns len(tensor) when found, or 1 when it hits an empty or non iterable.
"""
if isinstance(sample, torch.Tensor):
size = sample.size(0)
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Result.rename_keys | (self, map_dict: dict) |
Maps key values to the target values. Useful when renaming variables in mass.
Args:
map_dict:
|
Maps key values to the target values. Useful when renaming variables in mass. | def rename_keys(self, map_dict: dict):
"""
Maps key values to the target values. Useful when renaming variables in mass.
Args:
map_dict:
"""
meta = self.meta
for source, dest in map_dict.items():
# map the main keys
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616,
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631,
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JiraRestClient.get_boards | (self, start_at=0, max_results=100, board_type=None, name=None, project_key_or_id=None) |
Returns all boards. This only includes boards that the user has permission to view.
:param start_at: The starting index of the returned boards. Base index: 0.
:param max_results: The maximum number of boards to return per page. Default: 100.
:param board_type: Filters results to boards ... |
Returns all boards. This only includes boards that the user has permission to view.
:param start_at: The starting index of the returned boards. Base index: 0.
:param max_results: The maximum number of boards to return per page. Default: 100.
:param board_type: Filters results to boards ... | def get_boards(self, start_at=0, max_results=100, board_type=None, name=None, project_key_or_id=None):
"""
Returns all boards. This only includes boards that the user has permission to view.
:param start_at: The starting index of the returned boards. Base index: 0.
:param max_results: Th... | [
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JiraRestClient.get_users | (self, username='.', start_at=0, max_results=1000, include_active=True, include_inactive=False) |
Returns a list of users that match the search string. This resource cannot be accessed anonymously.
:param username: A query string used to search username, name or e-mail address. "." - search for all users.
:param start_at: the index of the first user to return (0-based).
:param max_r... |
Returns a list of users that match the search string. This resource cannot be accessed anonymously.
:param username: A query string used to search username, name or e-mail address. "." - search for all users.
:param start_at: the index of the first user to return (0-based).
:param max_r... | def get_users(self, username='.', start_at=0, max_results=1000, include_active=True, include_inactive=False):
"""
Returns a list of users that match the search string. This resource cannot be accessed anonymously.
:param username: A query string used to search username, name or e-mail address. "... | [
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JiraRestClient.issues_search | (self, jql='order by key', start_at=0, max_results=1000, fields=None) |
Searches for issues using JQL.
:param jql: a JQL query string
:param start_at: the index of the first issue to return (0-based)
:param max_results: the maximum number of issues to return (defaults to 50).
:param fields: the list of fields to return for each issue. By default, al... |
Searches for issues using JQL.
:param jql: a JQL query string
:param start_at: the index of the first issue to return (0-based)
:param max_results: the maximum number of issues to return (defaults to 50).
:param fields: the list of fields to return for each issue. By default, al... | def issues_search(self, jql='order by key', start_at=0, max_results=1000, fields=None):
"""
Searches for issues using JQL.
:param jql: a JQL query string
:param start_at: the index of the first issue to return (0-based)
:param max_results: the maximum number of issues to return (... | [
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117,
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] | python | en | ['en', 'error', 'th'] | False |
JiraRestClient.issues_search_parallel | (self, jql='order by key', max_results=1000, fields=None) |
Parallel version
|
Parallel version
| def issues_search_parallel(self, jql='order by key', max_results=1000, fields=None):
"""
Parallel version
"""
from multiprocessing import cpu_count
from multiprocessing.pool import ThreadPool
api_url = f'{self.host}/rest/api/2/search'
if max_results % BATCH_SIZE... | [
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JiraRestClient.create_user | (self, display_name=None, email=None, name='', password='', application_keys=None) |
Creates a user. This resource is retained for legacy compatibility.
As soon as a more suitable alternative is available this resource will be deprecated.
:param name: The username for the user. This property is deprecated because of privacy changes.
:param password: A password for the u... |
Creates a user. This resource is retained for legacy compatibility.
As soon as a more suitable alternative is available this resource will be deprecated.
:param name: The username for the user. This property is deprecated because of privacy changes.
:param password: A password for the u... | def create_user(self, display_name=None, email=None, name='', password='', application_keys=None):
"""
Creates a user. This resource is retained for legacy compatibility.
As soon as a more suitable alternative is available this resource will be deprecated.
:param name: The username for t... | [
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JiraRestClient.get_all_projects | (self) |
:return: Returns the projects list of all project types - all categories
|
:return: Returns the projects list of all project types - all categories
| def get_all_projects(self):
"""
:return: Returns the projects list of all project types - all categories
"""
api_url = f'{self.host}/rest/api/2/project'
response = self.get(api_url, 'Could not get the list of projects')
return response.json() | [
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SuperTensorTestCase.testNorm | (self) | Test norm function | Test norm function | def testNorm(self):
"Test norm function"
print(self.typeStr, "... ", file=sys.stderr)
norm = SuperTensor.__dict__[self.typeStr + "Norm"]
supertensor = np.arange(2*2*2*2, dtype=self.typeCode).reshape((2, 2, 2, 2))
#Note: cludge to get an answer of the same type as supertensor.
... | [
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28,
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36,
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SuperTensorTestCase.testNormBadList | (self) | Test norm function with bad list | Test norm function with bad list | def testNormBadList(self):
"Test norm function with bad list"
print(self.typeStr, "... ", file=sys.stderr)
norm = SuperTensor.__dict__[self.typeStr + "Norm"]
supertensor = [[[[0, "one"], [2, 3]], [[3, "two"], [1, 0]]], [[[0, "one"], [2, 3]], [[3, "two"], [1, 0]]]]
self.assertRais... | [
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39,
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44,
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SuperTensorTestCase.testNormWrongDim | (self) | Test norm function with wrong dimensions | Test norm function with wrong dimensions | def testNormWrongDim(self):
"Test norm function with wrong dimensions"
print(self.typeStr, "... ", file=sys.stderr)
norm = SuperTensor.__dict__[self.typeStr + "Norm"]
supertensor = np.arange(2*2*2, dtype=self.typeCode).reshape((2, 2, 2))
self.assertRaises(TypeError, norm, superte... | [
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47,
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52,
55
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SuperTensorTestCase.testNormWrongSize | (self) | Test norm function with wrong size | Test norm function with wrong size | def testNormWrongSize(self):
"Test norm function with wrong size"
print(self.typeStr, "... ", file=sys.stderr)
norm = SuperTensor.__dict__[self.typeStr + "Norm"]
supertensor = np.arange(3*2*2, dtype=self.typeCode).reshape((3, 2, 2))
self.assertRaises(TypeError, norm, supertensor) | [
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60,
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SuperTensorTestCase.testNormNonContainer | (self) | Test norm function with non-container | Test norm function with non-container | def testNormNonContainer(self):
"Test norm function with non-container"
print(self.typeStr, "... ", file=sys.stderr)
norm = SuperTensor.__dict__[self.typeStr + "Norm"]
self.assertRaises(TypeError, norm, None) | [
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67,
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] | python | en | ['en', 'en', 'en'] | True |
SuperTensorTestCase.testMax | (self) | Test max function | Test max function | def testMax(self):
"Test max function"
print(self.typeStr, "... ", file=sys.stderr)
max = SuperTensor.__dict__[self.typeStr + "Max"]
supertensor = [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]
self.assertEqual(max(supertensor), 8) | [
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75,
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] | python | en | ['en', 'en', 'en'] | True |
SuperTensorTestCase.testMaxBadList | (self) | Test max function with bad list | Test max function with bad list | def testMaxBadList(self):
"Test max function with bad list"
print(self.typeStr, "... ", file=sys.stderr)
max = SuperTensor.__dict__[self.typeStr + "Max"]
supertensor = [[[[1, "two"], [3, 4]], [[5, "six"], [7, 8]]], [[[1, "two"], [3, 4]], [[5, "six"], [7, 8]]]]
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78,
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83,
57
] | python | en | ['en', 'en', 'en'] | True |
SuperTensorTestCase.testMaxNonContainer | (self) | Test max function with non-container | Test max function with non-container | def testMaxNonContainer(self):
"Test max function with non-container"
print(self.typeStr, "... ", file=sys.stderr)
max = SuperTensor.__dict__[self.typeStr + "Max"]
self.assertRaises(TypeError, max, None) | [
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90,
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SuperTensorTestCase.testMaxWrongDim | (self) | Test max function with wrong dimensions | Test max function with wrong dimensions | def testMaxWrongDim(self):
"Test max function with wrong dimensions"
print(self.typeStr, "... ", file=sys.stderr)
max = SuperTensor.__dict__[self.typeStr + "Max"]
self.assertRaises(TypeError, max, [0, -1, 2, -3]) | [
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97,
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SuperTensorTestCase.testMin | (self) | Test min function | Test min function | def testMin(self):
"Test min function"
print(self.typeStr, "... ", file=sys.stderr)
min = SuperTensor.__dict__[self.typeStr + "Min"]
supertensor = [[[[9, 8], [7, 6]], [[5, 4], [3, 2]]], [[[9, 8], [7, 6]], [[5, 4], [3, 2]]]]
self.assertEqual(min(supertensor), 2) | [
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100,
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105,
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] | python | en | ['de', 'en', 'en'] | True |
SuperTensorTestCase.testMinBadList | (self) | Test min function with bad list | Test min function with bad list | def testMinBadList(self):
"Test min function with bad list"
print(self.typeStr, "... ", file=sys.stderr)
min = SuperTensor.__dict__[self.typeStr + "Min"]
supertensor = [[[["nine", 8], [7, 6]], [["five", 4], [3, 2]]], [[["nine", 8], [7, 6]], [["five", 4], [3, 2]]]]
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108,
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113,
57
] | python | en | ['en', 'en', 'en'] | True |
SuperTensorTestCase.testMinNonContainer | (self) | Test min function with non-container | Test min function with non-container | def testMinNonContainer(self):
"Test min function with non-container"
print(self.typeStr, "... ", file=sys.stderr)
min = SuperTensor.__dict__[self.typeStr + "Min"]
self.assertRaises(TypeError, min, True) | [
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120,
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SuperTensorTestCase.testMinWrongDim | (self) | Test min function with wrong dimensions | Test min function with wrong dimensions | def testMinWrongDim(self):
"Test min function with wrong dimensions"
print(self.typeStr, "... ", file=sys.stderr)
min = SuperTensor.__dict__[self.typeStr + "Min"]
self.assertRaises(TypeError, min, [[1, 3], [5, 7]]) | [
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127,
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] | python | en | ['en', 'en', 'en'] | True |
SuperTensorTestCase.testScale | (self) | Test scale function | Test scale function | def testScale(self):
"Test scale function"
print(self.typeStr, "... ", file=sys.stderr)
scale = SuperTensor.__dict__[self.typeStr + "Scale"]
supertensor = np.arange(3*3*3*3, dtype=self.typeCode).reshape((3, 3, 3, 3))
answer = supertensor.copy()*4
scale(supertensor, 4)
... | [
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SuperTensorTestCase.testScaleWrongType | (self) | Test scale function with wrong type | Test scale function with wrong type | def testScaleWrongType(self):
"Test scale function with wrong type"
print(self.typeStr, "... ", file=sys.stderr)
scale = SuperTensor.__dict__[self.typeStr + "Scale"]
supertensor = np.array([[[1, 0, 1], [0, 1, 0], [1, 0, 1]],
[[0, 1, 0], [1, 0, 1], [0, 1, 0]],
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SuperTensorTestCase.testScaleWrongDim | (self) | Test scale function with wrong dimensions | Test scale function with wrong dimensions | def testScaleWrongDim(self):
"Test scale function with wrong dimensions"
print(self.typeStr, "... ", file=sys.stderr)
scale = SuperTensor.__dict__[self.typeStr + "Scale"]
supertensor = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1],
[0, 1, 0], [1, 0, 1], [0, 1, 0]], ... | [
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SuperTensorTestCase.testScaleWrongSize | (self) | Test scale function with wrong size | Test scale function with wrong size | def testScaleWrongSize(self):
"Test scale function with wrong size"
print(self.typeStr, "... ", file=sys.stderr)
scale = SuperTensor.__dict__[self.typeStr + "Scale"]
supertensor = np.array([[[1, 0], [0, 1], [1, 0]],
[[0, 1], [1, 0], [0, 1]],
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SuperTensorTestCase.testScaleNonArray | (self) | Test scale function with non-array | Test scale function with non-array | def testScaleNonArray(self):
"Test scale function with non-array"
print(self.typeStr, "... ", file=sys.stderr)
scale = SuperTensor.__dict__[self.typeStr + "Scale"]
self.assertRaises(TypeError, scale, True) | [
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SuperTensorTestCase.testFloor | (self) | Test floor function | Test floor function | def testFloor(self):
"Test floor function"
print(self.typeStr, "... ", file=sys.stderr)
supertensor = np.arange(2*2*2*2, dtype=self.typeCode).reshape((2, 2, 2, 2))
answer = supertensor.copy()
answer[answer < 4] = 4
floor = SuperTensor.__dict__[self.typeStr + "Floor"]
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SuperTensorTestCase.testFloorWrongType | (self) | Test floor function with wrong type | Test floor function with wrong type | def testFloorWrongType(self):
"Test floor function with wrong type"
print(self.typeStr, "... ", file=sys.stderr)
floor = SuperTensor.__dict__[self.typeStr + "Floor"]
supertensor = np.ones(2*2*2*2, dtype='c').reshape((2, 2, 2, 2))
self.assertRaises(TypeError, floor, supertensor) | [
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SuperTensorTestCase.testFloorWrongDim | (self) | Test floor function with wrong type | Test floor function with wrong type | def testFloorWrongDim(self):
"Test floor function with wrong type"
print(self.typeStr, "... ", file=sys.stderr)
floor = SuperTensor.__dict__[self.typeStr + "Floor"]
supertensor = np.arange(2*2*2, dtype=self.typeCode).reshape((2, 2, 2))
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SuperTensorTestCase.testFloorNonArray | (self) | Test floor function with non-array | Test floor function with non-array | def testFloorNonArray(self):
"Test floor function with non-array"
print(self.typeStr, "... ", file=sys.stderr)
floor = SuperTensor.__dict__[self.typeStr + "Floor"]
self.assertRaises(TypeError, floor, object) | [
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208,
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SuperTensorTestCase.testCeil | (self) | Test ceil function | Test ceil function | def testCeil(self):
"Test ceil function"
print(self.typeStr, "... ", file=sys.stderr)
supertensor = np.arange(2*2*2*2, dtype=self.typeCode).reshape((2, 2, 2, 2))
answer = supertensor.copy()
answer[answer > 5] = 5
ceil = SuperTensor.__dict__[self.typeStr + "Ceil"]
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SuperTensorTestCase.testCeilWrongType | (self) | Test ceil function with wrong type | Test ceil function with wrong type | def testCeilWrongType(self):
"Test ceil function with wrong type"
print(self.typeStr, "... ", file=sys.stderr)
ceil = SuperTensor.__dict__[self.typeStr + "Ceil"]
supertensor = np.ones(2*2*2*2, 'c').reshape((2, 2, 2, 2))
self.assertRaises(TypeError, ceil, supertensor) | [
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SuperTensorTestCase.testCeilWrongDim | (self) | Test ceil function with wrong dimensions | Test ceil function with wrong dimensions | def testCeilWrongDim(self):
"Test ceil function with wrong dimensions"
print(self.typeStr, "... ", file=sys.stderr)
ceil = SuperTensor.__dict__[self.typeStr + "Ceil"]
supertensor = np.arange(2*2*2, dtype=self.typeCode).reshape((2, 2, 2))
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SuperTensorTestCase.testCeilNonArray | (self) | Test ceil function with non-array | Test ceil function with non-array | def testCeilNonArray(self):
"Test ceil function with non-array"
print(self.typeStr, "... ", file=sys.stderr)
ceil = SuperTensor.__dict__[self.typeStr + "Ceil"]
supertensor = np.arange(2*2*2*2, dtype=self.typeCode).reshape((2, 2, 2, 2)).tolist()
self.assertRaises(TypeError, ceil, ... | [
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SuperTensorTestCase.testLUSplit | (self) | Test luSplit function | Test luSplit function | def testLUSplit(self):
"Test luSplit function"
print(self.typeStr, "... ", file=sys.stderr)
luSplit = SuperTensor.__dict__[self.typeStr + "LUSplit"]
supertensor = np.ones(2*2*2*2, dtype=self.typeCode).reshape((2, 2, 2, 2))
answer_upper = [[[[0, 0], [0, 1]], [[0, 1], [1, 1]]], [[[... | [
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"supe... | [
246,
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] | [
255,
61
] | python | en | ['en', 'no', 'en'] | True |
Ball.draw | (self) |
draws the ball onto screen.
|
draws the ball onto screen.
| def draw(self):
"""
draws the ball onto screen.
"""
pygame.draw.circle(screen, (255, 0, 0), (self._xLoc, self._yLoc), self._radius) | [
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Ball.update | (self, paddle, brickwall) |
moves the ball at the screen.
contains some collision detection.
|
moves the ball at the screen.
contains some collision detection.
| def update(self, paddle, brickwall):
"""
moves the ball at the screen.
contains some collision detection.
"""
self._xLoc += self.__xVel
self._yLoc += self.__yVel
# left screen wall bounce
if self._xLoc <= self._radius:
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Paddle.draw | (self) |
draws the paddle onto screen.
|
draws the paddle onto screen.
| def draw(self):
"""
draws the paddle onto screen.
"""
pygame.draw.rect(screen, (0, 0, 0), (self._xLoc, self._yLoc, self._width, self._height), 0) | [
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Paddle.update | (self) |
moves the paddle at the screen via mouse
|
moves the paddle at the screen via mouse
| def update(self):
"""
moves the paddle at the screen via mouse
"""
x, y = pygame.mouse.get_pos()
if x >= 0 and x <= (self.__W - self._width):
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Brick.draw | (self) |
draws the brick onto screen.
color: rgb(56, 177, 237)
|
draws the brick onto screen.
color: rgb(56, 177, 237)
| def draw(self):
"""
draws the brick onto screen.
color: rgb(56, 177, 237)
"""
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Brick.add | (self, group) |
adds this brick to a given group.
|
adds this brick to a given group.
| def add(self, group):
"""
adds this brick to a given group.
"""
group.add(self)
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Brick.remove | (self, group) |
removes this brick from the given group.
|
removes this brick from the given group.
| def remove(self, group):
"""
removes this brick from the given group.
"""
group.remove(self)
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Brick.alive | (self) |
returns true when this brick belongs to the brick wall.
otherwise false
|
returns true when this brick belongs to the brick wall.
otherwise false
| def alive(self):
"""
returns true when this brick belongs to the brick wall.
otherwise false
"""
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Brick.collide | (self, ball) |
collision detection between ball and this brick
|
collision detection between ball and this brick
| def collide(self, ball):
"""
collision detection between ball and this brick
"""
brickX = self._xLoc
brickY = self._yLoc
brickW = self._width
brickH = self._height
ballX = ball._xLoc
ballY = ball._yLoc
ballXVel = ball.getXVel()
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BrickWall.add | (self, brick) |
adds a brick to this BrickWall (group)
|
adds a brick to this BrickWall (group)
| def add(self, brick):
"""
adds a brick to this BrickWall (group)
"""
self._bricks.append(brick) | [
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BrickWall.remove | (self, brick) |
removes a brick from this BrickWall (group)
|
removes a brick from this BrickWall (group)
| def remove(self, brick):
"""
removes a brick from this BrickWall (group)
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BrickWall.draw | (self) |
draws all bricks onto screen.
|
draws all bricks onto screen.
| def draw(self):
"""
draws all bricks onto screen.
"""
for brick in self._bricks:
if brick != None:
brick.draw() | [
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BrickWall.update | (self, ball) |
checks collision between ball and bricks.
|
checks collision between ball and bricks.
| def update(self, ball):
"""
checks collision between ball and bricks.
"""
for i in range(len(self._bricks)):
if ((self._bricks[i] != None) and self._bricks[i].collide(ball)):
self._bricks[i] = None
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BrickWall.hasWin | (self) |
Has player win the game?
|
Has player win the game?
| def hasWin(self):
"""
Has player win the game?
"""
return len(self._bricks) == 0 | [
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BrickWall.collide | (self, ball) |
check collisions between the ball and
any of the bricks.
|
check collisions between the ball and
any of the bricks.
| def collide(self, ball):
"""
check collisions between the ball and
any of the bricks.
"""
for brick in self._bricks:
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test_alexnet_backbone | () | Test alexnet backbone. | Test alexnet backbone. | def test_alexnet_backbone():
"""Test alexnet backbone."""
model = AlexNet(-1)
model.train()
imgs = torch.randn(1, 3, 256, 192)
feat = model(imgs)
assert feat.shape == (1, 256, 7, 5)
model = AlexNet(1)
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
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] | python | en | ['en', 'cy', 'en'] | True |
polyline | (off, scl) |
Returns an array representing a linear polynomial.
Parameters
----------
off, scl : scalars
The "y-intercept" and "slope" of the line, respectively.
Returns
-------
y : ndarray
This module's representation of the linear polynomial ``off +
scl*x``.
See Also
... |
Returns an array representing a linear polynomial. | def polyline(off, scl):
"""
Returns an array representing a linear polynomial.
Parameters
----------
off, scl : scalars
The "y-intercept" and "slope" of the line, respectively.
Returns
-------
y : ndarray
This module's representation of the linear polynomial ``off +
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98,
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] | [
129,
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] | python | en | ['en', 'error', 'th'] | False |
polyfromroots | (roots) |
Generate a monic polynomial with given roots.
Return the coefficients of the polynomial
.. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
where the `r_n` are the roots specified in `roots`. If a zero has
multiplicity n, then it must appear in `roots` n times. For instance,
if 2 is a... |
Generate a monic polynomial with given roots. | def polyfromroots(roots):
"""
Generate a monic polynomial with given roots.
Return the coefficients of the polynomial
.. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
where the `r_n` are the roots specified in `roots`. If a zero has
multiplicity n, then it must appear in `roots` n t... | [
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] | [
132,
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] | [
190,
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] | python | en | ['en', 'error', 'th'] | False |
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