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train | IoU | Calculates Intersection over Union
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
Returns:
MetricsLambda
Examples:
.. code-block:: python
train_evaluator = ...
cm = Confusi... | ignite/metrics/confusion_matrix.py | def IoU(cm, ignore_index=None):
"""Calculates Intersection over Union
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
Returns:
MetricsLambda
Examples:
.. code-block:: python
trai... | def IoU(cm, ignore_index=None):
"""Calculates Intersection over Union
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
Returns:
MetricsLambda
Examples:
.. code-block:: python
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train | cmAccuracy | Calculates accuracy using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
Returns:
MetricsLambda | ignite/metrics/confusion_matrix.py | def cmAccuracy(cm):
"""
Calculates accuracy using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
Returns:
MetricsLambda
"""
# Increase floating point precision
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"""
Calculates accuracy using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
Returns:
MetricsLambda
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train | cmPrecision | Calculates precision using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
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"""
Calculates precision using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
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MetricsLambda
... | def cmPrecision(cm, average=True):
"""
Calculates precision using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
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train | cmRecall | Calculates recall using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
Returns:
MetricsLambda | ignite/metrics/confusion_matrix.py | def cmRecall(cm, average=True):
"""
Calculates recall using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
Returns:
MetricsLambda
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... | def cmRecall(cm, average=True):
"""
Calculates recall using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
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MetricsLambda
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train | convert_tensor | Move tensors to relevant device. | ignite/utils.py | def convert_tensor(input_, device=None, non_blocking=False):
"""Move tensors to relevant device."""
def _func(tensor):
return tensor.to(device=device, non_blocking=non_blocking) if device else tensor
return apply_to_tensor(input_, _func) | def convert_tensor(input_, device=None, non_blocking=False):
"""Move tensors to relevant device."""
def _func(tensor):
return tensor.to(device=device, non_blocking=non_blocking) if device else tensor
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train | apply_to_type | Apply a function on a object of `input_type` or mapping, or sequence of objects of `input_type`. | ignite/utils.py | def apply_to_type(input_, input_type, func):
"""Apply a function on a object of `input_type` or mapping, or sequence of objects of `input_type`.
"""
if isinstance(input_, input_type):
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"""Apply a function on a object of `input_type` or mapping, or sequence of objects of `input_type`.
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train | to_onehot | Convert a tensor of indices of any shape `(N, ...)` to a
tensor of one-hot indicators of shape `(N, num_classes, ...)`. | ignite/utils.py | def to_onehot(indices, num_classes):
"""Convert a tensor of indices of any shape `(N, ...)` to a
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"""
onehot = torch.zeros(indices.shape[0], num_classes, *indices.shape[1:], device=indices.device)
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"""Convert a tensor of indices of any shape `(N, ...)` to a
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train | ProgressBar.attach | Attaches the progress bar to an engine object.
Args:
engine (Engine): engine object.
metric_names (list, optional): list of the metrics names to log as the bar progresses
output_transform (callable, optional): a function to select what you want to print from the engine's
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"""
Attaches the progress bar to an engine object.
Args:
engine (Engine): engine object.
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"""
Attaches the progress bar to an engine object.
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engine (Engine): engine object.
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train | create_supervised_tbptt_trainer | Create a trainer for truncated backprop through time supervised models.
Training recurrent model on long sequences is computationally intensive as
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However, when the training loss is computed over many outputs
(`X to many <https://karp... | ignite/contrib/engines/tbptt.py | def create_supervised_tbptt_trainer(
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prepare_batch=_prepare_batch
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train | _prepare_batch | Prepare batch for training: pass to a device with options. | ignite/engine/__init__.py | def _prepare_batch(batch, device=None, non_blocking=False):
"""Prepare batch for training: pass to a device with options.
"""
x, y = batch
return (convert_tensor(x, device=device, non_blocking=non_blocking),
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"""
x, y = batch
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train | create_supervised_trainer | Factory function for creating a trainer for supervised models.
Args:
model (`torch.nn.Module`): the model to train.
optimizer (`torch.optim.Optimizer`): the optimizer to use.
loss_fn (torch.nn loss function): the loss function to use.
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"""
Factory function for creating a trainer ... | def create_supervised_trainer(model, optimizer, loss_fn,
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train | create_supervised_evaluator | Factory function for creating an evaluator for supervised models.
Args:
model (`torch.nn.Module`): the model to train.
metrics (dict of str - :class:`~ignite.metrics.Metric`): a map of metric names to Metrics.
device (str, optional): device type specification (default: None).
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"""
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train | create_lr_scheduler_with_warmup | Helper method to create a LR scheduler with a linear warm-up.
Args:
lr_scheduler (ParamScheduler or subclass of `torch.optim.lr_scheduler._LRScheduler`): LR scheduler after
the warm-up.
warmup_start_value (float): LR start value of the warm-up phase.
warmup_end_value (float): LR... | ignite/contrib/handlers/param_scheduler.py | def create_lr_scheduler_with_warmup(lr_scheduler, warmup_start_value, warmup_end_value, warmup_duration,
save_history=False,
output_simulated_values=None):
"""
Helper method to create a LR scheduler with a linear warm-up.
Args:
... | def create_lr_scheduler_with_warmup(lr_scheduler, warmup_start_value, warmup_end_value, warmup_duration,
save_history=False,
output_simulated_values=None):
"""
Helper method to create a LR scheduler with a linear warm-up.
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train | ParamScheduler.simulate_values | Method to simulate scheduled values during num_events events.
Args:
num_events (int): number of events during the simulation.
**scheduler_kwargs : parameter scheduler configuration kwargs.
Returns:
list of pairs: [event_index, value]
Examples:
.. c... | ignite/contrib/handlers/param_scheduler.py | def simulate_values(cls, num_events, **scheduler_kwargs):
"""Method to simulate scheduled values during num_events events.
Args:
num_events (int): number of events during the simulation.
**scheduler_kwargs : parameter scheduler configuration kwargs.
Returns:
... | def simulate_values(cls, num_events, **scheduler_kwargs):
"""Method to simulate scheduled values during num_events events.
Args:
num_events (int): number of events during the simulation.
**scheduler_kwargs : parameter scheduler configuration kwargs.
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train | CosineAnnealingScheduler.get_param | Method to get current optimizer's parameter value | ignite/contrib/handlers/param_scheduler.py | def get_param(self):
"""Method to get current optimizer's parameter value
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cycle_progress = self.event_index / self.cycle_size
return self.start_value + ((self.end_value - self.start_value) / 2) * (1 - math.cos(math.pi * cycle_progress)) | def get_param(self):
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train | LRScheduler.get_param | Method to get current optimizer's parameter value | ignite/contrib/handlers/param_scheduler.py | def get_param(self):
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train | check_manual_seed | If manual seed is not specified, choose a random one and communicate it to the user. | examples/gan/dcgan.py | def check_manual_seed(seed):
""" If manual seed is not specified, choose a random one and communicate it to the user.
"""
seed = seed or random.randint(1, 10000)
random.seed(seed)
torch.manual_seed(seed)
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"""
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random.seed(seed)
torch.manual_seed(seed)
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train | BaseLogger.attach | Attach the logger to the engine and execute `log_handler` function at `event_name` events.
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engine (Engine): engine object.
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train | BaseOutputHandler._setup_output_metrics | Helper method to setup metrics to log | ignite/contrib/handlers/base_logger.py | def _setup_output_metrics(self, engine):
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train | Timer.attach | Register callbacks to control the timer.
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engine (Engine):
Engine that this timer will be attached to.
start (Events):
Event which should start (reset) the timer.
pause (Events):
Event which should pause the timer.
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engine (Engine):
Engine that this timer will be attached to.
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engine (Engine):
Engine that this timer will be attached to.
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train | _BaseVisDrawer.add_scalar | Helper method to log a scalar with VisdomLogger.
Args:
logger (VisdomLogger): visdom logger
k (str): scalar name which is used to set window title and y-axis label
v (int or float): scalar value, y-axis value
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Helper method to log a scalar with VisdomLogger.
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logger (VisdomLogger): visdom logger
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Helper method to log a scalar with VisdomLogger.
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train | _to_hours_mins_secs | Convert seconds to hours, mins, and seconds. | ignite/_utils.py | def _to_hours_mins_secs(time_taken):
"""Convert seconds to hours, mins, and seconds."""
mins, secs = divmod(time_taken, 60)
hours, mins = divmod(mins, 60)
return hours, mins, secs | def _to_hours_mins_secs(time_taken):
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mins, secs = divmod(time_taken, 60)
hours, mins = divmod(mins, 60)
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train | parse_args | Parse the arguments/options passed to the program on the command line. | coursera/commandline.py | def parse_args(args=None):
"""
Parse the arguments/options passed to the program on the command line.
"""
parse_kwargs = {
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conf_file_path = os.path.join(os.getcwd(), LOCAL_CONF_FILE_NAME)
if os.path.isfile(co... | def parse_args(args=None):
"""
Parse the arguments/options passed to the program on the command line.
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train | random_string | Return a pseudo-random string of specified length. | coursera/utils.py | def random_string(length):
"""
Return a pseudo-random string of specified length.
"""
valid_chars = string_ascii_letters + string_digits
return ''.join(random.choice(valid_chars) for i in range(length)) | def random_string(length):
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Return a pseudo-random string of specified length.
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valid_chars = string_ascii_letters + string_digits
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train | clean_filename | Sanitize a string to be used as a filename.
If minimal_change is set to true, then we only strip the bare minimum of
characters that are problematic for filesystems (namely, ':', '/' and
'\x00', '\n'). | coursera/utils.py | def clean_filename(s, minimal_change=False):
"""
Sanitize a string to be used as a filename.
If minimal_change is set to true, then we only strip the bare minimum of
characters that are problematic for filesystems (namely, ':', '/' and
'\x00', '\n').
"""
# First, deal with URL encoded stri... | def clean_filename(s, minimal_change=False):
"""
Sanitize a string to be used as a filename.
If minimal_change is set to true, then we only strip the bare minimum of
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train | normalize_path | Normalizes path on Windows OS. This means prepending
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See https://msdn.microsoft.com/en-us/library/aa365247%28v=vs.85%29.aspx#maxpath
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@type path: st... | coursera/utils.py | def normalize_path(path):
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Normalizes path on Windows OS. This means prepending
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See https://msdn.microsoft.com/en-us/library/aa365247%28v=vs.85%29.aspx#maxpath
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Normalizes path on Windows OS. This means prepending
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train | get_anchor_format | Extract the resource file-type format from the anchor. | coursera/utils.py | def get_anchor_format(a):
"""
Extract the resource file-type format from the anchor.
"""
# (. or format=) then (file_extension) then (? or $)
# e.g. "...format=txt" or "...download.mp4?..."
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Extract the resource file-type format from the anchor.
"""
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train | clean_url | Remove params, query and fragment parts from URL so that `os.path.basename`
and `os.path.splitext` can work correctly.
@param url: URL to clean.
@type url: str
@return: Cleaned URL.
@rtype: str | coursera/utils.py | def clean_url(url):
"""
Remove params, query and fragment parts from URL so that `os.path.basename`
and `os.path.splitext` can work correctly.
@param url: URL to clean.
@type url: str
@return: Cleaned URL.
@rtype: str
"""
parsed = urlparse(url.strip())
reconstructed = ParseResu... | def clean_url(url):
"""
Remove params, query and fragment parts from URL so that `os.path.basename`
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@param url: URL to clean.
@type url: str
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@rtype: str
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train | fix_url | Strip whitespace characters from the beginning and the end of the url
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train | make_coursera_absolute_url | If given url is relative adds coursera netloc,
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"""
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train | extend_supplement_links | Extends (merges) destination dictionary with supplement_links
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@param destination: Destination dictionary that will be extended.
@type destination: @see CourseraOnDemand._extract_links_from_text
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"""
Extends (merges) destination dictionary with supplement_links
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train | print_ssl_error_message | Print SSLError message with URL to instructions on how to fix it. | coursera/utils.py | def print_ssl_error_message(exception):
"""
Print SSLError message with URL to instructions on how to fix it.
"""
message = """
#####################################################################
# ATTENTION! PLEASE READ THIS!
#
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#
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"""
Print SSLError message with URL to instructions on how to fix it.
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message = """
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train | list_courses | List enrolled courses.
@param args: Command-line arguments.
@type args: namedtuple | coursera/coursera_dl.py | def list_courses(args):
"""
List enrolled courses.
@param args: Command-line arguments.
@type args: namedtuple
"""
session = get_session()
login(session, args.username, args.password)
extractor = CourseraExtractor(session)
courses = extractor.list_courses()
logging.info('Found %... | def list_courses(args):
"""
List enrolled courses.
@param args: Command-line arguments.
@type args: namedtuple
"""
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login(session, args.username, args.password)
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@return: Tuple of (bool, bool), where the first bool indicates whether
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Download all requested resources from the on-demand class given
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train | download_class | Try to download on-demand class.
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Try to download on-demand class.
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train | main | Main entry point for execution as a program (instead of as a module). | coursera/coursera_dl.py | def main():
"""
Main entry point for execution as a program (instead of as a module).
"""
args = parse_args()
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train | prepare_auth_headers | This function prepares headers with CSRF/CAUTH tokens that can
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@param session: Requests session.
@type session: requests.Session
@param include_cauth: Flag that indicates whether CAUTH cookies should be
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@type include_cauth:... | coursera/cookies.py | def prepare_auth_headers(session, include_cauth=False):
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@param session: Requests session.
@type session: requests.Session
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train | login | Login on coursera.org with the given credentials.
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sessionid, maestro_login, maestro_login_flag | coursera/cookies.py | def login(session, username, password, class_name=None):
"""
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This adds the following cookies to the session:
sessionid, maestro_login, maestro_login_flag
"""
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"""
Login on coursera.org with the given credentials.
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train | down_the_wabbit_hole | Authenticate on class.coursera.org | coursera/cookies.py | def down_the_wabbit_hole(session, class_name):
"""
Authenticate on class.coursera.org
"""
auth_redirector_url = AUTH_REDIRECT_URL.format(class_name=class_name)
r = session.get(auth_redirector_url)
logging.debug('Following %s to authenticate on class.coursera.org.',
auth_redir... | def down_the_wabbit_hole(session, class_name):
"""
Authenticate on class.coursera.org
"""
auth_redirector_url = AUTH_REDIRECT_URL.format(class_name=class_name)
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train | get_authentication_cookies | Get the necessary cookies to authenticate on class.coursera.org.
To access the class pages we need two cookies on class.coursera.org:
csrf_token, session | coursera/cookies.py | def get_authentication_cookies(session, class_name, username, password):
"""
Get the necessary cookies to authenticate on class.coursera.org.
To access the class pages we need two cookies on class.coursera.org:
csrf_token, session
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# First, check if we already have the .coursera.org coo... | def get_authentication_cookies(session, class_name, username, password):
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Get the necessary cookies to authenticate on class.coursera.org.
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train | do_we_have_enough_cookies | Check whether we have all the required cookies
to authenticate on class.coursera.org. | coursera/cookies.py | def do_we_have_enough_cookies(cj, class_name):
"""
Check whether we have all the required cookies
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"""
domain = 'class.coursera.org'
path = "/" + class_name
return cj.get('csrf_token', domain=domain, path=path) is not None | def do_we_have_enough_cookies(cj, class_name):
"""
Check whether we have all the required cookies
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"""
domain = 'class.coursera.org'
path = "/" + class_name
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train | validate_cookies | Checks whether we have all the required cookies
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"""
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train | make_cookie_values | Makes a string of cookie keys and values.
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"""
Makes a string of cookie keys and values.
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train | find_cookies_for_class | Return a RequestsCookieJar containing the cookies for
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train | load_cookies_file | Load cookies file.
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"""
Load cookies file.
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"""
logging.debug('Loading cookie file %s into memory.', cookies_file)
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Load cookies file.
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train | get_cookies_from_cache | Returns a RequestsCookieJar containing the cached cookies for the given
user. | coursera/cookies.py | def get_cookies_from_cache(username):
"""
Returns a RequestsCookieJar containing the cached cookies for the given
user.
"""
logging.debug('Trying to get cookies from the cache.')
path = get_cookies_cache_path(username)
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try:
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"""
Returns a RequestsCookieJar containing the cached cookies for the given
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"""
logging.debug('Trying to get cookies from the cache.')
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train | write_cookies_to_cache | Save RequestsCookieJar to disk in Mozilla's cookies.txt file format.
This prevents us from repeated authentications on the
accounts.coursera.org and class.coursera.org/class_name sites. | coursera/cookies.py | def write_cookies_to_cache(cj, username):
"""
Save RequestsCookieJar to disk in Mozilla's cookies.txt file format.
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authentication process has changed. | coursera/cookies.py | def get_cookies_for_class(session, class_name,
cookies_file=None,
username=None,
password=None):
"""
Get the cookies for the given class.
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train | get_reply | Download an HTML page using the requests session. Low-level function
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@param session: Requests session.
@type session: requests.Session
@param url: URL pattern with optional keywords to format.
@type url: str
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"""
Download an HTML page using the requests session. Low-level function
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@param session: Requests session.
@type session: requests.Session
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Download an HTML page using the requests session. Low-level function
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Download an HTML page using the requests session.
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train | get_page_and_url | Download an HTML page using the requests session and return
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"""
Download an HTML page using the requests session and return
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"""
reply = get_reply(session, url)
return reply.text, reply.url | def get_page_and_url(session, url):
"""
Download an HTML page using the requests session and return
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reply = get_reply(session, url)
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@type format_: str (e.g. html, txt, zip, pdf)
@param url: URL.
@type url: str
@return: True if format/url should be skipped, False otherwise.
@rtype bool | coursera/filtering.py | def skip_format_url(format_, url):
"""
Checks whether a give format/url should be skipped and not downloaded.
@param format_: Filename format (extension).
@type format_: str (e.g. html, txt, zip, pdf)
@param url: URL.
@type url: str
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"""
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train | find_resources_to_get | Select formats to download. | coursera/filtering.py | def find_resources_to_get(lecture, file_formats, resource_filter, ignored_formats=None):
"""
Select formats to download.
"""
resources_to_get = []
if ignored_formats is None:
ignored_formats = []
if len(ignored_formats):
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"""
Select formats to download.
"""
resources_to_get = []
if ignored_formats is None:
ignored_formats = []
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train | generate_readme_rst | Generate README.rst from README.md via pandoc.
In case of errors, we show a message having the error that we got and
exit the program. | setup.py | def generate_readme_rst():
"""
Generate README.rst from README.md via pandoc.
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exit the program.
"""
pandoc_cmd = [
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"""
Generate README.rst from README.md via pandoc.
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train | read_file | Read the contents of filename or give an alternative result instead. | setup.py | def read_file(filename, alt=None):
"""
Read the contents of filename or give an alternative result instead.
"""
lines = None
try:
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lines = f.read()
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lines = [] if alt is None else alt
return lines | def read_file(filename, alt=None):
"""
Read the contents of filename or give an alternative result instead.
"""
lines = None
try:
with open(filename, encoding='utf-8') as f:
lines = f.read()
except IOError:
lines = [] if alt is None else alt
return lines | [
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train | release_check | Check if there is a Git tag already in place | fabfile.py | def release_check():
"""Check if there is a Git tag already in place"""
tags = local("git tag", capture=True)
tags = set(tags.splitlines())
if env.version in tags:
raise Exception("Already released v. %r" % env.version) | def release_check():
"""Check if there is a Git tag already in place"""
tags = local("git tag", capture=True)
tags = set(tags.splitlines())
if env.version in tags:
raise Exception("Already released v. %r" % env.version) | [
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train | release | Release a new version | fabfile.py | def release():
"""Release a new version"""
release_check()
build()
print("Releasing %s version %s." % (env.projname, env.version))
local("git tag %s" % env.version)
local('gpg --detach-sign --armor dist/coursera-*.tar.gz*')
local('twine upload dist/coursera-*.tar.gz*')
local("git push")
... | def release():
"""Release a new version"""
release_check()
build()
print("Releasing %s version %s." % (env.projname, env.version))
local("git tag %s" % env.version)
local('gpg --detach-sign --armor dist/coursera-*.tar.gz*')
local('twine upload dist/coursera-*.tar.gz*')
local("git push")
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train | expand_specializations | Checks whether any given name is not a class but a specialization.
If it's a specialization, expand the list of class names with the child
class names. | coursera/api.py | def expand_specializations(session, class_names):
"""
Checks whether any given name is not a class but a specialization.
If it's a specialization, expand the list of class names with the child
class names.
"""
result = []
for class_name in class_names:
specialization = Specializatio... | def expand_specializations(session, class_names):
"""
Checks whether any given name is not a class but a specialization.
If it's a specialization, expand the list of class names with the child
class names.
"""
result = []
for class_name in class_names:
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train | MarkupToHTMLConverter._convert_markup_basic | Perform basic conversion of instructions markup. This includes
replacement of several textual markup tags with their HTML equivalents.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup | coursera/api.py | def _convert_markup_basic(self, soup):
"""
Perform basic conversion of instructions markup. This includes
replacement of several textual markup tags with their HTML equivalents.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup
"""
# Inject meta char... | def _convert_markup_basic(self, soup):
"""
Perform basic conversion of instructions markup. This includes
replacement of several textual markup tags with their HTML equivalents.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup
"""
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train | MarkupToHTMLConverter._convert_markup_images | Convert images of instructions markup. Images are downloaded,
base64-encoded and inserted into <img> tags.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup | coursera/api.py | def _convert_markup_images(self, soup):
"""
Convert images of instructions markup. Images are downloaded,
base64-encoded and inserted into <img> tags.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup
"""
# 6. Replace <img> assets with actual image c... | def _convert_markup_images(self, soup):
"""
Convert images of instructions markup. Images are downloaded,
base64-encoded and inserted into <img> tags.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup
"""
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train | MarkupToHTMLConverter._convert_markup_audios | Convert audios of instructions markup. Audios are downloaded,
base64-encoded and inserted as <audio controls> <source> tag.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup | coursera/api.py | def _convert_markup_audios(self, soup):
"""
Convert audios of instructions markup. Audios are downloaded,
base64-encoded and inserted as <audio controls> <source> tag.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup
"""
# 7. Replace <asset> audio a... | def _convert_markup_audios(self, soup):
"""
Convert audios of instructions markup. Audios are downloaded,
base64-encoded and inserted as <audio controls> <source> tag.
@param soup: BeautifulSoup instance.
@type soup: BeautifulSoup
"""
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train | OnDemandCourseMaterialItemsV1.create | Create an instance using a session and a course_name.
@param session: Requests session.
@type session: requests.Session
@param course_name: Course name (slug) from course json.
@type course_name: str
@return: Instance of OnDemandCourseMaterialItems
@rtype: OnDemandCour... | coursera/api.py | def create(session, course_name):
"""
Create an instance using a session and a course_name.
@param session: Requests session.
@type session: requests.Session
@param course_name: Course name (slug) from course json.
@type course_name: str
@return: Instance of On... | def create(session, course_name):
"""
Create an instance using a session and a course_name.
@param session: Requests session.
@type session: requests.Session
@param course_name: Course name (slug) from course json.
@type course_name: str
@return: Instance of On... | [
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train | CourseraOnDemand.list_courses | List enrolled courses.
@return: List of enrolled courses.
@rtype: [str] | coursera/api.py | def list_courses(self):
"""
List enrolled courses.
@return: List of enrolled courses.
@rtype: [str]
"""
reply = get_page(self._session, OPENCOURSE_MEMBERSHIPS, json=True)
course_list = reply['linked']['courses.v1']
slugs = [element['slug'] for element in ... | def list_courses(self):
"""
List enrolled courses.
@return: List of enrolled courses.
@rtype: [str]
"""
reply = get_page(self._session, OPENCOURSE_MEMBERSHIPS, json=True)
course_list = reply['linked']['courses.v1']
slugs = [element['slug'] for element in ... | [
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train | CourseraOnDemand.extract_links_from_lecture | Return the download URLs of on-demand course video.
@param video_id: Video ID.
@type video_id: str
@param subtitle_language: Subtitle language.
@type subtitle_language: str
@param resolution: Preferred video resolution.
@type resolution: str
@return: @see Cour... | coursera/api.py | def extract_links_from_lecture(self, course_id,
video_id, subtitle_language='en',
resolution='540p'):
"""
Return the download URLs of on-demand course video.
@param video_id: Video ID.
@type video_id: str
@pa... | def extract_links_from_lecture(self, course_id,
video_id, subtitle_language='en',
resolution='540p'):
"""
Return the download URLs of on-demand course video.
@param video_id: Video ID.
@type video_id: str
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train | CourseraOnDemand._get_lecture_asset_ids | Obtain a list of asset ids from a lecture. | coursera/api.py | def _get_lecture_asset_ids(self, course_id, video_id):
"""
Obtain a list of asset ids from a lecture.
"""
dom = get_page(self._session, OPENCOURSE_ONDEMAND_LECTURE_ASSETS_URL,
json=True, course_id=course_id, video_id=video_id)
# Note that we extract here "i... | def _get_lecture_asset_ids(self, course_id, video_id):
"""
Obtain a list of asset ids from a lecture.
"""
dom = get_page(self._session, OPENCOURSE_ONDEMAND_LECTURE_ASSETS_URL,
json=True, course_id=course_id, video_id=video_id)
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train | CourseraOnDemand._normalize_assets | Perform asset normalization. For some reason, assets that are sometimes
present in lectures, have "@1" at the end of their id. Such "uncut"
asset id when fed to OPENCOURSE_ASSETS_URL results in error that says:
"Routing error: 'get-all' not implemented". To avoid that, the last
two chara... | coursera/api.py | def _normalize_assets(self, assets):
"""
Perform asset normalization. For some reason, assets that are sometimes
present in lectures, have "@1" at the end of their id. Such "uncut"
asset id when fed to OPENCOURSE_ASSETS_URL results in error that says:
"Routing error: 'get-all' no... | def _normalize_assets(self, assets):
"""
Perform asset normalization. For some reason, assets that are sometimes
present in lectures, have "@1" at the end of their id. Such "uncut"
asset id when fed to OPENCOURSE_ASSETS_URL results in error that says:
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train | CourseraOnDemand._extract_links_from_lecture_assets | Extract links to files of the asset ids.
@param asset_ids: List of asset ids.
@type asset_ids: [str]
@return: @see CourseraOnDemand._extract_links_from_text | coursera/api.py | def _extract_links_from_lecture_assets(self, asset_ids):
"""
Extract links to files of the asset ids.
@param asset_ids: List of asset ids.
@type asset_ids: [str]
@return: @see CourseraOnDemand._extract_links_from_text
"""
links = {}
def _add_asset(name,... | def _extract_links_from_lecture_assets(self, asset_ids):
"""
Extract links to files of the asset ids.
@param asset_ids: List of asset ids.
@type asset_ids: [str]
@return: @see CourseraOnDemand._extract_links_from_text
"""
links = {}
def _add_asset(name,... | [
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train | CourseraOnDemand._get_asset_urls | Get list of asset urls and file names. This method may internally
use AssetRetriever to extract `asset` element types.
@param asset_id: Asset ID.
@type asset_id: str
@return List of dictionaries with asset file names and urls.
@rtype [{
'name': '<filename.ext>'
... | coursera/api.py | def _get_asset_urls(self, asset_id):
"""
Get list of asset urls and file names. This method may internally
use AssetRetriever to extract `asset` element types.
@param asset_id: Asset ID.
@type asset_id: str
@return List of dictionaries with asset file names and urls.
... | def _get_asset_urls(self, asset_id):
"""
Get list of asset urls and file names. This method may internally
use AssetRetriever to extract `asset` element types.
@param asset_id: Asset ID.
@type asset_id: str
@return List of dictionaries with asset file names and urls.
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train | CourseraOnDemand.extract_links_from_peer_assignment | Return a dictionary with links to supplement files (pdf, csv, zip,
ipynb, html and so on) extracted from peer assignment.
@param element_id: Element ID to extract files from.
@type element_id: str
@return: @see CourseraOnDemand._extract_links_from_text | coursera/api.py | def extract_links_from_peer_assignment(self, element_id):
"""
Return a dictionary with links to supplement files (pdf, csv, zip,
ipynb, html and so on) extracted from peer assignment.
@param element_id: Element ID to extract files from.
@type element_id: str
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train | CourseraOnDemand.extract_links_from_supplement | Return a dictionary with supplement files (pdf, csv, zip, ipynb, html
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@return: @see CourseraOnDemand._extract_links_from_text | coursera/api.py | def extract_links_from_supplement(self, element_id):
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@return: @see CourseraOnDemand._extract_links_from_text
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train | CourseraOnDemand._extract_asset_tags | Extract asset tags from text into a convenient form.
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@type text: str
@return: Asset map.
@rtype: {
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@param text: Text to extract asset tags from. This text contains HTML
code that is parsed by BeautifulSoup.
@type text: str
@return: Asset map.
@rtype: {
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Extract asset tags from text into a convenient form.
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@type text: str
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train | CourseraOnDemand._extract_asset_urls | Extract asset URLs along with asset ids.
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@type assertn: [str]
@return: List of dictionaries with asset URLs and ids.
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"""
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@param asset_ids: List of ids to get URLs for.
@type assertn: [str]
@return: List of dictionaries with asset URLs and ids.
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Extract asset URLs along with asset ids.
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train | CourseraOnDemand.extract_links_from_reference | Return a dictionary with supplement files (pdf, csv, zip, ipynb, html
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@return: @see CourseraOnDemand._extract_links_from_text | coursera/api.py | def extract_links_from_reference(self, short_id):
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@return: @see CourseraOnDemand._extract_links_from_text
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train | CourseraOnDemand._extract_programming_immediate_instructions_text | Extract assignment text (instructions).
@param element_id: Element id to extract assignment instructions from.
@type element_id: str
@return: List of assignment text (instructions).
@rtype: [str] | coursera/api.py | def _extract_programming_immediate_instructions_text(self, element_id):
"""
Extract assignment text (instructions).
@param element_id: Element id to extract assignment instructions from.
@type element_id: str
@return: List of assignment text (instructions).
@rtype: [str... | def _extract_programming_immediate_instructions_text(self, element_id):
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Extract assignment text (instructions).
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train | CourseraOnDemand._extract_notebook_text | Extract notebook text (instructions).
@param element_id: Element id to extract notebook links.
@type element_id: str
@return: Notebook URL.
@rtype: [str] | coursera/api.py | def _extract_notebook_text(self, element_id):
"""
Extract notebook text (instructions).
@param element_id: Element id to extract notebook links.
@type element_id: str
@return: Notebook URL.
@rtype: [str]
"""
headers = self._auth_headers_with_json()
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Extract notebook text (instructions).
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@type element_id: str
@return: Notebook URL.
@rtype: [str]
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train | CourseraOnDemand._extract_assignment_text | Extract assignment text (instructions).
@param element_id: Element id to extract assignment instructions from.
@type element_id: str
@return: List of assignment text (instructions).
@rtype: [str] | coursera/api.py | def _extract_assignment_text(self, element_id):
"""
Extract assignment text (instructions).
@param element_id: Element id to extract assignment instructions from.
@type element_id: str
@return: List of assignment text (instructions).
@rtype: [str]
"""
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"""
Extract assignment text (instructions).
@param element_id: Element id to extract assignment instructions from.
@type element_id: str
@return: List of assignment text (instructions).
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train | CourseraOnDemand._extract_peer_assignment_text | Extract peer assignment text (instructions).
@param element_id: Element id to extract peer assignment instructions from.
@type element_id: str
@return: List of peer assignment text (instructions).
@rtype: [str] | coursera/api.py | def _extract_peer_assignment_text(self, element_id):
"""
Extract peer assignment text (instructions).
@param element_id: Element id to extract peer assignment instructions from.
@type element_id: str
@return: List of peer assignment text (instructions).
@rtype: [str]
... | def _extract_peer_assignment_text(self, element_id):
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Extract peer assignment text (instructions).
@param element_id: Element id to extract peer assignment instructions from.
@type element_id: str
@return: List of peer assignment text (instructions).
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train | CourseraOnDemand._extract_links_from_text | Extract supplement links from the html text. Links may be provided
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2. <asset> tags with id attribute (requires additional request
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@param text: HTML text.
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"""
Extract supplement links from the html text. Links may be provided
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Extract supplement links from the html text. Links may be provided
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train | CourseraOnDemand._extract_links_from_asset_tags_in_text | Scan the text and extract asset tags and links to corresponding
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@param text: Page text.
@type text: str
@return: @see CourseraOnDemand._extract_links_from_text | coursera/api.py | def _extract_links_from_asset_tags_in_text(self, text):
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Scan the text and extract asset tags and links to corresponding
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@param text: Page text.
@type text: str
@return: @see CourseraOnDemand._extract_links_from_text
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# Extract asset tags fr... | def _extract_links_from_asset_tags_in_text(self, text):
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Scan the text and extract asset tags and links to corresponding
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train | CourseraOnDemand._extract_links_from_a_tags_in_text | Extract supplement links from the html text that contains <a> tags
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@type text: str
@return: Dictionary with supplement links grouped by extension.
@rtype: {
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"""
Extract supplement links from the html text that contains <a> tags
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@param text: HTML text.
@type text: str
@return: Dictionary with supplement links grouped by extension.
@rtype: {
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@param text: HTML text.
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train | create_m3u_playlist | Create M3U playlist with contents of `section_dir`/*.mp4. The playlist
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@param section_dir: Path where to scan for *.mp4 files.
@type section_dir: str | coursera/playlist.py | def create_m3u_playlist(section_dir):
"""
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@param section_dir: Path where to scan for *.mp4 files.
@type section_dir: str
"""
path_to_return = os.getcwd()
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"""
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train | CourseraExtractor.list_courses | List enrolled courses.
@return: List of enrolled courses.
@rtype: [str] | coursera/extractors.py | def list_courses(self):
"""
List enrolled courses.
@return: List of enrolled courses.
@rtype: [str]
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course = CourseraOnDemand(session=self._session,
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course_name=None)
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"""
List enrolled courses.
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@rtype: [str]
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course = CourseraOnDemand(session=self._session,
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train | CourseraExtractor._get_on_demand_syllabus | Get the on-demand course listing webpage. | coursera/extractors.py | def _get_on_demand_syllabus(self, class_name):
"""
Get the on-demand course listing webpage.
"""
url = OPENCOURSE_ONDEMAND_COURSE_MATERIALS_V2.format(
class_name=class_name)
page = get_page(self._session, url)
logging.debug('Downloaded %s (%d bytes)', url, le... | def _get_on_demand_syllabus(self, class_name):
"""
Get the on-demand course listing webpage.
"""
url = OPENCOURSE_ONDEMAND_COURSE_MATERIALS_V2.format(
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page = get_page(self._session, url)
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train | CourseraExtractor._parse_on_demand_syllabus | Parse a Coursera on-demand course listing/syllabus page.
@return: Tuple of (bool, list), where bool indicates whether
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@rtype: (bool, list) | coursera/extractors.py | def _parse_on_demand_syllabus(self, course_name, page, reverse=False,
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video_resolution=None,
download_quizzes=False,
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train | _iter_modules | This huge function generates a hierarchy with hopefully more
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"""
This huge function generates a hierarchy with hopefully more
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"""
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train | _walk_modules | Helper generator that traverses modules in returns a flattened
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Helper generator that traverses modules in returns a flattened
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@type url: str
@param fmt: Format of the resource (pdf, csv, etc)
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@type combined_section_lectures_nums: bool
@param section_dir: Path to current section directory.
@type section_dir: str
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secnum,
lecnum,
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title,
fmt):
"""
Prepare a destination lecture filename.
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train | format_bytes | Get human readable version of given bytes.
Ripped from https://github.com/rg3/youtube-dl | coursera/downloaders.py | def format_bytes(bytes):
"""
Get human readable version of given bytes.
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train | get_downloader | Decides which downloader to use. | coursera/downloaders.py | def get_downloader(session, class_name, args):
"""
Decides which downloader to use.
"""
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'curl': CurlDownloader,
'aria2': Aria2Downloader,
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for bin, class_ in iteritems(external):
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Decides which downloader to use.
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train | Downloader.download | Download the given url to the given file. When the download
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"""
Download the given url to the given file. When the download
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try:
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train | ExternalDownloader._prepare_cookies | Extract cookies from the requests session and add them to the command | coursera/downloaders.py | def _prepare_cookies(self, command, url):
"""
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train | DownloadProgress.report_progress | Report download progress. | coursera/downloaders.py | def report_progress(self):
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percent = self.calc_percent()
total = format_bytes(self._total)
speed = self.calc_speed()
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train | AbstractDownloader._download_wrapper | Actual download call. Calls the underlying file downloader,
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"""
Actual download call. Calls the underlying file downloader,
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try:
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For Windows platforms, there are several paths that can be tried to
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A brief recap of the situation (all fil... | coursera/credentials.py | def get_config_paths(config_name): # pragma: no test
"""
Return a list of config files paths to try in order, given config file
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train | authenticate_through_netrc | Return the tuple user / password given a path for the .netrc file.
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"""
Return the tuple user / password given a path for the .netrc file.
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for path in path... | def authenticate_through_netrc(path=None):
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train | get_credentials | Return valid username, password tuple.
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"""
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"""
if netrc:
path = None if netrc is True else netrc
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... | def get_credentials(username=None, password=None, netrc=None, use_keyring=False):
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Return valid username, password tuple.
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] | coursera-dl/coursera-dl | python | https://github.com/coursera-dl/coursera-dl/blob/9b434bcf3c4011bf3181429fe674633ae5fb7d4d/coursera/credentials.py#L141-L164 | [
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