code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def wrap_dqn(env, stack_frames=4, episodic_life=True,
reward_clipping=True, wrap_ndarray=False):
"""
Apply a common set of wrappers for Atari games.
"""
assert 'NoFrameskip' in env.spec.id
if episodic_life:
env = EpisodicLifeEnv(env)
env = NoopResetEnv(env, noop_max=30)
... |
Apply a common set of wrappers for Atari games.
| wrap_dqn | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env_fn, batch_size, thread_pool=4, max_episode_steps=1000):
"""
Args:
env_fn: function
Function to make an environment
batch_size: int
Batch size
thread_pool: int
Thread pool size
max_epis... |
Args:
env_fn: function
Function to make an environment
batch_size: int
Batch size
thread_pool: int
Thread pool size
max_episode_steps: int
Maximum step of an episode
| __init__ | python | keiohta/tf2rl | tf2rl/envs/multi_thread_env.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/multi_thread_env.py | MIT |
def step(self, actions, name=None):
"""
Args:
actions: tf.Tensor
Actions whose shape is float32[batch_size, dim_action]
name: str
Operator name
Returns:
obs: tf.Tensor
[batch_size, dim_obs]
reward: ... |
Args:
actions: tf.Tensor
Actions whose shape is float32[batch_size, dim_action]
name: str
Operator name
Returns:
obs: tf.Tensor
[batch_size, dim_obs]
reward: tf.Tensor
[batch_size]
... | step | python | keiohta/tf2rl | tf2rl/envs/multi_thread_env.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/multi_thread_env.py | MIT |
def py_step(self, actions):
"""
Args:
actions: np.array
Actions whose shape is [batch_size, dim_action]
Returns:
obs: np.array
reward: np.array
done: np.array
"""
def _process(offset):
for idx_env in ra... |
Args:
actions: np.array
Actions whose shape is [batch_size, dim_action]
Returns:
obs: np.array
reward: np.array
done: np.array
| py_step | python | keiohta/tf2rl | tf2rl/envs/multi_thread_env.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/multi_thread_env.py | MIT |
def experience(self, x):
"""Learn input values without computing the output values of them"""
if self.until is not None and self.count >= self.until:
return
count_x = x.shape[self.batch_axis]
if count_x == 0:
return
self.count += count_x
rate = ... | Learn input values without computing the output values of them | experience | python | keiohta/tf2rl | tf2rl/envs/normalizer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/normalizer.py | MIT |
def __call__(self, x, update=True):
"""Normalize mean and variance of values based on emprical values.
Args:
x (ndarray or Variable): Input values
update (bool): Flag to learn the input values
Returns:
ndarray or Variable: Normalized output values
"""
... | Normalize mean and variance of values based on emprical values.
Args:
x (ndarray or Variable): Input values
update (bool): Flag to learn the input values
Returns:
ndarray or Variable: Normalized output values
| __call__ | python | keiohta/tf2rl | tf2rl/envs/normalizer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/normalizer.py | MIT |
def make(id, **kwargs):
r"""
Make gym.Env with version tolerance
Args:
id (str) : Id specifying `gym.Env` registered to `gym.env.registry`.
Valid format is `"^(?:[\w:-]+\/)?([\w:.-]+)-v(\d+)$"`
See https://github.com/openai/gym/blob/v0.21.0/gym/envs/registratio... |
Make gym.Env with version tolerance
Args:
id (str) : Id specifying `gym.Env` registered to `gym.env.registry`.
Valid format is `"^(?:[\w:-]+\/)?([\w:.-]+)-v(\d+)$"`
See https://github.com/openai/gym/blob/v0.21.0/gym/envs/registration.py#L17-L19
Returns:
... | make | python | keiohta/tf2rl | tf2rl/envs/utils.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/utils.py | MIT |
def __init__(
self,
policy,
env,
args,
irl,
expert_obs,
expert_next_obs,
expert_act,
test_env=None):
"""
Initialize Trainer class
Args:
policy: Policy to be trained
... |
Initialize Trainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
irl
expert_obs
expert_next_obs
expert_act
... | __init__ | python | keiohta/tf2rl | tf2rl/experiments/irl_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/irl_trainer.py | MIT |
def __init__(self, *args, n_eval_episodes_per_model=5, **kwargs):
"""
Initialize ME-TRPO
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.... |
Initialize ME-TRPO
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.Env): Environment for test.
reward_fn (callable): Reward function... | __init__ | python | keiohta/tf2rl | tf2rl/experiments/me_trpo_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/me_trpo_trainer.py | MIT |
def predict_next_state(self, obses, acts, idx=None):
"""
Predict Next State
Args:
obses
acts
idx (int): Index number of dynamics mode to use. If ``None`` (default), choose randomly.
Returns:
np.ndarray: next state
"""
is_s... |
Predict Next State
Args:
obses
acts
idx (int): Index number of dynamics mode to use. If ``None`` (default), choose randomly.
Returns:
np.ndarray: next state
| predict_next_state | python | keiohta/tf2rl | tf2rl/experiments/me_trpo_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/me_trpo_trainer.py | MIT |
def finish_horizon(self, last_val=0):
"""
TODO: These codes are completly identical to the ones defined in on_policy_trainer.py. Use it.
"""
samples = self.local_buffer._encode_sample(
np.arange(self.local_buffer.get_stored_size()))
rews = np.append(samples["rew"], la... |
TODO: These codes are completly identical to the ones defined in on_policy_trainer.py. Use it.
| finish_horizon | python | keiohta/tf2rl | tf2rl/experiments/me_trpo_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/me_trpo_trainer.py | MIT |
def __init__(self, input_dim, output_dim, units=[32, 32], name="DymamicsModel", gpu=0):
"""
Initialize DynamicsModel
Args:
input_dim (int)
output_dim (int)
units (iterable of int): The default is ``[32, 32]``
name (str): The default is ``"Dynamics... |
Initialize DynamicsModel
Args:
input_dim (int)
output_dim (int)
units (iterable of int): The default is ``[32, 32]``
name (str): The default is ``"DynamicsModel"``
gpu (int): The default is ``0``.
| __init__ | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def call(self, inputs):
"""
Call Dynamics Model
Args:
inputs (tf.Tensor)
Returns:
tf.Tensor
"""
features = self.l1(inputs)
features = self.l2(features)
return self.l3(features) |
Call Dynamics Model
Args:
inputs (tf.Tensor)
Returns:
tf.Tensor
| call | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def get_action(self, obs):
"""
Get random action
Args:
obs
Returns:
float: action
"""
return np.random.uniform(
low=-self._max_action,
high=self._max_action,
size=self._act_dim) |
Get random action
Args:
obs
Returns:
float: action
| get_action | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def get_actions(self, obses):
"""
Get batch actions
Args:
obses
Returns:
np.dnarray: batch actions
"""
batch_size = obses.shape[0]
return np.random.uniform(
low=-self._max_action,
high=self._max_action,
... |
Get batch actions
Args:
obses
Returns:
np.dnarray: batch actions
| get_actions | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def __init__(
self,
policy,
env,
args,
reward_fn,
buffer_size=int(1e6),
n_dynamics_model=1,
lr=0.001,
**kwargs):
"""
Initialize MPCTrainer class
Args:
policy: Policy to be tra... |
Initialize MPCTrainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.Env): Environment for test.
reward_fn (callable): Reward... | __init__ | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def predict_next_state(self, obses, acts):
"""
Predict Next State
Args:
obses
acts
Returns:
np.ndarray: next state
"""
obs_diffs = np.zeros_like(obses)
inputs = np.concatenate([obses, acts], axis=1)
for dynamics_model ... |
Predict Next State
Args:
obses
acts
Returns:
np.ndarray: next state
| predict_next_state | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def collect_episodes(self, n_rollout=1):
"""
Collect Episodes
Args:
n_rollout (int): Number of rollout. The default is ``1``
"""
for _ in range(n_rollout):
obs = self._env.reset()
for _ in range(self._episode_max_steps):
act = ... |
Collect Episodes
Args:
n_rollout (int): Number of rollout. The default is ``1``
| collect_episodes | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def fit_dynamics(self, n_epoch=1):
"""
Fit dynamics
Args:
n_epocs (int): Number of epocs to fit
"""
inputs, labels = self._make_inputs_output_pairs(n_epoch)
dataset = tf.data.Dataset.from_tensor_slices((inputs, labels))
dataset = dataset.batch(self._... |
Fit dynamics
Args:
n_epocs (int): Number of epocs to fit
| fit_dynamics | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def evaluate_policy(self, total_steps):
"""
Evaluate policy
Args:
total_steps (int): Current total steps of training
"""
avg_test_return = 0.
avg_test_steps = 0
if self._save_test_path:
replay_buffer = get_replay_buffer(
se... |
Evaluate policy
Args:
total_steps (int): Current total steps of training
| evaluate_policy | python | keiohta/tf2rl | tf2rl/experiments/on_policy_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/on_policy_trainer.py | MIT |
def __init__(
self,
policy,
env,
args,
test_env=None):
"""
Initialize Trainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters... |
Initialize Trainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.Env): Environment for test.
| __init__ | python | keiohta/tf2rl | tf2rl/experiments/trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/trainer.py | MIT |
def evaluate_policy_continuously(self):
"""
Periodically search the latest checkpoint, and keep evaluating with the latest model until user kills process.
"""
if self._model_dir is None:
self.logger.error("Please specify model directory by passing command line argument `--mod... |
Periodically search the latest checkpoint, and keep evaluating with the latest model until user kills process.
| evaluate_policy_continuously | python | keiohta/tf2rl | tf2rl/experiments/trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/trainer.py | MIT |
def discount_cumsum(x, discount):
"""Forked from rllab for computing discounted cumulative sums of vectors.
Args:
x: np.ndarray or tf.Tensor
Vector of inputs
discount: float
Discount factor
Returns:
Discounted cumulative summation. If input is [x0, x1, x2], ... | Forked from rllab for computing discounted cumulative sums of vectors.
Args:
x: np.ndarray or tf.Tensor
Vector of inputs
discount: float
Discount factor
Returns:
Discounted cumulative summation. If input is [x0, x1, x2], then the output is:
[x0 + dis... | discount_cumsum | python | keiohta/tf2rl | tf2rl/misc/discount_cumsum.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/discount_cumsum.py | MIT |
def huber_loss(x, delta=1.):
"""
Args:
x: np.ndarray or tf.Tensor
Values to compute the huber loss.
delta: float
Positive floating point value. Represents the
maximum possible gradient magnitude.
Returns: tf.Tensor
The huber loss.
"""
del... |
Args:
x: np.ndarray or tf.Tensor
Values to compute the huber loss.
delta: float
Positive floating point value. Represents the
maximum possible gradient magnitude.
Returns: tf.Tensor
The huber loss.
| huber_loss | python | keiohta/tf2rl | tf2rl/misc/huber_loss.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/huber_loss.py | MIT |
def observe(self, x):
"""Compute next mean and std
Args:
x: float
Input data.
"""
self._n.assign_add(1)
numerator = x - self._mean
self._mean.assign_add((x - self._mean) / self._n)
self._mean_diff.assign_add(numerator * (x - self._mean... | Compute next mean and std
Args:
x: float
Input data.
| observe | python | keiohta/tf2rl | tf2rl/misc/normalizer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/normalizer.py | MIT |
def periodically(body, period, name="periodically"):
"""
Periodically performs a tensorflow op.
The body tensorflow op will be executed every `period` times the periodically
op is executed. More specifically, with `n` the number of times the op has
been executed, the body will be executed when `n` ... |
Periodically performs a tensorflow op.
The body tensorflow op will be executed every `period` times the periodically
op is executed. More specifically, with `n` the number of times the op has
been executed, the body will be executed when `n` is a non zero positive
multiple of `period` (i.e. there ... | periodically | python | keiohta/tf2rl | tf2rl/misc/periodic_ops.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/periodic_ops.py | MIT |
def is_return_code_zero(args):
"""
Return true if the given command's return code is zero.
All the messages to stdout or stderr are suppressed.
forked from https://github.com/chainer/chainerrl/blob/master/chainerrl/misc/is_return_code_zero.py
"""
with open(os.devnull, 'wb') as FNULL:
try... |
Return true if the given command's return code is zero.
All the messages to stdout or stderr are suppressed.
forked from https://github.com/chainer/chainerrl/blob/master/chainerrl/misc/is_return_code_zero.py
| is_return_code_zero | python | keiohta/tf2rl | tf2rl/misc/prepare_output_dir.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/prepare_output_dir.py | MIT |
def prepare_output_dir(args, user_specified_dir=None, argv=None,
time_format='%Y%m%dT%H%M%S.%f', suffix=""):
"""
Prepare a directory for outputting training results.
An output directory, which ends with the current datetime string,
is created. Then the following infomation is save... |
Prepare a directory for outputting training results.
An output directory, which ends with the current datetime string,
is created. Then the following infomation is saved into the directory:
args.txt: command line arguments
command.txt: command itself
environ.txt: environmental varia... | prepare_output_dir | python | keiohta/tf2rl | tf2rl/misc/prepare_output_dir.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/prepare_output_dir.py | MIT |
def _compute_dist(self, states):
"""
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.Categorical
Categorical distribution whose probabilities are
computed using softmax activation... |
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.Categorical
Categorical distribution whose probabilities are
computed using softmax activation of a neural network
| _compute_dist | python | keiohta/tf2rl | tf2rl/policies/tfp_categorical_actor.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/policies/tfp_categorical_actor.py | MIT |
def _compute_dist(self, states):
"""
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.MultivariateNormalDiag
Multivariate normal distribution object whose mean and
standard deviati... |
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.MultivariateNormalDiag
Multivariate normal distribution object whose mean and
standard deviation is output of a neural network
| _compute_dist | python | keiohta/tf2rl | tf2rl/policies/tfp_gaussian_actor.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/policies/tfp_gaussian_actor.py | MIT |
def call(self, states, test=False):
"""
Compute actions and log probabilities of the selected action
"""
dist = self._compute_dist(states)
if test:
raw_actions = dist.mean()
else:
raw_actions = dist.sample()
log_pis = dist.log_prob(raw_acti... |
Compute actions and log probabilities of the selected action
| call | python | keiohta/tf2rl | tf2rl/policies/tfp_gaussian_actor.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/policies/tfp_gaussian_actor.py | MIT |
def random_crop(input_imgs, output_size):
"""
Args:
input_imgs: np.ndarray
Images whose shape is (batch_size, width, height, channels)
output_size: Int
Output width and height size.
Returns:
"""
assert input_imgs.ndim == 4, f"The dimension of input images m... |
Args:
input_imgs: np.ndarray
Images whose shape is (batch_size, width, height, channels)
output_size: Int
Output width and height size.
Returns:
| random_crop | python | keiohta/tf2rl | tf2rl/tools/img_tools.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/tools/img_tools.py | MIT |
def center_crop(img, output_size):
"""
Args:
img: np.ndarray
Input image array. The shape is (width, height, channel)
output_size: int
Width and height size for output image
Returns:
"""
is_single_img = img.ndim == 3
h, w = img.shape[:2] if is_single_i... |
Args:
img: np.ndarray
Input image array. The shape is (width, height, channel)
output_size: int
Width and height size for output image
Returns:
| center_crop | python | keiohta/tf2rl | tf2rl/tools/img_tools.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/tools/img_tools.py | MIT |
def preprocess_img(img, bits=5):
"""Preprocessing image, see https://arxiv.org/abs/1807.03039."""
bins = 2 ** bits
if bits < 8:
obs = tf.cast(tf.floor(img / 2 ** (8 - bits)), dtype=tf.float32)
obs = obs / bins
obs = obs + tf.random.uniform(shape=obs.shape) / bins
obs = obs - 0.5
retu... | Preprocessing image, see https://arxiv.org/abs/1807.03039. | preprocess_img | python | keiohta/tf2rl | tf2rl/tools/img_tools.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/tools/img_tools.py | MIT |
def resolve_snakefile(path: Optional[Path], allow_missing: bool = False):
"""Get path to the snakefile.
Arguments
---------
path: Optional[Path] -- The path to the snakefile. If not provided, default locations will be tried.
"""
if path is None:
for p in SNAKEFILE_CHOICES:
i... | Get path to the snakefile.
Arguments
---------
path: Optional[Path] -- The path to the snakefile. If not provided, default locations will be tried.
| resolve_snakefile | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def workflow(
self,
resource_settings: ResourceSettings,
config_settings: Optional[ConfigSettings] = None,
storage_settings: Optional[StorageSettings] = None,
workflow_settings: Optional[WorkflowSettings] = None,
deployment_settings: Optional[DeploymentSettings] = None,
... | Create the workflow API.
Note that if provided, this also changes to the provided workdir.
It will change back to the previous working directory when the workflow API object is deleted.
Arguments
---------
config_settings: ConfigSettings -- The config settings for the workflow.... | workflow | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def print_exception(self, ex: Exception):
"""Print an exception during workflow execution in a human readable way
(with adjusted line numbers for exceptions raised in Snakefiles and stack
traces that hide Snakemake internals for better readability).
Arguments
---------
e... | Print an exception during workflow execution in a human readable way
(with adjusted line numbers for exceptions raised in Snakefiles and stack
traces that hide Snakemake internals for better readability).
Arguments
---------
ex: Exception -- The exception to print.
| print_exception | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def dag(
self,
dag_settings: Optional[DAGSettings] = None,
):
"""Create a DAG API.
Arguments
---------
dag_settings: DAGSettings -- The DAG settings for the DAG API.
"""
if dag_settings is None:
dag_settings = DAGSettings()
return... | Create a DAG API.
Arguments
---------
dag_settings: DAGSettings -- The DAG settings for the DAG API.
| dag | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def lint(self, json: bool = False):
"""Lint the workflow.
Arguments
---------
json: bool -- Whether to print the linting results as JSON.
Returns
-------
True if any lints were printed
"""
workflow = self._get_workflow(check_envvars=False)
... | Lint the workflow.
Arguments
---------
json: bool -- Whether to print the linting results as JSON.
Returns
-------
True if any lints were printed
| lint | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def execute_workflow(
self,
executor: str = "local",
execution_settings: Optional[ExecutionSettings] = None,
remote_execution_settings: Optional[RemoteExecutionSettings] = None,
scheduling_settings: Optional[SchedulingSettings] = None,
group_settings: Optional[GroupSettin... | Execute the workflow.
Arguments
---------
executor: str -- The executor to use.
execution_settings: ExecutionSettings -- The execution settings for the workflow.
resource_settings: ResourceSettings -- The resource settings for the workflow.
remote_execution_settings: Rem... | execute_workflow | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def create_report(
self,
reporter: str = "html",
report_settings: Optional[ReportSettingsBase] = None,
):
"""Create a report for the workflow.
Arguments
---------
report: Path -- The path to the report.
report_stylesheet: Optional[Path] -- The path to... | Create a report for the workflow.
Arguments
---------
report: Path -- The path to the report.
report_stylesheet: Optional[Path] -- The path to the report stylesheet.
reporter: str -- report plugin to use (default: html)
| create_report | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def conda_cleanup_envs(self):
"""Cleanup the conda environments of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.CONDA
)
self.workflow_api._workflow.conda_cleanup_envs() | Cleanup the conda environments of the workflow. | conda_cleanup_envs | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def conda_create_envs(self):
"""Only create the conda environments of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.CONDA
)
self.workflow_api._workflow.conda_create_envs() | Only create the conda environments of the workflow. | conda_create_envs | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def conda_list_envs(self):
"""List the conda environments of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.CONDA
)
self.workflow_api._workflow.conda_list_envs() | List the conda environments of the workflow. | conda_list_envs | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def container_cleanup_images(self):
"""Cleanup the container images of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.APPTAINER
)
self.workflow_api._workflow.container_cleanup_images() | Cleanup the container images of the workflow. | container_cleanup_images | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def timedelta_to_str(self, x):
"""Conversion of timedelta to str without fractions of seconds"""
mm, ss = divmod(x.seconds, 60)
hh, mm = divmod(mm, 60)
s = "%d:%02d:%02d" % (hh, mm, ss)
if x.days:
def plural(n):
return n, abs(n) != 1 and "s" or ""
... | Conversion of timedelta to str without fractions of seconds | timedelta_to_str | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def to_tsv(self, extended_fmt):
"""Return ``str`` with the TSV representation of this record"""
def to_tsv_str(x):
"""Conversion of value to str for TSV (None becomes "-")"""
if x is None:
return "-"
elif isinstance(x, float):
return f... | Return ``str`` with the TSV representation of this record | to_tsv | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def to_tsv_str(x):
"""Conversion of value to str for TSV (None becomes "-")"""
if x is None:
return "-"
elif isinstance(x, float):
return f"{x:.2f}"
else:
return str(x) | Conversion of value to str for TSV (None becomes "-") | to_tsv_str | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def to_json(self, extended_fmt):
"""Return ``str`` with the JSON representation of this record"""
import json
return json.dumps(
dict(zip(self.get_header(extended_fmt), self.get_benchmarks(extended_fmt))),
sort_keys=True,
) | Return ``str`` with the JSON representation of this record | to_json | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def benchmarked(pid=None, benchmark_record=None, interval=BENCHMARK_INTERVAL):
"""Measure benchmark parameters while within the context manager
Yields a ``BenchmarkRecord`` with the results (values are set after
leaving context).
If ``pid`` is ``None`` then the PID of the current process will be used.... | Measure benchmark parameters while within the context manager
Yields a ``BenchmarkRecord`` with the results (values are set after
leaving context).
If ``pid`` is ``None`` then the PID of the current process will be used.
If ``benchmark_record`` is ``None`` then a new ``BenchmarkRecord`` is
created... | benchmarked | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def print_benchmark_tsv(records, file_, extended_fmt):
"""Write benchmark records to file-like the object"""
logger.debug("Benchmarks in TSV format")
print("\t".join(BenchmarkRecord.get_header(extended_fmt)), file=file_)
for r in records:
print(r.to_tsv(extended_fmt), file=file_) | Write benchmark records to file-like the object | print_benchmark_tsv | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def write_benchmark_records(records, path, extended_fmt):
"""Write benchmark records to file at path"""
with open(path, "wt") as f:
if path.endswith(".jsonl"):
print_benchmark_jsonl(records, f, extended_fmt)
else:
print_benchmark_tsv(records, f, extended_fmt) | Write benchmark records to file at path | write_benchmark_records | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def parse_consider_ancient(
args: Optional[List[str]],
) -> Mapping[str, Set[Union[str, int]]]:
"""Parse command line arguments for marking input files as ancient.
Args:
args: List of RULE=INPUTITEMS pairs, where INPUTITEMS is a comma-separated list
of input item names or indices (0-b... | Parse command line arguments for marking input files as ancient.
Args:
args: List of RULE=INPUTITEMS pairs, where INPUTITEMS is a comma-separated list
of input item names or indices (0-based).
Returns:
A mapping of rules to sets of their ancient input items.
Raises:
... | parse_consider_ancient | python | snakemake/snakemake | src/snakemake/cli.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cli.py | MIT |
def generate_parser_metadata(parser, args):
"""Given a populated parser, generate the original command along with
metadata that can be handed to a logger to use as needed.
"""
command = "snakemake %s" % " ".join(
parser._source_to_settings["command_line"][""][1]
)
metadata = args.__dict_... | Given a populated parser, generate the original command along with
metadata that can be handed to a logger to use as needed.
| generate_parser_metadata | python | snakemake/snakemake | src/snakemake/cli.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cli.py | MIT |
def args_to_api(args, parser):
"""Convert argparse args to API calls."""
# handle legacy executor names
if args.dryrun:
args.executor = "dryrun"
elif args.touch:
args.executor = "touch"
elif args.executor is None:
args.executor = "local"
if args.report:
args.rep... | Convert argparse args to API calls. | args_to_api | python | snakemake/snakemake | src/snakemake/cli.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cli.py | MIT |
def cwl(
path,
basedir,
input,
output,
params,
wildcards,
threads,
resources,
log,
config,
rulename,
use_singularity,
bench_record,
jobid,
sourcecache_path,
runtime_sourcecache_path,
):
"""
Load cwl from the given basedir + path and execute it.
... |
Load cwl from the given basedir + path and execute it.
| cwl | python | snakemake/snakemake | src/snakemake/cwl.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cwl.py | MIT |
def job_to_cwl(job, dag, outputs, inputs):
"""Convert a job with its dependencies to a CWL workflow step."""
for f in job.output:
if os.path.isabs(f):
raise WorkflowError(
"All output files have to be relative to the working directory."
)
get_output_id = lamb... | Convert a job with its dependencies to a CWL workflow step. | job_to_cwl | python | snakemake/snakemake | src/snakemake/cwl.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cwl.py | MIT |
def dag_to_cwl(dag):
"""Convert a given DAG to a CWL workflow, which is returned as a JSON object."""
snakemake_cwl = {
"class": "CommandLineTool",
"id": "#snakemake-job",
"label": "Snakemake job executor",
"hints": [{"dockerPull": get_container_image(), "class": "DockerRequireme... | Convert a given DAG to a CWL workflow, which is returned as a JSON object. | dag_to_cwl | python | snakemake/snakemake | src/snakemake/cwl.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cwl.py | MIT |
def check_directory_outputs(self):
"""Check that no output file is contained in a directory output of the same or another rule."""
outputs = sorted(
{(os.path.abspath(f), job) for job in self.jobs for f in job.output}
)
for i in range(len(outputs) - 1):
(a, job_a)... | Check that no output file is contained in a directory output of the same or another rule. | check_directory_outputs | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def sanitize_local_storage_copies(self):
"""Remove local copies of storage files that will be recreated in this run."""
async with asyncio.TaskGroup() as tg:
for job in self.needrun_jobs():
if not self.finished(job):
for f in job.output:
... | Remove local copies of storage files that will be recreated in this run. | sanitize_local_storage_copies | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def check_incomplete(self):
"""Check if any output files are incomplete. This is done by looking up
markers in the persistence module."""
if not self.ignore_incomplete:
incomplete_files = await self.incomplete_files()
if any(incomplete_files):
if sel... | Check if any output files are incomplete. This is done by looking up
markers in the persistence module. | check_incomplete | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def incomplete_external_jobid(self, job) -> Optional[str]:
"""Return the external jobid of the job if it is marked as incomplete.
Returns None, if job is not incomplete, or if no external jobid has been
registered or if force_incomplete is True.
"""
if self.workflow.dag_settings... | Return the external jobid of the job if it is marked as incomplete.
Returns None, if job is not incomplete, or if no external jobid has been
registered or if force_incomplete is True.
| incomplete_external_jobid | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def needrun_jobs(self, exclude_finished=True):
"""Jobs that need to be executed."""
if exclude_finished:
return filterfalse(self.finished, self._needrun)
else:
return iter(self._needrun) | Jobs that need to be executed. | needrun_jobs | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def newversion_files(self):
"""Return list of files where the current version is newer than the
recorded version.
"""
return list(
chain(
*(
job.output
for job in filter(self.workflow.persistence.newversion, self.jobs)
... | Return list of files where the current version is newer than the
recorded version.
| newversion_files | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def check_and_touch_output(
self,
job: Job,
wait: int = 3,
ignore_missing_output: Union[List[_IOFile], bool] = False,
no_touch: bool = False,
wait_for_local: bool = True,
check_output_mtime: bool = True,
):
"""Raise exception if output files of j... | Raise exception if output files of job are missing. | check_and_touch_output | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def correctly_flagged_with_dir(f):
"""Check that files flagged as directories are in fact directories
In ambiguous cases, such as when f is managed by a storage backend, or f
doesn't exist and
ignore_missing_output is true, always return True
"""
... | Check that files flagged as directories are in fact directories
In ambiguous cases, such as when f is managed by a storage backend, or f
doesn't exist and
ignore_missing_output is true, always return True
| correctly_flagged_with_dir | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def unshadow_output(self, job, only_log=False, keep_shadow_dir=False):
"""Move files from shadow directory to real output paths."""
"""If shadow directory is kept, returns the path of it."""
if not job.shadow_dir or not job.output:
return
files = job.log if only_log else cha... | Move files from shadow directory to real output paths. | unshadow_output | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def check_periodic_wildcards(self, job):
"""Raise an exception if a wildcard of the given job appears to be periodic,
indicating a cyclic dependency."""
for wildcard, value in job.wildcards_dict.items():
periodic_substring = self.periodic_wildcard_detector.is_periodic(value)
... | Raise an exception if a wildcard of the given job appears to be periodic,
indicating a cyclic dependency. | check_periodic_wildcards | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def handle_protected(self, job):
"""Write-protect output files that are marked with protected()."""
for f in job.output:
if f in job.protected_output:
logger.info(f"Write-protecting output file {fmt_iofile(f)}.")
f.protect() | Write-protect output files that are marked with protected(). | handle_protected | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def handle_touch(self, job):
"""Touches those output files that are marked for touching."""
for f in job.output:
if f in job.touch_output:
f = job.shadowed_path(f)
logger.info(f"Touching output file {fmt_iofile(f)}.")
f.touch_or_create()
... | Touches those output files that are marked for touching. | handle_touch | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def temp_size(self, job):
"""Return the total size of temporary input files of the job.
If none, return 0.
"""
return sum([await f.size() for f in self.temp_input(job)]) | Return the total size of temporary input files of the job.
If none, return 0.
| temp_size | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def handle_temp(self, job):
"""Remove temp files if they are no longer needed. Update temp_mtimes."""
if self.workflow.storage_settings.notemp:
return
if job.is_group():
for j in job:
await self.handle_temp(j)
return
is_temp = l... | Remove temp files if they are no longer needed. Update temp_mtimes. | handle_temp | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def handle_storage(self, job, store_in_storage=True, store_only_log=False):
"""Remove local files if they are no longer needed and upload."""
if store_in_storage and (
self.workflow.remote_exec or self.workflow.is_main_process
):
# handle output files
fi... | Remove local files if they are no longer needed and upload. | handle_storage | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def jobid(self, job):
"""Return job id of given job."""
if job.is_group():
return job.jobid
else:
return self._jobid[job] | Return job id of given job. | jobid | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def update(
self,
jobs,
file=None,
visited=None,
known_producers=None,
progress=False,
create_inventory=False,
):
"""Update the DAG by adding given jobs and their dependencies."""
if visited is None:
visited = set()
if... | Update the DAG by adding given jobs and their dependencies. | update | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def update_(
self,
job,
visited=None,
known_producers=None,
progress=False,
create_inventory=False,
):
"""Update the DAG by adding the given job and its dependencies."""
if job in self._dependencies:
return
if visited is None:... | Update the DAG by adding the given job and its dependencies. | update_ | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def update_needrun(self, create_inventory=False):
"""Update the information whether a job needs to be executed."""
if create_inventory and self.workflow.is_main_process:
# Concurrently collect mtimes of all existing files.
await self.workflow.iocache.mtime_inventory(self.j... | Update the information whether a job needs to be executed. | update_needrun | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def _check_groups(self):
"""Check whether all groups are valid."""
# find paths of jobs that leave a group and then enter it again
# this is not allowed since then the group depends on itself
def dfs(job, group, visited, outside_jobs, outside_jobs_all, skip_this):
"""Inner f... | Check whether all groups are valid. | _check_groups | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def update_incomplete_input_expand_jobs(self):
"""Update (re-evaluate) all jobs which have incomplete input file expansions.
only filled in the second pass of postprocessing.
"""
updated = False
for job in list(self.jobs):
if job.incomplete_input_expand:
... | Update (re-evaluate) all jobs which have incomplete input file expansions.
only filled in the second pass of postprocessing.
| update_incomplete_input_expand_jobs | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def update_ready(self, jobs=None):
"""Update information whether a job is ready to execute.
Given jobs must be needrun jobs!
"""
if jobs is None:
jobs = self.needrun_jobs()
potential_new_ready_jobs = False
candidate_groups = set()
for job in jobs:
... | Update information whether a job is ready to execute.
Given jobs must be needrun jobs!
| update_ready | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def postprocess(
self,
update_needrun=True,
update_incomplete_input_expand_jobs=True,
check_initial=False,
):
"""Postprocess the DAG. This has to be invoked after any change to the
DAG topology."""
self.cleanup()
self.update_jobids()
if u... | Postprocess the DAG. This has to be invoked after any change to the
DAG topology. | postprocess | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def handle_pipes_and_services(self):
"""Use pipes and services to determine job groups. Check if every pipe has exactly
one consumer"""
visited = set()
for job in self.needrun_jobs():
candidate_groups = set()
user_groups = set()
if job.pipe_group is n... | Use pipes and services to determine job groups. Check if every pipe has exactly
one consumer | handle_pipes_and_services | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def _ready(self, job):
"""Return whether the given job is ready to execute."""
group = self._group.get(job, None)
if group is None:
return self._n_until_ready[job] == 0
else:
n_internal_deps = lambda job: sum(
self._group.get(dep) == group for dep... | Return whether the given job is ready to execute. | _ready | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def finish(self, job, update_checkpoint_dependencies=True):
"""Finish a given job (e.g. remove from ready jobs, mark depending jobs
as ready)."""
self._running.remove(job)
# turn off this job's Reason
if job.is_group():
for j in job:
self.reaso... | Finish a given job (e.g. remove from ready jobs, mark depending jobs
as ready). | finish | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def new_job(
self, rule, targetfile=None, format_wildcards=None, wildcards_dict=None
):
"""Create new job for given rule and (optional) targetfile.
This will reuse existing jobs with the same wildcards."""
product = rule.get_some_product()
if targetfile is None and wild... | Create new job for given rule and (optional) targetfile.
This will reuse existing jobs with the same wildcards. | new_job | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def replace_job(self, job, newjob, recursive=True):
"""Replace given job with new job."""
add_to_targetjobs = job in self.targetjobs
try:
jobid = self.jobid(job)
except KeyError:
# Job has been added while updating another checkpoint,
# jobid is ... | Replace given job with new job. | replace_job | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def collect_potential_dependencies(self, job, known_producers):
"""Collect all potential dependencies of a job. These might contain
ambiguities. The keys of the returned dict represent the files to be considered.
"""
# use a set to circumvent multiple jobs for the same file
... | Collect all potential dependencies of a job. These might contain
ambiguities. The keys of the returned dict represent the files to be considered.
| collect_potential_dependencies | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def bfs(self, direction, *jobs, stop=lambda job: False):
"""Perform a breadth-first traversal of the DAG."""
queue = deque(jobs)
visited = set(queue)
while queue:
job = queue.popleft()
if stop(job):
# stop criterion reached for this node
... | Perform a breadth-first traversal of the DAG. | bfs | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def level_bfs(self, direction, *jobs, stop=lambda job: False):
"""Perform a breadth-first traversal of the DAG, but also yield the
level together with each job."""
queue = [(job, 0) for job in jobs]
visited = set(jobs)
while queue:
job, level = queue.pop(0)
... | Perform a breadth-first traversal of the DAG, but also yield the
level together with each job. | level_bfs | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def dfs(self, direction, *jobs, stop=lambda job: False, post=True):
"""Perform depth-first traversal of the DAG."""
visited = set()
def _dfs(job):
"""Inner function for DFS traversal."""
if stop(job):
return
if not post:
yield ... | Perform depth-first traversal of the DAG. | dfs | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def new_wildcards(self, job):
"""Return wildcards that are newly introduced in this job,
compared to its ancestors."""
new_wildcards = set(job.wildcards.items())
for job_ in self._dependencies[job]:
if not new_wildcards:
return set()
for wildcard i... | Return wildcards that are newly introduced in this job,
compared to its ancestors. | new_wildcards | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def rule2job(self, targetrule):
"""Generate a new job from a given rule."""
if targetrule.has_wildcards():
raise WorkflowError(
"Target rules may not contain wildcards. "
"Please specify concrete files or a rule without wildcards at the command line, "
... | Generate a new job from a given rule. | rule2job | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def hsv_to_htmlhexrgb(h, s, v):
"""Convert hsv colors to hex-encoded rgb colors usable by html."""
import colorsys
hex_r, hex_g, hex_b = (round(255 * x) for x in colorsys.hsv_to_rgb(h, s, v))
return "#{hex_r:0>2X}{hex_g:0>2X}{hex_b:0>2X}".format(
hex_r=he... | Convert hsv colors to hex-encoded rgb colors usable by html. | hsv_to_htmlhexrgb | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def resolve_input_functions(input_files):
"""Iterate over all input files and replace input functions
with a fixed string.
"""
files = []
for f in input_files:
if callable(f):
files.append("<input function>")
... | Iterate over all input files and replace input functions
with a fixed string.
| resolve_input_functions | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def html_node(node_id, node, color):
"""Assemble a html style node for graphviz"""
input_files = resolve_input_functions(node._input)
output_files = [repr(f).strip("'") for f in node._output]
input_header = (
'<b><font point-size="14">↪ input</font><... | Assemble a html style node for graphviz | html_node | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def archive(self, path: Path):
"""Archives workflow such that it can be re-run on a different system.
Archiving includes git versioned files (i.e. Snakefiles, config files, ...),
ancestral input files and conda environments.
"""
if path.suffix == ".tar":
mode = "x"
... | Archives workflow such that it can be re-run on a different system.
Archiving includes git versioned files (i.e. Snakefiles, config files, ...),
ancestral input files and conda environments.
| archive | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def is_external_input(self, file, job, not_needrun_is_external=False):
"""Return True if the given file is an external input for the given job."""
consider = lambda job: True
if not_needrun_is_external:
consider = lambda job: self.needrun(job)
return not any(
file... | Return True if the given file is an external input for the given job. | is_external_input | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
async def clean(self, only_temp=False, dryrun=False):
"""Removes files generated by the workflow."""
for job in self.jobs:
for f in job.output:
if not only_temp or is_flagged(f, "temp"):
# The reason for the second check is that dangling
... | Removes files generated by the workflow. | clean | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def list_untracked(self):
"""List files in the workdir that are not in the dag."""
used_files = set()
files_in_cwd = set()
for job in self.jobs:
used_files.update(
os.path.relpath(file)
for file in chain(job.local_input, job.local_output, job.l... | List files in the workdir that are not in the dag. | list_untracked | python | snakemake/snakemake | src/snakemake/dag.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/dag.py | MIT |
def format_exception_to_string(ex, linemaps=None):
"""
Returns the error message for a given exception as a string.
Arguments
ex -- the exception
linemaps -- a dict of a dict that maps for each snakefile
the compiled lines to source code lines in the snakefile.
"""
if isinstance(ex,... |
Returns the error message for a given exception as a string.
Arguments
ex -- the exception
linemaps -- a dict of a dict that maps for each snakefile
the compiled lines to source code lines in the snakefile.
| format_exception_to_string | python | snakemake/snakemake | src/snakemake/exceptions.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/exceptions.py | MIT |
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