code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
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import mrl
from mrl.utils.misc import soft_update, flatten_state
from mrl.modules.model import PytorchModel
import numpy as np
import torch
import torch.nn.functional as F
import os
class QValuePolicy(mrl.Module):
""" For acting in the environment"""
def __init__(self):
super().__init__(
'policy',
... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/algorithms/discrete_off_policy.py | 0.775605 | 0.238572 | discrete_off_policy.py | pypi |
import mrl
from mrl.utils.misc import soft_update, flatten_state
from mrl.modules.model import PytorchModel
import numpy as np
import torch
import torch.nn.functional as F
import os
class ActorPolicy(mrl.Module):
"""Used for DDPG / TD3 and other deterministic policy variants"""
def __init__(self):
super().__i... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/algorithms/continuous_off_policy.py | 0.75392 | 0.158467 | continuous_off_policy.py | pypi |
import numpy as np
import random
import gym
import torch
from types import LambdaType
from scipy.linalg import block_diag
try:
import tensorflow as tf
except:
tf = None
def set_global_seeds(seed):
"""
set the seed for python random, tensorflow, numpy and gym spaces
:param seed: (int) the seed
"""
... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/utils/misc.py | 0.7181 | 0.32603 | misc.py | pypi |
from collections import OrderedDict
import numpy as np
from gym import spaces
from . import VecEnv
class DummyVecEnv(VecEnv):
"""
Creates a simple vectorized wrapper for multiple environments
:param env_fns: ([Gym Environment]) the list of environments to vectorize
"""
def __init__(self, env_fns):... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/utils/vec_env/dummy_vec_env.py | 0.831177 | 0.38743 | dummy_vec_env.py | pypi |
from abc import ABC, abstractmethod
import pickle
import cloudpickle
class AlreadySteppingError(Exception):
"""
Raised when an asynchronous step is running while
step_async() is called again.
"""
def __init__(self):
msg = 'already running an async step'
Exception.__init__(self, ms... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/utils/vec_env/base_vec_env.py | 0.8938 | 0.462412 | base_vec_env.py | pypi |
import torch
import numpy as np
from mrl.utils.misc import AnnotatedAttrDict
from mrl.utils.schedule import LinearSchedule
default_dqn_config = lambda: AnnotatedAttrDict(
device=('cuda' if torch.cuda.is_available() else 'cpu', 'torch device (cpu or gpu)'),
gamma=(0.99, 'discount factor'),
qvalue_lr=(1e-3, ... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/configs/discrete_off_policy.py | 0.841923 | 0.592784 | discrete_off_policy.py | pypi |
import torch
import numpy as np
from mrl.utils.misc import AnnotatedAttrDict
default_ddpg_config = lambda: AnnotatedAttrDict(
device=('cuda' if torch.cuda.is_available() else 'cpu', 'torch device (cpu or gpu)'),
gamma=(0.99, 'discount factor'),
actor_lr=(1e-3, 'actor learning rate'),
critic_lr=(1e-3, '... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/configs/continuous_off_policy.py | 0.770939 | 0.528533 | continuous_off_policy.py | pypi |
import mrl
import gym
from mrl.replays.core.replay_buffer import ReplayBuffer as Buffer
from typing import Optional
import numpy as np
import torch
import pickle
import os
class OldReplayBuffer(mrl.Module):
def __init__(self):
"""
A standard replay buffer (no prioritization / fancy stuff).
"""
sup... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/replays/old_replay_buffer.py | 0.798462 | 0.228382 | old_replay_buffer.py | pypi |
import mrl
import numpy as np
import gym
from mrl.replays.core.replay_buffer import RingBuffer
from mrl.utils.misc import AttrDict
import pickle
import os
from sklearn import mixture
from scipy.stats import rankdata
class EntropyPrioritizedOnlineHERBuffer(mrl.Module):
def __init__(
self,
module_name='pr... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/replays/prioritized_replay.py | 0.685529 | 0.337013 | prioritized_replay.py | pypi |
import numpy as np
from collections import OrderedDict
from mrl.utils.misc import AttrDict
from multiprocessing import RawValue
class RingBuffer(object):
"""This is a collections.deque in numpy, with pre-allocated memory"""
def __init__(self, maxlen, shape, dtype=np.float32, data=None):
"""
A buffer objec... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/mrl/replays/core/replay_buffer.py | 0.788909 | 0.520984 | replay_buffer.py | pypi |
import torch
import logging
import warnings
import torch.nn as nn
import torch.nn.functional as F
from ._base import BaseModule, BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .utils import operator as op
from .utils.logging import get_... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/fast_geometric.py | 0.924845 | 0.395864 | fast_geometric.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
from joblib import Parallel, delayed
from ._base import BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .utils import operator as op
__all__ = ["Baggin... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/bagging.py | 0.904133 | 0.330795 | bagging.py | pypi |
import abc
import torch
import logging
import warnings
import torch.nn as nn
import torch.nn.functional as F
from joblib import Parallel, delayed
from ._base import BaseModule, BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .utils impor... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/soft_gradient_boosting.py | 0.935832 | 0.403156 | soft_gradient_boosting.py | pypi |
import abc
import copy
import torch
import logging
import warnings
import numpy as np
import torch.nn as nn
from . import _constants as const
from .utils.io import split_data_target
from .utils.logging import get_tb_logger
def torchensemble_model_doc(header="", item="model"):
"""
A decorator on obtaining doc... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/_base.py | 0.908978 | 0.244916 | _base.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from ._base import BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .utils import operator as op
__all__ = ["FusionClassifier", "FusionRegressor"]
@torchensemble_model... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/fusion.py | 0.906307 | 0.297597 | fusion.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
from joblib import Parallel, delayed
from ._base import BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .utils import operator as op
__all__ = ["Voting... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/voting.py | 0.898715 | 0.29523 | voting.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
from joblib import Parallel, delayed
from ._base import BaseModule, BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .utils import operator as op
__all_... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/adversarial_training.py | 0.938096 | 0.407982 | adversarial_training.py | pypi |
import abc
import torch
import logging
import warnings
import torch.nn as nn
import torch.nn.functional as F
from ._base import BaseModule, BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .utils import operator as op
from .utils.logging ... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/gradient_boosting.py | 0.935273 | 0.447943 | gradient_boosting.py | pypi |
import math
import torch
import logging
import warnings
import torch.nn as nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import LambdaLR
from ._base import BaseModule, BaseClassifier, BaseRegressor
from ._base import torchensemble_model_doc
from .utils import io
from .utils import set_module
from .u... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/snapshot_ensemble.py | 0.927199 | 0.351701 | snapshot_ensemble.py | pypi |
__model_doc = """
Parameters
----------
estimator : torch.nn.Module
The class or object of your base estimator.
- If :obj:`class`, it should inherit from :mod:`torch.nn.Module`.
- If :obj:`object`, it should be instantiated from a class inherited
from :mod:`torch.nn.Module... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/_constants.py | 0.961326 | 0.697712 | _constants.py | pypi |
import importlib
def set_optimizer(model, optimizer_name, **kwargs):
"""
Set the parameter optimizer for the model.
Reference: https://pytorch.org/docs/stable/optim.html#algorithms
"""
torch_optim_optimizers = [
"Adadelta",
"Adagrad",
"Adam",
"AdamW",
"Ada... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/utils/set_module.py | 0.865537 | 0.440409 | set_module.py | pypi |
import os
import torch
def save(model, save_dir, logger):
"""Implement model serialization to the specified directory."""
if save_dir is None:
save_dir = "./"
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
# Decide the base estimator name
if isinstance(model.base_estimator_, ... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/deps/libraries/torchensemble/utils/io.py | 0.70253 | 0.180865 | io.py | pypi |
from typing import Iterable
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
from matplotlib.ticker import MaxNLocator
from rrt_ml.utilities.configs import *
from rrt_ml.utilities.hints import *
from rrt_ml.utilities.infos import *
from rrt_ml... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/utilities/maps.py | 0.912615 | 0.518668 | maps.py | pypi |
from coral_pytorch.losses import corn_loss
from rrt_ml.utilities.configs import *
from rrt_ml.utilities.hints import *
def get_ackermann_v_rf_lr_phi_lr(v_ref: float, phi_ref: float, cfg: MasterConfig) -> Vector6:
"""
Ackermann formula to get velocity for each wheel and steering angles.
:param v_ref: desi... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/utilities/formulas.py | 0.908214 | 0.744424 | formulas.py | pypi |
from typing import Any
import numpy as np
from rrt_ml.utilities.formulas import *
from rrt_ml.utilities.hints import *
from rrt_ml.utilities.stats import *
class RRTEpochInfo(BaseStats):
train_losses: list[float, ...] | None = None
val_losses: list[float, ...] | None = None
@classmethod
def new(cls... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/utilities/infos.py | 0.922774 | 0.3398 | infos.py | pypi |
from glob import glob
from pathlib import Path
from typing import Any
import numpy as np
from joblib import dump, load
from pydantic import BaseModel
from scipy.spatial.distance import euclidean
from rrt_ml.utilities.configs import *
from rrt_ml.utilities.hints import *
from rrt_ml.utilities.misc import *
class Bas... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/utilities/stats.py | 0.809163 | 0.325976 | stats.py | pypi |
from pathlib import Path
from pydantic import BaseModel
class Paths(BaseModel):
home: Path = Path(__file__).parents[1]
configs: Path = home / 'configs'
configs_rl = configs / 'rl'
configs_rrt = configs / 'rrt'
configs_sl = configs / 'sl'
configs_hyper = configs / 'hyper'
data: Path = h... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/utilities/paths.py | 0.740644 | 0.245266 | paths.py | pypi |
import numpy as np
import pandas as pd
import torch as t
from coral_pytorch.dataset import corn_label_from_logits
from torch.nn import functional as f
from rrt_ml.utilities.configs import *
from rrt_ml.utilities.paths import *
class CVAE(t.nn.Module):
def __init__(self, cfg: MasterConfig):
"""
I... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/utilities/models.py | 0.921025 | 0.431464 | models.py | pypi |
import numpy as np
import pandas as pd
import torch as t
from sklearn.preprocessing import StandardScaler
from torch.utils.data import Dataset
from rrt_ml.utilities.configs import *
from rrt_ml.utilities.paths import *
class NarrowCVAEDataset(Dataset):
def __init__(self, cfg: MasterConfig, train: bool):
... | /rrt_ml-0.0.8-py3-none-any.whl/rrt_ml/utilities/datasets.py | 0.841191 | 0.441252 | datasets.py | pypi |
from dataclasses import dataclass
from typing import Optional, List, Any
from enum import Enum
@dataclass
class ChannelMessage:
ChannelId: Optional[int] = None
ChannelMessageTranslations: Optional[List[Any]] = None
Message: Optional[str] = None
@dataclass
class Message:
URL: None
DetourId: None
... | /models/route.py | 0.836521 | 0.268496 | route.py | pypi |
from enum import Enum
from dataclasses import dataclass
from typing import Optional, List
from datetime import datetime
class ModeReportLabel(Enum):
Normal = "Normal"
class PropertyName(Enum):
RRTA = "RRTA"
class PStatusReportLabel(Enum):
Scheduled = "Scheduled"
class DirectionCode(Enum):
L = "L... | /models/stopdepartures.py | 0.837421 | 0.279988 | stopdepartures.py | pypi |
from enum import Enum
from dataclasses import dataclass
from typing import Optional, List, Any
class Dir(Enum):
L = "L"
@dataclass
class Direction:
DirectionDesc: None
DirectionIconFileName: None
Dir: Optional[Dir] = None
@dataclass
class ChannelMessage:
ChannelId: Optional[int] = None
Cha... | /models/routedetails.py | 0.838316 | 0.296158 | routedetails.py | pypi |
from __future__ import division
from builtins import map
from builtins import object
import numpy as np
import cv2
"""
#im1: object image, im2: scenery image
Tpt = cv2.perspectiveTransform(np.float32([[point]]), H) # point: [col,row] -> [x,y]
TM = cv2.getPerspectiveTransform(bx1,bx2) # box points: np.float32([Top_left... | /rrtools-1.0.0a2.tar.gz/rrtools-1.0.0a2/RRtoolbox/lib/arrayops/convert.py | 0.829527 | 0.461441 | convert.py | pypi |
import os
import re
import sys
from typing import List, Dict, Text, Union, Any
from loguru import logger
from rrtv_httprunner import exceptions, globalvar
from rrtv_httprunner.loader import load_project_meta, convert_relative_project_root_dir
from rrtv_httprunner.parser import parse_data
from rrtv_httprunner.utils im... | /rrtv-httprunner-2.8.13.tar.gz/rrtv-httprunner-2.8.13/rrtv_httprunner/compat.py | 0.466846 | 0.192179 | compat.py | pypi |
import csv
import importlib
import json
import os
import sys
import types
from typing import Tuple, Dict, Union, Text, List, Callable
import yaml
from loguru import logger
from pydantic import ValidationError
from rrtv_httprunner import builtin, utils
from rrtv_httprunner import exceptions
from rrtv_httprunner.models... | /rrtv-httprunner-2.8.13.tar.gz/rrtv-httprunner-2.8.13/rrtv_httprunner/loader.py | 0.460532 | 0.185652 | loader.py | pypi |
from elasticsearch5 import Elasticsearch, Transport
from loguru import logger
class ESHandler(Elasticsearch):
def __init__(self, hosts=None, transport_class=Transport, **kwargs):
"""
:arg hosts: list of nodes we should connect to. Node should be a
dictionary ({"host": "localhost", "po... | /rrtv-httprunner-2.8.13.tar.gz/rrtv-httprunner-2.8.13/rrtv_httprunner/es.py | 0.721841 | 0.349172 | es.py | pypi |
import re
from typing import Text, Any, Union, Dict
from deepdiff import DeepDiff
from jsonschema import validate
from loguru import logger
def equal(check_value: Any, expect_value: Any, message: Text = ""):
assert check_value == expect_value, message
def greater_than(
check_value: Union[int, float], e... | /rrtv-httprunner-2.8.13.tar.gz/rrtv-httprunner-2.8.13/rrtv_httprunner/builtin/comparators.py | 0.801509 | 0.69449 | comparators.py | pypi |
import json
import sys
from json.decoder import JSONDecodeError
from urllib.parse import unquote
import yaml
from loguru import logger
def load_har_log_entries(file_path):
""" load HAR file and return log entries list
Args:
file_path (str)
Returns:
list: entries
[
... | /rrtv-httprunner-2.8.13.tar.gz/rrtv-httprunner-2.8.13/rrtv_httprunner/ext/har2case/utils.py | 0.418816 | 0.26134 | utils.py | pypi |
import os
import sys
from typing import Text, NoReturn
from loguru import logger
from rrtv_httprunner.models import TStep, FunctionsMapping
from rrtv_httprunner.parser import parse_variables_mapping
try:
import filetype
from requests_toolbelt import MultipartEncoder
UPLOAD_READY = True
except ModuleNotF... | /rrtv-httprunner-2.8.13.tar.gz/rrtv-httprunner-2.8.13/rrtv_httprunner/ext/uploader/__init__.py | 0.473657 | 0.171685 | __init__.py | pypi |
from argparse import ArgumentParser, RawDescriptionHelpFormatter
import shutil
import subprocess
from tempfile import NamedTemporaryFile
import reciprocalspaceship as rs
def parse_arguments():
"""Parse commandline arguments"""
parser = ArgumentParser(
formatter_class=RawDescriptionHelpFormatter, desc... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/scaleit/scaleit.py | 0.715921 | 0.315393 | scaleit.py | pypi |
import argparse
import numpy as np
import reciprocalspaceship as rs
from rsbooster.diffmaps.weights import compute_weights
from rsbooster.utils.io import subset_to_FSigF
def parse_arguments():
"""Parse commandline arguments"""
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpForm... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/diffmaps/diffmap.py | 0.759582 | 0.296922 | diffmap.py | pypi |
import argparse
import numpy as np
import reciprocalspaceship as rs
import gemmi
from rsbooster.diffmaps.weights import compute_weights
from rsbooster.utils.io import subset_to_FSigF
def parse_arguments():
"""Parse commandline arguments"""
parser = argparse.ArgumentParser(
formatter_class=argparse.Ra... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/diffmaps/internaldiffmap.py | 0.715126 | 0.261739 | internaldiffmap.py | pypi |
from argparse import ArgumentParser
import reciprocalspaceship as rs
def parse_arguments():
desc = """Convert precognition ingegration results to `.mtz` files for mergning in Careless."""
parser = ArgumentParser(description=desc)
parser.add_argument(
"--remove-sys-absences",
action="store... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/io/precog2mtz.py | 0.868757 | 0.430028 | precog2mtz.py | pypi |
import argparse
import matplotlib.pyplot as plt
import reciprocalspaceship as rs
import seaborn as sns
from rsbooster.stats.parser import BaseParser
class ArgumentParser(BaseParser):
def __init__(self):
super().__init__(
description=__doc__
)
# Required arguments
... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/stats/ccanom.py | 0.524882 | 0.42937 | ccanom.py | pypi |
import argparse
import numpy as np
import reciprocalspaceship as rs
import gemmi
import matplotlib.pyplot as plt
import seaborn as sns
from rsbooster.stats.parser import BaseParser
class ArgumentParser(BaseParser):
def __init__(self):
super().__init__(
description=__doc__
)
... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/stats/ccsym.py | 0.632162 | 0.386358 | ccsym.py | pypi |
import argparse
import numpy as np
import reciprocalspaceship as rs
import gemmi
import matplotlib.pyplot as plt
import seaborn as sns
from rsbooster.stats.parser import BaseParser
class ArgumentParser(BaseParser):
def __init__(self):
super().__init__(
description=__doc__
)
#... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/stats/ccpred.py | 0.579519 | 0.286023 | ccpred.py | pypi |
import argparse
import matplotlib.pyplot as plt
import reciprocalspaceship as rs
import seaborn as sns
from rsbooster.stats.parser import BaseParser
class ArgumentParser(BaseParser):
def __init__(self):
super().__init__(
description=__doc__
)
# Required arguments
... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/stats/cchalf.py | 0.55447 | 0.402157 | cchalf.py | pypi |
import argparse
import reciprocalspaceship as rs
def rfree(cell, sg, dmin, rfraction, seed):
h, k, l = rs.utils.generate_reciprocal_asu(cell, sg, dmin).T
ds = (
rs.DataSet(
{
"H": h,
"K": k,
"L": l,
},
cell=cell,
... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/utils/rfree.py | 0.719778 | 0.262721 | rfree.py | pypi |
import argparse
import numpy as np
import reciprocalspaceship as rs
def parse_arguments():
"""Parse commandline arguments"""
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter, description=__doc__
)
# Required arguments
parser.add_argument(
"-on",
... | /rs-booster-0.1.1.tar.gz/rs-booster-0.1.1/rsbooster/esf/extrapolate.py | 0.676192 | 0.320768 | extrapolate.py | pypi |
import os
from os import rename
from os.path import join
import datatable as dt
import pandas as pd
from rs_datasets.data_loader import download_dataset, download_url
from rs_datasets.generic_dataset import Dataset, safe
class MillionSongDataset(Dataset):
def __init__(
self,
merge_kaggle... | /rs_datasets-0.5.1.tar.gz/rs_datasets-0.5.1/rs_datasets/msd.py | 0.515376 | 0.294786 | msd.py | pypi |
import os
from os import rename
from os.path import join
from typing import Tuple
import datatable as dt
from datatable import Frame
from rs_datasets.data_loader import download_dataset
from rs_datasets.generic_dataset import Dataset, safe
rating_cols = ['user_id', 'item_id', 'rating', 'timestamp']
item_cols = ['ite... | /rs_datasets-0.5.1.tar.gz/rs_datasets-0.5.1/rs_datasets/movielens.py | 0.604282 | 0.315921 | movielens.py | pypi |
import os
import tarfile
from os.path import splitext
from tarfile import TarFile
from typing import Union
from zipfile import ZipFile
from py7zr import SevenZipFile
def extract(archive_name: str, manage_folder: bool = True) -> None:
"""
Extract `archive_name` and put it inside a folder
if there are multi... | /rs_datasets-0.5.1.tar.gz/rs_datasets-0.5.1/rs_datasets/data_loader/archives.py | 0.557364 | 0.154472 | archives.py | pypi |
import sys
import platform
PYTHON_VERSION_INFO = sys.version_info
PY2 = sys.version_info[0] == 2
# Infos about python passed to the trace agent through the header
PYTHON_VERSION = platform.python_version()
PYTHON_INTERPRETER = platform.python_implementation()
stringify = str
if PY2:
from urllib import urlencode... | /rs-ddtrace-0.12.1.tar.gz/rs-ddtrace-0.12.1/ddtrace/compat.py | 0.449151 | 0.199776 | compat.py | pypi |
import json
import logging
# check msgpack CPP implementation; if the import fails, we're using the
# pure Python implementation that is really slow, so the ``Encoder`` should use
# a different encoding format.
try:
import msgpack
from msgpack._packer import Packer # noqa
from msgpack._unpacker import un... | /rs-ddtrace-0.12.1.tar.gz/rs-ddtrace-0.12.1/ddtrace/encoding.py | 0.559771 | 0.152663 | encoding.py | pypi |
import logging
from threading import Lock
from .compat import iteritems
log = logging.getLogger(__name__)
MAX_TRACE_ID = 2 ** 64
# Has to be the same factor and key as the Agent to allow chained sampling
KNUTH_FACTOR = 1111111111111111111
class AllSampler(object):
"""Sampler sampling all the traces"""
de... | /rs-ddtrace-0.12.1.tar.gz/rs-ddtrace-0.12.1/ddtrace/sampler.py | 0.756447 | 0.25159 | sampler.py | pypi |
import logging
import ddtrace
from ddtrace import config
from .constants import DEFAULT_SERVICE
from ...ext import http
from ...compat import parse
from ...propagation.http import HTTPPropagator
log = logging.getLogger(__name__)
def _extract_service_name(session, span, netloc=None):
"""Extracts the right ser... | /rs-ddtrace-0.12.1.tar.gz/rs-ddtrace-0.12.1/ddtrace/contrib/requests/connection.py | 0.429549 | 0.159185 | connection.py | pypi |
import asyncio
import ddtrace
from asyncio.base_events import BaseEventLoop
from .provider import CONTEXT_ATTR
from ...context import Context
_orig_create_task = BaseEventLoop.create_task
def set_call_context(task, ctx):
"""
Updates the ``Context`` for the given Task. Useful when you need to
pass the c... | /rs-ddtrace-0.12.1.tar.gz/rs-ddtrace-0.12.1/ddtrace/contrib/asyncio/helpers.py | 0.728845 | 0.370282 | helpers.py | pypi |
import wrapt
import inspect
from .deprecation import deprecated
def unwrap(obj, attr):
f = getattr(obj, attr, None)
if f and isinstance(f, wrapt.ObjectProxy) and hasattr(f, '__wrapped__'):
setattr(obj, attr, f.__wrapped__)
@deprecated('`wrapt` library is used instead', version='1.0.0')
def safe_pat... | /rs-ddtrace-0.12.1.tar.gz/rs-ddtrace-0.12.1/ddtrace/utils/wrappers.py | 0.474144 | 0.179279 | wrappers.py | pypi |
import warnings
from functools import wraps
class RemovedInDDTrace10Warning(DeprecationWarning):
pass
def format_message(name, message, version):
"""Message formatter to create `DeprecationWarning` messages
such as:
'fn' is deprecated and will be remove in future versions (1.0).
"""
re... | /rs-ddtrace-0.12.1.tar.gz/rs-ddtrace-0.12.1/ddtrace/utils/deprecation.py | 0.698227 | 0.252686 | deprecation.py | pypi |
import numpy
import numpy as np
import pandas as pd
import pkg_resources
from pyproj import Geod
g = Geod(ellps="WGS84")
def load_test_data():
"""Return a dataframe with a test ascent.
Contains all the necessary data.
"""
# This is a stream-like object. If you want the actual info, call
# stream... | /rs_drift-1.1.0-py3-none-any.whl/rs_drift/drift.py | 0.77373 | 0.325012 | drift.py | pypi |
import numpy
import numpy as np
import pandas as pd
import pkg_resources
from pyproj import Geod
g = Geod(ellps="WGS84")
def load_test_data():
"""Return a dataframe with a test ascent.
Contains all the necessary data.
"""
# This is a stream-like object. If you want the actual info, call
# stream... | /rs_drift-1.1.0-py3-none-any.whl/rs_drift/.ipynb_checkpoints/drift-checkpoint.py | 0.77373 | 0.325012 | drift-checkpoint.py | pypi |
from __future__ import annotations
import resource
import time
from collections.abc import Callable
from typing import Any
from fastapi import FastAPI
from starlette.middleware.base import RequestResponseEndpoint
from starlette.requests import Request
from starlette.responses import Response
from starlette.routing im... | /rs_fastapi_utils-0.4.0-py3-none-any.whl/rs_fastapi_utils/timing.py | 0.923644 | 0.29 | timing.py | pypi |
from __future__ import annotations
from functools import lru_cache
from typing import Any
from pydantic import BaseSettings
class APISettings(BaseSettings):
"""
This class enables the configuration of your FastAPI instance through the use of environment variables.
Any of the instance attributes can be ... | /rs_fastapi_utils-0.4.0-py3-none-any.whl/rs_fastapi_utils/api_settings.py | 0.891434 | 0.321353 | api_settings.py | pypi |
from __future__ import annotations
from collections.abc import Iterator
from contextlib import contextmanager
import sqlalchemy as sa
from sqlalchemy.orm import Session
class FastAPISessionMaker:
"""
A convenience class for managing a (cached) sqlalchemy ORM engine and sessionmaker.
Intended for use cr... | /rs_fastapi_utils-0.4.0-py3-none-any.whl/rs_fastapi_utils/session.py | 0.887951 | 0.240613 | session.py | pypi |
from __future__ import annotations
import inspect
from collections.abc import Callable
from typing import Any, TypeVar, get_type_hints
from fastapi import APIRouter, Depends
from pydantic.typing import is_classvar
from starlette.routing import Route, WebSocketRoute
T = TypeVar('T')
CBV_CLASS_KEY = '__cbv_class__'
... | /rs_fastapi_utils-0.4.0-py3-none-any.whl/rs_fastapi_utils/cbv.py | 0.850205 | 0.241176 | cbv.py | pypi |
from __future__ import annotations
import asyncio
import logging
from asyncio import ensure_future
from functools import wraps
from traceback import format_exception
from typing import Any, Callable, Coroutine, Union
from starlette.concurrency import run_in_threadpool
NoArgsNoReturnFuncT = Callable[[], None]
NoArgsN... | /rs_fastapi_utils-0.4.0-py3-none-any.whl/rs_fastapi_utils/tasks.py | 0.916009 | 0.176069 | tasks.py | pypi |
import pyvisa as visa
import numpy as np
import pandas as pd
from warnings import warn
warn("rs_fsl is deprecated. Use pymeasure instead.", DeprecationWarning)
def read_csv(filename):
"""Reads x and y values measured by the spectrum analyzer stored in a csv file."""
data = pd.read_csv(
filename, de... | /rs_fsl-0.2.tar.gz/rs_fsl-0.2/rs_fsl/rs_fsl.py | 0.77343 | 0.53692 | rs_fsl.py | pypi |
import math
def ray(io, eo, z, col, row):
x_ppa = io[0]
y_ppa = io[1]
focal_length = io[2]
pixel_size = io[3]
image_extent_x = io[4]
image_extent_y = io[5]
x0 = eo[0]
y0 = eo[1]
z0 = eo[2]
ome = eo[3]
phi = eo[4]
kap = eo[5]
o = math.radians(ome)
p = ma... | /rs_lib-0.0.15-py3-none-any.whl/rs_lib/rs_lib.py | 0.495606 | 0.345906 | rs_lib.py | pypi |
from functools import lru_cache
import numpy as np
import pandas as pd
from rs_metrics.helpers import flatten_list, pandas_to_dict, convert_pandas
from rs_metrics.parallel import user_mean, top_k, user_apply, user_mean_sub
from rs_metrics.statistics import item_pop
@convert_pandas
def ndcg(true, pred, k=10):
""... | /rs_metrics-0.5.0-py3-none-any.whl/rs_metrics/metrics.py | 0.814274 | 0.261823 | metrics.py | pypi |
from __future__ import annotations
from typing import List, Tuple
import matplotlib.pyplot as plt # type: ignore[import]
import numpy as np
from . import planner, primitives
# List of path endpoints to visualize Reeds-Shepp paths for with matplotlib.
# Format: (end x, end y, end yaw, turn radius, runway length)
_E... | /rs-path-1.0.0.tar.gz/rs-path-1.0.0/rs/demo.py | 0.890002 | 0.68125 | demo.py | pypi |
from __future__ import annotations
import dataclasses
import functools
from typing import Any, List, Literal, Optional, Tuple
import numpy as np
from . import helpers
@dataclasses.dataclass
class Path:
"""Reeds-Shepp path represented as its start/end points, turn radius (in meters),
and a list of Segments.... | /rs-path-1.0.0.tar.gz/rs-path-1.0.0/rs/primitives.py | 0.956145 | 0.610308 | primitives.py | pypi |
import pickle
import re
import shutil
import os
class FESReporter(object):
def __init__(self,file,reportInterval,metaD_wrapper,CV_list,
potentialEnergy=False, kineticEnergy=False,
totalEnergy=False, temperature=False):
self._reportInterval = reportInterval
self... | /rs-simtools-0.0.4.tar.gz/rs-simtools-0.0.4/rs_simtools/custom_reporters.py | 0.793066 | 0.187003 | custom_reporters.py | pypi |
from . import tabler
from typing import Iterable, Any
def from_plaintext(text: Iterable[Iterable[str]]) -> tabler.table.Table:
"""
Create a table from some plain text.
"""
tabler.table.Table(
list(
map(
lambda row: list(
map(lambda s: tabler.tabl... | /rs_tabler-0.1.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl/tabler/utils.py | 0.778944 | 0.372648 | utils.py | pypi |
from implicit.als import AlternatingLeastSquares
import numpy as np
from rs_tools.utils import encode, to_csc, dict_to_pandas
class Wrapper:
def fit(
self,
df,
show_progress=True,
user_col='user_id',
item_col='item_id',
rating_col='rating',
):
df, ue, i... | /rs_tools-0.2.tar.gz/rs_tools-0.2/rs_tools/implicit_wrapper.py | 0.693473 | 0.278508 | implicit_wrapper.py | pypi |
# *V*ae *A*ssisted *L*igand *D*isc*O*very (Valdo)
[](https://pypi.org/project/rs-valdo/)
Using variational Autoencoders to improve the signal-to-noise ratio of drug
fragment screens
- [*V*ae *A*ssisted *L*igand *D*isc*O*very (Valdo)](#vae-assisted-ligand-disc... | /rs-valdo-0.0.4.tar.gz/rs-valdo-0.0.4/README.md | 0.954489 | 0.910704 | README.md | pypi |
import numpy as np
import reciprocalspaceship as rs
from tqdm import tqdm
import pandas as pd
import re
import glob
from scipy.ndimage import gaussian_filter
import os
import gemmi
def generate_blobs(input_files, model_folder, diff_col, phase_col, output_folder, cutoff=5, negate=False, sample_rate=3):
"""
... | /rs-valdo-0.0.4.tar.gz/rs-valdo-0.0.4/valdo/blobs.py | 0.813757 | 0.704786 | blobs.py | pypi |
import pandas as pd
import reciprocalspaceship as rs
import numpy as np
import os
from tqdm import tqdm
def find_intersection(input_files, output_path, amplitude_col='F-obs-scaled'):
"""
Finds the intersection of `amplitude_col` from multiple input MTZ files.
Args:
input_files (list): List of... | /rs-valdo-0.0.4.tar.gz/rs-valdo-0.0.4/valdo/preprocessing.py | 0.823648 | 0.486088 | preprocessing.py | pypi |
import numpy as np
import reciprocalspaceship as rs
import pandas as pd
import time
def get_aniso_args_np(uaniso, reciprocal_cell_paras, hkl):
U11, U22, U33, U12, U13, U23 = uaniso
h, k, l = hkl.T
ar, br, cr, cos_alphar, cos_betar, cos_gammar = reciprocal_cell_paras
args = 2*np.pi**2*(
U11 * ... | /rs-valdo-0.0.4.tar.gz/rs-valdo-0.0.4/valdo/scaling.py | 0.568895 | 0.208562 | scaling.py | pypi |
import torch
import torch.nn as nn
import numpy as np
import numbers
class DenseLayer(nn.Module):
"""Just your regular densely-connected NN layer
Parameters
----------
in_features : int
Size of each input sample
out_features : int
Size of each output sample
activation : None o... | /rs-valdo-0.0.4.tar.gz/rs-valdo-0.0.4/valdo/vae_basics.py | 0.942612 | 0.690305 | vae_basics.py | pypi |
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from .vae_basics import DenseNet, sampling, elbo
from .helper import try_gpu
from tqdm import tqdm
import pickle
class VAE(nn.Module):
'''
Initialize a VAE model with assigned parameters
Param... | /rs-valdo-0.0.4.tar.gz/rs-valdo-0.0.4/valdo/vae_networks.py | 0.90902 | 0.574395 | vae_networks.py | pypi |
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import requests
from rs3_api.hiscores.exceptions import UserNotFoundException
from rs3_api.hiscores.seasonal_hiscore import UserSeasonalHiscore
from rs3_api.hiscores.types import Minigame, Skill, UserSeason
from rs3_api.utils.const impor... | /rs3_api-0.1.0-py3-none-any.whl/rs3_api/hiscores/user_hiscore/user_hiscore.py | 0.81231 | 0.152442 | user_hiscore.py | pypi |
from enum import Enum, unique
BASE_URL = "https://secure.runescape.com"
@unique
class Skills(str, Enum):
OVERALL = "overall",
ATTACK = "attack",
DEFENCE = "defence",
STRENGTH = "strength",
CONSTITUTION = "constitution",
RANGED = "ranged",
PRAYER = "prayer",
MAGIC = "magic",
COOKIN... | /rs3_api-0.1.0-py3-none-any.whl/rs3_api/utils/const.py | 0.523177 | 0.182389 | const.py | pypi |
# Rule Set 3
> Python package to predict the activity of CRISPR sgRNA sequences using Rule Set 3
## Install
You can install the latest release of rs3 from pypi using
`pip install rs3`
For mac users you may also have to brew install the OpenMP library
`brew install libomp`
or install lightgbm without Openmp
`pip... | /rs3-0.0.15.tar.gz/rs3-0.0.15/README.md | 0.609873 | 0.864768 | README.md | pypi |
import random as re
from math import sqrt
max = 10000000
def gcd(a, b):
'''for finding the Greatest Common Divisor or Highest Common Factor between two numbers.
It's purpose for this program is to check whether e and phi_of_n are co-prime or not,
which is possible iff their gcd is 1.
'''
while b !... | /rsa_algo_madhusree-0.0.1.tar.gz/rsa_algo_madhusree-0.0.1/rsa_algo_madhusree/__init__.py | 0.41052 | 0.488283 | __init__.py | pypi |
Library to work with Archer REST and Content APIs
===========================================
My original objective was to create Office365 mail to Archer Incidents application connector.Script captures the email, checks if there is an incident ID assigned and add the email to comments section (sub form) in archer reco... | /rsa_archer-0.1.9.tar.gz/rsa_archer-0.1.9/README.md | 0.453262 | 0.779112 | README.md | pypi |
[](https://opensource.org/licenses/MIT)
What is this for?
=================
If you need to use an [RSA SecurID](//en.wikipedia.org/wiki/RSA_SecurID) software token
to generate [one-time passwords](//en.wikipedia.org/wiki/One-time_password), and
have ... | /rsa_ct_kip-0.6.0.tar.gz/rsa_ct_kip-0.6.0/README.md | 0.461988 | 0.921605 | README.md | pypi |
from Crypto.Cipher import AES
from Crypto.Hash import CMAC
import math
import struct
def cmac(key, msg):
c = CMAC.new(key, ciphermod=AES)
c.update(msg)
return c.digest()
def ct_kip_prf_aes(key, *msg, dslen=16, pad=None):
assert (dslen // 16) < (2**32)
msg = b''.join(msg)
n = math.ceil(ds... | /rsa_ct_kip-0.6.0.tar.gz/rsa_ct_kip-0.6.0/rsa_ct_kip/ct_kip_prf_aes.py | 0.683631 | 0.229524 | ct_kip_prf_aes.py | pypi |
import re
from datetime import date, timedelta
from random import choice, randrange
from .constants import (
DATE_OF_BIRTH_FORMAT,
GENDER_FEMALE_MIN,
GENDER_FEMALE_MAX,
GENDER_MALE_MIN,
GENDER_MALE_MAX,
SA_CITIZEN_DIGIT,
PERMANENT_RESIDENT_DIGIT,
RACE_DIGIT,
Gender,
Citizenship,... | /rsa-id-number-0.0.3.tar.gz/rsa-id-number-0.0.3/src/rsaidnumber/random.py | 0.863017 | 0.423816 | random.py | pypi |
import logging
import re
from datetime import datetime
from .constants import (
DATE_OF_BIRTH_FORMAT,
GENDER_FEMALE_MAX,
GENDER_FEMALE_MIN,
PERMANENT_RESIDENT_DIGIT,
RSA_ID_LENGTH,
SA_CITIZEN_DIGIT,
Citizenship,
Gender,
)
from .random import generate
__version__ = "0.0.3"
__all__ = ["... | /rsa-id-number-0.0.3.tar.gz/rsa-id-number-0.0.3/src/rsaidnumber/__init__.py | 0.76769 | 0.202601 | __init__.py | pypi |
from rcj.utility import rmath
class Key:
"""
A class that holds the product of two primes and exponent of the public or private key.
Parameters:
Product (int): The product of two primes.
Exponent (int): The exponent of the public or private key.
"""
def __init__(self, product: int, expo... | /rsa-jpv-1.0.3.tar.gz/rsa-jpv-1.0.3/rcj/cryptosystem/rsa.py | 0.936836 | 0.501221 | rsa.py | pypi |
from dash.development.base_component import Component, _explicitize_args
class ScheduleCard(Component):
"""A ScheduleCard component.
Keyword arguments:
- id (string | dict; optional):
The ID used to identify this component in Dash callbacks.
- dailySchedule (boolean; optional)
- displayShiftEndTime (str... | /rsa_scheduler_components-0.1.8.tar.gz/rsa_scheduler_components-0.1.8/rsa_scheduler_components/ScheduleCard.py | 0.728265 | 0.229158 | ScheduleCard.py | pypi |
import numpy as np
import scipy.stats as ss
import scipy.linalg as sl
from scipy.spatial.distance import squareform
import pyrsa
def make_design(n_cond, n_part):
"""
Makes simple fMRI design with n_cond, each measures n_part times
Args:
n_cond (int): Number of conditions
n_part (... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/simulation/sim.py | 0.895785 | 0.57081 | sim.py | pypi |
import numpy as np
import tqdm
from collections.abc import Iterable
from pyrsa.rdm import compare
from pyrsa.inference import bootstrap_sample
from pyrsa.inference import bootstrap_sample_rdm
from pyrsa.inference import bootstrap_sample_pattern
from pyrsa.model import Model
from pyrsa.util.inference_util import input_c... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/inference/evaluate.py | 0.75392 | 0.384825 | evaluate.py | pypi |
import numpy as np
from pyrsa.util.rdm_utils import add_pattern_index
def sets_leave_one_out_pattern(rdms, pattern_descriptor):
""" generates training and test set combinations by leaving one level
of pattern_descriptor out as a test set.
This is only sensible if pattern_descriptor already defines larger ... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/inference/crossvalsets.py | 0.752286 | 0.313177 | crossvalsets.py | pypi |
import numpy as np
from pyrsa.util.inference_util import pool_rdm
from pyrsa.rdm import compare
from .crossvalsets import sets_leave_one_out_rdm
def cv_noise_ceiling(rdms, ceil_set, test_set, method='cosine',
pattern_descriptor='index'):
""" calculates the noise ceiling for crossvalidation.
... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/inference/noise_ceiling.py | 0.859899 | 0.601769 | noise_ceiling.py | pypi |
import numpy as np
from pyrsa.util.rdm_utils import add_pattern_index
def bootstrap_sample(rdms, rdm_descriptor='index', pattern_descriptor='index'):
"""Draws a bootstrap_sample from the data.
This function generates a bootstrap sample of RDMs resampled over
measurements and patterns. By default every pa... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/inference/bootstrap.py | 0.898197 | 0.568835 | bootstrap.py | pypi |
import numpy as np
import pyrsa.model
from pyrsa.util.file_io import write_dict_hdf5
from pyrsa.util.file_io import write_dict_pkl
from pyrsa.util.file_io import read_dict_hdf5
from pyrsa.util.file_io import read_dict_pkl
class Result:
""" Result class storing results for a set of models with the models,
the ... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/inference/result.py | 0.696991 | 0.507324 | result.py | pypi |
from collections.abc import Iterable
import numpy as np
from pyrsa.rdm.rdms import RDMs
from pyrsa.rdm.rdms import concat
from pyrsa.data import average_dataset_by
from pyrsa.util.matrix import pairwise_contrast_sparse
def calc_rdm(dataset, method='euclidean', descriptor=None, noise=None,
cv_descriptor=N... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/rdm/calc.py | 0.924262 | 0.439507 | calc.py | pypi |
import numpy as np
from scipy.stats import rankdata
from pyrsa.util.rdm_utils import batch_to_vectors
from pyrsa.util.rdm_utils import batch_to_matrices
from pyrsa.util.descriptor_utils import format_descriptor
from pyrsa.util.descriptor_utils import bool_index
from pyrsa.util.descriptor_utils import subset_descriptor
... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/rdm/rdms.py | 0.829803 | 0.292351 | rdms.py | pypi |
import numpy as np
import scipy.stats
from scipy.stats._stats import _kendall_dis
from pyrsa.util.matrix import pairwise_contrast_sparse
from pyrsa.util.rdm_utils import _get_n_from_reduced_vectors
from pyrsa.util.matrix import row_col_indicator_g
def compare(rdm1, rdm2, method='cosine', sigma_k=None):
"""calcula... | /rsa3-3.0.0.post20201106-py3-none-any.whl/pyrsa/rdm/compare.py | 0.890726 | 0.522263 | compare.py | pypi |
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