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
value |
|---|---|---|---|---|---|
import rlutils.tf as rlu
import tensorflow as tf
from rlutils.infra.runner import TFOffPolicyRunner, run_func_as_main
class SACAgent(tf.keras.Model):
def __init__(self,
obs_spec,
act_spec,
num_ensembles=2,
policy_mlp_hidden=256,
... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/mf/sac.py | 0.754553 | 0.236538 | sac.py | pypi |
import rlutils.tf as rlu
import tensorflow as tf
from rlutils.infra.runner import TFOffPolicyRunner, run_func_as_main
def gather_q_values(q_values, actions):
batch_size = tf.shape(actions)[0]
idx = tf.stack([tf.range(batch_size, dtype=actions.dtype), actions], axis=-1) # (None, 2)
q_values = tf.gather_nd... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/mf/dqn.py | 0.838944 | 0.429549 | dqn.py | pypi |
import time
import numpy as np
import tensorflow as tf
from rlutils.tf.utils import set_tf_allow_growth
set_tf_allow_growth()
from rlutils.infra.runner import TFRunner
from rlutils.tf.nn import AtariQNetworkDeepMind, hard_update
from rlutils.replay_buffers import PyUniformParallelEnvReplayBufferFrame
from rlutils.i... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/mf/images/dqn.py | 0.842345 | 0.414366 | dqn.py | pypi |
import os
import time
import gym
import numpy as np
import rlutils.tf as rlu
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.infra.runner import TFRunner
from rlutils.logx import EpochLogger
from rlutils.replay_buffers import PyUniformReplayBuffer
from tqdm.auto import tqdm, trange
tfd = tfp... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/offline/bracp.py | 0.750736 | 0.269163 | bracp.py | pypi |
import os
import time
import gym
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.tf.future import get_adam_optimizer, minimize
from rlutils.logx import EpochLogger
from rlutils.replay_buffers import PyUniformReplayBuffer
from rlutils.infra.runner import TFRunner
from rlutil... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/offline/plas.py | 0.656768 | 0.292513 | plas.py | pypi |
import time
import gym
import numpy as np
import tensorflow as tf
from rlutils.replay_buffers import PyUniformReplayBuffer
from rlutils.infra.runner import TFRunner, run_func_as_main
from rlutils.tf.distributions import apply_squash_log_prob
from rlutils.tf.functional import soft_update, hard_update, compute_target_va... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/offline/cql.py | 0.824144 | 0.258443 | cql.py | pypi |
import time
import gym.spaces
import numpy as np
import tensorflow as tf
from rlutils.replay_buffers import PyUniformReplayBuffer
from rlutils.infra.runner import TFRunner, run_func_as_main
from rlutils.tf.nn.models import EnsembleDynamicsModel
from rlutils.tf.nn.planners import RandomShooter
class PETSAgent(tf.kera... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/algos/tf/mb/pets.py | 0.744749 | 0.252284 | pets.py | pypi |
import numpy as np
import tensorflow as tf
EPS = 1e-6
def compute_accuracy(logits, labels):
num = tf.cast(tf.argmax(logits, axis=-1, output_type=tf.int32) == labels, dtype=tf.float32)
accuracy = tf.reduce_mean(num)
return accuracy
def expand_ensemble_dim(x, num_ensembles):
""" functionality for out... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/functional.py | 0.915202 | 0.438424 | functional.py | pypi |
import numpy as np
import rlutils.tf as rlu
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
tfl = tfp.layers
EPS = 1e-4
class CenteredBeta(tfd.TransformedDistribution):
def __init__(self,
concentration1,
concentration0,
... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/distributions.py | 0.867064 | 0.432303 | distributions.py | pypi |
import tensorflow as tf
from rlutils.tf.nn.functional import build_mlp
OUT_KERNEL_INIT = tf.keras.initializers.RandomUniform(minval=-1e-3, maxval=1e-3)
class EnsembleMinQNet(tf.keras.Model):
def __init__(self, ob_dim, ac_dim, mlp_hidden, num_ensembles=2, num_layers=3):
super(EnsembleMinQNet, self).__ini... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/nn/values.py | 0.860501 | 0.406273 | values.py | pypi |
import tensorflow as tf
from tensorflow.keras.regularizers import l2
from .layers import EnsembleDense, SqueezeLayer
def build_mlp(input_dim, output_dim, mlp_hidden, num_ensembles=None, num_layers=3,
activation='relu', out_activation=None, squeeze=False, dropout=None,
batch_norm=False, la... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/nn/functional.py | 0.927544 | 0.579817 | functional.py | pypi |
from abc import ABC, abstractmethod
import numpy as np
import rlutils.tf as rlu
import sklearn
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.tf.callbacks import EpochLoggerCallback
from rlutils.tf.generative_models.vae import ConditionalBetaVAE
tfd = tfp.distributions
tfl = tfp.layers
MIN... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/nn/behavior.py | 0.858541 | 0.356699 | behavior.py | pypi |
import sklearn
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.tf.callbacks import EpochLoggerCallback
from rlutils.tf.distributions import make_independent_normal_from_params, apply_squash_log_prob, \
make_independent_centered_beta_from_params, make_independent_truncated_normal, make_inde... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/nn/actors.py | 0.816589 | 0.30654 | actors.py | pypi |
import tensorflow as tf
from rlutils.np.functional import inverse_softplus
from rlutils.tf.functional import clip_by_value_preserve_gradient
from .initializer import _decode_initializer
class SqueezeLayer(tf.keras.layers.Layer):
def __init__(self, axis=-1):
super(SqueezeLayer, self).__init__()
se... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/nn/layers.py | 0.86771 | 0.277732 | layers.py | pypi |
import math
import tensorflow as tf
class _RandomGenerator(object):
"""Random generator that selects appropriate random ops."""
dtypes = tf.dtypes
def __init__(self, seed=None):
super(_RandomGenerator, self).__init__()
if seed is not None:
# Stateless random ops requires 2-in... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/nn/initializer.py | 0.921473 | 0.333693 | initializer.py | pypi |
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.tf.functional import compute_accuracy
tfd = tfp.distributions
class GAN(tf.keras.Model):
def __init__(self, n_critics=5, noise_dim=100):
super(GAN, self).__init__()
self.n_critics = n_critics
self.noise_dim = noise... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/generative_models/gan/base.py | 0.918822 | 0.341198 | base.py | pypi |
import tensorflow as tf
from rlutils.tf.functional import compute_accuracy
from tqdm.auto import tqdm
from .base import GAN, ACGAN
class WassersteinGANGradientPenalty(GAN):
def __init__(self, gp_weight=10, *args, **kwargs):
self.gp_weight = gp_weight
super(WassersteinGANGradientPenalty, self).__i... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/generative_models/gan/wgan_gp.py | 0.913464 | 0.311047 | wgan_gp.py | pypi |
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
class BetaVAE(tf.keras.Model):
def __init__(self, latent_dim, beta=1.):
super(BetaVAE, self).__init__()
self.latent_dim = latent_dim
self.beta = beta
self.encoder = self._make_encoder()
se... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/generative_models/vae/base.py | 0.83545 | 0.586671 | base.py | pypi |
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.tf.future import get_adam_optimizer
tfd = tfp.distributions
tfl = tfp.layers
eps = 1e-6
class Flow(tf.keras.Model):
"""
A flow is a function f that defines a forward (call) and backward path
"""
def call(self, x, training=No... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/generative_models/flow/base.py | 0.920397 | 0.615608 | base.py | pypi |
import tensorflow as tf
import tensorflow_probability as tfp
from rlutils.tf.distributions import make_independent_normal_from_params
from rlutils.tf.nn.functional import build_mlp
from .base import Flow, SequentialFlow, ConditionalFlowModel
tfd = tfp.distributions
tfl = tfp.layers
class AffineCouplingFlow(Flow):
... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/tf/generative_models/flow/realnvp.py | 0.8727 | 0.505554 | realnvp.py | pypi |
import os
import pprint
import random
from abc import abstractmethod, ABC
import numpy as np
import rlutils.gym
import rlutils.infra as rl_infra
from rlutils.logx import EpochLogger, setup_logger_kwargs
from rlutils.replay_buffers import PyUniformReplayBuffer, GAEBuffer
from tqdm.auto import trange
class BaseRunner(... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/infra/runner/base.py | 0.634204 | 0.206354 | base.py | pypi |
from abc import ABC, abstractmethod
import numpy as np
import rlutils.np as rln
from rlutils.gym.vector import VectorEnv
from tqdm.auto import trange
class Sampler(ABC):
def __init__(self, env: VectorEnv):
self.env = env
def reset(self):
pass
def set_logger(self, logger):
self.l... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/infra/samplers/base.py | 0.743541 | 0.368235 | base.py | pypi |
import multiprocessing as mp
import sys
import time
from copy import deepcopy
from enum import Enum
import numpy as np
from gym import logger
from gym.error import (AlreadyPendingCallError, NoAsyncCallError,
ClosedEnvironmentError)
from gym.vector.utils import (create_shared_memory, create_empty... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/gym/vector/async_vector_env.py | 0.410402 | 0.216156 | async_vector_env.py | pypi |
import numpy as np
from gym.vector.utils import create_empty_array
from .vector_env import VectorEnv
__all__ = ['SyncVectorEnv']
class SyncVectorEnv(VectorEnv):
"""Vectorized environment that serially runs multiple environments.
Parameters
----------
env_fns : iterable of callable
Functions... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/gym/vector/sync_vector_env.py | 0.862279 | 0.714441 | sync_vector_env.py | pypi |
try:
from collections.abc import Iterable
except ImportError:
Iterable = (tuple, list)
from .async_vector_env import AsyncVectorEnv
from .sync_vector_env import SyncVectorEnv
from .vector_env import VectorEnv
def make(id, num_envs=1, asynchronous=True, wrappers=None, **kwargs):
"""Create a vectorized env... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/gym/vector/__init__.py | 0.876423 | 0.462048 | __init__.py | pypi |
import inspect
import sys
import numpy as np
from .base import ModelBasedStaticFn
model_based_wrapper_dict = {}
class ReacherFn(ModelBasedStaticFn):
reward = False
terminate = True
env_name = ['Reacher-v2']
class HopperFn(ModelBasedStaticFn):
reward = False
terminate = True
env_name = ['H... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/gym/static/mujoco.py | 0.581184 | 0.678387 | mujoco.py | pypi |
import numpy as np
from .base import ModelBasedStaticFn
class InvertedPendulumBulletEnvFn(ModelBasedStaticFn):
env_name = ['InvertedPendulumBulletEnv-v0']
terminate = True
reward = True
@staticmethod
def terminate_fn_numpy_batch(states, actions, next_states):
cos_th, sin_th = next_states... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/gym/static/pybullet.py | 0.822118 | 0.630756 | pybullet.py | pypi |
class Schedule(object):
def value(self, t):
"""Value of the schedule at time t"""
raise NotImplementedError()
class ExponentialScheduler(Schedule):
def __init__(self, epsilon=1.0, decay=1e-4, minimum=0.01):
self.epsilon = epsilon
self.decay = decay
self.minimum = minimu... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/np/schedulers.py | 0.954563 | 0.588091 | schedulers.py | pypi |
import numpy as np
from rlutils.np.functional import discount_cumsum
from rlutils.np.functional import flatten_leading_dims
from .utils import combined_shape
class GAEBuffer(object):
"""
A buffer for storing trajectories experienced by a PPO agent interacting
with the environment, and using Generalized A... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/replay_buffers/pg_py.py | 0.767733 | 0.666669 | pg_py.py | pypi |
from abc import ABC, abstractmethod
from typing import Dict
import gym.spaces
import numpy as np
from gym.utils import seeding
from rlutils.np.functional import shuffle_dict_data
from .utils import combined_shape
class BaseReplayBuffer(ABC):
def __init__(self, seed=None):
self.set_seed(seed)
def re... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/replay_buffers/base.py | 0.895936 | 0.377225 | base.py | pypi |
from collections import deque
try:
import reverb
except:
print('Reverb is not installed.')
import tensorflow as tf
from .base import BaseReplayBuffer
class ReverbReplayBuffer(BaseReplayBuffer):
def __init__(self,
data_spec,
replay_capacity,
batch_size,
... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/replay_buffers/reverb.py | 0.802633 | 0.272454 | reverb.py | pypi |
from typing import Dict
import gym.spaces
import numpy as np
from .base import PyReplayBuffer
from .utils import segtree
EPS = np.finfo(np.float32).eps.item()
class PyPrioritizedReplayBuffer(PyReplayBuffer):
"""
A simple implementation of PER based on pure numpy. No advanced data structure is used.
"""... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/replay_buffers/prioritized_py.py | 0.911838 | 0.350727 | prioritized_py.py | pypi |
from typing import Union, Optional
import numpy as np
from numba import njit
class SegmentTree:
"""Implementation of Segment Tree.
The segment tree stores an array ``arr`` with size ``n``. It supports value
update and fast query of the sum for the interval ``[left, right)`` in
O(log n) time. The deta... | /rlutils-python-0.0.3.tar.gz/rlutils-python-0.0.3/rlutils/replay_buffers/utils/segtree.py | 0.942804 | 0.831622 | segtree.py | pypi |
from . import driver
import traceback
import weakref
class Engine(object):
"""
@ivar proxy: Proxy to a driver implementation
@type proxy: L{DriverProxy}
@ivar _connects: Array of subscriptions
@type _connects: list
@ivar _inLoop: Running an event loop or not
@type _inLoop: bool
@ivar _... | /rlvoice_1-1.1.1-py3-none-any.whl/rlvoice/engine.py | 0.699254 | 0.238129 | engine.py | pypi |
from ..voice import Voice
import time
def buildDriver(proxy):
'''
Builds a new instance of a driver and returns it for use by the driver
proxy.
@param proxy: Proxy creating the driver
@type proxy: L{driver.DriverProxy}
'''
return DummyDriver(proxy)
class DummyDriver(object):
'''
D... | /rlvoice_1-1.1.1-py3-none-any.whl/rlvoice/drivers/dummy.py | 0.641535 | 0.301908 | dummy.py | pypi |
# Reinforcement Learning Zoo
[](https://rlzoo.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/tensorflow/tensorflow/releases)
[![Dow... | /rlzoo-1.0.4.tar.gz/rlzoo-1.0.4/README.md | 0.933051 | 0.986244 | README.md | pypi |
import argparse
from pathlib import Path
from typing import List, Optional
import cv2
import numpy as np
import onnxruntime as rt
from huggingface_hub.file_download import hf_hub_download
SCALE: int = 255
def get_mask(
session_infer: rt.InferenceSession,
img: np.ndarray,
size_infer: int = 1024,
):
... | /rm_anime_bg-0.2.0-py3-none-any.whl/rm_anime_bg/cli.py | 0.739893 | 0.318989 | cli.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | /rm_gaussian_binomial_distributions-0.1.tar.gz/rm_gaussian_binomial_distributions-0.1/rm_gaussian_binomial_distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
import subprocess
# List if problem letters that have some problems to show in current version of simple
PROBLEM_LETTERS = "ěščřžýáíéúů"
# Current version of this module is trying to prevent SAS from crash by doing some edits
# to texts displayed on screen and adding "." after letters that wont render without it.
#... | /rm_pysas-0.0.1-py3-none-any.whl/rm_pySAS/__init__.py | 0.415729 | 0.288488 | __init__.py | pypi |
import boto3
import json
from pkg_resources import resource_filename
def get_region_name(region_code):
endpoint_file = resource_filename("botocore", "data/endpoints.json")
with open(endpoint_file, "r") as f:
endpoint_data = json.load(f)
region_name = endpoint_data["partitions"][0]["regions"][re... | /rm_runner-0.1.0-py3-none-any.whl/rm_runner/utils.py | 0.445288 | 0.213972 | utils.py | pypi |
port_service_map = {1: 'tcpmux', 2: 'compressnet', 3: 'compressnet', 5: 'rje', 7: 'echo', 9: 'discard', 11: 'systat',
13: 'daytime', 17: 'qotd', 18: 'msp', 19: 'chargen', 20: 'ftp-data', 21: 'ftp', 22: 'ssh',
23: 'telnet', 25: 'smtp', 27: 'nsw-fe', 29: 'msg-icp', 31: 'msg-auth', ... | /rm-sec-toolkit-0.2.4.tar.gz/rm-sec-toolkit-0.2.4/rmsectkf/core/network/port_service_map.py | 0.408631 | 0.316805 | port_service_map.py | pypi |
import numpy as np
import pandas as pd
from scipy.optimize import minimize
def vol_risk_parity(stockMeans, covar, b=None):
n = len(stockMeans)
# Function for Portfolio Volatility
def pvol(w):
x = np.array(w)
return np.sqrt(x.dot(covar).dot(x))
# Function for Component Standard... | /rm545_xd-0.7.2.tar.gz/rm545_xd-0.7.2/src/qrm545_xd/risk_parity.py | 0.649356 | 0.419053 | risk_parity.py | pypi |
import numpy as np
import pandas as pd
from . import cov_matrix
from scipy.stats import t, norm
from scipy.optimize import minimize
# Multivariate Normal Simulation
def multivariate_normal_simulation(covariance_matrix, n_samples, method='direct', mean = 0, explained_variance=1.0, seed=1234):
"""
A function to... | /rm545_xd-0.7.2.tar.gz/rm545_xd-0.7.2/src/qrm545_xd/simulation.py | 0.848361 | 0.830869 | simulation.py | pypi |
import numpy as np
import pandas as pd
def risk_contrib(w, covar):
risk_contrib = w * covar.dot(w) / np.sqrt(w.dot(covar).dot(w))
return risk_contrib
def expost_attribution(w, upReturns):
_stocks = list(upReturns.columns)
n = upReturns.shape[0]
pReturn = np.empty(n)
weights = np.empty((n, len(... | /rm545_xd-0.7.2.tar.gz/rm545_xd-0.7.2/src/qrm545_xd/risk_attribution.py | 0.818193 | 0.604457 | risk_attribution.py | pypi |
import numpy as np
# Exponentially Weighted Covariance Matrix
def exp_weighted_cov(returns, lambda_=0.97):
"""
Perform calculation on the input data set with a given λ for exponentially weighted covariance.
Parameters:
- data: input data set, a pandas DataFrame
- lambda_: fraction for unpdate... | /rm545_xd-0.7.2.tar.gz/rm545_xd-0.7.2/src/qrm545_xd/cov_matrix.py | 0.933688 | 0.824356 | cov_matrix.py | pypi |
INFINITY = float('inf')
NEGATIVE_INFINITY = -INFINITY
class IntervalSet:
__slots__ = ('intervals', 'size')
def __init__(self, intervals, disjoint=False):
self.intervals = intervals
if not disjoint:
self.intervals = union_overlapping(self.intervals)
self.size = sum(i.size for i in self.interva... | /rmap-7.5.tar.gz/rmap-7.5/graphite-dballe/intervals.py | 0.719482 | 0.318737 | intervals.py | pypi |
from hashlib import md5
from itertools import chain
import bisect
try:
import pyhash
hasher = pyhash.fnv1a_32()
def fnv32a(string, seed=0x811c9dc5):
return hasher(string, seed=seed)
except ImportError:
def fnv32a(string, seed=0x811c9dc5):
"""
FNV-1a Hash (http://isthe.com/chongo/tech/comp/fnv/) in ... | /rmap-7.5.tar.gz/rmap-7.5/graphite-dballe/render/hashing.py | 0.535827 | 0.237377 | hashing.py | pypi |
import json
class FloatEncoder(json.JSONEncoder):
def __init__(self, nan_str="null", **kwargs):
super(FloatEncoder, self).__init__(**kwargs)
self.nan_str = nan_str
def iterencode(self, o, _one_shot=False):
"""Encode the given object and yield each string
representation as avai... | /rmap-7.5.tar.gz/rmap-7.5/graphite-dballe/render/float_encoder.py | 0.660391 | 0.18717 | float_encoder.py | pypi |
import csv
import math
import pytz
from datetime import datetime
from time import time
from random import shuffle
from httplib import CannotSendRequest
from urllib import urlencode
from urlparse import urlsplit, urlunsplit
from cgi import parse_qs
from cStringIO import StringIO
try:
import cPickle as pickle
except I... | /rmap-7.5.tar.gz/rmap-7.5/graphite-dballe/render/views.py | 0.418459 | 0.159643 | views.py | pypi |
from pyparsing import (
ParserElement, Forward, Combine, Optional, Word, Literal, CaselessKeyword,
CaselessLiteral, Group, FollowedBy, LineEnd, OneOrMore, ZeroOrMore,
nums, alphas, alphanums, printables, delimitedList, quotedString,
__version__,
)
ParserElement.enablePackrat()
grammar = Forward()
expr... | /rmap-7.5.tar.gz/rmap-7.5/graphite-dballe/render/grammar.py | 0.745769 | 0.171165 | grammar.py | pypi |
__all__ = ["GeoJsonMapLayer"]
import json
from kivy.properties import StringProperty, ObjectProperty
from mapview.view import MapLayer
from mapview.downloader import Downloader
def flatten(l):
return [item for sublist in l for item in sublist]
class GeoJsonMapLayer(MapLayer):
source = StringProperty()
... | /rmap-7.5.tar.gz/rmap-7.5/mapview/geojson.py | 0.569134 | 0.253959 | geojson.py | pypi |
(function() {
var B = {
"B33194": {
"description": "[SIM] Space consistency",
"unit": "%"
},
"B33195": {
"description": "[SIM] MeteoDB variable ID",
"unit": "NUMERIC"
},
"B33196": {
"description": "[SIM] Data has... | /rmap-7.5.tar.gz/rmap-7.5/showdata/static/showdata/borinud.B.js | 0.503662 | 0.538923 | borinud.B.js | pypi |
from imagekit.models import ImageSpecField
from imagekit.models import ProcessedImageField
from imagekit.processors import ResizeToFill, Transpose, SmartResize, ResizeToFit
from djgeojson.fields import PointField
from django.db import models
from django.contrib.auth.models import User
from django.utils.translation impo... | /rmap-7.5.tar.gz/rmap-7.5/geoimage/models.py | 0.577614 | 0.280422 | models.py | pypi |
import json
import dballe
class BaseJSONEncoder(json.JSONEncoder):
"""Base JSON encoder."""
def default(self, o):
from datetime import datetime
if isinstance(o, datetime):
return o.isoformat()
else:
return super(BaseJSONEncoder, self).default(o)
class GeoJSONEnc... | /rmap-7.5.tar.gz/rmap-7.5/borinud/utils/codec.py | 0.588298 | 0.280382 | codec.py | pypi |
(function() {
var B = {
"B33194": {
"description": "[SIM] Space consistency",
"unit": "%"
},
"B33195": {
"description": "[SIM] MeteoDB variable ID",
"unit": "NUMERIC"
},
"B33196": {
"description": "[SIM] Data has... | /rmap-7.5.tar.gz/rmap-7.5/borinud/static/borinud/borinud.B.js | 0.503662 | 0.538923 | borinud.B.js | pypi |
from django.conf import settings
from django.contrib.sites.requests import RequestSite
from django.contrib.sites.models import Site
from registration import signals
from registration.models import RegistrationProfile
from registration.views import ActivationView as BaseActivationView
from registration.views import Reg... | /rmap-7.5.tar.gz/rmap-7.5/registration/backends/default/views.py | 0.802594 | 0.326218 | views.py | pypi |
import numpy as np
import scipy
from scipy import stats
import matplotlib.pylab as plt
class gaussian_kde_set_covariance(stats.gaussian_kde):
'''
from Anne Archibald in mailinglist:
http://www.nabble.com/Width-of-the-gaussian-in-stats.kde.gaussian_kde---td19558924.html#a19558924
'''
def __init__(se... | /rmats2sashimiplot-2.0.4-py3-none-any.whl/MISO/misopy/kde_subclass.py | 0.675444 | 0.533519 | kde_subclass.py | pypi |
from scipy import *
from numpy import *
def format_credible_intervals(event_name, samples,
confidence_level=0.95):
"""
Returns a list of print-able credible intervals for an NxM samples
matrix. Handles both the two isoform and multi-isoform cases.
"""
num_samples, num_... | /rmats2sashimiplot-2.0.4-py3-none-any.whl/MISO/misopy/credible_intervals.py | 0.840062 | 0.505432 | credible_intervals.py | pypi |
from numpy import *
from scipy import *
import time
import csv
def dictlist2csv(filename, dictlist, header_fields, delimiter='\t'):
"""
Serialize a list of dictionaries into the output
"""
str_header_fields = [str(f) for f in header_fields]
header = "\t".join(str_header_fields) + '\n'
output =... | /rmats2sashimiplot-2.0.4-py3-none-any.whl/MISO/misopy/parse_csv.py | 0.489259 | 0.375964 | parse_csv.py | pypi |
import os
import time
import scipy
import numpy
from scipy import *
from numpy import *
import misopy
import misopy.sam_utils as sam_utils
from misopy.Gene import load_genes_from_gff
from misopy.parse_csv import *
import pysam
def rpkm_per_region(region_lens, region_counts, read_len,
num_tota... | /rmats2sashimiplot-2.0.4-py3-none-any.whl/MISO/misopy/sam_rpkm.py | 0.517815 | 0.339307 | sam_rpkm.py | pypi |
import os
import sys
import glob
import matplotlib
# Add misopy path
miso_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.insert(0, miso_path)
# Use PDF backend
matplotlib.use("pdf")
from scipy import *
from numpy import *
import pysam
import shelve
import misopy
import... | /rmats2sashimiplot-2.0.4-py3-none-any.whl/MISO/misopy/sashimi_plot/sashimi_plot.py | 0.446253 | 0.235988 | sashimi_plot.py | pypi |
import os
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import rc
import misopy.sashimi_plot.plot_utils.plot_settings as plot_settings
import misopy.sashimi_plot.plot_utils.plotting as plotting
class Sashimi:
"""
Representation of a figure.
"""
def __init__(self, label, output_dir... | /rmats2sashimiplot-2.0.4-py3-none-any.whl/MISO/misopy/sashimi_plot/Sashimi.py | 0.642657 | 0.254677 | Sashimi.py | pypi |
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.axislines import SubplotZero
import scipy.stats as stats
from scipy import *
from numpy import array
from scipy import linalg
import sys
def plot_cumulative_bars(data, bins,
bar_color='k',
edgecolor='#ffffff',... | /rmats2sashimiplot-2.0.4-py3-none-any.whl/MISO/misopy/sashimi_plot/plot_utils/plotting.py | 0.438545 | 0.59658 | plotting.py | pypi |
import os
import numpy as np
from disorder.diffuse import scattering, space
from disorder.diffuse import displacive, magnetic
from disorder.material import crystal, symmetry
def factor(u, v, w, atms, occupancy, U11, U22, U33, U23, U13, U12,
a, b, c, alpha, beta, gamma, symops, dmin=0.3, source='neutron'):... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/material/structure.py | 0.856242 | 0.541227 | structure.py | pypi |
import os
import numpy as np
directory = os.path.abspath(os.path.dirname(__file__))
def magnetic_form_factor_coefficients_j0():
"""
Table of magnetic form factors zeroth-order :math:`j_0` coefficients.
Returns
-------
j0 : dict
Dictionary of magnetic form factors coefficients with magne... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/material/tables.py | 0.796055 | 0.513668 | tables.py | pypi |
import numpy as np
import matplotlib
import matplotlib.style as mplstyle
mplstyle.use('fast')
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.transforms as mtransforms
from matplotlib import ticker
from matplotlib.ticker import Locator
from matplotlib.patches import Polygon
fro... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/graphical/plots.py | 0.533884 | 0.403714 | plots.py | pypi |
import mayavi.mlab as mlab
import numpy as np
from scipy.stats import chi2
from scipy.spatial.transform.rotation import Rotation
from mayavi.sources.api import ParametricSurface
from mayavi.modules.api import Surface
class CrystalStructure:
def __init__(self):
self.fig = mlab.figure(fgc... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/graphical/visualization.py | 0.641871 | 0.33595 | visualization.py | pypi |
import re
import os
import numpy as np
from disorder.diffuse import experimental, space, filters, scattering
from disorder.diffuse import monocrystal, powder
from disorder.diffuse import magnetic, occupational, displacive, refinement
from disorder.material import crystal, symmetry, tables
import disorder.correlatio... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/graphical/model.py | 0.602062 | 0.358044 | model.py | pypi |
import numpy as np
from disorder.material import crystal
from disorder.material import symmetry
def reciprocal(h_range, k_range, l_range, mask, B, T=np.eye(3)):
nh, nk, nl = mask.shape[0], mask.shape[1], mask.shape[2]
h_, k_, l_ = np.meshgrid(np.linspace(h_range[0],h_range[1],nh),
... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/diffuse/space.py | 0.498291 | 0.533337 | space.py | pypi |
import numpy as np
from disorder.diffuse.displacive import number
def transform(U_r, A_r, H, K, L, nu, nv, nw, n_atm):
"""
Discrete Fourier transform of Taylor expansion displacement products and \
relative occupancy parameter.
Parameters
----------
U_r : 1d array
Displacement para... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/diffuse/nonmagnetic.py | 0.919326 | 0.823186 | nonmagnetic.py | pypi |
import numpy as np
def composition(nu, nv, nw, n_atm, value=0.5):
"""
Generate random relative site occupancies.
Parameters
----------
nu, nv, nw : int
Number of grid points :math:`N_1`, :math:`N_2`, :math:`N_3` along the
:math:`a`, :math:`b`, and :math:`c`-axis of the superce... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/diffuse/occupational.py | 0.928506 | 0.835919 | occupational.py | pypi |
import numpy as np
def expansion(nu, nv, nw, n_atm, value=1, fixed=True):
"""
Generate random displacement vectors.
Parameters
----------
nu, nv, nw : int
Number of grid points :math:`N_1`, :math:`N_2`, :math:`N_3` along the
:math:`a`, :math:`b`, and :math:`c`-axis of the supercel... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/diffuse/displacive.py | 0.926116 | 0.631225 | displacive.py | pypi |
import numpy as np
from scipy.special import erfc
from disorder.material import crystal
def __A(alpha,r):
c = 2*alpha/np.sqrt(np.pi)
return -(erfc(alpha*r)/r-c*np.exp(-alpha**2*r**2))/r**2
def __B(alpha,r):
c = 2*alpha/np.sqrt(np.pi)
return (erfc(alpha*r)/r+c*np.exp(-alpha**2*r**2))/r**2
def _... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/diffuse/interaction.py | 0.519521 | 0.568655 | interaction.py | pypi |
import numpy as np
from nexusformat.nexus import nxload
import pyvista as pv
from functools import reduce
from disorder.diffuse import filters
def data(filename):
data = nxload(filename)
signal = np.array(data.MDHistoWorkspace.data.signal.nxdata.T)
error_sq = np.array(data.MDHistoWorkspace.data.error... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/diffuse/experimental.py | 0.41182 | 0.567697 | experimental.py | pypi |
import numpy as np
from disorder.material import tables
def j0(Q, A, a, B, b, C, c, D):
"""
Appoximation of the zeroth-order spherical Bessesl function :math:`j_0(Q)`.
Parameters
----------
Q : 1d array
Magnitude of wavevector :math:`Q`.
A : float
:math:`A_0` constant.
a ... | /rmc_discord-0.0.4-cp36-cp36m-win_amd64.whl/disorder/diffuse/magnetic.py | 0.93739 | 0.823825 | magnetic.py | pypi |
class Preprocessor:
def __init__(self, fileName):
self.fileName = fileName
""" Pre processing class for receive data and return normalized data
Attributes: fileName
"""
def __repr__(self):
print(self.data)
return "Nome do Arquivo em estudo:... | /rmclino_preprocessor-1.0.tar.gz/rmclino_preprocessor-1.0/rmclino_preprocessor/preprocessor.py | 0.627609 | 0.396535 | preprocessor.py | pypi |
Rmdawn: a Python package for programmatic R markdown workflows
==============================================================
|Chat| |Build| |License| |PyPI| |Status| |Updates| |Versions|
Introduction
------------
The ``rmdawn`` Python package allows you to (de)construct, convert, and render `R Markdown <https://rma... | /rmdawn-0.1.2.tar.gz/rmdawn-0.1.2/README.rst | 0.898907 | 0.732296 | README.rst | pypi |
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import Polygon
def trapezoidal_rule(f, a: float, b: float,
n: int) -> float:
"""
Returns a numerical approximation of the definite integral of f
between a and b by the trapezoidal rule.
Parameters:
f... | /rmg_numerical_integration-4.1-py3-none-any.whl/rmg_numerical_integration/trapezoidal.py | 0.885018 | 0.879147 | trapezoidal.py | pypi |
import numpy as np
import matplotlib.pyplot as plt
def simpson_rule(f, a: float, b: float,
n: int) -> float:
"""
Returns a numerical approximation of the definite integral of f
between a and b by the Simpson rule.
Parameters:
f(function): function to be integrated
a(fl... | /rmg_numerical_integration-4.1-py3-none-any.whl/rmg_numerical_integration/simpson.py | 0.875321 | 0.85315 | simpson.py | pypi |
from scipy.special.orthogonal import p_roots
import numpy as np
import matplotlib.pyplot as plt
def gauss_rule(f, n: int, a: float, b: float) -> float:
"""
Returns a numerical approximation of the definite integral
of f between a and b by the Gauss quadrature rule.
Parameters:
f(function): fu... | /rmg_numerical_integration-4.1-py3-none-any.whl/rmg_numerical_integration/gaussian_quadrature.py | 0.949342 | 0.805747 | gaussian_quadrature.py | pypi |
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import Polygon
def midpoint_rule(f, a: float, b: float,
n: int) -> float:
"""
Returns a numerical approximation of the definite integral
of f between a and b by the midpoint rule.
Parameters:
f(function... | /rmg_numerical_integration-4.1-py3-none-any.whl/rmg_numerical_integration/midpoint.py | 0.893007 | 0.840815 | midpoint.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
EPSILON = 0.0005
class RMILoss(nn.Module):
"""
PyTorch Module which calculates the Region Mutual Information loss (https://arxiv.org/abs/1910.12037).
"""
def __init__(self,
with_logits,
radius=3,
... | /rmi-pytorch-0.1.1.tar.gz/rmi-pytorch-0.1.1/rmi/rmi.py | 0.948811 | 0.722233 | rmi.py | pypi |
codes = {
0: {
"message": "No error",
"response_code": 200
},
1: {
"message": "Unknown error",
"response_code": 500
},
2: {
"message": "Invalid input",
"response_code": 400
},
3: {
"message":"Insufficient permissions",
"response_code": 401
},
4: {
"message": "Bad ti... | /rmi_qb_sdk-0.4.1.tar.gz/rmi_qb_sdk-0.4.1/rmi_qb_sdk/error_codes.py | 0.557123 | 0.387632 | error_codes.py | pypi |
[](https://travis-ci.org/wouterboomsma/eigency)
# Eigency
Eigency is a Cython interface between Numpy arrays and Matrix/Array
objects from the Eigen C++ library. It is intended to simplify the
process of writing C++ extensions using the Eige... | /rmjarvis.eigency-1.77.1.tar.gz/rmjarvis.eigency-1.77.1/README.md | 0.790732 | 0.973418 | README.md | pypi |
import argparse
import dataclasses
import itertools
import os
from copy import deepcopy
from enum import Enum
from typing import Any, Dict, Optional, Sequence, Tuple, Union
class ConfigField(Enum):
ARGUMENT = "argument"
ATTRIBUTE = "attribute"
VARIABLE = "variable"
class PrefixSeparator(Enum):
ARGUM... | /rmk2_py-0.1.2-py3-none-any.whl/rmk2/config.py | 0.843122 | 0.232452 | config.py | pypi |
import datetime
import json
import logging
import os
from enum import Enum
from typing import Iterator, Union, Any
Expected = Union[bool, str, int, float, datetime.date, datetime.datetime, None]
Jsonified = Union[bool, str, int, float, None]
class WriteMode(Enum):
APPEND = "a"
CREATE = "x"
TRUNCATE = "w"... | /rmk2_py-0.1.2-py3-none-any.whl/rmk2/file.py | 0.665084 | 0.295725 | file.py | pypi |
import time
import os
import struct
import stat
import logging
logging.basicConfig(format='%(message)s')
log = logging.getLogger('resim')
def affine_map(x, a0, a1, b0, b1):
"""Map x in range (a0, a1) to (b0, b1)
Args:
x (float): input
a0 (float): input range start
a1 (float): input ran... | /rmkit-sim-0.0.2.tar.gz/rmkit-sim-0.0.2/remarkable_sim/evsim.py | 0.790652 | 0.254903 | evsim.py | pypi |
import array
import operator
from base64 import b64decode
import qrcode
from reportlab.lib.units import toLength
DEFAULT_PARAMS = {
'size': '5cm',
'padding': '2.5',
'fg': '#000000',
'bg': None,
'version': None,
'error_correction': 'L',
}
GENERATOR_PARAMS = {'size', 'padding', 'fg', 'bg', 'x', 'y'}
QR_PARAMS = ... | /rml_qrcode-1.1.0.tar.gz/rml_qrcode-1.1.0/rml_qrcode/__init__.py | 0.433502 | 0.245108 | __init__.py | pypi |
import logging
import traceback
from typing import List, Mapping
_Logger = logging.getLogger(__name__)
# ---- HTTP-related
class ClientError(Exception):
"""Client request is incorrect."""
pass
class AuthenticationError(Exception):
"""Failed to authenticate user."""
pass
class ForbiddenError(Exce... | /rmlab_errors-0.1.6-py3-none-any.whl/rmlab_errors/__init__.py | 0.910466 | 0.216964 | __init__.py | pypi |
import os, io
from dataclasses import dataclass
from typing import Callable, List, Optional
from inspect import signature, Parameter
from typing import Any, List, Mapping
from rmlab_errors import ValueError
from enum import Enum
import aiohttp
class EnumStrings(Enum):
@classmethod
def str_to_enum_value(cls... | /rmlab_http_client-0.4.0-py3-none-any.whl/rmlab_http_client/types.py | 0.890235 | 0.219819 | types.py | pypi |
from typing import Any, Mapping, Optional, Union
from rmlab_errors import ValueError
from rmlab_http_client import (
Endpoint,
AsyncEndpoint,
)
_EndpointType = Union[Endpoint, AsyncEndpoint]
class Cache:
"""Singleton cache to store credentials and endpoints,
meant to be initialized once.
Raise... | /rmlab_http_client-0.4.0-py3-none-any.whl/rmlab_http_client/cache.py | 0.918242 | 0.167083 | cache.py | pypi |
# RMM: RimWorld Mod Manager
Do you dislike DRM based platforms but love RimWorld and it's mods? RMM is cross platform mod manager that allows you to download, update, auto-sort, and configure mods for the game without relying on the Steam consumer client. RMM has a keyboard based interface that is easy to use and will... | /rmm-spoons-1.0.15.tar.gz/rmm-spoons-1.0.15/README.md | 0.678433 | 0.691484 | README.md | pypi |
from contextlib import contextmanager
import re
import shutil
import subprocess
import sys
import xml.etree.ElementTree as ET
from pathlib import Path
from typing import Generator, Optional, cast, Union, List
from xml.dom import minidom
def platform() -> Optional[str]:
return sys.platform
def execute(cmd) -> Ge... | /rmm-spoons-1.0.15.tar.gz/rmm-spoons-1.0.15/src/rmm/util.py | 0.57069 | 0.206574 | util.py | pypi |
import curses
class WindowSizeException(Exception):
pass
class AbortModOrderException(Exception):
pass
def multiselect_order_menu(stdscr, data):
data = [ ( n.packageid, n.enabled ) for n in data ]
k = 0
# Clear and refresh the screen for a blank canvas
stdscr.clear()
stdscr.refresh()... | /rmm-spoons-1.0.15.tar.gz/rmm-spoons-1.0.15/src/rmm/multiselect.py | 0.479016 | 0.351116 | multiselect.py | pypi |
from pathlib import Path
from typing import Optional, List
import rmm.util as util
class PathFinder:
DEFAULT_GAME_PATHS = [
("~/GOG Games/RimWorld", "linux"),
("~/games/rimworld", "linux"),
("~/.local/share/Steam/steamapps/common/RimWorld", "linux"),
("/Applications/RimWorld.app/M... | /rmm-spoons-1.0.15.tar.gz/rmm-spoons-1.0.15/src/rmm/path.py | 0.631481 | 0.262877 | path.py | pypi |
import torch.nn as nn
from .basic_layers import ResidualBlock
class AttentionModule(nn.Module):
def __init__(self, in_channels, out_channels, size1, size2, size3):
super(AttentionModule, self).__init__()
self.first_residual_blocks = ResidualBlock(in_channels, out_channels)
self.trunk_bra... | /rmn-3.1.1-py3-none-any.whl/models/attention_module.py | 0.946088 | 0.40592 | attention_module.py | pypi |
import torch
import torch.nn as nn
class PreActivateDoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(PreActivateDoubleConv, self).__init__()
self.double_conv = nn.Sequential(
nn.BatchNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.Conv2... | /rmn-3.1.1-py3-none-any.whl/models/brain_humor.py | 0.966036 | 0.455622 | brain_humor.py | pypi |
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import load_state_dict_from_url
__all__ = ["Inception3", "inception_v3"]
model_urls = {
# Inception v3 ported from TensorFlow
"inception_v3_google": "https://download.pytorch.org/models/incepti... | /rmn-3.1.1-py3-none-any.whl/models/inception.py | 0.954041 | 0.539226 | inception.py | pypi |
import torch.nn as nn
from .attention_module import AttentionModule
from .basic_layers import ResidualBlock
class ResidualAttentionModel(nn.Module):
def __init__(self, in_channels=3, num_classes=1000):
super(ResidualAttentionModel, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2... | /rmn-3.1.1-py3-none-any.whl/models/residual_attention_network.py | 0.906467 | 0.410402 | residual_attention_network.py | pypi |
import torch
import torch.nn as nn
from .masking import masking
from .resnet import BasicBlock, ResNet
from .utils import load_state_dict_from_url
model_urls = {
"resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth",
"resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
... | /rmn-3.1.1-py3-none-any.whl/models/resmasking_naive.py | 0.8777 | 0.393152 | resmasking_naive.py | pypi |
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