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 numpy as np
from sccloud import io, tools, demuxEM
def run_demuxEM_pipeline(input_adt_file, input_rna_file, output_name, **kwargs):
# load input data
adt = io.read_input(input_adt_file, genome="_ADT_")
print("ADT file is loaded.")
data = io.read_input(input_rna_file, genome=kwargs["genome"], co... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/pipeline/demuxEM_pipeline.py | 0.445288 | 0.423518 | demuxEM_pipeline.py | pypi |
import numpy as np
import pandas as pd
import time
from natsort import natsorted
import multiprocessing
from sklearn.cluster import KMeans
from typing import List
from anndata import AnnData
def estimate_background_probs(adt: AnnData, random_state: int = 0):
"""For cell-hashing data, estimate antibody backgroun... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/demuxEM/demuxEM.py | 0.860501 | 0.511839 | demuxEM.py | pypi |
import numpy as np
import pandas as pd
import json
import logging
from sys import stdout
from natsort import natsorted
from typing import List, Dict, Union
from anndata import AnnData
logger = logging.getLogger("sccloud")
class CellType:
def __init__(self, name: str, ignore_nonde: bool = False):
self.na... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/annotate_cluster/annotate_cluster.py | 0.653348 | 0.282048 | annotate_cluster.py | pypi |
import time
import numpy as np
import pandas as pd
import os.path
from scipy.io import mmread
from scipy.sparse import csr_matrix, issparse
import tables
import gzip
from typing import List, Tuple
from . import Array2D, MemData
import anndata
import logging
logger = logging.getLogger("sccloud")
def load_10x_h5_fi... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/io/io.py | 0.766556 | 0.391813 | io.py | pypi |
import time
import numpy as np
import pandas as pd
from anndata import AnnData
from joblib import Parallel, delayed, effective_n_jobs
from natsort import natsorted
import ctypes
import ctypes.util
try:
import louvain as louvain_module
except ImportError:
print("Need louvain!")
try:
import leidenalg
except... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/clustering.py | 0.790328 | 0.448306 | clustering.py | pypi |
import time
import numpy as np
import pandas as pd
from scipy.sparse import issparse
from sklearn.decomposition import PCA
from typing import Tuple
from anndata import AnnData
import logging
logger = logging.getLogger("sccloud")
def qc_metrics(
data: AnnData,
mito_prefix: str = "MT-",
min_genes: int =... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/preprocessing.py | 0.916025 | 0.538073 | preprocessing.py | pypi |
import time
import numpy as np
import pandas as pd
from scipy.sparse import issparse
from collections import defaultdict
from joblib import Parallel, delayed
import skmisc.loess as sl
from typing import List
from anndata import AnnData
import logging
logger = logging.getLogger("sccloud")
def estimate_feature_statis... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/hvf_selection.py | 0.698021 | 0.510192 | hvf_selection.py | pypi |
import numpy as np
import anndata
from typing import List
import logging
logger = logging.getLogger("sccloud")
def parse_subset_selections(subset_selections):
subsets_dict = {}
for subset_str in subset_selections:
attr, value_str = subset_str.split(":")
if attr in subsets_dict:
s... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/subcluster_utils.py | 0.488527 | 0.379637 | subcluster_utils.py | pypi |
import time
import numpy as np
from scipy.sparse import issparse
from anndata import AnnData
import logging
logger = logging.getLogger("sccloud")
from sccloud.tools import estimate_feature_statistics, select_features
def set_group_attribute(data: AnnData, attribute_string: str) -> None:
"""Set group attributes ... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/batch_correction.py | 0.837786 | 0.548855 | batch_correction.py | pypi |
import time
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
try:
import igraph
except ImportError as error:
print("Need python-igraph!")
from collections import deque
from typing import List
from anndata import AnnData
import logging
logger = logging.getLogger("sccloud")
def calc... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/pseudotime.py | 0.761804 | 0.556339 | pseudotime.py | pypi |
import numpy as np
import pandas as pd
import time
from scipy.sparse import issparse
from sccloud.io import read_input
def scp_write_coords(data, output_name):
cluster_labels = []
for col_name in data.obs.columns:
if col_name.find("labels") >= 0:
cluster_labels.append(col_name)
df_lab... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/scp_output.py | 0.5 | 0.333123 | scp_output.py | pypi |
import time
import numpy as np
import logging
from scipy.sparse import issparse
from scipy.sparse.csgraph import connected_components
from scipy.sparse.linalg import eigsh
from scipy.stats import entropy
from sklearn.decomposition import PCA
from sklearn.utils.extmath import randomized_svd
from typing import List, Tup... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/diffusion_map.py | 0.862207 | 0.513607 | diffusion_map.py | pypi |
import numpy as np
import pandas as pd
import os
import time
from subprocess import check_call
from typing import List
from anndata import AnnData
from sccloud.io import infer_file_format, read_input, write_output, MemData
def find_digits(value):
pos = len(value) - 1
while pos >= 0 and value[pos].isdigit():... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/tools/data_aggregation.py | 0.7181 | 0.656163 | data_aggregation.py | pypi |
import pandas as pd
from matplotlib import pyplot as pl
from sccloud.io import read_input
from .plot_utils import transform_basis
from .plot_qc import plot_qc_violin
from . import plot_library, iplot_library
pop_list = {
"composition": {
"basis",
"attrs",
"apply_to_all",
"group",
... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/plotting/run_plotting.py | 0.543711 | 0.408159 | run_plotting.py | pypi |
import matplotlib as mpl
mpl.use("Agg")
import numpy as np
import pandas as pd
import seaborn as sns
from natsort import natsorted
import matplotlib.pyplot as plt
# plot_type: gene, count, mito
def plot_qc_violin(
data,
plot_type,
out_file,
xattr="Channel",
hue=None,
inner=None,
dpi=500,
... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/plotting/plot_qc.py | 0.505127 | 0.367355 | plot_qc.py | pypi |
from .Base import Base
from sccloud.tools import aggregate_matrices
class AggregateMatrix(Base):
"""
Aggregate 10x matrices from each channel into one big matrix.
Usage:
sccloud aggregate_matrix <csv_file> <output_name> [--restriction <restriction>... --attributes <attributes> --google-cloud --select-only-sing... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/commands/AggregateMatrix.py | 0.784649 | 0.282181 | AggregateMatrix.py | pypi |
import os
from .Base import Base
from sccloud.plotting import make_static_plots
class Plotting(Base):
"""
Generate cluster composition plots.
Usage:
sccloud plot [options] [--restriction <restriction>...] <plot_type> <input_h5ad_file> <output_file>
sccloud plot -h
Arguments:
plot_type Only 2D... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/commands/Plotting.py | 0.769167 | 0.419945 | Plotting.py | pypi |
import os
from .Base import Base
from sccloud.tools import run_de_analysis
class DeAnalysis(Base):
"""
Perform DE analysis.
Usage:
sccloud de_analysis [options] <input_h5ad_file> <output_spreadsheet>
sccloud de_analysis -h
Arguments:
input_h5ad_file Single cell data with clustering calculated. DE r... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/commands/DeAnalysis.py | 0.514156 | 0.260331 | DeAnalysis.py | pypi |
from .Base import Base
from sccloud.pipeline import run_pipeline
class SubClustering(Base):
"""
Run sccloud to obtain subclusters.
Usage:
sccloud subcluster [options] --subset-selection <subset-selection>... <input_file> <output_name>
sccloud subcluster -h
Arguments:
input_file Single cell dat... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/commands/SubClustering.py | 0.748628 | 0.553143 | SubClustering.py | pypi |
from .Base import Base
from sccloud.pipeline import run_demuxEM_pipeline
class DemuxEM(Base):
"""
Run the demuxEM pipeline for cell-hashing/nuclei-hashing data.
Usage:
sccloud demuxEM [options] <input_adt_csv_file> <input_raw_gene_bc_matrices_h5.h5> <output_name>
sccloud demuxEM -h
Arguments:
input_adt_cs... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/commands/DemuxEM.py | 0.849316 | 0.509642 | DemuxEM.py | pypi |
import os
from .Base import Base
from sccloud.annotate_cluster import run_annotate_cluster, annotate_anndata_object
class AnnotateCluster(Base):
"""
Annotate potential cell types for each cluster. This command has two forms: the first form generates putative annotations and the second form write annotations into ... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/commands/AnnotateCluster.py | 0.619817 | 0.237333 | AnnotateCluster.py | pypi |
from .Base import Base
from sccloud.pipeline import run_pipeline
class Clustering(Base):
"""
Run sccloud.pipeline to obtain top-level clusters.
Usage:
sccloud cluster [options] <input_file> <output_name>
sccloud cluster -h
Arguments:
input_file Input HDF5 file in 10x or sccloud format. If first-pass... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/commands/Clustering.py | 0.742422 | 0.503418 | Clustering.py | pypi |
import numpy as np
import pandas as pd
from typing import List
from anndata import AnnData
from sccloud.io import read_input
def search_genes(
data: AnnData,
gene_list: List[str],
rec_key: str = "de_res",
measure: str = "percentage",
) -> pd.DataFrame:
"""Extract and display gene expressions for ... | /sccloud-0.14.0.tar.gz/sccloud-0.14.0/scCloud/misc/misc.py | 0.91351 | 0.756313 | misc.py | pypi |
import argparse
import importlib.metadata
import logging
import time
import adafruit_scd30 # type: ignore
import board # type: ignore
import busio # type: ignore
from prometheus_client import Gauge, Summary, start_http_server
logger = logging.getLogger(__name__)
METRIC_MEASUREMENT_TIME = Summary(
"scd30_meas... | /scd30_exporter-0.2.0-py3-none-any.whl/scd30_exporter/cli.py | 0.502441 | 0.1602 | cli.py | pypi |
from datetime import timedelta
import logging
import smbus2
import struct
import time
def interpret_as_float(integer: int):
return struct.unpack('!f', struct.pack('!I', integer))[0]
class SCD30:
"""Python I2C driver for the SCD30 CO2 sensor."""
def __init__(self):
self._i2c_addr = 0x61
... | /scd30_i2c-0.0.6-py3-none-any.whl/scd30_i2c/__init__.py | 0.873431 | 0.337163 | __init__.py | pypi |
____ ____ ____ _ _____ _____
/ ___\/ _\/ _ \/ \/ // /
| \| / | | \|| || __\| __\
\___ || \__| |_/|| || | | |
\____/\____/\____/\_/\_/ \_/
[](https://travis-ci.org/phoenixding/scdiff)
[
### Accepted to [International Conference on Intelligent Transport Systems 2023](https://2023.ieee-itsc.... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/README.md | 0.785966 | 0.994677 | README.md | pypi |
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Type
import numpy as np
from scenario_gym.entity import Entity
@dataclass
class Observation:
"""Base class for an observation."""
pass
@dataclass
class SingleEntityObservation(Observation):
... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/observation.py | 0.960491 | 0.528898 | observation.py | pypi |
from typing import Optional
import numpy as np
class Action:
"""Base class for actions that agents commnicate to controllers."""
pass
class TeleportAction(Action):
"""An action consiting of desired coordinates for the next pose."""
def __init__(
self,
x: float = 0.0,
y: fl... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/action.py | 0.978036 | 0.685976 | action.py | pypi |
from abc import ABC, abstractmethod
from typing import Optional, Union
import numpy as np
from scenario_gym.action import Action, TeleportAction, VehicleAction
from scenario_gym.entity import Entity
from scenario_gym.state import State
from scenario_gym.utils import ArrayLike
class Controller(ABC):
"""
Base... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/controller.py | 0.961362 | 0.711177 | controller.py | pypi |
from abc import ABC, abstractclassmethod
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union
from lxml.etree import Element
from scenariogeneration import xosc
from scenario_gym.utils import ArgsKwargs, load_properties_from_xml
@dataclass(frozen=True)
class Catalog:
"""A catalo... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/catalog_entry.py | 0.970479 | 0.443841 | catalog_entry.py | pypi |
from typing import Optional
from scenario_gym.action import Action, TeleportAction
from scenario_gym.controller import (
Controller,
PIDController,
ReplayTrajectoryController,
)
from scenario_gym.entity import Entity
from scenario_gym.observation import Observation
from scenario_gym.scenario import Scenari... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/agent.py | 0.965908 | 0.629917 | agent.py | pypi |
from __future__ import annotations
from copy import copy
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
from scipy.interpolate import interp1d
from scenario_gym.utils import ArrayLike, NDArray, cached_property
class Trajectory:
"""
A Scenario Gym representation of a trajectory... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/trajectory.py | 0.955121 | 0.844152 | trajectory.py | pypi |
from contextlib import suppress
from functools import lru_cache
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from lxml.etree import Element
from shapely.geometry import Polygon
from shapely.strtree import STRtree
try:
from functools import cached_property
except ImportError:
... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/utils.py | 0.945682 | 0.571826 | utils.py | pypi |
import inspect
import os
import warnings
from argparse import ArgumentParser
from typing import Any, Dict, List, Optional, Type, Union
import yaml
from scenario_gym.agent import Agent, ReplayTrajectoryAgent
from scenario_gym.controller import ReplayTrajectoryController
from scenario_gym.entity import Entity
from scen... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/manager.py | 0.907907 | 0.320848 | manager.py | pypi |
import os
from typing import Any, Callable, Dict, List, Optional, Type, Union
from scenario_gym.agent import Agent, _create_agent
from scenario_gym.entity import Entity
from scenario_gym.metrics import Metric
from scenario_gym.scenario import Scenario
from scenario_gym.state import State
from scenario_gym.viewer impor... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/scenario_gym.py | 0.89556 | 0.508971 | scenario_gym.py | pypi |
from __future__ import annotations
from copy import copy
from inspect import getfullargspec
from typing import Any, Dict, Optional, Type
import numpy as np
from shapely.geometry import Polygon
from scenario_gym.catalog_entry import BoundingBox, CatalogEntry
from scenario_gym.trajectory import Trajectory
from scenari... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/entity/base.py | 0.953329 | 0.513303 | base.py | pypi |
from typing import Dict, List, Optional, TypeVar
import numpy as np
from scipy.interpolate import interp1d
from scenario_gym.trajectory import Trajectory
from scenario_gym.utils import ArrayLike
from .base import Entity
State = TypeVar("State")
class BatchReplayEntity:
"""
A single object used to represen... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/entity/batch.py | 0.917363 | 0.473779 | batch.py | pypi |
from dataclasses import dataclass
from typing import Any, Dict, Optional
from lxml.etree import Element
from scenariogeneration import xosc
from scenario_gym.catalog_entry import (
ArgsKwargs,
BoundingBox,
Catalog,
CatalogEntry,
)
from scenario_gym.entity.base import Entity
from scenario_gym.trajector... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/entity/misc.py | 0.940742 | 0.248568 | misc.py | pypi |
from dataclasses import dataclass
from typing import Any, Dict, Optional
from lxml.etree import Element
from scenariogeneration import xosc
from scenario_gym.catalog_entry import (
ArgsKwargs,
BoundingBox,
Catalog,
CatalogEntry,
)
from scenario_gym.entity.base import Entity
from scenario_gym.trajector... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/entity/pedestrian.py | 0.943854 | 0.243755 | pedestrian.py | pypi |
from dataclasses import dataclass
from typing import Any, Dict, Optional
from lxml.etree import Element
from scenariogeneration import xosc
from scenario_gym.catalog_entry import (
ArgsKwargs,
BoundingBox,
Catalog,
CatalogEntry,
CatalogObject,
)
from scenario_gym.entity.base import Entity
from sce... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/entity/vehicle.py | 0.946621 | 0.336304 | vehicle.py | pypi |
import os
import warnings
from contextlib import suppress
from typing import Dict, List, Optional, Type
import numpy as np
from lxml import etree
from lxml.etree import Element
from scenario_gym.entity import Entity, Pedestrian, Vehicle
from scenario_gym.road_network import RoadNetwork
from scenario_gym.scenario impo... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/xosc_interface/read.py | 0.761627 | 0.252321 | read.py | pypi |
import os
from typing import List, Optional
from xml.etree import ElementTree as ET
import numpy as np
from scenariogeneration import xosc
from scenario_gym.entity import Entity
from scenario_gym.scenario import Scenario
from scenario_gym.trajectory import is_stationary
def write_scenario(
scenario: Scenario,
... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/xosc_interface/write.py | 0.856122 | 0.284677 | write.py | pypi |
import os
from collections import defaultdict
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Type
from lxml import etree
from lxml.etree import Element
from scenariogeneration import xosc
from scenario_gym.catalog_entry import Catalog, CatalogEntry
from scenario_gym.entity import DEFA... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/xosc_interface/catalogs.py | 0.800458 | 0.290327 | catalogs.py | pypi |
from enum import Enum
from typing import Any, Dict, List, Optional, Union
import numpy as np
from shapely.geometry import LineString, Polygon
from scenario_gym.utils import ArgsKwargs
from .base import RoadGeometry, RoadLike
class LaneType(Enum):
"""Enumerates OpenDrive standard lane types."""
none = 0
... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/road_network/objects.py | 0.930261 | 0.417271 | objects.py | pypi |
from typing import Any, Dict, Optional
import numpy as np
from shapely.geometry import LineString, Polygon
from shapely.validation import make_valid
from scenario_gym.utils import ArgsKwargs
from .utils import load_road_geometry_from_json, polygon_to_data
class RoadObject:
"""
Base class for an object in t... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/road_network/base.py | 0.964355 | 0.602705 | base.py | pypi |
import json
from contextlib import suppress
from functools import _lru_cache_wrapper, lru_cache, partial
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import numpy as np
from pyxodr.road_objects.network import RoadNetwork as xodrRoadNetwork
from scipy.interpolate import Line... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/road_network/road_network.py | 0.901759 | 0.442817 | road_network.py | pypi |
from typing import Dict, List, Optional, Tuple
import numpy as np
from pyxodr.road_objects.lane import Lane as xodrLane
from pyxodr.road_objects.network import RoadNetwork as xodrRoadNetwork
from shapely.geometry import LineString, Polygon
from scenario_gym.road_network import Lane, LaneType, Road
def xodr_lane_to_... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/road_network/xodr.py | 0.934813 | 0.550668 | xodr.py | pypi |
from abc import abstractmethod
from types import MethodType
from typing import Any, Callable, Optional, Tuple
from scenario_gym.agent import Agent
from scenario_gym.scenario_gym import ScenarioGym
try:
from dm_env import Environment, TimeStep, restart, termination, transition
except ImportError:
raise ImportE... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/integrations/deepmind_env.py | 0.903598 | 0.54468 | deepmind_env.py | pypi |
from __future__ import annotations
from math import inf
from types import MethodType
from typing import Callable, Dict, List, Optional, Tuple, Union
import numpy as np
from packaging import version
from scenario_gym.action import Action
from scenario_gym.agent import Agent
from scenario_gym.controller import Vehicle... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/integrations/openaigym.py | 0.962691 | 0.595316 | openaigym.py | pypi |
import json
from contextlib import suppress
from pathlib import Path
from typing import Dict
import numpy as np
from shapely.geometry import LineString, Polygon
from scenario_gym.catalog_entry import BoundingBox, Catalog, CatalogEntry
from scenario_gym.entity import Entity
from scenario_gym.road_network import (
... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/integrations/argoverse.py | 0.849535 | 0.433742 | argoverse.py | pypi |
from dataclasses import dataclass, field
from random import choice
from typing import Dict, Optional, Tuple
import numpy as np
from nuscenes import NuScenes
from nuscenes.prediction import PredictHelper
from nuscenes.prediction.input_representation.static_layers import load_all_maps
from scipy.spatial.transform import... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/integrations/nuScenes.py | 0.940803 | 0.425128 | nuScenes.py | pypi |
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple
import numpy as np
from shapely.geometry import MultiPolygon
from shapely.ops import unary_union
from shapely.prepared import prep
from shapely.vectorized import contains
from scenario_gym.entity import Entity
from scenario_gym.obs... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/sensor/map.py | 0.951762 | 0.747846 | map.py | pypi |
from dataclasses import dataclass
from typing import Dict, List
import numpy as np
from scenario_gym.entity import Entity
from scenario_gym.observation import (
Observation,
SingleEntityObservation,
combine_observations,
)
from scenario_gym.state import State, detect_collisions
from .base import Sensor
... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/sensor/common.py | 0.969971 | 0.661554 | common.py | pypi |
import random
from itertools import chain
from typing import Dict, List, Optional, Tuple
import numpy as np
from scenario_gym.road_network import RoadNetwork
class RouteFinder:
"""Find routes along walkable areas in the road network."""
def __init__(self, rn: RoadNetwork):
"""Construct the graph re... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/pedestrian/route.py | 0.858881 | 0.527438 | route.py | pypi |
from typing import Tuple, Union
import numpy as np
from shapely.geometry import MultiPolygon, Point, Polygon
from shapely.ops import nearest_points
from scenario_gym.agent import Agent
from scenario_gym.entity import Entity
from scenario_gym.pedestrian.behaviour import PedestrianBehaviour
from scenario_gym.pedestrian... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/pedestrian/social_force.py | 0.947174 | 0.527256 | social_force.py | pypi |
from typing import List
from scenario_gym.entity import Entity, Pedestrian
from scenario_gym.pedestrian.observation import PedestrianObservation
from scenario_gym.sensor import Sensor
from scenario_gym.state import State
class PedestrianSensor(Sensor):
"""Returns observation (complete state) for pedestrian entit... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/pedestrian/sensor.py | 0.959592 | 0.694665 | sensor.py | pypi |
from typing import List
import numpy as np
from shapely.geometry import LineString, Point
from scenario_gym.agent import Agent
from scenario_gym.entity import Entity
from scenario_gym.pedestrian.action import PedestrianAction
from scenario_gym.pedestrian.behaviour import PedestrianBehaviour
from scenario_gym.pedestri... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/pedestrian/agent.py | 0.947697 | 0.495239 | agent.py | pypi |
from __future__ import annotations
import warnings
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TypeVar, Union
import numpy as np
from shapely.geometry import MultiPolygon, Point, Polygon
from shapely.vectorized import contains
from scenario_gym.callback import State... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/state/state.py | 0.946929 | 0.589303 | state.py | pypi |
import math
from enum import Enum
from typing import List, Optional, Tuple
import numpy as np
from shapely.geometry import Polygon
from scenario_gym.entity import Entity
from scenario_gym.metrics.base import Metric
from scenario_gym.state import State
def angle_between(x: float, a_low: float, a_high: float) -> bool... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/metrics/collision.py | 0.936052 | 0.625038 | collision.py | pypi |
from abc import ABC, abstractmethod
from typing import Any, List, Optional, Type
from scenario_gym.callback import StateCallback
from scenario_gym.state import State
class Metric(ABC):
"""
Base class for a metric in scenario_gym.
All metrics implement reset and step methods to update internal states dur... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/metrics/base.py | 0.966553 | 0.494507 | base.py | pypi |
from typing import Dict, Iterable, List, Tuple
import numpy as np
from numpy.linalg import norm
def inverse_direction(vector: Iterable, normalised: bool = True) -> List[float]:
"""
Return the inverse of a 2D vector, Iterable -> Iterable.
Uses clockwise-rotating sign convention: (x, y) --> (y, -x)
O... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/metrics/rss/rss_utils.py | 0.919159 | 0.893913 | rss_utils.py | pypi |
import warnings
from collections import OrderedDict
from typing import Dict, List, Tuple
import numpy as np
from numpy.linalg import norm
from shapely.geometry import LineString, Polygon
from scenario_gym.callback import StateCallback
from scenario_gym.entity import Entity
from scenario_gym.metrics.rss.rss_utils impo... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/metrics/rss/callback.py | 0.918251 | 0.594728 | callback.py | pypi |
from enum import Enum
from typing import Dict, List
from scenario_gym.metrics import Metric
from scenario_gym.road_network import road_network
from scenario_gym.state import State
from .callback import RSSDistances
class Rules(Enum):
"""Enumerate the five rules."""
safe_longitudinal = 0
safe_lateral = ... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/metrics/rss/rss.py | 0.933104 | 0.676339 | rss.py | pypi |
from __future__ import annotations
import json
import warnings
from contextlib import suppress
from copy import copy
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Type
import matplotlib.pyplot as plt
import numpy as np
from scenario_gym.entity import Entity, MiscObject, Pedestrian, Ve... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/scenario/scenario.py | 0.910279 | 0.542742 | scenario.py | pypi |
from abc import ABC, abstractmethod
from copy import deepcopy
from typing import Any, Dict, Optional, TypeVar
import numpy as np
from scenario_gym.entity import Entity
State = TypeVar("State")
class ScenarioAction(ABC):
"""
Base class for scenario actions.
Actions are applied at the first timestamp wi... | /scenario_gym-0.4.5.tar.gz/scenario_gym-0.4.5/scenario_gym/scenario/actions.py | 0.945349 | 0.512632 | actions.py | pypi |
import logging
from .prepare.constraints import calculate_constraint_values_and_bounds
from .linear_programming_optimiser import optimise_scenarios_with_linear_programming
from .linear_programming_optimiser import errors
from .linear_interpolator.interpolate_scenarios import \
(linear_interpolate, interpolate_all_... | /scenario-optimiser-0.2.4.tar.gz/scenario-optimiser-0.2.4/src/scenario_optimiser/optimise.py | 0.87339 | 0.581689 | optimise.py | pypi |
from copy import deepcopy
import numpy as np
from scipy.optimize import minimize, LinearConstraint
def transform_to_concave(scenarios, constraint_metric, optimise_metric):
concave_scenarios = deepcopy(scenarios)
concave_optimise_metric, mse_deviation = _calculate_concave_scenarios(
scenarios[constrain... | /scenario-optimiser-0.2.4.tar.gz/scenario-optimiser-0.2.4/src/scenario_optimiser/linear_programming_optimiser/transform_to_concave.py | 0.726329 | 0.478894 | transform_to_concave.py | pypi |
import logging
import numpy as np
from scipy.optimize import linprog
from .intercepts_slopes import calculate_intercepts_and_slopes, scenarios_concave
from .errors import ScenariosNotConcaveError
log = logging.getLogger(__name__)
def optimise_scenarios_with_linear_programming(scenarios, settings):
"""
x = ... | /scenario-optimiser-0.2.4.tar.gz/scenario-optimiser-0.2.4/src/scenario_optimiser/linear_programming_optimiser/optimise_linear.py | 0.634317 | 0.50891 | optimise_linear.py | pypi |
from collections import namedtuple
import numpy as np
from mip import Model, xsum, maximize, BINARY, CONTINUOUS, LinExpr
from scenario_optimiser.mixed_integer_programming.piecewise_linear import (
PiecewiseLinearFunction,
)
def optimise_scenarios_with_mip(scenarios, settings):
constraint_metric = settings.ge... | /scenario-optimiser-0.2.4.tar.gz/scenario-optimiser-0.2.4/src/scenario_optimiser/mixed_integer_programming/mip_optimiser.py | 0.767864 | 0.525369 | mip_optimiser.py | pypi |
import numpy as np
class LinearInterpolator:
"""
Linear interpolator for a 2d-array
"""
def __init__(self, scenarios):
self.scenarios = scenarios
def get(self, from_metric, metric_values, to_metric):
verified_metric_values = self._verify_metric_values_are_in_scenario_range(
... | /scenario-optimiser-0.2.4.tar.gz/scenario-optimiser-0.2.4/src/scenario_optimiser/linear_interpolator/interpolate_linear.py | 0.913121 | 0.677887 | interpolate_linear.py | pypi |
# ScenarIO
[](https://isocpp.org)
[][scenario]
[][gym-ignition]
[... | /scenario-1.3.1.tar.gz/scenario-1.3.1/README.md | 0.750644 | 0.917562 | README.md | pypi |
import os
from typing import Optional, List
from kcu import sh, kpath
def create_scenes(
in_path: str,
output_folder_path: str,
threshold: float=0.5,
min_scene_duration: float=1.5,
max_scene_duration: float=30,
debug: bool=False
) -> Optional[List[str]]:
os.makedirs(output_folder_path,... | /scene_cutter-0.0.9-py3-none-any.whl/scene_cutter/scene_cutter.py | 0.721449 | 0.276275 | scene_cutter.py | pypi |
import trimesh
import numpy as np
import os
from scene_graph_predictor_pc.src.model.model import MMGNet
from scene_graph_predictor_pc.src.utils.config import Config
from scene_graph_predictor_pc.src.utils import util, define, util, op_utils, util_ply
from itertools import product
import torch
import torch.nn as nn
... | /scene_graph_predictor_pc-0.1.1-py3-none-any.whl/scene_graph_predictor_pc/inference.py | 0.445771 | 0.193986 | inference.py | pypi |
from torch.optim.lr_scheduler import _LRScheduler, EPOCH_DEPRECATION_WARNING
import warnings,types
class BatchMultiplicativeLR(_LRScheduler):
"""Multiply the learning rate of each parameter group by the factor given
in the specified function. When last_epoch=-1, sets initial lr as lr.
Args:
optimi... | /scene_graph_predictor_pc-0.1.1-py3-none-any.whl/scene_graph_predictor_pc/src/utils/optimizer.py | 0.946966 | 0.402979 | optimizer.py | pypi |
import os,sys,time,math,torch
import numpy as np
from torch_geometric.nn.conv import MessagePassing
def read_txt_to_list(file):
output = []
with open(file, 'r') as f:
for line in f:
entry = line.rstrip().lower()
output.append(entry)
return output
def rotation_matrix(ax... | /scene_graph_predictor_pc-0.1.1-py3-none-any.whl/scene_graph_predictor_pc/src/utils/op_utils.py | 0.655115 | 0.629945 | op_utils.py | pypi |
import torch
from scene_graph_predictor_pc.src.model.model_utils.model_base import BaseModel
from scene_graph_predictor_pc.src.utils import op_utils
from scene_graph_predictor_pc.src.utils.eval_utils import inference_triplet
from scene_graph_predictor_pc.src.model.model_utils.network_GNN import GraphEdgeAttenNetworkLay... | /scene_graph_predictor_pc-0.1.1-py3-none-any.whl/scene_graph_predictor_pc/src/model/vlsat/model.py | 0.622689 | 0.195825 | model.py | pypi |
import torch.nn as nn
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init__()
def init_weights(self, init_type='normal', gain=0.02, bias_value=0.0,
target_op = None):
'''
initialize network's weights
init_type: normal | xavier_no... | /scene_graph_predictor_pc-0.1.1-py3-none-any.whl/scene_graph_predictor_pc/src/model/model_utils/networks_base.py | 0.765111 | 0.29908 | networks_base.py | pypi |
import torch
from torch_geometric.nn.conv import MessagePassing
from scene_graph_predictor_pc.src.model.model_utils.networks_base import mySequential
def MLP(channels: list, do_bn=False, on_last=False, drop_out=None):
""" Multi-layer perceptron """
n = len(channels)
layers = []
offset = 0 if on_last el... | /scene_graph_predictor_pc-0.1.1-py3-none-any.whl/scene_graph_predictor_pc/src/model/model_utils/network_util.py | 0.824885 | 0.401101 | network_util.py | pypi |
import torch
import torch.nn as nn
from scene_graph_predictor_pc.src.model.model_utils.network_util import build_mlp, Gen_Index, Aggre_Index, MLP
from scene_graph_predictor_pc.src.model.model_utils.networks_base import BaseNetwork
import inspect
from collections import OrderedDict
import os
from scene_graph_predictor_p... | /scene_graph_predictor_pc-0.1.1-py3-none-any.whl/scene_graph_predictor_pc/src/model/model_utils/network_GNN.py | 0.764452 | 0.36815 | network_GNN.py | pypi |
from inspect import ismodule, ismethod
from typing import Optional, Set, Union, List
from aiogram import Dispatcher
from scene_manager import StorageSettings
from scene_manager.loader import utils
from scene_manager.loader.models import HandlersStorage, SceneModel
from scene_manager.loader.utils import get_class_attr... | /scene_manager-0.1.0.tar.gz/scene_manager-0.1.0/scene_manager/loader/loader.py | 0.786131 | 0.182426 | loader.py | pypi |
from functools import wraps
from typing import Callable, List, Union, Optional
from aiogram import types
from aiogram.types import ContentType
from scene_manager.utils import content_type_checker
from abc import ABC, abstractmethod
def context_types_filter(
context_types: List[Union[ContentType, str]], otherwis... | /scene_manager-0.1.0.tar.gz/scene_manager-0.1.0/scene_manager/tools/filters.py | 0.901864 | 0.281751 | filters.py | pypi |
import argparse
import glob
import logging
from math import atan2, degrees, fabs, sin, radians, cos
import numpy as np
import os
import cv2
from scene_text.detector import EASTDetector
from scene_text.recognizer import MORANRecognizer
log = logging.getLogger(__name__)
def sort_poly(p):
min_axis = np.argmin(np.s... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/scene_text.py | 0.497315 | 0.3975 | scene_text.py | pypi |
from collections import OrderedDict
import logging
import os
import cv2
from PIL import Image
import torch
from torch.autograd import Variable
from .MORAN_v2.tools import utils
from .MORAN_v2.tools import dataset
from .MORAN_v2.models.moran import MORAN
log = logging.getLogger(__name__)
class MORANRecognizer:
... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/recognizer/moran.py | 0.582254 | 0.167389 | moran.py | pypi |
import torch
from torch.autograd import Variable
import tools.utils as utils
import tools.dataset as dataset
from PIL import Image
from collections import OrderedDict
import cv2
from models.moran import MORAN
model_path = './demo.pth'
img_path = './demo/gtsoukas/6.png'
alphabet = '0:1:2:3:4:5:6:7:8:9:a:b:c:d:e:f:g:h:i... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/recognizer/MORAN_v2/demo.py | 0.422505 | 0.341363 | demo.py | pypi |
import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from scene_text.recognizer.MORAN_v2.models.fracPickup import fracPickup
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOu... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/recognizer/MORAN_v2/models/asrn_res.py | 0.906928 | 0.502136 | asrn_res.py | pypi |
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
class MORN(nn.Module):
def __init__(self, nc, targetH, targetW, inputDataType='torch.cuda.FloatTensor', maxBatch=256, CUDA=True):
super(MORN, self).__init__()
self.targetH = targetH
self.targetW = targ... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/recognizer/MORAN_v2/models/morn.py | 0.897288 | 0.59302 | morn.py | pypi |
import random
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from torch.utils.data import sampler
import lmdb
import six
import sys
from PIL import Image
import numpy as np
class lmdbDataset(Dataset):
def __init__(self, root=None, transform=None, reverse=False, alpha... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/recognizer/MORAN_v2/tools/dataset.py | 0.52829 | 0.296349 | dataset.py | pypi |
import torch
import torch.nn as nn
from torch.autograd import Variable
import collections
class strLabelConverterForAttention(object):
"""Convert between str and label.
NOTE:
Insert `EOS` to the alphabet for attention.
Args:
alphabet (str): set of the possible characters.
ignore_c... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/recognizer/MORAN_v2/tools/utils.py | 0.734596 | 0.353456 | utils.py | pypi |
import logging
import math
import numpy as np
import os
import time
import cv2
import tensorflow as tf
from keras.models import load_model, model_from_json
from .EAST import locality_aware_nms as nms_locality
from .EAST import lanms as lanms
from .EAST.model import *
from .EAST.losses import *
from .EAST.data_process... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/detector/east.py | 0.608827 | 0.286918 | east.py | pypi |
import cv2
import time
import math
import os
import argparse
import numpy as np
import tensorflow as tf
from keras.models import load_model, model_from_json
import detector.EAST.locality_aware_nms as nms_locality
import detector.EAST.lanms
parser = argparse.ArgumentParser()
parser.add_argument('--test_data_path', typ... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/detector/EAST/eval.py | 0.583678 | 0.241735 | eval.py | pypi |
from keras.optimizers import Optimizer
from keras import backend as K
import six
import copy
from six.moves import zip
from keras.utils.generic_utils import serialize_keras_object
from keras.utils.generic_utils import deserialize_keras_object
from keras.legacy import interfaces
class AdamW(Optimizer):
"""Adam opti... | /scene-text-0.2.3.tar.gz/scene-text-0.2.3/scene_text/detector/EAST/adamw.py | 0.919077 | 0.519521 | adamw.py | pypi |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.