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 |
|---|---|---|---|---|---|
from typing import Tuple, List
from pddl.pddl import Domain, Problem
from sam_learner.sam_models.comparable_predicate import ComparablePredicate
from sam_learner.sam_models.parameter_binding import ParameterBinding
class StateLiteral:
"""Represent the connection between a lifted and a grounded predicate."""
fact... | /sam_learner-2.1.9-py3-none-any.whl/sam_learner/sam_models/state.py | 0.921468 | 0.540742 | state.py | pypi |
import csv
import logging
import sys
from pathlib import Path
from typing import NoReturn, List, Dict, Any, Union
from pddl.parser import Parser
from pddl.pddl import Domain, Action
from sam_learner import SAMLearner, ESAMLearner
from sam_learner.core import DomainExporter
from sam_learner.core.trajectories_manager i... | /sam_learner-2.1.9-py3-none-any.whl/sam_learner/model_validation/action_model_statistics_extractor.py | 0.677794 | 0.447883 | action_model_statistics_extractor.py | pypi |
import csv
import logging
import os
import re
import sys
from pathlib import Path
from typing import Optional, NoReturn, Dict, List, Any
from task import Operator
from sam_learner.core import TrajectoryGenerator
from sam_learner.sam_models import Trajectory
from .fast_downward_solver import FastDownwardSolver
PLAN_S... | /sam_learner-2.1.9-py3-none-any.whl/sam_learner/model_validation/learned_domain_validator.py | 0.770724 | 0.305141 | learned_domain_validator.py | pypi |
import csv
import logging
from pathlib import Path
from typing import List, Set, NoReturn
from pddl.parser import Parser
from pddl.pddl import Domain, Action
from sam_learner.sam_models import ComparablePredicate
STATISTICS_COLUMNS_NAMES = ["domain_name", "domain_path", "action_name", "number_consistent_models"]
d... | /sam_learner-2.1.9-py3-none-any.whl/sam_learner/model_validation/consistent_models_calculator.py | 0.756537 | 0.324971 | consistent_models_calculator.py | pypi |
import logging
from typing import List, Any, Dict, NoReturn
from pddl.pddl import Domain
from sam_learner.sam_models import Trajectory, ComparablePredicate
def calculate_true_positive_value(
learned_predicates: List[ComparablePredicate], expected_predicates: List[ComparablePredicate]) -> int:
"""
:param learne... | /sam_learner-2.1.9-py3-none-any.whl/sam_learner/model_validation/action_precision_recall_calculator.py | 0.821331 | 0.650883 | action_precision_recall_calculator.py | pypi |
import os
import numpy as np
import cv2
import math
from skimage.feature import peak_local_max
from scipy.cluster.vq import kmeans
def find_max_subarray(array: np.ndarray, window_w: int, threshold: float) -> tuple:
assert len(array.shape) == 1
best_sum = -1
start_idx = None
array_cum = np.pad(np.cum... | /sam_lstm-1.0.1.tar.gz/sam_lstm-1.0.1/sam_lstm/cropping.py | 0.587352 | 0.424054 | cropping.py | pypi |
import keras.backend as K
from keras.layers import (
add,
Input,
Activation,
Conv2D,
MaxPooling2D,
ZeroPadding2D,
BatchNormalization,
)
from keras.models import Model
from keras.utils import get_file
from sam_lstm.config import TH_WEIGHTS_PATH_NO_TOP
def identity_block(input_tensor, kernel... | /sam_lstm-1.0.1.tar.gz/sam_lstm-1.0.1/sam_lstm/dcn_resnet.py | 0.875282 | 0.436442 | dcn_resnet.py | pypi |
import tensorflow as tf
import keras.backend as K
from keras.layers import Layer, InputSpec
from keras import initializers
class AttentiveConvLSTM(Layer):
"""
att_convlstm = AttentiveConvLSTM(
nb_filters_in=512, nb_filters_out=512, nb_filters_att=512, nb_cols=3, nb_rows=3
)(att_convlstm)
"""
... | /sam_lstm-1.0.1.tar.gz/sam_lstm-1.0.1/sam_lstm/attentive_convlstm.py | 0.858615 | 0.492798 | attentive_convlstm.py | pypi |
import cv2
import numpy as np
import scipy.io
import scipy.ndimage
from sam_lstm.config import gaussina_sigma
def padding(img, shape_r=240, shape_c=320, channels=3):
img_padded = np.zeros((shape_r, shape_c, channels), dtype=np.uint8)
if channels == 1:
img_padded = np.zeros((shape_r, shape_c), dtype=np... | /sam_lstm-1.0.1.tar.gz/sam_lstm-1.0.1/sam_lstm/utilities.py | 0.453262 | 0.358241 | utilities.py | pypi |
import tensorflow as tf
import numpy as np
import keras.backend as K
from keras.layers import Layer, InputSpec
from keras import initializers, regularizers, constraints
floatX = K.floatx()
class LearningPrior(Layer):
def __init__(
self,
nb_gaussian,
init="normal",
weights=None,
... | /sam_lstm-1.0.1.tar.gz/sam_lstm-1.0.1/sam_lstm/gaussian_prior.py | 0.865636 | 0.422326 | gaussian_prior.py | pypi |
import keras.backend as K
import numpy as np
from sam_lstm.config import *
from sam_lstm.dcn_resnet import dcn_resnet
from sam_lstm.gaussian_prior import LearningPrior
from sam_lstm.attentive_convlstm import AttentiveConvLSTM
from keras.layers import Lambda, concatenate, Conv2D, UpSampling2D
def repeat(x):
retu... | /sam_lstm-1.0.1.tar.gz/sam_lstm-1.0.1/sam_lstm/models.py | 0.816882 | 0.390708 | models.py | pypi |
import os
import sys
import time
import warnings
from datetime import timedelta
import numpy as np
import pandas as pd
# to deactivate pygame promt
os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
import pygame
from pkg_resources import resource_filename
from tqdm.auto import tqdm
from sam_ml.config import (
get... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/ClassifierTest.py | 0.417509 | 0.168754 | ClassifierTest.py | pypi |
from ConfigSpace import ConfigurationSpace, Float, Integer, Normal
from xgboost import XGBClassifier
from sam_ml.config import get_n_jobs
from .main_classifier import Classifier
class XGBC(Classifier):
""" SupportVectorClassifier Wrapper class """
def __init__(
self,
model_name: str = "XGBC... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/XGBoostClassifier.py | 0.697197 | 0.232779 | XGBoostClassifier.py | pypi |
from ConfigSpace import Beta, Categorical, ConfigurationSpace, Float, Integer
from sklearn.base import ClassifierMixin
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from .main_classifier import... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/AdaBoostClassifier.py | 0.807043 | 0.263289 | AdaBoostClassifier.py | pypi |
import warnings
from ConfigSpace import Beta, Categorical, ConfigurationSpace, Float, Integer
from sklearn.base import ClassifierMixin
from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sam_ml.... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/BaggingClassifier.py | 0.760651 | 0.248067 | BaggingClassifier.py | pypi |
import pickle
import time
from copy import deepcopy
from datetime import timedelta
import pandas as pd
from sam_ml.config import setup_logger
logger = setup_logger(__name__)
class Model:
""" Model parent class """
def __init__(self, model_object = None, model_name: str = "model", model_type: str = "Model")... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/main_model.py | 0.723016 | 0.254416 | main_model.py | pypi |
from ConfigSpace import Categorical, ConfigurationSpace, Float, Integer, Normal
from sklearn.ensemble import GradientBoostingClassifier
from .main_classifier import Classifier
class GBM(Classifier):
""" GradientBoostingMachine Wrapper class """
def __init__(
self,
model_name: str = "Gradient... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/GradientBoostingMachine.py | 0.923463 | 0.560132 | GradientBoostingMachine.py | pypi |
from ConfigSpace import Categorical, ConfigurationSpace, Integer, Normal
from sklearn.ensemble import RandomForestClassifier
from sam_ml.config import get_n_jobs
from .main_classifier import Classifier
class RFC(Classifier):
""" RandomForestClassifier Wrapper class """
def __init__(
self,
m... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/RandomForestClassifier.py | 0.87464 | 0.329257 | RandomForestClassifier.py | pypi |
from ConfigSpace import Categorical, ConfigurationSpace, Float
from sklearn.neural_network import MLPClassifier
from .main_classifier import Classifier
class MLPC(Classifier):
""" MLP Classifier Wrapper class """
def __init__(
self,
model_name: str = "MLP Classifier",
random_state: i... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/MLPClassifier.py | 0.898204 | 0.387864 | MLPClassifier.py | pypi |
import math
from sklearn.metrics import precision_score, recall_score
def samuel_function(x: float) -> float:
return math.sqrt(1/(1 + math.e**(12*(0.5-x))))
def lewis_function(x: float) -> float:
return 1-(0.5-0.5*math.cos((x-1)*math.pi))**4
def s_scoring(y_true: list, y_pred: list, scoring: str = None, p... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/scorer.py | 0.71123 | 0.558568 | scorer.py | pypi |
import inspect
import os
import sys
import warnings
from datetime import timedelta
from statistics import mean
import numpy as np
import pandas as pd
from ConfigSpace import Configuration, ConfigurationSpace
from matplotlib import pyplot as plt
from sklearn.exceptions import NotFittedError
from sklearn.metrics import ... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/main_classifier.py | 0.516352 | 0.194578 | main_classifier.py | pypi |
from ConfigSpace import Categorical, ConfigurationSpace, Integer, Normal
from sklearn.ensemble import ExtraTreesClassifier
from sam_ml.config import get_n_jobs
from .main_classifier import Classifier
class ETC(Classifier):
""" ExtraTreesClassifier Wrapper class """
def __init__(
self,
model... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/ExtraTreesClassifier.py | 0.869119 | 0.325306 | ExtraTreesClassifier.py | pypi |
import copy
import pandas as pd
from sam_ml.config import setup_logger
from sam_ml.data.preprocessing import (
Embeddings_builder,
Sampler,
SamplerPipeline,
Scaler,
Selector,
)
from .main_classifier import Classifier
from .RandomForestClassifier import RFC
logger = setup_logger(__name__)
class... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/main_pipeline.py | 0.727298 | 0.265202 | main_pipeline.py | pypi |
from ConfigSpace import (
Categorical,
ConfigurationSpace,
EqualsCondition,
Float,
ForbiddenAndConjunction,
ForbiddenEqualsClause,
ForbiddenInClause,
)
from sklearn.linear_model import LogisticRegression
from .main_classifier import Classifier
class LR(Classifier):
""" LogisticRegress... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/models/LogisticRegression.py | 0.884962 | 0.340485 | LogisticRegression.py | pypi |
import pandas as pd
from sklearn.preprocessing import (
MaxAbsScaler,
MinMaxScaler,
Normalizer,
PowerTransformer,
QuantileTransformer,
RobustScaler,
StandardScaler,
)
from sam_ml.config import setup_logger
from .main_data import DATA
logger = setup_logger(__name__)
class Scaler(DATA):
... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/data/preprocessing/scaler.py | 0.797833 | 0.312422 | scaler.py | pypi |
import concurrent.futures
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from tqdm.auto import tqdm
from sam_ml.config import setup_logger
from .main_data import DATA
logger = setup_logger(__na... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/data/preprocessing/embeddings.py | 0.76882 | 0.341116 | embeddings.py | pypi |
import pandas as pd
from imblearn.over_sampling import SMOTE, BorderlineSMOTE, RandomOverSampler
from imblearn.under_sampling import (
ClusterCentroids,
NearMiss,
OneSidedSelection,
RandomUnderSampler,
TomekLinks,
)
from sam_ml.config import setup_logger
from .main_data import DATA
logger = setup... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/data/preprocessing/sampling.py | 0.794185 | 0.428652 | sampling.py | pypi |
import pandas as pd
import statsmodels.api as sm
from sklearn.decomposition import PCA
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import (
RFE,
RFECV,
SelectFromModel,
SelectKBest,
SequentialFeatureSelector,
chi2,
)
from sklearn.linear_model import LogisticR... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/data/preprocessing/feature_selection.py | 0.762998 | 0.45538 | feature_selection.py | pypi |
import pandas as pd
from sam_ml.config import setup_logger
from .sampling import Sampler
logger = setup_logger(__name__)
class SamplerPipeline:
def __init__(self, algorithm: str | list[Sampler] = "SMOTE_rus_20_50"):
"""
Class uses multplie up- and down-sampling algorithms instead of only one
... | /sam_ml_py-0.13.0-py3-none-any.whl/sam_ml/data/preprocessing/sampling_pipeline.py | 0.77928 | 0.566798 | sampling_pipeline.py | pypi |
# Overview
If you author an [AWS Serverless Application Model (SAM)](https://aws.amazon.com/serverless/sam/) template you may wish to publish this as an [AWS CloudFormation](https://docs.aws.amazon.com/cloudformation/index.html) template to allow the user to deploy the solution from the console and remove the need for ... | /sam-publish-0.2.1.tar.gz/sam-publish-0.2.1/README.md | 0.828211 | 0.986442 | README.md | 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 ... | /sam_s_distributions-0.1.tar.gz/sam_s_distributions-0.1/distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
from abc import ABC, abstractmethod
from typing import Any, Optional, Set
class BaseArgType(ABC):
help: Optional[str] = None
@abstractmethod
def parse(self, value) -> Any:
pass
@abstractmethod
def help_repr(self) -> str:
pass
@abstractmethod
def global_help_repr(self, na... | /sam_slash_slack-0.1.2-py3-none-any.whl/sam_slash_slack/arg_types.py | 0.912592 | 0.221793 | arg_types.py | pypi |
import logging
from typing import Any, Callable, List, Optional, Set, Tuple, Union
import aiohttp
from sam_slash_slack.arg_types import (
BaseArgType,
FlagType,
StringType,
UnknownLengthListType,
)
from sam_slash_slack.blocks import _make_block_message
from sam_slash_slack.slash_slack_request import S... | /sam_slash_slack-0.1.2-py3-none-any.whl/sam_slash_slack/slash_slack_command.py | 0.793146 | 0.207014 | slash_slack_command.py | pypi |
import hashlib
import hmac
from time import time
from typing import Dict, Optional, Union
class Clock:
def now(self) -> float:
return time()
class SignatureVerifier:
def __init__(self, signing_secret: str, clock: Clock = Clock()):
"""Slack request signature verifier
Slack signs its r... | /sam_slash_slack-0.1.2-py3-none-any.whl/sam_slash_slack/signature_verifier.py | 0.931058 | 0.266947 | signature_verifier.py | pypi |
# sam_subseq - Extract GFF Features From Aligned Reads
`sam_subseq` takes two inputs:
1. SAM file with reads (or sequences in general) aligned to one or more references
2. GFF file defining features for the reference(s)
`sam_subseq` will project the GFF coordinates (which refer to the reference)
onto the reads, extr... | /sam_subseq-0.1.0.tar.gz/sam_subseq-0.1.0/README.md | 0.68215 | 0.870212 | README.md | pypi |
import re
class IndexMap:
"""
Build an index map, mapping reference coordinates to query coordinates.
This allows retrieval of mapped read segments using reference positions.
The index map is a list of tuples. Each list element corresponds to a
reference position. The tuple at this list element co... | /sam_subseq-0.1.0.tar.gz/sam_subseq-0.1.0/src/sam_subseq/IndexMap.py | 0.873498 | 0.652546 | IndexMap.py | pypi |
import sys
import argparse
import textwrap
from sam_subseq import io
from sam_subseq.SamRefAlignment import SamRefAlignment
def parse_args():
argparser = argparse.ArgumentParser(
formatter_class = argparse.RawTextHelpFormatter,
description = textwrap.dedent("""
Extract features (subsequen... | /sam_subseq-0.1.0.tar.gz/sam_subseq-0.1.0/src/sam_subseq/main.py | 0.486088 | 0.475423 | main.py | pypi |
import sys
import io
def stdin_or_fh(f):
"""
Read a line from stdin or a file on disk.
"""
if f is sys.stdin:
for line in f:
yield line
elif isinstance(f, str):
with open(f, "r") as fh:
for line in fh:
yield line
elif isinstance(f, io.Tex... | /sam_subseq-0.1.0.tar.gz/sam_subseq-0.1.0/src/sam_subseq/io.py | 0.433022 | 0.452838 | io.py | pypi |
from typing import List
from template_creator.util.constants import EVENT_TYPES
def create_lambda_function(name: str, handler: str, uri: str, variables, events, api) -> dict:
generic = {
'Type': 'AWS::Serverless::Function',
'Properties': {
'CodeUri': uri,
'Handler': handle... | /sam-template-creator-0.1.3.tar.gz/sam-template-creator-0.1.3/template_creator/writer/lambda_writer.py | 0.80456 | 0.371308 | lambda_writer.py | pypi |
import logging
import sys
from types import FrameType
from typing import List, cast
from loguru import logger
from pydantic import AnyHttpUrl, BaseSettings
class LoggingSettings(BaseSettings):
LOGGING_LEVEL: int = logging.INFO # logging levels are type int
class Settings(BaseSettings):
API_V1_STR: str = "... | /sam_tid_regression_model-0.0.6-py3-none-any.whl/api/app/config.py | 0.535827 | 0.163646 | config.py | pypi |
from typing import Any, List, Optional
from pydantic import BaseModel
from regression_model.processing.validation import SalesDataInputSchema
class PredictionResults(BaseModel):
errors: Optional[Any]
version: str
predictions: Optional[List[float]]
class MultipleSalesDataInputs(BaseModel):
inputs: L... | /sam_tid_regression_model-0.0.6-py3-none-any.whl/api/app/schemas/predict.py | 0.783947 | 0.27492 | predict.py | pypi |
from pathlib import Path
from typing import Dict, List, Sequence
from pydantic import BaseModel
from strictyaml import YAML, load
import regression_model
# Project Directories
PACKAGE_ROOT = Path(regression_model.__file__).resolve().parent
ROOT = PACKAGE_ROOT.parent
CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml"
DAT... | /sam_tid_regression_model-0.0.6-py3-none-any.whl/regression_model/config/core.py | 0.816736 | 0.282116 | core.py | pypi |
from typing import List, Optional, Tuple
import numpy as np
import pandas as pd
from pydantic import BaseModel, ValidationError
from regression_model.config.core import config
def drop_na_inputs(*, input_data: pd.DataFrame) -> pd.DataFrame:
"""Check model inputs for na values and filter."""
validated_data =... | /sam_tid_regression_model-0.0.6-py3-none-any.whl/regression_model/processing/validation.py | 0.831964 | 0.474936 | validation.py | pypi |
tsfresh
=========
tsfresh is a package that can be used to calculate many timeseries-related features used for analysing time series, especially based on physics, and use them as features in your models. It's pretty straightforward to use, because it has built-in functions that calculate all these features, and select... | /sam-3.1.9.tar.gz/sam-3.1.9/docs/source/general_documents/tsfresh.md | 0.638497 | 0.991032 | tsfresh.md | pypi |
Project approach
==================
In this document we describe the typical steps to take in a sensor analysis project.
## Before the project
Use the SAM package as much as possible. If relevant functionality is missing, let us add that and extend the package.
Tip: read the tips!
### General notes, tips and trick... | /sam-3.1.9.tar.gz/sam-3.1.9/docs/source/general_documents/project_approach.md | 0.949623 | 0.981058 | project_approach.md | pypi |
Weather data
============
Often, weather features are important predictors. For training the model, historic data might be relevant, but when making predictions, weather forecast can also be useful. However, there is a big difference in the availability of historic weather data vs forecasts, the resolution and frequen... | /sam-3.1.9.tar.gz/sam-3.1.9/docs/source/general_documents/weather_features.md | 0.953972 | 0.970352 | weather_features.md | pypi |
Feature Extraction
==================
This is the start of the documentation on which features to use when.
## Transforms
Best practices on types of transforms that you can apply.
### Summarizing
Summarizing a window prior to the prediction moment with some basic functions is always a good starting point:
- Basic: m... | /sam-3.1.9.tar.gz/sam-3.1.9/docs/source/general_documents/feature_extraction.md | 0.962081 | 0.988525 | feature_extraction.md | pypi |
# Feature engineering examples
This notebook contains some examples of feature engineering using SAM.
We use the following example dataset:
```
import pandas as pd
from sam.datasets import load_rainbow_beach
data = load_rainbow_beach()
```
## Simple feature engineering for timeseries data
The class `sam.feature_... | /sam-3.1.9.tar.gz/sam-3.1.9/examples/feature_engineering.ipynb | 0.465873 | 0.989879 | feature_engineering.ipynb | pypi |
# sam4onnx
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. **S**imple **A**ttribute and Constant **M**odifier for **ONNX**.
https://github.com/PINTO0309/simple-onnx-processing-tools
[:
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | /sama_probability-0.1.tar.gz/sama_probability-0.1/sama_probability/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.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 ... | /saman_distributions-0.1.tar.gz/saman_distributions-0.1/saman_distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
describe("map_events.js file", function () {
describe("deselectText", function () {});
describe("distanceSquared", function () {
it("is accurate", function () {
expect(distanceSquared(0, 0, 0, 10)).toEqual(100);
expect(distanceSquared(0, 0, 10, 0)).toEqual(100);
expect(distanceSquared(0, 10... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/map_events_spec.js | 0.895222 | 0.925432 | map_events_spec.js | pypi |
describe("table_filters.js file", function () {
beforeEach(function () {
g_known_tags = ["tag1", "tag2"];
g_known_envs = ["production", "dev", "inherit"];
});
describe("members", function () {
it("has filter types", function () {
expect(Object.keys(filters.private.types)).toContain("connections... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/table_filters_spec.js | 0.818664 | 0.778944 | table_filters_spec.js | pypi |
describe("map_render.js file", function () {
describe("fadeFont", function () {
it("works", function () {
expect(fadeFont("#FFFFFF", 1.0)).toEqual("rgba(255,255,255,1)");
expect(fadeFont("#706050", 0.25)).toEqual("rgba(112,96,80,0.25)");
});
});
describe("color_links", function () {
it("wo... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/map_render_spec.js | 0.764452 | 0.862352 | map_render_spec.js | pypi |
describe("metadata.js file", function () {
describe("normalizeIP", function () {
it("works with short IPs", function () {
expect(normalizeIP("110")).toEqual("110.0.0.0/8");
expect(normalizeIP("110.23")).toEqual("110.23.0.0/16");
expect(normalizeIP("110.23.45")).toEqual("110.23.45.0/24");
e... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/metadata_spec.js | 0.82994 | 0.784567 | metadata_spec.js | pypi |
describe("map_node.js file", function () {
describe("Node", function () {
beforeEach(function () {
n1 = new Node("bob", "192.168", 168, 24, 1, 1, 1, 10);
});
it("prepares details member", function () {
expect(n1.hasOwnProperty("details")).toEqual(true);
expect(n1.details.hasOwnProperty("... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/map_node_spec.js | 0.880463 | 0.74704 | map_node_spec.js | pypi |
describe("map.js file", function () {
describe("zoom levels", function() {
it("defined", function () {
expect(zNodes16).toBeDefined();
expect(zNodes24).toBeDefined();
expect(zNodes32).toBeDefined();
expect(zLinks16).toBeDefined();
expect(zLinks24).toBeDefined();
expect(zLinks... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/map_spec.js | 0.854171 | 0.822332 | map_spec.js | pypi |
describe("map_links file", function () {
describe("link_request_add", function () {
it("adds to the queue", function () {
m_link_requests = [];
link_request_add("1.2.3.4");
link_request_add("2.3.4.5");
link_request_add("3.4.5.6");
link_request_add("4.5.6.7");
let expected = ... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/map_links_spec.js | 0.859826 | 0.712401 | map_links_spec.js | pypi |
describe("map_ports.js file", function () {
describe("ports.loaded", function () {
beforeEach(function () {
get_mock_m_ports();
});
it("exists", function () {
expect(ports.loaded(443)).toEqual(true)
});
it("doesn't exist", function () {
expect(ports.loaded(444)).toEqual(false)
... | /samapper-0.3.2.tar.gz/samapper-0.3.2/spec/javascripts/map_ports_spec.js | 0.782455 | 0.743075 | map_ports_spec.js | pypi |
from sam import common, integrity
class DBPlugin(object):
@staticmethod
def checkIntegrity(db):
"""
Checks if the database is correct and returns the equivalent to false if db is consistent.
if db is healthy, return False,
examples: False, 0, [] or {}
if db is unhe... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/models/base.py | 0.69946 | 0.284843 | base.py | pypi |
import web
from sam import common
from sam.models.links import Links
class Nodes(object):
default_environments = {'production', 'dev', 'inherit'}
def __init__(self, db, subscription):
"""
:type db: web.DB
:type subscription: int
:param db:
:param subscription:
... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/models/nodes.py | 0.679072 | 0.262605 | nodes.py | pypi |
import os
import cPickle
import web
from sam.models.security import rule_template, rule
class Rules():
TABLE_FORMAT = "s{}_Rules"
def __init__(self, db, sub_id):
"""
:param db: database connection
:type db: web.DB
:param sub_id: subscription id
:type sub_id: int
... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/models/security/rules.py | 0.442637 | 0.151216 | rules.py | pypi |
from sam import common
import re
from sam import errors
import base
import sam.models.details
import sam.models.nodes
import sam.models.links
# This class is for getting the main selection details, such as ins, outs, and ports.
def nice_protocol(strings, p_in, p_out):
"""
:param p_in: comma-seperated p... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/pages/details.py | 0.621426 | 0.314169 | details.py | pypi |
import base
import sam.models.ports
from sam import errors
from sam import common
# This class is for getting the aliases for a port number
class Portinfo(base.headless_post):
"""
The expected GET data includes:
'port': comma-seperated list of port numbers
A request for ports 80, 443, and... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/pages/portinfo.py | 0.615435 | 0.375477 | portinfo.py | pypi |
from sam import errors
import re
import base64
import base
import sam.models.settings
import sam.models.datasources
import sam.models.livekeys
import sam.models.nodes
import sam.models.links
import sam.models.upload
from sam import common
def nice_name(s):
s = re.sub("([a-z])([A-Z]+)", lambda x: "{0} {1}".format(... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/pages/settings.py | 0.503906 | 0.156201 | settings.py | pypi |
import re
import base
import sam.models.nodes
from sam import errors
from sam import common
# This class is for getting the child nodes of all nodes in a node list, for the map
class Nodes(base.headless_post):
"""
The expected GET data includes:
'address': comma-seperated list of dotted-decimal IP ad... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/pages/nodes.py | 0.555797 | 0.423696 | nodes.py | pypi |
import math
import re
from datetime import datetime
from sam import errors, common
from sam.pages import base
from sam.models.security import alerts
def time_to_seconds(tstring):
"""
Converts a period of time (expressed as a string) to seconds.
:param tstring: string time period. use # of (years/weeks/da... | /samapper-0.3.2.tar.gz/samapper-0.3.2/sam/pages/alerts.py | 0.608594 | 0.368491 | alerts.py | pypi |
from importlib import import_module
import inspect
# Republishing for easy serialization
from pickle import load, loads, dump, dumps # noqa
def import_string(dotted_path):
"""
Import a dotted module path or a element from it if a `:` separator
is provided
:arg dotted_path: path to import (e.g. 'my_m... | /samarche-0.0.1.tar.gz/samarche-0.0.1/samarche.py | 0.708918 | 0.185892 | samarche.py | pypi |
import os
import pandas as pd
import numpy as np
from scipy.spatial.distance import cdist
from sewar.full_ref import mse, sam
def load_img(folder_path, tag=None):
"""
Reads all the images saved in a certain folder path and in the tag file
:param folder_path: Path of the folder where the images from micro... | /samba_metric-0.0.8.tar.gz/samba_metric-0.0.8/src/samba/SAMBA_metric.py | 0.731155 | 0.66238 | SAMBA_metric.py | pypi |
from collections import defaultdict
from pathlib import Path
from typing import List, Tuple, Union, Optional, Sequence
import array
import bz2
import csv
import functools
import itertools
import logging
import math
import pickle
import re
# Import local modules
from .newick import Node
# Define the path to the 'etc' ... | /samba_sampler-0.3.tar.gz/samba_sampler-0.3/src/samba_sampler/common.py | 0.941506 | 0.640854 | common.py | pypi |
# Import Python standard libraries
import argparse
import sys
# Import our library to leverage functions and classes
import samba_sampler as samba
# Define a dictionary for models and their parameters
models = {
"tiago1": {
"algorithm": "standard",
"freq_weight": 1.0,
"matrices": "gled.ma... | /samba_sampler-0.3.tar.gz/samba_sampler-0.3/src/samba_sampler/__main__.py | 0.550849 | 0.553083 | __main__.py | pypi |
# Samba
An extremly tiny PaaS (platform as a s service) to deploy multiple apps on a single servers with git, similar to Heroku or Dokku.
It is simple and compatible with current infrastucture.
It supports Python (Flask/Django), Nodejs, PHP and Static HTML.
### Features
- Easy command line setup
- Instant de... | /samba-0.0.0.tar.gz/samba-0.0.0/README.md | 0.572484 | 0.808275 | README.md | pypi |
from cobra.flux_analysis import flux_variability_analysis
import logging
import time
log = logging.getLogger(__name__)
def run_fva(model, rxnsOfInterest, proc, fraction_opt):
log.info("Starting FVA...")
start_time = time.time()
s = flux_variability_analysis(model, reaction_list=rxnsOfInterest, fraction_of... | /sambaflux-0.1.8-py3-none-any.whl/samba/fva/fva_functions.py | 0.73678 | 0.463687 | fva_functions.py | pypi |
from scipy.interpolate import interp1d
import numpy as np
class CorrelationIntegrands():
"""
Class to compute different integrands of correlation
functions for a specified x. In particular, the
integrands:
B2(x,f) = w(x)*x**2*f(x)*g2(x), (1)
B3(x,f) = x*f(x)*\int dv v**2*... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/correlationintegrands.py | 0.825379 | 0.780077 | correlationintegrands.py | pypi |
import numpy as np
def meshODE(t,sol):
"""
Given a 2D meshgrid array input and an OdeSolution
object, output the ODE solution held in OdeSolution
as a meshgrid compatible with the input.
Parameters
----------
t : 2D np.array
Form of the array should be like either XX or YY... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/meshode.py | 0.681939 | 0.739834 | meshode.py | pypi |
import numpy as np
import scipy.special as special
from .effectivepotential import twobody_value, twobody_derivative
from .effectivepotential import threebody_value, threebody_derivative_u
class wLowDt():
"""
Evaluate the w(r) function when D_t goes to 0 (is much smaller than D_r*sigma**2)
Attri... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/wlowDt.py | 0.891832 | 0.732687 | wlowDt.py | pypi |
import numpy as np
"""
Calculate effective potentials for ABP system, given a
specific w-function.
Methods
-------
twobody_value(r)
twobody_derivative(r)
threebody_value(u,v)
threebody_derivative_u(u,v)
"""
def twobody_value(r,fp,w,V_value):
"""
Compute effective two-body potential.
Para... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/effectivepotential.py | 0.917307 | 0.740667 | effectivepotential.py | pypi |
import numpy as np
import scipy.special as special
from scipy.integrate import solve_ivp, solve_bvp
from scipy.interpolate import interp1d
from .trimerbc import shiftedLJ
from .effectivepotential import twobody_value,twobody_derivative
from .meshode import meshODE
from .whardsphere import wHardSphere
class wXi(shifted... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/wxi.py | 0.836521 | 0.727044 | wxi.py | pypi |
class shiftedLJ():
"""
Simple class which allows for evaluation of Lennard-Jones (LJ)
potential with value of 0 at 2**(1./6.). For
r<2**(1./6.), this potential is equivalent to the
Weeks-Chandler-Anderson potential. Note that length
is measured in units of sigma. The explicit form of the
po... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/trimerbc.py | 0.942784 | 0.852045 | trimerbc.py | pypi |
import numpy as np
import scipy.special as special
from scipy.integrate import solve_ivp, solve_bvp
from .trimerbc import expPot
from .effectivepotential import twobody_value,twobody_derivative
from .meshode import meshODE
class wdiffPot(expPot):
"""
Evaluate the w(r) function which satisfies the differ... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/wdiffpot.py | 0.829734 | 0.575021 | wdiffpot.py | pypi |
import numpy as np
import scipy.special as special
from scipy.integrate import solve_ivp, solve_bvp
from .trimerbc import shiftedLJ
from .effectivepotential import twobody_value,twobody_derivative
from .meshode import meshODE
class wPerturb(shiftedLJ):
"""
Evaluate the w(r) function which satisfies the ... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/wperturb.py | 0.81309 | 0.714827 | wperturb.py | pypi |
import numpy as np
import scipy.special as special
from .trimerbc import shiftedLJ
from .effectivepotential import twobody_value, twobody_derivative
from .effectivepotential import threebody_value, threebody_derivative_u
class wHardSphere(shiftedLJ):
"""
Evaluate the w(r) function which satisfies the d... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/whardsphere.py | 0.893626 | 0.697648 | whardsphere.py | pypi |
import numpy as np
import math
from scipy.integrate import romb
class MayerInt():
"""
This class is focused on computing the integral of the first
correction in density to the radial distribution function for
the 2D active brownian particle system with effective two-body
potential V(r).
The r... | /sambristol_ssabp_w-0.0.15.tar.gz/sambristol_ssabp_w-0.0.15/sambristol_ssabp_w/mayerint.py | 0.722625 | 0.751101 | mayerint.py | pypi |
import networkx as nx
import itertools
import numpy as np
from math import factorial
def gen_expected_crick_angles(P, rep_len, start_ph1, ap=False):
step = 360 / P
if ap:
sign=-1
else:
sign=1
return [adj(start_ph1+(sign * i * float(step))) for i in range(rep_len)]
def adj2(ang):
ang = adj(ang)
if ang < -... | /samcc-turbo-0.0.2.tar.gz/samcc-turbo-0.0.2/samcc/helper_functions.py | 0.505859 | 0.593315 | helper_functions.py | pypi |
import itertools
import heapq
import numpy as np
import scipy.spatial.distance as distance
import scipy.optimize
import functools
from operator import attrgetter
from Bio import PDB
from .bundle import get_local_axis
def create_pymol_selection_from_socket_results(indices):
"""create pymol-readable selection from soc... | /samcc-turbo-0.0.2.tar.gz/samcc-turbo-0.0.2/samcc/layer_detection.py | 0.510252 | 0.5047 | layer_detection.py | pypi |
import argparse
import os
import json
from random import shuffle
from pathlib import Path
from flattenKern import flatten_gpos_kerning
from typing import Union
from fontTools.ttLib import TTFont
from defcon import Font
__all__ = ["SameWidther", "TTFont", "Font"]
class SameWidther:
def __init__(self, font: Union[... | /sameWidther-0.0.5.tar.gz/sameWidther-0.0.5/Lib/sameWidther.py | 0.762336 | 0.291321 | sameWidther.py | pypi |
import dash
from dash import dcc, html, Input, Output
import dash_bootstrap_components as dbc
import plotly.express as px
import pandas as pd
import matplotlib.font_manager as fm
def get_system_fonts():
font_list = fm.findSystemFonts(fontpaths=None, fontext='ttf')
font_names = [fm.FontProperties(fname=font_fil... | /sameh-stirling-0.0.6.tar.gz/sameh-stirling-0.0.6/sameh_stirling/stacked_bar.py | 0.68616 | 0.253476 | stacked_bar.py | pypi |
import dash
from dash import dcc, html, Input, Output
import dash_bootstrap_components as dbc
import plotly.express as px
import pandas as pd
import matplotlib.font_manager as fm
def get_system_fonts():
font_list = fm.findSystemFonts(fontpaths=None, fontext='ttf')
font_names = [fm.FontProperties(fname=font_fil... | /sameh-stirling-0.0.6.tar.gz/sameh-stirling-0.0.6/sameh_stirling/bubble_chart.py | 0.646349 | 0.301683 | bubble_chart.py | pypi |
# Check clients that are known to be incompatible with `SameSite=None`.
import re
def should_send_same_site_none(useragent):
return useragent is None or not is_same_site_none_incompatible(useragent)
# _classes of browsers known to be incompatible.
def is_same_site_none_incompatible(useragent):
return ha... | /samesite-compat-check-0.2.0.tar.gz/samesite-compat-check-0.2.0/samesite_compat_check/check.py | 0.767864 | 0.416856 | check.py | pypi |
import sys
import subprocess
import argparse
import re
from collections import defaultdict
parser = argparse.ArgumentParser(
description='This script is for parsing the BAM file and look for reads overlapping with the target genes and report the pileup.')
parser.add_argument('sample_id', help='sample ID')
parser.a... | /samestr-1.2023.4-py3-none-any.whl/samestr-1.2023.4.data/scripts/kpileup.py | 0.640636 | 0.37777 | kpileup.py | pypi |
import argparse
import numpy as np
from os.path import isdir, basename
from os import makedirs
# Input arguments
# ---------------
parser = argparse.ArgumentParser()
parser.add_argument('--kp', help='Kpileup alignments (.kp.txt)')
parser.add_argument('--map', help='Map of genomes to contigs (tab-delimited)')
parser.a... | /samestr-1.2023.4-py3-none-any.whl/samestr-1.2023.4.data/scripts/kp2np.py | 0.488527 | 0.307969 | kp2np.py | pypi |
# Samil Power inverter tool
[](https://pypi.org/project/samil/)
Get model and status data from Samil Power inverters over the network.
If you just need PVOutput.org uploading, you can also try the
[old version](https://github.com/mhvis/solar/tree/v1).
## Supported inverte... | /samil-2.2.1.tar.gz/samil-2.2.1/README.md | 0.435661 | 0.943504 | README.md | pypi |
<div align="center">
<img src="https://github.com/sepandhaghighi/samila/raw/master/otherfiles/logo.png" width=400 height=400>
<br/>
<h1>Samila</h1>
<br/>
<a href="https://www.python.org/"><img src="https://img.shields.io/badge/built%20with-Python3-green.svg" alt="built with Python3" /></a>
<a href="https://codecov.io/g... | /samila-1.1.tar.gz/samila-1.1/README.md | 0.523177 | 0.900836 | README.md | pypi |
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'DonationSuggestion'
db.create_table('samklang_payment_donationsuggestion', (
('id', self.gf('dja... | /samklang-payment-0.6.0.tar.gz/samklang-payment-0.6.0/samklang_payment/migrations/0003_auto__add_donationsuggestion__add_field_donationcampaign_default_amoun.py | 0.400867 | 0.150559 | 0003_auto__add_donationsuggestion__add_field_donationcampaign_default_amoun.py | pypi |
# SAML Reader
## **IMPORTANT**
Please **DO NOT** add any personally identifiable information (PII) when reporting an issue.
This means **DO NOT** upload any SAML data, even if it is yours. I don't want to be responsible
for it. :)
## Table of Contents
- [SAML Reader](#saml-reader)
- [**IMPORTANT**](#important)
... | /saml_reader-0.0.6.tar.gz/saml_reader-0.0.6/README.md | 0.494385 | 0.754146 | README.md | pypi |
import json
from urllib.parse import unquote
import haralyzer
class HarParsingError(Exception):
"""
Custom exception raised when we get any error from the HAR parser
"""
pass
class NoSAMLResponseFound(Exception):
"""
Custom exception if we don't find a SAML response
"""
pass
class... | /saml_reader-0.0.6.tar.gz/saml_reader-0.0.6/saml_reader/har.py | 0.450359 | 0.330924 | har.py | pypi |
from itertools import zip_longest
from cryptography import x509
from cryptography.hazmat.backends import default_backend
class Certificate(object):
"""
Wrapper around cryptography's x509 parser for PEM certificates with
helper functions to retrieve relevant data from the certificate
"""
def __ini... | /saml_reader-0.0.6.tar.gz/saml_reader-0.0.6/saml_reader/cert.py | 0.841565 | 0.249979 | cert.py | pypi |
import sys
import pyperclip
from saml_reader.cert import Certificate
from saml_reader.saml.parser import RegexSamlParser, StandardSamlParser
from saml_reader.saml.errors import SamlParsingError, SamlResponseEncryptedError, IsASamlRequest, DataTypeInvalid
from saml_reader.har import HarParser, HarParsingError, NoSAMLR... | /saml_reader-0.0.6.tar.gz/saml_reader-0.0.6/saml_reader/text_reader.py | 0.575588 | 0.239061 | text_reader.py | pypi |
from abc import ABC, abstractmethod
class BaseSamlParser(ABC):
"""
Generalized SAML response parser
"""
def __init__(self):
"""
Parses SAML response from base64 input.
Args:
response (basestring): SAML response as a base64-encoded string
Raises:
... | /saml_reader-0.0.6.tar.gz/saml_reader-0.0.6/saml_reader/saml/base.py | 0.92297 | 0.535584 | base.py | pypi |
from collections import defaultdict
import re
from onelogin.saml2.utils import OneLogin_Saml2_Utils as utils
from urllib.parse import unquote
from lxml import etree
from saml_reader.saml.base import BaseSamlParser
from saml_reader.saml.oli import OLISamlParser
from saml_reader.saml.errors import SamlResponseEncrypted... | /saml_reader-0.0.6.tar.gz/saml_reader-0.0.6/saml_reader/saml/parser.py | 0.817502 | 0.362715 | parser.py | pypi |
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