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 __future__ import annotations
import collections, itertools, re
from collections.abc import Sequence
from typing import Callable, Dict, List, Optional, Tuple, Union
# ---- QR Code symbol class ----
class QrCode:
"""A QR Code symbol, which is a type of two-dimension barcode.
Invented by Denso Wave and describ... | /qrcodegen-1.8.0.zip/qrcodegen-1.8.0/qrcodegen.py | 0.894807 | 0.60054 | qrcodegen.py | pypi |
from __future__ import annotations
import os
import requests
import tqdm
from yolov7_package import Yolov7Detector
_WEIGHTS_PATH = os.path.join(os.path.dirname(__file__), '.yolov7_qrdet', 'qrdet-yolov7.pt')
_WEIGHTS_URL = 'https://github.com/Eric-Canas/qrdet/releases/download/first_qrdet_yolov7/qrdet-yolov7.pt'
clas... | /qrdet-1.10.tar.gz/qrdet-1.10/qrdet.py | 0.794584 | 0.558026 | qrdet.py | pypi |
# qre - like re, but cuter
[](https://pypi.org/project/qre/)
[](https://opensource.org/licenses/MIT)
[](http... | /qre-22.10.2.tar.gz/qre-22.10.2/README.md | 0.456652 | 0.95561 | README.md | pypi |
from __future__ import annotations
from warnings import warn
import numpy as np
from pyzbar.pyzbar import decode as decodeQR, ZBarSymbol, Decoded
import cv2
from qrdet import QRDetector
_SHARPEN_KERNEL = np.array(((-1., -1., -1.), (-1., 9., -1.), (-1., -1., -1.)), dtype=np.float32)
class QReader:
def __init__(sel... | /qreader-2.13.tar.gz/qreader-2.13/qreader.py | 0.885576 | 0.488832 | qreader.py | pypi |
# QReader
<img alt="QReader" title="QReader" src="https://raw.githubusercontent.com/Eric-Canas/QReader/main/documentation/resources/logo.png" width="20%" align="left"> **QReader** is a **Robust** and **Straight-Forward** solution for reading **difficult** and **tricky** **QR** codes within images in **Python**. Powere... | /qreader-2.13.tar.gz/qreader-2.13/README.md | 0.682679 | 0.961244 | README.md | pypi |
Qree
====
Qree (read 'Curie') is a tiny but mighty Python templating engine, geared toward HTML. 'Qree' is short for: *Q*uote, *r*eplace, *e*xec(), *e*val().
The entire module is under 200 lines. Instead of using regular expressions or PEGs, Qree relies on Python's `exec()` and `eval()`. Thus, it supports *all langua... | /qree-0.0.4.tar.gz/qree-0.0.4/README.md | 0.53607 | 0.890532 | README.md | pypi |
import numpy as np
def count_tensor(vectors, tensor=[]):
# ASSIGNING TERMINATION CONDITION AT THE BEGINNING
# Check if single vector was passed
if len(vectors) == 1:
# Insert it's elements into target if so and return
for el in vectors[0]:
tensor.append(el)
return tenso... | /QREM-0.0.56.tar.gz/QREM-0.0.56/QREM/PyMaLi/countTensor.py | 0.614163 | 0.532243 | countTensor.py | pypi |
import copy
import random
import math
import numpy as np
from functions import functions_data_visualization
from tqdm import tqdm
class InfinityException(Exception):
"""Class for handling infinity"""
pass
class ClusterSizeError(NameError):
"""Class for handling too small max cluster size"""
pass
# ... | /QREM-0.0.56.tar.gz/QREM-0.0.56/QREM/functions/functions_noise_model_heuristic.py | 0.410284 | 0.349394 | functions_noise_model_heuristic.py | pypi |
# Quantum Detector Tomography
This notebook contains examples for using QDT module with Qiskit.
## Theoretical background
### Noisy quantum detectors
In quantum mechanics any measurement can be described by Positive Operator-Valued Measure (POVM).
A POVM $\mathbf{M}$ with $n$ outcomes is a list of $n$ operators
$... | /QREM-0.0.56.tar.gz/QREM-0.0.56/QREM/Tutorials/QDT/01_implementing_QDT.ipynb | 0.449634 | 0.993442 | 01_implementing_QDT.ipynb | pypi |
# Error Mitigation
This notebook contains theoretical background and examples for using our Error Mitigation module with Qiskit.
We recommend to first read our [Quantum Detector Tomography Tutorial](QDT_Tutorial.ipynb).
## Theoretical background
Here we describe main theoretical concepts related to mitigation proce... | /QREM-0.0.56.tar.gz/QREM-0.0.56/QREM/Tutorials/QDT/02_error_mitigation.ipynb | 0.536313 | 0.993667 | 02_error_mitigation.ipynb | pypi |
from typing import Optional, List, Dict, Union
import numpy as np
from functions import ancillary_functions as anf
class LabelsBaseDDOT:
def __init__(self,
number_of_qubits: int,
subsets: Union[Dict[str, List[int]], List[List[int]]],
maximal_circuits_amount: Opt... | /QREM-0.0.56.tar.gz/QREM-0.0.56/QREM/noise_characterization/tomography/LabelsBaseDDOT.py | 0.930774 | 0.425187 | LabelsBaseDDOT.py | pypi |
import copy
from typing import Optional, List, Dict, Union
import numpy as np
from QREM.noise_characterization.tomography.LabelsBaseDDOT import LabelsBaseDDOT
from functions import ancillary_functions as anf
from tqdm import tqdm
import time
from scipy.special import binom as binomial_coefficient
class LabelsCreatorD... | /QREM-0.0.56.tar.gz/QREM-0.0.56/QREM/noise_characterization/tomography/LabelsCreatorDDOT.py | 0.841272 | 0.326741 | LabelsCreatorDDOT.py | pypi |
from functions import ancillary_functions as anf
import numpy as np
from typing import Optional, Dict, List, Union
class GlobalNoiseMatrixCreator:
"""
This is class that, given noise matrices on clusters of qubits as function of input state of their
neighbors, constructs global noise model on all qubits.
... | /QREM-0.0.56.tar.gz/QREM-0.0.56/QREM/noise_characterization/modeling/GlobalNoiseMatrixCreator.py | 0.843992 | 0.570571 | GlobalNoiseMatrixCreator.py | pypi |
from wtforms import Form, StringField, PasswordField, RadioField, HiddenField, FieldList, FormField, BooleanField, DateTimeField, TextAreaField, SelectField
from wtforms import validators
from wtforms.fields.html5 import EmailField,IntegerField
from wtforms.validators import DataRequired, Optional
class RequiredIf(Dat... | /qresp-1.2.1.tar.gz/qresp-1.2.1/project/views.py | 0.632049 | 0.272905 | views.py | pypi |
from mongoengine import *
class Person(DynamicEmbeddedDocument):
""" Class mapping creators,PIs,authors of paper to mongo database
"""
firstName = StringField(max_length=50, required= True)
middleName = StringField(max_length=50)
lastName = StringField(max_length=50, required= True)
emailId = S... | /qresp-1.2.1.tar.gz/qresp-1.2.1/project/models.py | 0.659624 | 0.181608 | models.py | pypi |
import click
import qrcode
def get_error_correction(error_correction):
if error_correction in [0, 1, 2, 3]: # defined values are [0, 1, 2, 3], so we just accept it.
return error_correction
if error_correction == "7":
return qrcode.constants.ERROR_CORRECT_L
elif error_correction == "15":
... | /qrimg-0.2.0.tar.gz/qrimg-0.2.0/qrimg.py | 0.551574 | 0.301722 | qrimg.py | pypi |
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from qrl_graph.utils import batch_outer, get_masked_tensor
class Attention(nn.Module):
def __init__(self, n_hidden):
super(Attention, self).__init__()
self.size = 0
... | /qrl_graph-0.0.13-py3-none-any.whl/qrl_graph/agent/gpn.py | 0.943152 | 0.414751 | gpn.py | pypi |
from collections.abc import Collection
from io import StringIO
import logging
from sqlalchemy import Engine, MetaData, Table, Column, create_mock_engine, select, insert
from sqlalchemy.schema import CreateSchema, CreateTable
MAX_TABLE_SIZE = 10000
class Database:
def engine(self) -> Engine:
"""Return the ... | /qrlew_datasets-0.3.2.tar.gz/qrlew_datasets-0.3.2/datasets/database.py | 0.74008 | 0.217888 | database.py | pypi |
import numpy as np
import pandas as pd
from scipy.optimize import minimize
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.empt... | /qrmfinal_xd-0.2.1.tar.gz/qrmfinal_xd-0.2.1/src/qrmfinal_xd/risk_cal.py | 0.799638 | 0.521227 | risk_cal.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... | /qrmfinal_xd-0.2.1.tar.gz/qrmfinal_xd-0.2.1/src/qrmfinal_xd/matrix.py | 0.933688 | 0.824356 | matrix.py | pypi |
import numpy as np
import pandas as pd
from . import 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 sim... | /qrmfinal_xd-0.2.1.tar.gz/qrmfinal_xd-0.2.1/src/qrmfinal_xd/sim_copula.py | 0.797439 | 0.723468 | sim_copula.py | pypi |
<p align="center">
<img src="https://github.com/ozanerhansha/qRNG/blob/master/qRNG.png?raw=true" width="500px"/>
</p>
-----------------
[](https://zenodo.org/badge/latestdoi/164359657)
**qRNG** is an open-source quantum random number generator written in python. It achi... | /qrng-0.1.3.tar.gz/qrng-0.1.3/README.md | 0.605916 | 0.984032 | README.md | pypi |
# qronos-client
Python client for QRonos
## Installation
This package can be installed via pip:
```
pip install qronos-client
```
## Example Usage
### Authentication
```python
from qronos import QRonosClient
# Create client and login
qronos = QRonosClient(host='https://dev.qronos.xyz')
token, expiry = qronos.log... | /qronos-client-1.8.1.tar.gz/qronos-client-1.8.1/README.md | 0.551332 | 0.847495 | README.md | pypi |
import DI
import sys
from query import *
import operators
import data
# todo-list throughout the orm-project
'''
===important===
todo tests
===middle===
todo first connection - no logs, for logger not configured
todo insert values -> add array of dicts support
todo funcall - not working with psycorg2
todo connection ... | /qrookDB-1.3.3.1-py3-none-any.whl/qrook_db/DB.py | 0.462473 | 0.306968 | DB.py | pypi |
from abc import ABCMeta, abstractmethod, abstractproperty
from symbols import QRDB_IDENTIFIER, QRDB_LITERAL
class IQROperator:
"""
Abstract class representing sql operator.
The only purpose is to create the condition string and literals literals for query.
All operators are supposed to work like this
... | /qrookDB-1.3.3.1-py3-none-any.whl/qrook_db/operators.py | 0.87464 | 0.225097 | operators.py | pypi |
from abc import ABCMeta, abstractmethod, abstractproperty
from data import *
import operators as op
from collections.abc import Iterable
from symbols import *
def get_field(field_name: str, tables) -> QRField:
"""
:param field_name: field name
:param tables: iterable of QRTables
:return: QRField corre... | /qrookDB-1.3.3.1-py3-none-any.whl/qrook_db/query_parts.py | 0.512449 | 0.200108 | query_parts.py | pypi |
import inject
from abc import ABCMeta, abstractmethod, abstractproperty
from IConnector import IConnector
from data_formatter import IDataFormatter
from query_parts import *
from symbols import *
import qrlogging
class IQRQuery:
"""
Abstract class for db-queries like select, update, delete etc.
"""
... | /qrookDB-1.3.3.1-py3-none-any.whl/qrook_db/query.py | 0.473414 | 0.180467 | query.py | pypi |
## UTIL LIBRARIES
import numpy as np
import pandas as pd
from copy import deepcopy
import matplotlib.pyplot as plt
## QRS DETECTION LIBRARIES
from biosppy.signals import ecg
import neurokit2 as nk
from ecgdetectors import Detectors
# LOAD CATALOGUE AND ASSOCIATED METHODS
from qrs_wrapper.wrapper_map import CATALOGUE... | /qrs_wrapper-0.1.1.tar.gz/qrs_wrapper-0.1.1/qrs_wrapper/qrs_wrapper.py | 0.617743 | 0.391319 | qrs_wrapper.py | pypi |
# QRS DETECTORS LIBRARIES
from biosppy.signals import ecg
from ecgdetectors import Detectors
# CATALOGUE OF AVAILABLE LIBRARIES AND METHODS WITHIN EACH OF THEM
CATALOGUE_DETECTORS = {
"biosppy": ["default", "christov", "engzee", "gamboa", "hamilton", "ssf"],
"neurokit": ["default", "christov", "elgendi", "eng... | /qrs_wrapper-0.1.1.tar.gz/qrs_wrapper-0.1.1/qrs_wrapper/wrapper_map.py | 0.581897 | 0.248067 | wrapper_map.py | pypi |
import sys
import io
import zlib
from random import randint
from struct import pack, unpack
from math import log, floor, sqrt, ceil
from collections import defaultdict
from random import choice
DEFAULT_C = 0.1
DEFAULT_DELTA = 0.5
# Parameters for Pseudorandom Number Generator
PRNG_A = 16807
PRNG_M = (1 << 31) - 1
PRN... | /qrstreamer-0.1.2.tar.gz/qrstreamer-0.1.2/fountaincoding/fountaincoding.py | 0.61231 | 0.436622 | fountaincoding.py | pypi |
# Welcome to QRules!
```{title} Welcome
```
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
[](https://doi.org/10.5281/zenodo.5526360)
[](https://p... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/index.md | 0.847116 | 0.962108 | index.md | pypi |
from typing import TYPE_CHECKING, List
if TYPE_CHECKING:
from docutils import nodes
from sphinx.addnodes import pending_xref
from sphinx.environment import BuildEnvironment
__TARGET_SUBSTITUTIONS = {
"a set-like object providing a view on D's items": "typing.ItemsView",
"a set-like object providin... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/_relink_references.py | 0.782912 | 0.423637 | _relink_references.py | pypi |
```
# WARNING: advised to install a specific version, e.g. qrules==0.1.2
%pip install -q qrules[doc,viz] IPython
%config InlineBackend.figure_formats = ['svg']
import os
from IPython.display import display # noqa: F401
STATIC_WEB_PAGE = {"EXECUTE_NB", "READTHEDOCS"}.intersection(os.environ)
```
```{autolink-concat}... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/usage.ipynb | 0.66072 | 0.887497 | usage.ipynb | pypi |
```
# WARNING: advised to install a specific version, e.g. qrules==0.1.2
%pip install -q qrules[doc,viz] IPython
%config InlineBackend.figure_formats = ['svg']
import os
from IPython.display import display # noqa: F401
STATIC_WEB_PAGE = {"EXECUTE_NB", "READTHEDOCS"}.intersection(os.environ)
```
```{autolink-concat}... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/usage/ls-coupling.ipynb | 0.732018 | 0.918187 | ls-coupling.ipynb | pypi |
```
# WARNING: advised to install a specific version, e.g. qrules==0.1.2
%pip install -q qrules[doc,viz] IPython
%config InlineBackend.figure_formats = ['svg']
import os
from IPython.display import display # noqa: F401
STATIC_WEB_PAGE = {"EXECUTE_NB", "READTHEDOCS"}.intersection(os.environ)
```
```{autolink-concat}... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/usage/particle.ipynb | 0.749087 | 0.964355 | particle.ipynb | pypi |
```
# WARNING: advised to install a specific version, e.g. qrules==0.1.2
%pip install -q qrules[doc,viz] IPython
%config InlineBackend.figure_formats = ['svg']
import os
from IPython.display import display # noqa: F401
STATIC_WEB_PAGE = {"EXECUTE_NB", "READTHEDOCS"}.intersection(os.environ)
```
```{autolink-concat}... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/usage/conservation.ipynb | 0.775435 | 0.874774 | conservation.ipynb | pypi |
```
# WARNING: advised to install a specific version, e.g. qrules==0.1.2
%pip install -q qrules[doc,viz] IPython
%config InlineBackend.figure_formats = ['svg']
import os
from IPython.display import display # noqa: F401
STATIC_WEB_PAGE = {"EXECUTE_NB", "READTHEDOCS"}.intersection(os.environ)
```
```{autolink-concat}... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/usage/custom-topology.ipynb | 0.572245 | 0.843573 | custom-topology.ipynb | pypi |
```
# WARNING: advised to install a specific version, e.g. qrules==0.1.2
%pip install -q qrules[doc,viz] IPython
%config InlineBackend.figure_formats = ['svg']
import os
from IPython.display import display # noqa: F401
STATIC_WEB_PAGE = {"EXECUTE_NB", "READTHEDOCS"}.intersection(os.environ)
```
```{autolink-concat}... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/usage/visualize.ipynb | 0.76708 | 0.913869 | visualize.ipynb | pypi |
```
# WARNING: advised to install a specific version, e.g. qrules==0.1.2
%pip install -q qrules[doc,viz] IPython
%config InlineBackend.figure_formats = ['svg']
import os
from IPython.display import display # noqa: F401
STATIC_WEB_PAGE = {"EXECUTE_NB", "READTHEDOCS"}.intersection(os.environ)
```
```{autolink-concat}... | /qrules-0.10.0a1.tar.gz/qrules-0.10.0a1/docs/usage/reaction.ipynb | 0.774242 | 0.914863 | reaction.ipynb | 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 ... | /qs_distributions-0.1.tar.gz/qs_distributions-0.1/qs_distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
import os
from typing import List, Optional, Tuple, Union
import gdown
import numpy as np
import torch
from tqdm.auto import trange
from transformers import AutoTokenizer
from qs_kpa.pseudo_label.model_argument import PseudoLabelModelArguments
from qs_kpa.pseudo_label.models import PseudoLabelModel
from qs_kpa.utils.... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/KeyPointAnalysis.py | 0.895134 | 0.217878 | KeyPointAnalysis.py | pypi |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class BaseDataArguments:
"""Base Arguments pertaining to what data we are going to input our model for training and eval."""
directory: Optional[str] = field(default=None, metadata={"help": "The input data folder"})
test_dir... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/backbone/base_arguments.py | 0.941513 | 0.460168 | base_arguments.py | pypi |
import os
import random
import numpy as np
import torch
from qs_kpa.utils.logging import custom_logger
logger = custom_logger(__name__)
def seed_everything(seed: int) -> None:
"""
Seed for reproceducing.
Args:
seed (int): seed number
"""
random.seed(seed)
os.environ["PYTHONHASHSEED... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/train_utils/helpers.py | 0.832781 | 0.250832 | helpers.py | pypi |
import glob
import json
import os
from typing import Dict, Optional, Tuple
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from tqdm.auto import tqdm
from transformers import AdamW, get_linear_schedule_with_warmup
from qs_kpa.train_utils.helpe... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/train_utils/trainer.py | 0.898381 | 0.333965 | trainer.py | pypi |
from itertools import combinations
import numpy as np
import torch
def pdist(vectors):
distance_matrix = (
-2 * vectors.mm(torch.t(vectors))
+ vectors.pow(2).sum(dim=1).view(1, -1)
+ vectors.pow(2).sum(dim=1).view(-1, 1)
)
return distance_matrix
class PairSelector:
def __ini... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/train_utils/samplers.py | 0.849909 | 0.654867 | samplers.py | pypi |
import dataclasses
import json
from dataclasses import dataclass, field
from typing import Any, Dict
import torch
from qs_kpa.train_utils.helpers import cached_property
@dataclass
class TrainingArguments:
"""TrainingArguments is the subset of the arguments we use in our example
scripts."""
experiment:... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/train_utils/training_argument.py | 0.933975 | 0.386821 | training_argument.py | pypi |
import json
import os
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.metrics import average_precision_score
from qs_kpa.utils.logging import custom_logger
logger = custom_logger(__name__)
def get_ap(df: pd.DataFrame, label... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/utils/data.py | 0.764232 | 0.34494 | data.py | pypi |
import dataclasses
import json
import sys
from argparse import ArgumentParser
from enum import Enum
from pathlib import Path
from typing import Any, Iterable, NewType, Tuple, Union
DataClass = NewType("DataClass", Any)
DataClassType = NewType("DataClassType", Any)
class HfArgumentParser(ArgumentParser):
"""This... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/utils/hf_argparser.py | 0.757615 | 0.2957 | hf_argparser.py | pypi |
from typing import Optional
import torch
import torch.nn.functional as F
from pytorch_metric_learning import distances, losses, miners
from qs_kpa.backbone.base_model import BaseModel
from qs_kpa.pseudo_label.model_argument import PseudoLabelModelArguments
from qs_kpa.utils.logging import custom_logger
logger = cust... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/pseudo_label/models.py | 0.948082 | 0.352257 | models.py | pypi |
import random
from typing import Dict, List
import numpy as np
import pandas as pd
import torch
from transformers import PreTrainedTokenizer
from qs_kpa.backbone.base_dataset import BaseDataset
from qs_kpa.pseudo_label.data_argument import PseudoLabelDataArguments
from qs_kpa.utils.logging import custom_logger
logge... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/pseudo_label/datasets.py | 0.804444 | 0.2964 | datasets.py | pypi |
import torch
import torch.nn as nn
def cos_sim(a: torch.Tensor, b: torch.Tensor):
"""
Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.
:return: Matrix with res[i][j] = cos_sim(a[i], b[j])
"""
if not isinstance(a, torch.Tensor):
a = torch.tensor(a)
if not isinstance... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/losses/multiple_negatives_ranking_loss.py | 0.930584 | 0.743354 | multiple_negatives_ranking_loss.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
# TODO: compare negative vs positive
class HardNegativeTripletLoss(nn.Module):
def __init__(self, distance_metric, triplet_margin: float = 0.5):
"""
For each positive pair, takes the hardest negative sample (with the greatest trip... | /qs_kpa-0.0.1-py3-none-any.whl/qs_kpa/losses/online_triplet_loss.py | 0.807612 | 0.69118 | online_triplet_loss.py | pypi |
import json
import copy
import collections
class QueryError(Exception):
'''Query error base class'''
def __init__(self, message, status_code=None):
super(QueryError, self).__init__(message)
if status_code:
self.status_code = status_code
class QueryResourceDoesNotExist(QueryErro... | /qs_parse_rest-0.2.20170114-py3-none-any.whl/parse_rest/query.py | 0.677687 | 0.204025 | query.py | pypi |
from parse_rest.connection import API_ROOT
from parse_rest.datatypes import ParseResource
from parse_rest.query import QueryManager
class Installation(ParseResource):
ENDPOINT_ROOT = '/'.join([API_ROOT, 'installations'])
@classmethod
def _get_installation_url(cls, installation_id):
"""
G... | /qs_parse_rest-0.2.20170114-py3-none-any.whl/parse_rest/installation.py | 0.779406 | 0.21099 | installation.py | pypi |
alignment_location = [
None, None, (6, 18), (6, 22), (6, 26), (6, 30), (6, 34), (6, 22, 38), (6, 24, 42), (6, 26, 46), (6, 28, 50),
(6, 30, 54), (6, 32, 58), (6, 34, 62), (6, 26, 46, 66), (6, 26, 48, 70), (6, 26, 50, 74), (6, 30, 54, 78),
(6, 30, 56, 82), (6, 30, 58, 86), (6, 34, 62, 90), (6, 28, 50, 72, 94... | /qs-qrcode-1.2.tar.gz/qs-qrcode-1.2/qsqrcode/constant.py | 0.411347 | 0.347039 | constant.py | pypi |
from feature_engine.encoding import OrdinalEncoder, RareLabelEncoder
from feature_engine.imputation import (
AddMissingIndicator,
CategoricalImputer,
MeanMedianImputer,
)
from feature_engine.selection import DropFeatures
from feature_engine.transformation import LogTransformer
from feature_engine.wrappers i... | /qs-regression-model-0.0.1.tar.gz/qs-regression-model-0.0.1/regression_model/pipeline.py | 0.704364 | 0.202956 | pipeline.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... | /qs-regression-model-0.0.1.tar.gz/qs-regression-model-0.0.1/regression_model/config/core.py | 0.815049 | 0.348091 | 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 =... | /qs-regression-model-0.0.1.tar.gz/qs-regression-model-0.0.1/regression_model/processing/validation.py | 0.803598 | 0.42173 | validation.py | pypi |
import numpy as np
import pandas
import os
import json
import logging
from qsarmodelingpy.runGa import run as runGA
from qsarmodelingpy.runOPS import run as runOPS
from qsarmodelingpy.runExtVal import run as runExtVal
from qsarmodelingpy.filter import variance_cut, correlation_cut, autocorrelation_cut
from qsarmodeling... | /qsarmodelingpy_gui-0.2.5-py3-none-any.whl/qsarmodelingpy_gui/runCalculations.py | 0.709422 | 0.260966 | runCalculations.py | pypi |
from qsarmodelingpy.cross_validation_class import CrossValidation
import os
from typing import Dict, Callable, Optional
try:
from typing import TypedDict # Python 3.8+
except ImportError:
from typing_extensions import TypedDict # Python 3.7-
import logging
import pandas as pd
from MainHandler import Handler
im... | /qsarmodelingpy_gui-0.2.5-py3-none-any.whl/qsarmodelingpy_gui/ResultsHandler.py | 0.572125 | 0.193871 | ResultsHandler.py | pypi |
# qsearch
An implementation of a quantum gate synthesis algorithm based on A* and numerical optimization. It relies on [NumPy](https://numpy.org) and [SciPy](https://www.scipy.org). It can export code for [Qiskit](https://q... | /qsearch-2.6.0.tar.gz/qsearch-2.6.0/README.md | 0.647241 | 0.980224 | README.md | pypi |
import collections
import numpy
import itertools
def delistify(lst_obj):
if isinstance(lst_obj, list):
return lst_obj if (len(lst_obj) > 1 or not lst_obj) else lst_obj[0]
else:
return lst_obj
def delistify_single(iterable):
lst = list(iterable)
assert len(lst) == 1
return lst[0]
... | /qset-python-client-0.1.6.tar.gz/qset-python-client-0.1.6/qset/utils/builtin/collection.py | 0.413951 | 0.375535 | collection.py | pypi |
import re
import numpy as np
import pandas as pd
from dateutil.tz import tzutc
from dateutil.parser import parse as parse_date
from datetime import datetime, timedelta, timezone
from qset.utils.numeric import custom_round
# NOTE: slow
def parse_human_timestamp_re(hts, min_date_str="2000"):
"""
:param hts: Hum... | /qset-python-client-0.1.6.tar.gz/qset-python-client-0.1.6/qset/utils/time/dt.py | 0.575946 | 0.433742 | dt.py | pypi |
import calendar
from datetime import datetime, timedelta
def add_months(dt, n):
month = dt.month - 1 + n
year = dt.year + month // 12
month = month % 12 + 1
day = min(dt.day, calendar.monthrange(year, month)[1])
return datetime(year, month, day)
def iter_range_by_months(beg, end):
while True... | /qset-python-client-0.1.6.tar.gz/qset-python-client-0.1.6/qset/utils/time/ranges.py | 0.433502 | 0.418816 | ranges.py | pypi |
from decimal import (
ROUND_HALF_DOWN,
ROUND_DOWN,
ROUND_CEILING,
ROUND_05UP,
ROUND_FLOOR,
ROUND_HALF_EVEN,
ROUND_HALF_UP,
ROUND_UP,
Decimal as D,
)
ROUND_DIC = {
"nearest_half_even": ROUND_HALF_EVEN,
"floor": ROUND_FLOOR,
"ceil": ROUND_CEILING,
"down": ROUND_DOWN,
... | /qset-python-client-0.1.6.tar.gz/qset-python-client-0.1.6/qset/utils/numeric/numeric.py | 0.748628 | 0.231766 | numeric.py | pypi |
import numpy as np
import pandas as pd
import scipy.stats
import math
import qset_tslib as tslib
from datetime import datetime, timedelta
def _get_timeframe(td):
days = td.days
hours = td.seconds // 3600
minutes = (td.seconds // 60) % 60
seconds = td.seconds - (3600 * hours + 60 * minutes)
res =... | /qset-tslib-0.1.1.tar.gz/qset-tslib-0.1.1/qset_tslib/stats.py | 0.53777 | 0.557845 | stats.py | pypi |
import numpy as np
import pandas as pd
import qset_tslib as tslib
from qset_tslib.cython.neutralize import cs_neutralize
def cs_mean(df, as_series=False, *args, **kwargs):
res = df.mean(axis=1, *args, **kwargs)
if not as_series:
res = tslib.make_like(df, res)
return res
def cs_sum(df, as_series=... | /qset-tslib-0.1.1.tar.gz/qset-tslib-0.1.1/qset_tslib/cs.py | 0.524882 | 0.273096 | cs.py | pypi |
import pandas as pd
import numpy as np
from statsmodels.regression.quantile_regression import QuantReg
from sklearn.linear_model import Ridge, LinearRegression
def ts_ls_slope(df_x, df_y, window, min_periods=2, add_notna_mask=False, ddof=1):
"""Calculates running slope of simple linear regression between windows ... | /qset-tslib-0.1.1.tar.gz/qset-tslib-0.1.1/qset_tslib/ls.py | 0.675872 | 0.638652 | ls.py | pypi |
import pandas as pd
import numpy as np
import qset_tslib as tslib
def money_flow_index(high, low, close, volume, n=14):
up_or_down = tslib.ifelse(
tslib.ts_diff(close) > 0, tslib.make_like(close, 1), tslib.make_like(close, -1)
)
# 1 typical price
tp = (high + low + close) / 3.0
# 2 mon... | /qset-tslib-0.1.1.tar.gz/qset-tslib-0.1.1/qset_tslib/technical_indicators/momentum.py | 0.735167 | 0.540985 | momentum.py | pypi |
import qset_tslib as tslib
# Bollinger Bands
def bollinger_bands(close, periods, coef=1, min_periods=None):
"""returns [bb_low, bb_mid, bb_high]"""
c_mean = tslib.ts_mean(close, periods, min_periods=min_periods)
c_std = tslib.ts_std(close, periods, min_periods=min_periods)
return c_mean - coef * c_std... | /qset-tslib-0.1.1.tar.gz/qset-tslib-0.1.1/qset_tslib/technical_indicators/basic.py | 0.670285 | 0.574693 | basic.py | pypi |
import pandas as pd
import numpy as np
import qset_tslib as tslib
def macd(close, n_fast=12, n_slow=26):
"""Moving Average Convergence Divergence (MACD)
Is a trend-following momentum indicator that shows the relationship between
two moving averages of prices.
https://en.wikipedia.org/wiki/MACD
Ar... | /qset-tslib-0.1.1.tar.gz/qset-tslib-0.1.1/qset_tslib/technical_indicators/trend.py | 0.854915 | 0.724042 | trend.py | pypi |
try:
import numpy as np
except ImportError:
np = None
except Exception as ex:
print("Failed to import numpy. Please check your numpy installation.")
raise
# Tuples are json encoded differently in C#, this makes sure they are in the right format.
def map_tuples(obj):
"""
Given a Python object t... | /qsharp-core-0.25.222455b1.tar.gz/qsharp-core-0.25.222455b1/qsharp/serialization.py | 0.487063 | 0.415492 | serialization.py | pypi |
from sklearn.decomposition import PCA
from ..cla import ensemble
from ..vis import *
from collections import Counter
def fsse_cv(X_scaled,y, X_names = None, N = 30, base_learner=ensemble.create_elmcv_instance, \
WIDTHS = [1, 2, 10], ALPHAS = [0.5,0.75,1.0], display = True, verbose = True):
'''
Feature su... | /qsi_tk-1.0.5-py3-none-any.whl/qsi/fs/fsse.py | 0.78789 | 0.233859 | fsse.py | pypi |
from functools import wraps
import numpy as np
def _extract_from_singleton_iterable(inputs):
if len(inputs) == 1:
return inputs[0]
return tuple(inputs)
def _get_random_row_idxes(num_rows, subsampling_scheme, random_state):
if subsampling_scheme is None:
return range(num_rows)
elif is... | /qsi_tk-1.0.5-py3-none-any.whl/qsi/fs/glasso/_subsampling.py | 0.846038 | 0.531878 | _subsampling.py | pypi |
import warnings
from math import sqrt
import numpy as np
import numpy.linalg as la
from sklearn.exceptions import ConvergenceWarning
class FISTAProblem:
def __init__(
self, smooth_loss, proximable_loss, smooth_grad, prox, init_lipschitz
):
self.smooth_loss = smooth_loss
self.smooth_gr... | /qsi_tk-1.0.5-py3-none-any.whl/qsi/fs/glasso/_fista.py | 0.827619 | 0.417984 | _fista.py | pypi |
import warnings
from abc import ABC, abstractmethod
from math import sqrt
from numbers import Number
import numpy as np
import numpy.linalg as la
from scipy import sparse
from scipy.special import logsumexp
from sklearn.base import (BaseEstimator, ClassifierMixin, RegressorMixin,
TransformerM... | /qsi_tk-1.0.5-py3-none-any.whl/qsi/fs/glasso/_group_lasso.py | 0.892422 | 0.486697 | _group_lasso.py | pypi |
import numpy as np
from sklearn.preprocessing import StandardScaler
def expand_dataset(X, y, NX = 3):
'''
Parameters
----------
NX : how many times to expand the dataset.
'''
# use SMOTE to upsample
labels = set(y)
for label in labels:
X_grp = X[y == label]
new... | /qsi_tk-1.0.5-py3-none-any.whl/qsi/io/aug/VAE.py | 0.841011 | 0.698468 | VAE.py | pypi |
# Use %matplotlib notebook instead of %matplotlib inline to get embedded interactive figures in the IPython notebook.
# This requires recent versions of matplotlib (1.4+) and IPython (3.0+)
# Need to restart notebook or kernel and call %matplotlib notebook twice
from mpl_toolkits.mplot3d import Axes3D
import matplot... | /qsi_tk-1.0.5-py3-none-any.whl/qsi/vis/plot_components.py | 0.506836 | 0.652255 | plot_components.py | pypi |
import numpy as np
import matplotlib.pyplot as plt
from .unsupervised_dimension_reductions import unsupervised_dimension_reductions
# plot the importance for all features
def plot_feature_importance(feature_importances, feature_names, title = None, \
xtick_angle=None, xlabel = '', ylabel = '', row_size = None, fi... | /qsi_tk-1.0.5-py3-none-any.whl/qsi/vis/feature_importance.py | 0.421314 | 0.558568 | feature_importance.py | pypi |
# qsim and qsimh
Quantum circuit simulators qsim and qsimh. These simulators were used for cross
entropy benchmarking in
[[1]](https://www.nature.com/articles/s41586-019-1666-5).
[[1]](https://www.nature.com/articles/s41586-019-1666-5), F. Arute et al,
"Quantum Supremacy Using a Programmable Superconducting Processor... | /qsimcirq-0.9.5.tar.gz/qsimcirq-0.9.5/README.md | 0.445771 | 0.985524 | README.md | pypi |
import numpy as np
from qsimov.structures.qsystem import QSystem
from qsimov.structures.qgate import QGate
# np.zeros((h,w), dtype=complex) Inicializa una matriz de numeros complejos
# con alto h y ancho w
# La suma de matrices se realiza con +. A + B
# La multiplicacion por un escalar se hace con *. n * A
# Para mult... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/qsimov.py | 0.609757 | 0.550064 | qsimov.py | pypi |
import numpy as np
import qsimov.connectors.parser as gt
import sympy as sp
from sympy.functions import conjugate
from sympy.matrices import Matrix
from sympy.physics.quantum import TensorProduct
_pauli = [None for i in range(3)]
_pauli[0] = gt.PauliX() # σx
_pauli[1] = gt.PauliY() # σy
_pauli[2] = gt.PauliZ() # σz
... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/cores/bdoki.py | 0.600188 | 0.525369 | bdoki.py | pypi |
import doki
import numpy as np
class Funmatrix(object):
"""Functional Matrices related stuff in python."""
def __init__(self, fmatrix, name="Lazy Matrix", verbose=False):
"""Functional Matrix constructor.
Positional arguments:
fmatrix: pointer to C FunMatrix structure
Key... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/structures/funmatrix.py | 0.788665 | 0.337067 | funmatrix.py | pypi |
import doki
import numpy as np
import qsimov.connectors.parser as prs
import sympy as sp
from collections.abc import Iterable
from qsimov.structures.qbase import QBase
from sympy.matrices import Matrix
from sympy.codegen.cfunctions import log2
from sympy.physics.quantum.dagger import Dagger
class SimpleGate(QBase):
... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/structures/simple_gate.py | 0.792705 | 0.427576 | simple_gate.py | pypi |
from qsimov.structures.qregistry import QRegistry
from qsimov.structures.qstructure import _get_op_data
from qsimov.structures.qsystem import QSystem
from qsimov.structures.qdesign import QDesign
import qsimov.connectors.qsimovapi as qapi
import numpy as np
class QCircuit(QDesign):
"""Quantum Circuit, built from ... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/structures/qcircuit.py | 0.892907 | 0.505005 | qcircuit.py | pypi |
import numpy as np
import sympy as sp
from sympy.functions.elementary.complexes import arg
from qsimov.structures.funmatrix import Funmatrix
from qsimov.structures.simple_gate import SimpleGate
from qsimov.structures.qstructure import QStructure, _get_op_data, \
_get_qubit_set,... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/structures/qregistry.py | 0.728459 | 0.429489 | qregistry.py | pypi |
import numpy as np
from abc import abstractmethod
from collections.abc import Iterable
from qsimov.structures.qbase import QBase
from qsimov.structures.simple_gate import SimpleGate
class QStructure(QBase):
@abstractmethod
def __init__(self, num_qubits, data=None, doki=None, verbose=False):
pass
... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/structures/qstructure.py | 0.821546 | 0.321606 | qstructure.py | pypi |
import numpy as np
from qsimov.structures.qstructure import QStructure, _get_qubit_set, \
_get_op_data, _get_key_with_defaults
from qsimov.structures.qregistry import QRegistry, superposition
class QSystem(QStructure):
"""Quantum System, preferred over QRegistry (can save a... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/structures/qsystem.py | 0.61855 | 0.412412 | qsystem.py | pypi |
import re
import sympy as sp
from sympy.matrices import Matrix
from sympy.parsing.sympy_parser import parse_expr
__rep__ = re.compile(r"^([a-zA-Z0-9]+)(\(.*\))?(\-1)?$")
def parse_groups(groups):
"""Parse the result of get_groups function, passed as parameter."""
errored = False
g1 = groups[0]
g4 =... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/connectors/parser.py | 0.780621 | 0.517144 | parser.py | pypi |
import numpy as np
from qsimov.structures.qstructure import QStructure, _get_op_data
from qsimov.structures.qdesign import QDesign
def _check_classical(classical_reg, c_controls, c_anticontrols):
for k in c_controls:
if classical_reg[k] is None:
raise ValueError("Undefined value for classic " ... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/connectors/qsimovapi.py | 0.5144 | 0.459379 | qsimovapi.py | pypi |
import matplotlib.pyplot as plt
import numpy as np
def plot_arc3d(vector1, vector2, radius=0.2, ax=None, colour="C0", color=None):
""" Plot arc between two given vectors in 3D space. """
'''
https://stackoverflow.com/questions/47321839/how-to-show-the-angle-by-an-arc-between-two-3d-vectors-in-matplotlib
... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/utils/bloch.py | 0.839537 | 0.675865 | bloch.py | pypi |
from qsimov import QGate
import sympy as sp
def first_column_gate(num_qubits, vector):
"""Create a gate with normalized vector as first matrix column."""
gates = get_gates(vector, num_qubits)
# print("Gates:", gates)
# print("V:", vector)
vector_oracle = QGate(num_qubits, "Vector oracle")
if ... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/samples/state_gate.py | 0.455925 | 0.59302 | state_gate.py | pypi |
"""Deutsch-Jozsa with n qubit."""
from qsimov import Drewom, QCircuit, QGate
import sys
import time as t
def DJAlgCircuit(size, U_f):
"""Return Deutsch-Josza algorithm circuit.
U_f is the oracle, having x1..xn and y as input qubits.
x1..xn and y take only values either 0 or 1.
Once applied it return... | /qsimov-Mowstyl-5.1.1.tar.gz/qsimov-Mowstyl-5.1.1/qsimov/samples/djcircuit.py | 0.702836 | 0.611875 | djcircuit.py | pypi |
import sys
import os
import re
import subprocess
__version__ = '0.19.0'
__packagename__ = 'qsiprep-container'
__author__ = ''
__copyright__ = 'Copyright 2019, '
__credits__ = []
__license__ = '3-clause BSD'
__maintainer__ = ''
__email__ = ''
__url__ = 'https://github.com/pennbbl/qsiprep'
__bugreports__ = 'https://gith... | /qsiprep-container-0.19.0.tar.gz/qsiprep-container-0.19.0/qsiprep_docker.py | 0.481941 | 0.176228 | qsiprep_docker.py | pypi |
import sys
import os
import os.path as op
import re
import subprocess
__version__ = 'latest'
__packagename__ = 'qsiprep-container'
__author__ = ''
__copyright__ = 'Copyright 2019, '
__credits__ = []
__license__ = '3-clause BSD'
__maintainer__ = ''
__email__ = ''
__url__ = 'https://github.com/pennbbl/qsiprep'
__bugrepo... | /qsiprep-container-0.19.0.tar.gz/qsiprep-container-0.19.0/qsiprep_singularity.py | 0.476092 | 0.166913 | qsiprep_singularity.py | pypi |
# Python package about interferometry using two-mode quantum states
## This package provides functions that return various physical quantities related to interferometry experiments using twin Fock states $|n,n\rangle$ or two-mode squeezed vacuum states
### Functions contained in this package are derived in [this arti... | /qsipy-1.0.0.tar.gz/qsipy-1.0.0/README.md | 0.971578 | 0.995479 | README.md | pypi |
.. SPDX-FileCopyrightText: 2021-2022 Constantine Evans <const@costi.eu>
..
.. SPDX-License-Identifier: AGPL-3.0-only
Tutorial
========
Defining a new experiment
-------------------------
Experiments are generally defined by two parts: a temperature :any:`Protocol`, which tells the machine how to run the experiment, ... | /qslib-1.8.2.tar.gz/qslib-1.8.2/docs/tutorial.rst | 0.92948 | 0.741066 | tutorial.rst | pypi |
```
from qslib import *
protocol = Protocol(
[
Stage.stepped_ramp(
"80 °C", 60, "20 minutes"
), # A unitless temperature is degrees Celsius
Stage.hold_for("60 degC", total_time=120), # A unitless time is seconds
Stage.stepped_ramp(
from_temperature=60.0,
... | /qslib-1.8.2.tar.gz/qslib-1.8.2/examples/qslib-example.ipynb | 0.639173 | 0.309738 | qslib-example.ipynb | pypi |
# QSMxT: A Complete QSM Processing and Analysis Pipeline
QSMxT is an end-to-end software toolbox for QSM that excels at automatically reconstructing and processing large groups of participants using sensible defaults.
QSMxT produces:
... | /qsmxt-4.0.1.tar.gz/qsmxt-4.0.1/README.md | 0.433981 | 0.98355 | README.md | pypi |
# Tutorial
In this tutorial, we will give a brief introduction on the quantization and pruning techniques upon which QSPARSE is built. Using our library, we guide you through the building of a image classification neural network with channel pruning and both weights and activations quantized.
> If you are already fa... | /qsparse-2.0.1.tar.gz/qsparse-2.0.1/docs/tutorial.ipynb | 0.930506 | 0.987508 | tutorial.ipynb | pypi |
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