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fairseq/tasks/audio_pretraining.py | yyeboah/fairseq | 11 | 194329 | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import os
import sys
import torch
from argparse import Namespace
from dataclasses import dataclass, field
from typing import Optional, Any
from omegaconf import MISSING
from fairseq.data import AddTargetDataset, Dictionary, FileAudioDataset, encoders
from fairseq.data.data_utils import post_process
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.configs import GenerationConfig
from . import FairseqTask, register_task
from .. import utils
from ..logging import metrics
class LabelEncoder(object):
def __init__(self, dictionary):
self.dictionary = dictionary
def __call__(self, label):
return self.dictionary.encode_line(
label, append_eos=False, add_if_not_exist=False
)
@dataclass
class AudioPretrainingConfig(FairseqDataclass):
data: str = field(default=MISSING, metadata={"help": "path to data directory"})
labels: Optional[str] = field(
default=None,
metadata={"help": "extension of the label file to load, used for fine-tuning"},
)
sample_rate: int = field(
default=16_000,
metadata={
"help": "target sample rate. audio files will be up/down sampled to this rate"
},
)
normalize: bool = field(
default=False,
metadata={"help": "if set, normalizes input to have 0 mean and unit variance"},
)
enable_padding: bool = field(
default=False, metadata={"help": "pad shorter samples instead of cropping"}
)
max_sample_size: Optional[int] = field(
default=None, metadata={"help": "max sample size to crop to for batching"}
)
min_sample_size: Optional[int] = field(
default=None, metadata={"help": "min sample size to crop to for batching"}
)
# Options for reporting WER metrics during validation. Only applicable to
# Seq2Seq models during fine-tuning
eval_wer: bool = field(
default=False, metadata={"help": "compute WER for Seq2Seq models"}
)
eval_wer_config: GenerationConfig = field(
default_factory=lambda: GenerationConfig(),
metadata={"help": "beam search config for evaluating wer during training"},
)
eval_wer_tokenizer: Any = field(
default=None,
metadata={"help": "tokenizer config for evaluating wer during training"},
)
eval_wer_post_process: str = field(
default="letter",
metadata={
"help": "remove BPE tokens before scoring (can be sentencepiece, letter, and more)"
},
)
autoregressive: bool = field(
default=False,
metadata={
"help": "required for autoregressive decoders (like seq2seq models); "
"adds 'prev_output_tokens' to input and appends eos to target"
},
)
@register_task("audio_pretraining", dataclass=AudioPretrainingConfig)
class AudioPretrainingTask(FairseqTask):
""""""
cfg: AudioPretrainingConfig
def __init__(
self,
cfg: AudioPretrainingConfig,
source_dictionary=None,
target_dictionary=None,
):
super().__init__(cfg)
self._target_dictionary = target_dictionary
self._source_dictionary = source_dictionary
if cfg.eval_wer:
assert cfg.labels is not None, "eval_wer can only be set during fine-tuning"
self.blank_symbol = "<s>"
@classmethod
def setup_task(cls, cfg: AudioPretrainingConfig, **kwargs):
"""Setup the task (e.g., load dictionaries).
Args:
cfg (AudioPretrainingConfig): configuration of this task
"""
if cfg.labels:
dict_path = os.path.join(cfg.data, f"dict.{cfg.labels}.txt")
target_dictionary = Dictionary.load(dict_path)
else:
target_dictionary = None
return cls(cfg, target_dictionary=target_dictionary)
def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs):
data_path = self.cfg.data
task_cfg = task_cfg or self.cfg
# upgrade old task
if isinstance(task_cfg, Namespace):
if not hasattr(task_cfg, "autoregressive"):
task_cfg.autoregressive = not task_cfg.criterion == 'ctc'
manifest = os.path.join(data_path, "{}.tsv".format(split))
self.datasets[split] = FileAudioDataset(
manifest,
sample_rate=task_cfg.sample_rate,
max_sample_size=self.cfg.max_sample_size,
min_sample_size=self.cfg.max_sample_size,
min_length=self.cfg.min_sample_size,
pad=task_cfg.labels is not None or task_cfg.enable_padding,
normalize=task_cfg.normalize,
)
if task_cfg.labels:
label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}")
labels = []
with open(label_path, "r") as f:
labels = [
line for i, line in enumerate(f)
if i in self.datasets[split].line_inds
]
assert len(labels) == len(self.datasets[split]), (
f"labels length ({len(labels)}) and dataset length "
f"({len(self.datasets[split])}) do not match")
process_label = LabelEncoder(self.target_dictionary)
self.datasets[split] = AddTargetDataset(
self.datasets[split],
labels,
pad=self.target_dictionary.pad(),
eos=self.target_dictionary.eos(),
batch_targets=True,
process_label=process_label,
add_to_input=task_cfg.autoregressive,
)
@property
def source_dictionary(self):
return self._source_dictionary
@property
def target_dictionary(self):
"""Return the :class:`~fairseq.data.Dictionary` for the language
model."""
return self._target_dictionary
def max_positions(self):
"""Maximum input length supported by the encoder."""
return (sys.maxsize, sys.maxsize)
def filter_indices_by_size(
self,
indices,
dataset,
max_positions=None,
ignore_invalid_inputs=False,
):
# we do not need to filter by size in this task as dataloaders take care of this
return indices
def valid_step(self, sample, model, criterion):
loss, sample_size, logging_output = super().valid_step(sample, model, criterion)
if self.cfg.eval_wer and self.cfg.autoregressive:
metrics = self._inference_with_wer(self.sequence_generator, sample, model)
logging_output["_num_char_errors"] = metrics["num_char_errors"]
logging_output["_num_chars"] = metrics["num_chars"]
logging_output["_num_word_errors"] = metrics["num_word_errors"]
logging_output["_num_words"] = metrics["num_words"]
return loss, sample_size, logging_output
def build_model(self, model_cfg: FairseqDataclass):
model = super().build_model(model_cfg)
if self.cfg.eval_wer and self.cfg.autoregressive:
self.sequence_generator = self.build_generator(
[model],
self.cfg.eval_wer_config,
)
if self.cfg.eval_wer_tokenizer:
self.tokenizer = encoders.build_tokenizer(self.cfg.eval_wer_tokenizer)
else:
self.tokenizer = None
return model
def _inference_with_wer(self, generator, sample, model):
import editdistance
def decode(toks):
s = self.target_dictionary.string(
toks.int().cpu(),
self.cfg.eval_wer_post_process,
escape_unk=True,
)
if self.tokenizer:
s = self.tokenizer.decode(s)
return s
num_word_errors, num_char_errors = 0, 0
num_chars, num_words = 0, 0
gen_out = self.inference_step(generator, [model], sample, None)
for i in range(len(gen_out)):
hyp = decode(gen_out[i][0]["tokens"])
ref = decode(
utils.strip_pad(sample["target"][i], self.target_dictionary.pad()),
)
num_char_errors += editdistance.eval(hyp, ref)
num_chars += len(ref)
hyp_words = hyp.split()
ref_words = ref.split()
num_word_errors += editdistance.eval(hyp_words, ref_words)
num_words += len(ref_words)
return {
"num_char_errors": num_char_errors,
"num_chars": num_chars,
"num_word_errors": num_word_errors,
"num_words": num_words,
}
def reduce_metrics(self, logging_outputs, criterion):
super().reduce_metrics(logging_outputs, criterion)
zero = torch.scalar_tensor(0.0)
num_char_errors = sum(
log.get("_num_char_errors", zero) for log in logging_outputs
)
num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs)
num_word_errors = sum(
log.get("_num_word_errors", zero) for log in logging_outputs
)
num_words = sum(log.get("_num_words", zero) for log in logging_outputs)
metrics.log_scalar("_num_char_errors", num_char_errors)
metrics.log_scalar("_num_chars", num_chars)
metrics.log_scalar("_num_word_errors", num_word_errors)
metrics.log_scalar("_num_words", num_words)
if num_words > 0:
metrics.log_derived(
"uer",
lambda meters: meters["_num_char_errors"].sum
* 100.0
/ meters["_num_chars"].sum
if meters["_num_chars"].sum > 0
else float("nan"),
)
metrics.log_derived(
"wer",
lambda meters: meters["_num_word_errors"].sum
* 100.0
/ meters["_num_words"].sum
if meters["_num_words"].sum > 0
else float("nan"),
)
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pykintone/account.py | hykw/pykintone | 0 | 1615577 | from datetime import datetime
import yaml
import pytz
class Account(object):
def __init__(self, domain,
login_id="", login_password="",
basic_id="", basic_password=""):
self.domain = domain
self.login_id = login_id
self.login_password = <PASSWORD>
self.basic_id = basic_id
self.basic_password = <PASSWORD>
def to_header(self, api_token="", with_content_type=True):
header = {}
header["Host"] = "{0}.cybozu.com:443".format(self.domain)
def encode(user_id, password):
import base64
return base64.b64encode("{0}:{1}".format(user_id, password).encode(kintoneService.ENCODE))
if self.basic_id:
auth = encode(self.basic_id, self.basic_password)
header["Authorization"] = "Basic {0}".format(auth)
if api_token:
header["X-Cybozu-API-Token"] = api_token
elif self.login_id:
auth = encode(self.login_id, self.login_password)
header["X-Cybozu-Authorization"] = auth
if with_content_type:
header["Content-Type"] = "application/json"
return header
def kintone(self):
return kintoneService(self)
@classmethod
def load(cls, path):
apps = None
with open(path, "rb") as f:
a_dict = yaml.safe_load(f)
apps = cls.loads(a_dict)
return apps
@classmethod
def loads(cls, account_dict):
account = None
# create account
args = {
"domain": account_dict["domain"]
}
for k in ["login", "basic"]:
if k in account_dict:
args[k + "_id"] = account_dict[k]["id"]
args[k + "_password"] = account_dict[k]["password"]
account = Account(**args)
kintone = kintoneService(account)
# load kintone apps
apps = []
for name in account_dict["apps"]:
_a = account_dict["apps"][name]
token = "" if "token" not in _a else _a["token"]
kintone.app(int(_a["id"]), token, name)
return kintone
def __str__(self):
infos = []
infos.append("domain:\t {0}".format(self.domain))
infos.append("login:\t {0} / {1}".format(self.login_id, self.login_password))
infos.append("basic:\t {0} / {1}".format(self.basic_id, self.basic_password))
return "\n".join(infos)
class kintoneService(object):
ENCODE = "utf-8"
SELECT_LIMIT = 500
UPDATE_LIMIT = 100
DATE_FORMAT = "%Y-%m-%d"
TIME_FORMAT = "%H:%M"
DATETIME_FORMAT = "%Y-%m-%dT%H:%M:%SZ"
TIMESTAMP_FORMAT = "%Y-%m-%dT%H:%M:%S.%fZ"
from tzlocal import get_localzone
__TIME_ZONE = get_localzone()
def __init__(self, account):
self.account = account
self.__apps = []
def __len__(self):
return len(self.__apps)
def app(self, app_id="", api_token="", app_name=""):
from pykintone.application import Application
if not app_id:
return self.__apps[0]
else:
existed = [a for a in self.__apps if a.app_id == app_id]
# register if not exist
if len(existed) > 0:
return existed[0]
else:
_a = Application(self.account, app_id, api_token, app_name)
self.__apps.append(_a)
return _a
def administration(self,requests_options=()):
from pykintone.application_settings.administrator import Administrator
return Administrator(self.account, requests_options=requests_options)
def user_api(self, requests_options=()):
from pykintone.user_api import UserAPI
api = UserAPI(self.account, requests_options)
return api
@classmethod
def value_to_date(cls, value):
return value if not value else datetime.strptime(value, cls.DATE_FORMAT)
@classmethod
def value_to_time(cls, value):
return value if not value else datetime.strptime(value, cls.TIME_FORMAT)
@classmethod
def value_to_datetime(cls, value):
if value:
d = datetime.strptime(value, cls.DATETIME_FORMAT)
return cls._to_local(d)
else:
return None
@classmethod
def value_to_timestamp(cls, value):
if value:
d = datetime.strptime(value, cls.TIMESTAMP_FORMAT)
return cls._to_local(d)
else:
return None
@classmethod
def _to_local(cls, d):
utc = d.replace(tzinfo=pytz.utc) # configure timezone (on kintone, time is utc)
local = utc.astimezone(cls.__TIME_ZONE).replace(tzinfo=None) # to local, and to native
return local
@classmethod
def date_to_value(cls, date):
return date.strftime(cls.DATE_FORMAT)
@classmethod
def time_to_value(cls, time):
return time.strftime(cls.TIME_FORMAT)
@classmethod
def datetime_to_value(cls, dt):
local = dt.replace(tzinfo=cls.__TIME_ZONE)
utc = local.astimezone(pytz.utc)
value = utc.strftime(cls.DATETIME_FORMAT)
return value
@classmethod
def get_default_field_list(cls, as_str=False):
from pykintone.structure import FieldType
fields = [
FieldType.CATEGORY,
FieldType.STATUS,
FieldType.RECORD_NUMBER,
FieldType.CREATED_TIME,
FieldType.CREATOR,
FieldType.STATUS_ASSIGNEE,
FieldType.UPDATED_TIME,
FieldType.MODIFIER
]
if as_str:
str_fields = [f.value for f in fields]
return str_fields
else:
return fields
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13,
4706,
1683,
29901,
13,
9651,
736,
4235,
13,
13,
2
] |
web_frameworks/resources/forms.py | Minkov/python-web-frameworks-2020-11 | 4 | 108577 | from django import forms
from resources.models import Pet
class PetForm(forms.ModelForm):
class Meta:
model = Pet
fields = '__all__'
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] |
beginner/sum-of-all-numbers_ChingLingYeung.py | garvitsharma05/hacktoberithms | 16 | 36254 |
def sum_all(ls):
sum = 0
if(len(ls) != 2):
print("Invalid input")
else:
ls.sort()
start = ls[0]
end = ls[1]
if(start == end):
sum = 2 * start
else:
for i in range(start, end+1):
sum += i
return sum
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] |
src/hypertrace/agent/autoinstrumentation/sitecustomize.py | hypertrace/pythonagent | 4 | 101737 | <gh_stars>1-10
'''Enable instrumentationon all supported modules.''' # pylint: disable=R0401
import logging
from hypertrace.agent import Agent
from hypertrace.env_var_settings import get_env_value
# Initialize logger
logger = logging.getLogger(__name__) # pylint: disable=C0103
skip_modules = get_env_value('SKIP_MODULES')
if skip_modules:
logger.debug("[env] Loaded SKIP_MODULES from env")
if len(skip_modules) > 0:
skip_modules = skip_modules.replace(' ', '')
skip_modules = skip_modules.split(',')
else:
skip_modules = []
# Create Hypertrace agent
agent = Agent()
agent.instrument(None, skip_modules, auto_instrument=True)
| [
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13,
2
] |
dash/admin.py | ravshansk/ajans | 0 | 163566 | from django.contrib import admin
from .models import Actor
# Register your models here.
@admin.register(Actor)
class ActorAdmin(admin.ModelAdmin):
pass
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] |
writer1.py | mahongquan/ipy_rtf | 0 | 59369 | /// <summary>
/// RTF文档书写器
/// </summary>
/// <remarks>
/// 本书写器对生成RTF文档提供了基础的支持
/// 编制 袁永福 http://www.xdesigner.cn
/// </remarks>
public class RTFWriter : System.IDisposable
{
#region 测试代码 ******************************************************
[System.STAThread]
static void Main()
{
TestWriteFile();
TestClipboard();
}
/// <summary>
/// 测试生成RTF文件
/// 执行这个函数后可以使用 MS Word 打开文件 c:\a.rtf
/// </summary>
internal static void TestWriteFile( )
{
RTFWriter w = new RTFWriter( "c:\\a.rtf" ) ;
TestBuildRTF( w );
w.Close();
System.Windows.Forms.MessageBox.Show("好了,你可以打开文件 c:\\a.rtf 了.");
}
/// <summary>
/// 测试生成RTF文档并设置到系统剪切板中
/// 执行这个函数后就可以在 MS Word中使用粘贴操作来显示程序生成的文档了
/// </summary>
internal static void TestClipboard()
{
System.IO.StringWriter myStr = new System.IO.StringWriter();
RTFWriter w = new RTFWriter( myStr );
TestBuildRTF( w );
w.Close();
System.Windows.Forms.DataObject data = new System.Windows.Forms.DataObject();
data.SetData( System.Windows.Forms.DataFormats.Rtf , myStr.ToString());
System.Windows.Forms.Clipboard.SetDataObject( data , true );
System.Windows.Forms.MessageBox.Show("好了,你可以在MS Word 中粘贴文本了.");
}
/// <summary>
/// 测试生成RTF文档
/// </summary>
/// <param name="w">RTF文档书写器</param>
private static void TestBuildRTF( RTFWriter w )
{
w.Encoding = System.Text.Encoding.GetEncoding( 936 );
// 输出文件头
w.WriteStartGroup();
w.WriteKeyword("rtf1");
w.WriteKeyword("ansi");
w.WriteKeyword("ansicpg" + w.Encoding.CodePage );
// 输出字体表
w.WriteStartGroup();
w.WriteKeyword("fonttbl");
w.WriteStartGroup();
w.WriteKeyword("f0");
w.WriteText("隶书;");
w.WriteEndGroup();
w.WriteStartGroup();
w.WriteKeyword("f1");
w.WriteText("宋体;");
w.WriteEndGroup();
w.WriteEndGroup();
// 输出颜色表
w.WriteStartGroup();
w.WriteKeyword("colortbl");
w.WriteText(";");
w.WriteKeyword("red0");
w.WriteKeyword("green0");
w.WriteKeyword("blue255");
w.WriteText(";");
w.WriteEndGroup();
// 输出正文
w.WriteKeyword("qc"); // 设置居中对齐
w.WriteKeyword("f0"); // 设置字体
w.WriteKeyword("fs30"); // 字体大小
w.WriteText("这是第一段文本 ");
w.WriteKeyword("cf1"); // 设置颜色
w.WriteText("隶书 ");
w.WriteKeyword("cf0"); // 设置为默认颜色
w.WriteKeyword("f1"); // 设置字体
w.WriteText("居中对齐 ABC12345");
w.WriteKeyword("par"); // 开始新的段落
w.WriteKeyword("pard"); // 清除居中对齐
w.WriteKeyword("f1"); // 设置字体
w.WriteKeyword("fs20"); // 字体大小
w.WriteKeyword("cf1");
w.WriteText("这是第二段文本 宋体 左对齐 ABC12345");
// 结束输出
w.WriteEndGroup();
}
#endregion
/// <summary>
/// 初始化对象
/// </summary>
/// <param name="w">文本书写器</param>
public RTFWriter( System.IO.TextWriter w )
{
myWriter = w ;
}
/// <summary>
/// 初始化对象
/// </summary>
/// <param name="strFileName">文件名</param>
public RTFWriter( string strFileName )
{
myWriter = new System.IO.StreamWriter(
strFileName ,
false ,
System.Text.Encoding.ASCII );
}
private System.Text.Encoding myEncoding = System.Text.Encoding.GetEncoding( 936 ) ;
/// <summary>
/// 字符编码格式
/// </summary>
public System.Text.Encoding Encoding
{
get{ return myEncoding ;}
set{ myEncoding = value;}
}
/// <summary>
/// 内置的文本书写器
/// </summary>
private System.IO.TextWriter myWriter = null;
private bool bolIndent = false;
/// <summary>
/// 是否使用缩进
/// </summary>
/// <remarks>
/// RTF文档内部不能随便缩进，提供此选项只是用于生成便于阅读的RTF文档，便于程序的调试，
/// 在开发调试中可以设置该属性为true,方便开发者能直接查看生成的RTF文档，但在生成最终运行的
/// 程序时应当设置该属性为 false .
/// </remarks>
public bool Indent
{
get{ return bolIndent ;}
set{ bolIndent = value;}
}
private string strIndentString = " ";
/// <summary>
/// 缩进字符串
/// </summary>
public string IndentString
{
get{ return strIndentString ;}
set{ strIndentString = value;}
}
/// <summary>
/// 当前缩进层次
/// </summary>
private int intGroupLevel = 0 ;
/// <summary>
/// 关闭对象
/// </summary>
public void Close()
{
if(this.intGroupLevel > 0 )
throw new System.Exception("还有组未写完");
if( myWriter != null )
{
myWriter.Close();
myWriter = null;
}
}
/// <summary>
/// 输出一个组
/// </summary>
/// <param name="KeyWord">关键字</param>
public void WriteGroup( string KeyWord )
{
this.WriteStartGroup();
this.WriteKeyword( KeyWord );
this.WriteEndGroup();
}
/// <summary>
/// 开始输出组
/// </summary>
public void WriteStartGroup( )
{
if( bolIndent )
{
InnerWriteNewLine();
myWriter.Write("{");
}
else
myWriter.Write("{");
intGroupLevel ++ ;
}
/// <summary>
/// 结束输出组
/// </summary>
public void WriteEndGroup()
{
intGroupLevel -- ;
if( intGroupLevel < 0 )
throw new System.Exception("组不匹配");
if( bolIndent )
{
InnerWriteNewLine();
InnerWrite("}");
}
else
InnerWrite("}");
}
/// <summary>
/// 输出原始文本
/// </summary>
/// <param name="txt">文本值</param>
public void WriteRaw( string txt )
{
if( txt != null && txt.Length > 0 )
{
InnerWrite( txt );
}
}
/// <summary>
/// 输出关键字
/// </summary>
/// <param name="Keyword">关键字值</param>
public void WriteKeyword( string Keyword )
{
WriteKeyword( Keyword , false );
}
/// <summary>
/// 输出关键字
/// </summary>
/// <param name="Keyword">关键字值</param>
/// <param name="Ext">是否是扩展关键字</param>
public void WriteKeyword( string Keyword , bool Ext)
{
if( Keyword == null || Keyword.Length == 0)
throw new System.ArgumentNullException("值不得为空");
if( bolIndent == false && ( Keyword == "par" || Keyword == "pard" ) )
{
// par 或 pard 前可以输出空白行，不影响RTF文档显示
InnerWrite( System.Environment.NewLine );
}
if( this.bolIndent )
{
if( Keyword == "par" || Keyword == "pard" )
{
this.InnerWriteNewLine();
}
}
if( Ext )
InnerWrite("\\*\\");
else
InnerWrite("\\");
InnerWrite( Keyword );
}
public void WriteText( string Text )
{
if( Text == null || Text.Length == 0 )
return ;
InnerWrite(' ');
for( int iCount = 0 ; iCount < Text.Length ; iCount ++ )
{
char c = Text[ iCount ] ;
if( c == '\t')
{
this.WriteKeyword("tab");
InnerWrite(' ');
}
else if( c < 256 )
{
if( c > 32 && c < 127 )
{
// 出现特殊字符，需要斜线转义
if( c == '\\' || c == '{' || c == '}' )
InnerWrite( '\\');
InnerWrite( c );
}
else
{
InnerWrite("\\\'");
WriteByte( ( byte ) c );
}
}
else
{
byte[] bs = myEncoding.GetBytes( c.ToString());
for(int iCount2 = 0 ; iCount2 < bs.Length ; iCount2 ++ )
{
InnerWrite("\\\'");
WriteByte( bs[ iCount2 ] );
}
}
}//for( int iCount = 0 ; iCount < Text.Length ; iCount ++ )
}
/// <summary>
/// 当前位置
/// </summary>
private int intPosition = 0 ;
/// <summary>
/// 当前行的位置
/// </summary>
private int intLineHead = 0 ;
/// <summary>
/// 16进制字符组
/// </summary>
private const string Hexs = "0123456789abcdef";
/// <summary>
/// 输出字节数组
/// </summary>
/// <param name="bs">字节数组</param>
public void WriteBytes( byte[] bs )
{
if( bs == null || bs.Length == 0 )
return ;
WriteRaw( " " );
for( int iCount = 0 ; iCount < bs.Length ; iCount ++ )
{
if( ( iCount % 32 ) == 0 )
{
this.WriteRaw( System.Environment.NewLine );
this.WriteIndent();
}
else if( ( iCount % 8 ) == 0 )
{
this.WriteRaw(" ");
}
byte b = bs[ iCount ] ;
int h = ( b & 0xf0 ) >> 4 ;
int l = b & 0xf ;
myWriter.Write( Hexs[ h ] );
myWriter.Write( Hexs[ l ] );
intPosition += 2 ;
}
}
/// <summary>
/// 输出一个字节数据
/// </summary>
/// <param name="b">字节数据</param>
public void WriteByte( byte b )
{
int h = ( b & 0xf0 ) >> 4 ;
int l = b & 0xf ;
myWriter.Write( Hexs[ h ] );
myWriter.Write( Hexs[ l ] );
intPosition += 2 ;
//FixIndent();
}
#region 内部成员 ******************************************************
private void InnerWrite( char c )
{
intPosition ++ ;
myWriter.Write( c );
}
private void InnerWrite( string txt )
{
intPosition += txt.Length ;
myWriter.Write( txt );
}
private void FixIndent()
{
if( this.bolIndent )
{
if( intPosition - intLineHead > 100 )
InnerWriteNewLine();
}
}
private void InnerWriteNewLine()
{
if( this.bolIndent )
{
if( intPosition > 0 )
{
InnerWrite( System.Environment.NewLine );
intLineHead = intPosition ;
WriteIndent();
}
}
}
private void WriteIndent( )
{
if( bolIndent )
{
for( int iCount = 0 ; iCount < intGroupLevel ; iCount ++ )
{
InnerWrite( this.strIndentString );
}
}
}
#endregion
/// <summary>
/// 销毁对象
/// </summary>
public void Dispose()
{
this.Close();
}
} | [
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docs/examples/pyplot/tick-position.py | tdegeus/pyplot_ext | 0 | 1602765 | <reponame>tdegeus/pyplot_ext
import matplotlib.pyplot as plt
import numpy as np
plt.style.use(["goose", "goose-latex"])
n = 5
x = np.arange(n)
y = np.sin(np.linspace(-3, 3, n))
xlabels = ["Long ticklabel %i" % i for i in range(n)]
fig, ax = plt.subplots()
ax.plot(x, y, "o-")
ax.set_xticks(x)
labels = ax.set_xticklabels(xlabels)
for i, label in enumerate(labels):
label.set_y(label.get_position()[1] - (i % 2) * 0.075)
plt.savefig("tick-position.svg")
plt.close()
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Tests/Test_FetchUserStatuses.py | rohitgs28/FindMyEmployer | 0 | 103943 | <reponame>rohitgs28/FindMyEmployer
import unittest
import mock
from mock import MagicMock,patch
import os.path
import logging
import sys,os
from Test_Config import *
from MockData import Emailid,Password,statusData,Messages2
import sys
import sys, os
sys.path.append(os.path.abspath(os.path.join('..', 'extensions/')))
import extensions
sys.path.append(os.path.abspath(os.path.join('..', 'LoggingDatabase/')))
import LoggingErrorsinDatabase
sys.path.append(os.path.abspath(os.path.join('..', 'Databaselayer/')))
import FetchUserStatuses
class Test_FetchUserStatuses(unittest.TestCase):
def test_getUserStatuses_1(self):
statusData = ['My first status', 'felling thoughtful']
fetchuserstatuses = FetchUserStatuses.FetchUserStatuses(mysql,statusData,Messages2[0])
statusData,result = fetchuserstatuses.getUserStatuses()
#assert result == "pass"
def test_getUserStatuses_2(self):
statusData = []
fetchuserstatuses = FetchUserStatuses.FetchUserStatuses(mysql,statusData,Messages2[1])
statusData,result = fetchuserstatuses.getUserStatuses()
#assert result == "fail"
if __name__ == '__main__':
unittest.main()
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MachineLearning/TP3/lrCostFunction.py | piwithy/ENSTA_MACHINE_LEARNING | 0 | 1603636 | <gh_stars>0
import numpy as np
from sigmoid import sigmoid
def lrCostFunction(theta, X, y, Lambda):
"""computes the cost of using
theta as the parameter for regularized logistic regression.
"""
# preambule
m, n = X.shape # 5,4
theta = theta.reshape((n, 1)) # (4,1)
# ====================== YOUR CODE HERE ======================
# Instructions: Compute the cost of a particular choice of theta.
# You should set J to the cost.
#
# Hint: The computation of the cost function and gradients can be
# efficiently vectorized. For example, consider the computation
#
# sigmoid(X @ theta) or np.dot(X, theta)
#
# Each row of the resulting matrix will contain the value of the
# prediction for that example. You can make use of this to vectorize
# the cost function and gradient computations.
#
term1 = -(y.T @ (np.log(sigmoid(X @ theta))))
term2 = (1 - y).T @ (np.log(1 - sigmoid(X @ theta)))
term3 = (np.linalg.norm(theta[1:]) ** 2) * (Lambda / (2 * m))
J = (1 / m) * (term1 - term2) + term3
# =============================================================
return J
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polling_stations/apps/data_collection/management/commands/import_tower_hamlets.py | mtravis/UK-Polling-Stations | 0 | 20332 | from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter
class Command(BaseXpressDemocracyClubCsvImporter):
council_id = "E09000030"
addresses_name = "local.2018-05-03/Version 2/Democracy_Club__03May2018.tsv"
stations_name = "local.2018-05-03/Version 2/Democracy_Club__03May2018.tsv"
elections = ["local.2018-05-03"]
csv_delimiter = "\t"
csv_encoding = "windows-1252"
def address_record_to_dict(self, record):
uprn = record.property_urn.strip().lstrip("0")
if uprn == "6198433":
rec = super().address_record_to_dict(record)
rec["postcode"] = "E2 9DG"
return rec
if record.addressline6 == "E3 2LB" or record.addressline6 == "E3 5EG":
return None
return super().address_record_to_dict(record)
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omega_miya/plugins/maybe/__init__.py | rinrini001/omega-miya | 120 | 150698 | import datetime
import random
from nonebot import CommandGroup, logger
from nonebot.plugin.export import export
from nonebot.typing import T_State
from nonebot.adapters.cqhttp.bot import Bot
from nonebot.adapters.cqhttp.event import GroupMessageEvent
from nonebot.adapters.cqhttp.permission import GROUP
from nonebot.adapters.cqhttp.message import Message, MessageSegment
from omega_miya.utils.omega_plugin_utils import init_export, init_processor_state
from .utils import maybe, sp, sp_event
# from ._oldalmanac import old_almanac
# Custom plugin usage text
__plugin_custom_name__ = '求签'
__plugin_usage__ = r'''【求签】
求签, 求运势, 包括且不限于抽卡、吃饭、睡懒觉、DD
每个人每天求同一个东西的结果是一样的啦!
不要不信邪重新抽啦!
仅限群聊使用
**Permission**
Command & Lv.10
or AuthNode
**AuthNode**
basic
**Usage**
/求签 [所求之事]
/帮我选 [选项1 选项2 ...]'''
# Init plugin export
init_export(export(), __plugin_custom_name__, __plugin_usage__)
# 注册事件响应器
Maybe = CommandGroup(
'maybe',
# 使用run_preprocessor拦截权限管理, 在default_state初始化所需权限
state=init_processor_state(
name='maybe',
command=True,
level=10),
permission=GROUP,
priority=10,
block=True)
luck = Maybe.command('luck', aliases={'求签'})
# 修改默认参数处理
@luck.args_parser
async def parse(bot: Bot, event: GroupMessageEvent, state: T_State):
args = str(event.get_plaintext()).strip().split()
if not args:
await luck.reject('你似乎没有发送有效的参数呢QAQ, 请重新发送:')
state[state["_current_key"]] = args[0]
if state[state["_current_key"]] == '取消':
await luck.finish('操作已取消')
@luck.handle()
async def handle_first_receive(bot: Bot, event: GroupMessageEvent, state: T_State):
args = str(event.get_plaintext()).strip().split()
if not args:
pass
elif args and len(args) == 1:
state['draw'] = args[0]
else:
await luck.finish('参数错误QAQ')
@luck.got('draw', prompt='你想问什么事呢?')
async def handle_luck(bot: Bot, event: GroupMessageEvent, state: T_State):
user_id = event.user_id
_draw = state['draw']
# 求签者昵称, 优先使用群昵称
draw_user = event.sender.card
if not draw_user:
draw_user = event.sender.nickname
# 判断特殊事件
if _draw in sp.keys():
draw_result = sp_event(_draw)
else:
draw_result = maybe(draw=_draw, user_id=user_id)
# 向用户发送结果
today = datetime.date.today().strftime('%Y年%m月%d日')
msg = f'今天是{today}\n{draw_user}{draw_result}'
logger.info(f'{event.group_id}/{event.user_id} 进行了一次求签')
await luck.finish(msg)
help_choice = Maybe.command('choice', aliases={'帮我选', '选择困难症'})
# 修改默认参数处理
@help_choice.args_parser
async def parse(bot: Bot, event: GroupMessageEvent, state: T_State):
args = str(event.get_plaintext()).strip()
if not args:
await help_choice.reject('你似乎没有发送有效的参数呢QAQ, 请重新发送:')
state[state["_current_key"]] = args
if state[state["_current_key"]] == '取消':
await help_choice.finish('操作已取消')
@help_choice.handle()
async def handle_first_receive(bot: Bot, event: GroupMessageEvent, state: T_State):
args = str(event.get_plaintext()).strip()
if not args:
pass
else:
state['choices'] = args
@help_choice.got('choices', prompt='有啥选项, 发来我帮你选~')
async def handle_help_choice(bot: Bot, event: GroupMessageEvent, state: T_State):
choices = state['choices']
result = random.choice(str(choices).split())
result_text = f'''帮你从“{'”，“'.join(str(choices).split())}”中选择了：\n\n“{result}”'''
msg = Message(MessageSegment.at(user_id=event.user_id)).append(result_text)
await help_choice.finish(msg)
# almanac = Maybe.command('almanac', aliases={'DD老黄历', 'dd老黄历'})
#
#
# @almanac.handle()
# async def handle_first_receive(bot: Bot, event: GroupMessageEvent, state: T_State):
# args = str(event.get_plaintext()).strip().lower().split()
# if not args:
# pass
# else:
# await almanac.finish('参数错误QAQ')
#
# user_id = event.user_id
#
# # 求签者昵称, 优先使用群昵称
# draw_user = event.sender.card
# if not draw_user:
# draw_user = event.sender.nickname
#
# draw_result = old_almanac(user_id=user_id)
#
# # 向用户发送结果
# today = datetime.date.today().strftime('%Y年%m月%d日')
# msg = f"今天是{today}\n{draw_user}今日:\n{'='*12}\n{draw_result}"
# logger.info(f'{event.group_id}/{event.user_id} 进行了一次dd老黄历查询')
# await almanac.finish(msg)
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hard-gists/7578539/snippet.py | jjhenkel/dockerizeme | 21 | 3630 | from pylab import *
from numpy import *
from numpy.linalg import solve
from scipy.integrate import odeint
from scipy.stats import norm, uniform, beta
from scipy.special import jacobi
a = 0.0
b = 3.0
theta=1.0
sigma=sqrt(theta/(2*(a+b+2)))
tscale = 0.05
invariant_distribution = poly1d( [-1 for x in range(int(a))], True)*poly1d( [1 for x in range(int(b))], True)
def eigenvalue(n):
return theta*n*(n+a+b+1)/(a+b+2)
gaussian_var = norm()
def dW(dt):
return norm.rvs() / sqrt(dt)
def random_walk(y0, tmax, dt, times = None):
dt = dt * tscale
def rhs(y,t):
return -theta*(y-(a-b)/(a+b+2)) + sqrt(2*theta*(1-y*y)/(a+b+2))*dW(dt/tscale)
if (times is None):
times = arange(0,tmax,dt)
y = zeros(shape=times.shape, dtype=float)
y[0] = y0
for i in range(1,y.shape[0]):
y[i] = y[i-1] + rhs(y[i-1], times[i])*dt
if abs(y[i]) > 1:
y[i] = y[i] / abs(y[i])
return (times, y)
def beta_prior(s, f):
return poly1d(ones(shape=(s,)), True)*poly1d(-1*ones(shape=(f,)), True)
def poly_to_jacobi(x):
"""x is a poly1d object"""
xc = x.coeffs
N = x.order+1
matrix = zeros(shape=(N,N), dtype=float)
for i in range(N):
matrix[N-i-1:N, i] = jacobi(i,a,b).coeffs
return solve(matrix, xc)
def jacobi_to_poly(x):
result = poly1d([0])
for i in range(x.shape[0]):
result = result + (jacobi(i,a,b)*invariant_distribution)*x[i]
return result
def jacobi_to_poly_no_invariant(x):
result = poly1d([0])
for i in range(x.shape[0]):
result = result + jacobi(i,a,b)*x[i]
return result
def propagate_jacobi(pc, t):
"""Takes jacobi coefficients and propagates them"""
n = arange(pc.shape[0], dtype=float)
l = theta*n*(n+a+b+1.0)/(a+b+2.0)*tscale
return exp(-l*t)*pc
def truncate_unnecessary_jacobi(p):
p_normalized = p / (abs(p).sum())
cs = cumsum(abs(p_normalized[::-1]))[::-1]
return p_normalized[where(abs(cs) > 1e-4)]
def pde_solve(prior, t):
result = zeros(shape=(t.shape[0], prior.shape[0]), dtype=float)
result[0,:] = prior
for i in range(1,t.shape[0]):
result[i,:] = propagate_jacobi(result[i-1,:], t[i]-t[i-1])
return result
def transform_to_x(pdf, x):
result = zeros(shape=(pdf.shape[0], x.shape[0]), dtype=float)
for i in range(0, pdf.shape[0]):
p = jacobi_to_poly(pdf[i,:])
result[i,:] = p(x)
result[i,:] /= result[i,:].sum()
return result
tmax = 4
prior = beta_prior(40, 20)
prior_in_jacobi = poly_to_jacobi(prior)
dt = 0.1
times = arange(0,tmax,dt)
x = arange(-1,1,0.01)
rw_dt = 0.01
t, y = random_walk(0.35*2-1, tmax, rw_dt)
solution_as_x = zeros(shape=(times.size, x.size), dtype=float)
solution_as_jacobi = None
empirical_ctr = zeros(shape=(4,), dtype=float)
for i in range(0,4):
nt = int(1.0/dt)
prior = prior_in_jacobi
rnd = uniform(0,1)
if (i > 0):
nsamples = 40
r = rnd.rvs(nsamples)
ctr = (y[i/rw_dt]+1)/2.0
print "CTR: " + str(ctr)
success = (r < ctr).sum()
print "Empirical: " + str(success / float(nsamples))
evidence = beta_prior( nsamples - success, success)
prior = None
j = truncate_unnecessary_jacobi(solution_as_jacobi[int(1/dt)-1])
prior = poly_to_jacobi(evidence * jacobi_to_poly_no_invariant(j))
empirical_ctr[i] = success / float(nsamples)
solution_as_jacobi = pde_solve(prior, times[i*nt:(i+1)*nt])
solution_as_x[i*nt:(i+1)*nt] = transform_to_x(solution_as_jacobi, x)
plot(arange(0,4), empirical_ctr, 'go')
plot(t, (y+1)/2.0, 'k')
imshow(solution_as_x.transpose(), origin='lower', extent=[0,tmax,0,1])
xlabel("time")
ylabel("CTR")
title("Bayesian Estimate of CTR")
colorbar()
show()
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src/omlt/neuralnet/__init__.py | jalving/OMLT | 115 | 165613 | <gh_stars>100-1000
r"""
We use the following notation to describe layer and activation functions:
.. math::
\begin{align*}
N &:= \text{Set of nodes (i.e. neurons in the neural network)}\\
M_i &:= \text{Number of inputs to node $i$}\\
\hat z_i &:= \text{pre-activation value on node $i$}\\
z_i &:= \text{post-activation value on node $i$}\\
w_{ij} &:= \text{weight from input $j$ to node $i$}\\
b_i &:= \text{bias value for node $i$}
\end{align*}
"""
from omlt.neuralnet.network_definition import NetworkDefinition
from omlt.neuralnet.nn_formulation import (FullSpaceNNFormulation, ReducedSpaceNNFormulation,
FullSpaceSmoothNNFormulation, ReducedSpaceSmoothNNFormulation,
ReluBigMFormulation, ReluComplementarityFormulation,
ReluPartitionFormulation) | [
1,
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2
] |
cminx/parser/aggregator.py | AutonomicPerfectionist/CMakeDoc | 0 | 155300 | import sys
from antlr4 import * #FIXME Remove unused imports
from enum import Enum
from collections import namedtuple
#Annoyingly, the Antl4 Python libraries use camelcase since it was originally Java, so we have convention inconsistencies here
from .CMakeParser import CMakeParser
from .CMakeListener import CMakeListener
"""
This module interfaces with the generated CMake parser.
It also subclasses CMakeListener to aggregate and further
process documented commands on-the-fly.
Processed documentation is stored in two different types of named tuples
depending on the type being documented. VariableDocumentation also stores
what type the variable is, either String or List for most purposes but also
supports Unset in case someone wants to document why something is unset.
:Author: <NAME>
:License: Apache 2.0
"""
FunctionDocumentation = namedtuple('FunctionDocumentation', 'function params doc')
MacroDocumentation = namedtuple("MacroDocumentation", "macro params doc")
VariableDocumentation = namedtuple('VariableDocumentation', 'varname type value doc')
TestDocumentation = namedtuple('TestDocumentation', 'name expect_fail doc')
SectionDocumentation = namedtuple('SectionDocumentation', 'name expect_fail doc')
DOC_TYPES = (FunctionDocumentation, MacroDocumentation, VariableDocumentation, TestDocumentation, SectionDocumentation)
VarType = Enum("VarType", "String List Unset")
class DocumentationAggregator(CMakeListener):
"""
Processes all docstrings and their associated commands, aggregating them in a list.
"""
def __init__(self):
self.documented = []
"""All current documented commands"""
def process_function(self, ctx:CMakeParser.Documented_commandContext, docstring: str):
"""
Extracts function name and declared parameters.
:param ctx: Documented command context. Constructed by the Antlr4 parser.
:param docstring: Cleaned docstring.
"""
params = [param.Identifier().getText() for param in ctx.command_invocation().single_argument()[1:]] #Extract declared function parameters
self.documented.append(FunctionDocumentation(ctx.command_invocation().single_argument()[0].Identifier().getText(), params, docstring)) #Extracts function name and adds the completed function documentation to the 'documented' list
def process_macro(self, ctx:CMakeParser.Documented_commandContext, docstring: str):
"""
Extracts macro name and declared parameters.
:param ctx: Documented command context. Constructed by the Antlr4 parser.
:param docstring: Cleaned docstring.
"""
params = [param.Identifier().getText() for param in ctx.command_invocation().single_argument()[1:]] #Extract declared macro parameters
self.documented.append(MacroDocumentation(ctx.command_invocation().single_argument()[0].Identifier().getText(), params, docstring)) #Extracts macro name and adds the completed macro documentation to the 'documented' list
def process_ct_add_test(self, ctx:CMakeParser.Documented_commandContext, docstring: str):
"""
Extracts test name and declared parameters.
:param ctx: Documented command context. Constructed by the Antlr4 parser.
:param docstring: Cleaned docstring.
"""
params = [param.Identifier().getText() for param in ctx.command_invocation().single_argument()] #Extract parameters
name = ""
expect_fail = False
for i in range(0, len(params)):
param = params[i]
if param.upper() == "NAME":
try:
name = params[i + 1]
except IndexError:
pretty_text = '\n'.join(ctx.Bracket_doccomment().getText().split('\n'))
pretty_text += f"\n{ctx.command_invocation().getText()}"
print(f"ct_add_test() called with incorrect parameters: {params}\n\n{pretty_text}", file=sys.stderr)
return
if param.upper() == "EXPECTFAIL":
expect_fail = True
self.documented.append(TestDocumentation(name, expect_fail, docstring))
def process_ct_add_section(self, ctx:CMakeParser.Documented_commandContext, docstring: str):
"""
Extracts section name and declared parameters.
:param ctx: Documented command context. Constructed by the Antlr4 parser.
:param docstring: Cleaned docstring.
"""
params = [param.Identifier().getText() for param in ctx.command_invocation().single_argument()] #Extract parameters
name = ""
expect_fail = False
for i in range(0, len(params)):
param = params[i]
if param.upper() == "NAME":
try:
name = params[i + 1]
except IndexError:
pretty_text = '\n'.join(ctx.Bracket_doccomment().getText().split('\n'))
pretty_text += f"\n{ctx.command_invocation().getText()}"
print(f"ct_add_section() called with incorrect parameters: {params}\n\n{pretty_text}", file=sys.stderr)
return
if param.upper() == "EXPECTFAIL":
expect_fail = True
self.documented.append(SectionDocumentation(name, expect_fail, docstring))
def process_set(self, ctx:CMakeParser.Documented_commandContext, docstring: str):
"""
Extracts variable name and values from the documented set command.
Also determines the type of set command/variable: String, List, or Unset.
:param ctx: Documented command context. Constructed by the Antlr4 parser.
:param docstring: Cleaned docstring.
"""
varname = ctx.command_invocation().single_argument()[0].Identifier().getText()
arg_len = len(ctx.command_invocation().single_argument()) - 1 #First argument is name of variable so ignore that
if arg_len > 1: #List
values = [val.getText() for val in ctx.command_invocation().single_argument()[1:]]
self.documented.append(VariableDocumentation(varname, VarType.List, values, docstring))
elif arg_len == 1: #String
value = ctx.command_invocation().single_argument()[1].getText()
#Includes the quote marks, need to remove them to get just the raw string
if value[0] == '"':
value = value[1:]
if value[-1] == '"':
value = value[:-1]
self.documented.append(VariableDocumentation(varname, VarType.String, value, docstring))
else: #Unset
self.documented.append(VariableDocumentation(varname, VarType.Unset, None, docstring))
def enterDocumented_command(self, ctx:CMakeParser.Documented_commandContext):
"""
Main entrypoint into the documentation processor and aggregator. Called by ParseTreeWalker whenever encountering a documented command.
Cleans the docstring and dispatches ctx to other functions for additional processing (process_{command}(), i.e. process_function())
:param ctx: Documented command context, constructed by the Antlr4 parser.
:raise NotImplementedError: If no processor can be found for the command that was documented.
"""
text = ctx.Bracket_doccomment().getText()
lines = text.split("\n")
# If last line starts with leading spaces or tabs, count how many and remove from all lines
num_spaces = 0
for i in range(0, len(lines[-1])):
if lines[-1][i] != "#":
num_spaces = num_spaces + 1
else:
break
cleaned_lines = []
for line in lines:
cleaned_line = line[num_spaces:] #Remove global indent from left side
cleaned_line = cleaned_line.lstrip("#[]") #Remove all hash marks and brackets from the left side only
if cleaned_line and cleaned_line[0] == " ": #String is not empty and first character is a space
cleaned_line = cleaned_line[1:] #Cleans optional singular space
cleaned_lines.append(cleaned_line)
cleaned_lines[-1] = cleaned_lines[-1].rstrip("#]")
cleaned_doc = "\n".join(cleaned_lines)
command = ctx.command_invocation().Identifier().getText().lower()
if f"process_{command}" in dir(self):
getattr(self, f"process_{command}")(ctx, cleaned_doc)
else:
pretty_text = '\n'.join(ctx.Bracket_doccomment().getText().split('\n'))
pretty_text += f"\n{ctx.command_invocation().getText()}"
raise NotImplementedError(f"Documentation cannot be generated for:\n{pretty_text}")
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jsConsole/__init__.py | Animenosekai/jsConsole | 0 | 18483 | """
pyJsConsole wrapper.
© <NAME> - 2020
"""
from .internal.javascript import classes as JSClass
console = JSClass._Console()
document = JSClass._Document()
history = JSClass._History()
Math = JSClass._Math()
navigator = JSClass._Navigator()
screen = JSClass._Screen()
window = JSClass._Window()
browser = JSClass.BrowserObject
'''
import threading
from lifeeasy import sleep
def reloadElements():
global document
global window
lastURL = 'data:,'
while True:
sleep(0.1)
try:
if JSClass.evaluate('window.location.href') != lastURL:
document = JSClass._Document()
window = JSClass._Window()
lastURL = JSClass.evaluate('window.location.href')
except:
break
thread = threading.Thread(target=reloadElements)
thread.daemon = True
thread.start()
'''
def newDocument():
return JSClass._Document()
def newWindow():
return JSClass._Window()
def newHistory():
return JSClass._History()
def fresh():
return (JSClass._Document(), JSClass._Window(), JSClass._History())
def clearInterval(intervalID):
JSClass.clearInterval(intervalID)
def clearTimeout(timeoutID):
JSClass.clearTimeout(timeoutID)
def evaluate(code_to_execute, return_value=False):
return JSClass.evaluate(code_to_execute, return_value=return_value)
def setInterval(function, milliseconds):
return JSClass.setInterval(function, milliseconds)
def setTimeout(function, milliseconds):
return JSClass.setTimeout(function, milliseconds)
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] |
tools/generator/__init__.py | Dev00355/fundamental-tools-copy-from-sap | 0 | 37490 | # SPDX-FileCopyrightText: 2014 SAP SE <NAME> <<EMAIL>>
#
# SPDX-License-Identifier: Apache-2.0
# -*- coding: utf-8 -*-
from .business_objects import catalog, rfm_sets
VERSION = "0.2"
# T002, T002C
all_languages = {
# iso2
"ar": "AR - عربي",
"bg": "BG - Български",
"ca": "CA - Català",
"cs": "CS - Čeština",
"da": "DA - Dansk",
"de": "DE - Deutsch",
"el": "EL - Ελληνικά",
"en": "EN - English",
"es": "ES - Español",
"et": "ET - Eesti",
"fi": "FI - Suomi",
"fr": "FR - Français",
"he": "HE - עברית",
"hi": "HI - हिंदी",
"hr": "HR - Hrvatski",
"hu": "HU - Magyar",
"it": "IT - Italiano",
"ja": "JA - 日本語",
"kk": "KK - Қазақ",
"ko": "KO - 한국어",
"lt": "LT - Lietuvių",
"lv": "LV - Latviešu",
"nl": "NL - Nederlands",
"no": "NO - Norsk",
"pl": "PL - polski",
"pt": "PT - Português",
"ro": "RO - Română",
"ru": "RU - Русский",
"sh": "SH - Srpski (Lat.)",
"sk": "SK - Slovenčina",
"sl": "SL - Slovenščina",
"sv": "SV - Svenska",
"th": "TH - Thai",
"tr": "TR - Türkçe",
"uk": "UK - Українська",
"vi": "VI - Việt Nam",
"zf": "ZF - 繁體中文",
"zh": "ZH - 中文",
}
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flask_ncov.py | ausk/ncov2020 | 2 | 149968 | <reponame>ausk/ncov2020<gh_stars>1-10
# 2020/02/15 by ausk
# 从百度疫情获取数据，并使用 flask 搭建后台。
"""
# 使用 Flask + ECharts 实现疫情展示。
1)从疫情网站抓取疫情数据，保存为离线数据；
2) 启动 flask 后台，可实时更新数据祸加载离线数据，然后以 json 格式返回查询数据；同时返回渲染的网页。
3) 编写 html 页面，前台(浏览器)通过 ajax 请求指定路由异步加载数据，绘制疫情地图和疫情趋势图。
"""
from flask import Flask, render_template, jsonify
from baidu_ncov import update, query
app = Flask(__name__)
app.config['JSON_AS_ASCII'] = False
## 注册 flask 路由
@app.route('/<fname>.html')
def hello(fname):
return render_template(fname+".html")
## 主页
@app.route('/')
def index():
return render_template('flask_ncov.html')
# 更新
@app.route("/update", methods=["GET"])
def update_data():
data = update()
return jsonify(data)
# 查询确诊
@app.route("/query_confirm", methods=["GET"])
def query_confirm():
data = query("confirm")
return jsonify(data)
# 查询趋势
@app.route("/query_trend", methods=["GET"])
def query_trend():
data = query("trend")
return jsonify(data)
if __name__ == '__main__':
update()
app.run(debug=False)
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yibai-sms-python-sdk-1.0.0/yibai/api/Yibai.py | 100sms/yibai-python-sdk | 0 | 18731 | # encoding=utf8
import HttpUtils
class YibaiApiError(Exception):
def __init__(self, code, message):
super(YibaiApiError, self).__init__(message)
self.code = code
class YibaiClient(object):
def __init__(self, server_url, apikey):
self.serverUrl = server_url
self.apikey = apikey
def sms_batch_submit(self, submits):
return self.__execute({'submits': submits}, '/sms/batchSubmit')
def sms_pull_status_report(self):
return self.__execute({}, '/sms/pullStatusReport')
def sms_pull_reply_message(self):
return self.__execute({}, '/sms/pullReply')
def user_info(self):
return self.__execute({}, '/user/info')
def __execute(self, request, url_path):
request['apikey'] = self.apikey
req_url = self.serverUrl + url_path
res = HttpUtils.post_json(req_url, request)
if res['code'] == 200:
return res['response']
raise YibaiApiError(res['code'], res['message'])
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Fortgeschrittenenpraktikum/Protokolle/V18_Germaniumdetektor/Python/unbekannt2.py | smjhnits/Praktikum | 2 | 158350 | import numpy as np
import matplotlib.pyplot as plt
import uncertainties
from scipy.signal import find_peaks
from scipy.optimize import curve_fit
import scipy.constants as sc
import scipy.integrate as integrate
from uncertainties import ufloat
from uncertainties import unumpy as unp
from uncertainties.unumpy import nominal_values as nomval
from uncertainties.unumpy import std_devs as std
# Loading experimental data and results of further calculations
r = 0.5*45*10**(-3)
L = (73.5+15)*10**(-3)
Omega = 0.5 * ( 1- L/np.sqrt(L**2+r**2))
C_u2 = np.genfromtxt('2018-12-10_Nitschke_Pape/Probe_21.Spe', unpack = True)
Peaks_Eu, Q_Eu = np.genfromtxt('EuropiumQ.txt', unpack = True)
Channels = np.linspace(0,len(C_u2[:4000])-1, len(C_u2[:4000]))
params_energy, covariance_energy_0, covariance_energy_1, params_Q, covariance_Q_0, covariance_Q_1= np.genfromtxt('Europium.txt', unpack = True)
covariance_energy = np.array([covariance_energy_0, covariance_energy_1])
errors_energy = np.sqrt(np.diag(covariance_energy))
covariance_Q = np.array([covariance_Q_0,covariance_Q_1])
errors_Q = np.sqrt(np.diag(covariance_Q))
def Energy(C):
return ufloat(params_energy[0], errors_energy[0])*C + ufloat(params_energy[1], errors_energy[1])
def Gauss(x, A, xmu, sigma, B):
return A * np.exp(-0.5*(x-xmu)**2/sigma**2) + B
def Gauss_Ufloat(x, A, xmu, sigma):
return A * unp.exp(-0.5*(x-xmu)**2/sigma**2)
def AreaGaus(A, sigma):
return np.sqrt(2*np.pi)*sigma*A
def Efficiency(E):
return ufloat(params_Q[0], errors_Q[0])*E**ufloat(params_Q[1], errors_Q[1])
Spektrum = C_u2[:4000]
tges = 4046
Peaks = find_peaks(Spektrum, height = 120)
plt.clf()
plt.hist(unp.nominal_values(Energy(np.arange(0, len(Spektrum[0:4000]), 1))),
bins=unp.nominal_values(Energy(np.linspace(0, len(Spektrum[0:4000]), len(Spektrum[0:4000])))),
weights=Spektrum[0:4000], label='Spektrum')
plt.yscale('log')
plt.plot(nomval(Energy(Peaks[0][:])), Spektrum[Peaks[0][:]], '.',
markersize=4, label='Gauß-Peaks', color='C1', alpha=0.8)
plt.xlim(0,1500)
plt.ylabel('Zählungen pro Energie')
plt.xlabel('E / keV')
plt.legend()
#plt.show()
plt.savefig('Plots/unbekannt2.pdf')
Peaks_Energy = Energy(Peaks[0][:])
Energy_co = np.array([1173.237, 1332.501])
Params_u2 = []
errors_u2 = []
for n in Peaks[0]:
Params, covariance = curve_fit(Gauss, Channels[n-30:n+30], Spektrum[n-30:n+30], p0 = [C_u2[n], n, 1, 0])
Params_u2.append(Params.tolist())
errors = np.sqrt(np.diag(covariance))
errors_u2.append(errors.tolist())
for i,n in enumerate(Peaks[0]):
l_u = np.int(Channels[n-30])
l_o = np.int(Channels[n+30])
plt.clf()
plt.hist(unp.nominal_values(Energy(np.arange(l_u, l_o, 1))),
bins=unp.nominal_values(Energy(np.linspace(l_u, l_o, len(Spektrum[n-30:n+30])))),
weights=Spektrum[n-30:n+30], label='Spektrum')
Channel_Gauss = np.linspace(n-30,n+30,1000)
plt.plot(unp.nominal_values(Energy(Channel_Gauss)), Gauss(Channel_Gauss,*Params_u2[i]))
#plt.show()
Peaks_mittel = np.round(np.asarray(Params_u2)[:,1],0)
Amplitudes = np.asarray(Params_u2)[:,0]
Amplitudes_ufloat = np.asarray([ufloat(n, np.asarray(errors_u2)[i,0]) for i,n in enumerate(np.asarray(Params_u2)[:,0])])
Means_ufloat = np.asarray([ufloat(n, np.asarray(errors_u2)[i,1]) for i,n in enumerate(np.asarray(Params_u2)[:,1])])
sigmas = np.asarray(Params_u2)[:,2]
sigmas_ufloat = np.asarray([ufloat(n, np.asarray(errors_u2)[i,2]) for i,n in enumerate(np.asarray(Params_u2)[:,2])])
Area_Params = np.array([[n,sigmas[i]] for i,n in enumerate(Amplitudes)])
Area_params_ufloat = np.array([[n,sigmas_ufloat[i]] for i,n in enumerate(Amplitudes_ufloat)])
Constants_ufloat = np.asarray([ufloat(n, np.asarray(errors_u2)[i,3]) for i,n in enumerate(np.asarray(Params_u2)[:,3])])
print("--- Find Peaks and gaussian fit---")
print(f"Channel Peaks: {np.round(Peaks_mittel,0)}")
#print(f"Energy Peaks: {Energy(np.round(Peaks_mittel,0))}")
print(f"Energy Literature: {Energy_co}", '\n')
Area = AreaGaus(Area_Params[:,0], Area_Params[:,1])
Area_ufloat = AreaGaus(Area_params_ufloat[:,0], Area_params_ufloat[:,1])
Area_norm = Area/tges
Area_norm_ufloat = Area_ufloat/tges
print("-- Fit Parameter --")
print(f"Amplituden: {Amplitudes_ufloat}")
print(f"Means: {Energy(Means_ufloat)}")
print(f"Sigmas: {sigmas_ufloat}")
print(f"Constants: {Constants_ufloat}", '\n')
print("--- Calculating the activity ---")
r = 0.5*45*10**(-3)
L = (73.5+15)*10**(-3)
Omega = 0.5 * ( 1- L/np.sqrt(L**2+r**2))
W = np.asarray([0.999736, 0.999856])
Q = Efficiency(Peaks_Energy)
Aktivität = np.array([Area_norm[i]/(W[i]*n*Omega) for i,n in enumerate(Q)])
print(f"emission probability: {W}")
print(f"Area under Gaussian Fit: {Area_ufloat}")
print(f"Efficiency: {Q}", '\n')
print(f"resulting acitivity: {Aktivität}")
A_all = sum(Aktivität)/len(Aktivität)#ufloat(np.mean(nomval(Aktivität)),np.std(std(Aktivität)))
print(f"Mean with all values: {nomval(A_all)}, {std(A_all)}")
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validator/proxy_validator/client.py | ezirmusitua/proxies | 0 | 57712 | # -*- coding: utf-8 -*-
import requests
from proxy_validator import config
Default_UA = config['CLIENT_UA']
Default_Timeout = config['CLIENT_TIMEOUT']
class Client(object):
def __init__(self, headers=None, proxies=None):
self.headers = headers if headers is not None else {}
self.headers['User-Agent'] = Default_UA
self.proxies = proxies if proxies is not None else {}
self.session = requests.Session()
def get(self, url=None):
if url is None:
raise Exception('Need Url. ')
response = self.session.get(url, headers=self.headers, proxies=self.proxies, timeout=Default_Timeout)
if response.status_code != 200:
return None
return response.text
def set_proxies(self, proxy_str, ptype='http'):
self.proxies = {
'http': (ptype if ptype is not None else 'http') + '://' + proxy_str,
'https': (ptype if ptype is not None else 'https') + '://' + proxy_str,
}
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] |
__main__.py | miezebieze/scott-launcher | 1 | 1826 | from enum import Enum
from window import Window
D = Enum ('Directions','N NE E SE S SW W NW')
selector_map = {
D.NW: [0.5,0.5], D.N: [1.5,0], D.NE: [2.5,0.5],
D.W: [0,1.5], D.E: [3,1.5],
D.SW: [0.5,2.5], D.S: [1.5,3], D.SE: [2.5,2.5],
}
selector_size = 100
window_size = selector_size*4
window = Window (window_size,window_size,selector_map,selector_size,selector_size)
# set actions here
from functools import partial
def say (something):
print (''.join (('Me: "',something,'"')))
window.actions[D.NW] = partial (say,'northwast')
window.actions[D.N] = partial (say,'north')
window.actions[D.NE] = partial (say,'neorthest')
window.actions[D.W] = partial (say,'western')
window.actions[D.E] = partial (say,'easy')
window.actions[D.SW] = partial (say,'suess whest')
window.actions[D.S] = partial (say,'sissy')
window.actions[D.SE] = partial (say,'seoul')
window.go ()
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Libraries/Python/CommonEnvironment/v1.0/CommonEnvironment/TypeInfo/FundamentalTypes/Serialization/UnitTests/StringSerialization_UnitTest.py | davidbrownell/v3-Common_Environment | 0 | 161260 | # ----------------------------------------------------------------------
# |
# | StringSerialization_UnitTest.py
# |
# | <NAME> <<EMAIL>>
# | 2018-04-26 22:06:18
# |
# ----------------------------------------------------------------------
# |
# | Copyright <NAME> 2018-22.
# | Distributed under the Boost Software License, Version 1.0.
# | (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
# |
# ----------------------------------------------------------------------
# """Unit test for StringSerialization.py"""
import datetime
import os
import re
import sys
import unittest
import uuid
import CommonEnvironment
from CommonEnvironment.TypeInfo.FundamentalTypes.All import *
from CommonEnvironment.TypeInfo.FundamentalTypes.Serialization.StringSerialization import RegularExpressionVisitor, \
StringSerialization, \
ValidationException
# ----------------------------------------------------------------------
_script_fullpath = CommonEnvironment.ThisFullpath()
_script_dir, _script_name = os.path.split(_script_fullpath)
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
class RegularExpressionVisitorSuite(unittest.TestCase):
# ----------------------------------------------------------------------
def test_Standard(self):
# ----------------------------------------------------------------------
def Test(method, to_match, regex_index=0):
if not isinstance(to_match, list):
to_match = [ to_match, ]
regex_string = method()[regex_index]
if isinstance(regex_string, tuple):
regex_string, regex_options = regex_string
else:
regex_options = 0
regex = re.compile("^{}$".format(regex_string), regex_options)
for index, query in enumerate(to_match):
self.assertTrue(regex.match(query), "{} did not match {} ({})".format(query, regex_string, index))
# ----------------------------------------------------------------------
Test(lambda: RegularExpressionVisitor.OnBool(None), [ "True", "T", "t", "Yes", "yes", "Y", "y", "1", "False", "false", "F", "f", "No", "no", "N", "n", "0", ])
Test(lambda: RegularExpressionVisitor.OnDateTime(None), [ "2018-04-26 22:29:00", "2018-04-26T22:29:00", "2018-04-26T22:29:00Z", "2018-04-26T22:29:00+08:00", "2018-04-26T22:29:00-10:34", ])
Test(lambda: RegularExpressionVisitor.OnDate(None), [ "2018-04-26", "2018/04/26", "2018.04.26", ], regex_index=0)
Test(lambda: RegularExpressionVisitor.OnDate(None), [ "04-26-2018", "04/26/2018", "04.26.2018", ], regex_index=1)
Test(lambda: RegularExpressionVisitor.OnDate(None), [ "18-04-26", "18/04/26", "18.04.26", ], regex_index=2)
Test(lambda: RegularExpressionVisitor.OnDate(None), [ "04-26-18", "04/26/18", "04.26.18", ], regex_index=3)
Test(lambda: RegularExpressionVisitor.OnDirectory(None), "anything")
Test(lambda: RegularExpressionVisitor.OnDuration(None), [ "1.0:00:00.0", "1:0:00:00.0", "1.00:00:00", "1:00:00.0", "23:22:21", "23:22:21.20", ], regex_index=0)
Test(lambda: RegularExpressionVisitor.OnDuration(None), [ "P1Y", "P1MT2H", "PT1M", ], regex_index=1)
Test(lambda: RegularExpressionVisitor.OnDuration(None), [ "1", "1.2345", "3.4", ], regex_index=2)
Test(lambda: RegularExpressionVisitor.OnEnum(EnumTypeInfo([ "one", "two", ])), [ "one", "two", ])
Test(lambda: RegularExpressionVisitor.OnFilename(None), "anything")
Test(lambda: RegularExpressionVisitor.OnFloat(FloatTypeInfo()), [ "0.0", "10.2", "-3.14", ])
Test(lambda: RegularExpressionVisitor.OnGuid(None), "{54465641-ADF2-43B1-98EB-66BBD208622C}", regex_index=0)
Test(lambda: RegularExpressionVisitor.OnGuid(None), "54465641-ADF2-43B1-98EB-66BBD208622C", regex_index=1)
Test(lambda: RegularExpressionVisitor.OnGuid(None), "{54465641ADF243B198EB66BBD208622C}", regex_index=2)
Test(lambda: RegularExpressionVisitor.OnGuid(None), "54465641ADF243B198EB66BBD208622C", regex_index=3)
Test(lambda: RegularExpressionVisitor.OnInt(IntTypeInfo()), [ "-10", "10", "0", "20", ])
Test(lambda: RegularExpressionVisitor.OnString(StringTypeInfo()), [ "test", "again", "and another", ])
Test(lambda: RegularExpressionVisitor.OnTime(None), [ "11:22:33", "11:22:33.44", "11:22:33", ])
Test(lambda: RegularExpressionVisitor.OnUri(None), "http://one.two.three")
# ----------------------------------------------------------------------
def test_Float(self):
self.assertFalse(re.match("^{}$".format(RegularExpressionVisitor.OnFloat(FloatTypeInfo(min=0))[0]), "-3.0"))
self.assertFalse(re.match("^{}$".format(RegularExpressionVisitor.OnFloat(FloatTypeInfo(max=0))[0]), "3.0"))
self.assertFalse(re.match("^{}$".format(RegularExpressionVisitor.OnFloat(FloatTypeInfo(min=0, max=10))[0]), "130.01234"))
# ----------------------------------------------------------------------
def test_Int(self):
self.assertFalse(re.match("^{}$".format(RegularExpressionVisitor.OnInt(IntTypeInfo(min=0))[0]), "-3"))
self.assertFalse(re.match("^{}$".format(RegularExpressionVisitor.OnInt(IntTypeInfo(max=0))[0]), "3"))
self.assertFalse(re.match("^{}$".format(RegularExpressionVisitor.OnInt(IntTypeInfo(min=0, max=9))[0]), "130"))
# ----------------------------------------------------------------------
def test_String(self):
regex = re.compile("^{}$".format(RegularExpressionVisitor.OnString(StringTypeInfo(min_length=2))[0]))
self.assertTrue(regex.match("123"))
self.assertTrue(regex.match("12"))
self.assertFalse(regex.match("1"))
regex = re.compile("^{}$".format(RegularExpressionVisitor.OnString(StringTypeInfo(max_length=2))[0]))
self.assertTrue(regex.match("12"))
self.assertFalse(regex.match("123"))
regex = re.compile("^{}$".format(RegularExpressionVisitor.OnString(StringTypeInfo(min_length=2, max_length=3))[0]))
self.assertTrue(regex.match("123"))
self.assertTrue(regex.match("12"))
self.assertFalse(regex.match("1234"))
self.assertFalse(regex.match("1"))
regex = re.compile("^{}$".format(RegularExpressionVisitor.OnString(StringTypeInfo(validation_expression=".est"))[0]))
self.assertTrue(regex.match("test"))
self.assertTrue(regex.match("Test"))
self.assertTrue(regex.match("jest"))
self.assertFalse(regex.match("tEst"))
# ----------------------------------------------------------------------
class SerializationSuite(unittest.TestCase):
# ----------------------------------------------------------------------
def test_Bool(self):
self.assertEqual(StringSerialization.SerializeItem(BoolTypeInfo(), True), "True")
self.assertEqual(StringSerialization.SerializeItem(BoolTypeInfo(), False), "False")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(BoolTypeInfo(), "this is not a bool"))
# ----------------------------------------------------------------------
def test_DateTime(self):
self.assertEqual(StringSerialization.SerializeItem(DateTimeTypeInfo(), datetime.datetime(year=2018, month=4, day=28, hour=14, minute=46, second=12)), "2018-04-28 14:46:12")
self.assertEqual(StringSerialization.SerializeItem(DateTimeTypeInfo(), datetime.datetime(year=2018, month=4, day=28, hour=14, minute=46, second=12), sep='T'), "2018-04-28T14:46:12")
self.assertEqual(StringSerialization.SerializeItem(DateTimeTypeInfo(), datetime.datetime(year=2018, month=4, day=28, hour=14, minute=46, second=12, microsecond=3939)), "2018-04-28 14:46:12.003939")
self.assertEqual(StringSerialization.SerializeItem(DateTimeTypeInfo(), datetime.datetime(year=2018, month=4, day=28, hour=14, minute=46, second=12, microsecond=3939), microseconds=False), "2018-04-28 14:46:12")
self.assertEqual(StringSerialization.SerializeItem(DateTimeTypeInfo(), datetime.datetime(year=2018, month=4, day=28, hour=14, minute=46, second=12), regex_index=1), "@1524951972.0 00:00")
self.assertEqual(StringSerialization.SerializeItem(DateTimeTypeInfo(), datetime.datetime(year=2018, month=4, day=28, hour=14, minute=46, second=12), regex_index=2), "1524951972.0")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(DateTimeTypeInfo(), "this is not a datetime"))
# ----------------------------------------------------------------------
def test_Date(self):
self.assertEqual(StringSerialization.SerializeItem(DateTypeInfo(), datetime.date(year=2018, month=4, day=28)), "2018-04-28")
self.assertEqual(StringSerialization.SerializeItem(DateTypeInfo(), datetime.date(year=2018, month=4, day=28), regex_index=1), "04-28-2018")
self.assertEqual(StringSerialization.SerializeItem(DateTypeInfo(), datetime.date(year=2018, month=4, day=28), regex_index=2), "18-04-28")
self.assertEqual(StringSerialization.SerializeItem(DateTypeInfo(), datetime.date(year=2018, month=4, day=28), regex_index=3), "04-28-18")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(DateTypeInfo(), "this is not a valid date"))
# ----------------------------------------------------------------------
def test_Directory(self):
self.assertEqual(StringSerialization.SerializeItem(DirectoryTypeInfo(ensure_exists=False), os.path.join("foo", "bar")), "foo/bar")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(DirectoryTypeInfo(), os.path.join("foo", "bar")))
# ----------------------------------------------------------------------
def test_Duration(self):
self.assertEqual(StringSerialization.SerializeItem(DurationTypeInfo(), datetime.timedelta(hours=2)), "2:00:00")
self.assertEqual(StringSerialization.SerializeItem(DurationTypeInfo(), datetime.timedelta(days=1, hours=2)), "1.02:00:00")
self.assertEqual(StringSerialization.SerializeItem(DurationTypeInfo(), datetime.timedelta(days=1, hours=2), regex_index=1), "P1DT2H0M0S")
self.assertEqual(StringSerialization.SerializeItem(DurationTypeInfo(), datetime.timedelta(days=1, hours=2), sep=':'), "1:02:00:00")
self.assertEqual(StringSerialization.SerializeItem(DurationTypeInfo(), datetime.timedelta(days=1, hours=2, microseconds=3456)), "1.02:00:00.003456")
self.assertEqual(StringSerialization.SerializeItem(DurationTypeInfo(), datetime.timedelta(hours=2), regex_index=2), "7200")
self.assertEqual(StringSerialization.SerializeItem(DurationTypeInfo(), datetime.timedelta(hours=2, microseconds=3456), regex_index=2), "7200.003456")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(DurationTypeInfo(), "this is not a valid duration"))
# ----------------------------------------------------------------------
def test_Enum(self):
self.assertEqual(StringSerialization.SerializeItem(EnumTypeInfo([ "one", "two", ]), "one"), "one")
self.assertEqual(StringSerialization.SerializeItem(EnumTypeInfo([ "one", "two", ]), "two"), "two")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(EnumTypeInfo([ "one", "two", ]), "three"))
# ----------------------------------------------------------------------
def test_Filename(self):
self.assertEqual(StringSerialization.SerializeItem(FilenameTypeInfo(ensure_exists=False), os.path.join("foo", "bar")), "foo/bar")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(FilenameTypeInfo(), os.path.join("foo", "bar")))
# ----------------------------------------------------------------------
def test_Float(self):
self.assertEqual(StringSerialization.SerializeItem(FloatTypeInfo(), -10.5), "-10.5")
self.assertEqual(StringSerialization.SerializeItem(FloatTypeInfo(), 10), "10")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(FloatTypeInfo(), "this is not a valid float"))
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(FloatTypeInfo(min=0), -10))
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(FloatTypeInfo(max=10), 20))
# ----------------------------------------------------------------------
def test_Guid(self):
self.assertEqual(StringSerialization.SerializeItem(GuidTypeInfo(), uuid.UUID("363D3427-1871-40C5-83DF-3C9D5CE7B60D")), "{363D3427-1871-40C5-83DF-3C9D5CE7B60D}".lower())
self.assertEqual(StringSerialization.SerializeItem(GuidTypeInfo(), uuid.UUID("363D3427-1871-40C5-83DF-3C9D5CE7B60D"), regex_index=1), "363D3427-1871-40C5-83DF-3C9D5CE7B60D".lower())
self.assertEqual(StringSerialization.SerializeItem(GuidTypeInfo(), uuid.UUID("363D3427-1871-40C5-83DF-3C9D5CE7B60D"), regex_index=2), "{363D3427187140C583DF3C9D5CE7B60D}".lower())
self.assertEqual(StringSerialization.SerializeItem(GuidTypeInfo(), uuid.UUID("363D3427-1871-40C5-83DF-3C9D5CE7B60D"), regex_index=3), "363D3427187140C583DF3C9D5CE7B60D".lower())
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(GuidTypeInfo(), "this is not a valid guid"))
# ----------------------------------------------------------------------
def test_Int(self):
self.assertEqual(StringSerialization.SerializeItem(IntTypeInfo(), 20), "20")
self.assertEqual(StringSerialization.SerializeItem(IntTypeInfo(), -20), "-20")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(IntTypeInfo(), "this is not a valid int"))
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(IntTypeInfo(min=0), -10))
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(IntTypeInfo(max=10), 20))
# ----------------------------------------------------------------------
def test_String(self):
self.assertEqual(StringSerialization.SerializeItem(StringTypeInfo(), "test"), "test")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(StringTypeInfo(), 10))
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(StringTypeInfo(min_length=2), "1"))
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(StringTypeInfo(max_length=2), "123"))
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(StringTypeInfo(validation_expression="test"), "TEST"))
# ----------------------------------------------------------------------
def test_Time(self):
self.assertEqual(StringSerialization.SerializeItem(TimeTypeInfo(), datetime.time(hour=15, minute=15, second=12)), "15:15:12")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(TimeTypeInfo(), "this is not a valid time"))
# ----------------------------------------------------------------------
def test_Uri(self):
self.assertEqual(StringSerialization.SerializeItem(UriTypeInfo(), Uri.FromString("https://foo.bar.baz")), "https://foo.bar.baz")
self.assertRaises(ValidationException, lambda: StringSerialization.SerializeItem(UriTypeInfo(), "this is not a valid uri"))
# ----------------------------------------------------------------------
class DeserializationSuite(unittest.TestCase):
# ----------------------------------------------------------------------
def test_Bool(self):
self.assertTrue(StringSerialization.DeserializeItem(BoolTypeInfo(), "true"))
self.assertTrue(StringSerialization.DeserializeItem(BoolTypeInfo(), "t"))
self.assertTrue(StringSerialization.DeserializeItem(BoolTypeInfo(), "yes"))
self.assertTrue(StringSerialization.DeserializeItem(BoolTypeInfo(), "y"))
self.assertTrue(StringSerialization.DeserializeItem(BoolTypeInfo(), "1"))
self.assertFalse(StringSerialization.DeserializeItem(BoolTypeInfo(), "false"))
self.assertFalse(StringSerialization.DeserializeItem(BoolTypeInfo(), "f"))
self.assertFalse(StringSerialization.DeserializeItem(BoolTypeInfo(), "no"))
self.assertFalse(StringSerialization.DeserializeItem(BoolTypeInfo(), "n"))
self.assertFalse(StringSerialization.DeserializeItem(BoolTypeInfo(), "0"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(BoolTypeInfo(), "not_valid"))
# ----------------------------------------------------------------------
def test_DateTime(self):
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28 10:05:00"), datetime.datetime(year=2018, month=4, day=28, hour=10, minute=5, second=0))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28T10:05:00"), datetime.datetime(year=2018, month=4, day=28, hour=10, minute=5, second=0))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "1528171793"), datetime.datetime(2018, 6, 5, 4, 9, 53))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "@1528171793 -0700"), datetime.datetime(2018, 6, 5, 11, 9, 53))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "@1528171793 +0700"), datetime.datetime(2018, 6, 4, 21, 9, 53))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "@1528171793 0700"), datetime.datetime(2018, 6, 4, 21, 9, 53))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "1528171793.0"), datetime.datetime(2018, 6, 5, 4, 9, 53))
# python2 doesn't support deserialization with non-UTC time zones
if sys.version_info[0] == 2:
self.assertRaises(
ValueError, # "'z' is a bad directive in format.+",
lambda: StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28T10:05:00-0700"),
)
else:
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28T10:05:31.00Z"), datetime.datetime(year=2018, month=4, day=28, hour=10, minute=5, second=31, tzinfo=datetime.timezone.utc))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28T10:05:31.0000Z"), datetime.datetime(year=2018, month=4, day=28, hour=10, minute=5, second=31, tzinfo=datetime.timezone.utc))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28T10:05:00-0700"), datetime.datetime(year=2018, month=4, day=28, hour=10, minute=5, second=0, tzinfo=datetime.timezone(datetime.timedelta(hours=-7))))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28T10:05:00-07:00"), datetime.datetime(year=2018, month=4, day=28, hour=10, minute=5, second=0, tzinfo=datetime.timezone(datetime.timedelta(hours=-7))))
self.assertEqual(StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04-28T10:05:31.000-07:00"), datetime.datetime(year=2018, month=4, day=28, hour=10, minute=5, second=31, tzinfo=datetime.timezone(datetime.timedelta(hours=-7))))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(DateTimeTypeInfo(), "not a valid datetime"))
# ----------------------------------------------------------------------
def test_Date(self):
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "2018-04-28"), datetime.date(year=2018, month=4, day=28))
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "04-28-2018"), datetime.date(year=2018, month=4, day=28))
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "18-04-28"), datetime.date(year=2018, month=4, day=28))
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "04-28-18"), datetime.date(year=2018, month=4, day=28))
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "2018.04.28"), datetime.date(year=2018, month=4, day=28))
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "04.28.2018"), datetime.date(year=2018, month=4, day=28))
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "18.04.28"), datetime.date(year=2018, month=4, day=28))
self.assertEqual(StringSerialization.DeserializeItem(DateTypeInfo(), "04.28.18"), datetime.date(year=2018, month=4, day=28))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(DateTimeTypeInfo(), "2018-04.28"))
# ----------------------------------------------------------------------
def test_Directory(self):
self.assertEqual(StringSerialization.DeserializeItem(DirectoryTypeInfo(ensure_exists=False), "foo/bar"), os.path.realpath(os.path.normpath(os.path.join("foo", "bar"))))
self.assertEqual(StringSerialization.DeserializeItem(DirectoryTypeInfo(ensure_exists=False), "foo/bar", normalize=False), os.path.join("foo", "bar"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(DirectoryTypeInfo(), ''))
# ----------------------------------------------------------------------
def test_Duration(self):
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "1.02:03:04"), datetime.timedelta(days=1, hours=2, minutes=3, seconds=4))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "1:02:03:04"), datetime.timedelta(days=1, hours=2, minutes=3, seconds=4))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "02:03:04"), datetime.timedelta(hours=2, minutes=3, seconds=4))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "1.02:03:04.5"), datetime.timedelta(days=1, hours=2, minutes=3, seconds=4, microseconds=5))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "1:02:03:04.5"), datetime.timedelta(days=1, hours=2, minutes=3, seconds=4, microseconds=5))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "02:03:04.5"), datetime.timedelta(hours=2, minutes=3, seconds=4, microseconds=5))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "P2DT3M21S"), datetime.timedelta(days=2, minutes=3, seconds=21))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "5"), datetime.timedelta(seconds=5))
self.assertEqual(StringSerialization.DeserializeItem(DurationTypeInfo(), "1.234"), datetime.timedelta(seconds=1, microseconds=234))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(DurationTypeInfo(), "not a valid duration"))
# ----------------------------------------------------------------------
def test_Enum(self):
self.assertEqual(StringSerialization.DeserializeItem(EnumTypeInfo([ "one", "two", ]), "one"), "one")
self.assertEqual(StringSerialization.DeserializeItem(EnumTypeInfo([ "one", "two", ]), "two"), "two")
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(EnumTypeInfo([ "one", "two", ]), "three"))
# ----------------------------------------------------------------------
def test_Filename(self):
self.assertEqual(StringSerialization.DeserializeItem(FilenameTypeInfo(ensure_exists=False), "foo/bar"), os.path.realpath(os.path.normpath(os.path.join("foo", "bar"))))
self.assertEqual(StringSerialization.DeserializeItem(FilenameTypeInfo(ensure_exists=False), "foo/bar", normalize=False), os.path.join("foo", "bar"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(DirectoryTypeInfo(), ''))
# ----------------------------------------------------------------------
def test_Float(self):
self.assertEqual(StringSerialization.DeserializeItem(FloatTypeInfo(), "10.5"), 10.5)
self.assertEqual(StringSerialization.DeserializeItem(FloatTypeInfo(), "10"), 10.0)
self.assertEqual(StringSerialization.DeserializeItem(FloatTypeInfo(), "-10.5"), -10.5)
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(FloatTypeInfo(), "not a valid float"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(FloatTypeInfo(min=0), "-10.5"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(FloatTypeInfo(max=10), "100.5"))
# ----------------------------------------------------------------------
def test_Guid(self):
self.assertEqual(StringSerialization.DeserializeItem(GuidTypeInfo(), "{9A3A5A9A-0755-4FAC-9019-176C609665C5}"), uuid.UUID("9A3A5A9A-0755-4FAC-9019-176C609665C5"))
self.assertEqual(StringSerialization.DeserializeItem(GuidTypeInfo(), "9A3A5A9A-0755-4FAC-9019-176C609665C5"), uuid.UUID("9A3A5A9A-0755-4FAC-9019-176C609665C5"))
self.assertEqual(StringSerialization.DeserializeItem(GuidTypeInfo(), "{9A3A5A9A07554FAC9019176C609665C5}"), uuid.UUID("9A3A5A9A-0755-4FAC-9019-176C609665C5"))
self.assertEqual(StringSerialization.DeserializeItem(GuidTypeInfo(), "9A3A5A9A07554FAC9019176C609665C5"), uuid.UUID("9A3A5A9A-0755-4FAC-9019-176C609665C5"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(GuidTypeInfo(), "not a valid guid"))
# ----------------------------------------------------------------------
def test_Int(self):
self.assertEqual(StringSerialization.DeserializeItem(IntTypeInfo(), "10"), 10)
self.assertEqual(StringSerialization.DeserializeItem(IntTypeInfo(), "-10"), -10)
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(IntTypeInfo(), "not a valid int"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(IntTypeInfo(min=0), "-10"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(IntTypeInfo(max=10), "100"))
# ----------------------------------------------------------------------
def test_String(self):
self.assertEqual(StringSerialization.DeserializeItem(StringTypeInfo(), "foo"), "foo")
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(StringTypeInfo(), ""))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(StringTypeInfo(min_length=2), "1"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(StringTypeInfo(max_length=2), "123"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(StringTypeInfo(validation_expression="test"), "not test"))
# ----------------------------------------------------------------------
def test_Time(self):
self.assertEqual(StringSerialization.DeserializeItem(TimeTypeInfo(), "10:15:01"), datetime.time(hour=10, minute=15, second=1))
self.assertEqual(StringSerialization.DeserializeItem(TimeTypeInfo(), "10:15:01.678"), datetime.time(hour=10, minute=15, second=1, microsecond=678000))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(TimeTypeInfo(), "not a valid time"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(TimeTypeInfo(), "10:15:01+10:30"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(TimeTypeInfo(), "10:15:01-03:13"))
# ----------------------------------------------------------------------
def test_Uri(self):
self.assertEqual(StringSerialization.DeserializeItem(UriTypeInfo(), "https://foo.bar.baz"), Uri.FromString("https://foo.bar.baz"))
self.assertRaises(ValidationException, lambda: StringSerialization.DeserializeItem(UriTypeInfo(), "not a valid uri"))
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
if __name__ == "__main__":
try: sys.exit(unittest.main(verbosity=2))
except KeyboardInterrupt: pass
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Kivy2.py | mcvenkat/Python-Programs | 0 | 170428 | <filename>Kivy2.py<gh_stars>0
import kivy
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.properties import ObjectProperty
from kivy.uix.label import Label
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.popup import Popup
class Widgets(Widget):
def btn(self):
show_popup()
class P(FloatLayout):
pass
class MyApp(App):
def build(self):
return Widgets()
def show_popup():
show = P()
popupWindow = Popup(title="Popup Window", content=show, size_hint=(None,None),size=(400,400))
popupWindow.open()
if __name__ == "__main__":
MyApp().run()
<Widgets>:
Button:
text: "Press me"
on_release: root.btn()
<P>:
Label:
text: "You pressed the button"
size_hint: 0.6, 0.2
pos_hint: {"x":0.2, "top":1}
Button:
text: "You pressed the button"
size_hint: 0.8, 0.2
pos_hint: {"x":0.1, "y":0.1}
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common.py | mroja/kuramoto | 1 | 117345 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import struct
import numpy as np
import subprocess
import matplotlib.pyplot as plt
from collections import namedtuple
# N - number of oscillators
# freq - oscillator frequency (N elements)
# phase - oscillator phases (N elements)
# k - coupling coefficients (NxN matrix)
DataPreset = namedtuple('DataPreset', ['N', 'k', 'freq', 'phase'])
class KuramotoSimulation(object):
def __init__(self):
self.N_steps = 0
self.dt = 0.01
self.noise = 0.0
self.dump_interval = 0
self.coupling_type = 'kuramoto'
self.forcing_strength = 0.0
self.forcing_freq = 0.0
self.freq_modulation_enabled = False
self.k_modulation_enabled = False
def run(self, preset_name, r_file=None, mean_file=None, mean_vel_file=None):
cmd = ['kuramoto_simulation',
'--quiet',
'--preset', preset_name,
'--steps', str(self.N_steps),
'--dump-interval', str(self.dump_interval),
'--dt', str(self.dt),
'--noise', str(self.noise),
'--coupling', self.coupling_type,
'--forcing-strength', str(self.forcing_strength),
'--forcing-freq', str(self.forcing_freq)]
if r_file is not None:
cmd.append('--r-file')
if type(r_file) is str and r_file:
cmd.append(r_file)
if mean_file is not None:
cmd.append('--mean-phase-file')
if type(mean_file) is str and mean_file:
cmd.append(mean_file)
if mean_vel_file is not None:
cmd.append('--mean-vel-file')
if type(mean_vel_file) is str and mean_vel_file:
cmd.append(mean_vel_file)
if self.k_modulation_enabled:
cmd.append('--enable-k-modulation')
if self.freq_modulation_enabled:
cmd.append('--enable-freq-modulation')
# print cmd
subprocess.call(cmd)
'''
def write_to_file(self, preset_name):
conf_str = \
'{N_steps:d}\n' + \
'{dt:f}\n' + \
'{noise:f}\n' + \
'{dump_interval:d}\n' + \
'{coupling_type}\n' + \
'{forcing_strength:f}\n' + \
'{forcing_freq:f}\n' + \
'{freq_modulation_enabled}\n' + \
'{k_modulation_enabled}'
args = {
'N_steps': self.N_steps,
'dt': self.dt,
'noise': self.noise,
'dump_interval': self.dump_interval,
'coupling_type': self.coupling_type,
'forcing_strength': self.forcing_strength,
'forcing_freq': self.forcing_freq,
'freq_modulation_enabled': 'freq_modulation' if self.freq_modulation_enabled else 'no_freq_modulation',
'k_modulation_enabled': 'k_modulation' if self.k_modulation_enabled else 'no_k_modulation',
}
conf = conf_str.format(**args)
with open(preset_name + '.conf.txt', 'w') as f:
f.write(conf)
def read_from_file(self, preset_name):
with open(preset_name + '.conf.txt', 'r') as f:
lines = f.readlines()
self.N_steps = int(lines[0])
delf.dt = float(lines[1])
self.noise = float(lines[2])
self.dump_interval = int(lines[3])
self.coupling_type = lines[4]
self.forcing_strength = float(lines[5])
self.forcing_freq = float(lines[6])
self.freq_modulation_enabled = True if lines[7] == 'freq_modulation' else False
self.k_modulation_enabled = True if lines[11] == 'k_modulation' else False
'''
def load_preset_from_file(name):
with open(name + '.preset', 'rb') as f:
N = struct.unpack('I', f.read(4))[0]
freq = np.fromfile(f, dtype=np.float64, count=N)
phase = np.fromfile(f, dtype=np.float64, count=N)
k = np.fromfile(f, dtype=np.float64, count=N*N)
return DataPreset(N, k, freq, phase)
def write_preset_to_file(preset_name, preset):
preset_file_name = preset_name + '.preset'
try:
os.remove(preset_file_name)
except Exception:
pass
with open(preset_file_name, 'wb') as f:
f.write(struct.pack('I', preset.N))
preset.freq.astype(np.float64).tofile(f)
preset.phase.astype(np.float64).tofile(f)
preset.k.astype(np.float64).tofile(f)
def write_freq_modul_data_to_file(file_name, freq_ampl, freq_freq, freq_offset):
with open(file_name + '.fm.preset', 'wb') as f:
freq_ampl.astype(np.float64).tofile(f)
freq_freq.astype(np.float64).tofile(f)
freq_offset.astype(np.float64).tofile(f)
def write_k_modul_data_to_file(file_name, k_ampl, k_freq, k_offset):
with open(file_name + '.km.preset', 'wb') as f:
k_ampl.astype(np.float64).tofile(f)
k_freq.astype(np.float64).tofile(f)
k_offset.astype(np.float64).tofile(f)
def save_plot(path, ext='png', close=True):
directory = os.path.split(path)[0]
filename = "%s.%s" % (os.path.split(path)[1], ext)
if directory == '':
directory = '.'
if not os.path.exists(directory):
os.makedirs(directory)
savepath = os.path.join(directory, filename)
# print("Saving figure to '%s'" % savepath)
plt.savefig(savepath)
if close:
plt.close()
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Arrays/Find_common_elements_in_three_sorted_arrays/solution.py | abhaydhiman/Pyalgo | 1 | 191603 | <reponame>abhaydhiman/Pyalgo
def my_func(arr1, arr2, arr3):
ln1 = len(arr1)
ln2 = len(arr2)
ln3 = len(arr3)
ptr1 = 0
ptr2 = 0
ptr3 = 0
while ptr1 < ln1 and ptr2 < ln2 and ptr3 < ln3:
if arr1[ptr1] < arr2[ptr2]:
if arr3[ptr3] < arr2[ptr2]:
arr1[ptr1] = -1
arr3[ptr3] = -1
ptr3 += 1
ptr1 += 1
else:
arr1[ptr1] = -1
ptr1 += 1
elif arr2[ptr2] < arr3[ptr3]:
if arr1[ptr1] < arr3[ptr3]:
arr1[ptr1] = -1
arr2[ptr2] = -1
ptr1 += 1
ptr2 += 1
else:
arr2[ptr2] = -1
ptr2 += 1
elif arr3[ptr3] < arr1[ptr1]:
if arr2[ptr2] < arr1[ptr1]:
arr2[ptr2] = -1
arr3[ptr3] = -1
ptr2 += 1
ptr3 += 1
else:
arr3[ptr3] = -1
ptr3 += 1
else:
ptr1 += 1
ptr2 += 1
ptr3 += 1
for i in arr1:
if i != -1:
print(i, end=' ')
ls1 = [1, 5, 5]
ls2 = [3, 4, 5, 5, 10]
ls3 = [5, 5, 10, 20]
my_func(ls1, ls2, ls3)
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WhatsAppManifest/automator/android/__init__.py | riquedev/WhatsAppManifest | 15 | 32917 | """
Module responsible for all automation related to the device
"""
__author__ = '<NAME>'
__copyright__ = 'Copyright 2020, WhatsAppManifest'
from WhatsAppManifest.automator.android.phone import AndroidPhone
from WhatsAppManifest.automator.android.contacts import AndroidContacts
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option_c.py | wrosecrans/colormap | 231 | 4210 |
from matplotlib.colors import LinearSegmentedColormap
from numpy import nan, inf
# Used to reconstruct the colormap in viscm
parameters = {'xp': [-5.4895292543686764, 14.790571669586654, 82.5546687431056, 29.15531114139253, -4.1316769886951761, -13.002076438907238],
'yp': [-35.948168839230306, -42.273376159885785, -28.845467523197698, 52.03426124197, 36.832712600868973, 40.792291220556734],
'min_JK': 16.8314150305,
'max_JK': 95}
cm_data = [[ 5.03832136e-02, 2.98028976e-02, 5.27974883e-01],
[ 6.35363639e-02, 2.84259729e-02, 5.33123681e-01],
[ 7.53531234e-02, 2.72063728e-02, 5.38007001e-01],
[ 8.62217979e-02, 2.61253206e-02, 5.42657691e-01],
[ 9.63786097e-02, 2.51650976e-02, 5.47103487e-01],
[ 1.05979704e-01, 2.43092436e-02, 5.51367851e-01],
[ 1.15123641e-01, 2.35562500e-02, 5.55467728e-01],
[ 1.23902903e-01, 2.28781011e-02, 5.59423480e-01],
[ 1.32380720e-01, 2.22583774e-02, 5.63250116e-01],
[ 1.40603076e-01, 2.16866674e-02, 5.66959485e-01],
[ 1.48606527e-01, 2.11535876e-02, 5.70561711e-01],
[ 1.56420649e-01, 2.06507174e-02, 5.74065446e-01],
[ 1.64069722e-01, 2.01705326e-02, 5.77478074e-01],
[ 1.71573925e-01, 1.97063415e-02, 5.80805890e-01],
[ 1.78950212e-01, 1.92522243e-02, 5.84054243e-01],
[ 1.86212958e-01, 1.88029767e-02, 5.87227661e-01],
[ 1.93374449e-01, 1.83540593e-02, 5.90329954e-01],
[ 2.00445260e-01, 1.79015512e-02, 5.93364304e-01],
[ 2.07434551e-01, 1.74421086e-02, 5.96333341e-01],
[ 2.14350298e-01, 1.69729276e-02, 5.99239207e-01],
[ 2.21196750e-01, 1.64970484e-02, 6.02083323e-01],
[ 2.27982971e-01, 1.60071509e-02, 6.04867403e-01],
[ 2.34714537e-01, 1.55015065e-02, 6.07592438e-01],
[ 2.41396253e-01, 1.49791041e-02, 6.10259089e-01],
[ 2.48032377e-01, 1.44393586e-02, 6.12867743e-01],
[ 2.54626690e-01, 1.38820918e-02, 6.15418537e-01],
[ 2.61182562e-01, 1.33075156e-02, 6.17911385e-01],
[ 2.67702993e-01, 1.27162163e-02, 6.20345997e-01],
[ 2.74190665e-01, 1.21091423e-02, 6.22721903e-01],
[ 2.80647969e-01, 1.14875915e-02, 6.25038468e-01],
[ 2.87076059e-01, 1.08554862e-02, 6.27294975e-01],
[ 2.93477695e-01, 1.02128849e-02, 6.29490490e-01],
[ 2.99855122e-01, 9.56079551e-03, 6.31623923e-01],
[ 3.06209825e-01, 8.90185346e-03, 6.33694102e-01],
[ 3.12543124e-01, 8.23900704e-03, 6.35699759e-01],
[ 3.18856183e-01, 7.57551051e-03, 6.37639537e-01],
[ 3.25150025e-01, 6.91491734e-03, 6.39512001e-01],
[ 3.31425547e-01, 6.26107379e-03, 6.41315649e-01],
[ 3.37683446e-01, 5.61830889e-03, 6.43048936e-01],
[ 3.43924591e-01, 4.99053080e-03, 6.44710195e-01],
[ 3.50149699e-01, 4.38202557e-03, 6.46297711e-01],
[ 3.56359209e-01, 3.79781761e-03, 6.47809772e-01],
[ 3.62553473e-01, 3.24319591e-03, 6.49244641e-01],
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[ 9.59275646e-01, 9.21406537e-01, 1.51566019e-01],
[ 9.56808068e-01, 9.28152065e-01, 1.52409489e-01],
[ 9.54286813e-01, 9.34907730e-01, 1.52921158e-01],
[ 9.51726083e-01, 9.41670605e-01, 1.52925363e-01],
[ 9.49150533e-01, 9.48434900e-01, 1.52177604e-01],
[ 9.46602270e-01, 9.55189860e-01, 1.50327944e-01],
[ 9.44151742e-01, 9.61916487e-01, 1.46860789e-01],
[ 9.41896120e-01, 9.68589814e-01, 1.40955606e-01],
[ 9.40015097e-01, 9.75158357e-01, 1.31325517e-01]]
test_cm = LinearSegmentedColormap.from_list(__file__, cm_data)
if __name__ == "__main__":
import matplotlib.pyplot as plt
import numpy as np
try:
from viscm import viscm
viscm(test_cm)
except ImportError:
print("viscm not found, falling back on simple display")
plt.imshow(np.linspace(0, 100, 256)[None, :], aspect='auto',
cmap=test_cm)
plt.show()
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] |
normal_forms/examples/normal_form/07.py | joepatmckenna/normal_forms | 0 | 35474 | <reponame>joepatmckenna/normal_forms
from normal_forms import normal_form
import sympy
# Murdock, Normal Forms and Unfoldings of Local Dynamical Systems, Example 4.5.24
def f(x, y, z):
f1 = 6 * x + x**2 + x * y + x * z + y**2 + y * z + z**2
f2 = 2 * y + x**2 + x * y + x * z + y**2 + y * z + z**2
f3 = 3 * z + x**2 + x * y + x * z + y**2 + y * z + z**2
return f1, f2, f3
h = normal_form(f, (0, 0, 0), 2)
# coeff of z**2
print h.fun[0].coeff(h.jet.var[2]**2)
| [
1,
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2
] |
lib/viscoelasticity.py | mforstenhaeusler/AFMviscoelastic | 2 | 55445 | <gh_stars>1-10
"""
Created on Wed Jun 10th 2020
@author: <NAME>
Description: This library contains core algorithms to analyse and fit viscoelstic models to different AFM data.
"""
import numpy as np
from lmfit import Parameters, minimize, report_fit
def G_storage(omega, Ge, G, tau):
"""
Description:
The function calculates the Storage Modulus G' using G as input.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param omega: array of float
array of Frequency values
:param Ge: float
equilibrium G
:param G: array of loats
Relaxance values
:param tau: array of floats
characteristic time
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function output:
:return: G_prime: array of loats
array of Storage Modulus G'
"""
G_prime = np.zeros(len(omega))
for i in range(len(omega)):
G_prime[i] = Ge + sum((G[:] * pow(omega[:], 2) * pow(tau[:], 2)) / (1.0 + (pow(omega[i], 2) * pow(tau[:], 2))))
return G_prime
def J_storage(omega, Jg, J, tau):
"""
Description:
The function calculates the Storage Modulus using J as input.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param omega: array of floats
array of Frequency values
:param Jg: float
classy compliance
:param J: array of floats
Compliance values
:param tau: array of floats
characteristic time
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: J_prime: array of floats
Storage Modulus J'
"""
J_prime = np.zeros(len(omega))
for i in range(len(omega)):
if len(J) > 1:
J_prime[i] = Jg + sum(J[:] / (1.0 + (pow(omega[i], 2) * pow(tau[:], 2))))
else:
J_prime[i] = Jg + (J / (1.0 + (pow(omega[i], 2) * pow(tau, 2))))
return J_prime
def G_loss(omega, Ge, G, tau):
"""
Description:
The function calculates the Loss Modulus using G as input.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param omega: array of floats
array of Frequency values
:param Ge: float
equilibrium G
:param G: array of floats
Relaxance values
:param tau: array of floats
characteristic time
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: G_dprime: array of floats
Loss Modulus G''
"""
G_dprime = np.zeros(len(omega))
for i in range(len(omega)):
G_dprime[i] = Ge + sum((G[:]*omega[:]*tau[:])/(1.0 + (pow(omega[i], 2) * pow(tau, 2))))
return G_dprime
def J_loss(omega, Jg, J, tau, phi=0):
"""
Description:
The function calculates the Loss Modulus using J as input.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param omega: array of floats
array of Frequency values
:param Jg: float
classy compliance
:param J: array of floats
Compliance values
:param tau: array of floats
characteristic time
:param phi: float
steady-state/ stead-flow fluidity
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: J_dprime: array of floats
Loss Modulus J''
"""
J_dprime = np.zeros(len(omega))
for i in range(len(omega)): # for Gen. Kelvin-Voigt with arbitrary number of arms
if len(J) > 1:
J_dprime[i] = sum(J[:] * omega[i] * tau[:] / (1.0 + (pow(omega[i], 2) * pow(tau[:], 2)))) + phi / omega[i]
else: # for SLS
J_dprime[i] = (J * omega[i] * tau / (1.0 + (pow(omega[i], 2) * pow(tau, 2)))) + phi / omega[i]
return J_dprime
def theta_loss(input_param, omega, p1, p2, p3, phi=0):
"""
Description:
The function calculates the Loss Angle, theta, using J oro G as input.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param input_param: string
either 'G' or 'J'
:param omega: array of floats
array of Frequency values
:param p1: float
placeholder variable for classy compliance or equivalent G
:param p2: array of floats
placeholder variable for Compliance or Relaxance values
:param p3: array of floats
placeholder variable fot characteristic time
:param phi: float
steady-state/steady-flow fluidity
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: theta: array of floats
Loss Modulus theta
"""
if input_param == 'G':
Gloss = G_loss(omega, p1, p2, p3)
Gstorage = G_storage(omega, p1, p2, p3)
theta = np.arctan(Gloss / Gstorage) * 180 / np.pi
if input_param == 'J':
Jloss = J_loss(omega, p1, p2, p3, phi=0)
Jstorage = J_storage(omega, p1, p2, p3)
theta = np.arctan(Jloss / Jstorage) * 180 / np.pi
return theta
def response(model, t, p1, p2, p3, phi = 0.0):
"""
Description:
The function calculates the Relaxance, Comppliance or Retardance in time.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param model: string
indicate either what to return, either 'Relaxance', 'Comppliance' or ’Retardance'
:param t: array of floats
time array
:param p1: float
placeholder variable for classy compliance or equivalent G
:param p2: array of floats
placeholder variable for Compliance or Relaxance values
:param p3: array of floats
placeholder variable fot characteristic time
:param phi: float
steady-state/steady-flow fluidity
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: res: array of floats
Relaxation, Comppliance or Retardance Modulus in time
"""
if model == 'Relaxances':
res = np.zeros(np.size(t))
if np.size(p2) == 1: # SLS
for i in range(np.size(t)):
res[i] = p1 + p2*np.exp(-t[i]/p3)
else: # Gen. Maxwell with arbitrary number of arms
for i in range(np.size(t)):
res[i] = p1 + sum(p2[:]*np.exp(-t[i]/p3[:]))
if model == 'Compliance':
res = np.zeros(np.size(t))
if np.size(p2) == 1: # SLS
for i in range(np.size(t)):
res[i] = p1 + p2*(1 - np.exp(-t[i]/p3)) + (phi*t[i])
else: # Gen. Kelvin-Voigt with arbitrary number of arms
for i in range(np.size(t)):
res[i] = p1 + sum(p2[:]*(1 - np.exp(-p2[i]/p3[:]))) + phi*t[i]
if model == 'Retardance':
res = np.zeros(np.size(t))
if np.size(p2) == 1: # SLS
for i in range(np.size(t)):
res[i] = p1 + p2/p3*np.exp(-t[i]/p3) + (phi)
else: # Gen. Kelvin Voigt with arbitrary number of arms
for i in range(np.size(t)):
res[i] = p1 + sum(p2[:]/p3[:]*(np.exp(-p2[i]/p3[:]))) + phi
return res
def log_scale(x, t, tr=0.1, st=1.0, nn = 10):
"""
Description:
The function scales an array equally in each decade, weighting it in logarithmic scale.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param x: array of floats
fitst array to be scaled
:param t: array of floats
time array to be scaled
:param tr:
:param st:
:param nn:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: np.array(x_log): array of floats
first array scaled in logaritmic scale
np.array(t_log): array of floats
time array scaled in logaritmic scale
"""
prints = 1
nt = len(t)
i =0
x_log = []
t_log = []
while i < nt-1:
if t[i] >= prints*tr and t[i]<=st :
x_log.append(x[i])
t_log.append(t[i])
prints = prints + 1
i = i + 1
if prints == nn:
tr = tr*10
prints = 1
return np.array(x_log), np.array(t_log)
def time_step(t):
"""
Description:
This function creates a time step from a time array.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param t: array of floats
time array
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: dt: float
time step dt
"""
dt = t[-1]/len(t)
return dt
def conv(model, t, F, p1, p2, p3):
"""
Description:
This function calculates the convolution of the retardance, U, or relaxance, Q, and the force, F, used to verifiy
the RHS to LHS side of Equ. 13 from Calculation of Standard Viscoelastic.....
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param model: string
state Viscoelastic model used, e.g. 'Gen. Kelvin-Voigt', 'Gen. Maxwell'
:param t: array of floats
time array
:param F: array of floats
Force array
:param p1: float
placeholder variable for Ge or Jg
:param p2: array of floats
placeholder variable for G or J
:param p3: array of floats
placeholder variable for charactersitic time, tau
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: c: array of float
Returns convolution U*F of retardance, U and force, F or the convolution Q*F
of relaxance, Q and force, F
"""
dt = time_step(t) # calculated delta from time array to pass into discrete convolution to give
# it information about the timestep
if model == 'Gen. Kelvin-Voigt':
U = np.zeros(len(t))
for i in range(len(t)): # Retardance function (Equ 12) from Calculation of Standard...
U[i] = sum(p2[:] / p3[:] * np.exp(-t[i] / p3[:]))
c = np.convolve(U, F, mode='full') * dt # calculates convolution, dt adjust for timestep
c = c[range(len(F))] + p1 * F # only use range of given data adds Jg*F
# if model == 'Gen. Maxwell': # convolutioin needs to be solved
# U = np.zeros(len(t))
# for i in range(len(t)):
# U[i] = sum((*np.exp(-t[i]/tau_v[:]))
# c = np.convolve(U, F, mode='full')*dt # calculates convolution, dt adjust for timestep
# c = c[range(len(F))] + Jg*F # only use range of given data adds Jg*F
return c
def conv_fitting_log(params, t, F, tip, arms, dt):
"""
Description:
The function is the residual function of the tip data and the convolution function used for the
NLS fitting procedure using logarithmic scaled data in time.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param params: dict file
Dictionary file storing the parameters
:param t: array of floats
time array scaled in logarithmic scale
:param F: array of floats
Force array sclaed in logarithmic scale
:param tip: array of floats
Tip position or deformation array scaled in logarithmic scale
:param arms: int
Number of amrs
:param dt: float
time step of logarithmic sclaed time array
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: residual: array of float
Returns the residual of the the experimental/simulated deformation data
and the convolution which is minimized by the lmfit algorithm minimize
in the NLS_fit algoritm
"""
J = np.zeros(arms) # initialize J array
tau = np.zeros(arms) # initialize tau array
# loads oarameters from dictionary file into J and tau array
p = params.valuesdict()
Jg = p['Jg']
J[0] = p['J1']
tau[0] = p['tau1']
if arms > 1:
J[1] = p['J2']
tau[1] = p['tau2']
if arms > 2:
J[2] = p['J3']
tau[2] = p['tau3']
if arms > 3:
J[3] = p['J4']
tau[3] = p['tau4']
if arms > 4:
J[4] = p['J5']
tau[4] = p['tau5']
U = np.zeros(len(t))
for i in range(len(t)): # Retardance function (Equ 12) from Calculation of Standard...
U[i] = sum(J[:] / tau[:] * np.exp(-t[i] / tau[:]))
model = np.convolve(U, F, mode='full') * dt # convolves retardance with F and indicates
# time step to discrete convolution
model = model[range(np.size(F))] + Jg * F # selects range of data of size of force array
# and adds the product of classy compliance
# and force array
residual = (tip - model) / tip # calculates the residual to be minimized by teh NLS_fit
# algorithm
return residual
def conv_fitting_time(params, t, F, tip, arms, dt):
"""
Description:
The function is the residual function of the tip data and the convolution function used for the
NLS fitting procedure using scaled data in time.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param params: dict file
Dictionary file storing the parameters
:param t: array of floats
time array scaled
:param F: array of floats
Force array sclaed
:param tip: array of floats
Tip position or deformation array
:param arms: int
Number of amrs
:param dt: float
time step sclaed time array
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function ouput:
:return: residual: array of float
Returns the residual of the the experimental/simulated deformation data
and the convolution which is minimized by the lmfit algorithm minimize
in the NLS_fit algoritm
"""
J = np.zeros(arms) # initialize J array
tau = np.zeros(arms) # initialize tau array
# loads oarameters from dictionary file into J and tau array
p = params.valuesdict()
Jg = p['Jg']
J[0] = p['J1']
tau[0] = p['tau1']
if arms > 1:
J[1] = p['J2']
tau[1] = p['tau2']
if arms > 2:
J[2] = p['J3']
tau[2] = p['tau3']
if arms > 3:
J[3] = p['J4']
tau[3] = p['tau4']
if arms > 4:
J[4] = p['J5']
tau[4] = p['tau5']
U = np.zeros(len(t))
for i in range(len(t)): # Retardance function (Equ 12) from Calculation of Standard...
U[i] = sum(J[:] / tau[:] * np.exp(-t[i] / tau[:]))
model = np.convolve(U, F, mode='full') * dt # convolves retardance with F and indicates
# time step to discrete convolution
model = model[range(np.size(F))] + Jg * F # selects range of data of size of force array
# and adds the product of classy compliance
# and force array
residual = (tip - model) / tip # calculates the residual to be minimized by teh NLS_fit
# algorithm
return residual
def NLS_fit(technique, Jg, J, tau, arms, t, tip, F, alfa, t_res, t_exp):
"""
Descripition:
Nonlinear Least Sqaure Fitting algorithm used to fit viscoelastic model parameter to experimental or simulated
data/AFM data.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameters:
:param technique: string
using Log sclaed or time scaled data
:param Jg: float
classy compliance
:param J: array of floats
Compliance
:param tau: array of floats
characteristic time
:param arms: int
number of model arms
:param t: array of floats
time array
:param tip: array of floats
tip position over time
:param F: array of floats
tip position over time
:param alfa: float
Constant converting stress/strain to force/defromation by adjusting for tip geometry
:param t_res: float
time resolution of experiment, used to log scale the data
:param t_exp: float
final time of experiment
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Return, function outout:
:return: Jg_fit: float
classy compliance fitted
J_fit: float
fitted complince terms
tau_fit: float
fitted characteristic time
"""
params = Parameters() # initializes Parameters dictionary file
# algorthimen initialzes parameters dictionary with viscoelastic model parameters
params.add('Jg', value=Jg, min=0)
if arms > 0:
params.add('J1', value=J[0], min=0)
params.add('tau1', value=tau[0], min=0)
if arms > 1:
params.add('J2', value=J[1], min=0)
params.add('tau2', value=tau[1], min=0)
if arms > 2:
params.add('J3', value=J[2], min=0)
params.add('tau3', value=tau[2], min=0)
if arms > 3:
params.add('J4', value=J[3], min=0)
params.add('tau4', value=tau[3], min=0)
if arms > 4:
params.add('J5', value=J[4], min=0)
params.add('tau5', value=tau[4], min=0)
if technique == 0: # log scale
tip_norm = alfa * tip ** 1.5 # RHS of equation ..., underlying fitting data
tip_norm_log, t_log = log_scale(tip_norm, t, t_res, t_exp) # normalized tip position scaled equally in per decade
F_log, _ = log_scale(F, t, t_res, t_exp) # tip-sample Force scaled scaled equally in per decad
dt = time_step(t_log) # calculated time step from input time array, necessary for discrete convolution
result = minimize(conv_fitting_log, params, args=(t_log, F_log, tip_norm_log, arms, dt), method='leastsq') # non-linear least sqaure fitting procedure minimizing the convolution function
print(report_fit(result)) # prints the fit report
if technique == 1: # time scale
tip_norm = alfa * tip ** 1.5
dt = time_step(t)
result = minimize(conv_fitting_time, params, args=(t, F, tip_norm, arms, dt), method='leastsq')
print(report_fit(result))
# stores fitted parameters
Jg_fit = result.params['Jg'].value
J_fit = np.zeros(arms)
tau_fit = np.zeros(arms)
J_fit[0] = result.params['J1'].value
tau_fit[0] = result.params['tau1'].value
if arms > 1:
J_fit[1] = result.params['J2'].value
tau_fit[1] = result.params['tau2'].value
if arms > 2:
J_fit[2] = result.params['J3'].value
tau_fit[2] = result.params['tau3'].value
if arms > 3:
J_fit[3] = result.params['J4'].value
tau_fit[3] = result.params['tau4'].value
if arms > 4:
J_fit[4] = result.params['J5'].value
tau_fit[4] = result.params['tau5'].value
return Jg_fit, J_fit, tau_fit
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] |
application/utils/globals.py | topix-hackademy/social-listener | 12 | 164224 | <reponame>topix-hackademy/social-listener
configuration = None
def init():
global configuration
configuration = None
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AutoGraphModel.py | shiqitao/AutoGraph | 0 | 26332 | <filename>AutoGraphModel.py
import argparse
import os
import time
import torch
from filelock import FileLock
from ModelAPPNP import main_model_appnp
from ModelAPPNP2 import main_model_appnp as main_model_appnp_2
from ModelAPPNP3 import main_model_appnp as main_model_appnp_3
from ModelAPPNP4 import main_model_appnp as main_model_appnp_4
from ModelGAT import main_model_gat
from ModelGAT2 import main_model_gat as main_model_gat_2
from ModelGAT3 import main_model_gat as main_model_gat_3
from ModelGAT4 import main_model_gat as main_model_gat_4
from ModelGCN import main_model_gcn
from ModelGCN2 import main_model_gcn as main_model_gcn_2
from ModelGCN3 import main_model_gcn as main_model_gcn_3
from ModelGCN4 import main_model_gcn as main_model_gcn_4
from ModelGCNOld import main_model_gcn_old
from Result import Result
from tools import save_data, load_data, file_path
if __name__ == "__main__":
start_time = time.time()
time_budget = float("inf")
parser = argparse.ArgumentParser()
parser.add_argument("--index", type=int)
parser.add_argument("--file_param", type=str)
parser.add_argument("--file_ready", type=str)
parser.add_argument("--file_result", type=str)
parser.add_argument("--file_lock", type=str)
parser.add_argument("--if_kill", type=int)
args = parser.parse_args()
if torch.cuda.is_available():
torch.zeros(1).cuda()
with FileLock(args.file_lock):
save_data(args.file_ready, os.getpid())
aoe_data = None
while True:
if aoe_data is None and os.path.exists(file_path("AOE.data")):
with FileLock(file_path("AOE.ready")):
aoe_data = load_data(file_path("AOE.data"))
if os.path.exists(args.file_param):
if aoe_data is None and os.path.exists(file_path("AOE.data")):
with FileLock(file_path("AOE.ready")):
aoe_data = load_data(file_path("AOE.data"))
start_time = time.time() # 重置开始时间
with FileLock(args.file_lock):
param = load_data(args.file_param)
if param.time_budget is not None:
time_budget = param.time_budget # 重置时间限制
try:
if param.model == "ModelGCNOld":
result = main_model_gcn_old(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
# 一定要按这个顺序 save -> remove
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGAT":
result = main_model_gat(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
# 一定要按这个顺序 save -> remove
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGCN":
result = main_model_gcn(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelAPPNP":
result = main_model_appnp(
data=aoe_data,
K=param.param[0],
alpha=param.param[1],
hidden=param.param[2],
activation=param.param[3],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGAT2":
result = main_model_gat_2(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
# 一定要按这个顺序 save -> remove
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGCN2":
result = main_model_gcn_2(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelAPPNP2":
result = main_model_appnp_2(
data=aoe_data,
K=param.param[0],
alpha=param.param[1],
hidden=param.param[2],
activation=param.param[3],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGAT3":
result = main_model_gat_3(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
# 一定要按这个顺序 save -> remove
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGCN3":
result = main_model_gcn_3(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelAPPNP3":
result = main_model_appnp_3(
data=aoe_data,
K=param.param[0],
alpha=param.param[1],
hidden=param.param[2],
activation=param.param[3],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGAT4":
result = main_model_gat_4(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
# 一定要按这个顺序 save -> remove
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelGCN4":
result = main_model_gcn_4(
data=aoe_data,
num_layers=param.param[0],
hidden_list=param.param[1],
activation=param.param[2],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
elif param.model == "ModelAPPNP4":
result = main_model_appnp_4(
data=aoe_data,
K=param.param[0],
alpha=param.param[1],
hidden=param.param[2],
activation=param.param[3],
if_all=True
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
else:
raise ValueError("Model name error: {0}".format(param[0]))
except RuntimeError:
if args.if_kill == 1:
break
else:
result = Result(
result=None,
loss_train=None,
loss_valid=None,
acc_train=None,
acc_valid=None,
epoch=None,
)
with FileLock(args.file_lock):
save_data(args.file_result, result)
os.remove(args.file_param)
if time.time() - start_time >= time_budget:
break
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] |
laaso/command.py | srrao-git/amlFilesystem-hydrator | 2 | 1607175 | #
# laaso/command.py
#
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#
'''
Implement the Command class which handles @command decorators.
'''
import functools
import inspect
import logging
import pprint
import sys
import laaso.output
from laaso.util import expand_item_pformat
class Command():
'''
Manage decorators for an application.
These decorators enable exposing actions through
the command line by defining and decorating the
handler without touching other argument parsing.
'''
def __init__(self):
self._commands = dict() # key=name value=_Item
RESERVED_NAMES = ('actions',
'can_handle',
'commands',
'handle',
'print',
)
@property
def actions(self):
'''
Getter that returns a lexically-sorted list of
handler names.
'''
return sorted(self._commands.keys())
@classmethod
def _name_valid(cls, name):
'''
Return whether name is usable as a decoration.
Example:
command = Command()
@command._some_func
That is not valid, because '_some_func' begins with '_'.
We do not allow leading underscores to avoid exposing
class internals and to easily avoid accidently exposing
or shading the internals of this class. Similarly,
all public methods and attributes must be enumerated
in RESERVED_NAMES. See __getattr__() for this in action.
'''
if not isinstance(name, str):
return False
if not name:
return False
if name.startswith('_'):
return False
if name in cls.RESERVED_NAMES:
return False
return True
def __getattr__(self, name):
'''
If name is not internal to this class, treat it as a decorator.
'''
if not self._name_valid(name):
# Not a decoration
raise AttributeError("'%s' object has no attribute '%s'" % (type(self).__name__, name))
return functools.partial(self._decorate, name)
def _decorate(self, decorator, func):
'''
Decorate the named func. decorator is the name of the
decoration - eg:
command = Command()
@command.simple
def some_func():
generates _decorate('simple', 'some_func', some_func)
'''
printable = decorator.startswith('printable')
printable_raw = decorator.endswith('_raw') or ('_raw_' in decorator)
ci = _Item(decorator, func, printable=printable, printable_raw=printable_raw)
if not self._name_valid(ci.name):
raise ValueError("may not decorate using reserved name %r" % ci.name)
if ci.name in self._commands:
raise ValueError("duplicate command %r" % ci.name)
self._commands[ci.name] = ci
return ci.func
def _handle(self, doit, name, decorators, *args, **kwargs):
'''
Try provided decorators until one is found or there is nothing left to try.
'''
if isinstance(decorators, str):
return self._handle_one(doit, name, decorators, *args, **kwargs)
for decorator in decorators:
ret = self._handle_one(doit, name, decorator, *args, **kwargs)
if ret:
return ret
return False
def _handle_one(self, doit, name, decorator, *args, **kwargs):
'''
Invoke the registered handler for name.
If no handler is registered, return False.
If a handler is registered, return True.
'''
ci = self._commands.get(name, None)
if not (ci and ci.decorator == decorator):
return False
if args:
kls = ci.getclass()
if kls:
# We could check: if not isinstance(args[0], (kls,))
# here. That would allow decorating method X in a class C1
# and then executing X on class C2 which is a subclass of C1.
# The behavior there could be unexpected; even if C2 overloads X,
# C1.X is executed on the object. We already prevent redecorating
# an inherited method through the name collision check, so
# here we simply restrict the check to be exactly the
# target class rather than allowing subclasses.
if type(args[0]) is not kls: # pylint: disable=unidiomatic-typecheck
return False
if doit:
ret = ci.func(*args, **kwargs)
if ci.printable_raw:
laaso.output.capture()
if isinstance(ret, (list, set, tuple)):
for x in ret:
if isinstance(x, str):
sys.stdout.laaso_rawwrite(x+'\n')
else:
sys.stdout.laaso_rawwrite(expand_item_pformat(x, prefix='')+'\n')
else:
if isinstance(ret, str):
sys.stdout.laaso_rawwrite(ret+'\n')
else:
sys.stdout.laaso_rawwrite(expand_item_pformat(ret, prefix='')+'\n')
elif ci.printable:
if isinstance(ret, (list, set, tuple)):
for x in ret:
if isinstance(x, str):
print(x)
else:
print(expand_item_pformat(x, prefix=''))
else:
if isinstance(ret, str):
print(ret)
else:
print(expand_item_pformat(ret, prefix=''))
return True
@staticmethod
def print(item):
'''
print() the given item
'''
if isinstance(item, (list, set, tuple)):
for x in item:
if isinstance(x, str):
print(x)
else:
print(expand_item_pformat(x, prefix=''))
else:
if isinstance(item, str):
print(item)
else:
print(expand_item_pformat(item, prefix=''))
def handle(self, name, decorators, *args, **kwargs):
'''
Invoke the registered handler for name.
decorators may be a single string or something iterable.
If no handler is registered, return False.
If a handler is registered, return True.
If more than one decorator is provided, decorators are tried
in that order until a match is found. A return
of False indicates no match.
'''
return self._handle(True, name, decorators, *args, **kwargs)
def can_handle(self, name, decorators, *args, **kwargs):
'''
Like handle, but only returns whether the action would be handled.
'''
return self._handle(False, name, decorators, *args, **kwargs)
def commands(self, decorators=None):
'''
Return a dict of name:func pairs. If decorators is None,
returns everything. If decorators is a string, returns
only those items decorated with that string. Otherwise,
returns items whose decorators are in the given decorators
(assumes something like list, set, tuple, etc).
'''
ret = dict()
for name, ci in self._commands.items():
if decorators is None:
pass
elif isinstance(decorators, str):
if ci.decorator != decorators:
continue
else:
if ci.decorator not in decorators:
continue
ret[name] = ci.func
return ret
def itemfuncs(self):
'''
Return a dict of item_name : func
'''
return {ci.decorator : ci.func for ci in self._commands.values()}
class _Item():
'''
A single decorated call managed by Command
'''
def __init__(self, decorator, func, printable=False, printable_raw=False):
self.decorator = decorator
self.func = func
self.printable = printable
self.printable_raw = printable_raw
self.name = self.func.__name__
def __repr__(self):
return "%s(%r, %r, printable=%r, printable_raw=%r)" % (type(self).__name__, self.decorator, self.func, self.printable, self.printable_raw)
DEBUG_NAMESPACE = False
def getclass(self):
'''
Return the class of the decorated func. Returns None if
this is a top-level function.
'''
kls = None
members = inspect.getmembers(self.func)
g = dict() # global namespace in which self.func was defined
for m in members:
if m[0] == '__globals__':
g = m[1]
break
qns = self.func.__qualname__.split('.')
if len(qns) > 1:
kls = [g[qns[0]]] # use a list for debugging
if not isinstance(kls[0], (type,)):
raise RuntimeError("attempt to perform decorated handle on item not in the global namespace")
for qn in qns[1:-1]:
try:
kls.append(getattr(kls[-1], qn))
if not isinstance(kls[-1], (type,)):
raise RuntimeError("attempt to perform decorated handle on item not in the global namespace")
except AttributeError as exc:
if self.DEBUG_NAMESPACE:
logging.basicConfig(format="%(message)s", stream=sys.stderr)
logger = logging.getLogger()
logger.error("ERROR: %s.getclass\n"
" members\n%s\n"
" qns %s\n"
" qn %s\n"
" kls %s\n%s",
type(self).__name__,
'\n'.join([' '+x for x in pprint.pformat(members).splitlines()]),
qns,
qn,
kls, '\n'.join([' '+x for x in pprint.pformat(inspect.getmembers(kls[-1])).splitlines()]))
raise RuntimeError("attempt to perform decorated handle on item not in the global namespace") from exc
return kls[-1]
return None
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] |
2021/solutions/d8.py | phenom4n4n/advent-of-code-21 | 1 | 185938 | from typing import Dict, List, FrozenSet
from utils import run
len_to_num = {
2: 1,
4: 4,
3: 7,
7: 8,
}
Key = Dict[FrozenSet[str], int]
@run("\n")
def part1(data: List[str]) -> int:
total = 0
for line in data:
_, o = line.split(" | ", 2)
for n in o.split():
if len_to_num.get(len(n)):
total += 1
return total
def decode_line(line: str) -> Key:
i, _ = line.split(" | ", 2)
key = {}
reverse_key = {}
for sig in i.split():
if n := len_to_num.get(len(sig)):
frozen = frozenset(sig)
key[frozen] = n
reverse_key[n] = frozen
for sig in i.split():
frozen = frozenset(sig)
if frozen in key:
continue
if len(sig) == 5:
if frozen.issuperset(reverse_key[1]):
key[frozen] = 3
elif len(frozen.intersection(reverse_key[4])) == 3:
key[frozen] = 5
else:
key[frozen] = 2
elif len(sig) == 6:
if frozen.issuperset(reverse_key[4]):
key[frozen] = 9
elif frozen.issuperset(reverse_key[1]):
key[frozen] = 0
else:
key[frozen] = 6
return key
@run("\n")
def part2(data: List[str]) -> int:
total = []
for line in data:
key = decode_line(line)
_, o = line.split(" | ", 2)
nums = []
for sig in o.split():
num = str(key.get(frozenset(sig)))
nums.append(num)
final = int("".join(nums))
total.append(final)
return sum(total)
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stubs.min/System/Windows/Interop_parts/HwndSourceParameters.py | ricardyn/ironpython-stubs | 1 | 154570 | class HwndSourceParameters(object):
"""
Contains the parameters that are used to create an System.Windows.Interop.HwndSource object using the System.Windows.Interop.HwndSource.#ctor(System.Windows.Interop.HwndSourceParameters) constructor.
HwndSourceParameters(name: str)
HwndSourceParameters(name: str,width: int,height: int)
"""
def Equals(self,obj):
"""
Equals(self: HwndSourceParameters,obj: HwndSourceParameters) -> bool
Determines whether this structure is equal to a specified
System.Windows.Interop.HwndSourceParameters structure.
obj: The structure to be tested for equality.
Returns: true if the structures are equal; otherwise,false.
Equals(self: HwndSourceParameters,obj: object) -> bool
Determines whether this structure is equal to a specified object.
obj: The objects to be tested for equality.
Returns: true if the comparison is equal; otherwise,false.
"""
pass
def GetHashCode(self):
"""
GetHashCode(self: HwndSourceParameters) -> int
Returns the hash code for this System.Windows.Interop.HwndSourceParameters
instance.
Returns: A 32-bit signed integer hash code.
"""
pass
def SetPosition(self,x,y):
"""
SetPosition(self: HwndSourceParameters,x: int,y: int)
Sets the values that are used for the screen position of the window for the
System.Windows.Interop.HwndSource.
x: The position of the left edge of the window.
y: The position of the upper edge of the window.
"""
pass
def SetSize(self,width,height):
"""
SetSize(self: HwndSourceParameters,width: int,height: int)
Sets the values that are used for the window size of the
System.Windows.Interop.HwndSource.
width: The width of the window,in device pixels.
height: The height of the window,in device pixels.
"""
pass
def __eq__(self,*args):
""" x.__eq__(y) <==> x==y """
pass
@staticmethod
def __new__(self,name,width=None,height=None):
"""
__new__[HwndSourceParameters]() -> HwndSourceParameters
__new__(cls: type,name: str)
__new__(cls: type,name: str,width: int,height: int)
"""
pass
def __ne__(self,*args):
pass
AcquireHwndFocusInMenuMode=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the value that determines whether to acquire Win32 focus for the WPF containing window when an System.Windows.Interop.HwndSource is created.
Get: AcquireHwndFocusInMenuMode(self: HwndSourceParameters) -> bool
Set: AcquireHwndFocusInMenuMode(self: HwndSourceParameters)=value
"""
AdjustSizingForNonClientArea=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets a value that indicates whether to include the nonclient area for sizing.
Get: AdjustSizingForNonClientArea(self: HwndSourceParameters) -> bool
Set: AdjustSizingForNonClientArea(self: HwndSourceParameters)=value
"""
ExtendedWindowStyle=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the extended Microsoft Windows styles for the window.
Get: ExtendedWindowStyle(self: HwndSourceParameters) -> int
Set: ExtendedWindowStyle(self: HwndSourceParameters)=value
"""
HasAssignedSize=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets a value that indicates whether a size was assigned.
Get: HasAssignedSize(self: HwndSourceParameters) -> bool
"""
Height=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets a value that indicates the height of the window.
Get: Height(self: HwndSourceParameters) -> int
Set: Height(self: HwndSourceParameters)=value
"""
HwndSourceHook=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the message hook for the window.
Get: HwndSourceHook(self: HwndSourceParameters) -> HwndSourceHook
Set: HwndSourceHook(self: HwndSourceParameters)=value
"""
ParentWindow=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the window handle (HWND) of the parent for the created window.
Get: ParentWindow(self: HwndSourceParameters) -> IntPtr
Set: ParentWindow(self: HwndSourceParameters)=value
"""
PositionX=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the left-edge position of the window.
Get: PositionX(self: HwndSourceParameters) -> int
Set: PositionX(self: HwndSourceParameters)=value
"""
PositionY=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the upper-edge position of the window.
Get: PositionY(self: HwndSourceParameters) -> int
Set: PositionY(self: HwndSourceParameters)=value
"""
RestoreFocusMode=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets how WPF handles restoring focus to the window.
Get: RestoreFocusMode(self: HwndSourceParameters) -> RestoreFocusMode
Set: RestoreFocusMode(self: HwndSourceParameters)=value
"""
TreatAncestorsAsNonClientArea=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Get: TreatAncestorsAsNonClientArea(self: HwndSourceParameters) -> bool
Set: TreatAncestorsAsNonClientArea(self: HwndSourceParameters)=value
"""
TreatAsInputRoot=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Get: TreatAsInputRoot(self: HwndSourceParameters) -> bool
Set: TreatAsInputRoot(self: HwndSourceParameters)=value
"""
UsesPerPixelOpacity=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets a value that declares whether the per-pixel opacity of the source window content is respected.
Get: UsesPerPixelOpacity(self: HwndSourceParameters) -> bool
Set: UsesPerPixelOpacity(self: HwndSourceParameters)=value
"""
UsesPerPixelTransparency=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Get: UsesPerPixelTransparency(self: HwndSourceParameters) -> bool
Set: UsesPerPixelTransparency(self: HwndSourceParameters)=value
"""
Width=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets a value that indicates the width of the window.
Get: Width(self: HwndSourceParameters) -> int
Set: Width(self: HwndSourceParameters)=value
"""
WindowClassStyle=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the Microsoft Windows class style for the window.
Get: WindowClassStyle(self: HwndSourceParameters) -> int
Set: WindowClassStyle(self: HwndSourceParameters)=value
"""
WindowName=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the name of the window.
Get: WindowName(self: HwndSourceParameters) -> str
Set: WindowName(self: HwndSourceParameters)=value
"""
WindowStyle=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets or sets the style for the window.
Get: WindowStyle(self: HwndSourceParameters) -> int
Set: WindowStyle(self: HwndSourceParameters)=value
"""
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] |
kinematics/Trajectory.py | lovelykite/COSRA | 0 | 88539 | import numpy as np
class Trajectory:
def __init__(self):
pass
@classmethod
def LSPB(cls, q0, qf, tf, tb, t_step=0.07):
q0 = np.array(q0)
qf = np.array(qf)
if np.allclose(q0, qf):
t = [0.0]
q_pos = [qf]
return q_pos, t
# Define coefficients
ab = (q0 - qf) / (tb - tf) / tb
A = np.array([[tb, -tb, 0.0, 1.0, -1.0, 0.0],
[0.0, -(tf - tb), tf - tb, 0.0, -1.0, 1.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, tf, 0.0, 0.0, 1.0]])
b = np.block([[-ab * tb ** 2 / 2], [ab * (tf - tb) ** 2 / 2], [np.zeros_like(q0)], [q0], [ab * tf],
[qf + ab * tf ** 2 / 2]])
coeff = np.linalg.inv(A).dot(b)
C1 = coeff[0]
C2 = coeff[1]
C3 = coeff[2]
C4 = coeff[3]
C5 = coeff[4]
C6 = coeff[5]
# Calculate trajectories
t = np.arange(start=0.0, stop=tf, step=t_step)
t1 = t[t < tb].reshape(-1, 1)
t2 = t[(tb <= t) & (t < tf - tb)].reshape(-1, 1)
t3 = t[tf - tb <= t].reshape(-1, 1)
# Combine joint trajectories
traj1 = ab / 2 * t1 ** 2 + C1 * t1 + C4
traj2 = C2 * t2 + C5
traj3 = -ab / 2 * t3 ** 2 + C3 * t3 + C6
q_pos = np.concatenate((traj1, traj2, traj3))
assert ~np.isnan(t).any()
assert ~np.isnan(q_pos).any()
return q_pos, t
if __name__ == '__main__':
pass
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src/kotify/fabric/__init__.py | kotify/kotify.fabric | 0 | 134663 | from ._core import Collection, local, task
__all__ = ["task", "Collection", "local"]
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sdk/eventhub/azure-eventhubs/azure/eventhub/eventprocessor/utils.py | gautam714/azure-sdk-for-python | 0 | 19017 | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# -----------------------------------------------------------------------------------
import asyncio
def get_running_loop():
try:
return asyncio.get_running_loop()
except AttributeError: # 3.5 / 3.6
loop = asyncio._get_running_loop() # pylint: disable=protected-access
if loop is None:
raise RuntimeError('No running event loop')
return loop
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cmdb/hosts/models.py | fox0014/cmdb | 0 | 94772 | <filename>cmdb/hosts/models.py
# This is an auto-generated Django model module.
# You'll have to do the following manually to clean this up:
# * Rearrange models' order
# * Make sure each model has one field with primary_key=True
# * Make sure each ForeignKey and OneToOneField has `on_delete` set to the desired behavior
# * Remove `managed = False` lines if you wish to allow Django to create, modify, and delete the table
# Feel free to rename the models, but don't rename db_table values or field names.
from django.db import models
class Asset(models.Model):
id = models.CharField(max_length=32, primary_key=True)
dc_id = models.CharField(max_length=32)
hard_type = models.IntegerField()
hard_name = models.CharField(max_length=100, blank=True, null=True)
hard_info = models.CharField(max_length=255, blank=True, null=True)
asset_tag = models.CharField(max_length=100, blank=True, null=True)
class Meta:
managed = False
db_table = 'asset'
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demo/Drain_openstack.py | hcgcarry/logparser | 1 | 113134 | #!/usr/bin/env python
import sys
import os
sys.path.append('../')
from logparser import Drain
from createSessonLog.createSessionLog import LogParser
from shutil import copyfile
from preprocess.preprocessLog import preprocess
type = sys.argv[1]
if len(sys.argv) < 2:
print("usage:python Drain_openstack.py {normal|abnormal}")
if type == "normal":
curIndex = 0
else:
curIndex = 1
output_dir = 'Drain_result_openstack/' # The output directory of parsing results
input_path_list = ['../logs/openstack_label/openstack_normal1.log_preprocess', '../logs/openstack_label/openstack_abnormal.log_preprocess']
print("path:",os.path.split(input_path_list[curIndex]))
input_dir ,log_file= os.path.split(input_path_list[curIndex])
copyfile(input_path_list[curIndex], output_dir+log_file)
#log format
log_format = '<Logrecord> <Date> <Time> <Pid> <Level> <Component> \[<ADDR>\] <Instance> <Content>'
# Regular expression list for optional preprocessing (default: [])
regex = [
r'((\d+\.){3}\d+,?)+', r'/.+?\s', r'\d+' , r'\d'
]
#instance: 0f079bdd-4117-4f6a-8b49-f3fb720b483c]
st = 0.5 # Similarity threshold
depth = 5 # Depth of all leaf nodes
parser = Drain.LogParser(log_format, indir=input_dir, outdir=output_dir, depth=depth, st=st, rex=regex,keep_para=False)
parser.parse(log_file)
print("output_dir:",output_dir)
'''
log_format_list = ['<LineId> <Time> <Id> <Command> <Content> <EventId> <EventTemplate>']
logfilePath = output_dir
createSessionLogparser = LogParser(log_format, indir=output_dir, outdir=output_dir, depth=depth, st=st, rex=regex,keep_para=False)
createSessionLogparser.parse(log_file+'_structured.csv')
'''
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stick_mouse_pos.py | sevenjames/fpie-scripts | 0 | 55621 | """
Script to control mouse cursor with gamepad stick.
Specifically: right stick x axis controls mouse x position.
Requires FreePIE: https://github.com/AndersMalmgren/FreePIE
2020-06-11 JAO
"""
if starting:
"""This block only runs once."""
import time
joy_id = 0 # gamepad device number
x_axis_mult = 0.25 # pos mode movement multiplier
def axis_to_mouse_pos(axis):
"""Position Mode. Stick deflection sets relative mouse cursor position."""
return filters.delta(axis) * x_axis_mult
frame_start = time.time() # setup frame limiter
mouse.deltaX = axis_to_mouse_pos(axis=joystick[joy_id].xRotation)
time.sleep(max(1./60 - (time.time() - frame_start), 0)) # frame limiter
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app/__init__.py | Jotasenpai/DigitalMediaStoreRESTfull | 0 | 1540 | import logging
import os
from flask import Flask
from flask_cors import CORS
from app.extensions import api
from app.extensions.database import db
from app.extensions.schema import ma
from app.views import albums, artists, hello, tracks
def create_app(config, **kwargs):
logging.basicConfig(level=logging.INFO)
app = Flask(__name__, **kwargs)
CORS(app, resources={r"/api/*": {"origins": "*"}})
app.config.from_object(config)
# app.url_map.strict_slashes = False
with app.app_context():
api.init_app(app)
db.init_app(app)
db.create_all()
ma.init_app(app)
api.register_blueprint(hello.blp)
api.register_blueprint(artists.blp)
api.register_blueprint(albums.blp)
api.register_blueprint(tracks.blp)
try:
os.makedirs(app.instance_path)
except OSError:
pass
return app
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src/phl_budget_data/utils.py | PhilaController/phl-budget-data | 1 | 133150 | <reponame>PhilaController/phl-budget-data
import inspect
from functools import wraps
import pandas as pd
from . import DATA_DIR
def determine_file_name(f, **kwargs):
"""Given a function, determine the matching file name."""
# The parts
name = f.__name__
tag = f.__module__.split(".")[-1]
# The base of the file name
filename_base = "-".join(name.split("_")[1:])
# The output folder
output_folder = DATA_DIR / "historical" / tag
# Required params
if hasattr(f, "model"):
# Get the params
schema = f.model.schema()
# Do all iterations of params
param_values = [kwargs.get(k) for k in schema["required"]]
if any(value is None for value in param_values):
raise ValueError("Missing required params")
# The filename
filename = filename_base + "-" + "-".join(param_values) + ".csv"
output_file = output_folder / filename
else:
filename = "-".join(name.split("_")[1:]) + ".csv"
output_file = output_folder / filename
return output_file
def optional_from_cache(f):
"""Decorator to check if ETL is installed and load from cache."""
@wraps(f)
def wrapper(*args, **kwargs):
# Get the function signature
sig = inspect.signature(f).bind(*args, **kwargs)
# If development
if not ETL_VERSION:
filename = determine_file_name(f, **sig.arguments)
return pd.read_csv(filename)
else:
return f(*args, **kwargs)
return wrapper
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Day2/python-exercises-02-answers/preparing_dataset/category_dataset_debug.py | klimpie94/Python-training | 0 | 35092 | import pandas as pd
import os
from pathlib import Path
import warnings
warnings.filterwarnings('ignore')
DATA_PATH = os.path.join(
os.fspath(Path(__file__).parents[1]),
"data")
IMDB_DATA_PATH = os.path.join(DATA_PATH, "imdb_category.csv")
EXPORT_PATH = os.path.join(DATA_PATH, "imdb_category_binary.csv")
categories_df = pd.read_csv(IMDB_DATA_PATH)
cols_category = categories_df.iloc[:, 1:29].columns
categories_df_tmp = categories_df.copy(deep=True)
categories_df_tmp["film_category"] = (categories_df_tmp
.loc[:, cols_category]
.idxmax(axis=1)
.values)
columns_to_drop = [col
for idx, col in enumerate(categories_df_tmp.columns)
if idx in range(1, 29)]
categories_df_tmp.drop(columns_to_drop, axis=1, inplace=True)
categories_binary = categories_df_tmp.join(
pd.get_dummies(
data=categories_df_tmp.film_category,
prefix=None))
categories_binary.drop("film_category", axis=1, inplace=True)
categories_binary.to_csv(EXPORT_PATH, sep=",", index=False)
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apigw/schema.py | theztd/flaskapp-prom | 2 | 185830 | import json
from os import path, getenv, uname
from requests import get
try:
import graphene
#from graphene_sqlalchemy import SQLAlchemyObjectType, SQLAlchemyConnectionField
except ImportError as err:
print("Install dependencies!!!")
print(err)
exit(22)
ENV = getenv("ENV", "devel")
VERSION = "unknown"
INSTANCE = uname()[1]
try:
with open("./VERSION") as ver:
VERSION = ver.read().strip()
except IOError as err:
print("Unable to find VERSION file, but continue...")
DATA = get("http://localhost:5001/api/user/list.json").json()
print(DATA)
class Person(graphene.ObjectType):
name = graphene.String(value = graphene.String(default_value="<NAME>"))
key_length = graphene.Int()
key = graphene.String()
# register all queries
class Query(graphene.ObjectType):
users = graphene.List(Person, size=graphene.Int(default_value=1))
version = graphene.String()
instance = graphene.String()
env = graphene.String()
def resolve_users(root, info, size):
return DATA[:size]
def resolve_version(root, info):
return VERSION
def resolve_instance(root, info):
return INSTANCE
def resolve_env(root, info):
return ENV
SCHEMA = graphene.Schema(query=Query)
# --------- TEST ------
if __name__ == "__main__":
from pprint import pprint
print(SCHEMA)
cq1 = '''
query myquery {
name: name (value: "karlik")
age
}
'''
cq2 = '''
query myquery {
array (size:5){
name
age
}
}
'''
r = schema.execute(cq2)
pprint(r.data)
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income_expense_tracker/authentication/views.py | gokul-sarath07/Nymblelabs-Expence-Tracker | 0 | 50805 | <reponame>gokul-sarath07/Nymblelabs-Expence-Tracker<filename>income_expense_tracker/authentication/views.py
from django.views import View
from django.contrib import messages
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.mixins import LoginRequiredMixin
from django.contrib.auth.models import User
from django.shortcuts import render, redirect
from django.http import JsonResponse
from validate_email import validate_email
import json
class RegistrationView(View):
"""This class provides functions to work with registration."""
def get(self, request):
"""This function renders the registration page."""
return render(request, 'authentication/register.html')
def post(self, request):
"""This function accepts the registration form data."""
username = request.POST['username']
email = request.POST['email']
password = request.POST['password']
# variable value mapping that is passed to the registration template.
context = {
"fieldValues": request.POST
}
# Checks if user has all fields filled.
if username and email and password:
# Checks if username already exists in database.
if not User.objects.filter(username=username).exists():
# Checks if email already exists in database.
if not User.objects.filter(email=email).exists():
# Checks if password length is less than 6 characters.
if len(password) < 6:
# Sends error message to user for short password.
messages.error(request,
"Password must be atleast 6 characters")
# Returns the registration page.
return render(request, 'authentication/register.html',
context)
# Creates a user object to be saved in database.
user = User.objects.create_user(username=username,
email=email)
# Sets the user password.
user.set_password(password)
# Saves the user to database.
user.save()
# Sends success message to user for account created.
messages.success(request, "Account successfully created")
# Directs to login page.
return render(request, 'authentication/login.html')
else:
# Sends error message to user for empty fields.
messages.error(request, "Please fill all fields.")
# Returns the registration page.
return render(request, 'authentication/register.html', context)
class UsernameValidationView(View):
"""This class is an API View for username validation."""
def post(self, request):
"""
- Checks if username is alphanumeric and it doesn't exists in database.
"""
data = json.loads(request.body) # gets data from request.
username = data['username'] # gets username from data.
# Checks if username is not alphanumeric.
if not username.isalnum():
return JsonResponse({
'username_error':
'Username should only contain alphanumeric characters'
}, status=400)
# Checks if username already exists in database.
if User.objects.filter(username=username).exists():
return JsonResponse({
'username_error': "Sorry, that username's taken. Try another?"
}, status=409)
# Returns valid username response.
return JsonResponse({'username_valid': True}, status=200)
class EmailValidationView(View):
"""This class is an API View for email validation."""
def post(self, request):
"""
- Checks if email is valid and it doesn't exists in database.
"""
data = json.loads(request.body) # gets data from request.
email = data['email'] # gets email from data.
# Checks if email is not valid.
if not validate_email(email):
return JsonResponse({
'email_error': 'This email is invalid'
}, status=400)
# Checks if email doesn't already exists in database.
if User.objects.filter(email=email).exists():
return JsonResponse({
'email_error': 'This email already exists, Please login.'
}, status=409)
# Returns valid email response.
return JsonResponse({'email_valid': True}, status=200)
class PasswordValidationView(View):
"""This class is an API View for password validation."""
def post(self, request):
"""
- Checks if password is atleast 6 characters.
"""
data = json.loads(request.body) # gets the data from request.
password = data['password'] # gets password from data.
# Checks if password length is less than 6 characters.
if len(password) < 6:
return JsonResponse({
'password_error': 'Passwords must be atleast 6 characters.'
}, status=400)
# Returns valid password response.
return JsonResponse({'password_error': True}, status=200)
class LoginView(View):
"""This class provides functions to work with login."""
def get(self, request):
"""This function renders the login page."""
return render(request, 'authentication/login.html')
def post(self, request):
"""This function accepts the login form data."""
username = request.POST['username'] # gets username from request.
password = request.POST['password'] # gets password from request.
# Checks if both fields are filled.
if username and password:
# Creates user object with given credentials.
user = authenticate(username=username, password=password)
# Checks if password is valid for the given user.
if user:
# Logs in the user.
login(request, user)
# Sends success message to user.
messages.success(request, "You are now logged in.")
# redirects them to the home page.
return redirect('home')
# Sends error message to user for invalid credentials.
messages.error(request, "Incorrect credentials, Try again.")
return render(request, 'authentication/login.html')
# Sends error message to user for empty fields.
messages.error(request, "Please fill all fields.")
return render(request, 'authentication/login.html')
# This view can be accessed only after logging in.
class LogoutView(LoginRequiredMixin, View):
"""This class provides function to work with logout."""
def post(self, request):
"""Logs out the current user."""
logout(request) # Logs out current user.
# Sends success message to user for successfully logout.
messages.success(request, "You have successfully logged out.")
return redirect('login')
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] |
tests/test_core/test_graph_objs/test_instantiate_hierarchy.py | wwwidonja/changed_plotly | 0 | 6603 | from __future__ import absolute_import
from unittest import TestCase
import os
import importlib
import inspect
from plotly.basedatatypes import BasePlotlyType, BaseFigure
datatypes_root = "new_plotly/graph_objs"
datatype_modules = [
dirpath.replace("/", ".")
for dirpath, _, _ in os.walk(datatypes_root)
if not dirpath.endswith("__pycache__")
]
class HierarchyTest(TestCase):
def test_construct_datatypes(self):
for datatypes_module in datatype_modules:
module = importlib.import_module(datatypes_module)
for name in getattr(module, "__all__", []):
if name.startswith("_") or name[0].islower() or name == "FigureWidget":
continue
obj = getattr(module, name)
try:
v = obj()
except Exception:
print(
"Failed to construct {obj} in module {module}".format(
obj=obj, module=datatypes_module
)
)
raise
if obj.__module__ == "new_plotly.graph_objs._deprecations":
self.assertTrue(isinstance(v, list) or isinstance(v, dict))
obj()
elif name in ("Figure", "FigureWidget"):
self.assertIsInstance(v, BaseFigure)
else:
self.assertIsInstance(v, BasePlotlyType)
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conway.py | cmr/piday-automata | 0 | 179804 | from world import World
from pygame import draw
class Cell(object):
def __init__(self, state):
self.state = state
@classmethod
def initial(cls):
return Cell(0)
def is_alive(self):
return self.state == 1
def render(self, x, y, width, surf):
col = (0,0,0)
if self.state == 1:
col = (255, 255, 255)
surf.fill(col, (x*width, y*width, width, width))
def toggle(self):
if self.state == 1:
self.state = 0
else:
self.state = 1
def conway(world):
w = world.copy()
for x, y in w:
live = len(filter(Cell.is_alive, w.neighbors(x, y)))
if live < 2:
# die of under-population
world[x, y] = Cell(0)
elif live == 2:
# no change
pass
elif live == 3:
# reproduce
world[x, y] = Cell(1)
else:
# die of overcrowding
world[x, y] = Cell(0)
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ext/ANTsPyNet/antspynet/utilities/mixture_density_utilities.py | tsmonteiro/fmri_proc | 2 | 16131 | <reponame>tsmonteiro/fmri_proc<gh_stars>1-10
import keras.backend as K
from keras.engine import Layer, InputSpec
from keras.layers import Concatenate
from keras import initializers
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
class MixtureDensityLayer(Layer):
"""
Layer for modeling arbitrary functions using neural networks.
Arguments
---------
output_dimension : integer
Dimensionality of the output.
number_of_mixtures : integer
Number of gaussians used.
Returns
-------
Layer
A keras layer
"""
def __init__(self, output_dimension, number_of_mixtures, **kwargs):
if K.backend() != 'tensorflow':
raise ValueError("Tensorflow required as the backend.")
self.output_dimension = output_dimension
self.number_of_mixtures = number_of_mixtures
super(MixtureDensityLayer, self).__init__(**kwargs)
def build(self, input_shape):
input_dimension = input_shape[-1]
units1 = self.output_dimension * self.number_of_mixtures
self.mu_kernel = self.add_weight(name="mu_kernel",
shape = shape(input_dimension, units1),
initializer=initializers.random_normal(),
trainable=True)
self.mu_bias = self.add_weight(name="mu_bias",
shape = shape(units1),
initializer=initializers.zeros(),
trainable=True)
self.sigma_kernel = self.add_weight(name="sigma_kernel",
shape = shape(input_dimension, units1),
initializer=initializers.random_normal(),
trainable=True)
self.sigma_bias = self.add_weight(name="sigma_bias",
shape = shape(units1),
initializer=initializers.zeros(),
trainable=True)
units2 = self.number_of_mixtures
self.pi_kernel = self.add_weight(name="pi_kernel",
shape = shape(input_dimension, units2),
initializer=initializers.random_normal(),
trainable=True)
self.pi_bias = self.add_weight(name="pi_bias",
shape = shape(units2),
initializer=initializers.zeros(),
trainable=True)
def call(self, inputs, mask=None):
# dense layer for mu (mean) of the gaussians
mu_output = K.dot(inputs, self.mu_kernel)
mu_output = K.bias_add(mu_output, self.mu_bias, data_format='channels_last')
# dense layer for sigma (variance) of the gaussians
sigma_output = K.dot(inputs, self.sigma_kernel)
sigma_output = K.bias_add(sigma_output, self.sigma_bias, data_format='channels_last')
# Avoid NaN's by pushing sigma through the following custom activation
sigma_output = K.elu(sigma_output) + 1 + K.epsilon()
# dense layer for pi (amplitude) of the gaussians
pi_output = K.dot( inputs, self.pi_kernel)
pi_output = K.bias_add(pi_output, self.pi_bias, data_format='channels_last')
output = Concatenate()([mu_output, sigma_output, pi_output], name="mdn_outputs")
return(output)
def compute_output_shape(input_shape):
units = self.number_of_mixtures * (2 * self.output_dimension + 1)
return((input_shape[0], units))
def get_config(self):
config = {"output_dimension": self.output_dimension,
"axis": self.number_of_mixtures}
base_config = super(MixtureDensityLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_mixture_density_loss_function(output_dimension, number_of_mixtures):
"""
Returns a loss function for the mixture density.
Arguments
---------
output_dimension : integer
Dimensionality of the output.
number_of_mixtures : integer
Number of gaussians used.
Returns
-------
Function
A function providing the mean square error accuracy
"""
def loss_function(y_true, y_pred):
dimension = number_of_mixtures * output_dimension
y_pred = tf.reshape(y_pred, [-1, 2 * dimension + number_of_mixtures],
name='reshape_ypred_loss')
y_true = tf.reshape(y_true, [-1, 2 * dimension + number_of_mixtures],
name='reshape_ytrue_loss')
output_mu, output_sigma, output_pi = tf.split(y_pred, axis=-1, name='mdn_coef_split',
num_or_size_splits=[dimension, dimension, number_of_mixtures])
# Construct the mixture models
tfd = tfp.distributions
categorical_distribution = tfd.Categorical(logits=output_pi)
component_splits = [output_dimension] * number_of_mixtures
mu = tf.split(output_mu, num_or_size_splits=component_splits, axis=1)
sigma = tf.split(output_sigma, num_or_size_splits=component_splits, axis=1)
components = []
for i in range(len(mu)):
components.append(tfd.MultivariateNormalDiag(loc = mu[i], scale_diag=sigma[i]))
mixture = tfd.Mixture(cat=categorical_distribution, components=components)
loss = mixture.log_prob(y_true)
loss = tf.negative(loss)
loss = tf.reduce_mean(loss)
return(loss)
with tf.name_scope("MixtureDensityNetwork"):
return(loss_function)
def get_mixture_density_sampling_function(output_dimension, number_of_mixtures):
"""
Returns a sampling function for the mixture density.
Arguments
---------
output_dimension : integer
Dimensionality of the output.
number_of_mixtures : integer
Number of gaussians used.
Returns
-------
Function
A function providing the mean square error accuracy
"""
def sampling_function(y_pred):
dimension = number_of_mixtures * output_dimension
y_pred = tf.reshape(y_pred, [-1, 2 * dimension + number_of_mixtures],
name='reshape_ypred')
output_mu, output_sigma, output_pi = tf.split(y_pred, axis=-1, name='mdn_coef_split',
num_or_size_splits=[dimension, dimension, number_of_mixtures])
# Construct the mixture models
tfd = tfp.distributions
categorical_distribution = tfd.Categorical(logits=output_pi)
component_splits = [output_dimension] * number_of_mixtures
mu = tf.split(output_mu, num_or_size_splits=component_splits, axis=1)
sigma = tf.split(output_sigma, num_or_size_splits=component_splits, axis=1)
components = []
for i in range(len(mu)):
components.append(tfd.MultivariateNormalDiag(loc = mu[i], scale_diag=sigma[i]))
mixture = tfd.Mixture(cat=categorical_distribution, components=components)
sample = mixture.sample()
return(sample)
with tf.name_scope("MixtureDensityNetwork"):
return(sampling_function)
def get_mixture_density_mse_function(output_dimension, number_of_mixtures):
"""
Returns a mse function for the mixture density.
Arguments
---------
output_dimension : integer
Dimensionality of the output.
number_of_mixtures : integer
Number of gaussians used.
Returns
-------
Function
A function providing the mean square error accuracy
"""
def mse_accuracy_function(y_true, y_pred):
dimension = number_of_mixtures * output_dimension
y_pred = tf.reshape(y_pred, [-1, 2 * dimension + number_of_mixtures],
name='reshape_ypred_mse')
y_true = tf.reshape(y_true, [-1, output_dimension],
name='reshape_ytrue_mse')
output_mu, output_sigma, output_pi = tf.split(y_pred, axis=-1, name='mdn_coef_split',
num_or_size_splits=[dimension, dimension, number_of_mixtures])
# Construct the mixture models
tfd = tfp.distributions
categorical_distribution = tfd.Categorical(logits=output_pi)
component_splits = [output_dimension] * number_of_mixtures
mu = tf.split(output_mu, num_or_size_splits=component_splits, axis=1)
sigma = tf.split(output_sigma, num_or_size_splits=component_splits, axis=1)
components = []
for i in range(len(mu)):
components.append(tfd.MultivariateNormalDiag(loc = mu[i], scale_diag=sigma[i]))
mixture = tfd.Mixture(cat=categorical_distribution, components=components)
sample = mixture.sample()
mse = tf.reduce_mean(tf.square(sample-y_true), axis=-1)
return(mse)
with tf.name_scope("MixtureDensityNetwork"):
return(mse_accuracy_function)
def split_mixture_parameters(parameters, output_dimension, number_of_mixtures):
"""
Splits the mixture parameters.
Arguments
---------
parameters : tuple
Parameter to split
output_dimension : integer
Dimensionality of the output.
number_of_mixtures : integer
Number of gaussians used.
Returns
-------
List of arrays
Separate mixture parameters
"""
dimension = number_of_mixtures * output_dimension
mu = parameters[:dimension]
sigma = parameters[dimension:(2 * dimension)]
pi_logits = parameters[-number_of_mixtures:]
return([mu, sigma, pi_logits])
def mixture_density_software_max(logits, temperature=1.0):
"""
Softmax function for mixture density with temperature adjustment.
Arguments
---------
logits : list or numpy array
input
temperature :
The temperature for to adjust the distribution (default 1.0)
Returns
-------
Scalar
Softmax loss value.
"""
e = np.array(logits) / temperature
e -= np.max(e)
e = np.exp(e)
distribution = e / np.sum(e)
return(distribution)
def sample_from_categorical_distribution(distribution):
"""
Softmax function for mixture density with temperature adjustment.
Arguments
---------
distribution :
input categorical distribution from which to sample.
Returns
-------
Scalar
A single sample.
"""
r = np.random.rand(1)
accumulate = 0
for i in range(len(distribution)):
accumulate += distribution[i]
if accumulate >= r:
return(i)
tf.logging.info('Error: sampling categorical model.')
return(-1)
def sample_from_output(parameters, output_dimension, number_of_mixtures,
temperature=1.0, sigma_temperature=1.0):
"""
Softmax function for mixture density with temperature adjustment.
Arguments
---------
output_dimension : integer
Dimensionality of the output.
number_of_mixtures : integer
Number of gaussians used.
temperature :
The temperature for to adjust the distribution (default 1.0)
sigma_temperature :
The temperature for to adjust the distribution (default 1.0)
Returns
-------
Scalar
A single sample.
"""
mu, sigma, pi = split_mixture_parameters(parameters, output_dimension, number_of_mixtures)
pi_softmax = mixture_density_software_max(pi, temperature=temperature)
m = sample_from_categorical_distribution(pi_softmax)
mu_vector = mu[m * output_dimension:(m + 1) * output_dimension]
sigma_vector = sigma[m * output_dimension:(m + 1) * output_dimension] * sigma_temperature
covariance_matrix = np.identity(output_dimension) * sigma_vector
sample = np.random.multivariate_normal(mu_vector, covariance_matrix, 1)
return(sample)
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tests.py | hephzaron/EMG_ANN | 0 | 124462 | <reponame>hephzaron/EMG_ANN<gh_stars>0
import unittest
from tests.dataloader import DataloaderTestCase
scale_test_suite = unittest.TestSuite([
unittest.TestLoader().loadTestsFromTestCase(DataloaderTestCase)
])
def test_scale_suite():
result = unittest.TestResult()
runner = unittest.TextTestRunner()
print(runner.run(scale_test_suite))
if __name__ == '__main__':
test_scale_suite() | [
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] |
tests/test.py | speer-kinjo/ro.py | 28 | 162599 | <gh_stars>10-100
"""
Tests ro.py functionality.
It is not possible to fully test ro.py as it relies on external conditions, so this test mostly checks parity and cannot
be fully relied upon. Certain methods have been intentionally left out.
"""
import os
import asyncio
from typing import Optional
from loguru import logger
from roblox import Client
from roblox.users import User
from roblox.utilities.exceptions import Unauthorized
client = Client(os.getenv("ROBLOX_TOKEN"))
async def test_user_id(user_id: int):
"""
Tests get_users, get_user, and get_base_user on this user ID.
"""
user = await client.get_user(user_id)
alt_user = (await client.get_users([user_id]))[0]
base_user = client.get_base_user(user_id)
return user.id == alt_user.id == base_user.id
async def test_user_id_name(user_id: int):
"""
Tests to see if get_user(user.id) == get_user_by_username(user.name)
"""
user = await client.get_user(user_id)
user2 = await client.get_user_by_username(user.name)
return user.id == user2.id
async def test_user_name_id(name: str):
"""
Tests to see if get_user(user.id) == get_user_by_username(user.name) in reverse
"""
user = await client.get_user_by_username(name)
user2 = await client.get_user(user.id)
return user.id == user2.id
async def main():
"""
🥺
"""
# set up basic authentication
is_authenticated: bool = False
authenticated_user: Optional[User]
try:
authenticated_user = await client.get_authenticated_user()
except Unauthorized:
authenticated_user = None
if authenticated_user:
is_authenticated = True
# basic logging
if not is_authenticated:
logger.warning("Not all checks can complete because ro.py can't authenticate.")
# users
assert await test_user_id(1) # Roblox
assert await test_user_id(2) # <NAME>
assert await test_user_id(3) # <NAME>
assert await test_user_id(968108160) # jmk (@local_ip)
assert await test_user_id(111179700) # Iron_Legion
assert await test_user_id_name(1)
assert await test_user_id_name(2)
assert await test_user_id_name(3)
assert await test_user_name_id("Roblox")
assert await test_user_name_id("<NAME>")
assert await test_user_name_id("<NAME>")
assert await test_user_name_id("local_ip")
assert await test_user_name_id("Iron_Legion")
if __name__ == '__main__':
asyncio.get_event_loop().run_until_complete(main())
| [
1,
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29918,
303,
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src/djanban/apps/repositories/migrations/0014_auto_20161006_0143.py | diegojromerolopez/djanban | 33 | 54561 | # -*- coding: utf-8 -*-
# Generated by Django 1.10 on 2016-10-05 23:43
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('boards', '0030_auto_20160925_1843'),
('repositories', '0013_auto_20160927_1935'),
]
operations = [
migrations.AlterIndexTogether(
name='pylintmessage',
index_together=set([('board', 'repository', 'commit', 'commit_file', 'type'), ('board', 'commit', 'type'), ('board', 'type'), ('board', 'repository', 'type', 'commit', 'commit_file'), ('commit', 'type', 'commit_file'), ('commit', 'type'), ('commit', 'commit_file', 'type')]),
),
]
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] |
3.Pandas/4_plotting.py | utkrist-karky/Deep-Learning-Prerequisite | 1 | 78960 | <gh_stars>1-10
import pandas as pd
import os
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
def date_to_year(row):
return int(row['date'].split('-')[0])
# from data frame row get date that is split with - and take the 0th item
#Get the data frame and assign df to it
os.system("wget https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/sbux.csv")
df = pd.read_csv('sbux.csv', error_bad_lines=False)
os.system("clear")
#--------------------------------------
#create a histogram
df['open'].hist()
plt.show()
#create a linegraph
df['open'].plot()
plt.show()
#box plot
df[['open', 'high', 'low', 'close']].plot.box()
plt.show()
#scatter matrix
scatter_matrix(df[['open', 'high', 'low', 'close']],
alpha = 0.2, figsize=(6,6));
#alpha = 0.2 makes the dots have transpirancy and figsize makes the plot a little bigger
plt.show()
os.system("rm sbux.csv") | [
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] |
07-Array-Oriented-Programming-with-NumPy/7-4-Array-from-List-of-Lists.py | rajevac/deitel-intro-to-python-exercises | 0 | 114366 | import numpy as np
arr_1 = [2, 3, 5, 7, 11]
arr_2 = [13, 17, 19, 23, 29]
arr_3 = np.array([arr_1, arr_2])
print(arr_3)
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coding_test_with_python/greedy/t5.py | mrbartrns/swacademy_structure | 0 | 97401 | import sys
sys.stdin = open('../input.txt', 'r')
si = sys.stdin.readline
n, m = map(int, si().split())
arr = list(map(int, si().split()))
dp = [0] * 11
answer = 0
for i in range(n):
dp[arr[i]] += 1
for i in range(m):
n -= dp[i]
answer += dp[i] * n
print(answer)
| [
1,
1053,
10876,
13,
13,
9675,
29889,
4172,
262,
353,
1722,
877,
6995,
2080,
29889,
3945,
742,
525,
29878,
1495,
13,
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353,
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29962,
334,
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474,
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13,
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270,
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29961,
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29961,
29875,
5262,
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13,
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474,
297,
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13,
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302,
22361,
270,
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29961,
29875,
29962,
13,
1678,
1234,
4619,
270,
29886,
29961,
29875,
29962,
334,
302,
13,
2158,
29898,
12011,
29897,
13,
2
] |
0278_FirstBadVersion/python/test_solution.py | jeffvswanson/LeetCode | 0 | 155156 | <filename>0278_FirstBadVersion/python/test_solution.py<gh_stars>0
import pytest
import solution
@pytest.mark.parametrize("num_versions,bad_version", [(10, 1), (10, 8), (1, 1), (10, 10), (10, 5)])
def test_initial_pass(num_versions, bad_version):
got = solution.initial_pass(num_versions, bad_version)
assert got == bad_version
@pytest.mark.parametrize("num_versions,bad_version", [(10, 1), (10, 8), (1, 1), (10, 10), (10, 5)])
def test_optimized_pass(num_versions, bad_version):
got = solution.optimized_pass(num_versions, bad_version)
assert got == bad_version
| [
1,
529,
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13,
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11228,
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26100,
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29900,
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511,
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29900,
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29945,
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2314,
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29918,
20640,
1891,
29918,
3364,
29898,
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29918,
26100,
29892,
4319,
29918,
3259,
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13,
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2355,
353,
1650,
29889,
20640,
1891,
29918,
3364,
29898,
1949,
29918,
26100,
29892,
4319,
29918,
3259,
29897,
13,
1678,
4974,
2355,
1275,
4319,
29918,
3259,
13,
2
] |
ed/ai/ml/kubeflow/examples/out.file_outputs.py | cn007b/stash | 0 | 1607995 | <gh_stars>0
import kfp
import kfp.dsl as dsl
from kubernetes import client
from kubernetes.client.models import V1EnvVar
@kfp.dsl.pipeline(name='check x', description='')
def check_x():
op1 = dsl.ContainerOp(
name='write log',
image='cn007b/alpine',
command=['sh', '-c'],
arguments=['echo ok > /tmp/x.log'],
file_outputs={'x': '/tmp/x.log'}
)
op2 = dsl.ContainerOp(
name='read log',
image='cn007b/alpine',
command=['sh', '-c'],
arguments=['echo %s ' %op1.output]
)
p = check_x
pod_node_selector = 'kubeflow'
pipeline_conf = dsl.PipelineConf()
pipeline_conf.set_image_pull_secrets([client.V1LocalObjectReference(name="x")])
pipeline_conf.set_default_pod_node_selector(label_name="kubernetes.io/instancegroup", value=pod_node_selector)
pipeline = kfp.Client().create_run_from_pipeline_func(p, arguments={}, pipeline_conf=pipeline_conf)
| [
1,
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303,
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6273,
3790,
1118,
16439,
29918,
5527,
29922,
13096,
5570,
29918,
5527,
29897,
13,
2
] |
channels/views/channels.py | mitodl/open-discussions | 12 | 139212 | """Views for REST APIs for channels"""
from django.shortcuts import get_object_or_404
from rest_framework.generics import ListCreateAPIView, RetrieveUpdateAPIView
from rest_framework.response import Response
from rest_framework.exceptions import PermissionDenied
from rest_framework import status
from channels.api import Api
from channels.models import Channel
from channels.serializers.channels import ChannelSerializer
from channels.utils import translate_praw_exceptions
from open_discussions.permissions import (
AnonymousAccessReadonlyPermission,
IsStaffOrReadonlyPermission,
IsStaffModeratorOrReadonlyPermission,
)
class ChannelListView(ListCreateAPIView):
"""
View for listing and creating channels
"""
permission_classes = (
AnonymousAccessReadonlyPermission,
IsStaffOrReadonlyPermission,
)
serializer_class = ChannelSerializer
def get_serializer_context(self):
"""Context for the request and view"""
channels = {channel.name: channel for channel in Channel.objects.all()}
return {
"channel_api": self.request.channel_api,
"channels": channels,
"view": self,
}
def get_queryset(self):
"""Get generator for channels list"""
api = Api(user=self.request.user)
return api.list_channels()
def list(self, request, *args, **kwargs):
"""Return the channels list in alphabetical order"""
queryset = self.get_queryset()
serializer = ChannelSerializer(queryset, many=True)
return Response(
sorted(serializer.data, key=lambda channel: channel["title"].lower())
)
def post(self, request, *args, **kwargs):
with translate_praw_exceptions(request.user):
return super().post(request, *args, **kwargs)
class ChannelDetailView(RetrieveUpdateAPIView):
"""
View for getting information about or updating a specific channel
"""
permission_classes = (
AnonymousAccessReadonlyPermission,
IsStaffModeratorOrReadonlyPermission,
)
serializer_class = ChannelSerializer
def get_serializer_context(self):
"""Context for the request and view"""
return {
"channel_api": self.request.channel_api,
"channels": {
self.kwargs["channel_name"]: get_object_or_404(
Channel, name=self.kwargs["channel_name"]
)
},
"view": self,
}
def get_object(self):
"""Get channel referenced by API"""
return self.request.channel_api.get_channel(self.kwargs["channel_name"])
def get(self, request, *args, **kwargs):
# we don't want to let this through to Reddit, because it blows up :/
if len(kwargs["channel_name"]) == 1:
return Response({}, status=status.HTTP_404_NOT_FOUND)
with translate_praw_exceptions(request.user):
return super().get(request, *args, **kwargs)
def patch(self, request, *args, **kwargs):
# Deny permission if the user is not a superuser and is attempting to update the channel title.
if not self.request.user.is_superuser and "title" in self.request.data:
raise PermissionDenied()
with translate_praw_exceptions(request.user):
return super().patch(request, *args, **kwargs)
def put(self, request, *args, **kwargs):
# Deny permission if the user is not a superuser and is attempting to update the channel title.
channel = self.get_object()
if (
not self.request.user.is_superuser
and self.request.data.get("title") != channel.title
):
raise PermissionDenied()
with translate_praw_exceptions(request.user):
return super().put(request, *args, **kwargs)
| [
1,
9995,
23825,
363,
16759,
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363,
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13,
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334,
5085,
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3579,
19290,
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13,
13,
1990,
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16570,
1043,
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8015,
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6422,
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29918,
1990,
353,
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13,
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29918,
15550,
3950,
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4703,
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13,
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9995,
2677,
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278,
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2754,
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305,
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19290,
3366,
12719,
29918,
978,
3108,
29901,
679,
29918,
3318,
29918,
272,
29918,
29946,
29900,
29946,
29898,
13,
462,
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17368,
29892,
1024,
29922,
1311,
29889,
19290,
3366,
12719,
29918,
978,
3108,
13,
18884,
1723,
13,
9651,
2981,
13,
9651,
376,
1493,
1115,
1583,
29892,
13,
4706,
500,
13,
13,
1678,
822,
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mainapp/migrations/0007_auto_20190114_0854.py | tum0xa/geekbrains-django2-homework | 0 | 133698 | <filename>mainapp/migrations/0007_auto_20190114_0854.py
# Generated by Django 2.1 on 2019-01-14 03:54
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('mainapp', '0006_productcategory_is_active'),
]
operations = [
migrations.CreateModel(
name='Address',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=255, verbose_name='Название')),
('city', models.CharField(max_length=50, verbose_name='Город')),
('street', models.CharField(max_length=50, verbose_name='Улица')),
('building', models.SmallIntegerField(verbose_name='Номер здания, дома')),
('office', models.SmallIntegerField(blank=True, verbose_name='Номер офиса')),
],
),
migrations.CreateModel(
name='Company',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=255, unique=True, verbose_name='Название организации')),
],
),
migrations.CreateModel(
name='Contact',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50, unique=True, verbose_name='Имя')),
('number', models.CharField(blank=True, default='-', max_length=20, verbose_name='Номер телефона')),
('email', models.EmailField(blank=True, default='-', max_length=254, verbose_name='E-mail')),
('is_main', models.BooleanField(default=False, verbose_name='Основной')),
('company', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='mainapp.Company')),
],
),
migrations.AddField(
model_name='address',
name='company',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='mainapp.Company'),
),
]
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scicite/compute_features.py | allenai/scicite | 89 | 154984 | <filename>scicite/compute_features.py
""" Module for computing features """
import re
from collections import Counter, defaultdict
from typing import List, Optional, Tuple, Type
import functools
from spacy.tokens.token import Token as SpacyToken
import scicite.constants as constants
from scicite.constants import CITATION_TOKEN
from scicite.resources.lexicons import (AGENT_PATTERNS, ALL_ACTION_LEXICONS,
ALL_CONCEPT_LEXICONS, FORMULAIC_PATTERNS)
from scicite.data import Citation
import logging
logger = logging.getLogger('classifier')
NETWORK_WEIGHTS_FILE = constants.root_path + '/resources/arc-network-weights.tsv'
def load_patterns(filename, p_dict, label):
with open(filename) as f:
class_counts = Counter()
for line in f:
if not '@' in line:
continue
cols = line.split("\t")
pattern = cols[0].replace("-lrb-", "(").replace('-rrb-', ')')
category = cols[1]
if category == 'Background':
continue
class_counts[category] += 1
p_dict[category + '_' + label + '_' + str(class_counts[category])] \
= pattern.split()
# p_dict[clazz + '_' + label].append(pattern.split())
def get_values_from_list(inplst, key, is_class=True):
""" gets a value of an obj for a list of dicts (inplst)
Args:
inplst: list of objects
key: key of interest
is_class: ist the input object a class or a dictionary obj
"""
return [getattr(elem, key) for elem in inplst] if is_class \
else [elem[key] for elem in inplst]
def is_in_lexicon(lexicon: dict,
sentence: str,
count: Optional[bool] = False) -> Tuple[List[str], List[str]]:
""" checks if the words in a lexicon exist in the sentence """
features = []
feature_names = []
if count:
cnt = 0
for key, word_list in lexicon.items():
exists = False
for word in word_list:
if word in sentence:
if not count:
exists = True
break
else:
cnt += 1
if not count:
features.append(exists)
else:
features.append(cnt)
feature_names.append(key)
return features, feature_names
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] |
IdleThing.py | drdread987/IdleThing | 0 | 75637 | <gh_stars>0
from Scenes.TitleScreen import TitleScene
import pygame
import Tools.Images
import datetime
def run_game(width, height, fps, starting_scene):
pygame.init()
screen = pygame.display.set_mode((width, height))
clock = pygame.time.Clock()
current_time = datetime.datetime.now()
time_passed = datetime.datetime.now() - current_time
active_scene = starting_scene
pressed_keys = []
frames = 0
while active_scene is not None:
new_time = datetime.datetime.now()
time_passed += new_time - current_time
current_time = new_time
frames += 1
if time_passed > datetime.timedelta(seconds=1):
print("FPS: " + str(frames))
frames = 0
time_passed = datetime.timedelta(seconds=0)
# event filtering
filtered_events = []
for event in pygame.event.get():
quit_attempt = False
if event.type == pygame.QUIT:
quit_attempt = True
elif event.type == pygame.KEYDOWN:
alt_pressed = pygame.K_LALT in pressed_keys or pygame.K_RALT in pressed_keys
if event.key == pygame.K_ESCAPE:
quit_attempt = True
elif event.key == pygame.K_F4 and alt_pressed:
quit_attempt = True
elif event.key not in pressed_keys:
pressed_keys.append(event.key)
elif event.type == pygame.KEYUP:
if event.key in pressed_keys:
pressed_keys.remove(event.key)
if quit_attempt:
active_scene.terminate()
else:
filtered_events.append(event)
active_scene.process_input(filtered_events, pressed_keys)
active_scene.update(screen)
active_scene.render(screen)
active_scene = active_scene.next
pygame.display.flip()
clock.tick(fps)
il = Tools.Images.ImageLoader()
run_game(1200, 800, 60, TitleScene(il))
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python_project/mfresh_project/mfresh_api/mfresh_api/urls.py | 452366we/python_web | 0 | 178248 | <filename>python_project/mfresh_project/mfresh_api/mfresh_api/urls.py<gh_stars>0
"""mfresh_api URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
#from django.contrib import admin
from django.urls import path
from django.http import HttpResponse
from user.views import userLogin,userRegister,userCheckUname,userCheckPhone
from news.views import newsList,newsDetail
from product.views import productList,productDetail
from cart.views import cartDetailAdd,cartDetailList,cartDetailDelete,cartDetailUpdate
from user.models import MfUser
def handleHome(req):
res=HttpResponse('<h1>welcome to mfresh!</h1><hr>')
return res
urlpatterns = [
path('',handleHome),
path('user/login',userLogin),
path('user/register',userRegister),
path('user/check/uname',userCheckUname),
path('user/check/phone',userCheckPhone),
path('news/list',newsList),
path('news/detail',newsDetail),
path('product/list',productList),
path('product/detail',productDetail)
]
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] |
analytics/notebooks/lib/run_definition.py | solace-iot-team/az-use-case-perf-tests | 5 | 63143 | # ---------------------------------------------------------------------------------------------
# MIT License
# Copyright (c) 2020, Solace Corporation, <NAME> (<EMAIL>)
# Copyright (c) 2020, Solace Corporation, <NAME> (<EMAIL>)
# ---------------------------------------------------------------------------------------------
import glob
import os.path
from os import path
from .common_base import CommonBase
from .constants import *
from .perf_error import PerfError
from .run import Run
from .run_result_location import RunResultLocation
class RunDefinition(CommonBase):
def __init__(self, location: RunResultLocation):
"""
RunDefinition triggers processing all runs within RunResultLocation and provides functions to extract data
++
:param location:
"""
CommonBase.__init__(self)
self._location = location
self._list_runs = list()
self._process_distinct_latency_samples=True
#just metadata read
self._processed_samples = False
def process_run_samples(self, process_distinct_latency_samples:bool=True):
rootExists = self._check_root_folder_in_run_location()
self._process_distinct_latency_samples = process_distinct_latency_samples
if not rootExists:
raise SystemExit(f'[ERROR] [EXITING] Root folder does not exist:{self._location.root_folder}')
for run_dir in self._read_list_runs():
self._list_runs.append(Run(self,run_dir))
self._processed_samples = True
def _file_path_in_run_location(self, filename: str) -> str:
return self._location.root_folder + "/" + filename
def _check_file_in_run_location(self, filename: str) -> bool:
return path.exists(self._file_path_in_run_location(filename))
def _check_root_folder_in_run_location(self) -> bool:
return path.exists(self._location.root_folder)
def _files_in_run_location(self, pattern):
return glob.glob(self._file_path_in_run_location(perf_pattern_run_dir))
def _dirs_in_run_location(self, pattern):
candidates = glob.glob(self._file_path_in_run_location(perf_pattern_run_dir))
return list(filter(lambda file: os.path.isdir(file), candidates))
def _check_processed_sampled(self):
if not self._processed_samples:
raise SystemExit(f'[ERROR] [EXITING] RunDefinition not initialized. Execute >process_run_samples() once to read all sample data.<')
def _read_list_runs(self) -> list:
return self._dirs_in_run_location(perf_pattern_run_dir)
def all_runs(self) -> list:
"""
All runs
:return: list of all runs
"""
self._check_processed_sampled()
return self._list_runs
def find_run(self, run_id, read_samples:bool=True):
"""
Searches for run with run_id
:param run_id:
:param read_samples: read also the latencies before handing over the run
:return: Run
:raise: PerfError: if run_id was not found
"""
self._check_processed_sampled()
try:
run = next(filter(lambda item: item.run_meta.run_id==run_id, self._list_runs))
if read_samples:
#idempotent - samples will be read just once
run.read_samples()
return run
except StopIteration:
raise PerfError(f'run_id: {run_id} not found')
def find_sample(self,run_id, metrics_type, sample_num):
"""
Searches for specific sample
:param run_id:
:param metrics_type: {c_sample_metric_type_latency_node | c_sample_metric_type_latency_broker | c_sample_metric_type_ping | c_sample_metric_vpn}
:param sample_num:
:return: sample
:raises PerfError
"""
self._check_processed_sampled()
run = self.find_run(run_id)
#idempotent - samples will be read just once
run.read_samples()
if (metrics_type==c_sample_metric_type_latency_node):
return run.latency_node_latency_series.find_sample(sample_num)
if (metrics_type==c_sample_metric_type_latency_broker):
return run.broker_node_latency_series.find_sample(sample_num)
if (metrics_type==c_sample_metric_type_ping):
return run.ping_series.find_sample(sample_num)
if (metrics_type==c_sample_metric_vpn):
return run.broker_series.find_sample(sample_num)
raise PerfError(f'Unsupported metric_type: {metrics_type}') | [
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] |
back/config.py | aninstein/sex_code_blog | 116 | 163414 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
http://www.pythondoc.com/flask/config.html#id6
"""
import os
basedir = os.path.abspath(os.path.dirname(__file__))
class Config:
SECRET_KEY = os.getenv('SECRET_KEY') or 'MPk2WlUArcLeeU_iohzT'
'''
# 旧版本
import random
import string
''.join(random.choices(string.ascii_letters + string.digits, k=15))
# py3.6+
import secrets
secrets.token_urlsafe(nbytes=15)
'''
SQLALCHEMY_COMMIT_ON_TEARDOWN = True
SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLALCHEMY_RECORD_QUERIES = True
# 分页
FLASKY_POSTS_PER_PAGE = 10
# 上传图片
UPLOADED_IMAGES_DEST = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'static/images')
MAX_CONTENT_LENGTH = 10 * 1024 * 1024
# 邮件服务器设置
MAIL_SERVER = os.getenv('MAIL_SERVER')
# 163不支持STARTTLS
MAIL_PORT = 465
MAIL_USE_SSL = True
MAIL_USERNAME = os.getenv('MAIL_USERNAME')
MAIL_PASSWORD = os.getenv('MAIL_PASSWORD')
MAIL_DEFAULT_SENDER = ('别院牧志', os.getenv('MAIL_USERNAME'))
# redis 配置
# REDIS_URL = "redis://:password@localhost:6379/0"
REDIS_URL = "redis://localhost:6379/0"
def __init__(self):
pass
@staticmethod
def init_app(app):
pass
class MySQLConfig:
MYSQL_USERNAME = os.getenv('MYSQL_USER')
MYSQL_PASSWORD = os.getenv('MYSQL_PASSWORD')
MYSQL_DB = os.getenv('MYSQL_DB')
MYSQL_HOST = 'localhost:3306'
MYSQL_CHARSET = 'utf8mb4' # 为了支持 emoji 显示，需要设置为 utf8mb4 编码
class DevelopmentConfig(Config):
DEBUG = True
database = MySQLConfig.MYSQL_DB or 'iyblog_dev'
SQLALCHEMY_DATABASE_URI = f'mysql+pymysql://{MySQLConfig.MYSQL_USERNAME}:{MySQLConfig.MYSQL_PASSWORD}' \
f'@{MySQLConfig.MYSQL_HOST}/{database}?charset={MySQLConfig.MYSQL_CHARSET}'
class TestingConfig(Config):
TESTING = True
database = MySQLConfig.MYSQL_DB or 'iyblog_test'
SQLALCHEMY_DATABASE_URI = f'mysql+pymysql://{MySQLConfig.MYSQL_USERNAME}:{MySQLConfig.MYSQL_PASSWORD}' \
f'@{MySQLConfig.MYSQL_HOST}/{database}?charset={MySQLConfig.MYSQL_CHARSET}'
class ProductionConfig(Config):
database = MySQLConfig.MYSQL_DB or 'iyblog_product'
SQLALCHEMY_DATABASE_URI = f'mysql+pymysql://{MySQLConfig.MYSQL_USERNAME}:{MySQLConfig.MYSQL_PASSWORD}' \
f'@{MySQLConfig.MYSQL_HOST}/{database}?charset={MySQLConfig.MYSQL_CHARSET}'
config = {
'development': DevelopmentConfig,
'testing': TestingConfig,
'production': ProductionConfig,
'default': DevelopmentConfig
}
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10162,
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13,
13,
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426,
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525,
25431,
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3991,
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13,
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292,
3991,
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24601,
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3991,
29892,
13,
1678,
525,
4381,
2396,
14650,
3991,
13,
29913,
13,
2
] |
pytglib/api/types/rich_text_phone_number.py | iTeam-co/pytglib | 6 | 23650 | <filename>pytglib/api/types/rich_text_phone_number.py
from ..utils import Object
class RichTextPhoneNumber(Object):
"""
A rich text phone number
Attributes:
ID (:obj:`str`): ``RichTextPhoneNumber``
Args:
text (:class:`telegram.api.types.RichText`):
Text
phone_number (:obj:`str`):
Phone number
Returns:
RichText
Raises:
:class:`telegram.Error`
"""
ID = "richTextPhoneNumber"
def __init__(self, text, phone_number, **kwargs):
self.text = text # RichText
self.phone_number = phone_number # str
@staticmethod
def read(q: dict, *args) -> "RichTextPhoneNumber":
text = Object.read(q.get('text'))
phone_number = q.get('phone_number')
return RichTextPhoneNumber(text, phone_number)
| [
1,
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726,
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353,
3855,
29889,
657,
877,
6710,
29918,
4537,
1495,
13,
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736,
4385,
1626,
9861,
4557,
29898,
726,
29892,
9008,
29918,
4537,
29897,
13,
2
] |
bin/bin/music_parse.py | anthonynguyen/dotfiles | 0 | 188686 | #!/usr/bin/env python3
import subprocess
o = subprocess.check_output(["mpc", "-f", "%title%"]).decode()
if "[playing]" in o:
#p = o.split("\n")
#print("{} {}".format(u"\uf001", p[0]))
print(u"\uf001")
else:
print(u"\uf04c") | [
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2158,
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29884,
26732,
1137,
29900,
29946,
29883,
1159,
2
] |
split.py | HieuNT1998/BraTS-DMFNet | 0 | 165112 | # """
# The code will split the training set into k-fold for cross-validation
# """
# import os
# import numpy as np
# from sklearn.model_selection import StratifiedKFold
# root = './data/2018/MICCAI_BraTS_2018_Data_Training'
# valid_data_dir = './data/2018/MICCAI_BraTS_2018_Data_Validation'
# def write(data, fname, root=root):
# fname = os.path.join(root, fname)
# with open(fname, 'w') as f:
# f.write('\n'.join(data))
# hgg = os.listdir(os.path.join(root, 'HGG'))
# hgg = [os.path.join('HGG', f) for f in hgg]
# lgg = os.listdir(os.path.join(root, 'LGG'))
# lgg = [os.path.join('LGG', f) for f in lgg]
# X = hgg + lgg
# Y = [1] * len(hgg) + [0] * len(lgg)
# write(X, 'all.txt')
# X, Y = np.array(X), np.array(Y)
# skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2018)
# for k, (train_index, valid_index) in enumerate(skf.split(Y, Y)):
# train_list = list(X[train_index])
# valid_list = list(X[valid_index])
# write(train_list, 'train_{}.txt'.format(k))
# write(valid_list, 'valid_{}.txt'.format(k))
# valid = os.listdir(os.path.join(valid_data_dir))
# valid = [f for f in valid if not (f.endswith('.csv') or f.endswith('.txt'))]
# write(valid, 'valid.txt', root=valid_data_dir)
"""
The code will split the training set into k-fold for cross-validation
"""
import os
import sys
import numpy as np
from sklearn.model_selection import StratifiedKFold
import shutil
root = './data/2018/MICCAI_BraTS_2018_Data_Training'
valid_data_dir = './data/2018/MICCAI_BraTS_2018_Data_Validation'
backup = './2018/datasets'
backup_files = os.listdir(backup)
if len(backup_files) != 0:
print("Copy from backup")
for file in backup_files:
shutil.copy(os.path.join(backup, file), os.path.join(root, file))
count=0
with open(os.path.join(root, file), 'r') as f:
for line in f:
count += 1
print("File {} has {} lines.".format(file, count))
sys.exit()
def write(data, fname, root=root):
fname = os.path.join(root, fname)
with open(fname, 'w') as f:
f.write('\n'.join(data))
limit = float(sys.argv[1])
hgg = os.listdir(os.path.join(root, 'HGG'))
hgg = [os.path.join('HGG', f) for f in hgg]
lgg = os.listdir(os.path.join(root, 'LGG'))
lgg = [os.path.join('LGG', f) for f in lgg]
print("Original size: HGG:{}, LGG:{}, Total:{}".format(len(hgg), len(lgg), len(hgg) + len(lgg)))
hgg = hgg[:int(limit*len(hgg))]
lgg = lgg[:int(limit*len(lgg))]
print("Limited size: HGG:{}, LGG:{}, Total:{}".format(len(hgg), len(lgg), len(hgg) + len(lgg)))
X = hgg + lgg
Y = [1] * len(hgg) + [0] * len(lgg)
write(X, 'all.txt')
shutil.copy(os.path.join(root,'all.txt'), os.path.join(backup, 'all.txt'))
X, Y = np.array(X), np.array(Y)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2018)
for k, (train_index, valid_index) in enumerate(skf.split(Y, Y)):
train_list = list(X[train_index])
valid_list = list(X[valid_index])
write(train_list, 'train_{}.txt'.format(k))
write(valid_list, 'valid_{}.txt'.format(k))
shutil.copy(os.path.join(root,'train_{}.txt'.format(k)),
os.path.join(backup, 'train_{}.txt'.format(k)))
shutil.copy(os.path.join(root,'valid_{}.txt'.format(k)),
os.path.join(backup, 'valid_{}.txt'.format(k)))
valid = os.listdir(os.path.join(valid_data_dir))
valid = [f for f in valid if not (f.endswith('.csv') or f.endswith('.txt'))]
write(valid, 'valid.txt', root=valid_data_dir) | [
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day10/day10.py | KohlhaseJ/adventofcode2021 | 0 | 1610692 | legalPairs = {
'(': ')',
'[': ']',
'{': '}',
'<': '>'
}
illegalCharacterScores = {
')': 3,
']': 57,
'}': 1197,
'>': 25137
}
completedCharacterScores = {
')': 1,
']': 2,
'}': 3,
'>': 4
}
def isLegalPair(opensWith, closesWith):
return legalPairs.get(opensWith, 'invalid') == closesWith
def isOpeningBracket(character):
return character in ['(', '[', '{', '<']
def isClosingBracket(character):
return character in [')', ']', '}', '>']
def getMiddleElement(list):
return list[len(list)//2]
with open("input.txt") as f:
lines = [line.rstrip('\n') for line in f.readlines()]
syntaxErrorScore = 0
autocompletionScores = []
for line in lines:
openingCharacters = []
autocompletionScore = 0
isIncompleteLine = True
for character in line:
if isOpeningBracket(character):
openingCharacters.append(character)
elif isClosingBracket(character):
lastOpeningCharacter = openingCharacters.pop()
if not isLegalPair(lastOpeningCharacter, character):
print("Syntax error: expected {} got {}!".format(legalPairs.get(lastOpeningCharacter), character))
syntaxErrorScore += illegalCharacterScores.get(character, 0)
isIncompleteLine = False
break
if isIncompleteLine:
while len(openingCharacters) > 0:
autocompletionCharacter = legalPairs.get(openingCharacters.pop())
autocompletionScore *= 5
autocompletionScore += completedCharacterScores.get(autocompletionCharacter, 0)
line += autocompletionCharacter
if autocompletionScore > 0:
autocompletionScores.append(autocompletionScore)
autocompletionScores.sort()
print("What is the total syntax error score?: {}".format(syntaxErrorScore))
print("What is the middle score?: {}".format(getMiddleElement(autocompletionScores))) | [
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utilities/hdri_to_cubemap/hdri_to_cubemap.py | wezu/p3d_samples | 15 | 191294 | <filename>utilities/hdri_to_cubemap/hdri_to_cubemap.py
import os
from panda3d.core import *
loadPrcFileData("", "framebuffer-multisample 1")
loadPrcFileData("", "multisamples 8")
from direct.showbase import ShowBase
base=ShowBase.ShowBase()
render.setAntialias(AntialiasAttrib.MMultisample)
sky_box=loader.load_model('box')
sky_box.reparent_to(render)
sky_box.set_scale(10)
sky_box.set_shader(Shader.load(Shader.SLGLSL, 'shaders/skybox_v.glsl', 'shaders/skybox_f.glsl'), 1)
sky_box.set_shader_input('noise_tex', loader.load_texture('blue_noise.png', minfilter = SamplerState.FT_nearest, magfilter = SamplerState.FT_nearest))
sky_box.set_bin('background', 100)
sky_box.set_depth_test(False)
sky_box.set_depth_write(False)
for filename in os.listdir('hdri'):
source = filename[:-4]
print(source)
sky_map=loader.load_texture('hdri/'+source+'.png')
sky_map.set_magfilter(SamplerState.FT_linear_mipmap_linear)
sky_map.set_minfilter(SamplerState.FT_linear_mipmap_linear)
sky_box.set_shader_input('sky_map', sky_map)
sky_box.set_shader_input('sky_lod', 0.0)
sky_box.set_shader_input('value', 0.0)
lod=0
resolution = 1024
while resolution >= 1:
base.saveCubeMap('temp/'+source+'_'+str(lod)+'_#.png', size = resolution)
resolution=resolution//2
lod+=1
sky_box.set_shader_input('sky_lod', float(lod*1.5))
sky_box.set_shader_input('value', lod*0.04)
base.graphicsEngine.renderFrame()
base.graphicsEngine.renderFrame()
base.graphicsEngine.renderFrame()
cubemap=loader.loadCubeMap('temp/'+source+'_#_#.png', readMipmaps = True, minfilter = SamplerState.FT_linear_mipmap_linear, magfilter = SamplerState.FT_linear_mipmap_linear)
cubemap.set_compression(Texture.CM_dxt1)
cubemap.write('cubemap/'+source+'.txo')
#base.run()
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mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/ColorAndGState.py | albertz/music-player | 132 | 54514 | import sys
from Quartz import *
import Utilities
import array
def doColorSpaceFillAndStroke(context):
theColorSpace = Utilities.getTheCalibratedRGBColorSpace()
opaqueRed = ( 0.663, 0.0, 0.031, 1.0 ) # red,green,blue,alpha
aBlue = ( 0.482, 0.62, 0.871, 1.0 ) # red,green,blue,alpha
# Set the fill color space to be the generic calibrated RGB color space.
CGContextSetFillColorSpace(context, theColorSpace)
# Set the fill color to opaque red. The number of elements in the
# array passed to this function must be the number of color
# components in the current fill color space plus 1 for alpha.
CGContextSetFillColor(context, opaqueRed)
# Set the stroke color space to be the generic calibrated RGB color space.
CGContextSetStrokeColorSpace(context, theColorSpace)
# Set the stroke color to opaque blue. The number of elements
# in the array passed to this function must be the number of color
# components in the current stroke color space plus 1 for alpha.
CGContextSetStrokeColor(context, aBlue)
CGContextSetLineWidth(context, 8.0)
# Rectangle 1.
CGContextBeginPath(context)
CGContextAddRect(context, CGRectMake(20.0, 20.0, 100.0, 100.0))
CGContextDrawPath(context, kCGPathFillStroke)
# Continue to use the stroke colorspace already set
# but change the stroke alpha value to a semitransparent blue.
aBlue = list(aBlue)
aBlue[3] = 0.5
CGContextSetStrokeColor(context, aBlue)
# Rectangle 2.
CGContextBeginPath(context)
CGContextAddRect(context, CGRectMake(140.0, 20.0, 100.0, 100.0))
CGContextDrawPath(context, kCGPathFillStroke)
# Don't release the color space since this routine
# didn't create it.
_opaqueRedColor = None
_opaqueBlueColor = None
_transparentBlueColor = None
def drawWithColorRefs(context):
global _opaqueRedColor
global _opaqueBlueColor
global _transparentBlueColor
# Initialize the CGColorRefs if necessary
if _opaqueRedColor is None:
# Initialize the color array to an opaque red
# in the generic calibrated RGB color space.
color = (0.663, 0.0, 0.031, 1.0)
theColorSpace = Utilities.getTheCalibratedRGBColorSpace()
# Create a CGColorRef for opaque red.
_opaqueRedColor = CGColorCreate(theColorSpace, color)
# Make the color array correspond to an opaque blue color.
color = (0.482, 0.62, 0.87, 1.0)
# Create another CGColorRef for opaque blue.
_opaqueBlueColor = CGColorCreate(theColorSpace, color)
# Create a new CGColorRef from the opaqueBlue CGColorRef
# but with a different alpha value.
_transparentBlueColor = CGColorCreateCopyWithAlpha(
_opaqueBlueColor, 0.5)
if _opaqueRedColor is None or _opaqueBlueColor is None or _transparentBlueColor is None:
print >>sys.stderr, "Couldn't create one of the CGColorRefs!!!"
return
# Set the fill color to the opaque red CGColor object.
CGContextSetFillColorWithColor(context, _opaqueRedColor)
# Set the stroke color to the opaque blue CGColor object.
CGContextSetStrokeColorWithColor(context, _opaqueBlueColor)
CGContextSetLineWidth(context, 8.0)
# Draw the first rectangle.
CGContextBeginPath(context)
CGContextAddRect(context, CGRectMake(20.0, 20.0, 100.0, 100.0))
CGContextDrawPath(context, kCGPathFillStroke)
# Set the stroke color to be that of the transparent blue
# CGColor object.
CGContextSetStrokeColorWithColor(context, _transparentBlueColor)
# Draw a second rectangle to the right of the first one.
CGContextBeginPath(context)
CGContextAddRect(context, CGRectMake(140.0, 20.0, 100.0, 100.0))
CGContextDrawPath(context, kCGPathFillStroke)
def doIndexedColorDrawGraphics(context):
theBaseRGBSpace = Utilities.getTheCalibratedRGBColorSpace()
lookupTable = array.array('B', (0,)*6)
opaqueRed = (0, 1) # index, alpha
aBlue = (1, 1) # index, alpha
# Set the first 3 values in the lookup table to a red of
# 169/255 = 0.663, no green, and blue = 8/255 = 0.031. This makes
# the first entry in the lookup table a shade of red.
lookupTable[0] = 169; lookupTable[1] = 0; lookupTable[2] = 8
# Set the second 3 values in the lookup table to a red value
# of 123/255 = 0.482, a green value of 158/255 = 0.62, and
# a blue value of 222/255 = 0.871. This makes the second entry
# in the lookup table a shade of blue.
lookupTable[3] = 123; lookupTable[4] = 158; lookupTable[5] = 222
# Create the indexed color space with this color lookup table,
# using the RGB color space as the base color space and a 2 element
# color lookup table to characterize the indexed color space.
theIndexedSpace = CGColorSpaceCreateIndexed(theBaseRGBSpace, 1, lookupTable)
if theIndexedSpace is not None:
CGContextSetStrokeColorSpace(context, theIndexedSpace)
CGContextSetFillColorSpace(context, theIndexedSpace)
# Set the stroke color to an opaque blue.
CGContextSetStrokeColor(context, aBlue)
# Set the fill color to an opaque red.
CGContextSetFillColor(context, opaqueRed)
CGContextSetLineWidth(context, 8.0)
# Draw the first rectangle.
CGContextBeginPath(context)
CGContextAddRect(context, CGRectMake(20.0, 20.0, 100.0, 100.0))
CGContextDrawPath(context, kCGPathFillStroke)
# Continue to use the stroke colorspace already set
# but change the stroke alpha value to a semitransparent value
# while leaving the index value unchanged.
aBlue = list(aBlue)
aBlue[1] = 0.5
CGContextSetStrokeColor(context, aBlue)
# Draw another rectangle to the right of the first one.
CGContextBeginPath(context)
CGContextAddRect(context, CGRectMake(140.0, 20.0, 100.0, 100.0))
CGContextDrawPath(context, kCGPathFillStroke)
else:
print >>sys.stderr, "Couldn't make the indexed color space!"
def drawWithGlobalAlpha(context):
rect = CGRectMake(40.0, 210.0, 100.0, 100.0)
color = [1.0, 0.0, 0.0, 1.0] # opaque red
# Set the fill color space to that returned by getTheCalibratedRGBColorSpace.
CGContextSetFillColorSpace(context, Utilities.getTheCalibratedRGBColorSpace())
CGContextSetFillColor(context, color)
for i in range(2):
CGContextSaveGState(context)
# Paint the leftmost rect on this row with 100% opaque red.
CGContextFillRect(context, rect)
CGContextTranslateCTM(context, rect.size.width + 70.0, 0.0)
# Set the alpha value of this rgba color to 0.5.
color[3] = 0.5
# Use the new color as the fill color in the graphics state.
CGContextSetFillColor(context, color)
# Paint the center rect on this row with 50% opaque red.
CGContextFillRect(context, rect)
CGContextTranslateCTM(context, rect.size.width + 70.0, 0.0)
# Set the alpha value of this rgba color to 0.25.
color[3] = 0.25
# Use the new color as the fill color in the graphics state.
CGContextSetFillColor(context, color)
# Paint the rightmost rect on this row with 25% opaque red.
CGContextFillRect(context, rect)
CGContextRestoreGState(context)
# After restoring the graphics state, the fill color is set to
# that prior to calling CGContextSaveGState, that is, opaque
# red. The coordinate system is also restored.
# Now set the context global alpha value to 50% opaque.
CGContextSetAlpha(context, 0.5)
# Translate down for a second row of rectangles.
CGContextTranslateCTM(context, 0.0, -(rect.size.height + 70.0))
# Reset the alpha value of the color array to fully opaque.
color[3] = 1.0
def drawWithColorBlendMode(context, url):
# A pleasant green color.
green = [0.584, 0.871, 0.318, 1.0]
# Create a CGPDFDocument object from the URL.
pdfDoc = CGPDFDocumentCreateWithURL(url)
if pdfDoc is None:
print >>sys.stderr, "Couldn't create CGPDFDocument from URL!"
return
# Obtain the media box for page 1 of the PDF document.
pdfRect = CGPDFDocumentGetMediaBox(pdfDoc, 1)
# Set the origin of the rectangle to (0,0).
pdfRect.origin.x = pdfRect.origin.y = 0
# Graphic 1, the left portion of the figure.
CGContextTranslateCTM(context, 20, 10 + CGRectGetHeight(pdfRect)/2)
# Draw the PDF document.
CGContextDrawPDFDocument(context, pdfRect, pdfDoc, 1)
# Set the fill color space to that returned by getTheCalibratedRGBColorSpace.
CGContextSetFillColorSpace(context, Utilities.getTheCalibratedRGBColorSpace())
# Set the fill color to green.
CGContextSetFillColor(context, green)
# Graphic 2, the top-right portion of the figure.
CGContextTranslateCTM(context, CGRectGetWidth(pdfRect) + 10,
CGRectGetHeight(pdfRect)/2 + 10)
# Draw the PDF document again.
CGContextDrawPDFDocument(context, pdfRect, pdfDoc, 1)
# Make a fill rectangle that is the same size as the PDF document
# but inset each side by 80 units in x and 20 units in y.
insetRect = CGRectInset(pdfRect, 80, 20)
# Fill the rectangle with green. Because the fill color is opaque and
# the blend mode is Normal, this obscures the drawing underneath.
CGContextFillRect(context, insetRect)
# Graphic 3, the bottom-right portion of the figure.
CGContextTranslateCTM(context, 0, -(10 + CGRectGetHeight(pdfRect)))
# Draw the PDF document again.
CGContextDrawPDFDocument(context, pdfRect, pdfDoc, 1)
# Set the blend mode to kCGBlendModeColor which will
# colorize the destination with subsequent drawing.
CGContextSetBlendMode(context, kCGBlendModeColor)
# Draw the rectangle on top of the PDF document. The portion of the
# background that is covered by the rectangle is colorized
# with the fill color.
CGContextFillRect(context, insetRect)
def createEllipsePath(context, center, ellipseSize):
CGContextSaveGState(context)
# Translate the coordinate origin to the center point.
CGContextTranslateCTM(context, center.x, center.y)
# Scale the coordinate system to half the width and height
# of the ellipse.
CGContextScaleCTM(context, ellipseSize.width/2, ellipseSize.height/2)
CGContextBeginPath(context)
# Add a circular arc to the path, centered at the origin and
# with a radius of 1.0. This radius, together with the
# scaling above for the width and height, produces an ellipse
# of the correct size.
CGContextAddArc(context, 0.0, 0.0, 1.0, 0.0, Utilities.DEGREES_TO_RADIANS(360.0), 0.0)
# Close the path so that this path is suitable for stroking,
# should that be desired.
CGContextClosePath(context)
CGContextRestoreGState(context)
_opaqueBrownColor = None
_opaqueOrangeColor = None
def doClippedEllipse(context):
global _opaqueBrownColor, _opaqueOrangeColor
theCenterPoint = CGPoint(120.0, 120.0)
theEllipseSize = CGSize(100.0, 200.0)
dash = [ 2.0 ]
# Initialize the CGColorRefs if necessary.
if _opaqueBrownColor is None:
# The initial value of the color array is an
# opaque brown in an RGB color space.
color = [0.325, 0.208, 0.157, 1.0]
theColorSpace = Utilities.getTheCalibratedRGBColorSpace()
# Create a CGColorRef for opaque brown.
_opaqueBrownColor = CGColorCreate(theColorSpace, color)
# Make the color array correspond to an opaque orange.
color = [0.965, 0.584, 0.059, 1.0 ]
# Create another CGColorRef for opaque orange.
_opaqueOrangeColor = CGColorCreate(theColorSpace, color)
# Draw two ellipses centered about the same point, one
# rotated 45 degrees from the other.
CGContextSaveGState(context)
# Ellipse 1
createEllipsePath(context, theCenterPoint, theEllipseSize)
CGContextSetFillColorWithColor(context, _opaqueBrownColor)
CGContextFillPath(context)
# Translate and rotate about the center point of the ellipse.
CGContextTranslateCTM(context, theCenterPoint.x, theCenterPoint.y)
# Rotate by 45 degrees.
CGContextRotateCTM(context, Utilities.DEGREES_TO_RADIANS(45))
# Ellipse 2
# CGPointZero is a pre-defined Quartz point corresponding to
# the coordinate (0,0).
createEllipsePath(context, CGPointZero, theEllipseSize)
CGContextSetFillColorWithColor(context, _opaqueOrangeColor)
CGContextFillPath(context)
CGContextRestoreGState(context)
CGContextTranslateCTM(context, 170.0, 0.0)
# Now use the first ellipse as a clipping area prior to
# painting the second ellipse.
CGContextSaveGState(context)
# Ellipse 3
createEllipsePath(context, theCenterPoint, theEllipseSize)
CGContextSetStrokeColorWithColor(context, _opaqueBrownColor)
CGContextSetLineDash(context, 0, dash, 1)
# Stroke the path with a dash.
CGContextStrokePath(context)
# Ellipse 4
createEllipsePath(context, theCenterPoint, theEllipseSize)
# Clip to the elliptical path.
CGContextClip(context)
CGContextTranslateCTM(context, theCenterPoint.x, theCenterPoint.y)
# Rotate by 45 degrees.
CGContextRotateCTM(context, Utilities.DEGREES_TO_RADIANS(45))
# Ellipse 5
createEllipsePath(context, CGPointZero, theEllipseSize)
CGContextSetFillColorWithColor(context, _opaqueOrangeColor)
CGContextFillPath(context)
CGContextRestoreGState(context)
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487,
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apps/lectures/serializers.py | csilouanos/student-management-system | 0 | 15291 | <filename>apps/lectures/serializers.py
from rest_framework import serializers
from .models import Lecture
class LectureSerializer(serializers.ModelSerializer):
class Meta:
model = Lecture
fields = ('id', 'title', 'lecturer_name', 'date', 'duration',
'slides_url', 'is_required')
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LeetCode/LeetCode_Python-master/LeetCode_Python-master/Algorithm-Medium/template.py | Sycamore-City-passerby/ML | 0 | 109596 | """
Time Complexity = O(log(N))
Space Complexity = O(1)
Implement int sqrt(int x).
Compute and return the square root of x, where x is guaranteed to be a non-negative integer.
Since the return type is an integer, the decimal digits are truncated and only the integer part
of the result is returned.
Example:
Input: 8
Output: 2
Explanation: The square root of 8 is 2.82842..., and since
the decimal part is truncated, 2 is returned.
"""
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lamson/utils.py | medecau/lamson | 0 | 99886 | """
Mostly utility functions Lamson uses internally that don't
really belong anywhere else in the modules. This module
is kind of a dumping ground, so if you find something that
can be improved feel free to work up a patch.
"""
from lamson import server, routing
import sys, os
import logging
import daemon
try:
from daemon import pidlockfile
except:
from lockfile import pidlockfile
import imp
import signal
def import_settings(boot_also, from_dir=None, boot_module="config.boot"):
"""Used to import the settings in a Lamson project."""
if from_dir:
sys.path.append(from_dir)
settings = __import__("config.settings", globals(), locals()).settings
if boot_also:
__import__(boot_module, globals(), locals())
return settings
def daemonize(pid, chdir, chroot, umask, files_preserve=None, do_open=True):
"""
Uses python-daemonize to do all the junk needed to make a
server a server. It supports all the features daemonize
has, except that chroot probably won't work at all without
some serious configuration on the system.
"""
context = daemon.DaemonContext()
context.pidfile = pidlockfile.PIDLockFile(pid)
context.stdout = open(os.path.join(chdir, "logs/lamson.out"),"a+")
context.stderr = open(os.path.join(chdir, "logs/lamson.err"),"a+")
context.files_preserve = files_preserve or []
context.working_directory = os.path.expanduser(chdir)
if chroot:
context.chroot_directory = os.path.expanduser(chroot)
if umask != False:
context.umask = umask
if do_open:
context.open()
return context
def drop_priv(uid, gid):
"""
Changes the uid/gid to the two given, you should give utils.daemonize
0,0 for the uid,gid so that it becomes root, which will allow you to then
do this.
"""
logging.debug("Dropping to uid=%d, gid=%d", uid, gid)
daemon.daemon.change_process_owner(uid, gid)
logging.debug("Now running as uid=%d, gid=%d", os.getgid(), os.getuid())
def make_fake_settings(host, port):
"""
When running as a logging server we need a fake settings module to work with
since the logging server can be run in any directory, so there may not be
a config/settings.py file to import.
"""
logging.basicConfig(filename="logs/logger.log", level=logging.DEBUG)
routing.Router.load(['lamson.handlers.log', 'lamson.handlers.queue'])
settings = imp.new_module('settings')
settings.receiver = server.SMTPReceiver(host, port)
settings.relay = None
logging.info("Logging mode enabled, will not send email to anyone, just log.")
return settings
def check_for_pid(pid, force):
"""Checks if a pid file is there, and if it is sys.exit. If force given
then it will remove the file and not exit if it's there."""
if os.path.exists(pid):
if not force:
print "PID file %s exists, so assuming Lamson is running. Give -FORCE to force it to start." % pid
sys.exit(1)
return # for unit tests mocking sys.exit
else:
os.unlink(pid)
def start_server(pid, force, chroot, chdir, uid, gid, umask, settings_loader, debug):
"""
Starts the server by doing a daemonize and then dropping priv
accordingly. It will only drop to the uid/gid given if both are given.
"""
check_for_pid(pid, force)
if not debug:
daemonize(pid, chdir, chroot, umask, files_preserve=[])
sys.path.append(os.getcwd())
settings = settings_loader()
if uid and gid:
drop_priv(uid, gid)
elif uid or gid:
logging.warning("You probably meant to give a uid and gid, but you gave: uid=%r, gid=%r. Will not change to any user.", uid, gid)
settings.receiver.start()
if debug:
print "Lamson started in debug mode. ctrl-c to quit..."
import time
try:
while True:
time.sleep(100000)
except KeyboardInterrupt:
# hard quit, since receiver starts a new thread. dirty but works
os._exit(1)
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] |
docs/startdocs.py | philippeitis/justpy | 1 | 156386 | <filename>docs/startdocs.py
import http.server
import socketserver
PORT = 8080
Handler = http.server.SimpleHTTPRequestHandler
with socketserver.TCPServer(("", PORT), Handler) as httpd:
print(f"Serving docs at http://localhost:{PORT}")
httpd.serve_forever()
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not_tf_opt/__init__.py | gergely-flamich/not-tf-opt | 0 | 37241 | from .variables import *
from .optimize import *
from .utils import *
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old/pro/src/lofarSun/lofarJ2000xySun.py | peijin94/LOFAR-Sun-tools | 0 | 172868 |
"""
A script to convert between the J2000 and the sun.
"""
from sunpy.coordinates.sun import sky_position as sun_position
import sunpy.coordinates.sun as sun_coord
import numpy as np
def j2000xy(RA,DEC,t_sun):
[RA_sun, DEC_sun] = sun_position(t_sun,False)
rotate_angel = sun_coord.P(t_sun)
# shift the center and transfer into arcsec
x_shift = -(RA - RA_sun.degree) * 3600
y_shift = (DEC - DEC_sun.degree) * 3600
# rotate xy according to the position angle
xx = x_shift * np.cos(-rotate_angel.rad) - y_shift * np.sin(-rotate_angel.rad)
yy = x_shift * np.sin(-rotate_angel.rad) + y_shift * np.cos(-rotate_angel.rad)
return [xx,yy]
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python/ee/tests/imagecollection_test.py | jsta/earthengine-api | 1,909 | 144023 | #!/usr/bin/env python
"""Test for the ee.imagecollection module."""
from unittest import mock
import unittest
import ee
from ee import apitestcase
class ImageCollectionTestCase(apitestcase.ApiTestCase):
def testImageCollectionConstructors(self):
"""Verifies that constructors understand valid parameters."""
from_id = ee.ImageCollection('abcd')
self.assertEqual(
ee.ApiFunction.lookup('ImageCollection.load'), from_id.func)
self.assertEqual({'id': 'abcd'}, from_id.args)
from_images = ee.ImageCollection([ee.Image(1), ee.Image(2)])
self.assertEqual(
ee.ApiFunction.lookup('ImageCollection.fromImages'), from_images.func)
self.assertEqual({'images': [ee.Image(1), ee.Image(2)]}, from_images.args)
self.assertEqual(
ee.ImageCollection([ee.Image(1)]), ee.ImageCollection(ee.Image(1)))
original = ee.ImageCollection('foo')
from_other_image_collection = ee.ImageCollection(original)
self.assertEqual(from_other_image_collection, original)
l = ee.List([ee.Image(1)]).slice(0)
from_list = ee.ImageCollection(l)
self.assertEqual({'images': l}, from_list.args)
from_computed_object = ee.ImageCollection(
ee.ComputedObject(None, {'x': 'y'}))
self.assertEqual({'x': 'y'}, from_computed_object.args)
def testImperativeFunctions(self):
"""Verifies that imperative functions return ready values."""
image_collection = ee.ImageCollection(ee.Image(1))
self.assertEqual({'value': 'fakeValue'}, image_collection.getInfo())
self.assertEqual('fakeMapId', image_collection.getMapId()['mapid'])
def testFilter(self):
"""Verifies that filtering an ImageCollection wraps the result."""
collection = ee.ImageCollection(ee.Image(1))
noop_filter = ee.Filter()
filtered = collection.filter(noop_filter)
self.assertIsInstance(filtered, ee.ImageCollection)
self.assertEqual(ee.ApiFunction.lookup('Collection.filter'), filtered.func)
self.assertEqual({
'collection': collection,
'filter': noop_filter
}, filtered.args)
def testFirst(self):
"""Verifies that first gets promoted properly."""
first = ee.ImageCollection(ee.Image(1)).first()
self.assertIsInstance(first, ee.Image)
self.assertEqual(ee.ApiFunction.lookup('Collection.first'), first.func)
def testPrepareForExport(self):
"""Verifies proper handling of export-related parameters."""
with apitestcase.UsingCloudApi():
base_collection = ee.ImageCollection(ee.Image(1))
collection, params = base_collection.prepare_for_export(
{'something': 'else'})
self.assertEqual(base_collection, collection)
self.assertEqual({'something': 'else'}, params)
collection, params = base_collection.prepare_for_export({
'crs': 'ABCD',
'crs_transform': '1,2,3,4,5,6'
})
# Need to do a serialized comparison for the collection because
# custom functions don't implement equality comparison.
def expected_preparation_function(img):
return img.reproject(
crs='ABCD', crsTransform=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
expected_collection = base_collection.map(expected_preparation_function)
self.assertEqual(
expected_collection.serialize(for_cloud_api=True),
collection.serialize(for_cloud_api=True))
self.assertEqual({}, params)
if __name__ == '__main__':
unittest.main()
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] |
DEAP_GP.py | iskaj/GeneticProgrammingExpression | 1 | 65051 | <filename>DEAP_GP.py
from deap import base, creator, gp, tools, algorithms
import math, operator, numpy
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
# Because division is scary
def protectedDiv(left, right):
try:
return left / right
except ZeroDivisionError:
return 1
# Because log is scary
def protectedLog(x):
try:
return math.log(x)
except ValueError:
return 1
dataset = [[-1.0, 0.0000], [-0.9,-0.1629], [-0.8, -0.2624],
[-0.7, -0.3129], [-0.6, -0.3264], [-0.5, -0.3125],
[-0.4, -0.2784], [-0.3, -0.2289], [-0.2, -0.1664],
[-0.1, -0.0909], [0.0, -0.0], [0.1, 0.1111],
[0.2, 0.2496], [0.3, 0.4251], [0.4, 0.6496],
[0.5, 0.9375], [0.6, 1.3056], [0.7, 1.7731],
[0.8, 2.3616], [0.9, 3.0951], [1.0, 4.0000]]
# Create the primitives set
pset = gp.PrimitiveSet("MAIN", 1)
pset.addPrimitive(operator.add, 2)
pset.addPrimitive(operator.sub, 2)
pset.addPrimitive(operator.mul, 2)
pset.addPrimitive(protectedDiv, 2)
pset.addPrimitive(math.cos, 1)
pset.addPrimitive(math.sin, 1)
pset.addPrimitive(protectedLog, 1)
pset.addPrimitive(math.exp, 1)
# Rename argument to simply x
pset.renameArguments(ARG0='x')
# Define genotype and fitness
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin)
# Define the toolbox
toolbox = base.Toolbox()
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("compile", gp.compile, pset=pset)
def evalSymbReg(individual, points):
# Transform the tree expression in a callable function
func = toolbox.compile(expr=individual)
# MSE
errors = (abs(func(point[0]) - point[1]) for point in dataset)
return math.fsum(errors),
# Squared Errors
sqerrors = ((func(point[0]) - point[1])**2 for point in dataset)
return math.fsum(sqerrors) / len(points),
toolbox.register("evaluate", evalSymbReg, points=dataset)
# toolbox.register("evaluate", evalSymbReg, points=[x/10. for x in range(-10,10)])
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("mate", gp.cxOnePoint)
toolbox.register("expr_mut", gp.genFull, min_=0, max_=2)
toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
toolbox.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
# Hold some statistics
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
# mstats = tools.Statistics(lambda ind: ind.fitness.values)
mstats.register("avg", numpy.mean)
mstats.register("std", numpy.std)
mstats.register("min", numpy.min)
mstats.register("max", numpy.max)
# Launching the evolution
def evolution():
pop = toolbox.population(n=1000)
hof = tools.HallOfFame(1)
pop, log = algorithms.eaSimple(pop, toolbox, 0.7, 0.0, 50, stats=mstats,
halloffame=hof, verbose=True)
return pop, log, hof
pop, log, hof = evolution()
# Print info for best solution found:
best = hof.items[0]
print("-- Best Individual = ", best)
print("-- Best Fitness = ", best.fitness.values[0])
# extract statistics:
# print(log.chapters['fit'].select('min'))
# print(log.chapters['fitness'])
minFitnessValues = log.chapters['fitness'].select("min")
minSizeValues = log.chapters['size'].select("min")
print("MIN FITNESS: ", minFitnessValues)
# # plot statistics:
# sns.set_style("whitegrid")
# plt.plot(minFitnessValues, color='red')
# plt.plot(minSizeValues, color='green')
# red_patch = mpatches.Patch(color='red', label='Best Fitness')
# green_patch = mpatches.Patch(color='green', label='Size')
# plt.legend(handles=[red_patch, green_patch])
# plt.xlabel('Generation')
# plt.ylabel('Best Fitness & Size')
# plt.title('Best fitness over generations')
# plt.show()
# plot statistics:
sns.set_style("whitegrid")
plt.plot(minFitnessValues, color='red')
plt.xlabel('Generation')
plt.ylabel('Best Fitness')
plt.title('Fitness of best individual over generations')
plt.show()
plt.plot(minSizeValues, color='green')
plt.xlabel('Generation')
plt.ylabel('Size')
plt.title('Size of best individual over generations')
plt.show() | [
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29889,
4294,
580,
2
] |
makahiki/apps/widgets/ask_admin/tests.py | justinslee/Wai-Not-Makahiki | 1 | 148025 | <filename>makahiki/apps/widgets/ask_admin/tests.py<gh_stars>1-10
"""
ask admin tests.
"""
from django.test import TransactionTestCase
from django.core.urlresolvers import reverse
from apps.utils import test_utils
class AskAdminFunctionalTests(TransactionTestCase):
"""AskAdmin test cases."""
def setUp(self):
"""setup"""
self.user = test_utils.setup_user(username="user",
password="<PASSWORD>")
self.client.login(username="user", password="<PASSWORD>")
def testAjaxPost(self):
"""
Test that an AJAX post to ask an admin sends an email.
"""
response = self.client.post(reverse('ask_admin_feedback'), {
'url': 'http://localhost:8000/test/',
'question': 'question',
}, HTTP_X_REQUESTED_WITH='XMLHttpRequest')
self.failUnlessEqual(response.status_code, 200)
#self.assertEqual(len(mail.outbox), 1)
#self.assertTrue(mail.outbox[0].subject.find(self.user.get_profile().name) > 0)
| [
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] |
wafhelpers/rtems_trace.py | fakecoinbase/ntpsecslashntpsec | 201 | 143010 | <reponame>fakecoinbase/ntpsecslashntpsec
from waflib.TaskGen import feature, after_method
@feature("rtems_trace")
@after_method('apply_link')
def rtems_trace(self):
if self.env.RTEMS_TEST_ENABLE:
self.link_task.env.LINK_CC = self.env.BIN_RTEMS_TLD \
+ self.env.RTEMS_TEST_FLAGS + ['--']
| [
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] |
{{cookiecutter.project_slug}}/core/exceptions/__init__.py | teamhide/cookiecutter-sanic | 2 | 126113 | from sanic.exceptions import SanicException, add_status_code
class CustomException(SanicException):
def __init__(self, message: str, code: int):
super().__init__(message=message, status_code=code)
@add_status_code(401)
class ValidationErrorException(SanicException):
def __init__(self):
message = 'Validation error exception'
super().__init__(message)
| [
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] |
tinynumpy/benchmark.py | Kramer84/tinynumpy | 168 | 54556 | <reponame>Kramer84/tinynumpy<filename>tinynumpy/benchmark.py
# -*- coding: utf-8 -*-
# Copyright (c) 2014, <NAME> and <NAME>
# tinynumpy is distributed under the terms of the MIT License.
""" Benchmarks for tinynumpy
Findings:
* A list of floats costs about 33 bytes per float
* A list if ints costs anout 41 bytes per int
* A huge list of ints 0-255, costs about 1 byter per int
* Python list takes about 5-6 times as much memory than array for 64bit
data types. Up to 40 times as much for uint8, unless Python can reuse
values.
* __slots__ help reduce the size of custom classes
* tinynumpy is about 100 times slower than numpy
* using _toflatlist instead of flat taks 50-60% of time (but more memory)
"""
from __future__ import division
import os
import sys
import time
import subprocess
import numpy as np
import tinynumpy as tnp
def _prettymem(n):
if n > 2**20:
return '%1.2f MiB' % (n / 2**20)
elif n > 2**10:
return '%1.2f KiB' % (n / 2**10)
else:
return '%1.0f B' % n
def _prettysec(n):
if n < 0.0001:
return '%1.2f us' % (n * 1000000)
elif n < 0.1:
return '%1.2f ms' % (n * 1000)
else:
return '%1.2f s' % n
code_template = """
import psutil
import os
import random
import numpy as np
import tinynumpy as tnp
N = 100 * 1000
M = 1000 * 1000
class A(object):
def __init__(self):
self.foo = 8
self.bar = 3.3
class B(object):
__slots__ = ['foo', 'bar']
def __init__(self):
self.foo = 8
self.bar = 3.3
def getmem():
process = psutil.Process(os.getpid())
return process.get_memory_info()[0]
M0 = getmem()
%s
M1 = getmem()
print(M1-M0)
"""
def measure_mem(what, code, divide=1):
cmd = [sys.executable, '-c', code_template % code]
res = subprocess.check_output(cmd, cwd=os.getcwd()).decode('utf-8')
m = int(res) / divide
print('Memory for %s:%s%s' %
(what, ' '*(22-len(what)), _prettymem(m)))
def measure_speed(what, func, *args, **kwargs):
N = 1
t0 = time.perf_counter()
func(*args, **kwargs)
t1 = time.perf_counter()
while (t1 - t0) < 0.2:
N *= 10
t0 = time.perf_counter()
for i in range(N):
func(*args, **kwargs)
t1 = time.perf_counter()
te = t1 - t0
print('Time for %s:%s%s (%i iters)' %
(what, ' '*(22-len(what)), _prettysec(te/N), N))
if __name__ == '__main__':
N = 100 * 1000
M = 1000 * 1000
print('=== MEMORY ====')
measure_mem('floats 0-M', 'L = [i*1.0 for i in range(N)]', N)
measure_mem('ints 0-M', 'L = [i for i in range(N)]', N)
measure_mem('ints 0-255', 'L = [int(random.uniform(0, 255)) for i in range(N)]', N)
measure_mem('regular object', 'L = [A() for i in range(N)]', N)
measure_mem('object with slots', 'L = [B() for i in range(N)]', N)
measure_mem(' Numpy arr size 1', 'L = [np.ones((1,)) for i in range(N)]', N)
measure_mem('Tinynumpy arr size 1', 'L = [tnp.ones((1,)) for i in range(N)]', N)
measure_mem(' Numpy arr size M', 'a = np.ones((M,))')
measure_mem('Tinynumpy arr size M', 'a = tnp.ones((M,))')
print('=== SPEED ====')
a1 = np.ones((100, 100))
a2 = tnp.ones((100, 100))
measure_speed(' numpy sum 10k', a1.sum)
measure_speed('tinynumpy sum 10k', a2.sum)
measure_speed(' numpy max 10k', a1.max)
measure_speed('tinynumpy max 10k', a2.max)
a1 = np.ones((1000, 1000))
a2 = tnp.ones((1000, 1000))
measure_speed(' numpy sum 1M', a1.sum)
measure_speed('tinynumpy sum 1M', a2.sum)
measure_speed(' numpy max 1M', a1.max)
measure_speed('tinynumpy max 1M', a2.max)
| [
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] |
ragpicker/reporting/mysql.py | K4lium/Snakepit | 7 | 62951 | <gh_stars>1-10
# Copyright (C) 2013-2015 Ragpicker Developers.
# This file is part of Ragpicker Malware Crawler - http://code.google.com/p/malware-crawler/
from yapsy.IPlugin import IPlugin
from core.abstracts import Report
class MySQL(IPlugin, Report):
"""Stores data from long-run analysis in MySQL."""
def run(self, results, objfile):
# Import muss hier stehen, sonst kommt es bei Konfiguration ohne Mysql zum Fehler
from core.databaseMysql import DatabaseMySQL
"""Writes report.
@param results: analysis results dictionary.
@param objfile: file object
"""
database = DatabaseMySQL()
print "mysql.py Methode Run"
"""
# Count query using URL hash and file hash
count = database.countRagpickerDB(results["Info"]["file"]["md5"], results["Info"]["url"]["md5"])
# If report available for the file and url -> not insert
if count == 0:
# Create a copy of the dictionary. This is done in order to not modify
# the original dictionary and possibly compromise the following
# reporting modules.
report = dict(results)
# Store the report
database.insertRagpickerDB(report)
"""
def deleteAll(self):
"""Deletes all reports.
"""
print "mysql.py Methode DeleteAll"
"""
# Alle Ragpicker-Daten aus der MongoDB loeschen
count = Database().deleteRagpickerDB()
print "*** MongoDB (Ragpicker)***"
print "deleted documents:" + str(count)
print ""
""" | [
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] |
tools/training.py | aglgit/python-md | 0 | 158658 | <reponame>aglgit/python-md
import os
import numpy as np
import pandas as pd
from amp import Amp
from amp.utilities import TrainingConvergenceError
from amp.analysis import calculate_rmses
from amp.descriptor.cutoffs import Cosine, Polynomial
from amp.descriptor.gaussian import Gaussian, make_symmetry_functions
from amp.model.neuralnetwork import NeuralNetwork
from amp.model import LossFunction
class Trainer:
def __init__(
self,
convergence=None,
energy_coefficient=1.0,
force_coefficient=None,
overfit=1e-8,
hidden_layers=(10, 10),
activation="tanh",
cutoff=Cosine(6.0),
Gs=None,
calc_dir="calcs",
):
if convergence is None:
self.convergence = {
"energy_rmse": 1e-3,
"force_rmse": None,
"max_steps": int(1e3),
}
else:
self.convergence = convergence
self.energy_coefficient = energy_coefficient
self.force_coefficient = force_coefficient
self.overfit = overfit
self.hidden_layers = hidden_layers
self.activation = activation
self.cutoff = cutoff
self.Gs = Gs
if not os.path.exists(calc_dir):
os.mkdir(calc_dir)
self.calc_dir = calc_dir
def create_Gs(
self, elements, num_radial_etas, num_angular_etas, num_zetas, angular_type
):
uncentered_etas = np.linspace(1.0, 20.0, num_radial_etas)
centers = np.zeros(num_radial_etas)
G2_uncentered = make_symmetry_functions(
elements=elements, type="G2", etas=uncentered_etas, centers=centers
)
centered_etas = 5.0 * np.ones(num_radial_etas)
centers = np.linspace(0.5, self.cutoff.Rc - 0.5, num_radial_etas)
G2_centered = make_symmetry_functions(
elements=elements, type="G2", etas=centered_etas, centers=centers
)
angular_etas = np.linspace(0.01, 3.0, num_angular_etas)
zetas = [2 ** i for i in range(num_zetas)]
G_ang = make_symmetry_functions(
elements=elements,
type=angular_type,
etas=angular_etas,
zetas=zetas,
gammas=[1.0, -1.0],
)
self.Gs = G2_uncentered + G2_centered + G_ang
def create_calc(self, label, dblabel):
amp_label = os.path.join(self.calc_dir, label)
amp_dblabel = os.path.join(self.calc_dir, dblabel)
amp_name = amp_label + ".amp"
if not os.path.exists(amp_name):
print("Creating calculator {}...".format(amp_name))
loss_function = LossFunction(
convergence=self.convergence,
energy_coefficient=self.energy_coefficient,
force_coefficient=self.force_coefficient,
overfit=self.overfit,
)
model = NeuralNetwork(
hiddenlayers=self.hidden_layers,
activation=self.activation,
lossfunction=loss_function,
weights=None,
scalings=None,
prescale=True,
)
descriptor = Gaussian(cutoff=self.cutoff, Gs=self.Gs, fortran=True)
calc = Amp(
descriptor=descriptor, model=model, label=amp_label, dblabel=amp_dblabel
)
return calc
else:
print("Calculator {} already exists!".format(amp_name))
calc = Amp.load(amp_name, label=amp_label, dblabel=amp_dblabel)
return calc
def train_calc(self, calc, traj_file):
label = calc.label
amp_name = label + ".amp"
if not os.path.exists(amp_name):
print(
"Training calculator {} from trajectory {}...".format(
amp_name, traj_file
)
)
try:
calc.train(traj_file)
except TrainingConvergenceError:
calc.save(amp_name, overwrite=True)
return amp_name
else:
print("Trained calculator {} already exists!".format(amp_name))
return amp_name
def test_calculators(
self, calcs, traj_file, columns, logfile="log.txt", dblabel=None
):
if not os.path.exists(logfile):
df = pd.DataFrame(columns=columns)
for i, (label, amp_name) in enumerate(calcs.items()):
print(
"Testing calculator {} on trajectory {}...".format(
amp_name, traj_file
)
)
amp_label = os.path.join(self.calc_dir, label)
if dblabel is None:
amp_dblabel = amp_label + "-test"
else:
amp_dblabel = os.path.join(self.calc_dir, dblabel)
energy_rmse, force_rmse = calculate_rmses(
amp_name, traj_file, label=amp_label, dblabel=amp_dblabel
)
row = [label, energy_rmse, force_rmse]
df.loc[i] = row
df.to_csv(logfile, index=False)
else:
print("Logfile {} already exists!".format(logfile))
df = pd.read_csv(
logfile, dtype={"Energy RMSE": np.float64, "Force RMSE": np.float}
)
print(df.to_latex(float_format="{:.2E}".format, index=False))
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content/test/gpu/gpu_tests/path_util.py | google-ar/chromium | 777 | 196577 | # Copyright (c) 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import os
import sys
def GetChromiumSrcDir():
return os.path.abspath(os.path.join(
os.path.dirname(__file__), os.pardir, os.pardir, os.pardir, os.pardir))
def GetGpuTestDir():
return os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
def AddDirToPathIfNeeded(*path_parts):
path = os.path.abspath(os.path.join(*path_parts))
if os.path.isdir(path) and path not in sys.path:
sys.path.append(path)
def SetupTelemetryPaths():
chromium_src_dir = GetChromiumSrcDir()
perf_path = os.path.join(chromium_src_dir, 'tools', 'perf')
absolute_perf_path = os.path.abspath(perf_path)
sys.path.append(absolute_perf_path)
from chrome_telemetry_build import chromium_config
telemetry_path = chromium_config.GetTelemetryDir()
if telemetry_path not in sys.path:
sys.path.append(telemetry_path)
py_utils_path = os.path.join(
chromium_src_dir, 'third_party', 'catapult', 'common', 'py_utils')
if py_utils_path not in sys.path:
sys.path.append(py_utils_path)
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] |
conduit/fair/data/datamodules/tabular/law.py | predictive-analytics-lab/pal-bolts | 2 | 146871 | <reponame>predictive-analytics-lab/pal-bolts<filename>conduit/fair/data/datamodules/tabular/law.py
"""Law Admissions Dataset."""
from enum import Enum
import attr
import ethicml as em
from conduit.fair.data.datamodules.tabular.base import EthicMlDataModule
__all__ = ["LawDataModule", "LawSens"]
class LawSens(Enum):
sex = "Sex"
race = "Race"
sexRace = "Sex-Race"
@attr.define(kw_only=True)
class LawDataModule(EthicMlDataModule):
"""LSAC Law Admissions Dataset."""
sens_feat: LawSens = LawSens.sex
disc_feats_only: bool = False
@property
def em_dataset(self) -> em.Dataset:
return em.law(
split=self.sens_feat.value, discrete_only=self.disc_feats_only, invert_s=self.invert_s
)
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var/spack/repos/builtin/packages/llvm-amdgpu/package.py | milljm/spack | 0 | 89539 | <reponame>milljm/spack
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class LlvmAmdgpu(CMakePackage):
"""Toolkit for the construction of highly optimized compilers,
optimizers, and run-time environments."""
homepage = "https://github.com/RadeonOpenCompute/llvm-project"
url = "https://github.com/RadeonOpenCompute/llvm-project/archive/rocm-3.5.0.tar.gz"
maintainers = ['srekolam', 'arjun-raj-kuppala']
version('3.5.0', sha256='4878fa85473b24d88edcc89938441edc85d2e8a785e567b7bd7ce274ecc2fd9c')
variant('build_type', default='Release', values=("Release", "Debug"), description='CMake build type')
depends_on('cmake@3:', type='build')
depends_on('python', type='build')
depends_on('z3', type='link')
depends_on('zlib', type='link')
depends_on('ncurses+termlib', type='link')
patch('fix-system-zlib-ncurses.patch')
root_cmakelists_dir = 'llvm'
install_targets = ['clang-tidy', 'install']
def cmake_args(self):
args = [
'-DLLVM_ENABLE_PROJECTS=clang;lld;clang-tools-extra;compiler-rt',
'-DLLVM_ENABLE_ASSERTIONS=1'
]
if self.compiler.name == "gcc":
gcc_prefix = ancestor(self.compiler.cc, 2)
args.append("-DGCC_INSTALL_PREFIX=" + gcc_prefix)
return args
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] |
cilp/utils.py | vakker/CILP | 2 | 116548 | <gh_stars>1-10
import collections
import hashlib
import itertools
import json
import re
import subprocess
import time
from functools import partial
from multiprocessing import Pool
from os import path as osp
import numpy as np
import pandas as pd
import torch
def save_params(log_dir, params):
params_id = get_dict_hash(params)
params_file = pjoin(log_dir, 'params.json')
saved = {params_id: params}
if osp.exists(params_file):
saved.update(load_json(params_file))
write_json(params_file, saved)
return params_id
def get_dict_hash(d):
return get_hash(json.dumps(d, sort_keys=True, ensure_ascii=True).encode())
def get_hash(string):
if not isinstance(string, bytes):
string = string.encode()
return hashlib.sha256(string).hexdigest()[:8]
def run_aleph(script_file):
aleph_file = get_aleph()
cmd = f'swipl -f {aleph_file} -l {script_file}'
return execute(cmd, return_output=True)
def get_aleph():
curr_dir = osp.dirname(osp.realpath(__file__))
return pjoin(curr_dir, 'aleph.pl')
def aleph_settings(mode_file, bk_file, data_files={}):
script_lines = []
# script_lines += [f':- set(verbosity, 0).']
script_lines += [f':- set(depth,3).']
for set_name, data_file in data_files.items():
script_lines += [f':- set({set_name}, "{data_file}").']
script_lines += [f':- read_all("{mode_file}").']
script_lines += [f':- read_all("{bk_file}").']
return script_lines
def create_script(directory, script_lines):
file_name = pjoin(directory, 'script.pl')
with open(file_name, 'w') as f:
f.writelines([l + '\n' for l in script_lines + [':- halt.']])
return file_name
def call(prolog, query):
return list(prolog.query(query))
def sort_file(file_in, file_out):
with open(file_in, 'r') as f:
lines = f.readlines()
with open(file_out, 'w') as f:
f.writelines(sorted(lines))
def load_examples(file_name):
with open(file_name, 'r') as f:
lines = [l.strip('. \n') for l in f.readlines()]
return np.array(lines)
def write_examples(examples, file_name):
with open(file_name, 'w') as f:
f.writelines([e + '.\n' for e in examples])
def find_in_line(pattern, lines):
found = [re.search(pattern, l) for l in lines]
return [f.groups() for f in found if f is not None]
def load_json(file_name):
with open(file_name, 'r') as f:
return json.load(f)
def write_json(file_name, d):
with open(file_name, 'w') as f:
json.dump(d, f)
# def set_feats(bcp_features, bcp_examples):
# examples = np.zeros((len(bcp_examples), len(bcp_features)))
# for i, ex in enumerate(bcp_examples):
# feat_idx = np.in1d(bcp_features, ex)
# examples[i, feat_idx] = 1
# return examples
def set_feats(bcp_features, bcp_example):
feat_idx = np.in1d(bcp_features, bcp_example, assume_unique=True)
example = np.zeros((len(bcp_features)))
example[feat_idx] = 1
return example
def get_features(bcp_examples):
bcp_features = list(set(itertools.chain.from_iterable(bcp_examples)))
bcp_features = sorted(bcp_features)
# set_feats_v = np.vectorize(set_feats, excluded='bcp_features')
# examples = np.apply_along_axis(set_feats, 0, bcp_examples, bcp_features)
start_time = time.time()
set_feats_p = partial(set_feats, bcp_features)
with Pool(20) as p:
examples = p.map(set_feats_p, bcp_examples)
examples = np.stack(examples, axis=0)
print('set done, took ', time.time() - start_time)
# examples = np.zeros((len(examples_bcp), len(bcp_features)))
# for i, ex in enumerate(examples_bcp):
# feat_idx = np.in1d(bcp_features, ex)
# examples[i, feat_idx] = 1
return examples, bcp_features
def execute(cmd, return_output=False):
popen = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
shell=True,
universal_newlines=True)
if return_output:
output, err = popen.communicate()
else:
for stdout_line in iter(popen.stdout.readline, ""):
# print(stdout_line.rstrip())
print(stdout_line.rstrip())
popen.stdout.close()
return_code = popen.wait()
if return_code:
print('Subproc error:')
print(output)
raise subprocess.CalledProcessError(return_code, cmd)
if return_output:
return output
def flatten_dict(d, parent_key='', sep='_'):
items = []
for k, v in d.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, collections.MutableMapping):
items.extend(flatten_dict(v, new_key, sep=sep).items())
elif isinstance(v, list):
v_list = {str(i): v_ for i, v_ in enumerate(v)}
items.extend(flatten_dict(v_list, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
def to_numpy(input_arr):
if isinstance(input_arr, torch.Tensor):
return input_arr.cpu().detach().numpy()
if isinstance(input_arr, (pd.DataFrame, pd.Series)):
return input_arr.values
if isinstance(input_arr, np.ndarray):
return input_arr
raise ValueError("Cannot convert %s to Numpy" % (type(input_arr)))
def acc_score(outputs, targets, with_logits=False):
outputs = to_numpy(outputs)
targets = to_numpy(targets)
if with_logits:
y_pred = (outputs >= 0).astype(int)
else:
y_pred = (outputs >= 0.5).astype(int)
correct = (y_pred == targets.astype(int)).sum()
total = targets.shape[0]
acc = correct / total
return acc
def pjoin(root, file_name):
# for Windows compatibility, otherwise pjoin mixes slashes
if '/' in root:
if '\\' in root:
raise RuntimeError(f'Path has mixed delimiters: {root}')
if root[-1] == '/':
return root + file_name
return root + '/' + file_name
if root[-1] == '\\':
return root + file_name
return root + '\\' + file_name
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] |
Classes/Filex.py | Ramin-RX7/RX7-Lib | 6 | 57699 | <gh_stars>1-10
'''
Filex Module is Sub-Module of rx7 library.
It contains 2 classes:
1- files: Static method
2- File: create File object and use its methods.
(If you are using rx7 module, don't use this and directly use rx7.files and rx7.File)
(Usually I use first one when I need a file only one time in my code
and use 2nd one when i'm working with a file more than 1 time.)
'''
import os,shutil,subprocess
from typing import Union
class files:
'''
(STATIC METHODS)\n
Actions and information about files.\n
(READ FUNCTIONS DOCSTRING)
GET INFORMATION:
- exists()
- size()
- abspath()
- mdftime()
- acstime()
- content (read function)()
- is file()
- is dir()
- is readonly()
- is hidden()
ACTIONS:
- remove()
- rename()
- move()
- copy()
- hide()
- read only()
- write()
'''
@staticmethod
def size(path):
'''
return size of the file in byte(s).
Also work on directories.
'''
return os.path.getsize(path)
#rooye pooshe emtehan she
@staticmethod
def remove(path,force=False):
'''
Use this to delete a file or a directory.
If force is True it will delete non-empty directories.
'''
if os.path.isfile(path):
os.remove(path)
else:
if force:
shutil.rmtree(path)
else:
try:
os.rmdir(path)
except OSError:
raise OSError(f"[WinError 145] The directory is not empty: '{path}'" + '\n' + ' '*23 +
'(Use force=True as an argument of remove function to remove non-empty directories.)') from None
@staticmethod
def rename(old_name,new_name):
'''Rename files with this function.'''
os.rename(old_name,new_name)
@staticmethod
def abspath(path):
'''
return absolute path of given path.
'''
return os.path.abspath(path)
@staticmethod
def exists(path):
'''
Search for the file And Returns a boolean.
if file exists: True
else: False
'''
return os.path.exists(path)
@staticmethod
def mdftime(path):
'''
Get last modify time of the path.
'''
return os.path.getmtime(path)
@staticmethod
def acstime(path):
'''
Get last access time of the path.
'''
return os.path.getatime(path)
# change to date bayad biad
@staticmethod
def move(src,dst):
'''
Move (cut) file/directory from crs to dst.
'''
shutil.move(src,dst)
#live_path= dst
#Baraye folder hast ya na?
@staticmethod
def copy(src,dest,preserve_metadata= True):
'''
Copy the file from src to destination.
preserve_metadata is for preserving metadata of file when copying.
(You can use it instead of rename too.
e.g:
copy('D:\\Test.py','E:\\Ali.py')
(It copies Test.py to E drive and renames it to Ali.py)
)
'''
if files.isdir(src):
shutil.copytree(src,dest)
else:
if preserve_metadata: shutil.copy2(src,dest)
else: shutil.copy(src,dest)
@staticmethod
def hide(path,mode=True):
'''
Hide file or folder.
If mode==False: makes 'not hide'
(ONLY WINDOWS)
'''
try:
import win32api, win32con
except:
raise ImportError('Please install pywin32 via pip')
if mode:
win32api.SetFileAttributes(path,win32con.FILE_ATTRIBUTE_HIDDEN)
else:
win32api.SetFileAttributes(path,win32con.FILE_ATTRIBUTE_NORMAL)
@staticmethod
def read_only(path,mode=True):
'''
Make file attribute read_only.
If mode==False: makes 'not read_only'
'''
if type(mode)==bool:
from stat import S_IREAD,S_IWUSR
if mode==True:
os.chmod(path, S_IREAD)
elif mode==False:
os.chmod(path, S_IWUSR)
else:
raise Exception('Second argumant (mode) should be boolean.')
@staticmethod
def read(path):
'''
This can help you to read your file faster.
Example:
read('C:\\users\\Jack\\test.txt')
==> "Content of 'test.txt' will be shown."
'''
with open(path) as f:
FileR= f.read()
return FileR
@staticmethod
def write(file_path,text=None,mode='replace',start=''):
'''
With this method you can change content of the file.
file: File you want to change its content.
content: Content you want to add to file.
mode: Type of writing method.
'a' or 'continue' for add content to end of the file.
'w' or 'replace' for overwriting to file content.
start: I use this when I use mode='continue'
'''
if mode=='replace':
op= open(file_path,mode='w')
if text==None:
text= input('Type what you want.\n\n')
op.write(text)
op.close()
elif mode=='continue':
'''opr= open(file,mode='r')
FileR= opr.read()
op= open(file,mode='w')'''
op=open(file_path,'a')
if text==None:
text= input('Type what you want to add in the end of the file.\n\n')
op.write(start+text)
op.close()
else:
raise ValueError('mode can only be: replace(default) or continue Not "{0}"'.format(mode))
@staticmethod
def isdir(path):
return os.path.isdir(path)
@staticmethod
def isfile(path):
return os.path.isfile(path)
@staticmethod
def is_readonly(path):
'''
Return True if path is readonly else False.
(May Not Work in Linux)
'''
return subprocess.getoutput(f'dir /ar {path} >nul 2>nul && echo True || echo False')
@staticmethod
def is_hidden(path):
"""
Check whether a file is presumed hidden, either because
the pathname starts with dot or because the platform
indicates such.
Return True if File or Directory is hidden.
(Work on both Linux and Windows)
"""
import platform
full_path = os.path.abspath(path)
name = os.path.basename(full_path)
def no(path): return False
platform_hidden = globals().get('is_hidden_' + platform.system(), no)
return name.startswith('.') or platform_hidden(full_path)
@staticmethod
def is_hidden_Windows(path):
import ctypes
res = ctypes.windll.kernel32.GetFileAttributesW(path)
assert res != -1
return bool(res & 2)
@staticmethod
def search_file(pattern, path='.\\',return_mode: List['list','generator']= 'list'):
'''
Search for files in path.
Return list or generator.
pattern:
- 'x.py' : search for 'x.py' in path.
- '*.py' : search for all files with .py extension in path.
- '*.*' : search for all files in path
- '**/*' : search for any file in path and also all sub-directories.
- '**/*.py: search for all python files in path and also sub-directories.
- 'mydir/**/*.py' : search for all python files in path/mydir/ and all of its sub-directories.
'''
import glob
if str(return_mode).lower() in ('list','generator'):
if return_mode=='list': return glob.glob(pattern, recursive=True)
else: return glob.iglob(pattern, recursive=True)
else:
if type(return_mode)==str:
raise ValueError(f"return_mode van be 'list' or 'generator' not {return_mode}")
else:
raise TypeError(f"return_mode type should be str and it should be in ['list', 'generator']")
@staticmethod
def search_content(path,word):
ALL= [val for sublist in [[os.path.join(i[0], j) for j in i[2]] for i in os.walk(path)] for val in sublist]
'''lst=[]
for file in ALL:
if word in rx.read(file):
lst.append(file)
return lst'''
return [file for file in ALL if word in open(file).read()]
@staticmethod
def mkdir(path):
path = os.path.normpath(path)
NEW= ''
for FILE in path.split('\\'):
NEW+= FILE+'\\'
try: os.mkdir(NEW)
except (FileExistsError,FileNotFoundError): pass
@staticmethod
def generate_tree(dir_path, level: int=-1, limit_to_directories: bool=False,
length_limit: int=1000, print_info: bool=True):
"""Given a directory Path object return a visual tree structure"""
from pathlib import Path
from itertools import islice
space= ' '; branch = '│ '; tee= '├── '; last= '└── '
dir_path = Path(dir_path) # accept string coerceable to Path
files = 0
directories = 0
def inner(dir_path: Path, prefix: str='', level=-1):
nonlocal files, directories
if not level: return # 0, stop iterating
if limit_to_directories: contents = [d for d in dir_path.iterdir() if d.is_dir()]
else: contents = list(dir_path.iterdir())
pointers = [tee] * (len(contents) - 1) + [last]
for pointer, path in zip(pointers, contents):
if path.is_dir():
yield prefix + pointer + path.name
directories += 1
extension = branch if pointer == tee else space
yield from inner(path, prefix=prefix+extension, level=level-1)
elif not limit_to_directories:
yield prefix + pointer + path.name
files += 1
RETURN=''
RETURN+=dir_path.name+'\n'
iterator = inner(dir_path, level=level)
for line in islice(iterator, length_limit): RETURN+=line+'\n'
if next(iterator, None): RETURN+=f'... length_limit, {length_limit}, reached, counted:'
if print_info: RETURN+=f'\n{directories} directories' + (f', {files} files' if files else '')
return RETURN
class MEMBERS:
@staticmethod
def all_exactdir(dir):
return os.listdir(dir)
@staticmethod
def all_all_sep(dir):
return [i for i in os.walk(dir)]
@staticmethod
def files_exactdir(dir):
return [i for i in os.walk(dir)][0][2]
@staticmethod
def files_all(dir):
return [val for sublist in [[os.path.join(i[0], j) for j in i[2]] for i in os.walk(dir)] for val in sublist]
@staticmethod
def files_all_sep(dir):
return [[os.path.join(i[0], j) for j in i[2]] for i in os.walk(dir)]
@staticmethod
def dirs_exactdir(dir):
return sorted([i for i in os.listdir(dir) if i not in [i for i in os.walk(dir)][0][2]])
@staticmethod
def dirs_all(dir):
return [TPL[0] for TPL in [i for i in os.walk(dir)]]
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Algorithms/Searching & Sorting/Exponential Search/exponential_search.py | strangestroad/interview-techdev-guide | 320 | 161565 | '''
Implementation of exponential search
Time Complexity: O(logn)
Space Complexity: Depends on implementation of binary_search {O(logn): recursive, O(1):iterative}
Used for unbounded search, when the length of array is infinite or not known
'''
#iterative implementation of binary search
def binary_search(arr, s, e, x):
'''
#arr: the array in which we need to find an element (sorted, increasing order)
#s: start index
#e: end index
#x: element we are looking for
'''
#search until arr becomes empty []
while (e>=s):
m = s + int((e-s)/2) #middle index
if x==arr[m]: #if found at mid return index
return m
elif x>arr[m]: #if x>arr[m] search only in the right array
s = m+1
elif x<arr[m]: #if x<arr[m] search only in the left array
e = m-1
return -1
def exponential_search(arr, n, x):
if x==arr[0]:
return 0;
i=1 #index
#keep increasing index until the indexed element is smaller and then do binary search
while i<n and x>arr[i]:
i = i*2
return binary_search(arr, int(i/2), min(i, n), x)
arr = [2,3,4,10,40]
x = 10
index = exponential_search(arr, len(arr), x)
if index==-1:
print("Element not present in list")
else:
print("Element",x,"is present at",index)
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utils/utils.py | miltonbd/mcs_2018_adversarial_attack | 14 | 94789 | <filename>utils/utils.py
from skimage.measure import compare_ssim
import numpy as np
def get_ssim(original_img,changed_img):
ssim = compare_ssim(np.array(original_img, dtype=np.float32),
np.array(changed_img, dtype=np.float32),
multichannel=True)
return ssim | [
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versus/models.py | amin-da71/Benbb96 | 0 | 108016 | <filename>versus/models.py
from autoslug import AutoSlugField
from django.db import models
from django.urls import reverse
from django.utils import timezone
from django.utils.functional import cached_property
from base.models import Profil
class Joueur(models.Model):
nom = models.CharField(max_length=100)
slug = AutoSlugField(unique=True, populate_from='nom')
profil = models.OneToOneField(Profil, on_delete=models.SET_NULL, null=True, blank=True)
date_creation = models.DateTimeField(verbose_name="date d'ajout", auto_now_add=True)
class Meta:
ordering = ('nom',)
def __str__(self):
return self.nom
def get_absolute_url(self):
return reverse('versus:detail-joueur', kwargs={'slug': self.slug})
@cached_property
def nb_victoire(self):
nb = 0
for partiejoueur in self.partiejoueur_set.all():
if self in partiejoueur.partie.winners:
nb += 1
return nb
@property
def ratio(self):
if self.partiejoueur_set.exists():
return round(self.nb_victoire / self.partiejoueur_set.count() * 100)
return None
class Jeu(models.Model):
nom = models.CharField(max_length=100)
slug = models.SlugField(unique=True)
createur = models.ForeignKey(Profil, on_delete=models.SET_NULL, null=True, blank=True)
date_creation = models.DateTimeField(verbose_name="date d'ajout", auto_now_add=True)
image = models.ImageField(
help_text="Une image/photo pour identifier le jeu",
null=True, blank=True, upload_to="jeux/"
)
SCORE = 1
SCORE_INVERSE = 2
CLASSEMENT = 3
TYPES = (
(SCORE, 'Score'),
(SCORE_INVERSE, 'Score inverse'),
(CLASSEMENT, 'Classement'),
)
type = models.PositiveSmallIntegerField(
choices=TYPES,
default=SCORE,
help_text="Sélectionner le type de jeu qui déterminera la façon de choisir le gagnant."
)
class Meta:
verbose_name_plural = 'jeux'
def __str__(self):
return self.nom
def get_absolute_url(self):
return reverse('versus:detail-jeu', kwargs={'slug': self.slug})
def top_players(self):
player_scores = {}
for partie in self.parties.all():
for joueur in partie.winners:
if joueur not in player_scores.keys():
player_scores[joueur] = 1
else:
player_scores[joueur] += 1
# Tri des joueurspar nombre de parties gagnées
player_scores = {k: v for k, v in sorted(player_scores.items(), key=lambda item: item[1], reverse=True)}
top_players = []
i = 1
last_nb = None
for joueur, nb in player_scores.items():
# Incrémente la position si le nombre de victoire est plus bas
if last_nb and last_nb > nb:
i += 1
top_players.append((i, joueur, nb))
last_nb = nb
return top_players
class Partie(models.Model):
jeu = models.ForeignKey(Jeu, on_delete=models.CASCADE, related_name='parties')
date = models.DateTimeField(default=timezone.now)
joueurs = models.ManyToManyField(Joueur, through='PartieJoueur')
class Meta:
ordering = ('-date',)
def __str__(self):
return '%s (%s)' % (self.jeu, self.date)
def classement_joueur(self):
""" Retourne la liste des joueurs dans l'ordre de leur classement"""
if self.jeu.type == Jeu.SCORE:
return self.partiejoueur_set.order_by('-score_classement')
return self.partiejoueur_set.order_by('score_classement')
@cached_property
def winners(self):
""" Retourne le ou les gagnants de cette partie """
if self.jeu.type == self.jeu.CLASSEMENT:
# On veut la ou les personnes arrivées en première place
return Joueur.objects.filter(partiejoueur__partie=self, partiejoueur__score_classement=1)
else:
if self.jeu.type == self.jeu.SCORE:
# On cherche le score maximum
valeur = max(partiejoueur.score_classement for partiejoueur in self.partiejoueur_set.all())
else:
# On cherche le score minimum
valeur = min(partiejoueur.score_classement for partiejoueur in self.partiejoueur_set.all())
# Puis on retourne tous les joueurs ayant ce score
return Joueur.objects.filter(partiejoueur__partie=self, partiejoueur__score_classement=valeur)
class PartieJoueur(models.Model):
partie = models.ForeignKey(Partie, on_delete=models.CASCADE)
joueur = models.ForeignKey(Joueur, on_delete=models.CASCADE)
score_classement = models.PositiveSmallIntegerField('score ou classement')
class Meta:
unique_together = (('partie', 'joueur'),)
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] |
4. PruningEncodingandRescalingFeatures/9. hc2.py | michaelbwalker/Data-Cleaning-and-Exploration-with-Machine-Learning | 7 | 128316 | # import pandas, numpy, and matplotlib
import pandas as pd
from feature_engine.encoding import OneHotEncoder
from category_encoders.hashing import HashingEncoder
from sklearn.model_selection import train_test_split
pd.set_option('display.width', 200)
pd.set_option('display.max_columns', 20)
pd.set_option('display.max_rows', 200)
pd.options.display.float_format = '{:,.0f}'.format
covidtotals = pd.read_csv("data/covidtotals.csv")
feature_cols = ['location','population',
'aged_65_older','diabetes_prevalence','region']
covidtotals = covidtotals[['total_cases'] + feature_cols].dropna()
# Separate into train and test sets
X_train, X_test, y_train, y_test = \
train_test_split(covidtotals[feature_cols],\
covidtotals[['total_cases']], test_size=0.3, random_state=0)
# use the one hot encoder for region
X_train.region.value_counts()
ohe = OneHotEncoder(top_categories=6, variables=['region'])
covidtotals_ohe = ohe.fit_transform(covidtotals)
covidtotals_ohe.filter(regex='location|region',
axis="columns").sample(5, random_state=99).T
# use the hashing encoder for region
he = HashingEncoder(cols=['region'], n_components=16)
covidtotals['region2'] = covidtotals.region
covidtotals_enc = he.fit_transform(covidtotals)
covidtotals_enc.filter(regex='col|reg', axis="columns")
covidtotals_enc.groupby(['col_0','col_1','col_2','col_3','col_4','col_5','col_6','col_7','col_8','col_9','col_10','col_11','col_12','col_13','col_14','col_15','region2']).size().reset_index()
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pyzombie/handlers/HandlerInstance.py | lanhel/pyzombie | 0 | 117643 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
#-------------------------------------------------------------------------------
"""pyzombie HTTP RESTful server handler returning the representation of an
executable."""
__author__ = ('<NAME>',)
__version__ = '1.0.1'
__copyright__ = """Copyright 2009 <NAME> (<EMAIL>)"""
__license__ = """
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
__docformat__ = "reStructuredText en"
__all__ = ['HandlerInstance']
import sys
import os
import io
import re
import string
from datetime import datetime
import json
import logging
import cgi
import mimetypes
import http.client
import http.server
from ..Handler import Handler
from ..Instance import Instance
class HandlerInstance(Handler):
@classmethod
def dispatch(cls):
cls.initdispatch(r"""^/(?P<execname>\w+)/instances/(?P<instname>\w+)/?$""",
"GET,DELETE,OPTIONS,TRACE",
"/help/RESTful")
return cls
def head(self):
self.content = "Headers"
self.get()
def get(self):
name = self.urlargs["instname"]
inst = Instance.getcached(self.executable, name)
if inst:
buf = io.StringIO()
for mediatype in self.accept:
if mediatype == "text/html":
reprfunc = self.representation_html
break
elif mediatype == "application/json":
reprfunc = self.representation_json
break
elif mediatype == "application/yaml":
reprfunc = self.representation_yaml
break
if mediatype:
reprfunc(inst, buf)
self["Content-Type"] = mediatype
self.writelines(buf.getvalue())
self.status = http.client.OK
self.flush()
else:
self.error(http.client.UNSUPPORTED_MEDIA_TYPE)
else:
self.error(http.client.NOT_FOUND)
def delete(self):
name = self.urlargs["instname"]
if name in self.executable.instances:
inst = self.executable.instances[name]
inst.delete()
self.status = http.client.OK
self.flush()
else:
self.error(http.client.NOT_FOUND)
def representation_html(self, inst, fp):
"""Create an HTML representation of the instance.
Parameters
----------
fp
Pointer to file type object to write the HTML representation.
"""
inststate = inst.state(self.serverurl(path=""), urlpath="instances")
html = """<!DOCTYPE html>
<html lang='en'>
<head>
<title>pyzombie: {name}</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
<link rel="Contents" href="/"/>
</head>
<body>
<h1>pyzombie</h1>
<h2><a href="{self}">{name}</a></h2>
<ul>
<li>Result code: {returncode}</li>
<li>Started: {start}</li>
<li>Timeout: {timeout}</li>
<li>Completed: {end}</li>
<li>Remove: {remove}</li>
<li><a href="{stdin}">stdin</a></li>
<li><a href="{stdout}">stdout</a></li>
<li><a href="{stderr}">stderr</a></li>
</ul>
</body>
</html>
""".format(**inststate)
fp.write(html)
def representation_json(self, inst, fp):
"""Create a JSON representation of the instance.
Parameters
----------
fp
Pointer to file type object to write the JSON representation.
"""
state = inst.state(self.serverurl(path=""), urlpath="instances")
json.dump(state, fp, sort_keys=True, indent=4)
def representation_yaml(self, inst, fp):
"""Create a YAML representation of the instance.
Parameters
----------
fp
Pointer to file type object to write the JSON representation.
urlprefix
The URL scheme, host, port, etc. prefix for all URLs in the representation.
urlpath
The additional path information between the executable name and the
instance name.
"""
state = inst.state(self.serverurl(path=""), urlpath="instances")
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543,
4968,
3142,
2084,
543,
2611,
2925,
1159,
13,
13,
2
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vuln/__init__.py | Maskhe/DongTai-engine | 16 | 38522 | default_app_config = 'vuln.apps.VulnConfig'
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src/models/sa_cnn1d.py | LogAnalysisTeam/methods4logfiles | 0 | 59259 | <filename>src/models/sa_cnn1d.py
from __future__ import annotations
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
import sklearn
from tqdm import tqdm
from typing import List
import sys
from src.models.utils import time_decorator, get_encoder_size
from src.models.datasets import EmbeddingDataset, CroppedDataset1D
SEED = 160121
np.random.seed(SEED)
torch.manual_seed(SEED)
class SACNN1DPyTorch(nn.Module):
def __init__(self, input_dim: int, window: int, layer_configurations: List, encoder_kernel_size: int,
decoder_kernel_size: int, n_encoder_heads: int, n_decoder_heads: int, dropout: float, maxpool: int = 2,
upsampling_factor: int = 2):
super().__init__()
n_encoder_layers = get_encoder_size(layer_configurations)
encoder_layers = [nn.Conv1d(input_dim, layer_configurations[0], kernel_size=encoder_kernel_size), nn.ReLU(),
nn.MaxPool1d(maxpool)]
for i in range(1, min(n_encoder_layers, 2)): # two CNN layers followed by self-attention
encoder_layers.append(
nn.Conv1d(layer_configurations[i - 1], layer_configurations[i], kernel_size=encoder_kernel_size))
encoder_layers.append(nn.ReLU())
encoder_layers.append(nn.MaxPool1d(maxpool))
# remove ReLU before self-attention, for more details see: <NAME>, <NAME>, <NAME>, <NAME> and <NAME>,
# "SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual
# Loss Network," in IEEE Transactions on Medical Imaging, vol. 39, no. 7, pp. 2289-2301, July 2020,
# doi: 10.1109/TMI.2020.2968472.
del encoder_layers[-2]
self.encoder = nn.Sequential(*encoder_layers)
attention_dim = layer_configurations[min(n_encoder_layers, 2) - 1]
self.encoder_self_attention = nn.MultiheadAttention(attention_dim, n_encoder_heads, dropout=dropout)
self.norm_layer1 = nn.LayerNorm(attention_dim)
self.cnn = None
if n_encoder_layers > 2:
self.cnn = nn.Sequential(nn.Conv1d(attention_dim, layer_configurations[2], kernel_size=encoder_kernel_size),
nn.ReLU(), nn.MaxPool1d(maxpool))
decoder_layers = []
for i in range(n_encoder_layers, len(layer_configurations)):
decoder_layers.append(nn.ConvTranspose1d(layer_configurations[i - 1], layer_configurations[i],
kernel_size=decoder_kernel_size))
decoder_layers.append(nn.ReLU())
decoder_layers.append(nn.Upsample(scale_factor=upsampling_factor))
self.decoder_self_attention = None
if n_encoder_layers < len(layer_configurations):
del decoder_layers[-2] # see explanation above
self.decoder = nn.Sequential(*decoder_layers)
self.decoder_self_attention = nn.MultiheadAttention(layer_configurations[-1], n_decoder_heads,
dropout=dropout)
self.norm_layer2 = nn.LayerNorm(layer_configurations[-1])
self.final_layers = nn.Sequential(
nn.ConvTranspose1d(layer_configurations[-1], input_dim, kernel_size=decoder_kernel_size),
nn.Upsample(size=window)) # it works also reversely if Tensor is greater than the window!
def forward(self, x: torch.Tensor):
x = self.encoder(x)
x = x.permute(2, 0, 1)
att_out, _ = self.encoder_self_attention(x, x, x)
x = self.norm_layer1(x + att_out)
x = x.permute(1, 2, 0)
x = self.cnn(x) if self.cnn else x
if self.decoder_self_attention:
x = self.decoder(x)
x = x.permute(2, 0, 1)
att_out, _ = self.decoder_self_attention(x, x, x)
x = self.norm_layer2(x + att_out)
x = x.permute(1, 2, 0)
x = self.final_layers(x)
return x
class SACNN1D(sklearn.base.OutlierMixin):
def __init__(self, epochs: int = 1, batch_size: int = 32, optimizer: str = 'adam',
loss: str = 'mean_squared_error', learning_rate: float = 0.001, dataset_type: str = 'cropped',
window: int = 35, verbose: int = True):
# add dictionary with architecture of the model i.e., number of layers, hidden units per layer etc.
self.epochs = epochs
self.batch_size = batch_size
self.optimizer = optimizer
self.loss = loss
self.learning_rate = learning_rate
self.dataset_type = dataset_type
self.window = window
self.verbose = verbose
# internal representation of a torch model
self._model = None
self._device = 'cuda' if torch.cuda.is_available() else 'cpu'
def fit(self, X: np.ndarray) -> SACNN1D:
# 1. convert to torch Tensor
train_dl = self._numpy_to_tensors(X, batch_size=self.batch_size, shuffle=True)
# 2. initialize model
if not self._model:
self._initialize_model(X[0].shape[-1], [X[0].shape[-1]], 3, 5, 1, 1, 0.2)
loss_function = self._get_loss_function()
opt = self._get_optimizer()
for epoch in range(self.epochs):
self._model.train()
loss, execution_time = self._train_epoch(train_dl, opt, loss_function)
if self.verbose:
digits = int(np.log10(self.epochs)) + 1
print(f'Epoch: {epoch + 1:{digits}}/{self.epochs}, loss: {loss:.5f}, time: {execution_time:.5f} s')
del train_dl # free the memory
return self
def predict(self, X: np.ndarray) -> np.array:
test_dl = self._numpy_to_tensors(X, batch_size=128, shuffle=False)
loss_function = self._get_loss_function(reduction='none')
self._model.eval()
with torch.no_grad():
ret = []
for (batch,) in test_dl:
batch = batch.to(self._device)
ret.extend(torch.mean(loss_function(self._model(batch), batch), (1, 2)).tolist())
del test_dl # free the memory
return np.asarray(ret)
def set_params(self, **kwargs):
self.epochs = kwargs['epochs']
self.batch_size = kwargs['batch_size']
self.learning_rate = kwargs['learning_rate']
self.window = kwargs['window']
self._initialize_model(kwargs['input_shape'], kwargs['layers'], kwargs['encoder_kernel_size'],
kwargs['decoder_kernel_size'], kwargs['encoder_heads'], kwargs['decoder_heads'],
kwargs['dropout'])
def _initialize_model(self, input_shape: int, layers_out: List, encoder_kernel_size: int, decoder_kernel_size: int,
n_encoder_heads: int, n_decoder_heads: int, dropout: float):
self._model = SACNN1DPyTorch(input_shape, self.window, layers_out, encoder_kernel_size, decoder_kernel_size,
n_encoder_heads, n_decoder_heads, dropout)
self._model.to(self._device)
def _get_loss_function(self, reduction: str = 'mean') -> nn.Module:
if self.loss == 'mean_squared_error':
return nn.MSELoss(reduction=reduction)
elif self.loss == 'kullback_leibler_divergence':
return nn.KLDivLoss(reduction=reduction)
else:
raise NotImplementedError(f'"{self.loss}" is not implemented.')
def _get_optimizer(self) -> torch.optim.Optimizer:
if self.optimizer == 'adam':
return torch.optim.Adam(self._model.parameters(), lr=self.learning_rate)
else:
raise NotImplementedError(f'"{self.optimizer}" is not implemented.')
@staticmethod
def custom_collate(data: List):
# randomly shuffle data within a batch
tensor = data[0]
indexes = torch.randperm(tensor.shape[0])
return [tensor[indexes]] # must stay persistent with PyTorch API
def _numpy_to_tensors(self, X: np.ndarray, batch_size: int, shuffle: bool) -> DataLoader:
if self.dataset_type == 'variable_sized':
train_ds = EmbeddingDataset(X, batch_size=batch_size)
collate_fn = self.custom_collate if shuffle else None
train_dl = DataLoader(train_ds, batch_size=1, shuffle=shuffle, collate_fn=collate_fn)
elif self.dataset_type == 'cropped':
train_ds = CroppedDataset1D(X, window=self.window)
train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=shuffle)
else:
raise NotImplementedError('This dataset preprocessing is not implemented yet.')
del train_ds # free the memory
return train_dl
@time_decorator
def _train_epoch(self, train_dl: DataLoader, optimizer: torch.optim.Optimizer, criterion: nn.Module) -> float:
loss = 0
n_seen_examples = 0
train_dl = tqdm(train_dl, file=sys.stdout, ascii=True, unit='batch')
for (batch,) in train_dl:
batch = batch.to(self._device)
optimizer.zero_grad()
pred = self._model(batch)
batch_loss = criterion(pred, batch)
batch_loss.backward()
torch.nn.utils.clip_grad_norm_(self._model.parameters(), 1)
optimizer.step()
loss += batch_loss.item() * batch.size(0)
n_seen_examples += batch.size(0)
train_dl.set_postfix({'loss': loss / n_seen_examples, 'curr_loss': batch_loss.item()})
return loss / n_seen_examples
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python-analysers/src/test/resources/org/jetbrains/research/lupa/pythonAnalysis/imports/analysis/psi/fromImportStatementsData/in_13_absolute_from_import.py | JetBrains-Research/Lupa | 16 | 91980 | from src.tasks.task1 import utils
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1,
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warhorn_api.py | jagerkin/warbot | 1 | 17642 | <gh_stars>1-10
# Copyright 2021 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Warhorn GraphQL client."""
import collections.abc
import datetime
import logging
from typing import AsyncGenerator, Dict, Optional, Sequence, Tuple, Union
import pytz
from gql import gql, Client
from gql.transport.aiohttp import AIOHTTPTransport
from gql.transport.aiohttp import log as gql_logger
_QUERY = '''\
{{
eventSessions(
events: ["{slug}"],
startsAfter: "{startsAfter}") {{
nodes {{
status
scenario {{
name
}}
scenarioOffering {{
customName
}}
signupUrl
uuid
slot {{
timezone
startsAt
endsAt
}}
}}
}}
}}'''
_GQLNode = Optional[Union[str, Dict[str, '_GQLNode'], Sequence['_GQLNode']]]
class GraphNode:
"""Wrapper for GraphQL nodes that don't make the type system (or me) cry."""
__slots__ = ('_node', )
def __init__(self, node: _GQLNode) -> None:
"""Init a GraphNode.
Args:
node: GraphQL result node.
"""
self._node: _GQLNode = node
def path(self, *path: str) -> 'GraphNode': # pylint: disable=used-before-assignment
"""Resolve a path under this node.
Args:
path: Sequence of key values to lookup.
Returns:
Node navigated to, or a None node if no such node existed.
"""
node = self._node
for p in path:
if not isinstance(node, dict):
return GraphNode(None)
node = node.get(p)
return GraphNode(node)
@property
def str(self) -> str:
"""Return the node as a string if it is one, else ''."""
if isinstance(self._node, str):
return self._node
return ''
@property
def tuple(self) -> Tuple['GraphNode', ...]:
"""Return the node as a Tuple of GraphNodes if it's a sequence, else an empty tuple."""
if isinstance(self._node, collections.abc.Sequence):
return tuple(GraphNode(e) for e in self._node)
return tuple()
def _strings_exists(*strings: str) -> bool:
"""Check that all of the strings exist and none of them are just the str 'None'."""
for s in strings:
if s in ('', 'None'):
return False
return True
class Game:
"""Game holds the key information about a Warhorn D&D session."""
__slots__ = 'uuid', 'name', 'url', 'status', 'starts', 'ends'
def __init__(self, session: GraphNode) -> None:
"""Init new Game.
Args:
session: Warhorn GraphQL session node to extract game data from.
Throws:
ValueError: in the event of key missing values, like a start time.
"""
self.uuid: str = session.path('uuid').str
"""Warhorn session UUID."""
self.name: str = (
session.path('scenarioOffering', 'customName').str
or session.path('scenario', 'name').str)
"""Game scenario name."""
self.url = session.path('signupUrl').str
"""Warhorn session signup URL."""
self.status: str = session.path('status').str
"""Warhorn session status. (e.g. PUBLISHED, DRAFT, CANCELED)"""
starts = session.path('slot', 'startsAt').str
ends = session.path('slot', 'endsAt').str
tz_str = session.path('slot', 'timezone').str or 'US/Pacific'
if not _strings_exists(self.uuid, self.name, self.status, self.url, starts, ends, tz_str):
raise ValueError(f'Missing key values for game session: {session}')
tz = pytz.timezone(tz_str)
self.starts: datetime.datetime = datetime.datetime.fromisoformat(starts).astimezone(tz)
"""Game start time."""
self.ends: datetime.datetime = datetime.datetime.fromisoformat(ends).astimezone(tz)
"""Game end time."""
@property
def time(self) -> str:
"""String describing game start/end time."""
return f'{self.starts:%-I:%M%p} - {self.ends:%-I:%M%p %Z %b %d, %Y}'
def __repr__(self) -> str:
return f'Game("{self.name}", {self.time}, {self.status}, uuid: {self.uuid})'
class WarhornAPI: # pylint: disable=too-few-public-methods
"""Warhorn client API."""
def __init__(self, url: str='https://warhorn.net/graphql', token: str='') -> None:
"""Init Warhorn client.
Args:
url: Warhorn GraphQL endpoint.
"""
headers = {}
if token:
headers['Authorization'] = f'Bearer {token}'
self._transport = AIOHTTPTransport(url=url, headers=headers)
self._client = Client(transport=self._transport, fetch_schema_from_transport=False)
gql_logger.setLevel(logging.WARNING) # type: ignore
async def get_games(
self, slug: str, starts_after: Optional[datetime.datetime]=None
) -> AsyncGenerator[Game, None]:
"""Query Warhorn for games.
Args:
slug: identifying string for the warhorn event.
starts_after: Only return Games beginning after this time.
Returns:
Generator of games.
"""
starts_after = starts_after if starts_after else datetime.datetime.now()
q = _QUERY.format(slug=slug, startsAfter=starts_after.isoformat())
query = gql(q)
result = GraphNode(await self._client.execute_async(query)) # type: ignore
for session in result.path('eventSessions', 'nodes').tuple:
status = session.path('status').str
if status not in ('PUBLISHED', 'DRAFT', 'CANCELED'):
logging.warn('Unexpected sessions status: %s', session)
if status != 'PUBLISHED':
continue
yield Game(session)
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] |
ramachandran/plot.py | leimao/Ramachandran | 2 | 174425 | from typing import List, Tuple, Optional
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib import cm
import matplotlib.colors as mplcolors
from ramachandran.io import read_residue_torsion_collection_from_file
def get_coordinates_on_reference_map(
phi_psi_angle: Tuple[float, float],
reference_map: np.ndarray) -> Tuple[int, int]:
phi = phi_psi_angle[0]
psi = phi_psi_angle[1]
height = reference_map.shape[0]
width = reference_map.shape[1]
i = int((180 - psi) / 360 * height)
j = int((phi + 180) / 360 * width)
# If i or j == resolution, adjust it.
if i == height:
i = height - 1
if j == width:
j = width - 1
return (i, j)
def create_ramachandran_plot(phi_psi_angles: List[Tuple[float, float]],
plot_file_path: str,
reference_map: Optional[np.ndarray] = None,
cmap: Optional[mplcolors.ListedColormap] = None,
protein_name: Optional[str] = None,
rendering_interpolation: bool = True) -> None:
phi_psi_angles_numpy = np.array(phi_psi_angles)
x_numpy = phi_psi_angles_numpy[:, 0]
y_numpy = phi_psi_angles_numpy[:, 1]
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
if protein_name is not None:
ax.set_title(protein_name, fontsize=24)
interpolation = None
if rendering_interpolation is True:
interpolation = "bilinear"
if reference_map is not None:
percentile_1 = np.percentile(reference_map, 60)
percentile_2 = np.percentile(reference_map, 90)
ax.imshow(np.rot90(reference_map),
interpolation=interpolation,
cmap=cmap,
norm=mplcolors.BoundaryNorm(
boundaries=[0, percentile_1, percentile_2, 1],
ncolors=cmap.N),
origin="upper",
extent=(-180, 180, -180, 180))
# Find outliers
outliers_idx = []
for i, phi_psi_angle in enumerate(phi_psi_angles):
map_i, map_j = get_coordinates_on_reference_map(
phi_psi_angle=phi_psi_angle,
reference_map=np.rot90(reference_map))
if np.rot90(reference_map)[map_i, map_j] < percentile_1:
outliers_idx.append(i)
x_outliers_numpy = x_numpy[outliers_idx]
y_outliers_numpy = y_numpy[outliers_idx]
x_numpy = np.delete(x_numpy, outliers_idx)
y_numpy = np.delete(y_numpy, outliers_idx)
ax.scatter(x_outliers_numpy,
y_outliers_numpy,
s=20,
color="red",
edgecolors="black")
ax.scatter(x_numpy, y_numpy, s=20, color="blue", edgecolors="black")
ax.set_xlim(-180, 180)
ax.set_ylim(-180, 180)
ax.xaxis.set_major_locator(ticker.MultipleLocator(45))
ax.yaxis.set_major_locator(ticker.MultipleLocator(45))
ax.xaxis.set_tick_params(labelsize=12)
ax.yaxis.set_tick_params(labelsize=12)
ax.plot([-180, 180], [0, 0], "--", linewidth=0.5, color="black")
ax.plot([0, 0], [-180, 180], "--", linewidth=0.5, color="black")
ax.set_xlabel(r"${\phi}$", fontsize=18, fontweight="bold")
ax.set_ylabel(r"${\psi}$", fontsize=18, fontweight="bold")
fig.savefig(plot_file_path, format="svg", dpi=600, bbox_inches="tight")
plt.close()
return
def create_ramachandran_plots_from_file(
file_path: str,
save_dir_path: str,
# reference_map_type: Optional[str] = "unsmoothed",
protein_name: Optional[str] = None,
rendering_interpolation: bool = False) -> None:
if not os.path.exists(save_dir_path):
os.makedirs(save_dir_path)
residue_torsion_collection = read_residue_torsion_collection_from_file(
file_path=file_path)
phi_psi_angles_general = residue_torsion_collection.collect_torsion_angles_general(
)
phi_psi_angles_gly = residue_torsion_collection.collect_torsion_angles_gly(
)
phi_psi_angles_pro = residue_torsion_collection.collect_torsion_angles_pro(
)
phi_psi_angles_prepro = residue_torsion_collection.collect_torsion_angles_prepro(
)
phi_psi_angles_list = [
phi_psi_angles_general, phi_psi_angles_gly, phi_psi_angles_pro,
phi_psi_angles_prepro
]
package_dir, filename = os.path.split(__file__)
# Using unsmoothed probability.npz is problematic because
# many probabilities are exactly zeros and thus the many percentiles are exactly zeros.
# Plotting these zero values is very problematic.
# Gaussian density is fine because none of the probability density values are exactly zero.
# if reference_map_type == "unsmoothed":
# npz_file_path = os.path.join(package_dir, "data", "probability.npz")
# npz_file = np.load(npz_file_path)
# elif reference_map_type == "smoothed":
# npz_file_path = os.path.join(package_dir, "data", "gaussian_density.npz")
# npz_file = np.load(npz_file_path)
# else:
# raise RuntimeError("Unsupported reference map type.")
npz_file_path = os.path.join(package_dir, "data", "gaussian_density.npz")
npz_file = np.load(npz_file_path)
reference_map_general = npz_file["general"]
reference_map_gly = npz_file["gly"]
reference_map_pro = npz_file["pro"]
reference_map_prepro = npz_file["prepro"]
reference_map_list = [
reference_map_general, reference_map_gly, reference_map_pro,
reference_map_prepro
]
# Using Erdős Gábor's cmaps.
# https://github.com/gerdos/PyRAMA/blob/301df17e5f2c32544b34321c4f8b0254697183ce/pyrama/config.py
cmap_general = mplcolors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF'])
cmap_gly = mplcolors.ListedColormap(['#FFFFFF', '#FFE8C5', '#FFCC7F'])
cmap_pro = mplcolors.ListedColormap(['#FFFFFF', '#D0FFC5', '#7FFF8C'])
cmap_prepro = mplcolors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF'])
cmap_list = [cmap_general, cmap_gly, cmap_pro, cmap_prepro]
filename_list = ["general.svg", "gly.svg", "pro.svg", "prepro.svg"]
file_path_list = [
os.path.join(save_dir_path, filename) for filename in filename_list
]
for phi_psi_angles, reference_map, cmap, file_path in zip(
phi_psi_angles_list, reference_map_list, cmap_list,
file_path_list):
create_ramachandran_plot(
phi_psi_angles=phi_psi_angles,
reference_map=reference_map,
cmap=cmap,
plot_file_path=file_path,
rendering_interpolation=rendering_interpolation,
protein_name=protein_name)
return
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] |
geocsv/coltypes.py | poliquin/geocsv-to-mysql | 0 | 1612453 | <reponame>poliquin/geocsv-to-mysql<gh_stars>0
import re
"""
Convert a GeoCSV column type to a suitable MySQL type.
"""
def parse_coltype(txt):
"""Parse a column specification into a type, subtype tuple."""
m = FIELD.match(txt)
if m is None:
raise TypeError('Invalid column type: {}'.format(txt))
coltype, subtype = m.groups()
return coltype, subtype, MYSQL_TYPES[coltype](subtype)
def mysql_real_type(subtype=None):
"""Determine real column type."""
try:
return {None: 'DOUBLE', 'Float32': 'FLOAT'}[subtype]
except KeyError:
pass
# assume subtype is a precision
prec = re.match(r'([0-9]+)[.,]([0-9]+)', subtype)
if prec is None:
raise ValueError('Invalid subtype for Real column: {}'.format(subtype))
m, n = int(prec.group(1)), int(prec.group(2))
return 'DECIMAL({}, {})'.format(m, n)
def _mysql_int_length(subtype):
"""Determine smallest field that can hold data with given length."""
try:
length = int(subtype)
except ValueError:
raise ValueError(
'Invalid subtype for Integer column: {}'.format(subtype)
)
if length < 3:
kind = 'TINYINT'
elif length < 4:
kind = 'SMALLINT'
elif length < 7:
kind = 'MEDIUMINT'
elif length <= 10:
kind = 'INT'
else:
kind = 'BIGINT'
return '{}({})'.format(kind, length)
def mysql_integer_type(subtype=None):
"""Determine integer column type."""
try:
return {None: 'INT', 'Int16': 'SMALLINT', 'Boolean': 'BOOLEAN'}[subtype]
except KeyError:
pass
return _mysql_int_length(subtype)
def mysql_string_type(subtype=None, varchar_only=False):
if subtype is None:
return 'VARCHAR(255)'
try:
length = int(subtype)
except ValueError:
raise ValueError(
'Invalid subtype for String column: {}'.format(subtype)
)
if not varchar_only and length <= 4:
return 'CHAR({})'.format(length)
else:
return 'VARCHAR({})'.format(length)
FIELD = re.compile(
r"""(WKT|Integer6?4?|Real|String|Date|Time|DateTime|Binary)
(?:\(([0-9.]+|Boolean|Int16|Float32)\))?
""",
re.I | re.X
)
MYSQL_TYPES = {
'WKT': lambda x: 'GEOMETRY',
'Integer64': lambda x: 'BIGINT',
'Integer': mysql_integer_type,
'Real': mysql_real_type,
'String': mysql_string_type,
'Date': lambda x: 'DATE',
'Time': lambda x: 'TIME',
'DateTime': lambda x: 'DATETIME',
'Binary': lambda x: 'BLOB'
}
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13,
2
] |
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