xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

3b50de8a6fa564ccc2023b2e7a70e457342c238f

15,920

py

Python

app/main.py

gniewus/gpt3-bias-paraphrase

d203b4fe788b5dd59be4caca87e77847470b25aa

[ "MIT" ]

2

2022-01-22T01:20:16.000Z

2022-01-26T13:28:03.000Z

app/main.py

gniewus/gpt3-bias-paraphrase

d203b4fe788b5dd59be4caca87e77847470b25aa

[ "MIT" ]

null

app/main.py

gniewus/gpt3-bias-paraphrase

d203b4fe788b5dd59be4caca87e77847470b25aa

[ "MIT" ]

2

2022-01-15T18:27:37.000Z

2022-01-22T01:20:25.000Z

import os from fastapi import FastAPI, Request from fastapi.responses import HTMLResponse, JSONResponse from fastapi.staticfiles import StaticFiles from fastapi.responses import HTMLResponse from .library.helpers import * from pydantic import BaseModel import json import re import openai from dotenv import load_dotenv load_dotenv() openai.api_key = os.environ["OPENAI_API_KEY"] openai.organization = os.environ["OPENAI_ORGANIZATION"] _RE_COMBINE_WHITESPACE = re.compile(r"\s+") app = FastAPI() #PROMPT_BASE = "Paraphrase gender-biased passages to neutral text avoiding gender-biased words like active, affectionate, adventurous, childish, aggressive, aggression, cheerful, ambitioned, commitment, communal, assertive, compassionate, athletic, connected, autonomous, considerate, cooperative, challenge, dependent, dependable, compete, competitive, emotional, confident, empath, courageous, feminine, decided, flatterable, decisive, gentle, decision, honest, determined, interpersonal, dominant, interdependent, interpersonal, forceful, kind, greedy, kinship, headstrong, loyalty, hierarchy, modesty, hostility, nag, impulsive, nurture, independent, pleasant, individual, polite, intellectual, quiet, lead, reason, logic, logically, sensitive, masculine, submissive, objective, support, opinion, sympathy, outspoken, tender, persist, together, trustworthy, reckless, understandable, understanding,stubborn, warm-hearted, superior, self-confident, boast, proud, develop warm client relationships, men, woman, female, male, assist, assistant\n\nWe’re looking for a strong businessman.\nParaphrase: We’re looking for an exceptional business person.\nMen who thrive in a competitive atmosphere.\nParaphrase: Person who is motivated by high goals.\nWe are looking for a reliable and polite waitress.\nParaphrase: Our company seeks a reliable server who is respectful towards clients\nCandidates who are assertive.\nParaphrase: Candidates who are go-getters.\nHave a polite and pleasant style.\nParaphrase: Are professional and courteous.\nNurture and connect with customers.\nParaphrase: Provide great customer service.\nWe are a determined company that delivers superior plumbing.\nParaphrase: We are a plumbing company that delivers great service.\nDemonstrated ability to act decisively in emergencies.\nParaphrase: Demonstrated ability to make quick decisions in emergencies.\nGood salesmen with strong computer skills.\nParaphrase: Good salesperson who knows how to efficiently use the computer.\nStrong women who is not afraid to take risks.\nParaphrase: Person not afraid of challenges.\nSensitive men who know how to establish a good relationship with the customer\nParaphrase: Person who knows how to establish a great relationship with the customer\nA great salesman who is open to new challenges.\nParaphrase: Great salesperson who is open to new challenges.\nThe company boasts impressive salaries, allowing our employees with financial independence.\nParaphrase: Our company offers excellent benefits, allowing our employees to maintain financial independence.\nSupport office team and assist with departmental procedures so that work progresses more efficiently.\nParaphrase: Work closely with the office team to organize departmental procedures so that work progresses more efficiently.\n We boast a competitive compensation package.\nParaphrase: We offer excellent compensation packages.\nTake our sales challenge! Even if you have no previous experience, we will facilitate the acquisition of your sales abilities.\nParaphrase: We are a company that is committed to facilitating employees to enhance their sales abilities.\nStrong communicator.\nParaphrase: A person who is good at communicating with others.\nBe a leader in your store, representing our exclusive brand.\nParaphrase: Be a role model in your store, representing our exclusive brand.\nJoin our sales community! Even if you have no previous experience, we will help nurture and develop your sales talents.\nParaphrase: Join our company and help develop your sales abilities.\nWe are a dominant engineering firm that boasts many leading clients.\nParaphrase: We are an engineering company that has many leading clients.\nStrong communication and influencing skills.\nParaphrase: Communication and influencing skills.\nAnalyze problems logically and troubleshoot to determine needed repairs.\nParaphrase: Respond to problems and troubleshoot them to uncover needed repairs.\nSensitive to clients’ needs, can develop warm client relationships.\nParaphrase: Person who understands client needs and can establish great relationships with them.\n", #PROMPT_BASE="This bot can paraphrase gender-biased job postings to neutral descriptions. This bot does not use these words:\nactive, well-groomed, men, woman, women, female, male, feminine, assist, masculine, assistant, shaved affectionate, adventurous, childish, aggressive, aggression, cheerful, ambitioned, commitment, communal, assertive, compassionate, athletic, connected, considerate, cooperative, challenge, dependent, dependable, compete, competitive, emotional, confident, empathy, courageous, decided, flatterable, decisive, gentle, honest, determined, interpersonal, dominant, interdependent, interpersonal, forceful, kind, greedy, kinship, headstrong, loyalty, hierarchy, modesty, hostility, nag, nurture, independent, pleasant, polite, intellectual, quiet, lead, reason, logic, logically, sensitive, submissive, objective, supportive, sympathy, outspoken, tender, persisting, together, trustworthy, reckless, stubborn, warm-hearted, superior, self-confident, boast, proud, banter\n\nA: We’re looking for a strong businessman.\nB: We’re looking for an exceptional business person.\nA: Chairmen who thrive in a competitive atmosphere.\nB: CEO who is motivated by high goals.\nA: We are looking for a reliable and polite waitress.\nB: Our company seeks a reliable server who is respectful towards clients\nA: Candidates who are assertive.\nB: Candidates who are go-getters.\nA: Have a polite and pleasant style.\nB: Are professional and courteous.\nA: Nurture and connect with customers.\nB: Provide great customer service.\nA: We are a determined company that delivers superior plumbing.\nB: We are a plumbing company that delivers great service.\nA: Demonstrated ability to act decisively in emergencies.\nB: Demonstrated ability to make quick decisions in emergencies.\nA: Good salesmen with strong computer skills.\nB: Good salesperson who knows how to efficiently use the computer.\nA: A strong woman who is not afraid to take risks.\nB: Person not afraid of challenges.\nA: Sensitive men who know how to establish a good relationship with the customer\nB: Person who knows how to establish a great relationship with the customer\nA: A great salesman who is open to new challenges.\nB: Great salesperson who is open to new challenges.\nA: The company boasts impressive salaries, allowing our employees with financial independence.\nB: Our company offers excellent benefits, allowing our employees to maintain financial independence.\nA: Support office team and assist with departmental procedures so that work progresses more efficiently.\nB: Work closely with the office team to organize departmental procedures so that work progresses more efficiently.\nA: We boast a competitive compensation package.\nB: We offer excellent compensation packages.\nA: Take our sales challenge! Even if you have no previous experience, we will facilitate the acquisition of your sales abilities.\nB: We are a company that is committed to facilitating employees to enhance their sales abilities.\nA: Our company needs a social person; a strong communicator.\nB: A person who is good at communicating with others.\nA: Be a leader in your store, representing our exclusive brand.\nB: Be a role model in your store, representing our unique brand.\nA: Join our sales community! Even if you have no previous experience, we will help nurture and develop your sales talents.\nB: Join our company and help develop your sales abilities.\nA: We are a dominant engineering firm that boasts many leading clients.\nB: We are an engineering company that has many leading clients.\nA: Strong communication and influencing skills.\nB: Communication and influencing skills.\nA: Analyze problems logically and troubleshoot to determine needed repairs.\nB: Respond to problems and troubleshoot them to uncover needed repairs.\nA: Sensitive to clients’ needs, can develop warm client relationships.\nB: Person who responds to client needs and can establish great relationships with them.\nA: Chairman willing to accept the challenge of regaining customers' trusts.\nB: CEO who is willing to work on restoring customer’s confidence.\nA: For this role, we’re looking for a strong, ‘All-American boy’ type. Must be well-mannered, well-groomed, well-spoken and respectful to the customers.\nB: For this role, we’re looking for a type of person who is well-mannered, respectful to the customers and always willing to go beyond.\nA: Please note that the position requires filling in the responsibilities of a receptionist, so female candidates are preferred.\nB: Please note that the position requires filling in the responsibilities of a receptionist, so candidates with customer service skills are preferred.\nA: Ability to deal with male banter and be sociable but not distracting.\nB: Ability to work closely with clients; sociable and proffesional.\nA: We are looking for a nice and good-looking girl.\nB: We are looking for a nice girl.", prompt_file = open('./prompt.txt',mode='r') PROMPT_BASE = prompt_file.read() prompt_file.close() PROMPT_BASE="I can paraphrase gender-biased job postings to neutral descriptions. I don't use these words:\nwell-groomed, men, woman, women, female, male, feminine, masculine, assistant, aggressive, cheerful, ambitious, commitment, community, assertive, compassionate, athletic, challenge, dependent, dependable, compete, competitive, emotional, confident, empathetic, courageous, decided, decisive, gentle, honest, determined, dominant, interdependent, interpersonal, forceful, kind, loyal, hierarchy, nurture, independent, pleasant, polite, intellectual, quiet, lead, logically, sensitive, objective, supportive, sympathy, outspoken, tender, trustworthy, reckless, stubborn, warm-hearted, superior, self-confident, boast, proud\n\nA: We’re looking for a strong businessman.\nB: We’re looking for an exceptional business person.\nA: Chairmen who thrive in a competitive atmosphere.\nB: CEO who is motivated by high goals.\nA: We are looking for a reliable and polite waitress.\nB: Our company seeks a reliable server who is respectful towards clients\nA: Candidates who are assertive.\nB: Candidates who are go-getters.\nA: Have a polite and pleasant style.\nB: Are professional and courteous.\nA: Nurture and connect with customers.\nB: Provide great customer service.\nA: We are a determined company that delivers superior plumbing.\nB: We are a plumbing company that delivers great service.\nA: Demonstrated ability to act decisively in emergencies.\nB: Demonstrated ability to make quick decisions in emergencies.\nA: Good salesmen with strong computer skills.\nB: Good salesperson who knows how to efficiently use the computer.\nA: A strong woman who is not afraid to take risks.\nB: Person not afraid of challenges.\nA: Sensitive men who know how to establish a good relationship with the customer\nB: Person who knows how to establish a great relationship with the customer\nA: A great salesman who is open to new challenges.\nB: Great salesperson who is open to new challenges.\nA: The company boasts impressive salaries, allowing our employees with financial independence.\nB: Our company offers excellent benefits, allowing our employees to maintain financial independence.\nA: Support office team and assist with departmental procedures so that work progresses more efficiently.\nB: Work closely with the office team to organize departmental procedures so that work progresses more efficiently.\nA: We boast a competitive compensation package.\nB: We offer excellent compensation packages.\nA: Take our sales challenge! Even if you have no previous experience, we will facilitate the acquisition of your sales abilities.\nB: We are a company that is committed to facilitating employees to enhance their sales abilities.\nA: Our company needs a social person; a strong communicator.\nB: A person who is good at communicating with others.\nA: Be a leader in your store, representing our exclusive brand.\nB: Be a role model in your store, representing our unique brand.\nA: Join our sales community! Even if you have no previous experience, we will help nurture and develop your sales talents.\nB: Join our company and help develop your sales abilities.\nA: We are a dominant engineering firm that boasts many leading clients.\nB: We are an engineering company that has many leading clients.\nA: Strong communication and influencing skills.\nB: Communication and persuasive skills.\nA: Analyze problems logically and troubleshoot to determine needed repairs.\nB: Respond to problems and troubleshoot them to uncover needed repairs.\nA: Sensitive to clients’ needs, can develop warm client relationships.\nB: Person who responds to client needs and can establish great relationships with them.\nA: Chairman willing to accept the challenge of regaining customers' trust.\nB: CEO who is willing to work on restoring customer’s confidence.\nA: For this role, we’re looking for a strong, ‘All-American boy’ type. Must be well-mannered, well-groomed, well-spoken, and respectful to the customers.\nB: For this role, we’re looking for a type of person who is well-mannered, respectful to the customers, and always willing to go beyond.\nA: Ability to deal with male banter and be sociable but not distracting.\nB: Ability to work closely with clients; sociable and professional.\nA: We are looking for a nice and good-looking girl.\nB: We are looking for a nice girl.\nA: Polite; sensitive to the needs of other employees and clients.\nB:", def build_query(INPUT): if type(INPUT) == str: INPUT = INPUT.replace("\n", "") INPUT = _RE_COMBINE_WHITESPACE.sub(" ", INPUT).strip() #print("build_query") #print(PROMPT_BASE[0]) #print("{}{}\nParaphrase: ".format(PROMPT_BASE[0], INPUT.strip())) return "{}\nA: {}\nB:".format(PROMPT_BASE[0], INPUT.strip()) app.mount("/static", StaticFiles(directory="static"), name="static") @app.post('/api', response_class=JSONResponse) async def get_prediction(data: Request): print() d = await data.json() # return json.loads(d) text = d['data'] print("text",text) QUERY = build_query(text) api_response = openai.Completion.create( engine="davinci", prompt=QUERY, temperature=.85, max_tokens=117, top_p=1, n=2, frequency_penalty=0.80, presence_penalty=1, stop=["\n"] ) #print(QUERY) #print(api_response) print(api_response["choices"]) if api_response['choices'] and api_response["choices"][0].text: arr=[] for choice in api_response["choices"]: arr.append(choice) return json.dumps({"data": api_response["choices"][0].text,"_data":arr}) else: return json.dumps({'data': text,'_data':[{'text':text}]}) import glob app.mount("/assets", StaticFiles(directory="frontend/dist/assets"), name="ass") app.mount("/css", StaticFiles(directory="frontend/dist/css"), name="css") app.mount("/js", StaticFiles(directory="frontend/dist/js"), name="js") @app.get("/", response_class=HTMLResponse) async def home(request: Request): filepath = os.path.join("./frontend/dist/", 'index.html') with open(filepath, "r", encoding="utf-8") as input_file: text = input_file.read() return HTMLResponse(text)

173.043478

4,920

0.788442

2,266

15,920

5.5203

0.19241

0.008794

0.012311

0.010073

0.76441

0.732752

0.715565

0.69494

0.65313

0.608762

0

0.001238

0.1375

15,920

91

4,921

174.945055

0.909766

0.577889

0

0.016129

0.700194

0.003129

0

0.016129

1

0.016129

false

0.016129

0.193548

0

0.274194

0.048387

0

null

0

1

0

1

0

1

0

1

0

null

0

6

3b8b02caa045250ad40c99c0fcd2559d90921c4e

32

py

Python

initial.py

bhbline/MasterVan

a5e58c9710984ea239f600ed2f4b5d5daa5762c0

[ "MIT" ]

null

initial.py

bhbline/MasterVan

a5e58c9710984ea239f600ed2f4b5d5daa5762c0

[ "MIT" ]

null

initial.py

bhbline/MasterVan

a5e58c9710984ea239f600ed2f4b5d5daa5762c0

[ "MIT" ]

null

print("Hello, my name is Van.")

16

31

0.65625

6

32

3.5

1

0

0.15625

32

1

32

0.777778

0

0.6875

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

8e658b3e24e04877e598f1b7c3259313ba7d0e23

13,304

py

Python

connectomics/model/zoo/unet_2d.py

yixinliao/pytorch_connectomics

0f6de546e6da1e0f3258b2c84f7e16b3a993c70c

[ "MIT" ]

1

2020-05-17T08:01:56.000Z

connectomics/model/zoo/unet_2d.py

yixinliao/pytorch_connectomics

0f6de546e6da1e0f3258b2c84f7e16b3a993c70c

[ "MIT" ]

null

connectomics/model/zoo/unet_2d.py

yixinliao/pytorch_connectomics

0f6de546e6da1e0f3258b2c84f7e16b3a993c70c

[ "MIT" ]

3

2020-03-31T21:40:12.000Z

2021-06-09T02:26:43.000Z

import torch import torch.nn as nn from ..block import * from ..utils import * class unet_2d(nn.Module): def __init__(self, in_num=1, out_num=1, filters=[32,64,128,256], activation='sigmoid'): super(unet_2d, self).__init__() self.activation = activation print('final activation function: '+self.activation) # Encoding Path self.layer1_E = nn.Sequential( residual_block_2d_c2(in_num, filters[0], projection=True), residual_block_2d_c2(filters[0], filters[0], projection=False), residual_block_2d_c2(filters[0], filters[0], projection=True) #SELayer(channel=filters[0], channel_reduction=2, spatial_reduction=16) ) self.layer2_E = nn.Sequential( residual_block_2d_c2(filters[0], filters[1], projection=True), residual_block_2d_c2(filters[1], filters[1], projection=False), residual_block_2d_c2(filters[1], filters[1], projection=True) #SELayer(channel=filters[1], channel_reduction=4, spatial_reduction=8) ) self.layer3_E = nn.Sequential( residual_block_2d_c2(filters[1], filters[2], projection=True), residual_block_2d_c2(filters[2], filters[2], projection=False), residual_block_2d_c2(filters[2], filters[2], projection=True) #SELayer(channel=filters[2], channel_reduction=8, spatial_reduction=4) ) # Center Block self.center = nn.Sequential( bottleneck_dilated_2d(filters[2], filters[3], projection=True), bottleneck_dilated_2d(filters[3], filters[3], projection=False), bottleneck_dilated_2d(filters[3], filters[3], projection=True) #SELayer(channel=filters[3], channel_reduction=16, spatial_reduction=2, z_reduction=2) ) # Decoding Path self.layer1_D = nn.Sequential( residual_block_2d_c2(filters[0], filters[0], projection=True), residual_block_2d_c2(filters[0], filters[0], projection=False), residual_block_2d_c2(filters[0], filters[0], projection=True) #SELayer(channel=filters[0], channel_reduction=2, spatial_reduction=16) ) self.layer2_D = nn.Sequential( residual_block_2d_c2(filters[1], filters[1], projection=True), residual_block_2d_c2(filters[1], filters[1], projection=False), residual_block_2d_c2(filters[1], filters[1], projection=True) #SELayer(channel=filters[1], channel_reduction=4, spatial_reduction=8) ) self.layer3_D = nn.Sequential( residual_block_2d_c2(filters[2], filters[2], projection=True), residual_block_2d_c2(filters[2], filters[2], projection=False), residual_block_2d_c2(filters[2], filters[2], projection=True) #SELayer(channel=filters[2], channel_reduction=8, spatial_reduction=4) ) # down & up sampling self.down = nn.MaxPool2d(kernel_size=(2,2), stride=(2,2)) self.up = nn.Upsample(scale_factor=(2,2), mode='bilinear', align_corners=True) # convert to probability self.conv1 = conv2d_bn_elu(filters[1], filters[0], kernel_size=(1,1), padding=(0,0)) self.conv2 = conv2d_bn_elu(filters[2], filters[1], kernel_size=(1,1), padding=(0,0)) self.conv3 = conv2d_bn_elu(filters[3], filters[2], kernel_size=(1,1), padding=(0,0)) self.fconv = conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1)) # initialization for m in self.modules(): if isinstance(m, (nn.Conv2d, nn.Linear)): nn.init.xavier_uniform_(m.weight, gain=nn.init.calculate_gain('relu')) def forward(self, x): z1 = self.layer1_E(x) x = self.down(z1) z2 = self.layer2_E(x) x = self.down(z2) z3 = self.layer3_E(x) x = self.down(z3) x = self.center(x) # Decoding Path x = self.up(self.conv3(x)) x = x + z3 x = self.layer3_D(x) x = self.up(self.conv2(x)) x = x + z2 x = self.layer2_D(x) x = self.up(self.conv1(x)) x = x + z1 x = self.layer1_D(x) x = self.fconv(x) if self.activation == 'sigmoid': x = torch.sigmoid(x) elif self.activation == 'tanh': x = torch.tanh(x) return x class unet_2d_ds(unet_2d): # unet_2d with deep supervision def __init__(self, in_num=1, out_num=1, filters=[32,64,128,256], activation='sigmoid'): super().__init__(in_num, out_num, filters, activation) print('final activation function: ' + self.activation) self.so1_conv1 = conv2d_bn_elu(filters[1], filters[0], kernel_size=(3,3), padding=(1,1)) self.so1_fconv = conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1)) self.so2_conv1 = conv2d_bn_elu(filters[2], filters[0], kernel_size=(3,3), padding=(1,1)) self.so2_fconv = conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1)) self.so3_conv1 = conv2d_bn_elu(filters[3], filters[0], kernel_size=(3,3), padding=(1,1)) self.so3_fconv = conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1)) def forward(self, x): z1 = self.layer1_E(x) x = self.down(z1) z2 = self.layer2_E(x) x = self.down(z2) z3 = self.layer3_E(x) x = self.down(z3) x = self.center(x) # side output 3 so3_add = self.so3_conv1(x) so3 = self.so3_fconv(so3_add) so3 = torch.sigmoid(so3) # Decoding Path x = self.up(self.conv3(x)) x = x + z3 x = self.layer3_D(x) # side output 2 so2_add = self.so2_conv1(x) + self.up(so3_add) so2 = self.so2_fconv(so2_add) so2 = torch.sigmoid(so2) x = self.up(self.conv2(x)) x = x + z2 x = self.layer2_D(x) # side output 1 so1_add = self.so1_conv1(x) + self.up(so2_add) so1 = self.so1_fconv(so1_add) so1 = torch.sigmoid(so1) x = self.up(self.conv1(x)) x = x + z1 x = self.layer1_D(x) x = x + self.up(so1_add) x = self.fconv(x) if self.activation == 'sigmoid': x = 2.0 * (torch.sigmoid(x) - 0.5) elif self.activation == 'tanh': x = torch.tanh(x) return x, so1, so2, so3 class unet_2d_sk(unet_2d_ds): def __init__(self, in_num=1, out_num=1, filters=[32,64,128,256], activation='sigmoid'): super().__init__(in_num, out_num, filters, activation) self.map_conv1 = conv2d_bn_elu(filters[0], filters[0], kernel_size=(3,3), padding=(1,1)) self.map_fconv = conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1)) def forward(self, x): z1 = self.layer1_E(x) x = self.down(z1) z2 = self.layer2_E(x) x = self.down(z2) z3 = self.layer3_E(x) x = self.down(z3) x = self.center(x) # side output 3 so3_add = self.so3_conv1(x) so3 = self.so3_fconv(so3_add) so3 = torch.sigmoid(so3) # Decoding Path x = self.up(self.conv3(x)) x = x + z3 x = self.layer3_D(x) # side output 2 so2_add = self.so2_conv1(x) + self.up(so3_add) so2 = self.so2_fconv(so2_add) so2 = torch.sigmoid(so2) x = self.up(self.conv2(x)) x = x + z2 x = self.layer2_D(x) # side output 1 so1_add = self.so1_conv1(x) + self.up(so2_add) so1 = self.so1_fconv(so1_add) so1 = torch.sigmoid(so1) x = self.up(self.conv1(x)) x = x + z1 x = self.layer1_D(x) x = x + self.up(so1_add) # side output 0 so0 = self.fconv(x) so0 = torch.sigmoid(so0) # energy map x = self.map_conv1(x) x = self.map_fconv(x) if self.activation == 'sigmoid': x = 2.0 * (torch.sigmoid(x) - 0.5) elif self.activation == 'tanh': x = torch.tanh(x) # print('x', x.size() ) # print('0',so0.size()) # print('1',so1.size()) # print('2',so2.size()) # print('3',so3.size()) return x, so0, so1, so2, so3 class unet_2d_so1(unet_2d): def __init__(self, in_num=1, out_num=1, filters=[32,64,128,256], activation='sigmoid'): super().__init__(in_num, out_num, filters, activation) print('final activation function: ' + self.activation) self.side_out1 = nn.Sequential( conv2d_bn_elu(filters[1], filters[0], kernel_size=(3,3), padding=(1,1)), conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1))) def forward(self, x): z1 = self.layer1_E(x) x = self.down(z1) z2 = self.layer2_E(x) x = self.down(z2) z3 = self.layer3_E(x) x = self.down(z3) x = self.center(x) # Decoding Path x = self.up(self.conv3(x)) x = x + z3 x = self.layer3_D(x) x = self.up(self.conv2(x)) x = x + z2 x = self.layer2_D(x) # side output so1 = self.side_out1(x) so1 = torch.sigmoid(so1) x = self.up(self.conv1(x)) x = x + z1 x = self.layer1_D(x) x = self.fconv(x) if self.activation == 'sigmoid': x = torch.sigmoid(x) elif self.activation == 'tanh': x = torch.tanh(x) return x, so1 class unet_2d_so2(unet_2d_so1): def __init__(self, in_num=1, out_num=1, filters=[32,64,128,256], activation='sigmoid'): super().__init__(in_num, out_num, filters, activation) print('final activation function: ' + self.activation) self.side_out2 = nn.Sequential( conv2d_bn_elu(filters[2], filters[0], kernel_size=(3,3), padding=(1,1)), conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1))) def forward(self, x): z1 = self.layer1_E(x) x = self.down(z1) z2 = self.layer2_E(x) x = self.down(z2) z3 = self.layer3_E(x) x = self.down(z3) x = self.center(x) # Decoding Path x = self.up(self.conv3(x)) x = x + z3 x = self.layer3_D(x) # side output 2 so2 = self.side_out2(x) so2 = torch.sigmoid(so2) x = self.up(self.conv2(x)) x = x + z2 x = self.layer2_D(x) # side output 1 so1 = self.side_out1(x) so1 = torch.sigmoid(so1) x = self.up(self.conv1(x)) x = x + z1 x = self.layer1_D(x) x = self.fconv(x) if self.activation == 'sigmoid': x = torch.sigmoid(x) elif self.activation == 'tanh': x = torch.tanh(x) return x, so1, so2 class unet_2d_so3(unet_2d_so2): def __init__(self, in_num=1, out_num=1, filters=[32,64,128,256], activation='sigmoid'): super().__init__(in_num, out_num, filters, activation) print('final activation function: ' + self.activation) self.side_out3 = nn.Sequential( conv2d_bn_elu(filters[3], filters[0], kernel_size=(3,3), padding=(1,1)), conv2d_bn_non(filters[0], out_num, kernel_size=(3,3), padding=(1,1))) def forward(self, x): z1 = self.layer1_E(x) x = self.down(z1) z2 = self.layer2_E(x) x = self.down(z2) z3 = self.layer3_E(x) x = self.down(z3) x = self.center(x) # side output 3 so3 = self.side_out3(x) so3 = torch.sigmoid(so3) # Decoding Path x = self.up(self.conv3(x)) x = x + z3 x = self.layer3_D(x) # side output 2 so2 = self.side_out2(x) so2 = torch.sigmoid(so2) x = self.up(self.conv2(x)) x = x + z2 x = self.layer2_D(x) # side output 1 so1 = self.side_out1(x) so1 = torch.sigmoid(so1) x = self.up(self.conv1(x)) x = x + z1 x = self.layer1_D(x) x = self.fconv(x) if self.activation == 'sigmoid': x = torch.sigmoid(x) elif self.activation == 'tanh': x = torch.tanh(x) return x, so1, so2, so3 class SELayer(nn.Module): # Squeeze-and-excitation layer def __init__(self, channel, channel_reduction=4, spatial_reduction=4, z_reduction=1): super(SELayer, self).__init__() self.pool_size = (z_reduction, spatial_reduction, spatial_reduction) self.se = nn.Sequential( nn.AvgPool3d(kernel_size=self.pool_size, stride=self.pool_size), nn.Conv3d(channel, channel // channel_reduction, kernel_size=1), SynchronizedBatchNorm3d(channel // channel_reduction), nn.ELU(inplace=True), nn.Conv3d(channel // channel_reduction, channel, kernel_size=1), SynchronizedBatchNorm3d(channel), nn.Sigmoid(), nn.Upsample(scale_factor=self.pool_size, mode='trilinear', align_corners=True), ) def forward(self, x): y = self.se(x) z = x + y*x return z

33.012407

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1,888

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0

null

0

1

0

null

0

6

8e7ab83474f1122ab5d2aeee872258ecdac6dc30

5,132

py

Python

src/unittest/python/required_zip_file_tests.py

svaningelgem/required_files

9c50451100fb8dd73ce9b045a0f7dd14c6366c4a

[ "MIT" ]

null

src/unittest/python/required_zip_file_tests.py

svaningelgem/required_files

9c50451100fb8dd73ce9b045a0f7dd14c6366c4a

[ "MIT" ]

null

src/unittest/python/required_zip_file_tests.py

svaningelgem/required_files

9c50451100fb8dd73ce9b045a0f7dd14c6366c4a

[ "MIT" ]

null

from pathlib import Path from tempfile import TemporaryDirectory from unittest import TestCase, main, mock from common import ( TESTFILE_NAME, TEST_STRING, URL_ZIP_WITH_DIR_STRUCTURE, URL_ZIP_WITH_MULTIPLE_DIRS, URL_ZIP_WITH_SINGLE_DIR, URL_ZIP_WITHOUT_DIR, ) from required_files import RequiredZipFile class TestRequiredZipFile(TestCase): def setUp(self) -> None: self.tmp_dir = TemporaryDirectory() def tearDown(self) -> None: self.tmp_dir.cleanup() del self.tmp_dir def test_basic_zip_skip_dir_false(self): expected_file = ( Path( RequiredZipFile( URL_ZIP_WITHOUT_DIR, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=False ).check() ) / TESTFILE_NAME ) self.assertTrue(expected_file.exists()) self.assertEqual(expected_file.read_text(), TEST_STRING) def test_basic_zip_skip_dir_true(self): p = ( Path( RequiredZipFile( URL_ZIP_WITHOUT_DIR, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=True ).check() ) / TESTFILE_NAME ) self.assertTrue(p.exists()) self.assertEqual(p.read_text(), TEST_STRING) def test_single_dir_zip_skip_dir_false(self): expected_file = ( Path( RequiredZipFile( URL_ZIP_WITH_SINGLE_DIR, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=False ).check() ) / 'dir1' / TESTFILE_NAME ) self.assertTrue(expected_file.exists()) self.assertEqual(expected_file.read_text(), TEST_STRING) def test_single_dir_zip_skip_dir_true(self): expected_file = ( Path( RequiredZipFile( URL_ZIP_WITH_SINGLE_DIR, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=True ).check() ) / TESTFILE_NAME ) self.assertTrue(expected_file.exists()) self.assertEqual(expected_file.read_text(), TEST_STRING) def test_multi_dir_zip_skip_dir_false(self): expected_file = Path( RequiredZipFile( URL_ZIP_WITH_MULTIPLE_DIRS, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=False ).check() ) file1 = expected_file / 'dir1' / TESTFILE_NAME file2 = expected_file / 'dir2' / TESTFILE_NAME self.assertTrue(file1.exists()) self.assertEqual(file1.read_text(), TEST_STRING) self.assertTrue(file2.exists()) self.assertEqual(file2.read_text(), TEST_STRING) def test_multi_dir_zip_skip_dir_true(self): expected_file = Path( RequiredZipFile( URL_ZIP_WITH_MULTIPLE_DIRS, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=True ).check() ) file1 = expected_file / 'dir1' / TESTFILE_NAME file2 = expected_file / 'dir2' / TESTFILE_NAME self.assertTrue(file1.exists()) self.assertEqual(file1.read_text(), TEST_STRING) self.assertTrue(file2.exists()) self.assertEqual(file2.read_text(), TEST_STRING) def test_structured_dir_zip_skip_dir_false(self): p = Path( RequiredZipFile( URL_ZIP_WITH_DIR_STRUCTURE, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=False ).check() ) file1 = p / 'dir1' / TESTFILE_NAME file2 = p / 'dir1/dir2' / TESTFILE_NAME self.assertTrue(file1.exists()) self.assertTrue(file2.exists()) self.assertEqual(file1.read_text(), TEST_STRING) self.assertEqual(file2.read_text(), TEST_STRING) def test_structured_dir_zip_skip_dir_true(self): p = Path( RequiredZipFile( URL_ZIP_WITH_DIR_STRUCTURE, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=True ).check() ) file1 = p / TESTFILE_NAME file2 = p / 'dir2' / TESTFILE_NAME self.assertTrue(file1.exists()) self.assertTrue(file2.exists()) self.assertEqual(file1.read_text(), TEST_STRING) self.assertEqual(file2.read_text(), TEST_STRING) @mock.patch('required_files.required_files.ZipfileMixin._process_zip') def test_when_file_is_present(self, process_zip): target_file = Path(self.tmp_dir.name) / TESTFILE_NAME target_file.touch() expected_file = ( Path( RequiredZipFile( URL_ZIP_WITHOUT_DIR, self.tmp_dir.name, file_to_check=TESTFILE_NAME, skip_initial_dir=False ).check() ) / TESTFILE_NAME ) self.assertFalse(process_zip.called) self.assertEqual(expected_file, target_file) self.assertTrue(expected_file.exists()) self.assertEqual(expected_file.read_text(), '') if __name__ == '__main__': main()

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0

0.131783

0

null

0

1

0

null

0

6

8e87bb43fa04090c522b6523d8ba899957b5f461

22

py

Python

wedding/card/__init__.py

ackneal/wedday

b57b524e3aa237a2568bda4fadb2d5709773c507

[ "MIT" ]

null

wedding/card/__init__.py

ackneal/wedday

b57b524e3aa237a2568bda4fadb2d5709773c507

[ "MIT" ]

null

wedding/card/__init__.py

ackneal/wedday

b57b524e3aa237a2568bda4fadb2d5709773c507

[ "MIT" ]

null

from .route import bp

11

21

0.772727

4

22

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1

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22

0.944444

0

1

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0

1

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1

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null

0

1

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null

0

1

0

1

0

1

0

6

8ed172a64e7a16868b8323dc7a35649a230ca912

28

py

Python

cls/p2.py

sanchez0623/zsq.LearningPython

419df031a2a905fe7d7c2dfe14aa2f8729989a9a

[ "Apache-2.0" ]

null

cls/p2.py

sanchez0623/zsq.LearningPython

419df031a2a905fe7d7c2dfe14aa2f8729989a9a

[ "Apache-2.0" ]

null

cls/p2.py

sanchez0623/zsq.LearningPython

419df031a2a905fe7d7c2dfe14aa2f8729989a9a

[ "Apache-2.0" ]

null

import sub # 会先执行__init__文件

9.333333

16

0.821429

5

28

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null

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1

0

1

0

1

0

6

d93bfa57bf46a8addc7daab86b08521d0d6b2ac8

748

py

Python

setup.py

looking-for-a-job/mac-finder.py

67c7d614b560d92a67349c2139dd98c90542c07f

[ "Unlicense" ]

7

2019-10-30T21:25:32.000Z

2022-01-26T01:53:39.000Z

setup.py

looking-for-a-job/mac-finder.py

67c7d614b560d92a67349c2139dd98c90542c07f

[ "Unlicense" ]

null

setup.py

looking-for-a-job/mac-finder.py

67c7d614b560d92a67349c2139dd98c90542c07f

[ "Unlicense" ]

null

import setuptools setuptools.setup( name='mac-finder', version='2020.12.3', install_requires=open('requirements.txt').read().splitlines(), packages=setuptools.find_packages(), scripts=['scripts/.DS_Store','scripts/.finder-alias.applescript','scripts/.finder-close-bg.applescript','scripts/.finder-close-duplicates.applescript','scripts/.finder-comment.applescript','scripts/.finder-icon.applescript','scripts/.finder-reveal.applescript','scripts/.finder-selection.applescript','scripts/finder','scripts/finder-alias','scripts/finder-close-bg','scripts/finder-close-duplicates','scripts/finder-comment','scripts/finder-exec','scripts/finder-frontmost','scripts/finder-icon','scripts/finder-reveal','scripts/finder-selection'] )

74.8

552

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87

748

6.597701

0.37931

0.385017

0.292683

0.101045

0

0.009749

0.040107

748

9

553

83.111111

0.789694

0

0.695187

0.529412

0

1

0

true

0

0.125

0

0.125

0

null

1

0

1

0

1

null

0

1

0

6

d941c97715e433adcf300f44e67d44abff2cc48d

394

py

Python

src/openbiolink/graph_creation/file_reader/mapping/__init__.py

jerryhluo/OpenBioLink

6fc073af978daec0b0db5938b73beed37f57f495

[ "MIT" ]

97

2019-11-26T09:53:18.000Z

2022-03-19T10:33:10.000Z

src/openbiolink/graph_creation/file_reader/mapping/__init__.py

jerryhluo/OpenBioLink

6fc073af978daec0b0db5938b73beed37f57f495

[ "MIT" ]

67

2019-12-09T21:01:52.000Z

2021-12-21T15:19:41.000Z

src/openbiolink/graph_creation/file_reader/mapping/__init__.py

jerryhluo/OpenBioLink

6fc073af978daec0b0db5938b73beed37f57f495

[ "MIT" ]

20

2020-01-13T23:02:25.000Z

2022-03-16T21:43:31.000Z

from openbiolink.graph_creation.file_reader.mapping.mapDisGeNetReader import MapDisGeNetReader from openbiolink.graph_creation.file_reader.mapping.mapDrugCentralPubchemReader import MapDrugCentralPubchemReader from openbiolink.graph_creation.file_reader.mapping.mapStringReader import MapStringReader from openbiolink.graph_creation.file_reader.mapping.mapUniprotReader import MapUniprotReader

78.8

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394

8.85

0.3

0.169492

0.225989

0.316384

0.508475

0

0.040609

394

4

115

98.5

0.936508

0

1

0

true

0

1

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1

0

null

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null

0

1

0

1

0

1

0

6

d9812c151978d69263c4ff75e015e47980eb6b2f

4,976

py

Python

hallo/modules/channel_control/mute.py

SpangleLabs/Hallo

17145d8f76552ecd4cbc5caef8924bd2cf0cbf24

[ "MIT" ]

1

2022-01-27T13:25:01.000Z

hallo/modules/channel_control/mute.py

joshcoales/Hallo

17145d8f76552ecd4cbc5caef8924bd2cf0cbf24

[ "MIT" ]

75

2015-09-26T18:07:18.000Z

2022-01-04T07:15:11.000Z

hallo/modules/channel_control/mute.py

SpangleLabs/Hallo

17145d8f76552ecd4cbc5caef8924bd2cf0cbf24

[ "MIT" ]

1

2021-04-10T12:02:47.000Z

from hallo.events import EventMode from hallo.function import Function import hallo.modules.channel_control.channel_control from hallo.server import Server class Mute(Function): """ Mutes the current or a selected channel. IRC only. """ def __init__(self): """ Constructor """ super().__init__() # Name for use in help listing self.help_name = "mute" # Names which can be used to address the function self.names = {"mute"} # Help documentation, if it's just a single line, can be set here self.help_docs = ( "Mutes a given channel or current channel. Format: mute <channel>" ) def run(self, event): # Get server object server_obj = event.server # If server isn't IRC type, we can't mute channels if server_obj.type != Server.TYPE_IRC: return event.create_response( "Error, this function is only available for IRC servers." ) # Check if no arguments were provided if event.command_args.strip() == "": if event.channel is None: return event.create_response( "Error, you can't set mute on a private message." ) return event.create_response(self.mute_channel(event.channel)) # Get channel from user input target_channel = server_obj.get_channel_by_name(event.command_args.strip()) if target_channel is None: return event.create_response( "Error, {} is not known on {}.".format( event.command_args.strip(), server_obj.name ) ) return event.create_response(self.mute_channel(target_channel)) def mute_channel(self, channel): """ Sets mute on a given channel. :param channel: Channel to mute :type channel: destination.Channel :return: Response to send to requester :rtype: str """ # Check if in channel if not channel.in_channel: return "Error, I'm not in that channel." # Check if hallo has op in channel if not hallo.modules.channel_control.channel_control.hallo_has_op(channel): return "Error, I don't have power to mute {}.".format(channel.name) # Send invite mode_evt = EventMode(channel.server, channel, None, "+m", inbound=False) channel.server.send(mode_evt) return "Set mute in {}.".format(channel.name) class UnMute(Function): """ Mutes the current or a selected channel. IRC only. """ def __init__(self): """ Constructor """ super().__init__() # Name for use in help listing self.help_name = "unmute" # Names which can be used to address the function self.names = {"unmute", "un mute"} # Help documentation, if it's just a single line, can be set here self.help_docs = "Unmutes a given channel or current channel if none is given. Format: unmute <channel>" def run(self, event): # Get server object server_obj = event.server # If server isn't IRC type, we can't unmute channels if server_obj.type != Server.TYPE_IRC: return event.create_response( "Error, this function is only available for IRC servers." ) # Check if no arguments were provided if event.command_args.strip() == "": if event.channel is None: return event.create_response( "Error, you can't unset mute on a private message." ) return event.create_response(self.unmute_channel(event.channel)) # Get channel from user input target_channel = server_obj.get_channel_by_name(event.command_args.strip()) if target_channel is None: return event.create_response( "Error, {} is not known on {}.".format( event.command_args.strip(), server_obj.name ) ) return event.create_response(self.unmute_channel(target_channel)) def unmute_channel(self, channel): """ Sets mute on a given channel. :param channel: Channel to mute :type channel: destination.Channel :return: Response to send to requester :rtype: str """ # Check if in channel if not channel.in_channel: return "Error, I'm not in that channel." # Check if hallo has op in channel if not hallo.modules.channel_control.channel_control.hallo_has_op(channel): return "Error, I don't have power to unmute {}.".format(channel.name) # Send invite mode_evt = EventMode(channel.server, channel, None, "-m", inbound=False) channel.server.send(mode_evt) return "Unset mute in {}.".format(channel.name)

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0

null

0

1

0

null

0

6

794f0d30ddea85d733f4fa94de6749eb5a7ed8e3

9,191

py

Python

tests/snc/agents/activity_rate_to_mpc_actions/test_feedback_mip_feasible_mpc_policy.py

dmcnamee/snc

c2da8c1e9ecdc42c59b9de73224b3d50ee1c9786

[ "Apache-2.0" ]

5

2021-03-24T16:23:10.000Z

2021-11-17T12:44:51.000Z

tests/snc/agents/activity_rate_to_mpc_actions/test_feedback_mip_feasible_mpc_policy.py

dmcnamee/snc

c2da8c1e9ecdc42c59b9de73224b3d50ee1c9786

[ "Apache-2.0" ]

3

2021-03-26T01:16:08.000Z

2021-05-08T22:06:47.000Z

tests/snc/agents/activity_rate_to_mpc_actions/test_feedback_mip_feasible_mpc_policy.py

dmcnamee/snc

c2da8c1e9ecdc42c59b9de73224b3d50ee1c9786

[ "Apache-2.0" ]

2

2021-03-24T17:20:06.000Z

2021-04-19T09:01:12.000Z

import numpy as np from snc.agents.activity_rate_to_mpc_actions.feedback_mip_feasible_mpc_policy \ import FeedbackMipFeasibleMpcPolicy def get_mpc_policy_sirl(): # Simple reentrant line like environment. constituency_matrix = np.array([[1, 0, 1], [0, 1, 0]]) buffer_processing_matrix = np.array([[-1, 0, 0], [1, -1, 0], [0, 1, -1]]) return FeedbackMipFeasibleMpcPolicy(constituency_matrix, buffer_processing_matrix) def get_mpc_policy_routing(): constituency_matrix = np.eye(3) buffer_processing_matrix = np.array([[-1, -1, -1]]) return FeedbackMipFeasibleMpcPolicy(constituency_matrix, buffer_processing_matrix) def get_mpc_policy_simple_link_routing_from_book(): mu12 = 1 mu13 = 1 mu25 = 1 mu32 = 1 mu34 = 1 mu35 = 1 mu45 = 1 mu5 = 1 buffer_processing_matrix = np.array([[-mu12, -mu13, 0, 0, 0, 0, 0, 0], [mu12, 0, -mu25, mu32, 0, 0, 0, 0], [0, mu13, 0, -mu32, -mu34, -mu35, 0, 0], [0, 0, 0, 0, mu34, 0, -mu45, 0], [0, 0, mu25, 0, 0, mu35, mu45, -mu5]]) constituency_matrix = np.eye(8) return FeedbackMipFeasibleMpcPolicy(constituency_matrix, buffer_processing_matrix) def test_get_nonidling_resources_zero_action(): sum_actions = np.array([[0], [0], [0]]) mpc_policy = get_mpc_policy_sirl() nonidling_constituency_mat, nonidling_ones = mpc_policy.get_nonidling_resources(sum_actions) assert np.all(nonidling_constituency_mat == np.zeros((2, 3))) assert np.all(nonidling_ones == np.zeros((2, 1))) def test_get_nonidling_resources_zero_action_res_1(): sum_actions = np.array([[0], [1], [0]]) mpc_policy = get_mpc_policy_sirl() nonidling_constituency_mat, nonidling_ones = mpc_policy.get_nonidling_resources(sum_actions) assert np.all(nonidling_constituency_mat == np.array([[0, 0, 0], [0, 1, 0]])) assert np.all(nonidling_ones == np.array([[0], [1]])) def test_get_nonidling_resources_zero_action_res_2(): sum_actions = np.array([[1], [0], [0]]) mpc_policy = get_mpc_policy_sirl() nonidling_constituency_mat, nonidling_ones = mpc_policy.get_nonidling_resources(sum_actions) assert np.all(nonidling_constituency_mat == np.array([[1, 0, 1], [0, 0, 0]])) assert np.all(nonidling_ones == np.array([[1], [0]])) def test_get_nonidling_resources_both_active(): sum_actions = np.array([[0], [1], [1]]) mpc_policy = get_mpc_policy_sirl() nonidling_constituency_mat, nonidling_ones = mpc_policy.get_nonidling_resources(sum_actions) assert np.all(nonidling_constituency_mat == np.array([[1, 0, 1], [0, 1, 0]])) assert np.all(nonidling_ones == np.ones((2, 1))) def test_generate_actions_with_feedback_empty_buffers(): sum_actions = np.ones((3, 1)) state = np.zeros((3, 1)) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.zeros((3, 1))) def test_generate_actions_with_feedback_empty_buffer_1(): sum_actions = np.ones((3, 1)) state = np.array([[0], [1], [1]]) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.array([[0], [1], [1]])) def test_generate_actions_with_feedback_empty_buffer_1_no_action_buffer_2(): sum_actions = np.array([[1], [1], [0]]) state = np.array([[0], [1], [1]]) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.array([[0], [1], [1]])) def test_generate_actions_with_feedback_empty_buffers_1_and_3(): sum_actions = np.array([[0], [1], [0]]) state = np.array([[0], [1], [0]]) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.array([[0], [1], [0]])) def test_generate_actions_with_feedback_priority_buffer_1(): sum_actions = np.array([[1001], [1000], [1000]]) state = np.array([[1], [1], [1]]) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.array([[1], [1], [0]])) def test_generate_actions_with_feedback_priority_buffer_3(): sum_actions = np.array([[1000], [1000], [1001]]) state = np.array([[1], [1], [1]]) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.array([[0], [1], [1]])) def test_generate_actions_with_feedback_no_priority(): sum_actions = np.array([[1000], [1000], [1000]]) state = np.array([[1], [1], [1]]) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert action[1] == 1 assert action[0] == 1 or action[2] == 1 def test_generate_actions_with_feedback_priority_buffer_3_but_empty(): sum_actions = np.array([[1000], [1000], [1001]]) state = np.array([[1], [1], [0]]) mpc_policy = get_mpc_policy_sirl() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.array([[1], [1], [0]])) def test_generate_actions_with_feedback_routing_enough_items(): sum_actions = np.array([[1], [1], [1]]) state = np.array([[3]]) mpc_policy = get_mpc_policy_routing() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.all(action == np.ones((3, 1))) def test_generate_actions_with_feedback_routing_only_one_item(): sum_actions = np.array([[1], [1], [1]]) state = np.array([[1]]) mpc_policy = get_mpc_policy_routing() action = mpc_policy.generate_actions_with_feedback(sum_actions, state) assert np.sum(action) == 1 def test_get_actions_drain_each_buffer_routing(): mpc_policy = get_mpc_policy_routing() actions_drain_each_buffer = mpc_policy.get_actions_drain_each_buffer() assert np.all(actions_drain_each_buffer[0] == [np.array([0, 1, 2])]) def test_get_action_drain_each_buffer_simple_link_routing(): mpc_policy = get_mpc_policy_simple_link_routing_from_book() actions_drain_each_buffer = mpc_policy.get_actions_drain_each_buffer() assert np.all(actions_drain_each_buffer[0] == [np.array([0, 1])]) assert np.all(actions_drain_each_buffer[1] == [np.array([2])]) assert np.all(actions_drain_each_buffer[2] == [np.array([3, 4, 5])]) assert np.all(actions_drain_each_buffer[3] == [np.array([6])]) assert np.all(actions_drain_each_buffer[4] == [np.array([7])]) def test_update_bias_counter_routing_enough_items(): mpc_policy = get_mpc_policy_routing() state = np.array([[3]]) action = np.array([[1], [1], [1]]) sum_actions = np.ones((3, 1)) mpc_policy.update_bias_counter(state, action, sum_actions) assert np.all(mpc_policy._bias_counter.value == np.zeros((3, 1))) def test_update_bias_counter_routing_enough_items_not_required(): mpc_policy = get_mpc_policy_routing() state = np.array([[3]]) action = np.array([[1], [1], [1]]) sum_actions = np.zeros((3, 1)) mpc_policy.update_bias_counter(state, action, sum_actions) assert np.all(mpc_policy._bias_counter.value == np.zeros((3, 1))) def test_update_bias_counter_routing_not_enough_items_1(): mpc_policy = get_mpc_policy_routing() state = np.array([[2]]) action = np.array([[1], [1], [0]]) sum_actions = np.ones((3, 1)) mpc_policy.update_bias_counter(state, action, sum_actions) assert np.all(mpc_policy._bias_counter.value == np.array([[0], [0], [1]])) def test_update_bias_counter_routing_not_enough_items_1_not_required(): mpc_policy = get_mpc_policy_routing() state = np.array([[2]]) action = np.array([[1], [1], [0]]) sum_actions = np.array([[1], [1], [0]]) mpc_policy.update_bias_counter(state, action, sum_actions) assert np.all(mpc_policy._bias_counter.value == np.zeros((3, 1))) def test_update_bias_counter_routing_not_enough_items_1_other_action(): mpc_policy = get_mpc_policy_routing() state = np.array([[2]]) action = np.array([[0], [1], [1]]) sum_actions = np.ones((3, 1)) mpc_policy.update_bias_counter(state, action, sum_actions) assert np.all(mpc_policy._bias_counter.value == np.array([[1], [0], [0]])) def test_update_bias_counter_routing_not_enough_items_2(): mpc_policy = get_mpc_policy_routing() state = np.array([[1]]) action = np.array([[0], [1], [0]]) sum_actions = np.ones((3, 1)) mpc_policy.update_bias_counter(state, action, sum_actions) assert np.all(mpc_policy._bias_counter.value == np.array([[1], [0], [1]])) def test_update_bias_counter_simple_link_routing(): mpc_policy = get_mpc_policy_simple_link_routing_from_book() state = np.ones((5, 1)) action = np.array([1, 0, 1, 1, 0, 0, 1, 1])[:, None] sum_actions = np.ones_like(action) mpc_policy.update_bias_counter(state, action, sum_actions) assert np.all(mpc_policy._bias_counter.value == np.array([0, 1, 0, 0, 1, 1, 0, 0])[:, None])

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794fbb023063d4b88c35838db3913d2f58b25750

60

py

Python

cifar10/models/__init__.py

huangleiBuaa/IterNorm-pytorch

574b1247036106c40d199c73ab29785b16d05407

[ "BSD-2-Clause" ]

28

2019-04-23T14:40:47.000Z

2022-03-28T13:55:21.000Z

cifar10/models/__init__.py

huangleiBuaa/IterNorm-pytorch

574b1247036106c40d199c73ab29785b16d05407

[ "BSD-2-Clause" ]

2

2019-06-27T08:27:26.000Z

2021-07-03T14:40:44.000Z

cifar10/models/__init__.py

huangleiBuaa/IterNorm-pytorch

574b1247036106c40d199c73ab29785b16d05407

[ "BSD-2-Clause" ]

8

2019-04-10T13:20:25.000Z

2021-07-29T11:10:49.000Z

from .resnet import * from .vgg import * from .WRN import *

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79d6bc9fba6cb3efb39bb139a0b0e7d7984e2cc9

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py

Python

2019/07/02/Solutions/malcolm-smith/solution.py

WillDaSilva/daily-questions

6e86b3f625df5c60d9a57f1694fafdd24c4ff2c4

[ "MIT" ]

12

2019-07-02T22:17:49.000Z

2020-10-08T16:02:04.000Z

2019/07/02/Solutions/malcolm-smith/solution.py

WillDaSilva/daily-questions

6e86b3f625df5c60d9a57f1694fafdd24c4ff2c4

[ "MIT" ]

2

2019-07-03T12:22:22.000Z

2019-09-04T23:31:38.000Z

2019/07/02/Solutions/malcolm-smith/solution.py

WillDaSilva/daily-questions

6e86b3f625df5c60d9a57f1694fafdd24c4ff2c4

[ "MIT" ]

15

2019-07-02T23:29:07.000Z

2020-05-11T15:53:07.000Z

def pushZeroes(arr): return [i for i in arr if i != 0] + [i for i in arr if i == 0] print(pushZeroes([0, 1, 0, 3, 12]))

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py

Python

majora2/submodels/__init__.py

CLIMB-COVID/majora2

46ea1809a61e4a768f8cbacaf54cba5c4d82e1f2

[ "MIT" ]

29

2019-04-04T18:03:43.000Z

2022-02-09T12:47:30.000Z

majora2/submodels/__init__.py

CLIMB-COVID/majora2

46ea1809a61e4a768f8cbacaf54cba5c4d82e1f2

[ "MIT" ]

66

2019-04-02T16:18:40.000Z

2022-01-25T16:15:42.000Z

majora2/submodels/__init__.py

CLIMB-COVID/majora2

46ea1809a61e4a768f8cbacaf54cba5c4d82e1f2

[ "MIT" ]

6

2020-04-10T14:15:32.000Z

2022-01-18T13:08:35.000Z

from .supplemental import *

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6

8de98294dc0ea2c3f70705044f738a627e18bf64

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py

Python

simulation_pomegranate.py

malwash/Simulation_Calibration

fd0ebd54e78694aa0d256d3837fa67642a35c54b

[ "Apache-2.0" ]

null

simulation_pomegranate.py

malwash/Simulation_Calibration

fd0ebd54e78694aa0d256d3837fa67642a35c54b

[ "Apache-2.0" ]

null

simulation_pomegranate.py

malwash/Simulation_Calibration

fd0ebd54e78694aa0d256d3837fa67642a35c54b

[ "Apache-2.0" ]

null

import notears import numpy as np from pomegranate import BayesianNetwork from notears.nonlinear import notears_nonlinear, NotearsMLP from statsmodels.tools.eval_measures import bic def pomegranate_setup(train_data, training_n): model = BayesianNetwork.from_samples(train_data, state_names=train_data.columns.values, algorithm='exact') #print(model.structure) # model.plot() nt_sampling_train = model.sample(1000) nt_sampling_test = model.sample(1000) #print(nt_sampling_train) np.savetxt('X_est_train.csv', nt_sampling_train, delimiter=',') #np.savetxt('W_est_test.csv', nt_sampling_test, delimiter=',') return nt_sampling_train, nt_sampling_test def pomegranate_setup_b(train_data, training_n): model = BayesianNetwork.from_samples(train_data, state_names=train_data.columns.values, algorithm='greedy') #print(model.structure) # model.plot() nt_sampling_train = model.sample(1000) nt_sampling_test = model.sample(1000) #print(nt_sampling_train) np.savetxt('X_est_train.csv', nt_sampling_train, delimiter=',') #np.savetxt('W_est_test.csv', nt_sampling_test, delimiter=',') return nt_sampling_train, nt_sampling_test def pomegranate_setup_c(train_data, training_n): model = BayesianNetwork.from_samples(train_data, state_names=train_data.columns.values, algorithm='chow-liu’') #print(model.structure) # model.plot() nt_sampling_train = model.sample(1000) nt_sampling_test = model.sample(1000) #print(nt_sampling_train) np.savetxt('X_est_train.csv', nt_sampling_train, delimiter=',') #np.savetxt('W_est_test.csv', nt_sampling_test, delimiter=',') return nt_sampling_train, nt_sampling_test

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8df36a1face9d6c6b4a0e92f1d065d5bdef9cb4b

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py

Python

__init__.py

tekromancr/pypurdypixels

2a357bb5e423303d08c03dac871dbd0f98b63035

[ "MIT" ]

null

__init__.py

tekromancr/pypurdypixels

2a357bb5e423303d08c03dac871dbd0f98b63035

[ "MIT" ]

null

__init__.py

tekromancr/pypurdypixels

2a357bb5e423303d08c03dac871dbd0f98b63035

[ "MIT" ]

null

from strand import *

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6

5c2890399e7412165e09afb334e6d9e942573ac3

157

py

Python

python_mbills/exceptions.py

boris-savic/python-mbills

a591be0a3e07ee825503c2e0dd8a54d5c991f56f

[ "MIT" ]

null

python_mbills/exceptions.py

boris-savic/python-mbills

a591be0a3e07ee825503c2e0dd8a54d5c991f56f

[ "MIT" ]

null

python_mbills/exceptions.py

boris-savic/python-mbills

a591be0a3e07ee825503c2e0dd8a54d5c991f56f

[ "MIT" ]

null

class SignatureValidationException(Exception): pass class TransactionDoesNotExist(Exception): pass class InsufficientFunds(Exception): pass

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5c37d5938bc6e4ff26abc6a0454650dbb466b92e

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py

Python

datasets/__init__.py

rioyokotalab/ecl-isvr

ae274b1b81b1d1c10db008140c477f5893a0c1c3

[ "BSD-4-Clause-UC" ]

null

datasets/__init__.py

rioyokotalab/ecl-isvr

ae274b1b81b1d1c10db008140c477f5893a0c1c3

[ "BSD-4-Clause-UC" ]

null

datasets/__init__.py

rioyokotalab/ecl-isvr

ae274b1b81b1d1c10db008140c477f5893a0c1c3

[ "BSD-4-Clause-UC" ]

2

2021-09-30T02:13:40.000Z

2021-12-14T07:33:28.000Z

#! -*- coding:utf-8 from .datasets import *

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eb76ec1152ded3758f71170ddfc00815659ac7b8

187

py

Python

profiles_api/admin.py

jaesungreemei/profiles-rest-api

385d6b8ba1891397771a4f4d36f603358cf33517

[ "MIT" ]

null

profiles_api/admin.py

jaesungreemei/profiles-rest-api

385d6b8ba1891397771a4f4d36f603358cf33517

[ "MIT" ]

null

profiles_api/admin.py

jaesungreemei/profiles-rest-api

385d6b8ba1891397771a4f4d36f603358cf33517

[ "MIT" ]

null

from django.contrib import admin # Enable the model through imports from profiles_api import models admin.site.register(models.UserProfile) admin.site.register(models.ProfileFeedItem)

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ebe2de359eabe8105405f751046c2ddce10b0f70

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py

Python

datadog_checks_base/tests/openmetrics/test_config.py

vbarbaresi/integrations-core

ab26ab1cd6c28a97c1ad1177093a93659658c7aa

[ "BSD-3-Clause" ]

1

2021-06-06T23:49:17.000Z

datadog_checks_base/tests/openmetrics/test_config.py

vbarbaresi/integrations-core

ab26ab1cd6c28a97c1ad1177093a93659658c7aa

[ "BSD-3-Clause" ]

null

datadog_checks_base/tests/openmetrics/test_config.py

vbarbaresi/integrations-core

ab26ab1cd6c28a97c1ad1177093a93659658c7aa

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2020-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import pytest from datadog_checks.dev.testing import requires_py3 from .utils import get_check pytestmark = [requires_py3, pytest.mark.openmetrics, pytest.mark.openmetrics_config] class TestPrometheusEndpoint: def test_not_string(self, dd_run_check): check = get_check({'openmetrics_endpoint': 9000}) with pytest.raises(Exception, match='^The setting `openmetrics_endpoint` must be a string$'): dd_run_check(check, extract_message=True) def test_missing(self, dd_run_check): check = get_check({'openmetrics_endpoint': ''}) with pytest.raises(Exception, match='^The setting `openmetrics_endpoint` is required$'): dd_run_check(check, extract_message=True) class TestNamespace: def test_not_string(self, dd_run_check): check = get_check({'namespace': 9000}) check.__NAMESPACE__ = '' with pytest.raises(Exception, match='^Setting `namespace` must be a string$'): dd_run_check(check, extract_message=True) def test_not_string_override(self, dd_run_check): check = get_check({'namespace': 'foo'}) check.__NAMESPACE__ = 9000 with pytest.raises(Exception, match='^Setting `namespace` must be a string$'): dd_run_check(check, extract_message=True) def test_missing(self, dd_run_check): check = get_check({'openmetrics_endpoint': 'test'}) check.__NAMESPACE__ = '' with pytest.raises(Exception, match='^Setting `namespace` is required$'): dd_run_check(check, extract_message=True) class TestRawMetricPrefix: def test_not_string(self, dd_run_check): check = get_check({'raw_metric_prefix': 9000}) with pytest.raises(Exception, match='^Setting `raw_metric_prefix` must be a string$'): dd_run_check(check, extract_message=True) class TestHostnameLabel: def test_not_string(self, dd_run_check): check = get_check({'hostname_label': 9000}) with pytest.raises(Exception, match='^Setting `hostname_label` must be a string$'): dd_run_check(check, extract_message=True) class TestHostnameFormat: def test_not_string(self, dd_run_check): check = get_check({'hostname_format': 9000}) with pytest.raises(Exception, match='^Setting `hostname_format` must be a string$'): dd_run_check(check, extract_message=True) def test_no_placeholder(self, dd_run_check): check = get_check({'hostname_label': 'foo', 'hostname_format': 'bar'}) with pytest.raises( Exception, match='^Setting `hostname_format` does not contain the placeholder `<HOSTNAME>`$' ): dd_run_check(check, extract_message=True) class TestExcludeLabels: def test_not_array(self, dd_run_check): check = get_check({'exclude_labels': 9000}) with pytest.raises(Exception, match='^Setting `exclude_labels` must be an array$'): dd_run_check(check, extract_message=True) def test_entry_invalid_type(self, dd_run_check): check = get_check({'exclude_labels': [9000]}) with pytest.raises(Exception, match='^Entry #1 of setting `exclude_labels` must be a string$'): dd_run_check(check, extract_message=True) class TestRenameLabels: def test_not_mapping(self, dd_run_check): check = get_check({'rename_labels': 9000}) with pytest.raises(Exception, match='^Setting `rename_labels` must be a mapping$'): dd_run_check(check, extract_message=True) def test_value_not_string(self, dd_run_check): check = get_check({'rename_labels': {'foo': 9000}}) with pytest.raises(Exception, match='^Value for label `foo` of setting `rename_labels` must be a string$'): dd_run_check(check, extract_message=True) class TestExcludeMetrics: def test_not_array(self, dd_run_check): check = get_check({'exclude_metrics': 9000}) with pytest.raises(Exception, match='^Setting `exclude_metrics` must be an array$'): dd_run_check(check, extract_message=True) def test_entry_invalid_type(self, dd_run_check): check = get_check({'exclude_metrics': [9000]}) with pytest.raises(Exception, match='^Entry #1 of setting `exclude_metrics` must be a string$'): dd_run_check(check, extract_message=True) class TestExcludeMetricsByLabels: def test_not_mapping(self, dd_run_check): check = get_check({'exclude_metrics_by_labels': 9000}) with pytest.raises(Exception, match='^Setting `exclude_metrics_by_labels` must be a mapping$'): dd_run_check(check, extract_message=True) def test_value_not_string(self, dd_run_check): check = get_check({'exclude_metrics_by_labels': {'foo': [9000]}}) with pytest.raises( Exception, match='^Value #1 for label `foo` of setting `exclude_metrics_by_labels` must be a string$' ): dd_run_check(check, extract_message=True) def test_invalid_type(self, dd_run_check): check = get_check({'exclude_metrics_by_labels': {'foo': 9000}}) with pytest.raises( Exception, match='^Label `foo` of setting `exclude_metrics_by_labels` must be an array or set to `true`$' ): dd_run_check(check, extract_message=True) class TestTags: def test_not_array(self, dd_run_check): check = get_check({'tags': 9000}) with pytest.raises(Exception, match='^Setting `tags` must be an array$'): dd_run_check(check, extract_message=True) def test_entry_invalid_type(self, dd_run_check): check = get_check({'tags': [9000]}) with pytest.raises(Exception, match='^Entry #1 of setting `tags` must be a string$'): dd_run_check(check, extract_message=True) class TestRawLineFilters: def test_not_array(self, dd_run_check): check = get_check({'raw_line_filters': 9000}) with pytest.raises(Exception, match='^Setting `raw_line_filters` must be an array$'): dd_run_check(check, extract_message=True) def test_entry_invalid_type(self, dd_run_check): check = get_check({'raw_line_filters': [9000]}) with pytest.raises(Exception, match='^Entry #1 of setting `raw_line_filters` must be a string$'): dd_run_check(check, extract_message=True) def test_invalid_pattern(self, dd_run_check): check = get_check({'raw_line_filters': ['\\1']}) with pytest.raises(Exception, match='^invalid group reference'): dd_run_check(check, extract_message=True) class TestMetrics: def test_not_array(self, dd_run_check): check = get_check({'metrics': 9000}) with pytest.raises(Exception, match='^Setting `metrics` must be an array$'): dd_run_check(check, extract_message=True) def test_entry_invalid_type(self, dd_run_check): check = get_check({'metrics': [9000]}) with pytest.raises(Exception, match='^Entry #1 of setting `metrics` must be a string or a mapping$'): dd_run_check(check, extract_message=True) def test_mapped_value_not_string(self, dd_run_check): check = get_check({'metrics': [{'foo': 9000}]}) with pytest.raises( Exception, match='^Value of entry `foo` of setting `metrics` must be a string or a mapping$' ): dd_run_check(check, extract_message=True) def test_config_name_not_string(self, dd_run_check): check = get_check({'metrics': [{'foo': {'name': 9000}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: field `name` must be a string$' ): dd_run_check(check, extract_message=True) def test_config_type_not_string(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 9000}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: field `type` must be a string$' ): dd_run_check(check, extract_message=True) def test_config_type_unknown(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'bar'}}]}) with pytest.raises(Exception, match='^Error compiling transformer for metric `foo`: unknown type `bar`$'): dd_run_check(check, extract_message=True) class TestExtraMetrics: def test_not_array(self, dd_run_check): check = get_check({'extra_metrics': 9000}) with pytest.raises(Exception, match='^Setting `extra_metrics` must be an array$'): dd_run_check(check, extract_message=True) def test_entry_invalid_type(self, dd_run_check): check = get_check({'extra_metrics': [9000]}) with pytest.raises(Exception, match='^Entry #1 of setting `extra_metrics` must be a string or a mapping$'): dd_run_check(check, extract_message=True) def test_mapped_value_not_string(self, dd_run_check): check = get_check({'extra_metrics': [{'foo': 9000}]}) with pytest.raises( Exception, match='^Value of entry `foo` of setting `extra_metrics` must be a string or a mapping$' ): dd_run_check(check, extract_message=True) class TestTransformerCompilation: def test_temporal_percent_no_scale(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'temporal_percent'}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: the `scale` parameter is required$' ): dd_run_check(check, extract_message=True) def test_temporal_percent_unknown_scale(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'temporal_percent', 'scale': 'bar'}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: the `scale` parameter must be one of: ' ): dd_run_check(check, extract_message=True) def test_temporal_percent_scale_not_int(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'temporal_percent', 'scale': 1.23}}]}) with pytest.raises( Exception, match=( '^Error compiling transformer for metric `foo`: ' 'the `scale` parameter must be an integer representing parts of a second e.g. 1000 for millisecond$' ), ): dd_run_check(check, extract_message=True) def test_service_check_no_status_map(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'service_check'}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: the `status_map` parameter is required$' ): dd_run_check(check, extract_message=True) def test_service_check_status_map_not_dict(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'service_check', 'status_map': 5}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: the `status_map` parameter must be a mapping$', ): dd_run_check(check, extract_message=True) def test_service_check_status_map_empty(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'service_check', 'status_map': {}}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: the `status_map` parameter must not be empty$', ): dd_run_check(check, extract_message=True) def test_service_check_status_map_value_not_number(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'service_check', 'status_map': {True: 'OK'}}}]}) with pytest.raises( Exception, match=( '^Error compiling transformer for metric `foo`: ' 'value `True` of parameter `status_map` does not represent an integer$' ), ): dd_run_check(check, extract_message=True) def test_service_check_status_map_status_not_string(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'service_check', 'status_map': {'9000': 0}}}]}) with pytest.raises( Exception, match=( '^Error compiling transformer for metric `foo`: ' 'status `0` for value `9000` of parameter `status_map` is not a string$' ), ): dd_run_check(check, extract_message=True) def test_service_check_status_map_status_invalid(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'service_check', 'status_map': {'9000': '0k'}}}]}) with pytest.raises( Exception, match=( '^Error compiling transformer for metric `foo`: ' 'invalid status `0k` for value `9000` of parameter `status_map`$' ), ): dd_run_check(check, extract_message=True) def test_metadata_label_not_string(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'metadata', 'label': 9000}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: the `label` parameter must be a string$' ): dd_run_check(check, extract_message=True) def test_metadata_no_label(self, dd_run_check): check = get_check({'metrics': [{'foo': {'type': 'metadata'}}]}) with pytest.raises( Exception, match='^Error compiling transformer for metric `foo`: the `label` parameter is required$' ): dd_run_check(check, extract_message=True) class TestShareLabels: def test_not_mapping(self, dd_run_check): check = get_check({'share_labels': 9000}) with pytest.raises(Exception, match='^Setting `share_labels` must be a mapping$'): dd_run_check(check, extract_message=True) def test_invalid_type(self, dd_run_check): check = get_check({'share_labels': {'foo': 9000}}) with pytest.raises( Exception, match='^Metric `foo` of setting `share_labels` must be a mapping or set to `true`$' ): dd_run_check(check, extract_message=True) def test_values_not_array(self, dd_run_check): check = get_check({'share_labels': {'foo': {'values': 9000}}}) with pytest.raises( Exception, match='^Option `values` for metric `foo` of setting `share_labels` must be an array$' ): dd_run_check(check, extract_message=True) def test_values_entry_not_integer(self, dd_run_check): check = get_check({'share_labels': {'foo': {'values': [1.0]}}}) with pytest.raises( Exception, match=( '^Entry #1 of option `values` for metric `foo` of setting `share_labels` must represent an integer$' ), ): dd_run_check(check, extract_message=True) @pytest.mark.parametrize('option', ['labels', 'match']) def test_option_not_array(self, dd_run_check, option): check = get_check({'share_labels': {'foo': {option: 9000}}}) with pytest.raises( Exception, match='^Option `{}` for metric `foo` of setting `share_labels` must be an array$'.format(option) ): dd_run_check(check, extract_message=True) @pytest.mark.parametrize('option', ['labels', 'match']) def test_option_entry_not_string(self, dd_run_check, option): check = get_check({'share_labels': {'foo': {option: [9000]}}}) with pytest.raises( Exception, match=( '^Entry #1 of option `{}` for metric `foo` of setting `share_labels` must be a string$'.format(option) ), ): dd_run_check(check, extract_message=True)

39.195122

119

0.646422

2,026

16,070

4.851431

0.07305

0.049852

0.099705

0.146505

0.900295

0.893072

0.88595

0.873131

0.820938

0.762539

0

0.014519

0.232794

16,070

409

120

39.290954

0.782707

0.006721

0

0.527586

0

0.010345

0.268392

0.012533

0

1

0.168966

false

0

0.010345

0

0.231034

0

null

0

1

0

null

0

6

ccd9854e324566ae92a87c2fd96e8e156af84ee6

27

py

Python

gen2-mask-detection/depthai_utils/__init__.py

ibaiGorordo/depthai-experiments

cde67e277120ddac815cbad6360695759cca900f

[ "MIT" ]

381

2020-05-31T22:36:51.000Z

2022-03-31T15:39:36.000Z

gen2-mask-detection/depthai_utils/__init__.py

ibaiGorordo/depthai-experiments

cde67e277120ddac815cbad6360695759cca900f

[ "MIT" ]

211

2020-09-12T20:49:18.000Z

2022-03-31T17:22:52.000Z

gen2-mask-detection/depthai_utils/__init__.py

ibaiGorordo/depthai-experiments

cde67e277120ddac815cbad6360695759cca900f

[ "MIT" ]

189

2020-06-01T19:09:51.000Z

2022-03-31T15:39:28.000Z

from .gen2_depthai import *

27

0.814815

4

27

5.25

1

0

0.041667

0.111111

27

1

27

0.833333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ccdb03336fa4e60335500ce7924e6f29c4ccdec3

25

py

Python

src/tfmars/modules/__init__.py

Shakshi3104/tfmars

f34d3fff49d9eabf130cd7c5fb8f7c6d0f12b5e8

[ "MIT" ]

1

2022-03-19T11:14:04.000Z

src/tfmars/modules/__init__.py

Shakshi3104/tfmars

f34d3fff49d9eabf130cd7c5fb8f7c6d0f12b5e8

[ "MIT" ]

null

src/tfmars/modules/__init__.py

Shakshi3104/tfmars

f34d3fff49d9eabf130cd7c5fb8f7c6d0f12b5e8

[ "MIT" ]

null

from .attention import *

12.5

24

0.76

3

25

6.333333

1

0

0.16

25

1

25

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

692e2ec14110764034d7a064279febd7219b4110

53

py

Python

VersionDetermination/LastVersionDetector/__init__.py

grobbles/verion-determination

04600ff2c854b98849de12779e36b899cbff6679

[ "MIT" ]

null

VersionDetermination/LastVersionDetector/__init__.py

grobbles/verion-determination

04600ff2c854b98849de12779e36b899cbff6679

[ "MIT" ]

null

VersionDetermination/LastVersionDetector/__init__.py

grobbles/verion-determination

04600ff2c854b98849de12779e36b899cbff6679

[ "MIT" ]

null

from .LastVersionDetector import LastVersionDetector

26.5

52

0.90566

4

53

12

0.75

0

0.075472

53

1

53

0.979592

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

694698474062c3e2bc67478fdd66ae5beeefa20c

882

py

Python

python/ray/util/collective/__init__.py

Crissman/ray

2092b097eab41b118a117fdfadd0fe664db41f63

[ "Apache-2.0" ]

3

2021-06-22T19:57:41.000Z

2021-06-23T07:16:44.000Z

python/ray/util/collective/__init__.py

h453693821/ray

9eb79727aa6ad94b01f8b660b83e1182555a89f6

[ "Apache-2.0" ]

72

2021-02-06T08:07:16.000Z

2022-03-26T07:17:49.000Z

python/ray/util/collective/__init__.py

h453693821/ray

9eb79727aa6ad94b01f8b660b83e1182555a89f6

[ "Apache-2.0" ]

2

2021-05-05T21:05:16.000Z

2021-06-22T21:16:03.000Z

from ray.util.collective.collective import nccl_available, gloo_available, \ is_group_initialized, init_collective_group, destroy_collective_group, \ declare_collective_group, get_rank, get_world_size, allreduce, \ allreduce_multigpu, barrier, reduce, reduce_multigpu, broadcast, \ broadcast_multigpu, allgather, allgather_multigpu, reducescatter, \ reducescatter_multigpu, send, send_multigpu, recv, recv_multigpu __all__ = [ "nccl_available", "gloo_available", "is_group_initialized", "init_collective_group", "destroy_collective_group", "declare_collective_group", "get_rank", "get_world_size", "allreduce", "allreduce_multigpu", "barrier", "reduce", "reduce_multigpu", "broadcast", "broadcast_multigpu", "allgather", "allgather_multigpu", "reducescatter", "reducescatter_multigpu", "send", "send_multigpu", "recv", "recv_multigpu" ]

55.125

78

0.768707

93

882

6.817204

0.301075

0.141956

0.053628

0.082019

0.936909

0

0.11678

882

15

79

58.8

0.813864

0

0.35941

0.103175

0

1

0

false

0

0.071429

0

0.071429

0

null

0

1

0

null

0

6

c6573bc6895ef6da2022e09bd658f96f01d948ac

87,833

py

Python

teospy/seaice4.py

jarethholt/teospy

3bb23e67bbb765c0842aa8d4a73c1d55ea395d2f

[ "MIT" ]

null

teospy/seaice4.py

jarethholt/teospy

3bb23e67bbb765c0842aa8d4a73c1d55ea395d2f

[ "MIT" ]

null

teospy/seaice4.py

jarethholt/teospy

3bb23e67bbb765c0842aa8d4a73c1d55ea395d2f

[ "MIT" ]

null

"""Seawater-ice equilibrium functions. This module provides thermodynamic functions for seawater in equilibrium with ice (sea-ice), e.g. the enthalpy of melting. It also provides a Gibbs free energy function for sea-ice parcels, with primary variables being the total salinity (mass of salt per mass of salt, liquid, and ice), temperature, and pressure. :Examples: >>> temperature(salt=0.035,pres=1e5) 271.240373585159 >>> enthalpymelt(salt=0.035,pres=1e5) 329942.976285 >>> volumemelt(salt=0.035,pres=1e5) -9.10140854473e-5 >>> pressure(salt=0.035,temp=270.) 16132047.4385 >>> enthalpymelt(salt=0.035,temp=270.) 326829.393605 >>> volumemelt(salt=0.035,temp=270.) -9.67135426848e-5 >>> salinity(temp=270.,pres=1e5) 0.05602641503 >>> enthalpymelt(temp=270.,pres=1e5) 328249.119579 >>> volumemelt(temp=270.,pres=1e5) -9.18186917900e-5 >>> brinesalinity(270.,1e5) 0.05602641503 >>> meltingpressure(0.035,270.) 16132047.4385 >>> freezingtemperature(0.035,1e5) 271.240373585 >>> dtfdp(0.035,1e5) 7.48210942879e-8 >>> dtfds(0.035,1e5) -56.8751336296 >>> seaice_g(0,0,0,0.035,270.,1e5) -414.0175745 >>> seaice_g(0,1,0,0.035,270.,1e5) 500.445444181 >>> seaice_g(0,1,1,0.035,270.,1e5) -1.658664467e-05 >>> brinefraction(0.035,270.,1e5) 0.6247053284 >>> cp(0.035,270.,1e5) 62868.90151 >>> density(0.035,270.,1e5) 993.156434117 >>> enthalpy(0.035,270.,1e5) -135534.287504 >>> entropy(0.035,270.,1e5) -500.445444181 >>> expansion(0.035,270.,1e5) -1.647313287e-02 >>> kappa_t(0.035,270.,1e5) 1.56513441348e-9 :Functions: * :func:`eq_stp`: Calculate primary variables for sea-ice at any two of the seawater salinity, temperature, and pressure. * :func:`densityice`: Sea-ice ice density. * :func:`densitysea`: Sea-ice seawater density. * :func:`enthalpyice`: Sea-ice ice enthalpy. * :func:`enthalpysea`: Sea-ice seawater enthalpy. * :func:`entropyice`: Sea-ice ice entropy for sea ice. * :func:`entropysea`: Sea-ice seawater entropy. * :func:`pressure`: Sea-ice pressure. * :func:`temperature`: Sea-ice temperature. * :func:`salinity`: Sea-ice salinity. * :func:`enthalpymelt`: Enthalpy of melting. * :func:`volumemelt`: Specific volume of melting. * :func:`brinesalinity`: Salinity of seawater in equilibrium with ice. * :func:`meltingpressure`: Pressure of seawater in equilibrium with ice. * :func:`freezingtemperature`: Temperature of seawater in equilibrium with ice. * :func:`dtfdp`: Freezing point depression of seawater due to pressure. * :func:`dtfds`: Freezing point depression of seawater due to salinity. * :func:`eq_seaice`: Calculate primary variables for a sea-ice parcel at the given total salinity, temperature, and pressure. * :func:`seaice_g`: Sea-ice Gibbs free energy with derivatives. * :func:`brinefraction`: Sea-ice seawater mass fraction. * :func:`cp`: Sea-ice isobaric heat capacity. * :func:`density`: Sea-ice total density. * :func:`enthalpy`: Sea-ice specific enthalpy. * :func:`entropy`: Sea-ice specific entropy. * :func:`expansion`: Sea-ice thermal expansion coefficient. * :func:`kappa_t`: Sea-ice isothermal compressibility. """ __all__ = ['eq_stp','densityice','densitysea','enthalpyice','enthalpysea', 'entropyice','entropysea','pressure','temperature','salinity', 'enthalpymelt','volumemelt', 'brinesalinity','meltingpressure','freezingtemperature','dtfdp','dtfds', 'eq_seaice','seaice_g','brinefraction','cp','density','enthalpy','entropy', 'expansion','kappa_t'] import warnings import numpy from teospy import constants0 from teospy import flu1 from teospy import ice1 from teospy import flu2 from teospy import ice2 from teospy import sal2 from teospy import maths3 from teospy import flu3a from teospy import sea3a from teospy import maths4 _CHKTOL = constants0.CHKTOL _MSAL = constants0.MSAL _RUNIV = constants0.RUNIV _RWAT = constants0.RWAT _TTP = constants0.TTP _PTPE = constants0.PTPE _DLTP = constants0.DLTP _DITP = constants0.DITP _LILTP = constants0.LILTP _CLIQ = constants0.CLIQ _CICE = constants0.CICE _SAL0 = constants0.SAL0 _RSAL = _RUNIV / _MSAL _VLTP = _DLTP**(-1) _VITP = _DITP**(-1) _C_SP = 0.396166676603 _E = numpy.exp(1) _chkflubnds = constants0.chkflubnds _chkicebnds = constants0.chkicebnds _chksalbnds = constants0.chksalbnds _flu_f = flu1.flu_f _ice_g = ice1.ice_g _eq_pressure = flu2.eq_pressure _eq_chempot = flu2.eq_chempot _sal_g = sal2.sal_g _eq_liqpot = sal2.eq_liqpot _newton = maths3.newton _dliq_default = flu3a._dliq_default ## Equilibrium functions def _approx_st(salt,temp): """Approximate PDl at ST. Approximate the pressure and liquid water density of sea-ice with the given salinity and temperature. :arg float salt: Salinity in kg/kg. :arg float temp: Temperature in K. :returns: Pressure and liquid water density (both in SI units). """ dmu = ((_CLIQ-_CICE)*(temp - _TTP - temp*numpy.log(temp/_TTP)) + -_LILTP/_TTP*(temp - _TTP) - _RSAL*temp*salt) pres = _PTPE + dmu/(_VITP-_VLTP) dliq = _dliq_default(temp,pres) return pres, dliq def _approx_sp(salt,pres): """Approximate TDl at SP. Approximate the temperature and liquid water density of sea-ice with the given salinity and pressure. :arg float salt: Salinity in kg/kg. :arg float pres: Pressure in Pa. :returns: Temperature and liquid water density (both in SI units). """ CDIF = _CLIQ-_CICE R0 = _LILTP/_TTP / CDIF r1 = (pres-_PTPE) * (_VITP-_VLTP)/_TTP / CDIF r2 = _RSAL*salt / CDIF w = -(1 - R0 + r1) * numpy.exp(-(1 - R0 - r2)) negz = 1 - (1 + _E*w)**_C_SP temp = (1 - R0 + r1)*_TTP/negz dliq = _dliq_default(temp,pres) return temp, dliq def _approx_sp2(salt,pres): """Approximate TDl at SP. Approximate the temperature and liquid water density of sea-ice with the given salinity and pressure. :arg float salt: Salinity in kg/kg. :arg float pres: Pressure in Pa. :returns: Temperature and liquid water density (both in SI units). """ x = (_RSAL*_TTP*salt + (pres-_PTPE)*(_VITP-_VLTP)) / _LILTP temp = _TTP * (1-x) dliq = _dliq_default(temp,pres) return temp, dliq def _approx_tp(temp,pres,dliq): """Approximate S at TP. Approximate the salinity of sea-ice with the given temperature and pressure. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg float dliq: Liquid water density in kg/m3 (unused). :returns: Salinity in kg/kg. """ dmu = ((_CLIQ-_CICE) * (temp-_TTP-temp*numpy.log(temp/_TTP)) + -_LILTP/_TTP*(temp-_TTP) - (pres-_PTPE)*(_VITP-_VLTP)) salt = dmu / (_RSAL*temp) return salt def _diff_st(p,dl,salt,temp,useext=False): """Calculate sea-ice disequilibrium at ST. Calculate both sides of the equations given pressure = pressure of liquid water chemical potential of ice = potential of liquid water and their Jacobians with respect to pressure and liquid water density. Solving these equations gives equilibrium values at the given salinity and temperature. :arg float p: Pressure in Pa. :arg float dl: Liquid water density in kg/m3. :arg float salt: Salinity in kg/kg. :arg float temp: Temperature in K. :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :returns: Left-hand side of the equation, right-hand side, Jacobian of LHS, and Jacobian of RHS. :rtype: tuple(array(float)) """ pl = _eq_pressure(0,0,temp,dl) gi = _ice_g(0,0,temp,p) gl = _eq_chempot(0,0,temp,dl) gl += _eq_liqpot(0,0,0,salt,temp,p,useext=useext) lhs = numpy.array([p, gi]) rhs = numpy.array([pl, gl]) pl_d = _eq_pressure(0,1,temp,dl) gi_p = _ice_g(0,1,temp,p) gl_d = _eq_chempot(0,1,temp,dl) gl_p = _eq_liqpot(0,0,1,salt,temp,p,useext=useext) dlhs = numpy.array([[1.,0.], [gi_p,0.]]) drhs = numpy.array([[0.,pl_d], [gl_p,gl_d]]) return lhs, rhs, dlhs, drhs def _diff_sp(t,dl,salt,pres,useext=False): """Calculate sea-ice disequilibrium at SP. Calculate both sides of the equations given pressure = pressure of liquid water chemical potential of ice = potential of liquid water and their Jacobians with respect to temperature and liquid water density. Solving these equations gives equilibrium values at the given salinity and pressure. :arg float t: Temperature in K. :arg float dl: Liquid water density in kg/m3. :arg float salt: Salinity in kg/kg. :arg float pres: Pressure in Pa. :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :returns: Left-hand side of the equation, right-hand side, Jacobian of LHS, and Jacobian of RHS. :rtype: tuple(array(float)) """ pl = _eq_pressure(0,0,t,dl) gi = _ice_g(0,0,t,pres) gl = _eq_chempot(0,0,t,dl) gl += _eq_liqpot(0,0,0,salt,t,pres,useext=useext) lhs = numpy.array([pres, gi]) rhs = numpy.array([pl, gl]) pl_t = _eq_pressure(1,0,t,dl) pl_d = _eq_pressure(0,1,t,dl) gi_t = _ice_g(1,0,t,pres) gl_t = _eq_chempot(1,0,t,dl) gl_t += _eq_liqpot(0,1,0,salt,t,pres,useext=useext) gl_d = _eq_chempot(0,1,t,dl) dlhs = numpy.array([[0.,0.], [gi_t,0.]]) drhs = numpy.array([[pl_t,pl_d], [gl_t,gl_d]]) return lhs, rhs, dlhs, drhs def _diff_tp(s,temp,pres,dliq,useext=False): """Calculate sea-ice disequilibrium at TP. Calculate both sides of the equation chemical potential of ice = potential of liquid water and their derivatives with respect to salinity. Solving these equations gives the equilibrium salinity at the given temperature and pressure. :arg float s: Salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg float dliq: Liquid water density in kg/m3. :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :returns: Left-hand side of the equation, right-hand side, derivative of LHS, and derivative of RHS. :rtype: tuple(float) """ gi = _ice_g(0,0,temp,pres) gl = _eq_chempot(0,0,temp,dliq) gl += _eq_liqpot(0,0,0,s,temp,pres,useext=useext) lhs = gi rhs = gl gl_s = _eq_liqpot(1,0,0,s,temp,pres,useext=useext) dlhs = 0. drhs = gl_s return lhs, rhs, dlhs, drhs def eq_stp(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None,chkbnd=False, useext=False,mathargs=None): """Get primary sea-ice variables at STP. Get the values of all primary variables for sea-ice in equilibrium. At least two of the salinity, temperature, and pressure must be provided. If the calculation has already been done, the results can be passed to avoid unnecessary repeat calculations. If enough values are passed, they will be checked for consistency if chkvals is True. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Salinity, temperature, pressure, and seawater liquid density (all in SI units). :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. """ if sum(val is None for val in (salt,temp,pres)) > 1: errmsg = 'Must provide at least two of (salt,temp,pres)' raise ValueError(errmsg) if mathargs is None: mathargs = dict() fkwargs = {'useext': useext} if salt is None: dliq = flu3a.eq_tp_liq(temp,pres,dliq=dliq,dliq0=dliq0, mathargs=mathargs) fargs = (temp,pres,dliq) salt = _newton(_diff_tp,salt0,_approx_tp,fargs=fargs,fkwargs=fkwargs, **mathargs) elif temp is None: x0 = (temp0,dliq0) fargs = (salt,pres) x1 = _newton(_diff_sp,x0,_approx_sp,fargs=fargs,fkwargs=fkwargs, **mathargs) temp, dliq = x1 elif pres is None: x0 = (pres0,dliq0) fargs = (salt,temp) x1 = _newton(_diff_st,x0,_approx_st,fargs=fargs,fkwargs=fkwargs, **mathargs) pres, dliq = x1 elif dliq is None: dliq = flu3a.eq_tp_liq(temp,pres,dliq0=dliq0,mathargs=mathargs) _chkflubnds(temp,dliq,chkbnd=chkbnd) _chkicebnds(temp,pres,chkbnd=chkbnd) _chksalbnds(salt,temp,pres,chkbnd=chkbnd) if not chkvals: return salt, temp, pres, dliq lhs, rhs, __, __ = _diff_st(pres,dliq,salt,temp,useext=useext) errs = list() for (l,r) in zip(lhs,rhs): if abs(r) >= chktol: errs.append(abs(l/r-1)) else: errs.append(abs(l-r)) if max(errs) > chktol: warnmsg = ('Given values {0} and solutions {1} disagree to more than ' 'the tolerance {2}').format(lhs,rhs,chktol) warnings.warn(warnmsg,RuntimeWarning) return salt, temp, pres, dliq ## Thermodynamic functions def densityice(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice ice density. Calculate the density of ice in sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Density in kg/m3. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> densityice(salt=0.035,pres=1e5) 917.000739687 >>> densityice(salt=0.035,temp=270.) 918.898527655 >>> densityice(temp=270.,pres=1e5) 917.181167192 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) dice = ice2.density(temp,pres) return dice def densitysea(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice seawater density. Calculate the density of seawater in sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Density in kg/m3. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> densitysea(salt=0.035,pres=1e5) 1028.05199645 >>> densitysea(salt=0.035,temp=270.) 1035.73670169 >>> densitysea(temp=270.,pres=1e5) 1045.16805918 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) dsea = sea3a.density(salt,temp,pres,dliq=dliq,useext=useext) return dsea def enthalpyice(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice ice enthalpy. Calculate the specific enthalpy of ice in sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Enthalpy in J/kg. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> enthalpyice(salt=0.035,pres=1e5) -337351.999358 >>> enthalpyice(salt=0.035,temp=270.) -323205.968289 >>> enthalpyice(temp=270.,pres=1e5) -339929.555499 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) hice = ice2.enthalpy(temp,pres) return hice def enthalpysea(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice seawater enthalpy. Calculate the specific enthalpy of seawater in sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Enthalpy in J/kg. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> enthalpysea(salt=0.035,pres=1e5) -7613.193379 >>> enthalpysea(salt=0.035,temp=270.) 2832.949104 >>> enthalpysea(temp=270.,pres=1e5) -12742.86649 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) hsea = sea3a.enthalpy(salt,temp,pres,dliq=dliq,useext=useext) return hsea def entropyice(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice ice entropy. Calculate the specific entropy of ice in sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Entropy in J/kg/K. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> entropyice(salt=0.035,pres=1e5) -1235.44872812 >>> entropyice(salt=0.035,temp=270.) -1247.71314646 >>> entropyice(temp=270.,pres=1e5) -1244.97335506 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) sice = ice2.entropy(temp,pres) return sice def entropysea(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice seawater entropy. Calculate the specific entropy of seawater in sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Entropy in J/kg/K. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> entropysea(salt=0.035,pres=1e5) -27.9264598103 >>> entropysea(salt=0.035,temp=270.) -46.7361169560 >>> entropysea(temp=270.,pres=1e5) -53.1667911144 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) ssea = sea3a.entropy(salt,temp,pres,dliq=dliq,useext=useext) return ssea def pressure(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice pressure. Calculate the pressure of sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Pressure in Pa. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> pressure(salt=0.035,temp=270.) 16132047.4385 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) return pres def temperature(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice temperature. Calculate the temperature of sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Temperature in K. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> temperature(salt=0.035,pres=1e5) 271.240373585159 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) return temp def salinity(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice salinity. Calculate the salinity of sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Salinity in kg/kg. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> salinity(temp=270.,pres=1e5) 0.05602641503 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) return salt def enthalpymelt(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate the enthalpy of melting. Calculate the specific enthalpy of melting of sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Enthalpy in J/kg. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> enthalpymelt(salt=0.035,pres=1e5) 329942.976285 >>> enthalpymelt(salt=0.035,temp=270.) 326829.393605 >>> enthalpymelt(temp=270.,pres=1e5) 328249.119579 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) fl_t = _flu_f(1,0,temp,dliq) gs_t = _sal_g(0,1,0,salt,temp,pres,useext=useext) gs_st = _sal_g(1,1,0,salt,temp,pres,useext=useext) gi_t = _ice_g(1,0,temp,pres) hmelt = temp * (gi_t - (fl_t + gs_t - salt*gs_st)) return hmelt def volumemelt(salt=None,temp=None,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate the volume of melting. Calculate the specific volume of melting of sea-ice. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Specific volume in m3/kg. :raises ValueError: If fewer than two of salt, temp, and pres are provided. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> volumemelt(salt=0.035,pres=1e5) -9.10140854473e-5 >>> volumemelt(salt=0.035,temp=270.) -9.67135426848e-5 >>> volumemelt(temp=270.,pres=1e5) -9.18186917900e-5 """ salt, temp, pres, dliq = eq_stp(salt=salt,temp=temp,pres=pres,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,temp0=temp0,pres0=pres0, dliq0=dliq0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) gs_p = _sal_g(0,0,1,salt,temp,pres,useext=useext) gs_sp = _sal_g(1,0,1,salt,temp,pres,useext=useext) gi_p = _ice_g(0,1,temp,pres) vmelt = dliq**(-1) + gs_p - salt*gs_sp - gi_p return vmelt ## Thermodynamic functions of two variables def brinesalinity(temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,temp0=None,pres0=None,dliq0=None, chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice brine salinity. Calculate the salinity of seawater (brine) in equilibrium with ice of the given temperature and pressure. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Salinity in kg/kg. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> brinesalinity(270.,1e5) 0.05602641503 """ salt, __, __, dliq = eq_stp(temp=temp,pres=pres,salt=salt,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd, useext=useext,mathargs=mathargs) return salt def meltingpressure(salt,temp,pres=None,dliq=None,chkvals=False, chktol=_CHKTOL,pres0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice melting pressure. Calculate the pressure required to melt ice into seawater at the given salinity and temperature. :arg float salt: Salinity in kg/kg. :arg float temp: Temperature in K. :arg pres: Pressure in Pa. If unknown, pass None (default) and it will be calculated. :type pres: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg pres0: Initial guess for the pressure in Pa. If None (default) then `_approx_st` is used. :type pres0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Pressure in Pa. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> meltingpressure(0.035,270.) 16132047.4385 """ __, __, pres, dliq = eq_stp(temp=temp,pres=pres,salt=salt,dliq=dliq, chkvals=chkvals,chktol=chktol,pres0=pres0,dliq0=dliq0,chkbnd=chkbnd, useext=useext,mathargs=mathargs) return pres def freezingtemperature(salt,pres,temp=None,dliq=None,chkvals=False, chktol=_CHKTOL,temp0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice freezing temperature. Calculate the temperature required to freeze seawater at the given salinity and pressure. :arg float salt: Salinity in kg/kg. :arg float pres: Pressure in Pa. :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Temperature in K. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> freezingtemperature(0.035,1e5) 271.240373585 """ __, temp, __, dliq = eq_stp(temp=temp,pres=pres,salt=salt,dliq=dliq, chkvals=chkvals,chktol=chktol,temp0=temp0,dliq0=dliq0,chkbnd=chkbnd, useext=useext,mathargs=mathargs) return temp def dtfdp(salt,pres,temp=None,dliq=None,chkvals=False,chktol=_CHKTOL, temp0=None,dliq0=None,chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice freezing point pressure lowering. Calculate the effect of pressure on lowering the freezing point of sea-ice. :arg float salt: Salinity in kg/kg. :arg float pres: Pressure in Pa. :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Freezing point lowering in K/Pa. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> dtfdp(0.035,1e5) 7.48210942879e-8 """ __, temp, __, dliq = eq_stp(temp=temp,pres=pres,salt=salt,dliq=dliq, chkvals=chkvals,chktol=chktol,temp0=temp0,dliq0=dliq0,chkbnd=chkbnd, useext=useext,mathargs=mathargs) fl_t = _flu_f(1,0,temp,dliq) gs_t = _sal_g(0,1,0,salt,temp,pres,useext=useext) gs_p = _sal_g(0,0,1,salt,temp,pres,useext=useext) gs_st = _sal_g(1,1,0,salt,temp,pres,useext=useext) gs_sp = _sal_g(1,0,1,salt,temp,pres,useext=useext) gi_t = _ice_g(1,0,temp,pres) gi_p = _ice_g(0,1,temp,pres) dent = fl_t + gs_t - salt*gs_st - gi_t dvol = dliq**(-1) + gs_p - salt*gs_sp - gi_p dtfdp = dvol/dent return dtfdp def dtfds(salt,pres,temp=None,dliq=None,chkvals=False,chktol=_CHKTOL, temp0=None,dliq0=None,chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice freezing point salt lowering. Calculate the effect of salinity on lowering the freezing point of sea-ice. :arg float salt: Salinity in kg/kg. :arg float pres: Pressure in Pa. :arg temp: Temperature in K. If unknown, pass None (default) and it will be calculated. :type temp: float or None :arg dliq: Density of liquid water in seawater in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg temp0: Initial guess for the temperature in K. If None (default) then `_approx_sp` is used. :type temp0: float or None :arg dliq0: Initial guess for the liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Freezing point lowering in K/(kg/kg). :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :Examples: >>> dtfds(0.035,1e5) -56.8751336296 """ __, temp, __, dliq = eq_stp(temp=temp,pres=pres,salt=salt,dliq=dliq, chkvals=chkvals,chktol=chktol,temp0=temp0,dliq0=dliq0,chkbnd=chkbnd, useext=useext,mathargs=mathargs) fl_t = _flu_f(1,0,temp,dliq) gs_t = _sal_g(0,1,0,salt,temp,pres,useext=useext) gs_ss = _sal_g(2,0,0,salt,temp,pres,useext=useext) gs_st = _sal_g(1,1,0,salt,temp,pres,useext=useext) gi_t = _ice_g(1,0,temp,pres) dent = fl_t + gs_t - salt*gs_st - gi_t dtfds = salt*gs_ss / dent return dtfds ## Seawater-ice combined system def eq_seaice(sisal,temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Get primary sea-ice variables at SsiTP. Get the values of all primary variables for a seawater-ice parcel at the given total salinity, temperature, and pressure. Total salinity here is the ratio of the mass of salt to the total parcel mass (salt + liquid water + ice). If the calculation has already been done, the results can be passed to avoid unnecessary repeat calculations. If enough values are passed, they will be checked for consistency if chkvals is True. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Seawater salinity and liquid water density (both in SI units). :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. """ if salt is None or dliq is None: salt, __, __, dliq = eq_stp(temp=temp,pres=pres,salt=salt,dliq=dliq, chkvals=chkvals,chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd, useext=useext,mathargs=mathargs) if salt < sisal: warnmsg = ('Equilibrium salinity {0} is lower than the total parcel ' 'salinity {1}').format(salt,sisal) warnings.warn(warnmsg,RuntimeWarning) salt = sisal return salt, dliq def seaice_g(drvs,drvt,drvp,sisal,temp,pres,salt=None,dliq=None, chkvals=False,chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False, useext=False,mathargs=None): """Calculate sea-ice Gibbs free energy with derivatives. Calculate the specific Gibbs free energy of a sea-ice parcel or its derivatives with respect to total salinity, temperature, and pressure. :arg int drvs: Number of total salinity derivatives. :arg int drvt: Number of temperature derivatives. :arg int drvp: Number of pressure derivatives. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Gibbs free energy in units of (J/kg) / (kg/kg)^drvs / K^drvt / Pa^drvp. :raises ValueError: If any of (drvs,drvt,drvp) are negative or if (drvs+drvt+drvp) > 2. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> seaice_g(0,0,0,0.035,270.,1e5) -414.0175745 >>> seaice_g(1,0,0,0.035,270.,1e5) 96363.77305 >>> seaice_g(0,1,0,0.035,270.,1e5) 500.445444181 >>> seaice_g(0,0,1,0.035,270.,1e5) 1.00689072300e-3 >>> seaice_g(2,0,0,0.035,270.,1e5) 0. >>> seaice_g(1,1,0,0.035,270.,1e5) -21272.2260252 >>> seaice_g(1,0,1,0.035,270.,1e5) -2.383040378e-03 >>> seaice_g(0,2,0,0.035,270.,1e5) -232.847783380 >>> seaice_g(0,1,1,0.035,270.,1e5) -1.658664467e-05 >>> seaice_g(0,0,2,0.035,270.,1e5) -1.57591932118e-12 """ drvtup = (drvs,drvt,drvp) if any(drv < 0 for drv in drvtup) or sum(drvtup) > 2: errmsg = 'Derivatives {0} not recognized'.format(drvtup) raise ValueError(errmsg) salt, dliq = eq_seaice(sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) seaf = sisal/salt # Straightforward derivatives if (drvs,drvt,drvp) == (0,0,0): gl = _eq_chempot(0,0,temp,dliq) gs = _sal_g(0,0,0,salt,temp,pres,useext=useext) gi = _ice_g(0,0,temp,pres) g = seaf*(gl + gs) + (1-seaf)*gi return g elif (drvs,drvt,drvp) == (1,0,0): gs_s = _sal_g(1,0,0,salt,temp,pres,useext=useext) g_s = gs_s return g_s elif (drvs,drvt,drvp) == (0,1,0): fl_t = _flu_f(1,0,temp,dliq) gs_t = _sal_g(0,1,0,salt,temp,pres,useext=useext) gi_t = _ice_g(1,0,temp,pres) g_t = seaf*(fl_t + gs_t) + (1-seaf)*gi_t return g_t elif (drvs,drvt,drvp) == (0,0,1): gs_p = _sal_g(0,0,1,salt,temp,pres,useext=useext) gi_p = _ice_g(0,1,temp,pres) g_p = seaf*(dliq**(-1) + gs_p) + (1-seaf)*gi_p return g_p elif (drvs,drvt,drvp) == (2,0,0): g_ss = 0.0 return g_ss elif (drvs,drvt,drvp) == (1,1,0): fl_t = _flu_f(1,0,temp,dliq) gs_t = _sal_g(0,1,0,salt,temp,pres,useext=useext) gi_t = _ice_g(1,0,temp,pres) g_st = (fl_t + gs_t - gi_t)/salt return g_st elif (drvs,drvt,drvp) == (1,0,1): gs_p = _sal_g(0,0,1,salt,temp,pres,useext=useext) gi_p = _ice_g(0,1,temp,pres) g_sp = (dliq**(-1) + gs_p - gi_p)/salt return g_sp # Other derivatives require inversion cl = _eq_pressure(0,1,temp,dliq) gs_ss = _sal_g(2,0,0,salt,temp,pres,useext=useext) if drvt > 0: fl_t = _flu_f(1,0,temp,dliq) gs_t = _sal_g(0,1,0,salt,temp,pres,useext=useext) gs_st = _sal_g(1,1,0,salt,temp,pres,useext=useext) gi_t = _ice_g(1,0,temp,pres) dentr = fl_t + gs_t - salt*gs_st - gi_t if drvp > 0: gs_p = _sal_g(0,0,1,salt,temp,pres,useext=useext) gs_sp = _sal_g(1,0,1,salt,temp,pres,useext=useext) gi_p = _ice_g(0,1,temp,pres) dvol = dliq**(-1) + gs_p - salt*gs_sp - gi_p s_p = dvol / (salt*gs_ss) dl_p = cl**(-1) if (drvs,drvt,drvp) == (0,2,0): fl_tt = _flu_f(2,0,temp,dliq) fl_td = _flu_f(1,1,temp,dliq) gs_tt = _sal_g(0,2,0,salt,temp,pres,useext=useext) gi_tt = _ice_g(2,0,temp,pres) s_t = dentr / (salt*gs_ss) dl_t = -dliq**2*fl_td/cl gb_tt = fl_tt + fl_td*dl_t + gs_tt g_tt = -seaf/salt*dentr*s_t + seaf*gb_tt + (1-seaf)*gi_tt return g_tt elif (drvs,drvt,drvp) == (0,1,1): fl_td = _flu_f(1,1,temp,dliq) gs_tp = _sal_g(0,1,1,salt,temp,pres,useext=useext) gi_tp = _ice_g(1,1,temp,pres) gb_tp = fl_td*dl_p + gs_tp g_tp = -seaf/salt*dentr*s_p + seaf*gb_tp + (1-seaf)*gi_tp return g_tp elif (drvs,drvt,drvp) == (0,0,2): gs_pp = _sal_g(0,0,2,salt,temp,pres,useext=useext) gi_pp = _ice_g(0,2,temp,pres) gb_pp = -dl_p/dliq**2 + gs_pp g_pp = -seaf/salt*dvol*s_p + seaf*gb_pp + (1-seaf)*gi_pp return g_pp # Should not have made it this far! errmsg = 'Derivatives {0} not recognized'.format((drvs,drvt,drvp)) raise ValueError(errmsg) def brinefraction(sisal,temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice brine fraction. Calculate the mass fraction of seawater (brine) in a sea-ice parcel, the ratio of the mass of seawater (salt + liquid water) to the total mass (salt + liquid water + ice). :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Brine fraction in kg/kg. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> brinefraction(0.035,270.,1e5) 0.6247053284 """ salt, dliq = eq_seaice(sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) seaf = sisal/salt return seaf def cp(sisal,temp,pres,salt=None,dliq=None,chkvals=False,chktol=_CHKTOL, salt0=None,dliq0=None,chkbnd=False,useext=False,mathargs=None): """Calculate sea-ice isobaric heat capacity. Calculate the isobaric heat capacity of sea-ice. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Heat capacity in J/kg/K. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> cp(0.035,270.,1e5) 62868.90151 """ g_tt = seaice_g(0,2,0,sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) cp = -temp * g_tt return cp def density(sisal,temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice total density. Calculate the total density of a sea-ice parcel. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Density in kg/m3. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> density(0.035,270.,1e5) 993.156434117 """ g_p = seaice_g(0,0,1,sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) rho = g_p**(-1) return rho def enthalpy(sisal,temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice enthalpy. Calculate the specific enthalpy of a sea-ice parcel. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Enthalpy in J/kg. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> enthalpy(0.035,270.,1e5) -135534.287504 """ salt, dliq = eq_seaice(sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) g = seaice_g(0,0,0,sisal,temp,pres,salt=salt,dliq=dliq,useext=useext) g_t = seaice_g(0,1,0,sisal,temp,pres,salt=salt,dliq=dliq,useext=useext) h = g - temp*g_t return h def entropy(sisal,temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice entropy. Calculate the specific entropy of a sea-ice parcel. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Entropy in J/kg/K. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> entropy(0.035,270.,1e5) -500.445444181 """ g_t = seaice_g(0,1,0,sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) s = -g_t return s def expansion(sisal,temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice thermal expansion coefficient. Calculate the thermal expansion coefficient of a sea-ice parcel. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Expansion coefficient in 1/K. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> expansion(0.035,270.,1e5) -1.647313287e-02 """ salt, dliq = eq_seaice(sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) g_p = seaice_g(0,0,1,sisal,temp,pres,salt=salt,dliq=dliq,useext=useext) g_tp = seaice_g(0,1,1,sisal,temp,pres,salt=salt,dliq=dliq,useext=useext) alpha = g_tp / g_p return alpha def kappa_t(sisal,temp,pres,salt=None,dliq=None,chkvals=False, chktol=_CHKTOL,salt0=None,dliq0=None,chkbnd=False,useext=False, mathargs=None): """Calculate sea-ice isothermal compressibility. Calculate the isothermal compressibility of a sea-ice parcel. :arg float sisal: Total sea-ice salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Compressibility in 1/Pa. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> kappa_t(0.035,270.,1e5) 1.56513441348e-9 """ salt, dliq = eq_seaice(sisal,temp,pres,salt=salt,dliq=dliq,chkvals=chkvals, chktol=chktol,salt0=salt0,dliq0=dliq0,chkbnd=chkbnd,useext=useext, mathargs=mathargs) g_p = seaice_g(0,0,1,sisal,temp,pres,salt=salt,dliq=dliq,useext=useext) g_pp = seaice_g(0,0,2,sisal,temp,pres,salt=salt,dliq=dliq,useext=useext) kappa = -g_pp / g_p return kappa

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d687cf09eca45bb96738ee6a2dab0c2bbb4b8a46

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py

Python

kartverket_tide_api/__init__.py

matsjp/kartverket_tide_api

b4be15e9c8f077ef6ec0747fe67f0a64383cfa30

[ "MIT" ]

null

kartverket_tide_api/__init__.py

matsjp/kartverket_tide_api

b4be15e9c8f077ef6ec0747fe67f0a64383cfa30

[ "MIT" ]

null

kartverket_tide_api/__init__.py

matsjp/kartverket_tide_api

b4be15e9c8f077ef6ec0747fe67f0a64383cfa30

[ "MIT" ]

null

from .tide_api import TideApi

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6

ba95672ec341242f8978163c51676c13af938817

33

py

Python

qurry/visualization/__init__.py

LSaldyt/curry

9004a396ec2e351aa143a10a53156649a6747343

[ "MIT" ]

11

2018-07-28T17:08:23.000Z

2019-02-08T03:04:03.000Z

qurry/visualization/__init__.py

LSaldyt/Qurry

9004a396ec2e351aa143a10a53156649a6747343

[ "MIT" ]

33

2019-07-09T09:46:44.000Z

2019-09-23T23:44:37.000Z

qurry/visualization/__init__.py

LSaldyt/Qurry

9004a396ec2e351aa143a10a53156649a6747343

[ "MIT" ]

4

2019-05-28T01:27:49.000Z

2019-12-26T18:01:51.000Z

from .histogram import histogram

16.5

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1

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1

0

1

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6

bace3196bc905295c4d58e81efd6505cd963dc47

33

py

Python

python/recognitionService_api/__init__.py

Koisell/SmartCoffeeMachine

40844039970d177b20b9d3c6d3e7eedf7352885e

[ "MIT" ]

null

python/recognitionService_api/__init__.py

Koisell/SmartCoffeeMachine

40844039970d177b20b9d3c6d3e7eedf7352885e

[ "MIT" ]

null

python/recognitionService_api/__init__.py

Koisell/SmartCoffeeMachine

40844039970d177b20b9d3c6d3e7eedf7352885e

[ "MIT" ]

null

from .flask_app import add_route

16.5

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4.333333

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0.896552

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6

bae77b64464b436386a4f76548eec99e0d7a192d

193

py

Python

scripts/osmupdate-latvia-trolleybus/config.example.py

trolleway/osmot

6fceaacb3d64b3d7d1a13f0b6a132a239fbab547

[ "Unlicense" ]

12

2015-02-23T17:31:46.000Z

2021-03-11T21:52:31.000Z

scripts/osmupdate-latvia-trolleybus/config.example.py

trolleway/osmot

6fceaacb3d64b3d7d1a13f0b6a132a239fbab547

[ "Unlicense" ]

8

2015-02-10T19:04:34.000Z

2020-10-19T23:31:52.000Z

scripts/osmupdate-latvia-trolleybus/config.example.py

trolleway/osmot

6fceaacb3d64b3d7d1a13f0b6a132a239fbab547

[ "Unlicense" ]

1

2018-01-26T19:38:20.000Z

dbname='gis' user='trolleway' host='localhost' password='admin' dump_url='http://download.geofabrik.de/europe/latvia-latest.osm.pbf' poly_url='http://download.geofabrik.de/europe/latvia.poly'

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6

0312f47f49120369ec8b514ab92ab4305bb4f87a

33

py

Python

ale/base/__init__.py

paarongiroux/ale

aaefdf555c4f24c5610912625e81dff57c479b4c

[ "Unlicense" ]

4

2019-05-23T18:37:43.000Z

2022-01-10T20:03:56.000Z

ale/base/__init__.py

paarongiroux/ale

aaefdf555c4f24c5610912625e81dff57c479b4c

[ "Unlicense" ]

262

2018-12-12T16:33:00.000Z

2022-03-28T01:28:33.000Z

ale/base/__init__.py

paarongiroux/ale

aaefdf555c4f24c5610912625e81dff57c479b4c

[ "Unlicense" ]

20

2018-12-17T20:42:18.000Z

2022-03-07T20:48:16.000Z

from ale.base.base import Driver

16.5

32

0.818182

6

33

4.5

0.833333

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33

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null

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1

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1

0

1

0

6

03544b668855363e6d2313dde61f58f4b2160a8b

89

py

Python

numpyro/__init__.py

Anthonymcqueen21/numpyro

94efe3a35491465eba66465b4dd1d4fb870d6c8c

[ "MIT" ]

1

2019-06-24T04:27:18.000Z

numpyro/__init__.py

Anthonymcqueen21/numpyro

94efe3a35491465eba66465b4dd1d4fb870d6c8c

[ "MIT" ]

null

numpyro/__init__.py

Anthonymcqueen21/numpyro

94efe3a35491465eba66465b4dd1d4fb870d6c8c

[ "MIT" ]

null

import numpyro.patch # noqa: F401 from numpyro.version import __version__ # noqa: F401

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6

069beec455baa18fc5311f4fb043836a053d084c

24,511

py

Python

src/nexgen/command_line/nexus_generator.py

DominicOram/nexgen

5a0dbb7bc7f8f3876e4cd69c6cd4eb7df0587af8

[ "BSD-3-Clause" ]

null

src/nexgen/command_line/nexus_generator.py

DominicOram/nexgen

5a0dbb7bc7f8f3876e4cd69c6cd4eb7df0587af8

[ "BSD-3-Clause" ]

null

src/nexgen/command_line/nexus_generator.py

DominicOram/nexgen

5a0dbb7bc7f8f3876e4cd69c6cd4eb7df0587af8

[ "BSD-3-Clause" ]

null

""" Command line tool to generate NeXus files. """ import sys import glob import h5py import time import logging import argparse import freephil import numpy as np from pathlib import Path from datetime import datetime from . import ( version_parser, detectormode_parser, nexus_parser, demo_parser, add_tristan_spec, ) from .. import ( get_nexus_filename, get_filename_template, get_iso_timestamp, ) from ..nxs_write.NexusWriter import write_nexus, write_nexus_demo from ..nxs_write.NXclassWriters import write_NXnote # Define a logger object and a formatter logger = logging.getLogger("NeXusGenerator") logger.setLevel(logging.DEBUG) # formatter = logging.Formatter("%(levelname)s %(message)s") formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s %(message)s") # Phil scopes master_phil = freephil.parse( """ input { datafile = None .multiple = True .type = path .help = "HDF5 file. For now, assumes pattern filename_%0{6}d.h5" coordinate_frame = *mcstas imgcif .type = choice .help = "Which coordinate system is being used to provide input vectors." vds_writer = *None dataset file .type = choice .help = "If not None, write vds along with external link to data in NeXus file, or create _vds.h5 file." } include scope nexgen.command_line.nxs_phil.goniometer_scope include scope nexgen.command_line.nxs_phil.beamline_scope include scope nexgen.command_line.nxs_phil.detector_scope include scope nexgen.command_line.nxs_phil.module_scope include scope nexgen.command_line.nxs_phil.timestamp_scope """, process_includes=True, ) demo_phil = freephil.parse( """ output { master_filename = nexus_master.h5 .type = path .help = "Filename for master file" } input { coordinate_frame = *mcstas imgcif .type = choice .help = "Which coordinate system is being used to provide input vectors" vds_writer = *None dataset file .type = choice .help = "If not None, either write a vds in the nexus file or create also a _vds.h5 file." } include scope nexgen.command_line.nxs_phil.goniometer_scope include scope nexgen.command_line.nxs_phil.beamline_scope include scope nexgen.command_line.nxs_phil.detector_scope include scope nexgen.command_line.nxs_phil.module_scope """, process_includes=True, ) meta_phil = freephil.parse( """ input { metafile = None .type = path .help = "Path to _meta.h5 file for collection." datafile = None .multiple = True .type = path .help = "HDF5 file. For now, assumes pattern filename_%0{6}d.h5" coordinate_frame = *mcstas imgcif .type = choice .help = "Which coordinate system is being used to provide input vectors." vds_writer = *None dataset file .type = choice .help = "If not None, write vds along with external link to data in NeXus file, or create _vds.h5 file." } include scope nexgen.command_line.nxs_phil.goniometer_scope include scope nexgen.command_line.nxs_phil.beamline_scope include scope nexgen.command_line.nxs_phil.detector_scope include scope nexgen.command_line.nxs_phil.module_scope include scope nexgen.command_line.nxs_phil.timestamp_scope # include scope nexgen.command_line.nexus_generator.master_phil """, process_includes=True, ) # Parse command line arguments parser = argparse.ArgumentParser( description="Generate a NeXus file for data collection.", parents=[version_parser], ) parser.add_argument("--debug", action="store_const", const=True) parser.add_argument( "-c", "--show-config", action="store_true", default=False, dest="show_config", help="Show the configuration parameters.", ) # CLIs def write_NXmx_cli(args): cl = master_phil.command_line_argument_interpreter() working_phil = master_phil.fetch(cl.process_and_fetch(args.phil_args)) params = working_phil.extract() # Path to data file datafiles = [Path(d).expanduser().resolve() for d in params.input.datafile] # Get NeXus file name master_file = get_nexus_filename(datafiles[0]) # Start logger logfile = datafiles[0].parent / "generate_nexus.log" # Define a file handler for logging FH = logging.FileHandler(logfile, mode="a") FH.setLevel(logging.DEBUG) FH.setFormatter(formatter) # Add handlers to logger logger.addHandler(FH) # Add some information to logger logger.info("Create a NeXus file for %s" % datafiles[0]) logger.info( "Number of experiment data files in directory, linked to the Nexus file: %d" % len(datafiles) ) logger.info("NeXus file will be saved as %s" % master_file) # Load technical info from phil parser cf = params.input.coordinate_frame goniometer = params.goniometer detector = params.detector module = params.detector_module source = params.source beam = params.beam attenuator = params.attenuator timestamps = ( get_iso_timestamp(params.start_time), get_iso_timestamp(params.end_time), ) # If dealing with a tristan detector, add its specifications to detector scope. if "TRISTAN" in detector.description.upper(): add_tristan_spec(detector, params.tristanSpec) # Log information logger.info("Source information") logger.info(f"Facility: {source.name} - {source.type}.") logger.info(f"Beamline: {source.beamline_name}") if timestamps[0] is not None: logger.info(f"Collection start time: {timestamps[0]}") else: logger.warning("No collection start time recorded.") if timestamps[1] is not None: logger.info(f"Collection end time: {timestamps[1]}") else: logger.warning("No collection end time recorded.") logger.info("Coordinate system: %s" % cf) if cf == "imgcif": logger.warning( "Input coordinate frame is imgcif. They will be converted to mcstas." ) logger.info("Goniometer information") axes = goniometer.axes axis_vectors = goniometer.vectors for tu in zip(goniometer.types, goniometer.units): assert tu in ( ("translation", "mm"), ("rotation", "deg"), ), "Appropriate axis units should be: mm for translations, det for rotations" assert len(axis_vectors) == 3 * len( axes ), "Number of vectors does not match number of goniometer axes." for j in reversed(range(len(axes))): vector = axis_vectors[3 * j : 3 * j + 3] logger.info( f"Goniometer axis: {axes[j]} => {vector} ({goniometer.types[j]}) on {goniometer.depends[j]}. {goniometer.starts[j]} {goniometer.ends[j]} {goniometer.increments[j]}" ) logger.info("") logger.info( f"Detector information:\n {detector.description}, {detector.detector_type}" ) logger.info( f"Sensor made of {detector.sensor_material} x {detector.sensor_thickness}" ) logger.info(f"Trusted pixels > {detector.underload} and < {detector.overload}") logger.info( f"Image is a {detector.image_size} array of {detector.pixel_size} pixels" ) logger.info("Detector axes:") axes = detector.axes axis_vectors = detector.vectors for tu in zip(detector.types, detector.units): assert tu in ( ("translation", "mm"), ("rotation", "deg"), ), "Appropriate axis units should be: mm for translations, det for rotations" assert len(axis_vectors) == 3 * len( axes ), "Number of vectors does not match number of detector axes." for j in range(len(axes)): vector = axis_vectors[3 * j : 3 * j + 3] logger.info( f"Detector axis: {axes[j]} => {vector} ({detector.types[j]}) on {detector.depends[j]}. {detector.starts[j]}" ) if detector.flatfield is None: logger.info("No flatfield applied") else: logger.info(f"Flatfield correction data lives here {detector.flatfield}") if detector.pixel_mask is None: logger.info("No bad pixel mask for this detector") else: logger.info(f"Bad pixel mask lives here {detector.pixel_mask}") logger.info("Module information") logger.info(f"Number of modules: {module.num_modules}") logger.info(f"Fast axis at datum position: {module.fast_axis}") logger.info(f"Slow_axis at datum position: {module.slow_axis}") if module.module_offset == "0": logger.warning(f"module_offset field will not be written.") logger.info("") logger.info("Start writing NeXus file ...") try: with h5py.File(master_file, "x") as nxsfile: write_nexus( nxsfile, datafiles, goniometer, detector, module, source, beam, attenuator, timestamps, cf, params.input.vds_writer, ) # Check and save pump status if params.pump_probe.pump_status is True: logger.info( "Pump probe status is True, write relative metadata as NXnote." ) pump_info = { "pump_exposure_time": params.pump_probe.pump_exp, "pump_delay": params.pump_probe.pump_delay, } write_NXnote(nxsfile, "/entry/source/notes", pump_info) logger.info(f"{master_file} correctly written.") except Exception as err: logger.info( f"An error occurred and {master_file} couldn't be written correctly." ) logger.exception(err) # logger.error(err) logger.info("EOF") def write_demo_cli(args): cl = demo_phil.command_line_argument_interpreter() working_phil = demo_phil.fetch(cl.process_and_fetch(args.phil_args)) params = working_phil.extract() # Path to file master_file = Path(params.output.master_filename).expanduser().resolve() # Just in case ... if master_file.suffix == ".h5" and "master" not in master_file.stem: master_file = Path(master_file.as_posix().replace(".h5", "_master.h5")) # Start logger logfile = master_file.parent / "generate_demo.log" # Define a file handler for logging FH = logging.FileHandler(logfile, mode="w") FH.setLevel(logging.DEBUG) FH.setFormatter(formatter) # Add handlers to logger logger.addHandler(FH) # Images or events ? if args.events is True: num_events = args.force if args.force else 1 data_type = ("events", num_events) else: num_images = args.force if args.force else None data_type = ("images", num_images) # Get data file name template data_file_template = get_filename_template(master_file) # Add some information to logger logger.info("NeXus file will be saved as %s" % params.output.master_filename) logger.info("Data file(s) template: %s" % data_file_template) # Next: go through technical info (goniometer, detector, beamline etc ...) cf = params.input.coordinate_frame goniometer = params.goniometer detector = params.detector module = params.detector_module source = params.source beam = params.beam attenuator = params.attenuator # If dealing with a tristan detector, add its specifications to detector scope. if "TRISTAN" in detector.description.upper(): add_tristan_spec(detector, params.tristanSpec) # Log information logger.info("Data type: %s" % data_type[0]) logger.info("Source information") logger.info(f"Facility: {source.name} - {source.type}.") logger.info(f"Beamline: {source.beamline_name}") logger.info("Coordinate system: %s" % cf) if cf == "imgcif": logger.warning( "Input coordinate frame is imgcif. They will be converted to mcstas." ) logger.info("Goniometer information") axes = goniometer.axes axis_vectors = goniometer.vectors for tu in zip(goniometer.types, goniometer.units): assert tu in (("translation", "mm"), ("rotation", "deg")) assert len(axis_vectors) == 3 * len( axes ), "Number of vectors does not match number of axes." for j in reversed(range(len(axes))): vector = axis_vectors[3 * j : 3 * j + 3] logger.info( f"Goniometer axis: {axes[j]} => {vector} ({goniometer.types[j]}) on {goniometer.depends[j]}. {goniometer.starts[j]} {goniometer.ends[j]} {goniometer.increments[j]}" ) logger.info("") logger.info("Detector information:\n%s" % detector.description) logger.info( f"Sensor made of {detector.sensor_material} x {detector.sensor_thickness}" ) if data_type[0] == "images": logger.info(f"Trusted pixels > {detector.underload} and < {detector.overload}") logger.info( f"Image is a {detector.image_size} array of {detector.pixel_size} pixels" ) logger.info("Detector axes:") axes = detector.axes axis_vectors = detector.vectors for tu in zip(detector.types, detector.units): assert tu in (("translation", "mm"), ("rotation", "deg")) assert len(axis_vectors) == 3 * len(axes) for j in range(len(axes)): vector = axis_vectors[3 * j : 3 * j + 3] logger.info( f"Detector axis: {axes[j]} => {vector} ({detector.types[j]}) on {detector.depends[j]}. {detector.starts[j]}" ) if detector.flatfield is None: logger.info("No flatfield applied") else: logger.info(f"Flatfield correction data lives here {detector.flatfield}") if detector.pixel_mask is None: logger.info("No bad pixel mask for this detector") else: logger.info(f"Bad pixel mask lives here {detector.pixel_mask}") logger.info("Module information") logger.warning(f"module_offset field setting: {module.module_offset}") logger.info(f"Number of modules: {module.num_modules}") logger.info(f"Fast axis at datum position: {module.fast_axis}") logger.info(f"Slow_axis at datum position: {module.slow_axis}") logger.info("") # Record string with start_time start_time = datetime.fromtimestamp(time.time()).strftime("%A, %d. %B %Y %I:%M%p") logger.info("Start writing NeXus and data files ...") try: with h5py.File(master_file, "x") as nxsfile: write_nexus_demo( nxsfile, data_file_template, data_type, cf, goniometer, detector, module, source, beam, attenuator, params.input.vds_writer, ) # Check and save pump status if params.pump_probe.pump_status is True: logger.info( "Pump probe status is True, write relative metadata as NXnote." ) pump_info = { "pump_exposure_time": params.pump_probe.pump_exp, "pump_delay": params.pump_probe.pump_delay, } write_NXnote(nxsfile, "/entry/source/notes", pump_info) # Record string with end_time end_time = datetime.fromtimestamp(time.time()).strftime( "%A, %d. %B %Y %I:%M%p" ) # Write /entry/start_time and /entry/end_time nxsfile.create_dataset("/entry/start_time", data=np.string_(start_time)) nxsfile.create_dataset("/entry/end_time", data=np.string_(end_time)) logger.info(f"{master_file} correctly written.") except Exception as err: logger.info( f"An error occurred and {master_file} couldn't be written correctly." ) logger.exception(err) logger.info("EOF") def write_with_meta_cli(args): cl = meta_phil.command_line_argument_interpreter() working_phil = meta_phil.fetch(cl.process_and_fetch(args.phil_args)) params = working_phil.extract() # Path to meta file if params.input.metafile: metafile = Path(params.input.metafile).expanduser().resolve() else: sys.exit( "Please pass a _meta.h5 file. If not available use 'nexus' option instead." ) # Get NeXus filename master_file = get_nexus_filename(metafile) # If no datafile has been passed, look for them in the directory if params.input.datafile: datafiles = [Path(d).expanduser().resolve() for d in params.input.datafile] else: datafile_pattern = ( metafile.parent / f"{master_file.stem}_{6*'[0-9]'}.h5" ).as_posix() datafiles = [ Path(d).expanduser().resolve() for d in sorted(glob.glob(datafile_pattern)) ] # Start logger logfile = metafile.parent / "generate_nexus_from_meta.log" # Define a file handler for logging FH = logging.FileHandler(logfile, mode="a") FH.setLevel(logging.DEBUG) FH.setFormatter(formatter) # Add handlers to logger logger.addHandler(FH) # Add some information to logger logger.info("Create a NeXus file for %s" % datafiles[0]) logger.info( "Number of experiment data files in directory, linked to the Nexus file: %d" % len(datafiles) ) logger.info("Meta file for the collection: %s" % metafile) logger.info("NeXus file will be saved as %s" % master_file) # Load technical info from phil parser cf = params.input.coordinate_frame goniometer = params.goniometer detector = params.detector module = params.detector_module source = params.source beam = params.beam attenuator = params.attenuator timestamps = ( get_iso_timestamp(params.start_time), get_iso_timestamp(params.end_time), ) # If dealing with a tristan detector, add its specifications to detector scope. if "TRISTAN" in detector.description.upper(): add_tristan_spec(detector, params.tristanSpec) # Log information logger.info("Source information") logger.info(f"Facility: {source.name} - {source.type}.") logger.info(f"Beamline: {source.beamline_name}") if timestamps[0] is not None: logger.info(f"Collection start time: {timestamps[0]}") else: logger.warning("No collection start time recorded.") if timestamps[1] is not None: logger.info(f"Collection end time: {timestamps[1]}") else: logger.warning("No collection end time recorded.") logger.info("Coordinate system: %s" % cf) if cf == "imgcif": logger.warning( "Input coordinate frame is imgcif. They will be converted to mcstas." ) logger.info("Goniometer information") axes = goniometer.axes axis_vectors = goniometer.vectors for tu in zip(goniometer.types, goniometer.units): assert tu in ( ("translation", "mm"), ("rotation", "deg"), ), "Appropriate axis units should be: mm for translations, det for rotations" assert len(axis_vectors) == 3 * len( axes ), "Number of vectors does not match number of goniometer axes." for j in reversed(range(len(axes))): vector = axis_vectors[3 * j : 3 * j + 3] logger.info( f"Goniometer axis: {axes[j]} => {vector} ({goniometer.types[j]}) on {goniometer.depends[j]}. {goniometer.starts[j]} {goniometer.ends[j]} {goniometer.increments[j]}" ) logger.info("") if detector.description is None: logger.warning("No detector description provided, exit.") sys.exit("Please provide a detector description for identification.") logger.info( f"Detector information:\n {detector.description}, {detector.detector_type}" ) logger.info( f"Sensor made of {detector.sensor_material} x {detector.sensor_thickness}" ) logger.info(f"Trusted pixels > {detector.underload} and < {detector.overload}") logger.info( f"Image is a {detector.image_size} array of {detector.pixel_size} pixels" ) logger.info("Detector axes:") axes = detector.axes axis_vectors = detector.vectors for tu in zip(detector.types, detector.units): assert tu in ( ("translation", "mm"), ("rotation", "deg"), ), "Appropriate axis units should be: mm for translations, det for rotations" assert len(axis_vectors) == 3 * len( axes ), "Number of vectors does not match number of detector axes." for j in range(len(axes)): vector = axis_vectors[3 * j : 3 * j + 3] logger.info( f"Detector axis: {axes[j]} => {vector} ({detector.types[j]}) on {detector.depends[j]}. {detector.starts[j]}" ) if detector.flatfield is None: logger.info("No flatfield applied") else: logger.info(f"Flatfield correction data lives here {detector.flatfield}") if detector.pixel_mask is None: logger.info("No bad pixel mask for this detector") else: logger.info(f"Bad pixel mask lives here {detector.pixel_mask}") logger.info("Module information") logger.info(f"Number of modules: {module.num_modules}") logger.info(f"Fast axis at datum position: {module.fast_axis}") logger.info(f"Slow_axis at datum position: {module.slow_axis}") if module.module_offset == "0": logger.warning(f"module_offset field will not be written.") logger.info("") if args.no_ow: logger.warning(f"The following datasets will not be overwritten: {args.no_ow}") metainfo = (metafile, args.no_ow) else: metainfo = (metafile, None) logger.info("Start writing NeXus file ...") try: with h5py.File(master_file, "x") as nxsfile: write_nexus( nxsfile, datafiles, goniometer, detector, module, source, beam, attenuator, timestamps, cf, params.input.vds_writer, metainfo, ) # Check and save pump status if params.pump_probe.pump_status is True: logger.info( "Pump probe status is True, write relative metadata as NXnote." ) pump_info = { "pump_exposure_time": params.pump_probe.pump_exp, "pump_delay": params.pump_probe.pump_delay, } write_NXnote(nxsfile, "/entry/source/notes", pump_info) logger.info(f"{master_file} correctly written.") except Exception as err: logger.info( f"An error occurred and {master_file} couldn't be written correctly." ) logger.exception(err) logger.info("EOF") # Define subparsers subparsers = parser.add_subparsers( help="Choose whether to write a NXmx NeXus file for a collection or a demo. \ Run generate_nexus <command> --help to see the parameters for each sub-command.", required=True, dest="sub-command", ) parser_NXmx = subparsers.add_parser( "1", aliases=["nexus"], description=("Trigger NeXus file writing pointing to existing data."), parents=[nexus_parser], ) parser_NXmx.set_defaults(func=write_NXmx_cli) parser_NXmx_demo = subparsers.add_parser( "2", aliases=["demo"], description=("Trigger NeXus and blank data file writing."), parents=[demo_parser, detectormode_parser], ) parser_NXmx_demo.set_defaults(func=write_demo_cli) parser_NXmx_meta = subparsers.add_parser( "3", aliases=["meta"], description=( "Trigger NeXus file writing pointing to an existing collection with a meta file." ), parents=[nexus_parser], ) parser_NXmx_meta.add_argument( "-no", "--no_ow", nargs="+", help="List of datasets that should not be overwritten even if present in meta file", type=str, ) parser_NXmx_meta.set_defaults(func=write_with_meta_cli) def main(): # Define a stream handler CH = logging.StreamHandler(sys.stdout) CH.setLevel(logging.DEBUG) CH.setFormatter(formatter) logger.addHandler(CH) args = parser.parse_args() args.func(args) # main()

33.078273

176

0.637143

3,050

24,511

5.002623

0.114754

0.062262

0.034605

0.024577

0.759339

0.74669

0.735024

0.718705

0.71628

0.714117

0

0.003782

0.255763

24,511

740

177

33.122973

0.832639

0.056464

0

0.639535

1

0.011628

0.306926

0.048815

0

0.023256

1

0.007752

false

0.001938

0.027132

0

0.034884

0

null

0

1

0

1

0

null

0

6

234c7ba7dba7e1153eaebd2a9f976d8200caa41b

487

py

Python

src/pages/views.py

Neville-Loh/webdev_commerce

b025182e0df54a7dd156ee027efd57736a458b95

[ "bzip2-1.0.6" ]

null

src/pages/views.py

Neville-Loh/webdev_commerce

b025182e0df54a7dd156ee027efd57736a458b95

[ "bzip2-1.0.6" ]

null

src/pages/views.py

Neville-Loh/webdev_commerce

b025182e0df54a7dd156ee027efd57736a458b95

[ "bzip2-1.0.6" ]

null

from django.shortcuts import render def home_view(request): return render(request, "home.html", {}) def about_view(request): return render(request, "about.html", {}) def contact_view(request): return render(request, "contact.html", {}) # For debug purpose # ----------------------------------------------------------------- def self_note_view(request): return render(request, "self_note.html", {}) def self_note_view_1(request): return render(request, "self_note_1.html", {})

24.35

67

0.632444

59

487

5.033898

0.322034

0.218855

0.319865

0.43771

0.545455

0.228956

0

0.004587

0.104723

487

19

68

25.631579

0.676606

0.170431

0

0.15212

0

1

0.454545

false

0

0.090909

0.454545

1

0

null

1

0

null

0

1

0

1

0

6

000106decf226374650a69d21bc7a44c65206ab7

6,023

py

Python

tests/potato/test_potato_transferFrom.py

MostafaMhmod/potato-farm

fc99abbc162a61f432aae3ba054ab46fc6a901e1

[ "MIT" ]

null

tests/potato/test_potato_transferFrom.py

MostafaMhmod/potato-farm

fc99abbc162a61f432aae3ba054ab46fc6a901e1

[ "MIT" ]

null

tests/potato/test_potato_transferFrom.py

MostafaMhmod/potato-farm

fc99abbc162a61f432aae3ba054ab46fc6a901e1

[ "MIT" ]

null

#!/usr/bin/python3 import brownie def test_potato_sender_balance_decreases(accounts, potato): sender_balance = potato.balanceOf(accounts[0]) amount = sender_balance // 4 potato.approve(accounts[1], amount, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], amount, {'from': accounts[1]}) assert potato.balanceOf(accounts[0]) == sender_balance - amount def test_potato_receiver_balance_increases(accounts, potato): receiver_balance = potato.balanceOf(accounts[2]) amount = potato.balanceOf(accounts[0]) // 4 potato.approve(accounts[1], amount, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], amount, {'from': accounts[1]}) assert potato.balanceOf(accounts[2]) == receiver_balance + amount def test_potato_caller_balance_not_affected(accounts, potato): caller_balance = potato.balanceOf(accounts[1]) amount = potato.balanceOf(accounts[0]) potato.approve(accounts[1], amount, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], amount, {'from': accounts[1]}) assert potato.balanceOf(accounts[1]) == caller_balance def test_potato_caller_approval_affected(accounts, potato): approval_amount = potato.balanceOf(accounts[0]) transfer_amount = approval_amount // 4 potato.approve(accounts[1], approval_amount, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], transfer_amount, {'from': accounts[1]}) assert potato.allowance(accounts[0], accounts[1]) == approval_amount - transfer_amount def test_potato_receiver_approval_not_affected(accounts, potato): approval_amount = potato.balanceOf(accounts[0]) transfer_amount = approval_amount // 4 potato.approve(accounts[1], approval_amount, {'from': accounts[0]}) potato.approve(accounts[2], approval_amount, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], transfer_amount, {'from': accounts[1]}) assert potato.allowance(accounts[0], accounts[2]) == approval_amount def test_potato_total_supply_not_affected(accounts, potato): total_supply = potato.totalSupply() amount = potato.balanceOf(accounts[0]) potato.approve(accounts[1], amount, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], amount, {'from': accounts[1]}) assert potato.totalSupply() == total_supply def test_potato_returns_true(accounts, potato): amount = potato.balanceOf(accounts[0]) potato.approve(accounts[1], amount, {'from': accounts[0]}) tx = potato.transferFrom(accounts[0], accounts[2], amount, {'from': accounts[1]}) assert tx.return_value is True def test_potato_transfer_full_balance(accounts, potato): amount = potato.balanceOf(accounts[0]) receiver_balance = potato.balanceOf(accounts[2]) potato.approve(accounts[1], amount, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], amount, {'from': accounts[1]}) assert potato.balanceOf(accounts[0]) == 0 assert potato.balanceOf(accounts[2]) == receiver_balance + amount def test_potato_transfer_zero_potatos(accounts, potato): sender_balance = potato.balanceOf(accounts[0]) receiver_balance = potato.balanceOf(accounts[2]) potato.approve(accounts[1], sender_balance, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[2], 0, {'from': accounts[1]}) assert potato.balanceOf(accounts[0]) == sender_balance assert potato.balanceOf(accounts[2]) == receiver_balance def test_potato_transfer_zero_potatos_without_approval(accounts, potato): sender_balance = potato.balanceOf(accounts[0]) receiver_balance = potato.balanceOf(accounts[2]) potato.transferFrom(accounts[0], accounts[2], 0, {'from': accounts[1]}) assert potato.balanceOf(accounts[0]) == sender_balance assert potato.balanceOf(accounts[2]) == receiver_balance def test_potato_insufficient_balance(accounts, potato): balance = potato.balanceOf(accounts[0]) potato.approve(accounts[1], balance + 1, {'from': accounts[0]}) with brownie.reverts(): potato.transferFrom(accounts[0], accounts[2], balance + 1, {'from': accounts[1]}) def test_potato_insufficient_approval(accounts, potato): balance = potato.balanceOf(accounts[0]) print(balance) potato.approve(accounts[1], balance - 1, {'from': accounts[0]}) with brownie.reverts(): potato.transferFrom(accounts[0], accounts[2], balance, {'from': accounts[1]}) def test_potato_no_approval(accounts, potato): balance = potato.balanceOf(accounts[0]) with brownie.reverts(): potato.transferFrom(accounts[0], accounts[2], balance, {'from': accounts[1]}) def test_potato_revoked_approval(accounts, potato): balance = potato.balanceOf(accounts[0]) potato.approve(accounts[1], balance, {'from': accounts[0]}) potato.approve(accounts[1], 0, {'from': accounts[0]}) with brownie.reverts(): potato.transferFrom(accounts[0], accounts[2], balance, {'from': accounts[1]}) def test_potato_transfer_to_self(accounts, potato): sender_balance = potato.balanceOf(accounts[0]) amount = sender_balance // 4 potato.approve(accounts[0], sender_balance, {'from': accounts[0]}) potato.transferFrom(accounts[0], accounts[0], amount, {'from': accounts[0]}) assert potato.balanceOf(accounts[0]) == sender_balance assert potato.allowance(accounts[0], accounts[0]) == sender_balance - amount def test_potato_transfer_to_self_no_approval(accounts, potato): amount = potato.balanceOf(accounts[0]) with brownie.reverts(): potato.transferFrom(accounts[0], accounts[0], amount, {'from': accounts[0]}) def test_potato_transfer_event_fires(accounts, potato): amount = potato.balanceOf(accounts[0]) potato.approve(accounts[1], amount, {'from': accounts[0]}) tx = potato.transferFrom(accounts[0], accounts[2], amount, {'from': accounts[1]}) assert len(tx.events) == 1 assert tx.events["Transfer"].values() == [accounts[0], accounts[2], amount]

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000817092cd6b9ae5fbe118fd14a6d71e367a553

2,111

py

Python

src/sort_strategy.py

Amatsukan/TechnicalAssessment

b30eaa2b94b9e34e61ee2372b61eb151fa8aa95c

[ "Unlicense" ]

null

src/sort_strategy.py

Amatsukan/TechnicalAssessment

b30eaa2b94b9e34e61ee2372b61eb151fa8aa95c

[ "Unlicense" ]

null

src/sort_strategy.py

Amatsukan/TechnicalAssessment

b30eaa2b94b9e34e61ee2372b61eb151fa8aa95c

[ "Unlicense" ]

null

from ordered_set import * class Sort_strategy: def sort(self, books): pass def setComposition(self,composition): self.composition = composition return self def cleanComposition(self,composition): self.composition = None return self def desc_order(self, recursive = True): self.reverse = True; if(self.composition != None and recursive): self.composition.desc_order(recursive) def asc_order(self, recursive = True): self.reverse = False; if(self.composition != None and recursive): self.composition.asc_order(recursive) class Title_sort(Sort_strategy): def __init__(self, composition = None, reverse = False): self.composition = composition self.reverse = reverse self.id = "TITLE SORT" def sort(self, books): if(self.composition == None): return OrderedSet(sorted(books, key = lambda x: x.title, reverse = self.reverse)) else: return OrderedSet(sorted(self.composition.sort(books), key = lambda x: x.title, reverse = self.reverse)) class Author_sort(Sort_strategy): def __init__(self, composition = None, reverse = False): self.composition = composition self.reverse = reverse self.id = "AUTHOR SORT" def sort(self, books): if(self.composition == None): return OrderedSet(sorted(books, key = lambda x: x.author, reverse = self.reverse)) else: return OrderedSet(sorted(self.composition.sort(books), key = lambda x: x.author, reverse = self.reverse)) class Year_sort(Sort_strategy): def __init__(self, composition = None, reverse = False): self.composition = composition self.reverse = reverse self.id = "YEAR SORT" def sort(self, books): if(self.composition == None): return OrderedSet(sorted(books, key = lambda x: x.ed_year, reverse = self.reverse)) else: return OrderedSet(sorted(self.composition.sort(books), key = lambda x: x.ed_year, reverse = self.reverse))

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1

0

null

0

1

0

6

0023bc2a40c6b9f6445e225b7289953888297c9d

217

py

Python

istock_app/resources.py

Jonas1015/ShoppySm

52c8edf958ca705b489f8f4efb547bdd635cefd2

[ "MIT" ]

null

istock_app/resources.py

Jonas1015/ShoppySm

52c8edf958ca705b489f8f4efb547bdd635cefd2

[ "MIT" ]

null

istock_app/resources.py

Jonas1015/ShoppySm

52c8edf958ca705b489f8f4efb547bdd635cefd2

[ "MIT" ]

null

# from import_export import resources from .models import sales # class salesResource(resources.ModelResource): # class Meta: # model = sales # fields = ['sales_name', 'quantity', 'date_of_sale']

27.125

61

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6

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7

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0.824885

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null

0

1

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1

0

1

0

6

003e6f4c7bd179448c6e83447f86aa9321c675b4

96

py

Python

venv/lib/python3.8/site-packages/pip/_vendor/chardet/__init__.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/pip/_vendor/chardet/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/pip/_vendor/chardet/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/99/66/5a/5a6bd9921c1f044013f4ed58ea74537cace14fb1478504d302e8dba940

96

0.895833

9

96

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null

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null

0

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0

1

0

1

0

null

1

0

1

0

6

cc8767a7d431d9c6df0105ead6e11ccf39bfffda

109

py

Python

aula#22/desafio115/teste.py

daramariabs/exercicios-python

0d9785a9cccd5442a190572c58ab8dd6e2fe0cce

[ "MIT" ]

null

aula#22/desafio115/teste.py

daramariabs/exercicios-python

0d9785a9cccd5442a190572c58ab8dd6e2fe0cce

[ "MIT" ]

null

aula#22/desafio115/teste.py

daramariabs/exercicios-python

0d9785a9cccd5442a190572c58ab8dd6e2fe0cce

[ "MIT" ]

null

arquivo = open("/home/daramariabs/Documentos/GITHUB/exercicios-python/aula#22/desafio115/contatos.txt", "a")

54.5

108

0.788991

14

109

6.142857

1

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0.047619

0.036697

109

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109

0.771429

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0.788991

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0

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null

0

1

0

1

0

1

null

0

6

ccaf4e0e199ba5b6f44d8a76e29133fde5daff1f

110

py

Python

flaskeddit/user/__init__.py

aqche/flaskedd

04edbf2e22c7a63c944cca91176df9119983eab2

[ "MIT" ]

1

2019-03-23T03:21:35.000Z

flaskeddit/user/__init__.py

aqche/flaskedd

04edbf2e22c7a63c944cca91176df9119983eab2

[ "MIT" ]

7

2020-03-24T18:05:13.000Z

2022-01-13T01:57:43.000Z

flaskeddit/user/__init__.py

aqche/flaskedd

04edbf2e22c7a63c944cca91176df9119983eab2

[ "MIT" ]

null

from flask import Blueprint user_blueprint = Blueprint("user", __name__) from flaskeddit.user import routes

18.333333

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14

110

6

0.571429

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null

0

1

0

1

0

6

ccb3c2aef88ab43ffb7170b22436644354627886

452

py

Python

wynncraft/__init__.py

martinkovacs/wynncraft-python

0c35e3eb4d080aa32b997543d21998933ffa7819

[ "0BSD" ]

null

wynncraft/__init__.py

martinkovacs/wynncraft-python

0c35e3eb4d080aa32b997543d21998933ffa7819

[ "0BSD" ]

null

wynncraft/__init__.py

martinkovacs/wynncraft-python

0c35e3eb4d080aa32b997543d21998933ffa7819

[ "0BSD" ]

null

from wynncraft.version import __version__ import wynncraft.utils import wynncraft.cache from wynncraft.wynncraft import Guild from wynncraft.wynncraft import Ingredient from wynncraft.wynncraft import Item from wynncraft.wynncraft import Leaderboard from wynncraft.wynncraft import Network from wynncraft.wynncraft import Player from wynncraft.wynncraft import Recipe from wynncraft.wynncraft import Search from wynncraft.wynncraft import Territory

28.25

43

0.869469

56

452

6.946429

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0.33419

0.508997

0.647815

0

0.103982

452

15

44

30.133333

0.960494

0

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true

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1

0

null

0

1

0

1

0

1

0

6

ccc6109cd2681baeee129a990454d03dc1c8648e

1,266

py

Python

tests/dags/test_sub_provider_update_workflow.py

lyu4321/openverse-catalog

cd5be8fbff402a7420e772a803abdc2a20fd7235

[ "MIT" ]

null

tests/dags/test_sub_provider_update_workflow.py

lyu4321/openverse-catalog

cd5be8fbff402a7420e772a803abdc2a20fd7235

[ "MIT" ]

1

2021-10-07T19:21:21.000Z

tests/dags/test_sub_provider_update_workflow.py

lyu4321/openverse-catalog

cd5be8fbff402a7420e772a803abdc2a20fd7235

[ "MIT" ]

null

import os from airflow.models import DagBag FILE_DIR = os.path.abspath(os.path.dirname(__file__)) def test_flickr_dag_loads_with_no_errors(tmpdir): tmp_directory = str(tmpdir) dag_bag = DagBag(dag_folder=tmp_directory, include_examples=False) dag_bag.process_file( os.path.join( FILE_DIR, "../../dags/flickr_sub_provider_update_workflow.py", ) ) assert len(dag_bag.import_errors) == 0 assert len(dag_bag.dags) == 1 def test_europeana_dag_loads_with_no_errors(tmpdir): tmp_directory = str(tmpdir) dag_bag = DagBag(dag_folder=tmp_directory, include_examples=False) dag_bag.process_file( os.path.join( FILE_DIR, "../../dags/europeana_sub_provider_update_workflow.py", ) ) assert len(dag_bag.import_errors) == 0 assert len(dag_bag.dags) == 1 def test_smithsonian_dag_loads_with_no_errors(tmpdir): tmp_directory = str(tmpdir) dag_bag = DagBag(dag_folder=tmp_directory, include_examples=False) dag_bag.process_file( os.path.join( FILE_DIR, "../../dags/smithsonian_sub_provider_update_workflow.py", ) ) assert len(dag_bag.import_errors) == 0 assert len(dag_bag.dags) == 1

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1,266

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0.222222

0

null

0

1

0

null

0

6

4e02bc1fdc8cad1317b4aee089994315c1fff569

212

py

Python

Gamify/status/admin.py

Londa-LG/Gamify

2fd8557650b584bf47ffaa6acfdc61c8fdbf64f6

[ "MIT" ]

null

Gamify/status/admin.py

Londa-LG/Gamify

2fd8557650b584bf47ffaa6acfdc61c8fdbf64f6

[ "MIT" ]

5

2021-03-30T14:10:33.000Z

2021-09-22T19:33:59.000Z

Gamify/status/admin.py

Londa-LG/Gamify

2fd8557650b584bf47ffaa6acfdc61c8fdbf64f6

[ "MIT" ]

null

from django.contrib import admin from .models import Challenge_Body, Challenge_Mind, Challenge_Skill admin.site.register(Challenge_Body) admin.site.register(Challenge_Mind) admin.site.register(Challenge_Skill)

26.5

67

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29

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6.034483

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0.291429

0.445714

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7

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true

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null

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1

0

6

d1c4f0253e3e6f6c94b8bf4b96b5e73e1562811c

943

py

Python

test/test_upgrade_notification.py

dcompane/controlm_py

c521208be2f00303383bb32ca5eb2b7ff91999d3

[ "MIT" ]

2

2020-03-20T18:24:23.000Z

2021-03-05T22:05:04.000Z

test/test_upgrade_notification.py

dcompane/controlm_py

c521208be2f00303383bb32ca5eb2b7ff91999d3

[ "MIT" ]

null

test/test_upgrade_notification.py

dcompane/controlm_py

c521208be2f00303383bb32ca5eb2b7ff91999d3

[ "MIT" ]

1

2021-05-27T15:54:37.000Z

# coding: utf-8 """ Control-M Services Provides access to BMC Control-M Services # noqa: E501 OpenAPI spec version: 9.20.220 Contact: customer_support@bmc.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import controlm_py from controlm_py.models.upgrade_notification import UpgradeNotification # noqa: E501 from controlm_py.rest import ApiException class TestUpgradeNotification(unittest.TestCase): """UpgradeNotification unit test stubs""" def setUp(self): pass def tearDown(self): pass def testUpgradeNotification(self): """Test UpgradeNotification""" # FIXME: construct object with mandatory attributes with example values # model = controlm_py.models.upgrade_notification.UpgradeNotification() # noqa: E501 pass if __name__ == '__main__': unittest.main()

23.575

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6.149533

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0.198303

943

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false

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1

0

1

0

6

d1c9ec3e52a28dbdd9f4961c2c882911edcd85ec

4,215

py

Python

ksteta3pi/PotentialBackgrounds/MC_12_11164011_MagUp.py.py

Williams224/davinci-scripts

730642d2ff13543eca4073a4ce0932631195de56

[ "MIT" ]

null

ksteta3pi/PotentialBackgrounds/MC_12_11164011_MagUp.py.py

Williams224/davinci-scripts

730642d2ff13543eca4073a4ce0932631195de56

[ "MIT" ]

null

ksteta3pi/PotentialBackgrounds/MC_12_11164011_MagUp.py.py

Williams224/davinci-scripts

730642d2ff13543eca4073a4ce0932631195de56

[ "MIT" ]

null

#-- GAUDI jobOptions generated on Fri Jul 24 17:00:56 2015 #-- Contains event types : #-- 11164011 - 17 files - 264994 events - 57.72 GBytes #-- Extra information about the data processing phases: #-- Processing Pass Step-124834 #-- StepId : 124834 #-- StepName : Reco14a for MC #-- ApplicationName : Brunel #-- ApplicationVersion : v43r2p7 #-- OptionFiles : $APPCONFIGOPTS/Brunel/DataType-2012.py;$APPCONFIGOPTS/Brunel/MC-WithTruth.py;$APPCONFIGOPTS/Persistency/Compression-ZLIB-1.py #-- DDDB : fromPreviousStep #-- CONDDB : fromPreviousStep #-- ExtraPackages : AppConfig.v3r164 #-- Visible : Y #-- Processing Pass Step-124620 #-- StepId : 124620 #-- StepName : Digi13 with G4 dE/dx #-- ApplicationName : Boole #-- ApplicationVersion : v26r3 #-- OptionFiles : $APPCONFIGOPTS/Boole/Default.py;$APPCONFIGOPTS/Boole/DataType-2012.py;$APPCONFIGOPTS/Boole/Boole-SiG4EnergyDeposit.py;$APPCONFIGOPTS/Persistency/Compression-ZLIB-1.py #-- DDDB : fromPreviousStep #-- CONDDB : fromPreviousStep #-- ExtraPackages : AppConfig.v3r164 #-- Visible : Y #-- Processing Pass Step-124632 #-- StepId : 124632 #-- StepName : TCK-0x409f0045 Flagged for Sim08 2012 #-- ApplicationName : Moore #-- ApplicationVersion : v14r8p1 #-- OptionFiles : $APPCONFIGOPTS/Moore/MooreSimProductionWithL0Emulation.py;$APPCONFIGOPTS/Conditions/TCK-0x409f0045.py;$APPCONFIGOPTS/Moore/DataType-2012.py;$APPCONFIGOPTS/L0/L0TCK-0x0045.py #-- DDDB : fromPreviousStep #-- CONDDB : fromPreviousStep #-- ExtraPackages : AppConfig.v3r164 #-- Visible : Y #-- Processing Pass Step-124630 #-- StepId : 124630 #-- StepName : Stripping20-NoPrescalingFlagged for Sim08 #-- ApplicationName : DaVinci #-- ApplicationVersion : v32r2p1 #-- OptionFiles : $APPCONFIGOPTS/DaVinci/DV-Stripping20-Stripping-MC-NoPrescaling.py;$APPCONFIGOPTS/DaVinci/DataType-2012.py;$APPCONFIGOPTS/DaVinci/InputType-DST.py;$APPCONFIGOPTS/Persistency/Compression-ZLIB-1.py #-- DDDB : fromPreviousStep #-- CONDDB : fromPreviousStep #-- ExtraPackages : AppConfig.v3r164 #-- Visible : Y #-- Processing Pass Step-126435 #-- StepId : 126435 #-- StepName : Sim08e - 2012 - MU - Pythia8 #-- ApplicationName : Gauss #-- ApplicationVersion : v45r7 #-- OptionFiles : $APPCONFIGOPTS/Gauss/Sim08-Beam4000GeV-mu100-2012-nu2.5.py;$DECFILESROOT/options/@{eventType}.py;$LBPYTHIA8ROOT/options/Pythia8.py;$APPCONFIGOPTS/Gauss/G4PL_FTFP_BERT_EmNoCuts.py;$APPCONFIGOPTS/Persistency/Compression-ZLIB-1.py #-- DDDB : dddb-20130929-1 #-- CONDDB : sim-20130522-1-vc-mu100 #-- ExtraPackages : AppConfig.v3r193;DecFiles.v27r22 #-- Visible : Y from Gaudi.Configuration import * from GaudiConf import IOHelper IOHelper('ROOT').inputFiles(['LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000001_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000002_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000003_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000004_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000005_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000006_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000007_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000008_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000009_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000010_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000011_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000012_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000013_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000014_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000015_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000016_1.allstreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00035796/0000/00035796_00000017_1.allstreams.dst' ], clear=True)

45.322581

247

0.759431

507

4,215

6.240631

0.287968

0.139697

0.048357

0.069848

0.496839

0.481669

0.46713

0

0.21054

0.095136

4,215

92

248

45.815217

0.619035

0.601186

0

1

0.85

0.849323

0.846863

0

1

0

true

0

0.1

0

0.1

0

null

0

1

0

1

null

0

1

0

6

d1d6852d9425380edd3198c3036ae6e94053de0c

116

py

Python

src/utils/torch_utils.py

YetAnotherPolicy/RMIX

dbc8f0a6560aa3f274765fa78aaaca22351ab8ad

[ "Apache-2.0" ]

6

2021-11-18T16:21:43.000Z

2021-12-31T02:22:23.000Z

src/utils/torch_utils.py

YetAnotherPolicy/RMIX

dbc8f0a6560aa3f274765fa78aaaca22351ab8ad

[ "Apache-2.0" ]

null

src/utils/torch_utils.py

YetAnotherPolicy/RMIX

dbc8f0a6560aa3f274765fa78aaaca22351ab8ad

[ "Apache-2.0" ]

1

2022-02-22T02:37:30.000Z

import torch as th def huber(x, k=1.0): return th.where(x.abs() < k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k))

19.333333

73

0.517241

27

116

2.222222

0.592593

0.133333

0

0.078652

0.232759

116

5

74

23.2

0.595506

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ae393a5beae32d41f4772ac1a0542ef95d370d06

23

py

Python

river_model_io/__init__.py

joelrahman/river-model-io

f4ff3fabde40906ab798ae2a668afb68d4bcfae3

[ "0BSD" ]

null

river_model_io/__init__.py

joelrahman/river-model-io

f4ff3fabde40906ab798ae2a668afb68d4bcfae3

[ "0BSD" ]

null

river_model_io/__init__.py

joelrahman/river-model-io

f4ff3fabde40906ab798ae2a668afb68d4bcfae3

[ "0BSD" ]

null

from .bigmod import *

7.666667

21

0.695652

3

23

5.333333

1

0

0.217391

23

2

22

11.5

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ae764cae0bb7c0087bbdf84868e0a40e8aa0d5a9

223

py

Python

jotdx/__init__.py

jojoquant/jotdx

bc1cf478ddb262883d779d3c494877a767ebedd5

[ "MIT" ]

null

jotdx/__init__.py

jojoquant/jotdx

bc1cf478ddb262883d779d3c494877a767ebedd5

[ "MIT" ]

null

jotdx/__init__.py

jojoquant/jotdx

bc1cf478ddb262883d779d3c494877a767ebedd5

[ "MIT" ]

null

from jotdx import config from jotdx.consts import EX_HOSTS from jotdx.consts import GP_HOSTS from jotdx.consts import HQ_HOSTS from jotdx.server import server from jotdx.utils import get_config_path __version__ = '0.1.11'

24.777778

39

0.829596

38

223

4.631579

0.447368

0.306818

0.255682

0.357955

0.295455

0

0.020513

0.125561

223

8

40

27.875

0.882051

0

0.026906

0

1

0

false

0

0.857143

0

0.857143

0

null

1

0

1

0

null

0

1

0

1

0

6

884340d1890b3b844cae63e031d0cdc6ac2fda77

84,078

py

Python

duke-cs671-fall21-coupon-recommendation/outputs/rules/ID3/10_features/maxdepth_5/0/rules.py

apcarrik/kaggle

6e2d4db58017323e7ba5510bcc2598e01a4ee7bf

[ "MIT" ]

null

duke-cs671-fall21-coupon-recommendation/outputs/rules/ID3/10_features/maxdepth_5/0/rules.py

apcarrik/kaggle

6e2d4db58017323e7ba5510bcc2598e01a4ee7bf

[ "MIT" ]

null

duke-cs671-fall21-coupon-recommendation/outputs/rules/ID3/10_features/maxdepth_5/0/rules.py

apcarrik/kaggle

6e2d4db58017323e7ba5510bcc2598e01a4ee7bf

[ "MIT" ]

null

def findDecision(obj): #obj[0]: Passanger, obj[1]: Time, obj[2]: Coupon, obj[3]: Education, obj[4]: Occupation, obj[5]: Bar, obj[6]: Coffeehouse, obj[7]: Restaurant20to50, obj[8]: Direction_same, obj[9]: Distance # {"feature": "Coupon", "instances": 8147, "metric_value": 0.4744, "depth": 1} if obj[2]>1: # {"feature": "Coffeehouse", "instances": 5889, "metric_value": 0.4587, "depth": 2} if obj[6]>0.0: # {"feature": "Distance", "instances": 4432, "metric_value": 0.4358, "depth": 3} if obj[9]<=2: # {"feature": "Passanger", "instances": 4015, "metric_value": 0.4266, "depth": 4} if obj[0]<=2: # {"feature": "Time", "instances": 2626, "metric_value": 0.4479, "depth": 5} if obj[1]<=3: # {"feature": "Occupation", "instances": 2123, "metric_value": 0.4553, "depth": 6} if obj[4]>0: # {"feature": "Direction_same", "instances": 2109, "metric_value": 0.4565, "depth": 7} if obj[8]<=0: # {"feature": "Restaurant20to50", "instances": 1161, "metric_value": 0.4673, "depth": 8} if obj[7]<=3.0: # {"feature": "Education", "instances": 1126, "metric_value": 0.4704, "depth": 9} if obj[3]<=3: # {"feature": "Bar", "instances": 1058, "metric_value": 0.4722, "depth": 10} if obj[5]>0.0: return 'True' elif obj[5]<=0.0: return 'True' else: return 'True' elif obj[3]>3: # {"feature": "Bar", "instances": 68, "metric_value": 0.4281, "depth": 10} if obj[5]<=2.0: return 'True' elif obj[5]>2.0: return 'True' else: return 'True' else: return 'True' elif obj[7]>3.0: # {"feature": "Bar", "instances": 35, "metric_value": 0.3255, "depth": 9} if obj[5]>2.0: # {"feature": "Education", "instances": 22, "metric_value": 0.4329, "depth": 10} if obj[3]>1: return 'True' elif obj[3]<=1: return 'True' else: return 'True' elif obj[5]<=2.0: # {"feature": "Education", "instances": 13, "metric_value": 0.1346, "depth": 10} if obj[3]>0: return 'True' elif obj[3]<=0: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[8]>0: # {"feature": "Bar", "instances": 948, "metric_value": 0.4386, "depth": 8} if obj[5]>-1.0: # {"feature": "Restaurant20to50", "instances": 940, "metric_value": 0.4379, "depth": 9} if obj[7]<=2.0: # {"feature": "Education", "instances": 841, "metric_value": 0.4443, "depth": 10} if obj[3]<=2: return 'True' elif obj[3]>2: return 'True' else: return 'True' elif obj[7]>2.0: # {"feature": "Education", "instances": 99, "metric_value": 0.3718, "depth": 10} if obj[3]<=2: return 'True' elif obj[3]>2: return 'True' else: return 'True' else: return 'True' elif obj[5]<=-1.0: # {"feature": "Education", "instances": 8, "metric_value": 0.375, "depth": 9} if obj[3]<=1: # {"feature": "Restaurant20to50", "instances": 4, "metric_value": 0.375, "depth": 10} if obj[7]<=1.0: return 'False' else: return 'False' elif obj[3]>1: # {"feature": "Restaurant20to50", "instances": 4, "metric_value": 0.375, "depth": 10} if obj[7]<=2.0: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'True' elif obj[4]<=0: # {"feature": "Education", "instances": 14, "metric_value": 0.1143, "depth": 7} if obj[3]<=0: return 'True' elif obj[3]>0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.2667, "depth": 8} if obj[8]<=0: # {"feature": "Bar", "instances": 3, "metric_value": 0.4444, "depth": 9} if obj[5]<=2.0: # {"feature": "Restaurant20to50", "instances": 3, "metric_value": 0.4444, "depth": 10} if obj[7]<=1.0: return 'True' else: return 'True' else: return 'True' elif obj[8]>0: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[1]>3: # {"feature": "Bar", "instances": 503, "metric_value": 0.4078, "depth": 6} if obj[5]<=1.0: # {"feature": "Occupation", "instances": 337, "metric_value": 0.3791, "depth": 7} if obj[4]<=13.168241765136806: # {"feature": "Education", "instances": 281, "metric_value": 0.3606, "depth": 8} if obj[3]<=3: # {"feature": "Restaurant20to50", "instances": 268, "metric_value": 0.3706, "depth": 9} if obj[7]<=1.0: # {"feature": "Direction_same", "instances": 182, "metric_value": 0.3831, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]>1.0: # {"feature": "Direction_same", "instances": 86, "metric_value": 0.3442, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[3]>3: # {"feature": "Restaurant20to50", "instances": 13, "metric_value": 0.1399, "depth": 9} if obj[7]<=1.0: # {"feature": "Direction_same", "instances": 11, "metric_value": 0.1653, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]>1.0: return 'True' else: return 'True' else: return 'True' elif obj[4]>13.168241765136806: # {"feature": "Education", "instances": 56, "metric_value": 0.4456, "depth": 8} if obj[3]<=2: # {"feature": "Restaurant20to50", "instances": 42, "metric_value": 0.4179, "depth": 9} if obj[7]<=2.0: # {"feature": "Direction_same", "instances": 40, "metric_value": 0.4387, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]>2.0: return 'True' else: return 'True' elif obj[3]>2: # {"feature": "Restaurant20to50", "instances": 14, "metric_value": 0.45, "depth": 9} if obj[7]>0.0: # {"feature": "Direction_same", "instances": 10, "metric_value": 0.48, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[7]<=0.0: # {"feature": "Direction_same", "instances": 4, "metric_value": 0.375, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[5]>1.0: # {"feature": "Education", "instances": 166, "metric_value": 0.4524, "depth": 7} if obj[3]>0: # {"feature": "Occupation", "instances": 115, "metric_value": 0.4565, "depth": 8} if obj[4]<=19: # {"feature": "Restaurant20to50", "instances": 112, "metric_value": 0.4647, "depth": 9} if obj[7]>0.0: # {"feature": "Direction_same", "instances": 103, "metric_value": 0.4751, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]<=0.0: # {"feature": "Direction_same", "instances": 9, "metric_value": 0.3457, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[4]>19: return 'False' else: return 'False' elif obj[3]<=0: # {"feature": "Occupation", "instances": 51, "metric_value": 0.3037, "depth": 8} if obj[4]>4: # {"feature": "Restaurant20to50", "instances": 39, "metric_value": 0.2501, "depth": 9} if obj[7]<=1.0: # {"feature": "Direction_same", "instances": 22, "metric_value": 0.1653, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]>1.0: # {"feature": "Direction_same", "instances": 17, "metric_value": 0.3599, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[4]<=4: # {"feature": "Restaurant20to50", "instances": 12, "metric_value": 0.4242, "depth": 9} if obj[7]<=1.0: # {"feature": "Direction_same", "instances": 11, "metric_value": 0.4628, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[7]>1.0: return 'False' else: return 'False' else: return 'False' else: return 'True' else: return 'True' else: return 'True' elif obj[0]>2: # {"feature": "Occupation", "instances": 1389, "metric_value": 0.382, "depth": 5} if obj[4]<=18.52567473260329: # {"feature": "Bar", "instances": 1292, "metric_value": 0.3906, "depth": 6} if obj[5]<=3.0: # {"feature": "Restaurant20to50", "instances": 1225, "metric_value": 0.3841, "depth": 7} if obj[7]<=1.0: # {"feature": "Education", "instances": 789, "metric_value": 0.4048, "depth": 8} if obj[3]<=3: # {"feature": "Time", "instances": 745, "metric_value": 0.4128, "depth": 9} if obj[1]>0: # {"feature": "Direction_same", "instances": 580, "metric_value": 0.4101, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[1]<=0: # {"feature": "Direction_same", "instances": 165, "metric_value": 0.4224, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[3]>3: # {"feature": "Time", "instances": 44, "metric_value": 0.2645, "depth": 9} if obj[1]>0: # {"feature": "Direction_same", "instances": 33, "metric_value": 0.2975, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[1]<=0: # {"feature": "Direction_same", "instances": 11, "metric_value": 0.1653, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[7]>1.0: # {"feature": "Time", "instances": 436, "metric_value": 0.3377, "depth": 8} if obj[1]>0: # {"feature": "Education", "instances": 337, "metric_value": 0.373, "depth": 9} if obj[3]<=3: # {"feature": "Direction_same", "instances": 317, "metric_value": 0.3805, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]>3: # {"feature": "Direction_same", "instances": 20, "metric_value": 0.255, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[1]<=0: # {"feature": "Education", "instances": 99, "metric_value": 0.2115, "depth": 9} if obj[3]<=3: # {"feature": "Direction_same", "instances": 94, "metric_value": 0.2227, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]>3: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[5]>3.0: # {"feature": "Time", "instances": 67, "metric_value": 0.4397, "depth": 7} if obj[1]>0: # {"feature": "Restaurant20to50", "instances": 51, "metric_value": 0.4057, "depth": 8} if obj[7]>2.0: # {"feature": "Education", "instances": 26, "metric_value": 0.4253, "depth": 9} if obj[3]>3: # {"feature": "Direction_same", "instances": 17, "metric_value": 0.4152, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]<=3: # {"feature": "Direction_same", "instances": 9, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' elif obj[7]<=2.0: # {"feature": "Education", "instances": 25, "metric_value": 0.3, "depth": 9} if obj[3]<=0: # {"feature": "Direction_same", "instances": 16, "metric_value": 0.2188, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]>0: # {"feature": "Direction_same", "instances": 9, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[1]<=0: # {"feature": "Education", "instances": 16, "metric_value": 0.4563, "depth": 8} if obj[3]>0: # {"feature": "Restaurant20to50", "instances": 9, "metric_value": 0.4167, "depth": 9} if obj[7]>0.0: # {"feature": "Direction_same", "instances": 8, "metric_value": 0.4688, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[7]<=0.0: return 'True' else: return 'True' elif obj[3]<=0: # {"feature": "Restaurant20to50", "instances": 7, "metric_value": 0.3429, "depth": 9} if obj[7]<=2.0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.48, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[7]>2.0: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'True' elif obj[4]>18.52567473260329: # {"feature": "Bar", "instances": 97, "metric_value": 0.2374, "depth": 6} if obj[5]<=1.0: # {"feature": "Restaurant20to50", "instances": 69, "metric_value": 0.2928, "depth": 7} if obj[7]<=1.0: # {"feature": "Time", "instances": 62, "metric_value": 0.2567, "depth": 8} if obj[1]>0: # {"feature": "Education", "instances": 49, "metric_value": 0.3174, "depth": 9} if obj[3]<=2: # {"feature": "Direction_same", "instances": 38, "metric_value": 0.3615, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]>2: # {"feature": "Direction_same", "instances": 11, "metric_value": 0.1653, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[1]<=0: return 'True' else: return 'True' elif obj[7]>1.0: # {"feature": "Education", "instances": 7, "metric_value": 0.2143, "depth": 8} if obj[3]>0: # {"feature": "Time", "instances": 4, "metric_value": 0.3333, "depth": 9} if obj[1]>0: # {"feature": "Direction_same", "instances": 3, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[1]<=0: return 'False' else: return 'False' elif obj[3]<=0: return 'True' else: return 'True' else: return 'True' elif obj[5]>1.0: # {"feature": "Education", "instances": 28, "metric_value": 0.0663, "depth": 7} if obj[3]>0: # {"feature": "Time", "instances": 14, "metric_value": 0.1224, "depth": 8} if obj[1]<=2: return 'True' elif obj[1]>2: # {"feature": "Restaurant20to50", "instances": 7, "metric_value": 0.2286, "depth": 9} if obj[7]>1.0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.32, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]<=1.0: return 'True' else: return 'True' else: return 'True' elif obj[3]<=0: return 'True' else: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[9]>2: # {"feature": "Passanger", "instances": 417, "metric_value": 0.4857, "depth": 4} if obj[0]>0: # {"feature": "Time", "instances": 404, "metric_value": 0.4863, "depth": 5} if obj[1]>0: # {"feature": "Bar", "instances": 347, "metric_value": 0.4936, "depth": 6} if obj[5]>-1.0: # {"feature": "Education", "instances": 344, "metric_value": 0.4944, "depth": 7} if obj[3]<=3: # {"feature": "Restaurant20to50", "instances": 318, "metric_value": 0.4935, "depth": 8} if obj[7]>-1.0: # {"feature": "Occupation", "instances": 316, "metric_value": 0.495, "depth": 9} if obj[4]<=7.639240506329114: # {"feature": "Direction_same", "instances": 202, "metric_value": 0.4992, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[4]>7.639240506329114: # {"feature": "Direction_same", "instances": 114, "metric_value": 0.4875, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' elif obj[7]<=-1.0: return 'False' else: return 'False' elif obj[3]>3: # {"feature": "Occupation", "instances": 26, "metric_value": 0.337, "depth": 8} if obj[4]>1: # {"feature": "Restaurant20to50", "instances": 14, "metric_value": 0.2286, "depth": 9} if obj[7]<=1.0: # {"feature": "Direction_same", "instances": 10, "metric_value": 0.32, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]>1.0: return 'True' else: return 'True' elif obj[4]<=1: # {"feature": "Restaurant20to50", "instances": 12, "metric_value": 0.3704, "depth": 9} if obj[7]<=3.0: # {"feature": "Direction_same", "instances": 9, "metric_value": 0.4938, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[7]>3.0: return 'False' else: return 'False' else: return 'False' else: return 'True' elif obj[5]<=-1.0: return 'False' else: return 'False' elif obj[1]<=0: # {"feature": "Education", "instances": 57, "metric_value": 0.3851, "depth": 6} if obj[3]<=2: # {"feature": "Bar", "instances": 43, "metric_value": 0.4289, "depth": 7} if obj[5]>0.0: # {"feature": "Occupation", "instances": 29, "metric_value": 0.3557, "depth": 8} if obj[4]>2: # {"feature": "Restaurant20to50", "instances": 24, "metric_value": 0.3021, "depth": 9} if obj[7]<=1.0: # {"feature": "Direction_same", "instances": 16, "metric_value": 0.2188, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[7]>1.0: # {"feature": "Direction_same", "instances": 8, "metric_value": 0.4688, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' elif obj[4]<=2: # {"feature": "Restaurant20to50", "instances": 5, "metric_value": 0.4, "depth": 9} if obj[7]<=1.0: # {"feature": "Direction_same", "instances": 4, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[7]>1.0: return 'True' else: return 'True' else: return 'True' elif obj[5]<=0.0: # {"feature": "Restaurant20to50", "instances": 14, "metric_value": 0.3365, "depth": 8} if obj[7]>0.0: # {"feature": "Occupation", "instances": 9, "metric_value": 0.0, "depth": 9} if obj[4]<=19: return 'True' elif obj[4]>19: return 'False' else: return 'False' elif obj[7]<=0.0: # {"feature": "Occupation", "instances": 5, "metric_value": 0.0, "depth": 9} if obj[4]>1: return 'False' elif obj[4]<=1: return 'True' else: return 'True' else: return 'False' else: return 'True' elif obj[3]>2: # {"feature": "Occupation", "instances": 14, "metric_value": 0.0, "depth": 7} if obj[4]<=16: return 'False' elif obj[4]>16: return 'True' else: return 'True' else: return 'False' else: return 'False' elif obj[0]<=0: # {"feature": "Restaurant20to50", "instances": 13, "metric_value": 0.2051, "depth": 5} if obj[7]<=1.0: return 'True' elif obj[7]>1.0: # {"feature": "Bar", "instances": 6, "metric_value": 0.0, "depth": 6} if obj[5]>1.0: return 'True' elif obj[5]<=1.0: return 'False' else: return 'False' else: return 'True' else: return 'True' else: return 'False' elif obj[6]<=0.0: # {"feature": "Passanger", "instances": 1457, "metric_value": 0.495, "depth": 3} if obj[0]<=1: # {"feature": "Distance", "instances": 933, "metric_value": 0.4888, "depth": 4} if obj[9]<=1: # {"feature": "Time", "instances": 499, "metric_value": 0.4911, "depth": 5} if obj[1]>0: # {"feature": "Direction_same", "instances": 325, "metric_value": 0.478, "depth": 6} if obj[8]>0: # {"feature": "Education", "instances": 170, "metric_value": 0.4853, "depth": 7} if obj[3]>1: # {"feature": "Restaurant20to50", "instances": 103, "metric_value": 0.4845, "depth": 8} if obj[7]<=2.0: # {"feature": "Occupation", "instances": 101, "metric_value": 0.4875, "depth": 9} if obj[4]>5: # {"feature": "Bar", "instances": 61, "metric_value": 0.4748, "depth": 10} if obj[5]>0.0: return 'True' elif obj[5]<=0.0: return 'False' else: return 'False' elif obj[4]<=5: # {"feature": "Bar", "instances": 40, "metric_value": 0.432, "depth": 10} if obj[5]<=0.0: return 'False' elif obj[5]>0.0: return 'False' else: return 'False' else: return 'False' elif obj[7]>2.0: return 'False' else: return 'False' elif obj[3]<=1: # {"feature": "Bar", "instances": 67, "metric_value": 0.4411, "depth": 8} if obj[5]<=1.0: # {"feature": "Restaurant20to50", "instances": 55, "metric_value": 0.3984, "depth": 9} if obj[7]<=1.0: # {"feature": "Occupation", "instances": 46, "metric_value": 0.439, "depth": 10} if obj[4]<=12: return 'True' elif obj[4]>12: return 'True' else: return 'True' elif obj[7]>1.0: return 'True' else: return 'True' elif obj[5]>1.0: # {"feature": "Restaurant20to50", "instances": 12, "metric_value": 0.3429, "depth": 9} if obj[7]<=1.0: # {"feature": "Occupation", "instances": 7, "metric_value": 0.2286, "depth": 10} if obj[4]>1: return 'False' elif obj[4]<=1: return 'False' else: return 'False' elif obj[7]>1.0: # {"feature": "Occupation", "instances": 5, "metric_value": 0.4667, "depth": 10} if obj[4]>10: return 'True' elif obj[4]<=10: return 'False' else: return 'False' else: return 'True' else: return 'False' else: return 'True' elif obj[8]<=0: # {"feature": "Bar", "instances": 155, "metric_value": 0.4437, "depth": 7} if obj[5]<=0.0: # {"feature": "Occupation", "instances": 82, "metric_value": 0.3864, "depth": 8} if obj[4]<=6: # {"feature": "Education", "instances": 51, "metric_value": 0.4422, "depth": 9} if obj[3]>0: # {"feature": "Restaurant20to50", "instances": 26, "metric_value": 0.359, "depth": 10} if obj[7]<=1.0: return 'True' elif obj[7]>1.0: return 'True' else: return 'True' elif obj[3]<=0: # {"feature": "Restaurant20to50", "instances": 25, "metric_value": 0.4562, "depth": 10} if obj[7]>0.0: return 'True' elif obj[7]<=0.0: return 'False' else: return 'False' else: return 'True' elif obj[4]>6: # {"feature": "Education", "instances": 31, "metric_value": 0.2497, "depth": 9} if obj[3]>0: # {"feature": "Restaurant20to50", "instances": 16, "metric_value": 0.1042, "depth": 10} if obj[7]<=0.0: return 'True' elif obj[7]>0.0: return 'True' else: return 'True' elif obj[3]<=0: # {"feature": "Restaurant20to50", "instances": 15, "metric_value": 0.3778, "depth": 10} if obj[7]>0.0: return 'True' elif obj[7]<=0.0: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[5]>0.0: # {"feature": "Restaurant20to50", "instances": 73, "metric_value": 0.447, "depth": 8} if obj[7]>0.0: # {"feature": "Occupation", "instances": 61, "metric_value": 0.4307, "depth": 9} if obj[4]<=6: # {"feature": "Education", "instances": 34, "metric_value": 0.4847, "depth": 10} if obj[3]>0: return 'False' elif obj[3]<=0: return 'True' else: return 'True' elif obj[4]>6: # {"feature": "Education", "instances": 27, "metric_value": 0.3281, "depth": 10} if obj[3]>1: return 'True' elif obj[3]<=1: return 'True' else: return 'True' else: return 'True' elif obj[7]<=0.0: # {"feature": "Occupation", "instances": 12, "metric_value": 0.25, "depth": 9} if obj[4]>8: return 'False' elif obj[4]<=8: # {"feature": "Education", "instances": 6, "metric_value": 0.4, "depth": 10} if obj[3]<=2: return 'False' elif obj[3]>2: return 'True' else: return 'True' else: return 'True' else: return 'False' else: return 'True' else: return 'True' elif obj[1]<=0: # {"feature": "Occupation", "instances": 174, "metric_value": 0.4654, "depth": 6} if obj[4]<=6: # {"feature": "Education", "instances": 94, "metric_value": 0.4397, "depth": 7} if obj[3]<=4: # {"feature": "Bar", "instances": 93, "metric_value": 0.4348, "depth": 8} if obj[5]>-1.0: # {"feature": "Restaurant20to50", "instances": 92, "metric_value": 0.4372, "depth": 9} if obj[7]>-1.0: # {"feature": "Direction_same", "instances": 91, "metric_value": 0.4405, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[7]<=-1.0: return 'False' else: return 'False' elif obj[5]<=-1.0: return 'True' else: return 'True' elif obj[3]>4: return 'True' else: return 'True' elif obj[4]>6: # {"feature": "Direction_same", "instances": 80, "metric_value": 0.4492, "depth": 7} if obj[8]>0: # {"feature": "Restaurant20to50", "instances": 42, "metric_value": 0.4, "depth": 8} if obj[7]>-1.0: # {"feature": "Bar", "instances": 40, "metric_value": 0.4154, "depth": 9} if obj[5]<=2.0: # {"feature": "Education", "instances": 39, "metric_value": 0.4251, "depth": 10} if obj[3]>0: return 'True' elif obj[3]<=0: return 'True' else: return 'True' elif obj[5]>2.0: return 'True' else: return 'True' elif obj[7]<=-1.0: return 'True' else: return 'True' elif obj[8]<=0: # {"feature": "Education", "instances": 38, "metric_value": 0.4605, "depth": 8} if obj[3]<=3: # {"feature": "Bar", "instances": 36, "metric_value": 0.4611, "depth": 9} if obj[5]<=2.0: # {"feature": "Restaurant20to50", "instances": 30, "metric_value": 0.491, "depth": 10} if obj[7]<=1.0: return 'False' elif obj[7]>1.0: return 'True' else: return 'True' elif obj[5]>2.0: # {"feature": "Restaurant20to50", "instances": 6, "metric_value": 0.25, "depth": 10} if obj[7]>1.0: return 'False' elif obj[7]<=1.0: return 'False' else: return 'False' else: return 'False' elif obj[3]>3: return 'True' else: return 'True' else: return 'False' else: return 'True' else: return 'False' elif obj[9]>1: # {"feature": "Education", "instances": 434, "metric_value": 0.4739, "depth": 5} if obj[3]>1: # {"feature": "Bar", "instances": 258, "metric_value": 0.4526, "depth": 6} if obj[5]<=1.0: # {"feature": "Occupation", "instances": 202, "metric_value": 0.4655, "depth": 7} if obj[4]<=9: # {"feature": "Time", "instances": 134, "metric_value": 0.4758, "depth": 8} if obj[1]>0: # {"feature": "Direction_same", "instances": 107, "metric_value": 0.4829, "depth": 9} if obj[8]<=0: # {"feature": "Restaurant20to50", "instances": 89, "metric_value": 0.4818, "depth": 10} if obj[7]>0.0: return 'False' elif obj[7]<=0.0: return 'True' else: return 'True' elif obj[8]>0: # {"feature": "Restaurant20to50", "instances": 18, "metric_value": 0.3723, "depth": 10} if obj[7]<=1.0: return 'False' elif obj[7]>1.0: return 'False' else: return 'False' else: return 'False' elif obj[1]<=0: # {"feature": "Direction_same", "instances": 27, "metric_value": 0.3608, "depth": 9} if obj[8]<=0: # {"feature": "Restaurant20to50", "instances": 17, "metric_value": 0.2647, "depth": 10} if obj[7]>0.0: return 'False' elif obj[7]<=0.0: return 'False' else: return 'False' elif obj[8]>0: # {"feature": "Restaurant20to50", "instances": 10, "metric_value": 0.4762, "depth": 10} if obj[7]>0.0: return 'False' elif obj[7]<=0.0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[4]>9: # {"feature": "Direction_same", "instances": 68, "metric_value": 0.4059, "depth": 8} if obj[8]<=0: # {"feature": "Time", "instances": 54, "metric_value": 0.3641, "depth": 9} if obj[1]>0: # {"feature": "Restaurant20to50", "instances": 47, "metric_value": 0.4144, "depth": 10} if obj[7]<=2.0: return 'False' elif obj[7]>2.0: return 'False' else: return 'False' elif obj[1]<=0: return 'False' else: return 'False' elif obj[8]>0: # {"feature": "Time", "instances": 14, "metric_value": 0.381, "depth": 9} if obj[1]>0: # {"feature": "Restaurant20to50", "instances": 12, "metric_value": 0.419, "depth": 10} if obj[7]>0.0: return 'False' elif obj[7]<=0.0: return 'False' else: return 'False' elif obj[1]<=0: return 'True' else: return 'True' else: return 'False' else: return 'False' elif obj[5]>1.0: # {"feature": "Occupation", "instances": 56, "metric_value": 0.3569, "depth": 7} if obj[4]>3: # {"feature": "Restaurant20to50", "instances": 43, "metric_value": 0.4027, "depth": 8} if obj[7]<=1.0: # {"feature": "Time", "instances": 30, "metric_value": 0.4593, "depth": 9} if obj[1]<=1: # {"feature": "Direction_same", "instances": 21, "metric_value": 0.4417, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[1]>1: # {"feature": "Direction_same", "instances": 9, "metric_value": 0.4938, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' elif obj[7]>1.0: # {"feature": "Direction_same", "instances": 13, "metric_value": 0.141, "depth": 9} if obj[8]<=0: # {"feature": "Time", "instances": 12, "metric_value": 0.1111, "depth": 10} if obj[1]<=1: return 'False' elif obj[1]>1: return 'False' else: return 'False' elif obj[8]>0: return 'True' else: return 'True' else: return 'False' elif obj[4]<=3: # {"feature": "Time", "instances": 13, "metric_value": 0.1282, "depth": 8} if obj[1]<=1: return 'False' elif obj[1]>1: # {"feature": "Restaurant20to50", "instances": 6, "metric_value": 0.25, "depth": 9} if obj[7]>1.0: # {"feature": "Direction_same", "instances": 4, "metric_value": 0.375, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[7]<=1.0: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[3]<=1: # {"feature": "Occupation", "instances": 176, "metric_value": 0.483, "depth": 6} if obj[4]>3: # {"feature": "Restaurant20to50", "instances": 115, "metric_value": 0.4881, "depth": 7} if obj[7]<=1.0: # {"feature": "Time", "instances": 89, "metric_value": 0.492, "depth": 8} if obj[1]>0: # {"feature": "Bar", "instances": 72, "metric_value": 0.4884, "depth": 9} if obj[5]<=2.0: # {"feature": "Direction_same", "instances": 65, "metric_value": 0.4941, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[5]>2.0: # {"feature": "Direction_same", "instances": 7, "metric_value": 0.3714, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'True' else: return 'True' else: return 'False' elif obj[1]<=0: # {"feature": "Direction_same", "instances": 17, "metric_value": 0.4189, "depth": 9} if obj[8]<=0: # {"feature": "Bar", "instances": 11, "metric_value": 0.4545, "depth": 10} if obj[5]<=2.0: return 'True' elif obj[5]>2.0: return 'False' else: return 'False' elif obj[8]>0: # {"feature": "Bar", "instances": 6, "metric_value": 0.25, "depth": 10} if obj[5]<=0.0: return 'True' elif obj[5]>0.0: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[7]>1.0: # {"feature": "Time", "instances": 26, "metric_value": 0.4259, "depth": 8} if obj[1]>0: # {"feature": "Bar", "instances": 23, "metric_value": 0.3844, "depth": 9} if obj[5]>0.0: # {"feature": "Direction_same", "instances": 19, "metric_value": 0.4613, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'False' else: return 'False' elif obj[5]<=0.0: return 'True' else: return 'True' elif obj[1]<=0: # {"feature": "Bar", "instances": 3, "metric_value": 0.3333, "depth": 9} if obj[5]>0.0: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.0, "depth": 10} if obj[8]>0: return 'True' elif obj[8]<=0: return 'False' else: return 'False' elif obj[5]<=0.0: return 'False' else: return 'False' else: return 'False' else: return 'True' elif obj[4]<=3: # {"feature": "Restaurant20to50", "instances": 61, "metric_value": 0.4312, "depth": 7} if obj[7]>0.0: # {"feature": "Direction_same", "instances": 41, "metric_value": 0.4253, "depth": 8} if obj[8]<=0: # {"feature": "Time", "instances": 35, "metric_value": 0.4114, "depth": 9} if obj[1]>0: # {"feature": "Bar", "instances": 30, "metric_value": 0.4528, "depth": 10} if obj[5]<=1.0: return 'False' elif obj[5]>1.0: return 'False' else: return 'False' elif obj[1]<=0: return 'False' else: return 'False' elif obj[8]>0: # {"feature": "Time", "instances": 6, "metric_value": 0.2222, "depth": 9} if obj[1]>0: # {"feature": "Bar", "instances": 3, "metric_value": 0.4444, "depth": 10} if obj[5]<=0.0: return 'True' else: return 'True' elif obj[1]<=0: return 'True' else: return 'True' else: return 'True' elif obj[7]<=0.0: # {"feature": "Time", "instances": 20, "metric_value": 0.3, "depth": 8} if obj[1]<=3: # {"feature": "Direction_same", "instances": 16, "metric_value": 0.3333, "depth": 9} if obj[8]<=0: # {"feature": "Bar", "instances": 12, "metric_value": 0.4444, "depth": 10} if obj[5]<=0.0: return 'False' else: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[1]>3: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[0]>1: # {"feature": "Distance", "instances": 524, "metric_value": 0.4635, "depth": 4} if obj[9]>1: # {"feature": "Occupation", "instances": 349, "metric_value": 0.4481, "depth": 5} if obj[4]>1.5048799563075503: # {"feature": "Time", "instances": 290, "metric_value": 0.4613, "depth": 6} if obj[1]<=2: # {"feature": "Education", "instances": 169, "metric_value": 0.4664, "depth": 7} if obj[3]<=2: # {"feature": "Restaurant20to50", "instances": 140, "metric_value": 0.4914, "depth": 8} if obj[7]<=1.0: # {"feature": "Bar", "instances": 114, "metric_value": 0.4767, "depth": 9} if obj[5]<=2.0: # {"feature": "Direction_same", "instances": 110, "metric_value": 0.494, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[5]>2.0: return 'True' else: return 'True' elif obj[7]>1.0: # {"feature": "Bar", "instances": 26, "metric_value": 0.4796, "depth": 9} if obj[5]<=2.0: # {"feature": "Direction_same", "instances": 17, "metric_value": 0.4983, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[5]>2.0: # {"feature": "Direction_same", "instances": 9, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[3]>2: # {"feature": "Restaurant20to50", "instances": 29, "metric_value": 0.3155, "depth": 8} if obj[7]<=2.0: # {"feature": "Bar", "instances": 27, "metric_value": 0.2963, "depth": 9} if obj[5]<=2.0: # {"feature": "Direction_same", "instances": 24, "metric_value": 0.2778, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[5]>2.0: # {"feature": "Direction_same", "instances": 3, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[7]>2.0: # {"feature": "Bar", "instances": 2, "metric_value": 0.5, "depth": 9} if obj[5]<=0.0: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'True' elif obj[1]>2: # {"feature": "Restaurant20to50", "instances": 121, "metric_value": 0.4185, "depth": 7} if obj[7]<=1.0: # {"feature": "Education", "instances": 96, "metric_value": 0.4508, "depth": 8} if obj[3]<=3: # {"feature": "Bar", "instances": 91, "metric_value": 0.444, "depth": 9} if obj[5]>-1.0: # {"feature": "Direction_same", "instances": 89, "metric_value": 0.454, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[5]<=-1.0: return 'True' else: return 'True' elif obj[3]>3: # {"feature": "Bar", "instances": 5, "metric_value": 0.2667, "depth": 9} if obj[5]<=0.0: # {"feature": "Direction_same", "instances": 3, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[5]>0.0: return 'False' else: return 'False' else: return 'False' elif obj[7]>1.0: # {"feature": "Education", "instances": 25, "metric_value": 0.24, "depth": 8} if obj[3]<=2: # {"feature": "Bar", "instances": 20, "metric_value": 0.1444, "depth": 9} if obj[5]<=3.0: # {"feature": "Direction_same", "instances": 18, "metric_value": 0.1049, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[5]>3.0: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[3]>2: # {"feature": "Bar", "instances": 5, "metric_value": 0.48, "depth": 9} if obj[5]<=0.0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.48, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[4]<=1.5048799563075503: # {"feature": "Time", "instances": 59, "metric_value": 0.3448, "depth": 6} if obj[1]>0: # {"feature": "Restaurant20to50", "instances": 45, "metric_value": 0.399, "depth": 7} if obj[7]>0.0: # {"feature": "Education", "instances": 33, "metric_value": 0.3463, "depth": 8} if obj[3]<=2: # {"feature": "Bar", "instances": 28, "metric_value": 0.4011, "depth": 9} if obj[5]<=2.0: # {"feature": "Direction_same", "instances": 26, "metric_value": 0.3935, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[5]>2.0: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[3]>2: return 'True' else: return 'True' elif obj[7]<=0.0: # {"feature": "Education", "instances": 12, "metric_value": 0.375, "depth": 8} if obj[3]<=2: # {"feature": "Bar", "instances": 8, "metric_value": 0.375, "depth": 9} if obj[5]<=0.0: # {"feature": "Direction_same", "instances": 8, "metric_value": 0.375, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[3]>2: # {"feature": "Bar", "instances": 4, "metric_value": 0.0, "depth": 9} if obj[5]>-1.0: return 'False' elif obj[5]<=-1.0: return 'True' else: return 'True' else: return 'False' else: return 'True' elif obj[1]<=0: # {"feature": "Education", "instances": 14, "metric_value": 0.125, "depth": 7} if obj[3]<=0: # {"feature": "Restaurant20to50", "instances": 8, "metric_value": 0.2, "depth": 8} if obj[7]<=1.0: # {"feature": "Bar", "instances": 5, "metric_value": 0.32, "depth": 9} if obj[5]<=0.0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.32, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[7]>1.0: return 'True' else: return 'True' elif obj[3]>0: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[9]<=1: # {"feature": "Occupation", "instances": 175, "metric_value": 0.4681, "depth": 5} if obj[4]>0: # {"feature": "Education", "instances": 171, "metric_value": 0.4675, "depth": 6} if obj[3]<=2: # {"feature": "Restaurant20to50", "instances": 138, "metric_value": 0.4827, "depth": 7} if obj[7]>0.0: # {"feature": "Time", "instances": 97, "metric_value": 0.4603, "depth": 8} if obj[1]<=3: # {"feature": "Bar", "instances": 69, "metric_value": 0.4857, "depth": 9} if obj[5]<=1.0: # {"feature": "Direction_same", "instances": 45, "metric_value": 0.48, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[5]>1.0: # {"feature": "Direction_same", "instances": 24, "metric_value": 0.4965, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[1]>3: # {"feature": "Bar", "instances": 28, "metric_value": 0.3652, "depth": 9} if obj[5]<=2.0: # {"feature": "Direction_same", "instances": 23, "metric_value": 0.3403, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[5]>2.0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.48, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[7]<=0.0: # {"feature": "Bar", "instances": 41, "metric_value": 0.4671, "depth": 8} if obj[5]<=1.0: # {"feature": "Time", "instances": 35, "metric_value": 0.4959, "depth": 9} if obj[1]<=3: # {"feature": "Direction_same", "instances": 28, "metric_value": 0.4974, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[1]>3: # {"feature": "Direction_same", "instances": 7, "metric_value": 0.4898, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[5]>1.0: # {"feature": "Time", "instances": 6, "metric_value": 0.2222, "depth": 9} if obj[1]>3: # {"feature": "Direction_same", "instances": 3, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[1]<=3: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[3]>2: # {"feature": "Time", "instances": 33, "metric_value": 0.3561, "depth": 7} if obj[1]<=2: # {"feature": "Restaurant20to50", "instances": 25, "metric_value": 0.3043, "depth": 8} if obj[7]>-1.0: # {"feature": "Bar", "instances": 23, "metric_value": 0.2849, "depth": 9} if obj[5]<=1.0: # {"feature": "Direction_same", "instances": 19, "metric_value": 0.2659, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[5]>1.0: # {"feature": "Direction_same", "instances": 4, "metric_value": 0.375, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' elif obj[7]<=-1.0: # {"feature": "Bar", "instances": 2, "metric_value": 0.0, "depth": 9} if obj[5]<=-1.0: return 'False' elif obj[5]>-1.0: return 'True' else: return 'True' else: return 'False' elif obj[1]>2: # {"feature": "Restaurant20to50", "instances": 8, "metric_value": 0.4286, "depth": 8} if obj[7]<=1.0: # {"feature": "Bar", "instances": 7, "metric_value": 0.381, "depth": 9} if obj[5]<=1.0: # {"feature": "Direction_same", "instances": 6, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[5]>1.0: return 'True' else: return 'True' elif obj[7]>1.0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[4]<=0: return 'True' else: return 'True' else: return 'False' else: return 'True' else: return 'True' elif obj[2]<=1: # {"feature": "Bar", "instances": 2258, "metric_value": 0.4643, "depth": 2} if obj[5]>0.0: # {"feature": "Time", "instances": 1296, "metric_value": 0.4911, "depth": 3} if obj[1]>0: # {"feature": "Passanger", "instances": 933, "metric_value": 0.4879, "depth": 4} if obj[0]<=2: # {"feature": "Coffeehouse", "instances": 743, "metric_value": 0.4889, "depth": 5} if obj[6]>1.0: # {"feature": "Restaurant20to50", "instances": 409, "metric_value": 0.4944, "depth": 6} if obj[7]<=2.0: # {"feature": "Education", "instances": 342, "metric_value": 0.4925, "depth": 7} if obj[3]<=2: # {"feature": "Occupation", "instances": 258, "metric_value": 0.4826, "depth": 8} if obj[4]>5: # {"feature": "Distance", "instances": 167, "metric_value": 0.4785, "depth": 9} if obj[9]<=2: # {"feature": "Direction_same", "instances": 123, "metric_value": 0.4944, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'True' else: return 'True' elif obj[9]>2: # {"feature": "Direction_same", "instances": 44, "metric_value": 0.4339, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[4]<=5: # {"feature": "Distance", "instances": 91, "metric_value": 0.4667, "depth": 9} if obj[9]>1: # {"feature": "Direction_same", "instances": 60, "metric_value": 0.4856, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'True' else: return 'True' elif obj[9]<=1: # {"feature": "Direction_same", "instances": 31, "metric_value": 0.3871, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[3]>2: # {"feature": "Occupation", "instances": 84, "metric_value": 0.4264, "depth": 8} if obj[4]>5: # {"feature": "Distance", "instances": 64, "metric_value": 0.416, "depth": 9} if obj[9]<=2: # {"feature": "Direction_same", "instances": 48, "metric_value": 0.4296, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[9]>2: # {"feature": "Direction_same", "instances": 16, "metric_value": 0.375, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' elif obj[4]<=5: # {"feature": "Direction_same", "instances": 20, "metric_value": 0.4118, "depth": 9} if obj[8]<=0: # {"feature": "Distance", "instances": 17, "metric_value": 0.4759, "depth": 10} if obj[9]>1: return 'True' elif obj[9]<=1: return 'False' else: return 'False' elif obj[8]>0: return 'True' else: return 'True' else: return 'True' else: return 'False' elif obj[7]>2.0: # {"feature": "Occupation", "instances": 67, "metric_value": 0.4501, "depth": 7} if obj[4]<=17: # {"feature": "Education", "instances": 63, "metric_value": 0.4717, "depth": 8} if obj[3]<=3: # {"feature": "Distance", "instances": 49, "metric_value": 0.4783, "depth": 9} if obj[9]>1: # {"feature": "Direction_same", "instances": 26, "metric_value": 0.4583, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'True' else: return 'True' elif obj[9]<=1: # {"feature": "Direction_same", "instances": 23, "metric_value": 0.4522, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'True' else: return 'True' else: return 'True' elif obj[3]>3: # {"feature": "Distance", "instances": 14, "metric_value": 0.3393, "depth": 9} if obj[9]>1: # {"feature": "Direction_same", "instances": 8, "metric_value": 0.2083, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'True' else: return 'True' elif obj[9]<=1: # {"feature": "Direction_same", "instances": 6, "metric_value": 0.25, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'False' else: return 'False' else: return 'True' else: return 'True' elif obj[4]>17: return 'True' else: return 'True' else: return 'True' elif obj[6]<=1.0: # {"feature": "Education", "instances": 334, "metric_value": 0.4654, "depth": 6} if obj[3]>0: # {"feature": "Restaurant20to50", "instances": 219, "metric_value": 0.4311, "depth": 7} if obj[7]<=2.0: # {"feature": "Direction_same", "instances": 215, "metric_value": 0.4335, "depth": 8} if obj[8]<=0: # {"feature": "Occupation", "instances": 188, "metric_value": 0.4427, "depth": 9} if obj[4]>2.8018865567551456: # {"feature": "Distance", "instances": 151, "metric_value": 0.425, "depth": 10} if obj[9]>1: return 'False' elif obj[9]<=1: return 'False' else: return 'False' elif obj[4]<=2.8018865567551456: # {"feature": "Distance", "instances": 37, "metric_value": 0.4994, "depth": 10} if obj[9]>1: return 'False' elif obj[9]<=1: return 'True' else: return 'True' else: return 'False' elif obj[8]>0: # {"feature": "Occupation", "instances": 27, "metric_value": 0.2729, "depth": 9} if obj[4]>4: # {"feature": "Distance", "instances": 19, "metric_value": 0.3792, "depth": 10} if obj[9]>1: return 'False' elif obj[9]<=1: return 'False' else: return 'False' elif obj[4]<=4: return 'False' else: return 'False' else: return 'False' elif obj[7]>2.0: return 'True' else: return 'True' elif obj[3]<=0: # {"feature": "Occupation", "instances": 115, "metric_value": 0.479, "depth": 7} if obj[4]>5: # {"feature": "Restaurant20to50", "instances": 77, "metric_value": 0.4753, "depth": 8} if obj[7]<=2.0: # {"feature": "Distance", "instances": 72, "metric_value": 0.4825, "depth": 9} if obj[9]>1: # {"feature": "Direction_same", "instances": 49, "metric_value": 0.4548, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'True' else: return 'True' elif obj[9]<=1: # {"feature": "Direction_same", "instances": 23, "metric_value": 0.4174, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'False' else: return 'False' else: return 'True' elif obj[7]>2.0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.2, "depth": 9} if obj[8]<=0: return 'False' elif obj[8]>0: # {"feature": "Distance", "instances": 2, "metric_value": 0.0, "depth": 10} if obj[9]<=1: return 'False' elif obj[9]>1: return 'True' else: return 'True' else: return 'False' else: return 'False' elif obj[4]<=5: # {"feature": "Restaurant20to50", "instances": 38, "metric_value": 0.417, "depth": 8} if obj[7]>0.0: # {"feature": "Direction_same", "instances": 29, "metric_value": 0.484, "depth": 9} if obj[8]<=0: # {"feature": "Distance", "instances": 27, "metric_value": 0.4825, "depth": 10} if obj[9]<=2: return 'False' elif obj[9]>2: return 'False' else: return 'False' elif obj[8]>0: # {"feature": "Distance", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[9]<=2: return 'False' else: return 'False' else: return 'False' elif obj[7]<=0.0: # {"feature": "Distance", "instances": 9, "metric_value": 0.1481, "depth": 9} if obj[9]<=2: return 'False' elif obj[9]>2: # {"feature": "Direction_same", "instances": 3, "metric_value": 0.4444, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[0]>2: # {"feature": "Occupation", "instances": 190, "metric_value": 0.4413, "depth": 5} if obj[4]<=19.70715618872958: # {"feature": "Coffeehouse", "instances": 179, "metric_value": 0.4572, "depth": 6} if obj[6]>1.0: # {"feature": "Restaurant20to50", "instances": 98, "metric_value": 0.4155, "depth": 7} if obj[7]<=3.0: # {"feature": "Distance", "instances": 93, "metric_value": 0.4014, "depth": 8} if obj[9]>1: # {"feature": "Education", "instances": 82, "metric_value": 0.4254, "depth": 9} if obj[3]<=3: # {"feature": "Direction_same", "instances": 79, "metric_value": 0.4416, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]>3: return 'True' else: return 'True' elif obj[9]<=1: # {"feature": "Education", "instances": 11, "metric_value": 0.1558, "depth": 9} if obj[3]>0: # {"feature": "Direction_same", "instances": 7, "metric_value": 0.2449, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]<=0: return 'True' else: return 'True' else: return 'True' elif obj[7]>3.0: # {"feature": "Education", "instances": 5, "metric_value": 0.0, "depth": 8} if obj[3]>2: return 'False' elif obj[3]<=2: return 'True' else: return 'True' else: return 'False' elif obj[6]<=1.0: # {"feature": "Restaurant20to50", "instances": 81, "metric_value": 0.4778, "depth": 7} if obj[7]<=1.0: # {"feature": "Distance", "instances": 58, "metric_value": 0.4855, "depth": 8} if obj[9]>1: # {"feature": "Education", "instances": 48, "metric_value": 0.4896, "depth": 9} if obj[3]<=2: # {"feature": "Direction_same", "instances": 44, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[3]>2: # {"feature": "Direction_same", "instances": 4, "metric_value": 0.375, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[9]<=1: # {"feature": "Education", "instances": 10, "metric_value": 0.4, "depth": 9} if obj[3]<=0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.32, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[3]>0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.48, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[7]>1.0: # {"feature": "Distance", "instances": 23, "metric_value": 0.3581, "depth": 8} if obj[9]>1: # {"feature": "Education", "instances": 17, "metric_value": 0.4471, "depth": 9} if obj[3]>0: # {"feature": "Direction_same", "instances": 12, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[3]<=0: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.32, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' else: return 'True' elif obj[9]<=1: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[4]>19.70715618872958: return 'True' else: return 'True' else: return 'True' elif obj[1]<=0: # {"feature": "Passanger", "instances": 363, "metric_value": 0.4495, "depth": 4} if obj[0]<=1: # {"feature": "Distance", "instances": 333, "metric_value": 0.4386, "depth": 5} if obj[9]<=1: # {"feature": "Restaurant20to50", "instances": 170, "metric_value": 0.3751, "depth": 6} if obj[7]<=1.0: # {"feature": "Coffeehouse", "instances": 102, "metric_value": 0.4339, "depth": 7} if obj[6]<=2.0: # {"feature": "Occupation", "instances": 81, "metric_value": 0.4508, "depth": 8} if obj[4]>5: # {"feature": "Education", "instances": 50, "metric_value": 0.4767, "depth": 9} if obj[3]<=3: # {"feature": "Direction_same", "instances": 48, "metric_value": 0.4965, "depth": 10} if obj[8]<=1: return 'True' else: return 'True' elif obj[3]>3: return 'True' else: return 'True' elif obj[4]<=5: # {"feature": "Education", "instances": 31, "metric_value": 0.3653, "depth": 9} if obj[3]>0: # {"feature": "Direction_same", "instances": 22, "metric_value": 0.4339, "depth": 10} if obj[8]<=1: return 'True' else: return 'True' elif obj[3]<=0: # {"feature": "Direction_same", "instances": 9, "metric_value": 0.1975, "depth": 10} if obj[8]<=1: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[6]>2.0: # {"feature": "Education", "instances": 21, "metric_value": 0.2721, "depth": 8} if obj[3]>1: # {"feature": "Occupation", "instances": 14, "metric_value": 0.3429, "depth": 9} if obj[4]<=7: # {"feature": "Direction_same", "instances": 10, "metric_value": 0.48, "depth": 10} if obj[8]<=1: return 'True' else: return 'True' elif obj[4]>7: return 'True' else: return 'True' elif obj[3]<=1: return 'True' else: return 'True' else: return 'True' elif obj[7]>1.0: # {"feature": "Education", "instances": 68, "metric_value": 0.2603, "depth": 7} if obj[3]>1: # {"feature": "Occupation", "instances": 40, "metric_value": 0.1636, "depth": 8} if obj[4]>8: # {"feature": "Coffeehouse", "instances": 22, "metric_value": 0.2773, "depth": 9} if obj[6]<=3.0: # {"feature": "Direction_same", "instances": 20, "metric_value": 0.255, "depth": 10} if obj[8]<=1: return 'True' else: return 'True' elif obj[6]>3.0: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[8]<=1: return 'False' else: return 'False' else: return 'False' elif obj[4]<=8: return 'True' else: return 'True' elif obj[3]<=1: # {"feature": "Occupation", "instances": 28, "metric_value": 0.2313, "depth": 8} if obj[4]>5: # {"feature": "Coffeehouse", "instances": 21, "metric_value": 0.1633, "depth": 9} if obj[6]<=2.0: # {"feature": "Direction_same", "instances": 14, "metric_value": 0.2449, "depth": 10} if obj[8]<=1: return 'True' else: return 'True' elif obj[6]>2.0: return 'True' else: return 'True' elif obj[4]<=5: # {"feature": "Coffeehouse", "instances": 7, "metric_value": 0.2381, "depth": 9} if obj[6]<=2.0: # {"feature": "Direction_same", "instances": 6, "metric_value": 0.2778, "depth": 10} if obj[8]<=1: return 'False' else: return 'False' elif obj[6]>2.0: return 'True' else: return 'True' else: return 'False' else: return 'True' else: return 'True' elif obj[9]>1: # {"feature": "Restaurant20to50", "instances": 163, "metric_value": 0.4737, "depth": 6} if obj[7]>0.0: # {"feature": "Education", "instances": 140, "metric_value": 0.4638, "depth": 7} if obj[3]<=3: # {"feature": "Direction_same", "instances": 130, "metric_value": 0.475, "depth": 8} if obj[8]<=0: # {"feature": "Occupation", "instances": 128, "metric_value": 0.4767, "depth": 9} if obj[4]<=13.844008971972023: # {"feature": "Coffeehouse", "instances": 107, "metric_value": 0.4879, "depth": 10} if obj[6]>1.0: return 'True' elif obj[6]<=1.0: return 'True' else: return 'True' elif obj[4]>13.844008971972023: # {"feature": "Coffeehouse", "instances": 21, "metric_value": 0.3878, "depth": 10} if obj[6]>0.0: return 'True' elif obj[6]<=0.0: return 'True' else: return 'True' else: return 'True' elif obj[8]>0: return 'False' else: return 'False' elif obj[3]>3: # {"feature": "Coffeehouse", "instances": 10, "metric_value": 0.1333, "depth": 8} if obj[6]>0.0: return 'True' elif obj[6]<=0.0: # {"feature": "Occupation", "instances": 3, "metric_value": 0.3333, "depth": 9} if obj[4]<=2: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[4]>2: return 'True' else: return 'True' else: return 'True' else: return 'True' elif obj[7]<=0.0: # {"feature": "Coffeehouse", "instances": 23, "metric_value": 0.4099, "depth": 7} if obj[6]<=2.0: # {"feature": "Education", "instances": 16, "metric_value": 0.3254, "depth": 8} if obj[3]<=2: # {"feature": "Occupation", "instances": 9, "metric_value": 0.1111, "depth": 9} if obj[4]>3: return 'False' elif obj[4]<=3: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' elif obj[3]>2: # {"feature": "Occupation", "instances": 7, "metric_value": 0.3429, "depth": 9} if obj[4]<=12: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.48, "depth": 10} if obj[8]<=0: return 'True' else: return 'True' elif obj[4]>12: return 'False' else: return 'False' else: return 'False' elif obj[6]>2.0: # {"feature": "Occupation", "instances": 7, "metric_value": 0.2143, "depth": 8} if obj[4]<=6: # {"feature": "Education", "instances": 4, "metric_value": 0.0, "depth": 9} if obj[3]>0: return 'False' elif obj[3]<=0: return 'True' else: return 'True' elif obj[4]>6: return 'True' else: return 'True' else: return 'True' else: return 'False' else: return 'True' elif obj[0]>1: # {"feature": "Restaurant20to50", "instances": 30, "metric_value": 0.35, "depth": 5} if obj[7]>0.0: # {"feature": "Occupation", "instances": 28, "metric_value": 0.3333, "depth": 6} if obj[4]<=14: # {"feature": "Education", "instances": 21, "metric_value": 0.375, "depth": 7} if obj[3]<=2: # {"feature": "Coffeehouse", "instances": 16, "metric_value": 0.4219, "depth": 8} if obj[6]>1.0: # {"feature": "Distance", "instances": 8, "metric_value": 0.375, "depth": 9} if obj[9]>1: # {"feature": "Direction_same", "instances": 6, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[9]<=1: return 'True' else: return 'True' elif obj[6]<=1.0: # {"feature": "Distance", "instances": 8, "metric_value": 0.3, "depth": 9} if obj[9]>1: # {"feature": "Direction_same", "instances": 5, "metric_value": 0.4, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'False' else: return 'False' elif obj[9]<=1: return 'False' else: return 'False' else: return 'False' elif obj[3]>2: return 'False' else: return 'False' elif obj[4]>14: return 'False' else: return 'False' elif obj[7]<=0.0: return 'True' else: return 'True' else: return 'False' else: return 'True' elif obj[5]<=0.0: # {"feature": "Restaurant20to50", "instances": 962, "metric_value": 0.4124, "depth": 3} if obj[7]<=2.0: # {"feature": "Distance", "instances": 914, "metric_value": 0.4034, "depth": 4} if obj[9]<=2: # {"feature": "Coffeehouse", "instances": 746, "metric_value": 0.423, "depth": 5} if obj[6]<=3.0: # {"feature": "Occupation", "instances": 703, "metric_value": 0.4307, "depth": 6} if obj[4]>1.302144119170582: # {"feature": "Education", "instances": 535, "metric_value": 0.4453, "depth": 7} if obj[3]>0: # {"feature": "Time", "instances": 366, "metric_value": 0.4225, "depth": 8} if obj[1]>0: # {"feature": "Direction_same", "instances": 243, "metric_value": 0.3874, "depth": 9} if obj[8]<=0: # {"feature": "Passanger", "instances": 219, "metric_value": 0.4188, "depth": 10} if obj[0]<=2: return 'False' elif obj[0]>2: return 'False' else: return 'False' elif obj[8]>0: # {"feature": "Passanger", "instances": 24, "metric_value": 0.0417, "depth": 10} if obj[0]<=1: return 'False' elif obj[0]>1: return 'False' else: return 'False' else: return 'False' elif obj[1]<=0: # {"feature": "Passanger", "instances": 123, "metric_value": 0.4629, "depth": 9} if obj[0]>0: # {"feature": "Direction_same", "instances": 113, "metric_value": 0.4733, "depth": 10} if obj[8]>0: return 'False' elif obj[8]<=0: return 'False' else: return 'False' elif obj[0]<=0: # {"feature": "Direction_same", "instances": 10, "metric_value": 0.32, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[3]<=0: # {"feature": "Passanger", "instances": 169, "metric_value": 0.4772, "depth": 8} if obj[0]<=1: # {"feature": "Time", "instances": 129, "metric_value": 0.4896, "depth": 9} if obj[1]>0: # {"feature": "Direction_same", "instances": 87, "metric_value": 0.4833, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[1]<=0: # {"feature": "Direction_same", "instances": 42, "metric_value": 0.4818, "depth": 10} if obj[8]<=0: return 'True' elif obj[8]>0: return 'False' else: return 'False' else: return 'True' elif obj[0]>1: # {"feature": "Time", "instances": 40, "metric_value": 0.408, "depth": 9} if obj[1]<=3: # {"feature": "Direction_same", "instances": 25, "metric_value": 0.4608, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[1]>3: # {"feature": "Direction_same", "instances": 15, "metric_value": 0.32, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[4]<=1.302144119170582: # {"feature": "Education", "instances": 168, "metric_value": 0.3565, "depth": 7} if obj[3]<=4: # {"feature": "Time", "instances": 164, "metric_value": 0.3511, "depth": 8} if obj[1]<=2: # {"feature": "Passanger", "instances": 86, "metric_value": 0.3908, "depth": 9} if obj[0]<=1: # {"feature": "Direction_same", "instances": 64, "metric_value": 0.3584, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'False' else: return 'False' elif obj[0]>1: # {"feature": "Direction_same", "instances": 22, "metric_value": 0.4502, "depth": 10} if obj[8]<=0: return 'False' elif obj[8]>0: return 'True' else: return 'True' else: return 'False' elif obj[1]>2: # {"feature": "Direction_same", "instances": 78, "metric_value": 0.2774, "depth": 9} if obj[8]<=0: # {"feature": "Passanger", "instances": 72, "metric_value": 0.2519, "depth": 10} if obj[0]>0: return 'False' elif obj[0]<=0: return 'False' else: return 'False' elif obj[8]>0: # {"feature": "Passanger", "instances": 6, "metric_value": 0.0, "depth": 10} if obj[0]<=1: return 'False' elif obj[0]>1: return 'True' else: return 'True' else: return 'False' else: return 'False' elif obj[3]>4: # {"feature": "Passanger", "instances": 4, "metric_value": 0.25, "depth": 8} if obj[0]<=1: return 'True' elif obj[0]>1: # {"feature": "Time", "instances": 2, "metric_value": 0.0, "depth": 9} if obj[1]<=0: return 'True' elif obj[1]>0: return 'False' else: return 'False' else: return 'True' else: return 'True' else: return 'False' elif obj[6]>3.0: # {"feature": "Education", "instances": 43, "metric_value": 0.2063, "depth": 6} if obj[3]<=0: # {"feature": "Occupation", "instances": 23, "metric_value": 0.3327, "depth": 7} if obj[4]<=7: # {"feature": "Direction_same", "instances": 12, "metric_value": 0.35, "depth": 8} if obj[8]<=0: # {"feature": "Time", "instances": 10, "metric_value": 0.3429, "depth": 9} if obj[1]<=3: # {"feature": "Passanger", "instances": 7, "metric_value": 0.4762, "depth": 10} if obj[0]>1: return 'False' elif obj[0]<=1: return 'False' else: return 'False' elif obj[1]>3: return 'False' else: return 'False' elif obj[8]>0: return 'True' else: return 'True' elif obj[4]>7: # {"feature": "Passanger", "instances": 11, "metric_value": 0.0909, "depth": 8} if obj[0]>0: return 'False' elif obj[0]<=0: # {"feature": "Time", "instances": 2, "metric_value": 0.0, "depth": 9} if obj[1]>0: return 'True' elif obj[1]<=0: return 'False' else: return 'False' else: return 'True' else: return 'False' elif obj[3]>0: return 'False' else: return 'False' else: return 'False' elif obj[9]>2: # {"feature": "Passanger", "instances": 168, "metric_value": 0.2854, "depth": 5} if obj[0]<=1: # {"feature": "Occupation", "instances": 164, "metric_value": 0.2716, "depth": 6} if obj[4]>0: # {"feature": "Education", "instances": 159, "metric_value": 0.256, "depth": 7} if obj[3]<=4: # {"feature": "Coffeehouse", "instances": 158, "metric_value": 0.2545, "depth": 8} if obj[6]>0.0: # {"feature": "Time", "instances": 111, "metric_value": 0.2183, "depth": 9} if obj[1]<=1: # {"feature": "Direction_same", "instances": 104, "metric_value": 0.233, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[1]>1: return 'False' else: return 'False' elif obj[6]<=0.0: # {"feature": "Time", "instances": 47, "metric_value": 0.3288, "depth": 9} if obj[1]<=1: # {"feature": "Direction_same", "instances": 44, "metric_value": 0.3512, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' elif obj[1]>1: return 'False' else: return 'False' else: return 'False' elif obj[3]>4: return 'True' else: return 'True' elif obj[4]<=0: # {"feature": "Coffeehouse", "instances": 5, "metric_value": 0.4, "depth": 7} if obj[6]<=2.0: # {"feature": "Time", "instances": 4, "metric_value": 0.5, "depth": 8} if obj[1]<=1: # {"feature": "Education", "instances": 4, "metric_value": 0.5, "depth": 9} if obj[3]<=0: # {"feature": "Direction_same", "instances": 4, "metric_value": 0.5, "depth": 10} if obj[8]<=0: return 'False' else: return 'False' else: return 'False' else: return 'False' elif obj[6]>2.0: return 'True' else: return 'True' else: return 'True' elif obj[0]>1: # {"feature": "Coffeehouse", "instances": 4, "metric_value": 0.0, "depth": 6} if obj[6]>-1.0: return 'True' elif obj[6]<=-1.0: return 'False' else: return 'False' else: return 'True' else: return 'False' elif obj[7]>2.0: # {"feature": "Education", "instances": 48, "metric_value": 0.3929, "depth": 4} if obj[3]<=0: # {"feature": "Occupation", "instances": 25, "metric_value": 0.2743, "depth": 5} if obj[4]<=4: # {"feature": "Passanger", "instances": 14, "metric_value": 0.4286, "depth": 6} if obj[0]<=2: # {"feature": "Time", "instances": 12, "metric_value": 0.4375, "depth": 7} if obj[1]<=2: # {"feature": "Direction_same", "instances": 8, "metric_value": 0.4375, "depth": 8} if obj[8]>0: # {"feature": "Coffeehouse", "instances": 4, "metric_value": 0.25, "depth": 9} if obj[6]>2.0: return 'True' elif obj[6]<=2.0: # {"feature": "Distance", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[9]<=2: return 'False' else: return 'False' else: return 'False' elif obj[8]<=0: # {"feature": "Coffeehouse", "instances": 4, "metric_value": 0.3333, "depth": 9} if obj[6]<=2.0: # {"feature": "Distance", "instances": 3, "metric_value": 0.3333, "depth": 10} if obj[9]>1: return 'True' elif obj[9]<=1: return 'True' else: return 'True' elif obj[6]>2.0: return 'False' else: return 'False' else: return 'False' elif obj[1]>2: # {"feature": "Coffeehouse", "instances": 4, "metric_value": 0.25, "depth": 8} if obj[6]<=2.0: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 9} if obj[8]<=0: # {"feature": "Distance", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[9]<=2: return 'False' else: return 'False' else: return 'False' elif obj[6]>2.0: return 'False' else: return 'False' else: return 'False' elif obj[0]>2: return 'True' else: return 'True' elif obj[4]>4: return 'True' else: return 'True' elif obj[3]>0: # {"feature": "Coffeehouse", "instances": 23, "metric_value": 0.3581, "depth": 5} if obj[6]<=2.0: # {"feature": "Time", "instances": 17, "metric_value": 0.4118, "depth": 6} if obj[1]<=3: # {"feature": "Passanger", "instances": 14, "metric_value": 0.4848, "depth": 7} if obj[0]<=2: # {"feature": "Occupation", "instances": 11, "metric_value": 0.4848, "depth": 8} if obj[4]>5: # {"feature": "Direction_same", "instances": 8, "metric_value": 0.5, "depth": 9} if obj[8]<=0: # {"feature": "Distance", "instances": 6, "metric_value": 0.5, "depth": 10} if obj[9]>1: return 'True' elif obj[9]<=1: return 'True' else: return 'True' elif obj[8]>0: # {"feature": "Distance", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[9]<=2: return 'False' else: return 'False' else: return 'False' elif obj[4]<=5: # {"feature": "Direction_same", "instances": 3, "metric_value": 0.3333, "depth": 9} if obj[8]<=0: # {"feature": "Distance", "instances": 2, "metric_value": 0.0, "depth": 10} if obj[9]<=1: return 'True' elif obj[9]>1: return 'False' else: return 'False' elif obj[8]>0: return 'False' else: return 'False' else: return 'False' elif obj[0]>2: # {"feature": "Occupation", "instances": 3, "metric_value": 0.3333, "depth": 8} if obj[4]>5: # {"feature": "Direction_same", "instances": 2, "metric_value": 0.5, "depth": 9} if obj[8]<=0: # {"feature": "Distance", "instances": 2, "metric_value": 0.5, "depth": 10} if obj[9]<=2: return 'True' else: return 'True' else: return 'True' elif obj[4]<=5: return 'True' else: return 'True' else: return 'True' elif obj[1]>3: return 'False' else: return 'False' elif obj[6]>2.0: return 'False' else: return 'False' else: return 'False' else: return 'True' else: return 'False' else: return 'False'

39.753191

212

0.494446

10,505

84,078

3.899857

0.049595

0.125122

0.136497

0.122535

0.922696

0.842438

0.819737

0.724639

0.667912

0.61221

0

0.105832

0.315362

84,078

2,114

213

39.771996

0.605875

0.44222

0

0.962985

0

0.079259

0

1

0.000607

false

0

0.21784

0

null

0

1

0

1

0

1

0

null

0

6

8872eed8fe157740b389e61010fddf9443825d2b

1,134

py

Python

nnunet/training/network_training/nnUNetTrainerV2_TransferLess.py

ZXLam/nnUNet

0cf7c8a857c248d6be171e4945427b405f6ac258

[ "Apache-2.0" ]

null

nnunet/training/network_training/nnUNetTrainerV2_TransferLess.py

ZXLam/nnUNet

0cf7c8a857c248d6be171e4945427b405f6ac258

[ "Apache-2.0" ]

null

nnunet/training/network_training/nnUNetTrainerV2_TransferLess.py

ZXLam/nnUNet

0cf7c8a857c248d6be171e4945427b405f6ac258

[ "Apache-2.0" ]

1

2022-03-15T03:15:02.000Z

from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 class nnUNetTrainerV2TransferLess(nnUNetTrainerV2): def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, unpack_data=True, deterministic=True, fp16=False): super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, deterministic, fp16) self.max_num_epochs = 50 self.initial_lr = 1e-4 self.num_batches_per_epoch = 50 class nnUNetTrainerTransferLess(nnUNetTrainer): def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, unpack_data=True, deterministic=True, fp16=False): super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, deterministic, fp16) self.max_num_epochs = 50 self.initial_lr = 1e-4 self.num_batches_per_epoch = 50

54

113

0.719577

134

1,134

5.716418

0.313433

0.046997

0.067885

0.099217

0.793734

0.707572

0

0.02649

0.201058

1,134

21

114

54

0.818985

0

0.777778

0

1

0.111111

false

0

0.111111

0

0.333333

0

null

0

1

0

1

0

null

0

6

88857f939de5dcf31149a4f73b41342f6634dd1b

48

py

Python

lib/west_tools/trajtree/__init__.py

ajoshpratt/westpa

545a42a5ae4cfa77de0e125a38a5b1ec2b9ab218

[ "MIT" ]

1

2019-12-21T09:11:54.000Z

lib/west_tools/trajtree/__init__.py

ajoshpratt/westpa

545a42a5ae4cfa77de0e125a38a5b1ec2b9ab218

[ "MIT" ]

1

2019-04-25T14:10:33.000Z

lib/west_tools/trajtree/__init__.py

ajoshpratt/westpa

545a42a5ae4cfa77de0e125a38a5b1ec2b9ab218

[ "MIT" ]

1

2020-04-14T20:42:11.000Z

import trajtree from trajtree import TrajTreeSet

24

32

0.895833

6

48

7.166667

0.666667

0

0.104167

48

2

32

24

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8897efa17268f45911b9e3024b5f78e0c4d3801e

79

py

Python

rmsectkf/core/modules/local/local_module.py

MartinR2295/rm-sec-toolkit

c3906dc97a8b778a29421efa982c3ab9d72873ff

[ "Apache-2.0" ]

null

rmsectkf/core/modules/local/local_module.py

MartinR2295/rm-sec-toolkit

c3906dc97a8b778a29421efa982c3ab9d72873ff

[ "Apache-2.0" ]

15

2021-05-07T07:44:48.000Z

2021-05-09T08:59:19.000Z

rmsectkf/core/modules/local/local_module.py

MartinR2295/rm-sec-toolkit

c3906dc97a8b778a29421efa982c3ab9d72873ff

[ "Apache-2.0" ]

null

from ..base_module import BaseModule class LocalModule(BaseModule): pass

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null

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88ab2f9e4b65b20b973f678eed6a4f7087f7b862

183

py

Python

test.py

pangbo13/BiliSpider

7f2b14b620f5abafed462b3edbe2437d59d0e17b

[ "MIT" ]

1

2019-07-30T14:17:17.000Z

test.py

pangbo13/BiliSpider

7f2b14b620f5abafed462b3edbe2437d59d0e17b

[ "MIT" ]

null

test.py

pangbo13/BiliSpider

7f2b14b620f5abafed462b3edbe2437d59d0e17b

[ "MIT" ]

null

# from bilispider.bilispider import * # s = spider(54,{'tid':(54,),'debug':True,'output':2}) # s.auto_run() from bilispider.gui import gui_config print(gui_config({'tid':30}).get())

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28

183

4.357143

0.642857

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0

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0.092896

183

6

55

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0

1

0

null

0

1

0

1

0

1

0

6

ee42990447a820842892b6708b0af588d3800d82

46

py

Python

scripts/mell/layers/gpt2.py

zhenhua32/EasyTransfer

07940087b80b7dc001fb688c81d9420a2055a2bd

[ "Apache-2.0" ]

806

2020-09-02T03:05:24.000Z

2022-03-26T03:45:23.000Z

scripts/mell/layers/gpt2.py

zhenhua32/EasyTransfer

07940087b80b7dc001fb688c81d9420a2055a2bd

[ "Apache-2.0" ]

48

2020-09-16T12:53:32.000Z

2022-03-09T09:34:44.000Z

scripts/mell/layers/gpt2.py

zhenhua32/EasyTransfer

07940087b80b7dc001fb688c81d9420a2055a2bd

[ "Apache-2.0" ]

151

2020-09-16T12:31:06.000Z

2022-03-24T08:51:47.000Z

from transformers import GPT2Model, GPT2Config

46

0.891304

5

46

8.2

1

0

0.047619

0.086957

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1

46

0.928571

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6

ee4f7a577586f8c5f04a68206b967f7e49c3ffe3

83

py

Python

src/advance/expression_evaluator/compiler/node_traversal/__init__.py

chuanhao01/Python_Expression_Evaluator

016f7c0a593f47a2139df5949b26d7f93c23fffe

[ "MIT" ]

null

src/advance/expression_evaluator/compiler/node_traversal/__init__.py

chuanhao01/Python_Expression_Evaluator

016f7c0a593f47a2139df5949b26d7f93c23fffe

[ "MIT" ]

null

src/advance/expression_evaluator/compiler/node_traversal/__init__.py

chuanhao01/Python_Expression_Evaluator

016f7c0a593f47a2139df5949b26d7f93c23fffe

[ "MIT" ]

null

from .pre_order import PreOrderTraversal from .post_order import PostOrderTraversal

41.5

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1

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c9cbc3d5ffc4a66cf02c11146fed4de8cd985e1e

47

py

Python

reader.py

JKLako/welltopreader-1

f78e0f76363cef38d8bf4b4c793e5a824119c0ec

[ "MIT" ]

null

reader.py

JKLako/welltopreader-1

f78e0f76363cef38d8bf4b4c793e5a824119c0ec

[ "MIT" ]

null

reader.py

JKLako/welltopreader-1

f78e0f76363cef38d8bf4b4c793e5a824119c0ec

[ "MIT" ]

null

import numpy as np ##Testing to commit changes

15.666667

27

0.787234

8

47

4.625

1

0

0.170213

47

3

27

15.666667

0.948718

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0

6

4e72be3bff6b9b73bdbe26671d49a040142593ed

377

py

Python

iCount/mapping/__init__.py

genialis/iCount

80dba0f7292a364c62843d71e76c1b22e6268e14

[ "MIT" ]

null

iCount/mapping/__init__.py

genialis/iCount

80dba0f7292a364c62843d71e76c1b22e6268e14

[ "MIT" ]

1

2021-09-30T12:55:37.000Z

iCount/mapping/__init__.py

ulelab/iCount

b9dc1b21b80e4dae77b3ac33734514091fbe3151

[ "MIT" ]

4

2021-03-23T12:38:55.000Z

2021-05-14T10:10:00.000Z

""".. Line to protect from pydocstyle D205, D400. Mapping ======= .. automodule:: iCount.mapping.indexstar :members: .. automodule:: iCount.mapping.mapstar :members: .. automodule:: iCount.mapping.filters :members: .. automodule:: iCount.mapping.xlsites :members: """ from . import filters from . import mapstar from . import indexstar from . import xlsites

15.708333

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0.4

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0.351145

0.343511

0

0.019048

0.164456

377

23

50

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true

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null

0

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1

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1

0

6

4ea4f2ba46af99b658edf74a1e9557ee0d84297f

89,165

py

Python

cottonformation/res/iotwireless.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

null

cottonformation/res/iotwireless.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

null

cottonformation/res/iotwireless.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- """ This module """ import attr import typing from ..core.model import ( Property, Resource, Tag, GetAtt, TypeHint, TypeCheck, ) from ..core.constant import AttrMeta #--- Property declaration --- @attr.s class WirelessDeviceSessionKeysAbpV11(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice.SessionKeysAbpV11" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html Property Document: - ``rp_AppSKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-appskey - ``rp_FNwkSIntKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-fnwksintkey - ``rp_NwkSEncKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-nwksenckey - ``rp_SNwkSIntKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-snwksintkey """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice.SessionKeysAbpV11" rp_AppSKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AppSKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-appskey""" rp_FNwkSIntKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "FNwkSIntKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-fnwksintkey""" rp_NwkSEncKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "NwkSEncKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-nwksenckey""" rp_SNwkSIntKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "SNwkSIntKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv11.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv11-snwksintkey""" @attr.s class DeviceProfileLoRaWANDeviceProfile(Property): """ AWS Object Type = "AWS::IoTWireless::DeviceProfile.LoRaWANDeviceProfile" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html Property Document: - ``p_ClassBTimeout``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-classbtimeout - ``p_ClassCTimeout``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-classctimeout - ``p_MacVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-macversion - ``p_MaxDutyCycle``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-maxdutycycle - ``p_MaxEirp``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-maxeirp - ``p_PingSlotDr``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-pingslotdr - ``p_PingSlotFreq``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-pingslotfreq - ``p_PingSlotPeriod``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-pingslotperiod - ``p_RegParamsRevision``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-regparamsrevision - ``p_RfRegion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-rfregion - ``p_Supports32BitFCnt``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supports32bitfcnt - ``p_SupportsClassB``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supportsclassb - ``p_SupportsClassC``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supportsclassc - ``p_SupportsJoin``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supportsjoin """ AWS_OBJECT_TYPE = "AWS::IoTWireless::DeviceProfile.LoRaWANDeviceProfile" p_ClassBTimeout: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "ClassBTimeout"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-classbtimeout""" p_ClassCTimeout: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "ClassCTimeout"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-classctimeout""" p_MacVersion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "MacVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-macversion""" p_MaxDutyCycle: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "MaxDutyCycle"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-maxdutycycle""" p_MaxEirp: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "MaxEirp"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-maxeirp""" p_PingSlotDr: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "PingSlotDr"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-pingslotdr""" p_PingSlotFreq: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "PingSlotFreq"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-pingslotfreq""" p_PingSlotPeriod: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "PingSlotPeriod"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-pingslotperiod""" p_RegParamsRevision: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "RegParamsRevision"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-regparamsrevision""" p_RfRegion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "RfRegion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-rfregion""" p_Supports32BitFCnt: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "Supports32BitFCnt"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supports32bitfcnt""" p_SupportsClassB: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "SupportsClassB"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supportsclassb""" p_SupportsClassC: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "SupportsClassC"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supportsclassc""" p_SupportsJoin: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "SupportsJoin"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-deviceprofile-lorawandeviceprofile.html#cfn-iotwireless-deviceprofile-lorawandeviceprofile-supportsjoin""" @attr.s class TaskDefinitionLoRaWANGatewayVersion(Property): """ AWS Object Type = "AWS::IoTWireless::TaskDefinition.LoRaWANGatewayVersion" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawangatewayversion.html Property Document: - ``p_Model``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawangatewayversion.html#cfn-iotwireless-taskdefinition-lorawangatewayversion-model - ``p_PackageVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawangatewayversion.html#cfn-iotwireless-taskdefinition-lorawangatewayversion-packageversion - ``p_Station``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawangatewayversion.html#cfn-iotwireless-taskdefinition-lorawangatewayversion-station """ AWS_OBJECT_TYPE = "AWS::IoTWireless::TaskDefinition.LoRaWANGatewayVersion" p_Model: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Model"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawangatewayversion.html#cfn-iotwireless-taskdefinition-lorawangatewayversion-model""" p_PackageVersion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "PackageVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawangatewayversion.html#cfn-iotwireless-taskdefinition-lorawangatewayversion-packageversion""" p_Station: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Station"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawangatewayversion.html#cfn-iotwireless-taskdefinition-lorawangatewayversion-station""" @attr.s class PartnerAccountSidewalkAccountInfo(Property): """ AWS Object Type = "AWS::IoTWireless::PartnerAccount.SidewalkAccountInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-partneraccount-sidewalkaccountinfo.html Property Document: - ``rp_AppServerPrivateKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-partneraccount-sidewalkaccountinfo.html#cfn-iotwireless-partneraccount-sidewalkaccountinfo-appserverprivatekey """ AWS_OBJECT_TYPE = "AWS::IoTWireless::PartnerAccount.SidewalkAccountInfo" rp_AppServerPrivateKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AppServerPrivateKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-partneraccount-sidewalkaccountinfo.html#cfn-iotwireless-partneraccount-sidewalkaccountinfo-appserverprivatekey""" @attr.s class WirelessGatewayLoRaWANGateway(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessGateway.LoRaWANGateway" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessgateway-lorawangateway.html Property Document: - ``rp_GatewayEui``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessgateway-lorawangateway.html#cfn-iotwireless-wirelessgateway-lorawangateway-gatewayeui - ``rp_RfRegion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessgateway-lorawangateway.html#cfn-iotwireless-wirelessgateway-lorawangateway-rfregion """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessGateway.LoRaWANGateway" rp_GatewayEui: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "GatewayEui"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessgateway-lorawangateway.html#cfn-iotwireless-wirelessgateway-lorawangateway-gatewayeui""" rp_RfRegion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "RfRegion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessgateway-lorawangateway.html#cfn-iotwireless-wirelessgateway-lorawangateway-rfregion""" @attr.s class TaskDefinitionLoRaWANUpdateGatewayTaskCreate(Property): """ AWS Object Type = "AWS::IoTWireless::TaskDefinition.LoRaWANUpdateGatewayTaskCreate" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html Property Document: - ``p_CurrentVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-currentversion - ``p_SigKeyCrc``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-sigkeycrc - ``p_UpdateSignature``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-updatesignature - ``p_UpdateVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-updateversion """ AWS_OBJECT_TYPE = "AWS::IoTWireless::TaskDefinition.LoRaWANUpdateGatewayTaskCreate" p_CurrentVersion: typing.Union['TaskDefinitionLoRaWANGatewayVersion', dict] = attr.ib( default=None, converter=TaskDefinitionLoRaWANGatewayVersion.from_dict, validator=attr.validators.optional(attr.validators.instance_of(TaskDefinitionLoRaWANGatewayVersion)), metadata={AttrMeta.PROPERTY_NAME: "CurrentVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-currentversion""" p_SigKeyCrc: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "SigKeyCrc"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-sigkeycrc""" p_UpdateSignature: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "UpdateSignature"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-updatesignature""" p_UpdateVersion: typing.Union['TaskDefinitionLoRaWANGatewayVersion', dict] = attr.ib( default=None, converter=TaskDefinitionLoRaWANGatewayVersion.from_dict, validator=attr.validators.optional(attr.validators.instance_of(TaskDefinitionLoRaWANGatewayVersion)), metadata={AttrMeta.PROPERTY_NAME: "UpdateVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskcreate-updateversion""" @attr.s class WirelessDeviceOtaaV11(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice.OtaaV11" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav11.html Property Document: - ``rp_AppKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav11.html#cfn-iotwireless-wirelessdevice-otaav11-appkey - ``rp_JoinEui``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav11.html#cfn-iotwireless-wirelessdevice-otaav11-joineui - ``rp_NwkKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav11.html#cfn-iotwireless-wirelessdevice-otaav11-nwkkey """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice.OtaaV11" rp_AppKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AppKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav11.html#cfn-iotwireless-wirelessdevice-otaav11-appkey""" rp_JoinEui: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "JoinEui"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav11.html#cfn-iotwireless-wirelessdevice-otaav11-joineui""" rp_NwkKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "NwkKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav11.html#cfn-iotwireless-wirelessdevice-otaav11-nwkkey""" @attr.s class WirelessDeviceSessionKeysAbpV10x(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice.SessionKeysAbpV10x" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv10x.html Property Document: - ``rp_AppSKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv10x.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv10x-appskey - ``rp_NwkSKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv10x.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv10x-nwkskey """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice.SessionKeysAbpV10x" rp_AppSKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AppSKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv10x.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv10x-appskey""" rp_NwkSKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "NwkSKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-sessionkeysabpv10x.html#cfn-iotwireless-wirelessdevice-sessionkeysabpv10x-nwkskey""" @attr.s class ServiceProfileLoRaWANServiceProfile(Property): """ AWS Object Type = "AWS::IoTWireless::ServiceProfile.LoRaWANServiceProfile" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html Property Document: - ``p_AddGwMetadata``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-addgwmetadata - ``p_ChannelMask``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-channelmask - ``p_DevStatusReqFreq``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-devstatusreqfreq - ``p_DlBucketSize``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-dlbucketsize - ``p_DlRate``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-dlrate - ``p_DlRatePolicy``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-dlratepolicy - ``p_DrMax``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-drmax - ``p_DrMin``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-drmin - ``p_HrAllowed``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-hrallowed - ``p_MinGwDiversity``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-mingwdiversity - ``p_NwkGeoLoc``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-nwkgeoloc - ``p_PrAllowed``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-prallowed - ``p_RaAllowed``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-raallowed - ``p_ReportDevStatusBattery``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-reportdevstatusbattery - ``p_ReportDevStatusMargin``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-reportdevstatusmargin - ``p_TargetPer``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-targetper - ``p_UlBucketSize``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-ulbucketsize - ``p_UlRate``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-ulrate - ``p_UlRatePolicy``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-ulratepolicy """ AWS_OBJECT_TYPE = "AWS::IoTWireless::ServiceProfile.LoRaWANServiceProfile" p_AddGwMetadata: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "AddGwMetadata"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-addgwmetadata""" p_ChannelMask: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "ChannelMask"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-channelmask""" p_DevStatusReqFreq: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DevStatusReqFreq"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-devstatusreqfreq""" p_DlBucketSize: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DlBucketSize"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-dlbucketsize""" p_DlRate: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DlRate"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-dlrate""" p_DlRatePolicy: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DlRatePolicy"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-dlratepolicy""" p_DrMax: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DrMax"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-drmax""" p_DrMin: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "DrMin"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-drmin""" p_HrAllowed: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "HrAllowed"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-hrallowed""" p_MinGwDiversity: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "MinGwDiversity"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-mingwdiversity""" p_NwkGeoLoc: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "NwkGeoLoc"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-nwkgeoloc""" p_PrAllowed: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "PrAllowed"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-prallowed""" p_RaAllowed: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "RaAllowed"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-raallowed""" p_ReportDevStatusBattery: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "ReportDevStatusBattery"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-reportdevstatusbattery""" p_ReportDevStatusMargin: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "ReportDevStatusMargin"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-reportdevstatusmargin""" p_TargetPer: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "TargetPer"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-targetper""" p_UlBucketSize: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "UlBucketSize"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-ulbucketsize""" p_UlRate: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "UlRate"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-ulrate""" p_UlRatePolicy: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "UlRatePolicy"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-serviceprofile-lorawanserviceprofile.html#cfn-iotwireless-serviceprofile-lorawanserviceprofile-ulratepolicy""" @attr.s class WirelessDeviceOtaaV10x(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice.OtaaV10x" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav10x.html Property Document: - ``rp_AppEui``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav10x.html#cfn-iotwireless-wirelessdevice-otaav10x-appeui - ``rp_AppKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav10x.html#cfn-iotwireless-wirelessdevice-otaav10x-appkey """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice.OtaaV10x" rp_AppEui: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AppEui"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav10x.html#cfn-iotwireless-wirelessdevice-otaav10x-appeui""" rp_AppKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AppKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-otaav10x.html#cfn-iotwireless-wirelessdevice-otaav10x-appkey""" @attr.s class PartnerAccountSidewalkUpdateAccount(Property): """ AWS Object Type = "AWS::IoTWireless::PartnerAccount.SidewalkUpdateAccount" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-partneraccount-sidewalkupdateaccount.html Property Document: - ``p_AppServerPrivateKey``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-partneraccount-sidewalkupdateaccount.html#cfn-iotwireless-partneraccount-sidewalkupdateaccount-appserverprivatekey """ AWS_OBJECT_TYPE = "AWS::IoTWireless::PartnerAccount.SidewalkUpdateAccount" p_AppServerPrivateKey: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "AppServerPrivateKey"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-partneraccount-sidewalkupdateaccount.html#cfn-iotwireless-partneraccount-sidewalkupdateaccount-appserverprivatekey""" @attr.s class WirelessDeviceAbpV11(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice.AbpV11" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv11.html Property Document: - ``rp_DevAddr``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv11.html#cfn-iotwireless-wirelessdevice-abpv11-devaddr - ``rp_SessionKeys``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv11.html#cfn-iotwireless-wirelessdevice-abpv11-sessionkeys """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice.AbpV11" rp_DevAddr: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "DevAddr"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv11.html#cfn-iotwireless-wirelessdevice-abpv11-devaddr""" rp_SessionKeys: typing.Union['WirelessDeviceSessionKeysAbpV11', dict] = attr.ib( default=None, converter=WirelessDeviceSessionKeysAbpV11.from_dict, validator=attr.validators.instance_of(WirelessDeviceSessionKeysAbpV11), metadata={AttrMeta.PROPERTY_NAME: "SessionKeys"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv11.html#cfn-iotwireless-wirelessdevice-abpv11-sessionkeys""" @attr.s class TaskDefinitionUpdateWirelessGatewayTaskCreate(Property): """ AWS Object Type = "AWS::IoTWireless::TaskDefinition.UpdateWirelessGatewayTaskCreate" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate.html Property Document: - ``p_LoRaWAN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate.html#cfn-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate-lorawan - ``p_UpdateDataRole``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate.html#cfn-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate-updatedatarole - ``p_UpdateDataSource``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate.html#cfn-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate-updatedatasource """ AWS_OBJECT_TYPE = "AWS::IoTWireless::TaskDefinition.UpdateWirelessGatewayTaskCreate" p_LoRaWAN: typing.Union['TaskDefinitionLoRaWANUpdateGatewayTaskCreate', dict] = attr.ib( default=None, converter=TaskDefinitionLoRaWANUpdateGatewayTaskCreate.from_dict, validator=attr.validators.optional(attr.validators.instance_of(TaskDefinitionLoRaWANUpdateGatewayTaskCreate)), metadata={AttrMeta.PROPERTY_NAME: "LoRaWAN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate.html#cfn-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate-lorawan""" p_UpdateDataRole: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "UpdateDataRole"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate.html#cfn-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate-updatedatarole""" p_UpdateDataSource: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "UpdateDataSource"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate.html#cfn-iotwireless-taskdefinition-updatewirelessgatewaytaskcreate-updatedatasource""" @attr.s class TaskDefinitionLoRaWANUpdateGatewayTaskEntry(Property): """ AWS Object Type = "AWS::IoTWireless::TaskDefinition.LoRaWANUpdateGatewayTaskEntry" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskentry.html Property Document: - ``p_CurrentVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskentry.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskentry-currentversion - ``p_UpdateVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskentry.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskentry-updateversion """ AWS_OBJECT_TYPE = "AWS::IoTWireless::TaskDefinition.LoRaWANUpdateGatewayTaskEntry" p_CurrentVersion: typing.Union['TaskDefinitionLoRaWANGatewayVersion', dict] = attr.ib( default=None, converter=TaskDefinitionLoRaWANGatewayVersion.from_dict, validator=attr.validators.optional(attr.validators.instance_of(TaskDefinitionLoRaWANGatewayVersion)), metadata={AttrMeta.PROPERTY_NAME: "CurrentVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskentry.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskentry-currentversion""" p_UpdateVersion: typing.Union['TaskDefinitionLoRaWANGatewayVersion', dict] = attr.ib( default=None, converter=TaskDefinitionLoRaWANGatewayVersion.from_dict, validator=attr.validators.optional(attr.validators.instance_of(TaskDefinitionLoRaWANGatewayVersion)), metadata={AttrMeta.PROPERTY_NAME: "UpdateVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-taskdefinition-lorawanupdategatewaytaskentry.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskentry-updateversion""" @attr.s class WirelessDeviceAbpV10x(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice.AbpV10x" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv10x.html Property Document: - ``rp_DevAddr``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv10x.html#cfn-iotwireless-wirelessdevice-abpv10x-devaddr - ``rp_SessionKeys``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv10x.html#cfn-iotwireless-wirelessdevice-abpv10x-sessionkeys """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice.AbpV10x" rp_DevAddr: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "DevAddr"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv10x.html#cfn-iotwireless-wirelessdevice-abpv10x-devaddr""" rp_SessionKeys: typing.Union['WirelessDeviceSessionKeysAbpV10x', dict] = attr.ib( default=None, converter=WirelessDeviceSessionKeysAbpV10x.from_dict, validator=attr.validators.instance_of(WirelessDeviceSessionKeysAbpV10x), metadata={AttrMeta.PROPERTY_NAME: "SessionKeys"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-abpv10x.html#cfn-iotwireless-wirelessdevice-abpv10x-sessionkeys""" @attr.s class WirelessDeviceLoRaWANDevice(Property): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice.LoRaWANDevice" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html Property Document: - ``p_AbpV10x``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-abpv10x - ``p_AbpV11``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-abpv11 - ``p_DevEui``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-deveui - ``p_DeviceProfileId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-deviceprofileid - ``p_OtaaV10x``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-otaav10x - ``p_OtaaV11``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-otaav11 - ``p_ServiceProfileId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-serviceprofileid """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice.LoRaWANDevice" p_AbpV10x: typing.Union['WirelessDeviceAbpV10x', dict] = attr.ib( default=None, converter=WirelessDeviceAbpV10x.from_dict, validator=attr.validators.optional(attr.validators.instance_of(WirelessDeviceAbpV10x)), metadata={AttrMeta.PROPERTY_NAME: "AbpV10x"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-abpv10x""" p_AbpV11: typing.Union['WirelessDeviceAbpV11', dict] = attr.ib( default=None, converter=WirelessDeviceAbpV11.from_dict, validator=attr.validators.optional(attr.validators.instance_of(WirelessDeviceAbpV11)), metadata={AttrMeta.PROPERTY_NAME: "AbpV11"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-abpv11""" p_DevEui: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DevEui"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-deveui""" p_DeviceProfileId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DeviceProfileId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-deviceprofileid""" p_OtaaV10x: typing.Union['WirelessDeviceOtaaV10x', dict] = attr.ib( default=None, converter=WirelessDeviceOtaaV10x.from_dict, validator=attr.validators.optional(attr.validators.instance_of(WirelessDeviceOtaaV10x)), metadata={AttrMeta.PROPERTY_NAME: "OtaaV10x"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-otaav10x""" p_OtaaV11: typing.Union['WirelessDeviceOtaaV11', dict] = attr.ib( default=None, converter=WirelessDeviceOtaaV11.from_dict, validator=attr.validators.optional(attr.validators.instance_of(WirelessDeviceOtaaV11)), metadata={AttrMeta.PROPERTY_NAME: "OtaaV11"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-otaav11""" p_ServiceProfileId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "ServiceProfileId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-iotwireless-wirelessdevice-lorawandevice.html#cfn-iotwireless-wirelessdevice-lorawandevice-serviceprofileid""" #--- Resource declaration --- @attr.s class ServiceProfile(Resource): """ AWS Object Type = "AWS::IoTWireless::ServiceProfile" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html Property Document: - ``p_LoRaWAN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#cfn-iotwireless-serviceprofile-lorawan - ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#cfn-iotwireless-serviceprofile-name - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#cfn-iotwireless-serviceprofile-tags """ AWS_OBJECT_TYPE = "AWS::IoTWireless::ServiceProfile" p_LoRaWAN: typing.Union['ServiceProfileLoRaWANServiceProfile', dict] = attr.ib( default=None, converter=ServiceProfileLoRaWANServiceProfile.from_dict, validator=attr.validators.optional(attr.validators.instance_of(ServiceProfileLoRaWANServiceProfile)), metadata={AttrMeta.PROPERTY_NAME: "LoRaWAN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#cfn-iotwireless-serviceprofile-lorawan""" p_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#cfn-iotwireless-serviceprofile-name""" p_Tags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#cfn-iotwireless-serviceprofile-tags""" @property def rv_LoRaWANUlRate(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.UlRate") @property def rv_LoRaWANUlBucketSize(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.UlBucketSize") @property def rv_LoRaWANUlRatePolicy(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.UlRatePolicy") @property def rv_LoRaWANDlRate(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.DlRate") @property def rv_LoRaWANDlBucketSize(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.DlBucketSize") @property def rv_LoRaWANDlRatePolicy(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.DlRatePolicy") @property def rv_LoRaWANDevStatusReqFreq(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.DevStatusReqFreq") @property def rv_LoRaWANReportDevStatusBattery(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.ReportDevStatusBattery") @property def rv_LoRaWANReportDevStatusMargin(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.ReportDevStatusMargin") @property def rv_LoRaWANDrMin(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.DrMin") @property def rv_LoRaWANDrMax(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.DrMax") @property def rv_LoRaWANChannelMask(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.ChannelMask") @property def rv_LoRaWANPrAllowed(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.PrAllowed") @property def rv_LoRaWANHrAllowed(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.HrAllowed") @property def rv_LoRaWANRaAllowed(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.RaAllowed") @property def rv_LoRaWANNwkGeoLoc(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.NwkGeoLoc") @property def rv_LoRaWANTargetPer(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.TargetPer") @property def rv_LoRaWANMinGwDiversity(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="LoRaWAN.MinGwDiversity") @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="Arn") @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-serviceprofile.html#aws-resource-iotwireless-serviceprofile-return-values""" return GetAtt(resource=self, attr_name="Id") @attr.s class WirelessDevice(Resource): """ AWS Object Type = "AWS::IoTWireless::WirelessDevice" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html Property Document: - ``rp_DestinationName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-destinationname - ``rp_Type``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-type - ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-description - ``p_LastUplinkReceivedAt``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-lastuplinkreceivedat - ``p_LoRaWAN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-lorawan - ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-name - ``p_ThingArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-thingarn - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-tags """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessDevice" rp_DestinationName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "DestinationName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-destinationname""" rp_Type: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Type"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-type""" p_Description: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Description"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-description""" p_LastUplinkReceivedAt: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "LastUplinkReceivedAt"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-lastuplinkreceivedat""" p_LoRaWAN: typing.Union['WirelessDeviceLoRaWANDevice', dict] = attr.ib( default=None, converter=WirelessDeviceLoRaWANDevice.from_dict, validator=attr.validators.optional(attr.validators.instance_of(WirelessDeviceLoRaWANDevice)), metadata={AttrMeta.PROPERTY_NAME: "LoRaWAN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-lorawan""" p_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-name""" p_ThingArn: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "ThingArn"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-thingarn""" p_Tags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#cfn-iotwireless-wirelessdevice-tags""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#aws-resource-iotwireless-wirelessdevice-return-values""" return GetAtt(resource=self, attr_name="Arn") @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#aws-resource-iotwireless-wirelessdevice-return-values""" return GetAtt(resource=self, attr_name="Id") @property def rv_ThingName(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessdevice.html#aws-resource-iotwireless-wirelessdevice-return-values""" return GetAtt(resource=self, attr_name="ThingName") @attr.s class WirelessGateway(Resource): """ AWS Object Type = "AWS::IoTWireless::WirelessGateway" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html Property Document: - ``rp_LoRaWAN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-lorawan - ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-description - ``p_LastUplinkReceivedAt``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-lastuplinkreceivedat - ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-name - ``p_ThingArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-thingarn - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-tags """ AWS_OBJECT_TYPE = "AWS::IoTWireless::WirelessGateway" rp_LoRaWAN: typing.Union['WirelessGatewayLoRaWANGateway', dict] = attr.ib( default=None, converter=WirelessGatewayLoRaWANGateway.from_dict, validator=attr.validators.instance_of(WirelessGatewayLoRaWANGateway), metadata={AttrMeta.PROPERTY_NAME: "LoRaWAN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-lorawan""" p_Description: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Description"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-description""" p_LastUplinkReceivedAt: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "LastUplinkReceivedAt"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-lastuplinkreceivedat""" p_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-name""" p_ThingArn: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "ThingArn"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-thingarn""" p_Tags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#cfn-iotwireless-wirelessgateway-tags""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#aws-resource-iotwireless-wirelessgateway-return-values""" return GetAtt(resource=self, attr_name="Arn") @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#aws-resource-iotwireless-wirelessgateway-return-values""" return GetAtt(resource=self, attr_name="Id") @property def rv_ThingName(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-wirelessgateway.html#aws-resource-iotwireless-wirelessgateway-return-values""" return GetAtt(resource=self, attr_name="ThingName") @attr.s class Destination(Resource): """ AWS Object Type = "AWS::IoTWireless::Destination" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html Property Document: - ``rp_Expression``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-expression - ``rp_ExpressionType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-expressiontype - ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-name - ``rp_RoleArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-rolearn - ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-description - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-tags """ AWS_OBJECT_TYPE = "AWS::IoTWireless::Destination" rp_Expression: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Expression"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-expression""" rp_ExpressionType: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "ExpressionType"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-expressiontype""" rp_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-name""" rp_RoleArn: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "RoleArn"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-rolearn""" p_Description: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Description"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-description""" p_Tags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#cfn-iotwireless-destination-tags""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-destination.html#aws-resource-iotwireless-destination-return-values""" return GetAtt(resource=self, attr_name="Arn") @attr.s class DeviceProfile(Resource): """ AWS Object Type = "AWS::IoTWireless::DeviceProfile" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html Property Document: - ``p_LoRaWAN``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#cfn-iotwireless-deviceprofile-lorawan - ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#cfn-iotwireless-deviceprofile-name - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#cfn-iotwireless-deviceprofile-tags """ AWS_OBJECT_TYPE = "AWS::IoTWireless::DeviceProfile" p_LoRaWAN: typing.Union['DeviceProfileLoRaWANDeviceProfile', dict] = attr.ib( default=None, converter=DeviceProfileLoRaWANDeviceProfile.from_dict, validator=attr.validators.optional(attr.validators.instance_of(DeviceProfileLoRaWANDeviceProfile)), metadata={AttrMeta.PROPERTY_NAME: "LoRaWAN"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#cfn-iotwireless-deviceprofile-lorawan""" p_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#cfn-iotwireless-deviceprofile-name""" p_Tags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#cfn-iotwireless-deviceprofile-tags""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#aws-resource-iotwireless-deviceprofile-return-values""" return GetAtt(resource=self, attr_name="Arn") @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-deviceprofile.html#aws-resource-iotwireless-deviceprofile-return-values""" return GetAtt(resource=self, attr_name="Id") @attr.s class PartnerAccount(Resource): """ AWS Object Type = "AWS::IoTWireless::PartnerAccount" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html Property Document: - ``p_AccountLinked``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-accountlinked - ``p_Fingerprint``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-fingerprint - ``p_PartnerAccountId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-partneraccountid - ``p_PartnerType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-partnertype - ``p_Sidewalk``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-sidewalk - ``p_SidewalkUpdate``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-sidewalkupdate - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-tags """ AWS_OBJECT_TYPE = "AWS::IoTWireless::PartnerAccount" p_AccountLinked: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "AccountLinked"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-accountlinked""" p_Fingerprint: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Fingerprint"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-fingerprint""" p_PartnerAccountId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "PartnerAccountId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-partneraccountid""" p_PartnerType: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "PartnerType"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-partnertype""" p_Sidewalk: typing.Union['PartnerAccountSidewalkAccountInfo', dict] = attr.ib( default=None, converter=PartnerAccountSidewalkAccountInfo.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PartnerAccountSidewalkAccountInfo)), metadata={AttrMeta.PROPERTY_NAME: "Sidewalk"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-sidewalk""" p_SidewalkUpdate: typing.Union['PartnerAccountSidewalkUpdateAccount', dict] = attr.ib( default=None, converter=PartnerAccountSidewalkUpdateAccount.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PartnerAccountSidewalkUpdateAccount)), metadata={AttrMeta.PROPERTY_NAME: "SidewalkUpdate"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-sidewalkupdate""" p_Tags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#cfn-iotwireless-partneraccount-tags""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-partneraccount.html#aws-resource-iotwireless-partneraccount-return-values""" return GetAtt(resource=self, attr_name="Arn") @attr.s class TaskDefinition(Resource): """ AWS Object Type = "AWS::IoTWireless::TaskDefinition" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html Property Document: - ``rp_AutoCreateTasks``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-autocreatetasks - ``p_LoRaWANUpdateGatewayTaskEntry``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskentry - ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-name - ``p_TaskDefinitionType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-taskdefinitiontype - ``p_Update``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-update - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-tags """ AWS_OBJECT_TYPE = "AWS::IoTWireless::TaskDefinition" rp_AutoCreateTasks: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "AutoCreateTasks"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-autocreatetasks""" p_LoRaWANUpdateGatewayTaskEntry: typing.Union['TaskDefinitionLoRaWANUpdateGatewayTaskEntry', dict] = attr.ib( default=None, converter=TaskDefinitionLoRaWANUpdateGatewayTaskEntry.from_dict, validator=attr.validators.optional(attr.validators.instance_of(TaskDefinitionLoRaWANUpdateGatewayTaskEntry)), metadata={AttrMeta.PROPERTY_NAME: "LoRaWANUpdateGatewayTaskEntry"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-lorawanupdategatewaytaskentry""" p_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-name""" p_TaskDefinitionType: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "TaskDefinitionType"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-taskdefinitiontype""" p_Update: typing.Union['TaskDefinitionUpdateWirelessGatewayTaskCreate', dict] = attr.ib( default=None, converter=TaskDefinitionUpdateWirelessGatewayTaskCreate.from_dict, validator=attr.validators.optional(attr.validators.instance_of(TaskDefinitionUpdateWirelessGatewayTaskCreate)), metadata={AttrMeta.PROPERTY_NAME: "Update"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-update""" p_Tags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#cfn-iotwireless-taskdefinition-tags""" @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#aws-resource-iotwireless-taskdefinition-return-values""" return GetAtt(resource=self, attr_name="Id") @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-iotwireless-taskdefinition.html#aws-resource-iotwireless-taskdefinition-return-values""" return GetAtt(resource=self, attr_name="Arn")

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0.882317

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0.881018

0

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0.098862

89,165

1,294

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68.906492

0.849421

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0.492637

0

0.089577

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1

0.042838

false

0

0.005355

0

0.299866

0.002677

0

null

0

1

0

null

0

6

14d7ddf6e1e6127dd8e0b04f9ab07d85c5142473

241

py

Python

fcos_core/modeling/discriminator/__init__.py

CityU-AIM-Group/SIGMA

19f89777db8d42f750a9b87756d3326c7efd18f5

[ "MIT" ]

5

2022-03-02T02:57:44.000Z

2022-03-25T02:48:43.000Z

fcos_core/modeling/discriminator/__init__.py

CityU-AIM-Group/SCAN

c42d67416fda5c44ee023e14307b63b1ad9890a2

[ "MIT" ]

null

fcos_core/modeling/discriminator/__init__.py

CityU-AIM-Group/SCAN

c42d67416fda5c44ee023e14307b63b1ad9890a2

[ "MIT" ]

null

from .fcos_head_discriminator import FCOSDiscriminator from .fcos_head_discriminator_CA import FCOSDiscriminator_CA from .fcos_head_discriminator_out import FCOSDiscriminator_out from .fcos_head_discriminator_con import FCOSDiscriminator_con

60.25

62

0.921162

30

241

6.933333

0.3

0.153846

0.230769

0.480769

0

0.062241

241

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60.25

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true

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1

0

null

0

1

0

1

0

1

0

6

0903f76474baec12080e8aa26a1c099fb0755aa4

29

py

Python

Models/__init__.py

educauchy/kaggle-tab-playground-apr21

c28d007970bcc3b3ddd4a1cf4811eddaa765ee38

[ "MIT" ]

null

Models/__init__.py

educauchy/kaggle-tab-playground-apr21

c28d007970bcc3b3ddd4a1cf4811eddaa765ee38

[ "MIT" ]

null

Models/__init__.py

educauchy/kaggle-tab-playground-apr21

c28d007970bcc3b3ddd4a1cf4811eddaa765ee38

[ "MIT" ]

null

from .MetaClassifier import *

29

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0923e33444074cfc54719898da419415ea161152

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py

Python

sirena_client/base/connection/__init__.py

utair-digital/sirena-client

7cbdca66531f3c86b6780878b22a891e0df754ae

[ "Apache-2.0" ]

11

2022-03-04T14:37:50.000Z

2022-03-04T14:57:12.000Z

sirena_client/base/connection/__init__.py

utair-digital/sirena-client

7cbdca66531f3c86b6780878b22a891e0df754ae

[ "Apache-2.0" ]

null

sirena_client/base/connection/__init__.py

utair-digital/sirena-client

7cbdca66531f3c86b6780878b22a891e0df754ae

[ "Apache-2.0" ]

null

from .async_connection import AsyncConnection # noqa from .async_pool import AsyncConnectionPool # noqa

36.666667

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0.8

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110

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0.666667

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110

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true

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0

null

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1

0

1

0

6

11b344a9e1bb465ec536f5bac14696fdf00681dc

95

py

Python

python/smqtk/algorithms/nn_index/lsh/functors/_plugins.py

joshanderson-kw/SMQTK

594e7c733fe7f4e514a1a08a7343293a883a41fc

[ "BSD-3-Clause" ]

82

2015-01-07T15:33:29.000Z

2021-08-11T18:34:05.000Z

python/smqtk/algorithms/nn_index/lsh/functors/_plugins.py

joshanderson-kw/SMQTK

594e7c733fe7f4e514a1a08a7343293a883a41fc

[ "BSD-3-Clause" ]

230

2015-04-08T14:36:51.000Z

2022-03-14T17:55:30.000Z

python/smqtk/algorithms/nn_index/lsh/functors/_plugins.py

joshanderson-kw/SMQTK

594e7c733fe7f4e514a1a08a7343293a883a41fc

[ "BSD-3-Clause" ]

65

2015-01-04T15:00:16.000Z

2021-11-19T18:09:11.000Z

from .itq import ItqFunctor # noqa: F401 from .simple_rp import SimpleRPFunctor # noqa: F401

31.666667

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95

5.538462

0.692308

0.222222

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95

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1

0

6

ee9808c9424ece166d71e967cbebcc233b9cee15

155

py

Python

helpers_calc.py

ahammadshawki8/Proggraming-Terms

264156b6cfb347fc1b3aaa966c44aeab8dca26c2

[ "MIT" ]

1

2021-06-07T00:22:28.000Z

helpers_calc.py

ahammadshawki8/Proggraming-Terms

264156b6cfb347fc1b3aaa966c44aeab8dca26c2

[ "MIT" ]

2

2021-03-03T02:22:42.000Z

2021-04-24T03:26:42.000Z

helpers_calc.py

ahammadshawki8/Proggraming-Terms

264156b6cfb347fc1b3aaa966c44aeab8dca26c2

[ "MIT" ]

null

print("Imported helpers_calc!") def add(x,y): return x+y def sub(x,y): return x-y def multiply(x,y): return x*y def devide(x,y): return x/y

17.222222

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0.625806

32

155

3

0.375

0.166667

0.333333

0.375

0.510417

0.40625

0

0.212903

155

9

32

17.222222

0.786885

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0.141026

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0.444444

false

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1

0

1

0

null

0

1

0

1

0

6

eeba5060e61f89875f63c3a4669a5265d78d2e18

149

py

Python

viperdriver/src/__init__.py

Tesqos/viperdriver

41f618221031c646e59adc146e7eb748a8ff5383

[ "Apache-2.0" ]

null

viperdriver/src/__init__.py

Tesqos/viperdriver

41f618221031c646e59adc146e7eb748a8ff5383

[ "Apache-2.0" ]

null

viperdriver/src/__init__.py

Tesqos/viperdriver

41f618221031c646e59adc146e7eb748a8ff5383

[ "Apache-2.0" ]

null

import logging # logger = logging.getLogger(__name__).addHandler(logging.NullHandler()) - remove upon testing logger = logging.getLogger(__name__)

29.8

95

0.791946

16

149

6.875

0.625

0.236364

0.4

0.472727

0

0.100671

149

4

96

37.25

0.820896

0.624161

0

1

0

false

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1

0

null

1

0

1

0

null

0

1

0

6

eedcf6750dfeb0ff038ffdecd67f8ea9f2ee086f

131

py

Python

app/blog/__init__.py

ralphdc/blog

fe3ebe3aea4da38a3cf592c90b0dd9914e47f8de

[ "Apache-2.0" ]

null

app/blog/__init__.py

ralphdc/blog

fe3ebe3aea4da38a3cf592c90b0dd9914e47f8de

[ "Apache-2.0" ]

null

app/blog/__init__.py

ralphdc/blog

fe3ebe3aea4da38a3cf592c90b0dd9914e47f8de

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 from flask import Blueprint blog = Blueprint('blog', __name__) from . import msgboard from . import index

16.375

34

0.748092

18

131

5.222222

0.666667

0.276596

0

0.009009

0.152672

131

8

35

16.375

0.837838

0.160305

0

0.036364

0

1

0

false

0

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1

0

1

0

null

0

1

0

1

0

6

eeff0450682e17379d80de0bcaa86e4f0bbf9a1d

210

py

Python

wrapclib/re2/__init__.py

sdpython/wrapclib

41212ec526f3eaef29333e8947c8d6ca9816362d

[ "MIT" ]

null

wrapclib/re2/__init__.py

sdpython/wrapclib

41212ec526f3eaef29333e8947c8d6ca9816362d

[ "MIT" ]

4

2019-03-15T10:42:36.000Z

2021-01-09T12:06:27.000Z

wrapclib/re2/__init__.py

sdpython/wrapclib

41212ec526f3eaef29333e8947c8d6ca9816362d

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ @file @brief Shortcut to *re2*. """ from .re2 import compile # pylint: disable=W0622 from .re2 import findall, search, match, fullmatch, Set, UNANCHORED, ANCHOR_START, ANCHOR_BOTH

23.333333

94

0.695238

28

210

5.142857

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0.180556

0

0.044944

0.152381

210

8

95

26.25

0.764045

0.361905

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1

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true

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null

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null

0

1

0

1

0

1

0

6

e1114c7c54c13f847c09a49cfd8bb6e90bfcc7bf

215

py

Python

timm/loss/__init__.py

david-yd-hao/timm-biased-loss

dce603249a6c216aebab8cc5a504a8ac188ae32b

[ "Apache-2.0" ]

null

timm/loss/__init__.py

david-yd-hao/timm-biased-loss

dce603249a6c216aebab8cc5a504a8ac188ae32b

[ "Apache-2.0" ]

null

timm/loss/__init__.py

david-yd-hao/timm-biased-loss

dce603249a6c216aebab8cc5a504a8ac188ae32b

[ "Apache-2.0" ]

null

from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, BiasedLossCrossEntropy from .jsd import JsdCrossEntropy from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel

71.666667

101

0.906977

17

215

11.352941

0.764706

0

0.065116

215

3

102

71.666667

0.960199

0

1

0

true

0

1

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1

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1

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1

null

0

1

0

null

0

1

0

1

0

1

0

6

e1295f1ac08e51c5e38b0f44b5adb2ee4511e636

114

py

Python

lang/py/cookbook/v2/source/cb2_1_23_sol_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_1_23_sol_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_1_23_sol_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

def encode_for_xml(unicode_data, encoding='ascii'): return unicode_data.encode(encoding, 'xmlcharrefreplace')

38

61

0.798246

14

114

6.214286

0.714286

0.252874

0

0.087719

114

2

62

57

0.836538

0

0.192982

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

011e5c65f17050df6c0beccce92d3cae32ba5cc7

25

py

Python

flowbee/workers/__init__.py

blitzagency/flowbee

35e8977827eda34474aa5edc95bbfec86c61b33a

[ "MIT" ]

null

flowbee/workers/__init__.py

blitzagency/flowbee

35e8977827eda34474aa5edc95bbfec86c61b33a

[ "MIT" ]

null

flowbee/workers/__init__.py

blitzagency/flowbee

35e8977827eda34474aa5edc95bbfec86c61b33a

[ "MIT" ]

null

from .base import Worker

12.5

24

0.8

4

25

5

1

0

0.16

25

1

25

0.952381

0

1

0

true

0

1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

017e734ac3251dff1577cce59896d63f5935c575

40

py

Python

cougar/structures/__init__.py

Swall0w/cougar

9161b2b1d0c256f4bb952ec190351684f28ec1b7

[ "MIT" ]

1

2019-11-23T12:20:50.000Z

cougar/structures/__init__.py

Swall0w/cougar

9161b2b1d0c256f4bb952ec190351684f28ec1b7

[ "MIT" ]

1

2022-01-13T01:41:34.000Z

cougar/structures/__init__.py

Swall0w/cougar

9161b2b1d0c256f4bb952ec190351684f28ec1b7

[ "MIT" ]

null

from cougar.structures import image_list

40

0.9

6

40

5.833333

1

0

0.075

40

1

40

0.945946

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0199ee794d2e26dbb466994ddc39414941ab741c

9,495

py

Python

tests/test_lookup.py

mkorman9/yt-archiver

eaa9eb4b98c1a9b9262e1d6f83522370b95c5d8a

[ "MIT" ]

6

2018-05-31T19:26:52.000Z

2022-02-16T22:58:57.000Z

tests/test_lookup.py

mkorman9/yt-archiver

eaa9eb4b98c1a9b9262e1d6f83522370b95c5d8a

[ "MIT" ]

1

2020-01-27T15:34:11.000Z

tests/test_lookup.py

mkorman9/yt-archiver

eaa9eb4b98c1a9b9262e1d6f83522370b95c5d8a

[ "MIT" ]

null

import logging from datetime import datetime from unittest import TestCase from mock import MagicMock, create_autospec, call from ytarchiver.api import YoutubeAPI, YoutubeChannel from ytarchiver.common import Context, RecordersController, StorageManager, ContentItem, EventBus, Event, PluginsManager from ytarchiver.lookup import lookup CHANNEL_1 = YoutubeChannel('id1', 'uploads_playlistid1') VIDEO_1 = ContentItem( video_id='video1', channel_id='channel_id', timestamp=datetime.utcnow(), title='video #1', channel_name='some channel' ) VIDEO_2 = ContentItem( video_id='video2', channel_id='channel_id', timestamp=datetime.utcnow(), title='video #2', channel_name='some channel' ) LIVESTREAM_1 = ContentItem( video_id='livestream', channel_id='channel_id', timestamp=datetime.utcnow(), title='livestream #live', channel_name='some channel' ) class LookupTest(TestCase): def test_should_search_for_videos_and_save_results(self): # given context, storage = _create_context_and_storage() context.config.archive_all = False context.config.monitor_livestreams = False storage.video_exist.return_value = False context.video_recorders.is_recording_active.return_value = False context.livestream_recorders.is_recording_active.return_value = False context.api.find_channels.return_value = [CHANNEL_1] context.api.find_channel_uploaded_videos.return_value = [VIDEO_1, VIDEO_2] # when lookup(context, is_first_run=True) # then storage.add_video.assert_has_calls([ call(context.api.find_channel_uploaded_videos.return_value[0]), call(context.api.find_channel_uploaded_videos.return_value[1]) ], any_order=True) storage.add_livestream.assert_not_called() context.livestream_recorders.start_recording.assert_not_called() context.video_recorders.start_recording.assert_not_called() context.bus.add_event.assert_has_calls([ call(Event(type=Event.NEW_VIDEO, content=context.api.find_channel_uploaded_videos.return_value[0])), call(Event(type=Event.NEW_VIDEO, content=context.api.find_channel_uploaded_videos.return_value[1])) ], any_order=True) context.bus.retrieve_events.assert_called_once() storage.commit.assert_called() def test_should_search_for_all_content_and_save_results_and_start_recording_livestream(self): # given context, storage = _create_context_and_storage() context.config.archive_all = False context.config.monitor_livestreams = True storage.video_exist.return_value = False context.video_recorders.is_recording_active.return_value = False context.livestream_recorders.is_recording_active.return_value = False context.api.find_channels.return_value = [CHANNEL_1] context.api.find_channel_uploaded_videos.return_value = [VIDEO_1, VIDEO_2] context.api.fetch_channel_livestream.return_value = LIVESTREAM_1 # when lookup(context, is_first_run=True) # then storage.add_video.assert_has_calls([ call(context.api.find_channel_uploaded_videos.return_value[0]), call(context.api.find_channel_uploaded_videos.return_value[1]) ], any_order=True) storage.add_livestream.assert_has_calls([ call(context.api.fetch_channel_livestream.return_value) ], any_order=True) context.livestream_recorders.start_recording.assert_has_calls([ call(context, context.api.fetch_channel_livestream.return_value) ], any_order=True) context.video_recorders.start_recording.assert_not_called() context.bus.add_event.assert_has_calls([ call(Event(type=Event.LIVESTREAM_STARTED, content=context.api.fetch_channel_livestream.return_value)) ], any_order=True) context.bus.retrieve_events.assert_called_once() storage.commit.assert_called() def test_should_search_for_videos_and_archive_all(self): # given context, storage = _create_context_and_storage() context.config.archive_all = True context.config.monitor_livestreams = False storage.video_exist.return_value = False context.video_recorders.is_recording_active.return_value = False context.livestream_recorders.is_recording_active.return_value = False context.api.find_channels.return_value = [CHANNEL_1] context.api.find_channel_uploaded_videos.return_value = [VIDEO_1, VIDEO_2] # when lookup(context, is_first_run=True) # then storage.add_video.assert_has_calls([ call(context.api.find_channel_uploaded_videos.return_value[0]), call(context.api.find_channel_uploaded_videos.return_value[1]) ], any_order=True) storage.add_livestream.assert_not_called() context.video_recorders.start_recording.assert_has_calls([ call(context, context.api.find_channel_uploaded_videos.return_value[0]), call(context, context.api.find_channel_uploaded_videos.return_value[1]) ], any_order=True) context.livestream_recorders.start_recording.assert_not_called() context.bus.add_event.assert_has_calls([ call(Event(type=Event.NEW_VIDEO, content=context.api.find_channel_uploaded_videos.return_value[0])), call(Event(type=Event.NEW_VIDEO, content=context.api.find_channel_uploaded_videos.return_value[1])) ], any_order=True) context.bus.retrieve_events.assert_called_once() storage.commit.assert_called() def test_should_download_newly_fetched_videos(self): # given context, storage = _create_context_and_storage() context.config.archive_all = False context.config.monitor_livestreams = False storage.video_exist.return_value = False context.video_recorders.is_recording_active.return_value = False context.livestream_recorders.is_recording_active.return_value = False context.api.find_channels.return_value = [CHANNEL_1] context.api.find_channel_uploaded_videos.return_value = [VIDEO_1, VIDEO_2] # when lookup(context, is_first_run=False) # then storage.add_video.assert_has_calls([ call(context.api.find_channel_uploaded_videos.return_value[0]), call(context.api.find_channel_uploaded_videos.return_value[1]) ], any_order=True) storage.add_livestream.assert_not_called() context.video_recorders.start_recording.assert_has_calls([ call(context, context.api.find_channel_uploaded_videos.return_value[0]), call(context, context.api.find_channel_uploaded_videos.return_value[1]) ], any_order=True) context.livestream_recorders.start_recording.assert_not_called() context.bus.add_event.assert_has_calls([ call(Event(type=Event.NEW_VIDEO, content=context.api.find_channel_uploaded_videos.return_value[0])), call(Event(type=Event.NEW_VIDEO, content=context.api.find_channel_uploaded_videos.return_value[1])) ], any_order=True) context.bus.retrieve_events.assert_called_once() storage.commit.assert_called() def test_should_not_download_if_video_already_exist(self): # given context, storage = _create_context_and_storage() context.config.archive_all = False context.config.monitor_livestreams = False storage.video_exist.return_value = True context.video_recorders.is_recording_active.return_value = False context.livestream_recorders.is_recording_active.return_value = False context.api.find_channels.return_value = [CHANNEL_1] context.api.find_channel_uploaded_videos.return_value = [VIDEO_1, VIDEO_2] # when lookup(context, is_first_run=False) # then storage.add_video.assert_not_called() storage.add_livestream.assert_not_called() context.video_recorders.start_recording.assert_not_called() context.livestream_recorders.start_recording.assert_not_called() context.bus.add_event.assert_not_called() context.bus.retrieve_events.assert_called_once() storage.commit.assert_called() def _create_context_and_storage(): config = MagicMock() config.output_dir = 'fake_output_directory' logger = create_autospec(logging.Logger, spec_set=True) api = create_autospec(YoutubeAPI, spec_set=True) video_recorders_controller = create_autospec(RecordersController, spec_set=True) livestream_recorders_controller = create_autospec(RecordersController, spec_set=True) event_bus = create_autospec(EventBus, spec_set=True) plugins_mananger = create_autospec(PluginsManager, spec_set=True) storage_manager = create_autospec(StorageManager, spec_set=True) storage_manager.open.return_value.__enter__.return_value = MagicMock() storage = storage_manager.open.return_value.__enter__.return_value context = Context( config, logger, api=api, video_recorders_controller=video_recorders_controller, livestream_recorders_controller=livestream_recorders_controller, storage_manager=storage_manager, bus=event_bus, plugins=plugins_mananger ) return context, storage

40.751073

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0.729647

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9,495

5.624674

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false

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0.087209

0

null

0

1

0

null

0

6

6dc090ba9a2d3ef1eebb45e3d8efbc0ce8b6ac30

40

py

Python

rgc/__init__.py

sievetech/rgc

33f44b33e8b303e156c002983411203b44e9aebf

[ "BSD-3-Clause" ]

null

rgc/__init__.py

sievetech/rgc

33f44b33e8b303e156c002983411203b44e9aebf

[ "BSD-3-Clause" ]

null

rgc/__init__.py

sievetech/rgc

33f44b33e8b303e156c002983411203b44e9aebf

[ "BSD-3-Clause" ]

null

#coding: utf-8 from rgc import collect

10

23

0.75

7

40

4.285714

1

0

0.030303

0.175

40

3

24

13.333333

0.878788

0.325

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

61cac778e20cbd4888cc273942ca2f1ec25ee40b

168

py

Python

skgaip/flood/gaip/__init__.py

danielsuo/toy_flood

471d3c4091d86d4a00fbf910937d4e60fdaf79a1

[ "MIT" ]

1

2020-04-30T07:42:12.000Z

skgaip/flood/gaip/__init__.py

danielsuo/toy_flood

471d3c4091d86d4a00fbf910937d4e60fdaf79a1

[ "MIT" ]

3

2020-09-25T22:37:57.000Z

2022-02-09T23:38:23.000Z

skgaip/flood/gaip/__init__.py

danielsuo/toy_flood

471d3c4091d86d4a00fbf910937d4e60fdaf79a1

[ "MIT" ]

null

# from ealstm.gaip.flood_lstm import FloodLSTM from ealstm.gaip.flood_data import FloodData # from ealstm.gaip.ar_stateless import ARStateless __all__ = ["FloodData"]

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61cc168afb34e1b77bec3b26a135c92d29bba7ae

25,449

py

Python

post_optimization_studies/mad_analyses/ma100MeV_L1pt8-2pt4TeV_deta2pt6/Output/Histos/MadAnalysis5job_0/selection_6.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

post_optimization_studies/mad_analyses/ma100MeV_L1pt8-2pt4TeV_deta2pt6/Output/Histos/MadAnalysis5job_0/selection_6.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

post_optimization_studies/mad_analyses/ma100MeV_L1pt8-2pt4TeV_deta2pt6/Output/Histos/MadAnalysis5job_0/selection_6.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

def selection_6(): # Library import import numpy import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec # Library version matplotlib_version = matplotlib.__version__ numpy_version = numpy.__version__ # Histo binning xBinning = numpy.linspace(0.0,15.0,76,endpoint=True) # Creating data sequence: middle of each bin xData = numpy.array([0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.5,1.7,1.9,2.1,2.3,2.5,2.7,2.9,3.1,3.3,3.5,3.7,3.9,4.1,4.3,4.5,4.7,4.9,5.1,5.3,5.5,5.7,5.9,6.1,6.3,6.5,6.7,6.9,7.1,7.3,7.5,7.7,7.9,8.1,8.3,8.5,8.7,8.9,9.1,9.3,9.5,9.7,9.9,10.1,10.3,10.5,10.7,10.9,11.1,11.3,11.5,11.7,11.9,12.1,12.3,12.5,12.7,12.9,13.1,13.3,13.5,13.7,13.9,14.1,14.3,14.5,14.7,14.9]) # Creating weights for histo: y7_DELTAR_0 y7_DELTAR_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.746530323921,1.61866183504,2.49079294616,3.26916606684,4.07761158445,4.6030155309,5.04173148619,5.36015945374,4.96389549413,4.56586353469,4.16960357507,3.88125000446,3.57697683547,3.08518688559,2.8976697047,2.63408453156,2.36165375932,1.95477740079,1.81679301485,1.52844104424,1.24362747326,1.14456188336,0.925202305712,0.792525119233,0.633312335459,0.49886634916,0.399800599256,0.31311816809,0.291889810253,0.201669339448,0.180440981611,0.132677186479,0.093758550445,0.0406876758535,0.024766409476,0.0212283498366,0.0106141749183,0.00353805843943,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_1 y7_DELTAR_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.403963891944,0.955309720248,1.45438208153,1.852842767,2.35299866323,2.78260608244,3.01447137079,3.09573928985,3.04752378311,2.82748385389,2.7121063665,2.54953535647,2.28876474493,2.18952557338,1.85396227323,1.60391869768,1.56337666476,1.36465051949,1.17755748683,1.01085897202,0.851687647823,0.756533215185,0.612290372702,0.51609916778,0.388952596341,0.350492181545,0.278914006598,0.220104997278,0.176283942195,0.146392166552,0.104721674548,0.0673212135287,0.0619848738415,0.0363259628544,0.0277816482816,0.0128212360556,0.00641566200093,0.00106772357368,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_2 y7_DELTAR_2_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.22431137196,0.567375731428,0.86530020044,1.23753188667,1.49656634667,1.73754480249,1.87782643498,2.06047007729,1.94866165139,1.95699485332,1.78893521439,1.70490519493,1.55142915938,1.46392673911,1.30836710307,1.18822507524,1.05141624355,0.939607817653,0.820160189984,0.65696135218,0.568070131589,0.522235720972,0.421538497646,0.353481361881,0.289590827082,0.246534137108,0.227783652764,0.163198677804,0.118753067508,0.0854188597867,0.0763908576954,0.052084692065,0.0479178910998,0.0194449485043,0.0180560241825,0.00555569728694,0.00138892472173,0.000694462560867,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_3 y7_DELTAR_3_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16690277194,0.352771789782,0.593168584593,0.796581447895,1.00189071179,1.16405196225,1.29586720327,1.43858804769,1.43100164533,1.32052321095,1.28306519929,1.19250117111,1.07917833584,0.971544702344,0.916542685227,0.818392654683,0.739208630041,0.634420197431,0.562348575002,0.476526548295,0.427214132949,0.361306592438,0.310097776502,0.233758712745,0.201990302859,0.16690277194,0.146039925447,0.115693956004,0.0848738664127,0.0621143793299,0.0592694584446,0.0440964537228,0.0260785601156,0.0199145341974,0.0109055793938,0.00379324478045,0.0033190894329,0.000474155747557,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_4 y7_DELTAR_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_5 y7_DELTAR_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0529581672,0.0,0.0,0.0,1.0521138287,0.0,1.05462838872,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_6 y7_DELTAR_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4605753905,1.15126882793,2.30383643573,1.15235088832,2.30326773922,3.6875657254,2.0723035641,4.14416446714,2.53284898276,0.690760297698,1.61153220386,1.38315290808,0.690937438976,0.6907560709,0.230455998676,0.0,0.46143957864,0.0,0.230350866673,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_7 y7_DELTAR_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.359930673019,1.02467163757,1.52287197843,2.16014969574,2.21554021106,2.79640758282,3.23978560499,3.82157971045,3.54384966239,2.46399524558,1.6893643612,1.24662301162,1.10786224032,0.858736679932,0.553717036599,0.304495109752,0.13843947284,0.193792287905,0.0830513811122,0.055299842367,0.0277194204404,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_8 y7_DELTAR_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.151084201488,0.48423614004,0.504377516496,0.675359702004,1.13907599672,1.41120160532,1.5830356947,1.69408174933,1.36118388312,0.887213066367,0.71563869268,0.413348077695,0.312530816245,0.201576026187,0.141078169117,0.0302685953236,0.0604550221859,0.0504230667906,0.0301680161753,0.0201517590157,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_9 y7_DELTAR_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0961516802609,0.18110331181,0.24614779823,0.373490429927,0.53758102364,0.676174258702,0.905334192949,1.01844234252,0.69036572705,0.59690386983,0.339506699398,0.209366171586,0.104671408976,0.0905198764959,0.0197879608031,0.01697604866,0.01131334269,0.0113283436484,0.00282343621867,0.00565237189102,0.0,0.0,0.00283193740298,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_10 y7_DELTAR_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0136848571629,0.0152216980958,0.0365169671737,0.039632448391,0.0594495704809,0.0914595798965,0.1552297714,0.187437010429,0.118771828975,0.0517749827387,0.0304819702114,0.013714783041,0.00916503115889,0.00458334140609,0.00152202212957,0.00151823561399,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_11 y7_DELTAR_11_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00216659567611,0.00198641060761,0.00378777066194,0.00541646608365,0.00704192465245,0.0142617587561,0.0287097800078,0.0370157756304,0.0186005318984,0.00794641419169,0.00270888195285,0.00126479882879,0.000180814223357,0.000361067379374,0.0,0.00018006256488,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_12 y7_DELTAR_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0243067199767,0.0,0.0,0.0242945760233,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_13 y7_DELTAR_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0301235473445,0.0301201301501,0.0602001795444,0.07029003463,0.170647867166,0.0702986705867,0.110519502419,0.070259953403,0.080279563865,0.0903759888888,0.110412193426,0.0903980539647,0.0100457331979,0.0602205091649,0.0702763989089,0.0100369732801,0.0100340932506,0.0201103993878,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_14 y7_DELTAR_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00549610751918,0.0220212053763,0.082501273114,0.109972893147,0.236463551029,0.236452744324,0.36850044573,0.467458665392,0.58839179223,0.594141284488,0.544418658146,0.506137732657,0.594260320754,0.407120685516,0.390558678292,0.31348980595,0.285914344042,0.253089667205,0.275022769494,0.120959006053,0.104542239323,0.0605015023909,0.0660052030425,0.0384962917511,0.00551434485012,0.00550890493341,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_15 y7_DELTAR_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0345292436295,0.107563798304,0.166786103988,0.242751902195,0.364121319786,0.436184490394,0.53092754537,0.599046762949,0.595042485216,0.610876818306,0.528911377502,0.52204839018,0.443090767205,0.411544754578,0.287165194352,0.235871799492,0.155924731113,0.146079418662,0.101652137586,0.0651205787258,0.0384885764514,0.0286232305864,0.0138173916229,0.00986914171184,0.00197291004404,0.00197407244699,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_16 y7_DELTAR_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0191538962283,0.0531793492939,0.089488529985,0.142421133628,0.176961603723,0.242768152703,0.289898212478,0.340056317918,0.327185653453,0.297188232115,0.246267803847,0.227384019486,0.179238929645,0.131319939962,0.0965389658765,0.0620093386667,0.0491618803762,0.0274785991608,0.0166351060603,0.0103367982856,0.00352906736996,0.00201567710607,0.000756651720394,0.000756508082177,0.0,0.000504346990318,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_17 y7_DELTAR_17_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01487905372,0.0260706394856,0.0466383758843,0.0853450869514,0.101317762802,0.146563121676,0.196712213959,0.219053654567,0.20100598004,0.152575913859,0.124260872544,0.0916329093138,0.0658572356715,0.0434977489888,0.0294825773694,0.0163354469461,0.0108738948652,0.00515803109849,0.000859068447659,0.000858927878233,0.00142991998416,0.000285189269689,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_18 y7_DELTAR_18_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00229000058071,0.00472975209873,0.00732024700378,0.0110771301609,0.0173614532999,0.0256745733096,0.0359121027543,0.0412880452271,0.0333957775386,0.021764430436,0.0148130830232,0.00958823766524,0.00509825353625,0.00267870432109,0.00151258304132,0.000970540792045,0.000302361286533,0.000172846893268,0.000129587066735,6.47756164652e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_19 y7_DELTAR_19_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000424383966984,0.000994375917234,0.00102261162147,0.00181470109613,0.00285953548127,0.00484562833142,0.00870869645677,0.0104268159111,0.00671779866065,0.0032323279934,0.00164597397055,0.000623735902071,0.000511575986831,0.000311105472207,0.00011365054761,2.84378684386e-05,8.50595298734e-05,0.0,2.84575189595e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating a new Canvas fig = plt.figure(figsize=(12,6),dpi=80) frame = gridspec.GridSpec(1,1,right=0.7) pad = fig.add_subplot(frame[0]) # Creating a new Stack pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights+y7_DELTAR_17_weights+y7_DELTAR_18_weights+y7_DELTAR_19_weights,\ label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights+y7_DELTAR_17_weights+y7_DELTAR_18_weights,\ label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights+y7_DELTAR_17_weights,\ label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights,\ label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights,\ label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights,\ label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights,\ label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights,\ label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights,\ label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights,\ label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights,\ label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights,\ label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights,\ label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights,\ label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights,\ label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#758991", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights,\ label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#688296", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights,\ label="$signal\_2pt4TeVL$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#6d7a84", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights,\ label="$signal\_2pt2TeVL$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7c99d1", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights,\ label="$signal\_2TeVL$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7f7f9b", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights,\ label="$signal\_1pt8TeVL$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#aaa5bf", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") # Axis plt.rc('text',usetex=False) plt.xlabel(r"\Delta R [ j_{1} , j_{2} ] ",\ fontsize=16,color="black") plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\ fontsize=16,color="black") # Boundary of y-axis ymax=(y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights+y7_DELTAR_17_weights+y7_DELTAR_18_weights+y7_DELTAR_19_weights).max()*1.1 ymin=0 # linear scale #ymin=min([x for x in (y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights+y7_DELTAR_17_weights+y7_DELTAR_18_weights+y7_DELTAR_19_weights) if x])/100. # log scale plt.gca().set_ylim(ymin,ymax) # Log/Linear scale for X-axis plt.gca().set_xscale("linear") #plt.gca().set_xscale("log",nonposx="clip") # Log/Linear scale for Y-axis plt.gca().set_yscale("linear") #plt.gca().set_yscale("log",nonposy="clip") # Legend plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.) # Saving the image plt.savefig('../../HTML/MadAnalysis5job_0/selection_6.png') plt.savefig('../../PDF/MadAnalysis5job_0/selection_6.png') plt.savefig('../../DVI/MadAnalysis5job_0/selection_6.eps') # Running! if __name__ == '__main__': selection_6()

116.738532

760

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25,449

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0.112465

0.240122

0.351908

0.458854

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0.592199

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0.585613

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0

0.37683

0.07403

25,449

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761

117.276498

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0.171875

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false

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0.039063

0

null

1

0

1

0

1

0

null

0

6

61e126316f971a0e63d3f73f30ad3959f32beed2

37

py

Python

src/lit_tracking/converter/__init__.py

Actis92/lit-tracking

9e7b243ba77c80ca260bff479e54db271d10c195

[ "MIT" ]

null

src/lit_tracking/converter/__init__.py

Actis92/lit-tracking

9e7b243ba77c80ca260bff479e54db271d10c195

[ "MIT" ]

14

2021-11-01T08:48:23.000Z

2022-01-08T14:20:17.000Z

src/lit_tracking/converter/__init__.py

Actis92/lit-tracking

9e7b243ba77c80ca260bff479e54db271d10c195

[ "MIT" ]

null

from .mot_to_coco import Mot20ToCoco

18.5

36

0.864865

6

37

5

1

0

0.060606

0.108108

37

1

37

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true

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1

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1

0

null

0

1

0

1

0

1

0

6

11320fbed288ea9e813dfc48a063f201b7628dd8

8,777

py

Python

demo/DeepForest/callbacks.py

bw4sz/SpeciesClassification

f661cf4409381a71b3ff7acf1383741c9eb19cdd

[ "MIT" ]

1

2020-05-18T07:14:31.000Z

demo/DeepForest/callbacks.py

bw4sz/SpeciesClassification

f661cf4409381a71b3ff7acf1383741c9eb19cdd

[ "MIT" ]

4

2019-11-12T02:48:19.000Z

2020-02-07T18:01:25.000Z

Weinstein2019/DeepForest/callbacks.py

weecology/NeonTreeEvaluation_analysis

a426a1a6a621b67f11dc4e6cc46eb9df9d0fc677

[ "MIT" ]

1

2021-04-18T22:12:58.000Z

''' Callback for evaluation. Modified in part from keras-retinanet by FizyR. ''' import keras from .evaluation import neonRecall from .evalmAP import evaluate class Evaluate(keras.callbacks.Callback): """ Evaluation callback for arbitrary datasets. """ def __init__(self, generator, iou_threshold=0.5, score_threshold=0.05, max_detections=300, suppression_threshold=0.2,save_path=None, weighted_average=False, verbose=1,experiment=None,DeepForest_config=None): """ Evaluate a given dataset using a given model at the end of every epoch during training. # Arguments generator : The generator that represents the dataset to evaluate. iou_threshold : The threshold used to consider when a detection is positive or negative. score_threshold : The score confidence threshold to use for detections. max_detections : The maximum number of detections to use per image. suppression_threshold: Percent overlap allowed among boxes save_path : The path to save images with visualized detections to. verbose : Set the verbosity level, by default this is set to 1. Experiment : Comet ml experiment for online logging """ self.generator = generator self.iou_threshold = iou_threshold self.score_threshold = score_threshold self.max_detections = max_detections self.suppression_threshold=suppression_threshold self.save_path = save_path self.weighted_average = weighted_average self.verbose = verbose self.experiment = experiment self.DeepForest_config = DeepForest_config super(Evaluate, self).__init__() def on_epoch_end(self, epoch, logs=None): logs = logs or {} # run evaluation average_precisions = evaluate( self.generator, self.model, iou_threshold=self.iou_threshold, score_threshold=self.score_threshold, max_detections=self.max_detections, save_path=self.save_path, experiment=self.experiment ) # compute per class average precision total_instances = [] precisions = [] for label, (average_precision, num_annotations ) in average_precisions.items(): if self.verbose == 1: print('{:.0f} instances of class'.format(num_annotations), self.generator.label_to_name(label), 'with average precision: {:.3f}'.format(average_precision)) total_instances.append(num_annotations) precisions.append(average_precision) if self.weighted_average: self.mean_ap = sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances) else: self.mean_ap = sum(precisions) / sum(x > 0 for x in total_instances) logs['mAP'] = self.mean_ap if self.verbose == 1: print('mAP: {:.3f}'.format(self.mean_ap)) self.experiment.log_metric("mAP", self.mean_ap) # Neon Recall class recallCallback(keras.callbacks.Callback): """ Evaluation callback for NEON stem maps """ def __init__(self, generator=None, score_threshold=0.05, max_detections=300, suppression_threshold=0.2,save_path=None, weighted_average=False, verbose=1,experiment=None, sites=None): """ Evaluate a given dataset using a given model at the end of every epoch during training. # Arguments generator : The generator that represents the dataset to evaluate. iou_threshold : The threshold used to consider when a detection is positive or negative. score_threshold : The score confidence threshold to use for detections. max_detections : The maximum number of detections to use per image. suppression_threshold: Percent overlap allowed among boxes save_path : The path to save images with visualized detections to. verbose : Set the verbosity level, by default this is set to 1. Experiment : Comet ml experiment for online logging """ self.generator = generator self.score_threshold = score_threshold self.max_detections = max_detections self.suppression_threshold=suppression_threshold self.save_path = save_path self.weighted_average = weighted_average self.verbose = verbose self.experiment = experiment self.sites = sites super(recallCallback, self).__init__() def on_epoch_end(self, epoch, logs=None): logs = logs or {} recall=neonRecall( self.sites, self.generator, self.model, score_threshold=self.score_threshold, save_path=self.save_path, max_detections=self.max_detections, experiment=self.experiment, ) print("Recall is {}".format(recall)) self.experiment.log_metric("Recall", recall) #Hand annotated mAP class NEONmAP(keras.callbacks.Callback): """ Evaluation callback for arbitrary datasets. """ def __init__(self, generator, iou_threshold=0.5, score_threshold=0.05, max_detections=300, save_path=None, weighted_average=False, verbose=1, experiment=None, DeepForest_config=None): """ Evaluate a given dataset using a given model at the end of every epoch during training. # Arguments generator : The generator that represents the dataset to evaluate. iou_threshold : The threshold used to consider when a detection is positive or negative. score_threshold : The score confidence threshold to use for detections. max_detections : The maximum number of detections to use per image. save_path : The path to save images with visualized detections to. verbose : Set the verbosity level, by default this is set to 1. Experiment : Comet ml experiment for online logging """ self.generator = generator self.iou_threshold = iou_threshold self.score_threshold = score_threshold self.max_detections = max_detections self.save_path = save_path self.weighted_average = weighted_average self.verbose = verbose self.experiment = experiment self.DeepForest_config = DeepForest_config super(NEONmAP, self).__init__() def on_epoch_end(self, epoch, logs=None): logs = logs or {} print("computing NEON mAP scores") # run evaluation average_precisions = evaluate( self.generator, self.model, iou_threshold=self.iou_threshold, score_threshold=self.score_threshold, max_detections=self.max_detections, save_path=self.save_path, experiment=self.experiment ) # print evaluation # compute per class average precision total_instances = [] precisions = [] for label, (average_precision, num_annotations ) in average_precisions.items(): if self.verbose == 1: print('{:.0f} instances of class'.format(num_annotations), self.generator.label_to_name(label), 'with average precision: {:.3f}'.format(average_precision)) total_instances.append(num_annotations) precisions.append(average_precision) if self.weighted_average: self.NEON_map = sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances) else: self.NEON_map = sum(precisions) / sum(x > 0 for x in total_instances) logs['NEON_mAP'] = self.NEON_map print('Neon mAP: {:.3f}'.format(self.NEON_map)) self.experiment.log_metric("Neon mAP", self.NEON_map) class shuffle_inputs(keras.callbacks.Callback): """Randomize order of tiles and windows """ def __init__(self, generator): """ # Arguments generator : The generator that represents the dataset to evaluate. """ self.generator = generator super(shuffle_inputs, self).__init__() #Before epoch, randomize tile order def on_epoch_begin(self,epoch,logs=None): self.generator.image_data, self.generator.image_names =self.generator.define_groups(self.generator.windowdf,shuffle=True) self.generator.group_images()

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null

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1

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null

0

6

114b2c69786d06e5b502c67bc505fb5dc84be52f

165

py

Python

canteen/admin.py

vasundhara7/College-EWallet

0a4c32bc08218650635a04fb9a9e28446fd4f3e1

[ "Apache-2.0" ]

2

2019-07-28T00:34:09.000Z

2020-06-18T11:58:03.000Z

canteen/admin.py

vasundhara7/College-EWallet

0a4c32bc08218650635a04fb9a9e28446fd4f3e1

[ "Apache-2.0" ]

null

canteen/admin.py

vasundhara7/College-EWallet

0a4c32bc08218650635a04fb9a9e28446fd4f3e1

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Payment, Order, card_pay admin.site.register(Payment) admin.site.register(Order) admin.site.register(card_pay)

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3a0f450a7bcebdc7c3b582246d0cef132e9ad897

23

py

Python

grid_utils/tiler/__init__.py

claydodo/grid_utils

1a08cb8ca226bb22ddac01be2a0863919d736767

[ "Unlicense" ]

null

grid_utils/tiler/__init__.py

claydodo/grid_utils

1a08cb8ca226bb22ddac01be2a0863919d736767

[ "Unlicense" ]

1

2018-11-07T08:05:41.000Z

grid_utils/tiler/__init__.py

claydodo/grid_utils

1a08cb8ca226bb22ddac01be2a0863919d736767

[ "Unlicense" ]

null

from .xy_tiler import *

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null

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1

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0

6

28c1d0e84f2c4ff9653db6c3987ac19f0875b589

231

py

Python

03.Inheritance/Lab/multiple_inheritance/project/teacher.py

nmoskova/Python-OOP

07327bcb93eee3a7db5d7c0bbdd1b54eb9e8b864

[ "MIT" ]

null

03.Inheritance/Lab/multiple_inheritance/project/teacher.py

nmoskova/Python-OOP

07327bcb93eee3a7db5d7c0bbdd1b54eb9e8b864

[ "MIT" ]

null

03.Inheritance/Lab/multiple_inheritance/project/teacher.py

nmoskova/Python-OOP

07327bcb93eee3a7db5d7c0bbdd1b54eb9e8b864

[ "MIT" ]

null

from inheritance.Lab.multiple_inheritance.project.employee import Employee from inheritance.Lab.multiple_inheritance.project.person import Person class Teacher(Person, Employee): def teach(self): return "teaching..."

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null

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1

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6

e910378db320015322ed2b7546d113a8d39c4ed3

138

py

Python

tensorboardX/__init__.py

rococostudio/Deep-Exemplar-based-Video-Colorization

f9ded4ccfd276251ad8426c5628af46e619e0a0e

[ "MIT" ]

402

2016-12-11T00:19:59.000Z

2022-03-20T04:03:11.000Z

tensorboardX/__init__.py

avalonstrel/tensorboard-pytorch

1cb71ccfe9016578c6ffd1802d13a888dca58a59

[ "MIT" ]

55

2016-12-11T19:53:23.000Z

2020-03-24T15:07:51.000Z

tensorboardX/__init__.py

avalonstrel/tensorboard-pytorch

1cb71ccfe9016578c6ffd1802d13a888dca58a59

[ "MIT" ]

74

2016-12-11T03:39:05.000Z

2022-03-31T02:04:16.000Z

"""A module for visualization with tensorboard """ from .writer import FileWriter, SummaryWriter from .record_writer import RecordWriter

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6

3a7dcc9b7ad07981edad0b8c1c0a15b440b1c262

596

py

Python

thualign/optimizers/__init__.py

bryant1410/Mask-Align

329690919d6885a8fcdf13beef6cf98ff6a2d51a

[ "BSD-3-Clause" ]

27

2021-05-11T07:24:59.000Z

2022-03-25T05:23:45.000Z

thualign/optimizers/__init__.py

bryant1410/Mask-Align

329690919d6885a8fcdf13beef6cf98ff6a2d51a

[ "BSD-3-Clause" ]

11

2021-10-02T05:56:01.000Z

2022-03-30T02:32:36.000Z

thualign/optimizers/__init__.py

bryant1410/Mask-Align

329690919d6885a8fcdf13beef6cf98ff6a2d51a

[ "BSD-3-Clause" ]

11

2021-06-04T05:23:39.000Z

2022-03-19T19:40:55.000Z

from thualign.optimizers.optimizers import AdamOptimizer from thualign.optimizers.optimizers import AdadeltaOptimizer from thualign.optimizers.optimizers import SGDOptimizer from thualign.optimizers.optimizers import MultiStepOptimizer from thualign.optimizers.optimizers import LossScalingOptimizer from thualign.optimizers.schedules import LinearWarmupRsqrtDecay from thualign.optimizers.schedules import PiecewiseConstantDecay from thualign.optimizers.schedules import LinearExponentialDecay from thualign.optimizers.clipping import ( adaptive_clipper, global_norm_clipper, value_clipper)

54.181818

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null

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1

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1

0

6

3a8016ae7719491d9c333b3d295c359ed2e23cff

3,167

py

Python

common/user_query.py

cedadev/download-stats

3d18b08ce239e82e53c5a9bd4dd77b35a1f040bc

[ "BSD-3-Clause" ]

null

common/user_query.py

cedadev/download-stats

3d18b08ce239e82e53c5a9bd4dd77b35a1f040bc

[ "BSD-3-Clause" ]

6

2019-08-29T10:35:09.000Z

2021-04-07T12:24:37.000Z

common/user_query.py

cedadev/access-stats

3d18b08ce239e82e53c5a9bd4dd77b35a1f040bc

[ "BSD-3-Clause" ]

1

2018-11-01T16:31:16.000Z

from common.query_builder import QueryBuilder class UserQuery(QueryBuilder): def get_size(self): return 0 def update_aggs(self): self.group_by() def group_by_main(self): self.generated_aggs["group_by_field"] = {} self.generated_aggs["group_by_field"]["terms"] = {} self.generated_aggs["group_by_field"]["terms"]["field"] = "user_data.field.keyword.terms.value" self.generated_aggs["group_by_field"]["aggs"] = {} self.generated_aggs["group_by_field"]["aggs"]["users"] = {} self.generated_aggs["group_by_field"]["aggs"]["users"]["cardinality"] = {} self.generated_aggs["group_by_field"]["aggs"]["users"]["cardinality"]["field"] = "user.keyword.terms.value" self.generated_aggs["group_by_country"] = {} self.generated_aggs["group_by_country"]["terms"] = {} self.generated_aggs["group_by_country"]["terms"]["field"] = "user_data.isocode.keyword.terms.value" self.generated_aggs["group_by_country"]["aggs"] = {} self.generated_aggs["group_by_country"]["aggs"]["users"] = {} self.generated_aggs["group_by_country"]["aggs"]["users"]["cardinality"] = {} self.generated_aggs["group_by_country"]["aggs"]["users"]["cardinality"]["field"] = "user.keyword.terms.value" self.generated_aggs["group_by_institute_type"] = {} self.generated_aggs["group_by_institute_type"]["terms"] = {} self.generated_aggs["group_by_institute_type"]["terms"]["field"] = "user_data.type.keyword.terms.value" self.generated_aggs["group_by_institute_type"]["aggs"] = {} self.generated_aggs["group_by_institute_type"]["aggs"]["users"] = {} self.generated_aggs["group_by_institute_type"]["aggs"]["users"]["cardinality"] = {} self.generated_aggs["group_by_institute_type"]["aggs"]["users"]["cardinality"]["field"] = "user.keyword.terms.value" if "user" in self.filters: self.generated_aggs["group_by_oda_type"] = {} self.generated_aggs["group_by_oda_type"]["terms"] = {} self.generated_aggs["group_by_oda_type"]["terms"]["field"] = "user_data.oda_country.keyword.terms.value" self.generated_aggs["group_by_oda_type"]["aggs"] = {} self.generated_aggs["group_by_oda_type"]["aggs"]["users"] = {} self.generated_aggs["group_by_oda_type"]["aggs"]["users"]["cardinality"] = {} self.generated_aggs["group_by_oda_type"]["aggs"]["users"]["cardinality"]["field"] = "user.keyword.terms.value" self.generated_aggs["group_by_area"] = {} self.generated_aggs["group_by_area"]["terms"] = {} self.generated_aggs["group_by_area"]["terms"]["field"] = "user_data.area.keyword.terms.value" self.generated_aggs["group_by_area"]["aggs"] = {} self.generated_aggs["group_by_area"]["aggs"]["users"] = {} self.generated_aggs["group_by_area"]["aggs"]["users"]["cardinality"] = {} self.generated_aggs["group_by_area"]["aggs"]["users"]["cardinality"]["field"] = "user.keyword.terms.value" def base_aggs(self): return {}

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null

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6

3a80ac0a1f58619078f9f3f3f6aec6b69e08303d

62

py

Python

copper/core/engine/__init__.py

cinepost/Copperfield_FX

1900b506d0a407a3fb5774ab129b984a547ee0b5

[ "Unlicense" ]

6

2016-07-28T13:59:34.000Z

2021-12-28T05:44:15.000Z

copper/core/engine/__init__.py

cinepost/Copperfield_FX

1900b506d0a407a3fb5774ab129b984a547ee0b5

[ "Unlicense" ]

5

2016-06-30T10:19:25.000Z

2022-03-11T23:19:01.000Z

copper/core/engine/__init__.py

cinepost/Copperfield_FX

1900b506d0a407a3fb5774ab129b984a547ee0b5

[ "Unlicense" ]

3

2019-03-18T05:17:10.000Z

2020-02-14T06:56:40.000Z

from .engine_signals import signals from .engine import Engine

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1

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6

aaf45d6dbb32cea95347efbbbf9946f0d33619d0

110

py

Python

info/modules/user/__init__.py

Signss/The-news-program

602c1c4970e4f96ec6772349f7628d179c3a00fa

[ "MIT" ]

null

info/modules/user/__init__.py

Signss/The-news-program

602c1c4970e4f96ec6772349f7628d179c3a00fa

[ "MIT" ]

null

info/modules/user/__init__.py

Signss/The-news-program

602c1c4970e4f96ec6772349f7628d179c3a00fa

[ "MIT" ]

null

from flask import Blueprint user_blue = Blueprint('user', __name__, url_prefix='/user') from . import views

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6

c945025354277c15cdb2f546fd5709b3a9bf6987

124

py

Python

wooey/models/__init__.py

fridmundklaus/wooey

4a2e31c282bfe86edf77b0ff8f58f4177eeab9dd

[ "BSD-3-Clause" ]

1,572

2015-06-19T21:31:41.000Z

2022-03-30T23:37:13.000Z

wooey/models/__init__.py

fridmundklaus/wooey

4a2e31c282bfe86edf77b0ff8f58f4177eeab9dd

[ "BSD-3-Clause" ]

309

2015-07-08T02:33:08.000Z

2022-02-08T00:37:11.000Z

wooey/models/__init__.py

fridmundklaus/wooey

4a2e31c282bfe86edf77b0ff8f58f4177eeab9dd

[ "BSD-3-Clause" ]

220

2015-07-01T10:30:27.000Z

2022-02-05T04:10:54.000Z

from __future__ import absolute_import, unicode_literals from .core import * from .favorite import * from .widgets import *

24.8

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6

c9839fa4ef1ed8b86f362d58198ab5f1c3c48662

108

py

Python

historical_weather/__init__.py

Shom770/xmacis

5e9694d4f2ba37ab93fc83e03b88c707026e886a

[ "MIT" ]

null

historical_weather/__init__.py

Shom770/xmacis

5e9694d4f2ba37ab93fc83e03b88c707026e886a

[ "MIT" ]

null

historical_weather/__init__.py

Shom770/xmacis

5e9694d4f2ba37ab93fc83e03b88c707026e886a

[ "MIT" ]

null

from ._wrapped_endpoints import * from .elements import * from .oni import * from .teleconnections import *

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1

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6

a33e0d9a2b5191b4ff7ae42bd5192ccd28e74918

21,704

py

Python

Src/models/new_vat.py

SivanKe/SyntheticDataHandwrittenCharacterRecognition

c2b299009ddc24eab1bc2074787e82e566f79abc

[ "MIT" ]

7

2019-07-14T06:49:15.000Z

2021-11-03T12:13:37.000Z

Src/models/new_vat.py

SivanKe/SyntheticDataHandwrittenCharacterRecognition

c2b299009ddc24eab1bc2074787e82e566f79abc

[ "MIT" ]

2

2019-07-16T07:44:37.000Z

2019-07-18T10:57:23.000Z

Src/models/new_vat.py

SivanKe/SyntheticDataHandwrittenCharacterRecognition

c2b299009ddc24eab1bc2074787e82e566f79abc

[ "MIT" ]

1

2020-09-03T14:51:53.000Z

import contextlib import torch import torch.nn as nn import torch.nn.functional as F from warpctc_pytorch import CTCLoss from torch.autograd import Variable import numpy as np @contextlib.contextmanager def _disable_tracking_bn_stats(model): def switch_attr(m): if hasattr(m, 'track_running_stats'): m.track_running_stats ^= True model.apply(switch_attr) yield model.apply(switch_attr) def _l2_normalize(d): d_reshaped = d.view(d.shape[0], -1, *(1 for _ in range(d.dim() - 2))) d /= torch.norm(d_reshaped, dim=1, keepdim=True) + 1e-8 return d def _entropy(logits, mask): p = F.softmax(logits, dim=1) return -torch.mean(torch.sum((p * F.log_softmax(logits, dim=1)), dim=1) * mask) def _kl_div(log_probs, probs, mask=None): # pytorch KLDLoss is averaged over all dim if size_average=True if mask is not None: kld = F.kl_div(log_probs, probs, size_average=False, reduce=False) kld = mask.view(-1) * kld.sum(1) kld = kld.sum() / mask.sum() else: kld = F.kl_div(log_probs, probs, size_average=False) kld = kld / log_probs.shape[0] return kld class LabeledATLoss(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(LabeledATLoss, self).__init__() self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, labels_flatten, img_seq_lens, label_lens, batch_size): with _disable_tracking_bn_stats(model): # calc adversarial direction # prepare random unit tensor d = torch.rand(x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d = _l2_normalize(d) for _ in range(self.ip): d.requires_grad_() loss_function = CTCLoss() preds = model.forward(x + self.xi * d, img_seq_lens) adv_loss_ctc = loss_function(preds, labels_flatten, Variable(torch.IntTensor(np.array(img_seq_lens))), label_lens) / batch_size adv_loss_ctc.backward() d = d.grad model.zero_grad() # calc LDS r_adv = torch.sign(d) * self.eps pred_hat = model.forward(x + r_adv, img_seq_lens) lds = loss_function(pred_hat, labels_flatten, Variable(torch.IntTensor(np.array(img_seq_lens))), label_lens) / batch_size return lds class LabeledAtAndUnlabeledTestVatLoss(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1, unlabeled_ratio=10.): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(LabeledAtAndUnlabeledTestVatLoss, self).__init__() self.xi = xi self.eps = eps self.ip = ip self.unlabeled_ratio = unlabeled_ratio def forward(self, model, train_x, train_labels_flatten, train_img_seq_lens, train_label_lens, batch_size, test_x, test_seq_len, test_mask ): with _disable_tracking_bn_stats(model): # TRAIN # calc adversarial direction # prepare random unit tensor train_d = torch.rand(train_x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) train_d = _l2_normalize(train_d) for _ in range(self.ip): train_d.requires_grad_() train_loss_function = CTCLoss() train_preds = model.forward(train_x + self.xi * train_d, train_img_seq_lens) train_adv_loss_ctc = train_loss_function(train_preds, train_labels_flatten, Variable(torch.IntTensor(np.array(train_img_seq_lens))), train_label_lens) / batch_size train_adv_loss_ctc.backward() train_d = train_d.grad model.zero_grad() #TEST with torch.no_grad(): test_pred = model.vat_forward(test_x, test_seq_len) test_pred = test_pred * test_mask test_pred = F.softmax(test_pred, dim=2).view(-1, test_pred.size()[-1]) # prepare random unit tensor test_d = torch.rand(test_x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) test_d = _l2_normalize(test_d) with _disable_tracking_bn_stats(model): # calc adversarial direction for _ in range(self.ip): test_d.requires_grad_() test_pred_hat = model.vat_forward(test_x + self.xi * test_d, test_seq_len) test_pred_hat = test_pred_hat * test_mask test_pred_hat = F.log_softmax(test_pred_hat, dim=2).view(-1, test_pred_hat.size()[-1]) # pred_hat = model(x + self.xi * d) # adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) test_adv_distance = _kl_div(test_pred_hat, test_pred) test_adv_distance.backward() test_d = _l2_normalize(test_d.grad) model.zero_grad() #TRAIN # calc LDS train_r_adv = torch.sign(train_d) * self.eps train_pred_hat = model.forward(train_x + train_r_adv, train_img_seq_lens) train_lds = train_loss_function(train_pred_hat, train_labels_flatten, Variable(torch.IntTensor(np.array(train_img_seq_lens))), train_label_lens) / batch_size #TEST # calc LDS test_d = torch.sign(test_d) test_r_adv = test_d * self.eps test_pred_hat = model.vat_forward(test_x + test_r_adv, test_seq_len) test_pred_hat = test_pred_hat * test_mask test_pred_hat = F.log_softmax(test_pred_hat, dim=2).view(-1, test_pred_hat.size()[-1]) #pred_hat = model(x + r_adv) #lds = _kl_div(F.log_softmax(pred_hat, dim=1), pred) test_lds = _kl_div(test_pred_hat, test_pred) return train_lds, test_lds class VATLoss(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(VATLoss, self).__init__() self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, seq_len, mask): with torch.no_grad(): pred = model.vat_forward(x, seq_len) pred = pred * mask pred = F.softmax(pred, dim=2).view(-1, pred.size()[-1]) # prepare random unit tensor d = torch.rand(x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d = _l2_normalize(d) with _disable_tracking_bn_stats(model): # calc adversarial direction for _ in range(self.ip): d.requires_grad_() pred_hat = model.vat_forward(x + self.xi * d, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + self.xi * d) #adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) adv_distance = _kl_div(pred_hat, pred) adv_distance.backward() d = _l2_normalize(d.grad) model.zero_grad() # calc LDS r_adv = d * self.eps pred_hat = model.vat_forward(x + r_adv, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + r_adv) #lds = _kl_div(F.log_softmax(pred_hat, dim=1), pred) lds = _kl_div(pred_hat, pred) return lds class VATonRnnSign(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(VATonRnnSign, self).__init__() self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, seq_len, mask): with torch.no_grad(): x_features = model.vat_forward_cnn(x) pred = model.vat_forward_rnn(x_features, seq_len) pred = pred * mask pred = F.softmax(pred, dim=2).view(-1, pred.size()[-1]) # prepare random unit tensor d = torch.rand(x_features.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d = _l2_normalize(d) with _disable_tracking_bn_stats(model): # calc adversarial direction for _ in range(self.ip): d.requires_grad_() pred_hat = model.vat_forward_rnn(x_features + self.xi * d, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + self.xi * d) #adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) adv_distance = _kl_div(pred_hat, pred) adv_distance.backward() d = _l2_normalize(d.grad) model.zero_grad() # calc LDS d = torch.sign(d) r_adv = d * self.eps pred_hat = model.vat_forward_rnn(x_features + r_adv, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + r_adv) #lds = _kl_div(F.log_softmax(pred_hat, dim=1), pred) lds = _kl_div(pred_hat, pred) return lds class VATonCnnSign(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(VATonCnnSign, self).__init__() self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, seq_len, mask): with torch.no_grad(): x_features = model.vat_forward(x) pred = model.vat_forward_rnn(x, seq_len) pred = pred * mask pred = F.softmax(pred, dim=2).view(-1, pred.size()[-1]) # prepare random unit tensor d = torch.rand(x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d = _l2_normalize(d) with _disable_tracking_bn_stats(model): # calc adversarial direction for _ in range(self.ip): d.requires_grad_() pred_hat = model.vat_forward(x + self.xi * d, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + self.xi * d) #adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) adv_distance = _kl_div(pred_hat, pred) adv_distance.backward() d = _l2_normalize(d.grad) model.zero_grad() # calc LDS d = torch.sign(d) r_adv = d * self.eps pred_features_hat = model.vat_forward_cnn(x + r_adv, seq_len) pred_features_hat = pred_features_hat * mask l2_loss = torch.nn.MSELoss() lds = l2_loss(pred_features_hat, x_features) return lds class VATonRnnCnnSign(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(VATonRnnCnnSign, self).__init__() self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, seq_len, mask): with torch.no_grad(): x_features = model.vat_forward_cnn(x) x_pred = model.vat_forward_rnn(x.size(0), x_features, seq_len) x_pred = x_pred * mask x_pred = F.softmax(x_pred, dim=2).view(-1, x_pred.size()[-1]) # prepare random unit tensor d_rnn = torch.rand(x_features.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d_rnn = _l2_normalize(d_rnn) d_cnn = torch.rand(x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d_cnn = _l2_normalize(d_cnn) with _disable_tracking_bn_stats(model): ### Calc rnn d for _ in range(self.ip): d_rnn.requires_grad_() pred_hat = model.vat_forward_rnn(x.size(0), x_features + self.xi * d_rnn, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + self.xi * d) #adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) adv_distance = _kl_div(pred_hat, x_pred) adv_distance.backward() d_rnn = _l2_normalize(d_rnn.grad) model.zero_grad() # calc LDS d_rnn = torch.sign(d_rnn) r_adv_rnn = d_rnn * self.eps ### Calc Cnn d for _ in range(self.ip): d_cnn.requires_grad_() pred_hat = model.vat_forward(x + self.xi * d_cnn, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + self.xi * d) #adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) adv_distance = _kl_div(pred_hat, x_pred) adv_distance.backward() d_cnn = _l2_normalize(d_cnn.grad) model.zero_grad() d_cnn = torch.sign(d_cnn) r_adv_cnn = d_cnn * self.eps #calc rnn lds pred_hat = model.vat_forward_rnn(x.size(0), x_features + r_adv_rnn, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) rnn_lds = _kl_div(pred_hat, x_pred) # calc cnn lds pred_features_hat = model.vat_forward_cnn(x + r_adv_cnn) pred_features_hat = pred_features_hat * mask l2_loss = torch.nn.L1Loss() cnn_lds = l2_loss(pred_features_hat, x_features) lds = cnn_lds + rnn_lds return lds, cnn_lds, rnn_lds class VATLossSign(nn.Module): def __init__(self, do_test_entropy, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(VATLossSign, self).__init__() self.do_test_entropy = do_test_entropy self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, seq_len, mask): with torch.no_grad(): pred = model.vat_forward(x, seq_len) pred = pred * mask pred = F.softmax(pred, dim=2).view(-1, pred.size()[-1]) # prepare random unit tensor d = torch.rand(x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d = _l2_normalize(d) with _disable_tracking_bn_stats(model): # calc adversarial direction for _ in range(self.ip): d.requires_grad_() pred_hat = model.vat_forward(x + self.xi * d, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + self.xi * d) #adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) adv_distance = _kl_div(pred_hat, pred, mask) adv_distance.backward() d = _l2_normalize(d.grad) model.zero_grad() # calc LDS d = torch.sign(d) r_adv = d * self.eps pred_hat = model.vat_forward(x + r_adv, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + r_adv) #lds = _kl_div(F.log_softmax(pred_hat, dim=1), pred) lds = _kl_div(pred_hat, pred, mask) if self.do_test_entropy: lds += _entropy(pred_hat, mask) return lds class VATLossSignOld(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(VATLossSign, self).__init__() self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, seq_len, mask): with torch.no_grad(): pred = model.vat_forward(x, seq_len) pred = pred * mask pred = F.softmax(pred, dim=2).view(-1, pred.size()[-1]) # prepare random unit tensor d = torch.rand(x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) d = _l2_normalize(d) with _disable_tracking_bn_stats(model): # calc adversarial direction for _ in range(self.ip): d.requires_grad_() pred_hat = model.vat_forward(x + self.xi * d, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + self.xi * d) #adv_distance = _kl_div(F.log_softmax(pred_hat, dim=1), pred) adv_distance = _kl_div(pred_hat, pred) adv_distance.backward() d = _l2_normalize(d.grad) model.zero_grad() # calc LDS d = torch.sign(d) r_adv = d * self.eps pred_hat = model.vat_forward(x + r_adv, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + r_adv) #lds = _kl_div(F.log_softmax(pred_hat, dim=1), pred) lds = _kl_div(pred_hat, pred) return lds class RandomLoss(nn.Module): def __init__(self, xi=10.0, eps=1.0, ip=1): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(RandomLoss, self).__init__() self.xi = xi self.eps = eps self.ip = ip def forward(self, model, x, seq_len, mask): with torch.no_grad(): pred = model.vat_forward(x, seq_len) pred = pred * mask pred = F.softmax(pred, dim=2).view(-1, pred.size()[-1]) # prepare random unit tensor d = torch.rand(x.shape).to( torch.device('cuda' if torch.cuda.is_available() else 'cpu')) # calc LDS d = torch.sign(d) r_adv = d * self.eps pred_hat = model.vat_forward(x + r_adv, seq_len) pred_hat = pred_hat * mask pred_hat = F.log_softmax(pred_hat, dim=2).view(-1, pred_hat.size()[-1]) #pred_hat = model(x + r_adv) #lds = _kl_div(F.log_softmax(pred_hat, dim=1), pred) lds = _kl_div(pred_hat, pred) return lds class PseudoLabel(nn.Module): def __init__(self, confidence_thresh): """VAT loss :param xi: hyperparameter of VAT (default: 10.0) :param eps: hyperparameter of VAT (default: 1.0) :param ip: iteration times of computing adv noise (default: 1) """ super(PseudoLabel, self).__init__() self.confidence_thresh = confidence_thresh def forward(self, model, x, seq_len, mask): pred = model.vat_forward(x, seq_len) pred = pred * mask pred = F.softmax(pred, dim=2).view(-1, pred.size()[-1]) np_preds = pred.cpu().data.numpy() indices, classes = np.where(np_preds > self.confidence_thresh) if len(indices) > 0: indices = Variable(torch.from_numpy(indices).cuda()) labels = Variable(torch.from_numpy(classes).cuda()) strong_preds = pred[indices] nll_loss = torch.nn.NLLLoss() loss = nll_loss(strong_preds, labels) return loss.cpu() else: return 0

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a3719461c944d994c377c4499b9b5bafd7b358e2

26

py

Python

Python/Subtitle_Downloader/__init__.py

CharvyJain/Rotten-Scripts

c9b8f7dde378620e4a82eae7aacec53f1eeea3c5

[ "MIT" ]

7

2020-07-18T15:29:20.000Z

2021-03-23T15:09:51.000Z

Python/Subtitle_Downloader/__init__.py

SKAUL05/Rotten-Scripts

c44e69754bbecb8a547fe2cc3a29be5acf97c46a

[ "MIT" ]

3

2022-01-15T07:33:28.000Z

2022-03-24T04:23:03.000Z

Python/Subtitle_Downloader/__init__.py

SKAUL05/Rotten-Scripts

c44e69754bbecb8a547fe2cc3a29be5acf97c46a

[ "MIT" ]

1

2021-01-28T07:58:26.000Z

from .subtitle import main

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100

py

Python

blendSupports/Meshs/__init__.py

sbaker-dev/blendSupports

42f9913f409c9e0d6bc39bde11ba6431b1a2ff30

[ "MIT" ]

null

blendSupports/Meshs/__init__.py

sbaker-dev/blendSupports

42f9913f409c9e0d6bc39bde11ba6431b1a2ff30

[ "MIT" ]

null

blendSupports/Meshs/__init__.py

sbaker-dev/blendSupports

42f9913f409c9e0d6bc39bde11ba6431b1a2ff30

[ "MIT" ]

null

from .mesh_ref import make_mesh from .text import make_text from .graph_axis import make_graph_axis

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19

py

Python

multimodal/clip/__init__.py

sithu31296/multimodal

78f57956cc84273579eb9e2e2be2a58fa1f38814

[ "MIT" ]

385

2020-10-26T13:12:11.000Z

2021-10-07T15:14:48.000Z

multimodal/clip/__init__.py

sithu31296/multimodal

78f57956cc84273579eb9e2e2be2a58fa1f38814

[ "MIT" ]

24

2020-10-29T13:16:31.000Z

2021-08-31T06:47:33.000Z

multimodal/clip/__init__.py

sithu31296/multimodal

78f57956cc84273579eb9e2e2be2a58fa1f38814

[ "MIT" ]

45

2020-10-29T15:25:19.000Z

2021-09-05T21:50:57.000Z

from .clip import *

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6e0bea132e4b61f3ac97b8c32a145b7faf76df4f

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py

Python

tests/test_carveme.py

acbweiss/carveme

08f44e3e180730a881fbd73f8af03fa8e1f5895a

[ "Apache-2.0" ]

84

2018-01-13T15:38:26.000Z

2022-02-12T14:31:05.000Z

tests/test_carveme.py

acbweiss/carveme

08f44e3e180730a881fbd73f8af03fa8e1f5895a

[ "Apache-2.0" ]

133

2017-09-19T14:58:23.000Z

2022-03-16T12:04:15.000Z

tests/test_carveme.py

acbweiss/carveme

08f44e3e180730a881fbd73f8af03fa8e1f5895a

[ "Apache-2.0" ]

48

2017-09-19T16:00:47.000Z

2022-03-24T13:47:54.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for `carveme` package.""" import unittest from carveme import carveme class TestCarveme(unittest.TestCase): """Tests for `carveme` package.""" pass

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0.111888

0.20979

0.307692

0

0.005587

0.175115

217

14

39

15.5

0.793296

0.460829

0

1

0

true

0.25

0.5

0

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0

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null

0

1

0

null

0

1

0

6

6e3ddb397d1bf64d7c08134c0c6c33c64b211aee

1,051

py

Python

bukber/models.py

ppabcd/django-bukber

8a5d272e988a63082977deb5ba026876d4c70ee4

[ "BSD-3-Clause" ]

null

bukber/models.py

ppabcd/django-bukber

8a5d272e988a63082977deb5ba026876d4c70ee4

[ "BSD-3-Clause" ]

null

bukber/models.py

ppabcd/django-bukber

8a5d272e988a63082977deb5ba026876d4c70ee4

[ "BSD-3-Clause" ]

null

from django.db import models from django.utils import timezone # Create your models here. class Kelas(models.Model): nama = models.CharField(max_length=200) created_at = models.DateTimeField(editable=False) updated_at = models.DateTimeField() def save(self): if self.id: self.updated_at = timezone.now() else: self.updated_at = timezone.now() self.created_at = timezone.now() super().save() def __str__(self): return self.nama class Peserta(models.Model): nama = models.CharField(max_length=200) kelas = models.ForeignKey('Kelas', on_delete=models.CASCADE) nominal = models.IntegerField() created_at = models.DateTimeField(editable=False) updated_at = models.DateTimeField() def save(self): if self.id: self.updated_at = timezone.now() else: self.updated_at = timezone.now() self.created_at = timezone.now() super().save() def __str__(self): return self.nama

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0.633682

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5.16

0.328

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0.130233

0.741085

0.610853

0

0.007702

0.258801

1,051

39

65

26.948718

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0.133333

false

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0

1

0

1

0

null

0

1

0

6

280e5c9629fe9185c4c9dae7b7d2676ce16a85a7

38

py

Python

moda/models/stl/__init__.py

Patte1808/moda

312c9594754ae0f6d17cbfafaa2c4c790c58efe5

[ "MIT" ]

null

moda/models/stl/__init__.py

Patte1808/moda

312c9594754ae0f6d17cbfafaa2c4c790c58efe5

[ "MIT" ]

null

moda/models/stl/__init__.py

Patte1808/moda

312c9594754ae0f6d17cbfafaa2c4c790c58efe5

[ "MIT" ]

null

from moda.models.stl import stl_model

19

37

0.842105

7

38

4.428571

0.857143

0

0.105263

38

1

38

0.911765

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2832a65a5061020c837ca4e4d03271dc5d234dc2

151

py

Python

continual_learning/backbone_networks/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

continual_learning/backbone_networks/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

continual_learning/backbone_networks/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

from .resnet.resnet import * from .resnet.torch_resnet import * from .alexnet import AlexNet from .lenet import LeNet, LeNet_300_100 from .vgg import *

30.2

39

0.794702

23

151

5.086957

0.391304

0.17094

0.273504

0

0.045802

0.13245

151

5

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30.2

0.847328

0

1

0

true

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1

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1

0

null

0

1

0

1

0

1

0

6

28397f5d1262c53eaf574f2535bad6003f1068cc

21

py

Python

models/__init__.py

hhj1897/fan_training

5882f9edf2f1a07c80a6d1f3341a7cf1d348e217

[ "MIT" ]

1

2021-12-11T21:31:57.000Z

models/__init__.py

hhj1897/fan_training

5882f9edf2f1a07c80a6d1f3341a7cf1d348e217

[ "MIT" ]

null

models/__init__.py

hhj1897/fan_training

5882f9edf2f1a07c80a6d1f3341a7cf1d348e217

[ "MIT" ]

1

2021-12-11T21:31:49.000Z

from .fan import FAN

10.5

20

0.761905

4

21

4

0.75

0

0.190476

21

1

21

0.941176

0

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true

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1

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null

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null

0

1

0

1

0

1

0

6

284092cc0cdfbe3cc230105f4b35fef6cb577976

5,783

py

Python

suspect/processing/phase.py

hjiang1/suspect

f8b320b16bbd73a95d58eea1660921d6cad16f36

[ "MIT" ]

16

2016-08-31T21:05:06.000Z

2022-02-06T12:48:33.000Z

suspect/processing/phase.py

hjiang1/suspect

f8b320b16bbd73a95d58eea1660921d6cad16f36

[ "MIT" ]

141

2016-07-28T21:34:17.000Z

2022-03-30T09:00:36.000Z

suspect/processing/phase.py

hjiang1/suspect

f8b320b16bbd73a95d58eea1660921d6cad16f36

[ "MIT" ]

21

2016-08-04T14:54:19.000Z

2022-03-29T16:04:08.000Z

import lmfit import numpy as np def mag_real(data, *args, range_hz=None, range_ppm=None): """ Estimates the zero and first order phase parameters which minimise the difference between the real part of the spectrum and the magnitude. Note that these are the phase correction terms, designed to be used directly in the adjust_phase() function without negation. Parameters ---------- data: MRSBase The data to be phased range_hz: tuple (low, high) The frequency range in Hertz over which to compare the spectra range_ppm: tuple (low, high) The frequency range in PPM over which to compare the spectra. range_hz and range_ppm cannot both be defined. Returns ------- phi0 : float The estimated zero order phase correction phi1 : float The estimated first order phase correction """ if range_hz is not None and range_ppm is not None: raise KeyError("Cannot specify both range_hz and range_ppm") if range_hz is not None: frequency_slice = data.slice_hz(*range_hz) elif range_hz is not None: frequency_slice = data.slice_ppm(*range_ppm) else: frequency_slice = slice(0, data.np) def single_spectrum_version(spectrum): def residual(pars): par_vals = pars.valuesdict() phased_data = spectrum.adjust_phase(par_vals['phi0'], par_vals['phi1']) diff = np.real(phased_data) - np.abs(spectrum) return diff[frequency_slice] params = lmfit.Parameters() params.add('phi0', value=0, min=-np.pi, max=np.pi) params.add('phi1', value=0.0, min=-0.01, max=0.25) result = lmfit.minimize(residual, params) return result.params['phi0'].value, result.params['phi1'].value return np.apply_along_axis(single_spectrum_version, axis=-1, arr=data.spectrum()) def ernst(data): """ Estimates the zero and first order phase using the ACME algorithm, which minimises the integral of the imaginary part of the spectrum. Note that these are the phase correction terms, designed to be used directly in the adjust_phase() function without negation. Parameters ---------- data: MRSBase The data to be phased range_hz: tuple (low, high) The frequency range in Hertz over which to compare the spectra range_ppm: tuple (low, high) The frequency range in PPM over which to compare the spectra. range_hz and range_ppm cannot both be defined. Returns ------- phi0 : float The estimated zero order phase correction phi1 : float The estimated first order phase correction """ def residual(pars): par_vals = pars.valuesdict() phased_data = data.adjust_phase(par_vals['phi0'], par_vals['phi1']) return np.sum(phased_data.spectrum().imag) params = lmfit.Parameters() params.add('phi0', value=0, min=-np.pi, max=np.pi) params.add('phi1', value=0.0, min=-0.005, max=0.1) result = lmfit.minimize(residual, params, method='simplex') return result.params['phi0'].value, result.params['phi1'].value def acme(data, *args, range_hz=None, range_ppm=None, gamma=100): """ Estimates the zero and first order phase using the ACME algorithm, which minimises the entropy of the real part of the spectrum. Note that these are the phase correction terms, designed to be used directly in the adjust_phase() function without negation. Parameters ---------- data : MRSBase The data to be phased range_hz : tuple (low, high) The frequency range in Hertz over which to compare the spectra range_ppm : tuple (low, high) The frequency range in PPM over which to compare the spectra. range_hz and range_ppm cannot both be defined. gamma : float Weighting factor for penalty function. Returns ------- phi0 : float The estimated zero order phase correction phi1 : float The estimated first order phase correction """ if range_hz is not None and range_ppm is not None: raise KeyError("Cannot specify both range_hz and range_ppm") if range_hz is not None: frequency_slice = data.slice_hz(*range_hz) elif range_hz is not None: frequency_slice = data.slice_ppm(*range_ppm) else: frequency_slice = slice(0, data.np) def single_spectrum_version(spectrum): def residual(pars): par_vals = pars.valuesdict() phased_data = spectrum.adjust_phase(par_vals['phi0'], par_vals['phi1']) r = phased_data.real[frequency_slice] r = r / np.sum(r) derivative = np.abs((r[1:] - r[:-1])) derivative_norm = derivative / np.sum(derivative) # make sure the entropy doesn't blow up by removing 0 values derivative_norm[derivative_norm == 0] = 1 entropy = -np.sum(derivative_norm * np.log(derivative_norm)) # penalty function p = np.sum(r[r < 0] ** 2) return entropy + gamma * p params = lmfit.Parameters() params.add('phi0', value=0.0, min=-np.pi, max=np.pi) params.add('phi1', value=0.001, min=-0.005, max=0.25) result = lmfit.minimize(residual, params, method='simplex') return result.params['phi0'].value, result.params['phi1'].value return np.apply_along_axis(single_spectrum_version, -1, data.spectrum())

35.478528

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4.564369

0.174252

0.035897

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0.025641

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0.758405

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0

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0.289296

5,783

162

79

35.697531

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false

0

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0

0.268657

0

null

0

1

0

null

0

6

2843ba3aa64e127ac4da4877930f346f9d4cad44

7,761

py

Python

tests/test_response_handler.py

Alex-Weatherhead/riot_api

2d589f57cd46e0f7c54de29245078c730acd710f

[ "MIT" ]

null

tests/test_response_handler.py

Alex-Weatherhead/riot_api

2d589f57cd46e0f7c54de29245078c730acd710f

[ "MIT" ]

null

tests/test_response_handler.py

Alex-Weatherhead/riot_api

2d589f57cd46e0f7c54de29245078c730acd710f

[ "MIT" ]

null

import unittest from riot_api.api import _response_handler class ResponseMock: def __init__ (self, status_code, headers=None): self.status_code = status_code self.headers = headers class TestResponseHandler (unittest.TestCase): def test_handle_yields_successful_equals_true_when_status_code_is_200 (self): STATUS_CODE = 200 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertTrue(result["successful"]) def test_handle_yields_response_object_when_status_code_is_200 (self): STATUS_CODE = 200 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertEqual(result["response"], RESPONSE) def test_handle_yields_successful_equals_false_when_status_code_is_400 (self): STATUS_CODE = 400 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_false_when_status_code_is_400 (self): STATUS_CODE = 400 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["retry"]) def test_handle_yields_successful_equals_false_when_status_code_is_401 (self): STATUS_CODE = 401 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_false_when_status_code_is_401 (self): STATUS_CODE = 401 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["retry"]) def test_handle_yields_successful_equals_false_when_status_code_is_403 (self): STATUS_CODE = 403 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_false_when_status_code_is_403 (self): STATUS_CODE = 403 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["retry"]) def test_handle_yields_successful_equals_false_when_status_code_is_404 (self): STATUS_CODE = 404 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_true_when_status_code_is_404 (self): STATUS_CODE = 404 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertTrue(result["retry"]) def test_handle_yields_default_delay_when_status_code_is_404 (self): STATUS_CODE = 404 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertEqual(result["delay"], _response_handler._DEFAULT_DELAY_FOR_STATUS_CODE_404) def test_handle_yields_successful_equals_false_when_status_code_is_415 (self): STATUS_CODE = 415 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_false_when_status_code_is_415 (self): STATUS_CODE = 415 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["retry"]) def test_handle_yields_successful_equals_false_when_status_code_is_429 (self): STATUS_CODE = 429 HEADERS = { "Retry-After": 0 } RESPONSE = ResponseMock(STATUS_CODE, headers=HEADERS) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_true_when_status_code_is_429 (self): STATUS_CODE = 429 HEADERS = { "Retry-After": 0 } RESPONSE = ResponseMock(STATUS_CODE, headers=HEADERS) result = _response_handler.handle(RESPONSE) self.assertTrue(result["retry"]) def test_handle_yields_retry_after_header_as_delay_when_status_code_is_429 (self): STATUS_CODE = 429 HEADERS = { "Retry-After": 10 } RESPONSE = ResponseMock(STATUS_CODE, headers=HEADERS) result = _response_handler.handle(RESPONSE) self.assertEqual(result["delay"], HEADERS["Retry-After"]) def test_handle_yields_successful_equals_false_when_status_code_is_500 (self): STATUS_CODE = 500 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_true_when_status_code_is_500 (self): STATUS_CODE = 500 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertTrue(result["retry"]) def test_handle_yields_default_delay_when_status_code_is_500 (self): STATUS_CODE = 500 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertEqual(result["delay"], _response_handler._DEFAULT_DELAY_FOR_STATUS_CODE_500) def test_handle_yields_successful_equals_false_when_status_code_is_502 (self): STATUS_CODE = 502 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_true_when_status_code_is_502 (self): STATUS_CODE = 502 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertTrue(result["retry"]) def test_handle_yields_default_delay_when_status_code_is_502 (self): STATUS_CODE = 502 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertEqual(result["delay"], _response_handler._DEFAULT_DELAY_FOR_STATUS_CODE_502) def test_handle_yields_successful_equals_false_when_status_code_is_503 (self): STATUS_CODE = 503 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_true_when_status_code_is_503 (self): STATUS_CODE = 503 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertTrue(result["retry"]) def test_handle_yields_default_delay_when_status_code_is_503 (self): STATUS_CODE = 503 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertEqual(result["delay"], _response_handler._DEFAULT_DELAY_FOR_STATUS_CODE_503) def test_handle_yields_successful_equals_false_when_status_code_is_504 (self): STATUS_CODE = 504 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertFalse(result["successful"]) def test_handle_yields_retry_equals_true_when_status_code_is_504 (self): STATUS_CODE = 504 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertTrue(result["retry"]) def test_handle_yields_default_delay_when_status_code_is_504 (self): STATUS_CODE = 504 RESPONSE = ResponseMock(STATUS_CODE) result = _response_handler.handle(RESPONSE) self.assertEqual(result["delay"], _response_handler._DEFAULT_DELAY_FOR_STATUS_CODE_504)

31.677551

95

0.718335

900

7,761

5.707778

0.057778

0.179093

0.08176

0.103562

0.949971

0.948024

0.934787

0.930504

0

0.030461

0.208994

7,761

245

96

31.677551

0.80632

0

0.698718

0

0.031178

0

0.179487

1

0.185897

false

0

0.012821

0

0.211538

0

null

0

1

0

null

0

6

285470d5580301217271de4b90c77c082d47b943

201

py

Python

papyruslib/__init__.py

hrmorley34/papyrusplusplus

bdda49f586b9780aef41002bc519b273187fb6f7

[ "MIT" ]

null

papyruslib/__init__.py

hrmorley34/papyrusplusplus

bdda49f586b9780aef41002bc519b273187fb6f7

[ "MIT" ]

null

papyruslib/__init__.py

hrmorley34/papyrusplusplus

bdda49f586b9780aef41002bc519b273187fb6f7

[ "MIT" ]

null

from .bases import Definition, PlayerMarker, Spreadsheet, Remote, Webhook from . import helpers __all__ = ["Definition", "PlayerMarker", "Spreadsheet", "Remote", "Webhook", "Definition", "helpers"]

28.714286

101

0.736318

19

201

7.578947

0.526316

0.305556

0.458333

0.541667

0.638889

0

0.119403

201

6

102

33.5

0.813559

0

0.313433

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

6

95556148fadeaee463dfe202a1320951679e9473

26

py

Python

src/dwriteshapepy/__init__.py

microsoft/DWriteShapePy

ced3341bf2c76a09135491660311c09d725a8b35

[ "MIT" ]

9

2021-03-26T08:20:24.000Z

2022-03-29T12:35:12.000Z

src/dwriteshapepy/__init__.py

paullinnerud/DWriteShapePy

f351de0de47818a7cff00ae9ba16d58086a76878

[ "MIT" ]

2

2021-07-14T13:39:59.000Z

2021-12-22T11:48:24.000Z

src/dwriteshapepy/__init__.py

paullinnerud/DWriteShapePy

f351de0de47818a7cff00ae9ba16d58086a76878

[ "MIT" ]

2

2021-03-30T06:00:08.000Z

2021-07-14T13:33:22.000Z

from .dwriteshape import *

26

0.807692

3

26

7

1

0

0.115385

26

1

26

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

95fe5b95a0c6e28964ab48ef953dc6c1ce110af9

4,021

py

Python

test/unit/test_should_investigate.py

nicolaslazo/chess-puzzle-maker

17ca484ab40f1e82d53b7ae93239b2abd37e4cdd

[ "MIT" ]

34

2019-08-28T16:49:45.000Z

2022-03-04T18:05:20.000Z

test/unit/test_should_investigate.py

nicolaslazo/chess-puzzle-maker

17ca484ab40f1e82d53b7ae93239b2abd37e4cdd

[ "MIT" ]

10

2019-11-27T14:15:25.000Z

2021-03-30T07:46:24.000Z

test/unit/test_should_investigate.py

nicolaslazo/chess-puzzle-maker

17ca484ab40f1e82d53b7ae93239b2abd37e4cdd

[ "MIT" ]

14

2019-12-06T12:41:28.000Z

2021-12-27T01:40:27.000Z

import unittest from chess import Board from chess.engine import Cp, Mate from puzzlemaker.puzzle_finder import should_investigate board = Board() class TestShouldInvestigate(unittest.TestCase): def test_investigating_moderate_score_changes(self): score_changes = [ [0, 200], [50, 200], [-50, 200], ] for a, b in score_changes: a = Cp(a) b = Cp(b) self.assertTrue(should_investigate(a, b, board)) def test_investigating_major_score_changes(self): score_changes = [ [0, 500], [100, 500], [100, -100], ] for a, b in score_changes: a = Cp(a) b = Cp(b) self.assertTrue(should_investigate(a, b, board)) def test_investigating_even_position_to_mate(self): a = Cp(0) b = Mate(5) self.assertTrue(should_investigate(a, b, board)) a = Cp(0) b = Mate(-5) self.assertTrue(should_investigate(a, b, board)) def test_investigating_minor_advantage_to_mate(self): a = Cp(100) b = Mate(5) self.assertTrue(should_investigate(a, b, board)) a = Cp(-100) b = Mate(-5) self.assertTrue(should_investigate(a, b, board)) def test_investigating_major_advantage_to_getting_mated(self): a = Cp(700) b = Mate(-5) self.assertTrue(should_investigate(a, b, board)) a = Cp(-700) b = Mate(5) self.assertTrue(should_investigate(a, b, board)) def test_investigating_major_advantage_to_major_disadvantage(self): a = Cp(700) b = Cp(-700) self.assertTrue(should_investigate(a, b, board)) a = Cp(-700) b = Cp(700) self.assertTrue(should_investigate(a, b, board)) def test_investigating_major_advantage_to_even_position(self): a = Cp(700) b = Cp(0) self.assertTrue(should_investigate(a, b, board)) a = Cp(-700) b = Cp(0) self.assertTrue(should_investigate(a, b, board)) def test_investigating_mate_threat_to_major_disadvantage(self): a = Mate(5) b = Cp(-700) self.assertTrue(should_investigate(a, b, board)) a = Mate(-5) b = Cp(700) self.assertTrue(should_investigate(a, b, board)) def test_investigating_mate_threat_to_even_position(self): a = Mate(5) b = Cp(0) self.assertTrue(should_investigate(a, b, board)) a = Mate(-5) b = Cp(0) self.assertTrue(should_investigate(a, b, board)) def test_investigating_mate_threat_to_getting_mated(self): a = Mate(1) b = Mate(-1) self.assertTrue(should_investigate(a, b, board)) a = Mate(-1) b = Mate(1) self.assertTrue(should_investigate(a, b, board)) def test_investigating_mate_threat_to_checkmate(self): a = Mate(1) b = Mate(0) self.assertFalse(should_investigate(a, b, board)) def test_not_investigating_insignificant_score_changes(self): score_changes = [ [0, 0], [-50, 50], [50, -50], [-70, -70], [70, 70], ] for a, b in score_changes: a = Cp(a) b = Cp(b) self.assertFalse(should_investigate(a, b, board)) def test_not_investigating_major_advantage_to_mate_threat(self): a = Cp(900) b = Mate(5) self.assertFalse(should_investigate(a, b, board)) a = Cp(-900) b = Mate(-5) self.assertFalse(should_investigate(a, b, board)) def test_not_investigating_even_position(self): board = Board("4k3/8/3n4/3N4/8/8/4K3/8 w - - 0 1") a = Cp(0) b = Cp(0) self.assertFalse(should_investigate(a, b, board)) a = Cp(9) b = Cp(9) self.assertFalse(should_investigate(a, b, board)) if __name__ == '__main__': unittest.main()

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py

Python

tests/test_package_import.py

rolando/parsel-cli

c211fe5ddfc53ac965acaeffde00082ebc57081d

[ "MIT" ]

5

2016-07-26T18:26:29.000Z

2017-04-28T14:47:05.000Z

tests/test_package_import.py

rmax/parsel-cli

c211fe5ddfc53ac965acaeffde00082ebc57081d

[ "MIT" ]

2

2016-07-28T17:27:27.000Z

2016-08-15T20:35:21.000Z

tests/test_package_import.py

rmax/parsel-cli

c211fe5ddfc53ac965acaeffde00082ebc57081d

[ "MIT" ]

3

2016-07-26T19:50:45.000Z

2017-03-17T17:36:44.000Z

import parsel_cli def test_package_metadata(): assert parsel_cli.__author__ assert parsel_cli.__email__ assert parsel_cli.__version__

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254ee93de9badfce23215ecadcfc63e4dc555223

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py

Python

tests/test_app_routers_files_GET.py

BoostryJP/ibet-Prime

924e7f8da4f8feea0a572e8b5532e09bcdf2dc99

[ "Apache-2.0" ]

2

2021-08-19T12:35:25.000Z

2022-02-16T04:13:38.000Z

tests/test_app_routers_files_GET.py

BoostryJP/ibet-Prime

924e7f8da4f8feea0a572e8b5532e09bcdf2dc99

[ "Apache-2.0" ]

46

2021-09-02T03:22:05.000Z

2022-03-31T09:20:00.000Z

tests/test_app_routers_files_GET.py

BoostryJP/ibet-Prime

924e7f8da4f8feea0a572e8b5532e09bcdf2dc99

[ "Apache-2.0" ]

1

2021-11-17T23:18:27.000Z

""" Copyright BOOSTRY Co., Ltd. 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. SPDX-License-Identifier: Apache-2.0 """ from datetime import datetime from app.model.db import UploadFile class TestAppRoutersFilesGET: # target API endpoint base_url = "/files" issuer_address = "0x1234567890123456789012345678900000000001" token_address = "0x1234567890123456789012345678900000000011" file_content = """test data 12345 67890 あいうえお　かきくけこ 😃😃😃😃 abc def""" ########################################################################### # Normal Case ########################################################################### # <Normal_1> # 0 record def test_normal_1(self, client, db): # request target api resp = client.get(self.base_url) # assertion assert resp.status_code == 200 assert resp.json() == { "result_set": { "count": 0, "offset": None, "limit": None, "total": 0 }, "files": [] } # <Normal_2> # 1 record def test_normal_2(self, client, db): file_content_1_bin = self.file_content.encode() # prepare data _upload_file = UploadFile() _upload_file.file_id = "file_id_1" _upload_file.issuer_address = self.issuer_address _upload_file.relation = self.token_address _upload_file.file_name = "file_name_1" _upload_file.content = file_content_1_bin _upload_file.content_size = len(file_content_1_bin) _upload_file.description = "description_1" _upload_file.created = datetime.strptime("2022/01/01 15:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) # request target api resp = client.get( self.base_url, ) # assertion assert resp.status_code == 200 assert resp.json() == { "result_set": { "count": 1, "offset": None, "limit": None, "total": 1 }, "files": [ { "file_id": "file_id_1", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_1", "content_size": len(file_content_1_bin), "description": "description_1", "created": "2022-01-02T00:20:30.000001+09:00", }, ] } # <Normal_3> # 2 record def test_normal_3(self, client, db): file_content_bin = self.file_content.encode() # prepare data _upload_file = UploadFile() _upload_file.file_id = "file_id_1" _upload_file.issuer_address = self.issuer_address _upload_file.relation = self.token_address _upload_file.file_name = "file_name_1" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_1" _upload_file.created = datetime.strptime("2022/01/01 15:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) _upload_file = UploadFile() _upload_file.file_id = "file_id_2" _upload_file.issuer_address = "0x1234567890123456789012345678900000000001" _upload_file.relation = self.token_address _upload_file.file_name = "file_name_2" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_2" _upload_file.created = datetime.strptime("2022/01/02 00:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) # request target api resp = client.get( self.base_url, ) # assertion assert resp.status_code == 200 assert resp.json() == { "result_set": { "count": 2, "offset": None, "limit": None, "total": 2 }, "files": [ { "file_id": "file_id_2", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_2", "content_size": len(file_content_bin), "description": "description_2", "created": "2022-01-02T09:20:30.000001+09:00", }, { "file_id": "file_id_1", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_1", "content_size": len(file_content_bin), "description": "description_1", "created": "2022-01-02T00:20:30.000001+09:00", }, ] } # <Normal_4_1> # Search Filter # issuer_address def test_normal_4_1(self, client, db): file_content_bin = self.file_content.encode() # prepare data _upload_file = UploadFile() _upload_file.file_id = "file_id_1" _upload_file.issuer_address = self.issuer_address _upload_file.relation = self.token_address _upload_file.file_name = "file_name_1" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_1" _upload_file.created = datetime.strptime("2022/01/01 15:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) _upload_file = UploadFile() _upload_file.file_id = "file_id_2" _upload_file.issuer_address = "0x1234567890123456789012345678900000000002" # not target _upload_file.relation = self.token_address _upload_file.file_name = "file_name_2" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_2" _upload_file.created = datetime.strptime("2022/01/02 00:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) # request target api resp = client.get( self.base_url, headers={ "issuer-address": self.issuer_address, }, ) # assertion assert resp.status_code == 200 assert resp.json() == { "result_set": { "count": 1, "offset": None, "limit": None, "total": 2 }, "files": [ { "file_id": "file_id_1", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_1", "content_size": len(file_content_bin), "description": "description_1", "created": "2022-01-02T00:20:30.000001+09:00", }, ] } # <Normal_4_2> # Search Filter # relation def test_normal_4_2(self, client, db): file_content_bin = self.file_content.encode() # prepare data _upload_file = UploadFile() _upload_file.file_id = "file_id_1" _upload_file.issuer_address = self.issuer_address _upload_file.relation = self.token_address _upload_file.file_name = "file_name_1" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_1" _upload_file.created = datetime.strptime("2022/01/01 15:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) _upload_file = UploadFile() _upload_file.file_id = "file_id_2" _upload_file.issuer_address = self.issuer_address _upload_file.relation = "uuid_test_foo_bar" # not target _upload_file.file_name = "file_name_2" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_2" _upload_file.created = datetime.strptime("2022/01/02 00:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) # request target api resp = client.get( self.base_url, params={ "relation": self.token_address, }, ) # assertion assert resp.status_code == 200 assert resp.json() == { "result_set": { "count": 1, "offset": None, "limit": None, "total": 2 }, "files": [ { "file_id": "file_id_1", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_1", "content_size": len(file_content_bin), "description": "description_1", "created": "2022-01-02T00:20:30.000001+09:00", }, ] } # <Normal_4_3> # Search Filter # file_name def test_normal_4_3(self, client, db): file_content_bin = self.file_content.encode() # prepare data _upload_file = UploadFile() _upload_file.file_id = "file_id_1" _upload_file.issuer_address = self.issuer_address _upload_file.relation = self.token_address _upload_file.file_name = "file_name_1" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_1" _upload_file.created = datetime.strptime("2022/01/01 15:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) _upload_file = UploadFile() _upload_file.file_id = "file_id_2" _upload_file.issuer_address = self.issuer_address _upload_file.relation = self.token_address _upload_file.file_name = "test_foo_bar_1" # not target _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_2" _upload_file.created = datetime.strptime("2022/01/02 00:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) # request target api resp = client.get( self.base_url, params={ "file_name": "name", }, ) # assertion assert resp.status_code == 200 assert resp.json() == { "result_set": { "count": 1, "offset": None, "limit": None, "total": 2 }, "files": [ { "file_id": "file_id_1", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_1", "content_size": len(file_content_bin), "description": "description_1", "created": "2022-01-02T00:20:30.000001+09:00", }, ] } # <Normal_5> # Pagination def test_normal_5(self, client, db): file_content_bin = self.file_content.encode() # prepare data _upload_file = UploadFile() _upload_file.file_id = "file_id_1" _upload_file.issuer_address = self.issuer_address _upload_file.relation = self.token_address _upload_file.file_name = "file_name_1" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_1" _upload_file.created = datetime.strptime("2022/01/01 15:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) _upload_file = UploadFile() _upload_file.file_id = "file_id_2" _upload_file.issuer_address = "0x1234567890123456789012345678900000000001" _upload_file.relation = self.token_address _upload_file.file_name = "file_name_2" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_2" _upload_file.created = datetime.strptime("2022/01/02 00:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/02 db.add(_upload_file) _upload_file = UploadFile() _upload_file.file_id = "file_id_3" _upload_file.issuer_address = "0x1234567890123456789012345678900000000001" _upload_file.relation = self.token_address _upload_file.file_name = "file_name_3" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_3" _upload_file.created = datetime.strptime("2022/01/02 15:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/03 db.add(_upload_file) _upload_file = UploadFile() _upload_file.file_id = "file_id_4" _upload_file.issuer_address = "0x1234567890123456789012345678900000000001" _upload_file.relation = self.token_address _upload_file.file_name = "file_name_4" _upload_file.content = file_content_bin _upload_file.content_size = len(file_content_bin) _upload_file.description = "description_4" _upload_file.created = datetime.strptime("2022/01/03 00:20:30.000001", '%Y/%m/%d %H:%M:%S.%f') # JST 2022/01/03 db.add(_upload_file) # request target api resp = client.get( self.base_url, params={ "offset": 1, "limit": 2 }, ) # assertion assert resp.status_code == 200 assert resp.json() == { "result_set": { "count": 4, "offset": 1, "limit": 2, "total": 4 }, "files": [ { "file_id": "file_id_3", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_3", "content_size": len(file_content_bin), "description": "description_3", "created": "2022-01-03T00:20:30.000001+09:00", }, { "file_id": "file_id_2", "issuer_address": self.issuer_address, "relation": self.token_address, "file_name": "file_name_2", "content_size": len(file_content_bin), "description": "description_2", "created": "2022-01-02T09:20:30.000001+09:00", }, ] } ########################################################################### # Error Case ########################################################################### # <Error_1> # Parameter Error # Query def test_error_1(self, client, db): # request target API resp = client.get( self.base_url, params={ "offset": "test", "limit": "test" }, ) # assertion assert resp.status_code == 422 assert resp.json() == { "meta": { "code": 1, "title": "RequestValidationError" }, "detail": [ { "loc": ["query", "offset"], "msg": "value is not a valid integer", "type": "type_error.integer" }, { "loc": ["query", "limit"], "msg": "value is not a valid integer", "type": "type_error.integer" }, ] } # <Error_2> # Parameter Error # Header def test_error_2(self, client, db): # request target API resp = client.get( self.base_url, headers={ "issuer-address": "test", }, ) # assertion assert resp.status_code == 422 assert resp.json() == { "meta": { "code": 1, "title": "RequestValidationError" }, "detail": [ { "loc": ["header", "issuer-address"], "msg": "issuer-address is not a valid address", "type": "value_error" } ] }

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