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173b1c09a24ba29b73e806482460e8a77d27c133
| 4,051
|
py
|
Python
|
alipay/aop/api/domain/AlipayOverseasTaxNeworderStatusSyncModel.py
|
antopen/alipay-sdk-python-all
|
8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c
|
[
"Apache-2.0"
] | 213
|
2018-08-27T16:49:32.000Z
|
2021-12-29T04:34:12.000Z
|
alipay/aop/api/domain/AlipayOverseasTaxNeworderStatusSyncModel.py
|
antopen/alipay-sdk-python-all
|
8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c
|
[
"Apache-2.0"
] | 29
|
2018-09-29T06:43:00.000Z
|
2021-09-02T03:27:32.000Z
|
alipay/aop/api/domain/AlipayOverseasTaxNeworderStatusSyncModel.py
|
antopen/alipay-sdk-python-all
|
8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c
|
[
"Apache-2.0"
] | 59
|
2018-08-27T16:59:26.000Z
|
2022-03-25T10:08:15.000Z
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
| 30.923664
| 87
| 0.602567
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.constant.ParamConstants import *
class AlipayOverseasTaxNeworderStatusSyncModel(object):
def __init__(self):
self._doc_id = None
self._extend_param = None
self._status = None
self._status_change_time = None
self._status_msg = None
self._tax_order_no = None
self._tax_payment_no = None
@property
def doc_id(self):
return self._doc_id
@doc_id.setter
def doc_id(self, value):
self._doc_id = value
@property
def extend_param(self):
return self._extend_param
@extend_param.setter
def extend_param(self, value):
self._extend_param = value
@property
def status(self):
return self._status
@status.setter
def status(self, value):
self._status = value
@property
def status_change_time(self):
return self._status_change_time
@status_change_time.setter
def status_change_time(self, value):
self._status_change_time = value
@property
def status_msg(self):
return self._status_msg
@status_msg.setter
def status_msg(self, value):
self._status_msg = value
@property
def tax_order_no(self):
return self._tax_order_no
@tax_order_no.setter
def tax_order_no(self, value):
self._tax_order_no = value
@property
def tax_payment_no(self):
return self._tax_payment_no
@tax_payment_no.setter
def tax_payment_no(self, value):
self._tax_payment_no = value
def to_alipay_dict(self):
params = dict()
if self.doc_id:
if hasattr(self.doc_id, 'to_alipay_dict'):
params['doc_id'] = self.doc_id.to_alipay_dict()
else:
params['doc_id'] = self.doc_id
if self.extend_param:
if hasattr(self.extend_param, 'to_alipay_dict'):
params['extend_param'] = self.extend_param.to_alipay_dict()
else:
params['extend_param'] = self.extend_param
if self.status:
if hasattr(self.status, 'to_alipay_dict'):
params['status'] = self.status.to_alipay_dict()
else:
params['status'] = self.status
if self.status_change_time:
if hasattr(self.status_change_time, 'to_alipay_dict'):
params['status_change_time'] = self.status_change_time.to_alipay_dict()
else:
params['status_change_time'] = self.status_change_time
if self.status_msg:
if hasattr(self.status_msg, 'to_alipay_dict'):
params['status_msg'] = self.status_msg.to_alipay_dict()
else:
params['status_msg'] = self.status_msg
if self.tax_order_no:
if hasattr(self.tax_order_no, 'to_alipay_dict'):
params['tax_order_no'] = self.tax_order_no.to_alipay_dict()
else:
params['tax_order_no'] = self.tax_order_no
if self.tax_payment_no:
if hasattr(self.tax_payment_no, 'to_alipay_dict'):
params['tax_payment_no'] = self.tax_payment_no.to_alipay_dict()
else:
params['tax_payment_no'] = self.tax_payment_no
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = AlipayOverseasTaxNeworderStatusSyncModel()
if 'doc_id' in d:
o.doc_id = d['doc_id']
if 'extend_param' in d:
o.extend_param = d['extend_param']
if 'status' in d:
o.status = d['status']
if 'status_change_time' in d:
o.status_change_time = d['status_change_time']
if 'status_msg' in d:
o.status_msg = d['status_msg']
if 'tax_order_no' in d:
o.tax_order_no = d['tax_order_no']
if 'tax_payment_no' in d:
o.tax_payment_no = d['tax_payment_no']
return o
| 0
| 1,473
| 0
| 2,440
| 0
| 0
| 0
| 21
| 68
|
dd0d1e45a6b03586912ed2af58c3ca9521a86418
| 618
|
py
|
Python
|
migrations/versions/4b01613bfbed_.py
|
J4LP/J4OAuth
|
ca757958f1e7069f08e0dae3becd70b90507c871
|
[
"MIT"
] | 1
|
2015-12-15T03:17:15.000Z
|
2015-12-15T03:17:15.000Z
|
migrations/versions/4b01613bfbed_.py
|
J4LP/J4OAuth
|
ca757958f1e7069f08e0dae3becd70b90507c871
|
[
"MIT"
] | null | null | null |
migrations/versions/4b01613bfbed_.py
|
J4LP/J4OAuth
|
ca757958f1e7069f08e0dae3becd70b90507c871
|
[
"MIT"
] | 1
|
2015-12-15T03:17:19.000Z
|
2015-12-15T03:17:19.000Z
|
"""empty message
Revision ID: 4b01613bfbed
Revises: 406cccb640c3
Create Date: 2014-02-07 00:29:22.150808
"""
# revision identifiers, used by Alembic.
revision = '4b01613bfbed'
down_revision = '406cccb640c3'
| 22.888889
| 88
| 0.697411
|
"""empty message
Revision ID: 4b01613bfbed
Revises: 406cccb640c3
Create Date: 2014-02-07 00:29:22.150808
"""
# revision identifiers, used by Alembic.
revision = '4b01613bfbed'
down_revision = '406cccb640c3'
from alembic import op
import sqlalchemy as sa
def upgrade():
### commands auto generated by Alembic - please adjust! ###
op.add_column('client', sa.Column('homepage', sa.String(length=255), nullable=True))
### end Alembic commands ###
def downgrade():
### commands auto generated by Alembic - please adjust! ###
op.drop_column('client', 'homepage')
### end Alembic commands ###
| 0
| 0
| 0
| 0
| 0
| 312
| 0
| 3
| 91
|
963b388fd46ae6be2ff0ea7cc00541ff85694744
| 529
|
py
|
Python
|
django_auth2/tasks.py
|
Nick1994209/django-auth2
|
b8678f06ade985d2b5b0606e6e49bd9d2a49931a
|
[
"MIT"
] | null | null | null |
django_auth2/tasks.py
|
Nick1994209/django-auth2
|
b8678f06ade985d2b5b0606e6e49bd9d2a49931a
|
[
"MIT"
] | null | null | null |
django_auth2/tasks.py
|
Nick1994209/django-auth2
|
b8678f06ade985d2b5b0606e6e49bd9d2a49931a
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import # for python 2.7
try:
from celery.task import task
except ImportError:
task = func_add_delay
| 24.045455
| 78
| 0.720227
|
from __future__ import absolute_import # for python 2.7
from django.conf import settings
from django.core.mail import send_mail as django_send_mail
try:
from celery.task import task
except ImportError:
def func_add_delay(func):
def wrapper(*args, **kwargs):
return func(*args, **kwargs)
wrapper.delay = func
return wrapper
task = func_add_delay
@task
def send_mail(subject, message, to_emails):
django_send_mail(subject, message, settings.DEFAULT_FROM_EMAIL, to_emails)
| 0
| 107
| 0
| 0
| 0
| 136
| 0
| 48
| 94
|
c603a2f92a55764ce391c8c6d64d7603bd21bea4
| 2,938
|
py
|
Python
|
main/summary.py
|
tucan9389/MobileHumanPose
|
94183778c8384a2412729fec179e66ca2cd15b60
|
[
"MIT"
] | 137
|
2021-04-13T14:33:32.000Z
|
2022-03-24T22:28:15.000Z
|
main/summary.py
|
tucan9389/MobileHumanPose
|
94183778c8384a2412729fec179e66ca2cd15b60
|
[
"MIT"
] | 27
|
2021-05-16T08:52:03.000Z
|
2022-03-30T11:49:38.000Z
|
main/summary.py
|
tucan9389/MobileHumanPose
|
94183778c8384a2412729fec179e66ca2cd15b60
|
[
"MIT"
] | 13
|
2021-04-13T17:18:28.000Z
|
2022-03-22T12:49:03.000Z
|
import torch
import os
import os.path as osp
import torch.backends.cudnn as cudnn
from torchsummary import summary
from torch.nn.parallel.data_parallel import DataParallel
from config import cfg
from model import get_pose_net
from thop import profile
from thop import clever_format
from ptflops import get_model_complexity_info
# argument parsing
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
# joint set
joint_num = args.joint
joints_name = ('Head_top', 'Thorax', 'R_Shoulder', 'R_Elbow', 'R_Wrist', 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Hip', 'R_Knee', 'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Pelvis', 'Spine', 'Head', 'R_Hand', 'L_Hand', 'R_Toe', 'L_Toe')
flip_pairs = ( (2, 5), (3, 6), (4, 7), (8, 11), (9, 12), (10, 13), (17, 18), (19, 20) )
if joint_num == 18:
skeleton = ( (0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), (0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6) )
if joint_num == 21:
skeleton = ( (0, 16), (16, 1), (1, 15), (15, 14), (14, 8), (14, 11), (8, 9), (9, 10), (10, 19), (11, 12), (12, 13), (13, 20), (1, 2), (2, 3), (3, 4), (4, 17), (1, 5), (5, 6), (6, 7), (7, 18) )
# snapshot load
model_path = os.path.join(cfg.model_dir, 'snapshot_%d.pth.tar' % args.test_epoch)
assert osp.exists(model_path), 'Cannot find model at ' + model_path
model = get_pose_net(args.backbone, args.frontbone, False, joint_num)
model = DataParallel(model).cuda()
ckpt = torch.load(model_path)
model.load_state_dict(ckpt['network'])
single_model = model.module
summary(single_model, (3, 256, 256))
input = torch.randn(1, 3, 256, 256).cuda()
macs, params = profile(single_model, inputs=(input,))
macs, params = clever_format([macs, params], "%.3f")
flops, params1 = get_model_complexity_info(single_model, (3, 256, 256),as_strings=True, print_per_layer_stat=False)
print('{:<30} {:<8}'.format('Computational complexity: ', flops))
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
print('{:<30} {:<8}'.format('Number of parameters: ', params1))
| 41.971429
| 233
| 0.642954
|
import torch
import argparse
import os
import os.path as osp
import torch.backends.cudnn as cudnn
from torchsummary import summary
from torch.nn.parallel.data_parallel import DataParallel
from config import cfg
from model import get_pose_net
from thop import profile
from thop import clever_format
from ptflops import get_model_complexity_info
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str, dest='gpu_ids')
parser.add_argument('--epoch', type=int, dest='test_epoch')
parser.add_argument('--jointnum', type=int, dest='joint')
parser.add_argument('--backbone', type=str, dest='backbone')
args = parser.parse_args()
# test gpus
if not args.gpu_ids:
assert 0, print("Please set proper gpu ids")
if not args.joint:
assert print("please insert number of joint")
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = 0 if not gpus[0].isdigit() else int(gpus[0])
gpus[1] = len(mem_info()) if not gpus[1].isdigit() else int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
return args
# argument parsing
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
# joint set
joint_num = args.joint
joints_name = ('Head_top', 'Thorax', 'R_Shoulder', 'R_Elbow', 'R_Wrist', 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Hip', 'R_Knee', 'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Pelvis', 'Spine', 'Head', 'R_Hand', 'L_Hand', 'R_Toe', 'L_Toe')
flip_pairs = ( (2, 5), (3, 6), (4, 7), (8, 11), (9, 12), (10, 13), (17, 18), (19, 20) )
if joint_num == 18:
skeleton = ( (0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), (0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6) )
if joint_num == 21:
skeleton = ( (0, 16), (16, 1), (1, 15), (15, 14), (14, 8), (14, 11), (8, 9), (9, 10), (10, 19), (11, 12), (12, 13), (13, 20), (1, 2), (2, 3), (3, 4), (4, 17), (1, 5), (5, 6), (6, 7), (7, 18) )
# snapshot load
model_path = os.path.join(cfg.model_dir, 'snapshot_%d.pth.tar' % args.test_epoch)
assert osp.exists(model_path), 'Cannot find model at ' + model_path
model = get_pose_net(args.backbone, args.frontbone, False, joint_num)
model = DataParallel(model).cuda()
ckpt = torch.load(model_path)
model.load_state_dict(ckpt['network'])
single_model = model.module
summary(single_model, (3, 256, 256))
input = torch.randn(1, 3, 256, 256).cuda()
macs, params = profile(single_model, inputs=(input,))
macs, params = clever_format([macs, params], "%.3f")
flops, params1 = get_model_complexity_info(single_model, (3, 256, 256),as_strings=True, print_per_layer_stat=False)
print('{:<30} {:<8}'.format('Computational complexity: ', flops))
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
print('{:<30} {:<8}'.format('Number of parameters: ', params1))
| 0
| 0
| 0
| 0
| 0
| 792
| 0
| -6
| 45
|
eadd411cee7bade1921682585bda0bf351b1063a
| 3,302
|
py
|
Python
|
fastflix/models/encode.py
|
benedicteb/FastFlix
|
45208b7c74a21758cb528c949422effcd0c01f44
|
[
"MIT"
] | null | null | null |
fastflix/models/encode.py
|
benedicteb/FastFlix
|
45208b7c74a21758cb528c949422effcd0c01f44
|
[
"MIT"
] | null | null | null |
fastflix/models/encode.py
|
benedicteb/FastFlix
|
45208b7c74a21758cb528c949422effcd0c01f44
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
| 22.161074
| 56
| 0.633253
|
# -*- coding: utf-8 -*-
from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Union
from fastflix.models.base import BaseDataClass
@dataclass
class AudioTrack(BaseDataClass):
index: int
outdex: int
codec: str = ""
downmix: int = 0
title: str = ""
language: str = ""
conversion_bitrate: str = ""
conversion_codec: str = ""
@dataclass
class SubtitleTrack(BaseDataClass):
index: int
outdex: int
disposition: str = ""
burn_in: bool = False
language: str = ""
@dataclass
class AttachmentTrack(BaseDataClass):
outdex: int
index: Union[int, None] = None
attachment_type: str = "cover"
file_path: Union[Path, None] = None
filename: Union[str, None] = None
@dataclass
class EncoderSettings(BaseDataClass):
max_muxing_queue_size: str = "1024"
pix_fmt: str = "yuv420p10le"
extra: str = ""
@dataclass
class x265Settings(EncoderSettings):
name = "HEVC (x265)" # MUST match encoder main.name
preset: str = "medium"
intra_encoding: bool = False
profile: str = "default"
tune: str = "default"
hdr10: bool = False
hdr10_opt: bool = False
dhdr10_opt: bool = False
repeat_headers: bool = False
aq_mode: int = 2
hdr10plus_metadata: str = ""
crf: Union[int, None] = None
bitrate: Union[str, None] = None
x265_params: List[str] = field(default_factory=list)
bframes: int = 4
lossless: bool = False
b_adapt: int = 2
intra_refresh: bool = False
intra_smoothing: bool = True
frame_threads: int = 0
@dataclass
class x264Settings(EncoderSettings):
name = "AVC (x264)"
preset: str = "medium"
profile: str = "default"
tune: str = "default"
pix_fmt: str = "yuv420p"
crf: Union[int, None] = None
bitrate: Union[str, None] = None
@dataclass
class rav1eSettings(EncoderSettings):
name = "AV1 (rav1e)"
speed: str = "-1"
tile_columns: str = "-1"
tile_rows: str = "-1"
tiles: str = "0"
single_pass: bool = False
qp: Union[int, None] = None
bitrate: Union[str, None] = None
@dataclass
class SVTAV1Settings(EncoderSettings):
name = "AV1 (SVT AV1)"
tile_columns: str = "0"
tile_rows: str = "0"
tier: str = "main"
# scene_detection: str = "false"
single_pass: bool = False
speed: str = "7"
qp: Union[int, None] = None
bitrate: Union[str, None] = None
@dataclass
class VP9Settings(EncoderSettings):
name = "VP9"
profile: int = 2
quality: str = "good"
speed: str = "0"
row_mt: int = 0
single_pass: bool = False
crf: Union[int, None] = None
bitrate: Union[str, None] = None
@dataclass
class AOMAV1Settings(EncoderSettings):
name = "AV1 (AOM)"
tile_columns: str = "0"
tile_rows: str = "0"
usage: str = "good"
row_mt: str = "enabled"
cpu_used: str = "4"
crf: Union[int, None] = None
bitrate: Union[str, None] = None
@dataclass
class WebPSettings(EncoderSettings):
name = "WebP"
lossless: str = "0"
compression: str = "3"
preset: str = "none"
qscale: int = 15
@dataclass
class GIFSettings(EncoderSettings):
name = "GIF"
fps: int = 15
dither: str = "sierra2_4a"
@dataclass
class CopySettings(EncoderSettings):
name = "Copy"
| 0
| 2,821
| 0
| 0
| 0
| 0
| 0
| 56
| 388
|
8929dca934368386b0442cdca9cb8e9309b1777f
| 17,130
|
py
|
Python
|
scripts/featureGenerator.py
|
dcompgriff/cs839_Entity_Extractor
|
a117256061cc75850c1da1ce837a2992c15db0fb
|
[
"MIT"
] | null | null | null |
scripts/featureGenerator.py
|
dcompgriff/cs839_Entity_Extractor
|
a117256061cc75850c1da1ce837a2992c15db0fb
|
[
"MIT"
] | null | null | null |
scripts/featureGenerator.py
|
dcompgriff/cs839_Entity_Extractor
|
a117256061cc75850c1da1ce837a2992c15db0fb
|
[
"MIT"
] | null | null | null |
import numpy as np
import pandas as pd
import argparse
import glob
import os
import time
import re
from multiprocessing import Pool
'''
****************************************************************
GLOBAL VARIABLES
****************************************************************
'''
MAX_ENTITY_LENGTH = 20
MAX_ENTITY_WORD_LENGTH = 8
NUM_FEATURES = 20
globalVerbSet = set()
with open('../data/verbs.txt', 'r') as f:
for line in f:
globalVerbSet.add(line.strip())
instituteKeywords = re.compile(r'\b(Inc|Incorporation|Corp|Corporation|Institute|\
University|School|College|Department|Org|Organization|Times|Committee|Foundation|\
Party|Agency|Council|News)\b', re.I)
badKeywords = re.compile(r'\b(the|an|as|be|am|is|are|was|were|has|have|had|\
at|from|to|in|under|before|after|near|far|away|\
that|them|there|their|they|his|her|hers|him|it|you|your|yours|\
ceo|chairman|founder|head|director|\
email)\b', re.I)
#allow . - _ & : ' ` and " inside entity words. As these are there while marking up
badpunc = re.compile(r'\~|\!|\@|\#|\$|\%|\^|\*|\(|\)|\+|\=|\{|\}|\[|\]|\;|\<|\>|\,|\?|\/|\\')
endSentence = re.compile(r'(\d\s*\.\s*\D)|([a-z]\s*\.)')
domain = re.compile(r'\.\s*(com|org)\b')
globalCount = 0
missedTuples = []
'''
****************************************************************
PROGRAM FUNCTION SCRIPTS
****************************************************************
'''
'''
This function accepts a list of strings, where each
string represents a single line in a text file (which has
been pre-processed with at most 1 word on each
line.
@:param fileContents List with every line of the file contents.
@:return A pandas dataframe object that has
'''
return pd.DataFrame(tupleList, columns=tupleColumns)
def F0(tuple, fileContents):
try:
if fileContents[tuple.start - 1].strip().lower() == 'the' or fileContents[tuple.start - 2].strip().lower() == 'the':
return 1
else:
return 0
except IndexError:
return 0
def F1(tuple, fileContents):
return sum(1 for char in tuple.string if char.isupper())
'''
Feature list:
F0: "[The]" occurs 1 or two lines before string.
F1: Number of capitol Letters.
F2: Verb occurs 1 or two lines after the string.
F3: Total character length
F4: Total number of words
F5: Number of capitol letters before the string.
F5: Number of capitol letters in line after this string.
F6: "on" comes before
F7: "called" comes before # shouldn't the verb have taken care of it?
F8: "they" comes after
F9: .?! comes in the middle of and entry# should no longer be true ever
F10: Number of "."s
F11: "," is in the raw string "NOTE: This feature reliably improves precision!", #should no longer be True ever
F12: "," is in the first or last raw string position "NOTE: This feature reliably improves precision!", #should no lenger be True ever
F13: "." is in the first or last raw string position.
F14: "as", "a", "an" is in the raw string., # Invalid as discussed, to be removed
F15: The faction of the number of words where only the first character is capitalized to all words.
F16: The rawString has a Single capitalized word after it.
F17: Contains a keyword
F18: fraction of capital letters to wordCount
F19: Contains bad punctuation in raw string.
Each "tuple" object is a Pandas series with first entry tuple[0] the index, and
all following entries the entries of each row from the string tuples dataframe.
'''
'''
****************************************************************
PROGRAM RUNNING AND MANAGEMENT SCRIPTS
****************************************************************
'''
'''
For each file in the directory provided to the program, generate all of the
possible feature sets.
'''
if __name__ == '__main__':
#Parse command line arguments
parser = argparse.ArgumentParser(description="""Fake news feature generator. Generates features from files
whos' words have been split to multiple lines. It also handles files where entities have been pre-marked.""")
parser.add_argument('FileFolder', metavar='f', type=str)
parser.add_argument('Mode', metavar='m', type=str, help="""U is for update, and C is for create""")
parser.add_argument('UpdateListString', metavar='--l', type=str, default="", help="""Use a string 'F0 F12 F13' of features, or '' empty string if no features. """)
args = parser.parse_args()
main(args)
| 39.837209
| 167
| 0.588266
|
import numpy as np
import pandas as pd
import argparse
import glob
import os
import time
import re
from multiprocessing import Pool
'''
****************************************************************
GLOBAL VARIABLES
****************************************************************
'''
MAX_ENTITY_LENGTH = 20
MAX_ENTITY_WORD_LENGTH = 8
NUM_FEATURES = 20
globalVerbSet = set()
with open('../data/verbs.txt', 'r') as f:
for line in f:
globalVerbSet.add(line.strip())
instituteKeywords = re.compile(r'\b(Inc|Incorporation|Corp|Corporation|Institute|\
University|School|College|Department|Org|Organization|Times|Committee|Foundation|\
Party|Agency|Council|News)\b', re.I)
badKeywords = re.compile(r'\b(the|an|as|be|am|is|are|was|were|has|have|had|\
at|from|to|in|under|before|after|near|far|away|\
that|them|there|their|they|his|her|hers|him|it|you|your|yours|\
ceo|chairman|founder|head|director|\
email)\b', re.I)
#allow . - _ & : ' ` and " inside entity words. As these are there while marking up
badpunc = re.compile(r'\~|\!|\@|\#|\$|\%|\^|\*|\(|\)|\+|\=|\{|\}|\[|\]|\;|\<|\>|\,|\?|\/|\\')
endSentence = re.compile(r'(\d\s*\.\s*\D)|([a-z]\s*\.)')
domain = re.compile(r'\.\s*(com|org)\b')
globalCount = 0
missedTuples = []
'''
****************************************************************
PROGRAM FUNCTION SCRIPTS
****************************************************************
'''
'''
This function accepts a list of strings, where each
string represents a single line in a text file (which has
been pre-processed with at most 1 word on each
line.
@:param fileContents List with every line of the file contents.
@:return A pandas dataframe object that has
'''
def generateStringTuples(fileContents, fileName):
global globalCount
# Create initial pandas dataframe for data objects.
# rawString: as read form the file after removing entity markers
# string: after stripping punctuations from inside rawString
# wordCount: number of words in 'string' field
# start, end: index in file
# class: class label if marked entity
#tupleDF = pd.DataFrame(columns=['rawString', 'file', 'start', 'end', 'string', 'wordCount' 'class'])
# Create native python list for appending to, which is faster than pandas DF append or concat.
tupleList = []
reg = re.compile(r'[a-zA-Z0-9_\’\']+')# use to strip inner punctuations, except _ and \’
tupleColumns=['rawString', 'file', 'start', 'end', 'string', 'wordCount', 'label']
global missedTuples
for entityLength in range(1, MAX_ENTITY_LENGTH):
for i in range(len(fileContents)-entityLength):#reversed order here to prevent i+entityLength overflow
# For each possible entityLength, generate string from each index.
# Strip punctuations in order to get wordCount
# make tuples only from those whose word count is <= MAX_ENTITY_WORD_LENGTH, >=0 and unique
try:
tuple = ['', fileName, i, i+entityLength, '', 0, '-']
entityList = list(map(lambda item: str(item).strip(), fileContents[i:i+entityLength]))
# Set class to positive if '<[>' in first list word, and '<]>' in last word in list.
if '<[>' in entityList[0].strip() and '<]>' in entityList[-1].strip():
# If '<[>' and '<]>' appear in any other places internally in the string, then the
# string isn't a single entity, and is actually two entities that have been grouped
# together. Ex '<[>Project Veritas<]> shows how the <[>Clinton campaign<]>'.
# Count the number of times left and right tags occur in the string.
lCount = 0#sum(map(lambda item: 1 if '<[>' in item else 0, entityList))
rCount = 0#sum(map(lambda item: 1 if '<]>' in item else 0, entityList))
for cStr in entityList:
if '<[>' in cStr:
lCount += 1
if '<]>' in cStr:
rCount += 1
if lCount + rCount == 2:
tuple[-1] = '+'
globalCount += 1
else:
tuple[-1] = '-'
# Remove any entity tags from the string.
entityList = list(map(lambda item: item.replace('<[>', ''), entityList))
entityList = list(map(lambda item: item.replace('<]>', ''), entityList))
# Update the rest of the tuple information.
tuple[0] = ' '.join(entityList).strip()#rawString
#groups of only continuous alpha numeric characters. Not including '.' as a separate group.
words = re.findall(reg, tuple[0])
tuple[4] = ' '.join(words).strip()# string after stripping inner punctuations
tuple[5] = len(words)# wordCount
#################################
# PRE-PROCESSING RULES
#################################
#if ',' in tuple[0].strip().split()[0] or ',' in tuple[0].strip().split()[-1]:
# continue
# #if ('.' in tuple[0].strip().split()[0] or '.' in tuple[0].strip().split()[-1]) and len(entityList):
# # continue
# if ('-' in tuple[0].strip()):
# continue
#if ('(' in tuple[0].strip() or ')' in tuple[0].strip()):
# continue
# if 'as' in tuple[0].lower() or 'a' in tuple[0].lower() or 'an' in tuple[0].lower():
# continue
failed = False# use this to remove negative entries
#empty or too long remaining string
failed = failed or tuple[5]==0 or tuple[5]>MAX_ENTITY_WORD_LENGTH
#begins with a .
failed = failed or tuple[0][0]=='.'
#full tuple contains any unwanted punctuations
failed = failed or len(re.findall(badpunc, tuple[0]))>0
#Want atleast 2 english chars. Removes number only cases
failed = failed or len(re.findall(r'[a-zA-Z]', tuple[4]))<2
#Looks like end of a sentence, except when a domain name
failed = failed or len(re.findall(endSentence, tuple[0])) - len(re.findall(domain, tuple[0]))>0
#contains a bad keyword
failed = failed or len(re.findall(badKeywords, tuple[4]))
if failed:
if tuple[-1] == '+': missedTuples.append(tuple)
continue
tupleList.append(tuple)
except IndexError:
continue
return pd.DataFrame(tupleList, columns=tupleColumns)
def F0(tuple, fileContents):
try:
if fileContents[tuple.start - 1].strip().lower() == 'the' or fileContents[tuple.start - 2].strip().lower() == 'the':
return 1
else:
return 0
except IndexError:
return 0
def F1(tuple, fileContents):
return sum(1 for char in tuple.string if char.isupper())
def F2(tuple, fileContents):
try:
if fileContents[tuple.end].strip().lower() in globalVerbSet:
return 1
else:
return 0
except IndexError:
return 0
def F3(tuple, fileContents):
return len(tuple.string.strip())
def F4(tuple, fileContents):
return tuple.wordCount#len(tuple.string.strip().split())
def F5(tuple, fileContents):
try:
return sum(1 for char in fileContents[tuple.start - 1] if char.isupper())
except:
return -1
def F6(tuple, fileContents):
try:
if fileContents[tuple.start - 1].strip().lower() == 'on':
return 1
else:
return 0
except IndexError:
return 0
def F7(tuple, fileContents):
try:
if fileContents[tuple.start - 1].strip().lower() == 'called':
return 1
else:
return 0
except IndexError:
return 0
def F8(tuple, fileContents):
try:
if fileContents[tuple.end].strip().lower() == 'they':
return 1
else:
return 0
except IndexError:
return 0
def F9(tuple, fileContents):
try:
if "." in tuple.rawString.split()[1:-1] or "!" in tuple.rawString.split()[1:-1] or "?" in tuple.rawString.split()[1:-1]:
return 1
else:
return 0
except IndexError:
return 0
def F10(tuple, fileContents):
return tuple.rawString.count('.')
def F11(tuple, fileContents):
if ',' in tuple.rawString:
return 1
else:
return 0
def F12(tuple, fileContents):
if ',' in tuple.rawString.strip().split()[0] or ',' in tuple.rawString.strip().split()[-1]:
return 1
else:
return 0
def F13(tuple, fileContents):
if '.' in tuple.rawString.strip().split()[0] or '.' in tuple.rawString.strip().split()[-1]:
return 1
else:
return 0
def F14(tuple, fileContents):
if 'as' in tuple.rawString.lower() or 'a' in tuple.rawString.lower() or 'an' in tuple.rawString.lower():
return 1
else:
return 0
def F15(tuple, fileContents):
count = 0
for word in tuple.rawString.strip().split():
if word[0].isupper() and word[1:] == word[1:].lower():
count += 1
return count / len(tuple.rawString.strip().split())
def F16(tuple, fileContents):
try:
if fileContents[tuple.end][0].isupper() and fileContents[tuple.end][1:] == fileContents[tuple.end][1:].lower():
return 1
else:
return 0
except:
return 0
def F17(tuple, fileContents):
return 1 if len(re.findall(instituteKeywords, tuple.string))>0 else 0#case ignoring search criteria
def F18(tuple, fileContents):
try:
return sum(1 for char in tuple.string if char.isupper())*1.0/tuple.wordCount
except:
return -1
def F19(tuple, fileContents):
if ":" in tuple.rawString or "-" in tuple.rawString or '"' in tuple.rawString or "&" in tuple.rawString:
return 1
else:
return 0
'''
Feature list:
F0: "[The]" occurs 1 or two lines before string.
F1: Number of capitol Letters.
F2: Verb occurs 1 or two lines after the string.
F3: Total character length
F4: Total number of words
F5: Number of capitol letters before the string.
F5: Number of capitol letters in line after this string.
F6: "on" comes before
F7: "called" comes before # shouldn't the verb have taken care of it?
F8: "they" comes after
F9: .?! comes in the middle of and entry# should no longer be true ever
F10: Number of "."s
F11: "," is in the raw string "NOTE: This feature reliably improves precision!", #should no longer be True ever
F12: "," is in the first or last raw string position "NOTE: This feature reliably improves precision!", #should no lenger be True ever
F13: "." is in the first or last raw string position.
F14: "as", "a", "an" is in the raw string., # Invalid as discussed, to be removed
F15: The faction of the number of words where only the first character is capitalized to all words.
F16: The rawString has a Single capitalized word after it.
F17: Contains a keyword
F18: fraction of capital letters to wordCount
F19: Contains bad punctuation in raw string.
Each "tuple" object is a Pandas series with first entry tuple[0] the index, and
all following entries the entries of each row from the string tuples dataframe.
'''
def generateFeaturesFromFile(fileContents, fileName):
tuplesDF = generateStringTuples(fileContents, fileName)
allFeaturesList = []
# Call each feature generation function on each dataframe tuple.
for i in range(0, NUM_FEATURES):
featureList = []
for tuple in tuplesDF.itertuples():
featureList.append(eval('F' + str(i) + '(tuple, fileContents)'))
allFeaturesList.append(featureList)
allFeaturesList.append(tuplesDF['label'].tolist())
# TODO: write to a csv file the entire matrix of examples and features. Randomize. Remove some to ensure almost even split b/w + and -
return pd.DataFrame(np.array(allFeaturesList).T, columns=['F' + str(i) for i in range(NUM_FEATURES)] + ['label']), tuplesDF
def updateFeaturesFromFile(fileContents, fileName, functionName):
tuplesDF = generateStringTuples(fileContents, fileName)
featureList = []
for tuple in tuplesDF.itertuples():
featureList.append(eval(functionName + '(tuple, fileContents)'))
return featureList
'''
****************************************************************
PROGRAM RUNNING AND MANAGEMENT SCRIPTS
****************************************************************
'''
'''
For each file in the directory provided to the program, generate all of the
possible feature sets.
'''
def main(args):
if args.Mode == "C":
# Get sorted file list names from the given directory.
fileList = sorted(filter(lambda item: '.txt' in str(item), os.listdir(args.FileFolder)), key=lambda item: int(item.split('_')[0]))
startTime = time.time()
global missedTuples
missedTuples = []
global globalCount
globalCount = 0
fullDF = pd.DataFrame(columns=['F' + str(i) for i in range(NUM_FEATURES)] + ['label'])
tuplesDF = pd.DataFrame(columns=['rawString', 'file', 'start', 'end', 'string', 'wordCount', 'label'])
# For each file, parse into tuples, then parse into features, and create a full pandas data frame object.
print('Performing featurization...')
for file in fileList:
if '.txt' in file:
with open(args.FileFolder + file, "r", encoding="ISO-8859-1") as f:
print(file)
fileDF, fileTuplesDF = generateFeaturesFromFile(f.readlines(), file)
fullDF = pd.concat([fullDF, fileDF])
tuplesDF = pd.concat([tuplesDF, fileTuplesDF])
endTime = time.time()
print(fullDF.shape)
print('Done!')
print("Total time to run: %s seconds." %str(endTime-startTime))
# Save the entire pandas data frame object of features and classes.
print('Saving the full dataframe...')
fullDF.to_csv('../data/featurized_instances.csv')
# Update tuples index to full data set.
tuplesDF.index = pd.Series(list(range(0, fullDF.shape[0])))
tuplesDF.to_csv('../data/tuples_instances.csv')
print('Done!')
print(globalCount)
if len(missedTuples)>0:
print("Missed", len(missedTuples), "items overall")
elif args.Mode == "U":
fullDF = pd.read_csv('../data/featurized_instances.csv', index_col=0)
tuplesDF = pd.read_csv('../data/tuples_instances.csv', index_col=0)
fileList = sorted(filter(lambda item: '.txt' in str(item), os.listdir(args.FileFolder)), key=lambda item: int(item.split('_')[0]))
# For each file, parse into tuples, then parse into features, and create a full pandas data frame object.
print('Performing featurization...')
startTime = time.time()
for functionName in args.UpdateListString.strip().split():
print(functionName)
featureList = []
for file in fileList:
if '.txt' in file:
print(file)
with open(args.FileFolder + file, "r", encoding="ISO-8859-1") as f:
newList = updateFeaturesFromFile(f.readlines(), file, functionName)
featureList.extend(newList)
# All features for current function have been generated, so update the full data frame.
fullDF.loc[:, functionName] = pd.Series(featureList, index=fullDF.index)
endTime = time.time()
print('Done!')
print("Total time to run: %s seconds." % str(endTime - startTime))
columnsList = list(fullDF.columns)
columnsList.remove('label')
fullDF = fullDF[columnsList + ['label']]
# Save the entire pandas data frame object of features and classes.
print('Saving the full dataframe...')
fullDF.to_csv('../data/featurized_instances.csv')
tuplesDF.to_csv('../data/tuples_instances.csv')
if __name__ == '__main__':
#Parse command line arguments
parser = argparse.ArgumentParser(description="""Fake news feature generator. Generates features from files
whos' words have been split to multiple lines. It also handles files where entities have been pre-marked.""")
parser.add_argument('FileFolder', metavar='f', type=str)
parser.add_argument('Mode', metavar='m', type=str, help="""U is for update, and C is for create""")
parser.add_argument('UpdateListString', metavar='--l', type=str, default="", help="""Use a string 'F0 F12 F13' of features, or '' empty string if no features. """)
args = parser.parse_args()
main(args)
| 6
| 0
| 0
| 0
| 0
| 12,141
| 0
| 0
| 503
|
27ea8f89b590262da723db9961dd54d30a7fc92d
| 9,134
|
py
|
Python
|
zodiacy/corpus.py
|
greenify/zodiacy
|
faf46a10b9b70869cb4caca02027921f1418cfcf
|
[
"MIT"
] | 1
|
2015-10-16T10:24:53.000Z
|
2015-10-16T10:24:53.000Z
|
zodiacy/corpus.py
|
greenify/zodiacy
|
faf46a10b9b70869cb4caca02027921f1418cfcf
|
[
"MIT"
] | null | null | null |
zodiacy/corpus.py
|
greenify/zodiacy
|
faf46a10b9b70869cb4caca02027921f1418cfcf
|
[
"MIT"
] | null | null | null |
import logging
"""corpus.py: Generates horoscopes based provided corpuses"""
__author__ = "Project Zodiacy"
__copyright__ = "Copyright 2015, Project Zodiacy"
logger = logging.getLogger('root')
| 38.868085
| 115
| 0.600285
|
import collections
import logging
from math import sqrt
from wordnik import swagger, WordApi
from .utils import weighted_choice
"""corpus.py: Generates horoscopes based provided corpuses"""
__author__ = "Project Zodiacy"
__copyright__ = "Copyright 2015, Project Zodiacy"
logger = logging.getLogger('root')
class Corpus():
"""
Generates a corpus from the provided database
Args:
zodiac_sign: select only entries for a specific zodiac sign
keyword: select only entries for a specific keyword
with_rating: weight entries after a predefined rating
threshold: minimal amount of entries needed for a valid corpus
with_synonyms: query wordnik for synonyms
wordnik_api_url: Wordnik API URL
wordnik_api_key: Wordnik API Key
"""
def __init__(self, conn, with_rating=False, with_synonyms=False,
zodiac_sign=None, keyword=None, threshold=5,
wordnik_api_url=None, wordnik_api_key=None):
kws = locals()
for key, val in kws.items():
if key != "conn":
self.__dict__[key] = val
assert conn is not None
self.cursor = conn.cursor()
self.synonym_influence = 0.2
self.zodiac_sign_ordinal = self._get_zodiac_sign(self.zodiac_sign)
self._table_name = "horoscopes"
def __str__(self):
""" dumps the class on print - useful for debugging """
return str(self.__dict__)
def _get_zodiac_sign(self, zodiac_sign=None):
""" converts the string representation of a zodiac sign into a ordinal one
Arguments:
zodiac_sign: sign as string
Returns:
ordinal zodiac sign (from 0 to 12)
"""
zodiac_signs = dict(zip(['general', 'aries', 'taurus', 'gemini', 'cancer', 'leo', 'virgo',
'libra', 'scorpio', 'sagittarius', 'capricorn', 'aquarius', 'pisces'], range(13)))
if zodiac_sign not in zodiac_signs:
if zodiac_sign is not None:
raise ValueError('Invalid zodiac sign')
else:
return zodiac_signs[zodiac_sign]
def _add_filter(self, filter_sql, filter_value):
""" help method to add a new filter
Arguments:
filter_sql: SQL string (with '?' for parameters)
filter_value: parameters to bind to the SQL string (either a single value or list)
"""
self._filters.append(filter_sql)
if isinstance(filter_value, collections.MutableSequence):
assert filter_sql.count("?") == len(filter_value)
self._filters_values += filter_value
else:
assert filter_sql.count("?") == 1
self._filters_values.append(filter_value)
def _create_filters(self):
""" builds all filters """
self._filters = []
self._filters_values = []
if self.zodiac_sign is not None:
self._add_filter("sign=?", self.zodiac_sign_ordinal)
if self.with_synonyms:
present_synonyms = self.get_present_synonyms()
if len(present_synonyms) == 0:
logger.warn("No related synonyms found")
present_synonyms.append(self.keyword)
synonyms_sql_array = ','.join(('?' for _ in present_synonyms))
self._add_filter('keyword in (%s)' %
synonyms_sql_array, present_synonyms)
elif self.keyword is not None:
# synonyms is already filtering on keyword
self._add_filter("keyword=?", self.keyword)
def _build_filters(self):
""" concatenates all available filter to SQL """
filters = ""
if len(self._filters) > 0:
filters += " WHERE "
filters += " AND ".join(self._filters)
return filters
def _execute_and_log(self, base_stmt, values=[]):
""" execute logs the entire SQL string
This is expensive as we need to make a request to our SQLite database.
Hence it is only performed when the debugging is enabled - the level
of the root logger needs to be logging.DEBUG or less"""
if logger.getEffectiveLevel() <= logging.DEBUG:
sql_with_vals = base_stmt
if len(values) > 0:
self.cursor.execute(
"SELECT " + ", ".join(["quote(?)" for i in values]), values)
quoted_values = self.cursor.fetchone()
for quoted_value in quoted_values:
sql_with_vals = sql_with_vals.replace(
'?', str(quoted_value), 1)
logger.debug("query: %s", sql_with_vals)
self.cursor.execute(base_stmt, values)
def _execute_query(self):
""" Builds and executes the SQL query to fetch the corpus """
columns = 'interp'
columns += ',rating' if self.with_rating or self.with_synonyms else ''
columns += ',keyword' if self.keyword else ''
base_stmt = 'SELECT %s from %s' % (columns, self._table_name)
base_stmt += self._build_filters()
self._execute_and_log(base_stmt, self._filters_values)
def _count_entries(self):
""" Returns the number of found entries in the database
Reason:
cursor.rowcount returns -1 until all results have been fetched
"""
base_stmt = 'SELECT COUNT(*) from %s' % self._table_name
base_stmt += self._build_filters()
self.cursor.execute(base_stmt, self._filters_values)
return self.cursor.fetchone()[0]
def _build(self):
""" Returns a cursor with all horoscopes for the given parameters """
self._create_filters()
nr_entries = self._count_entries()
if nr_entries < self.threshold:
raise ValueError("Found %d matches" % nr_entries)
logger.debug("%d entries found in corpus db", nr_entries)
self._execute_query()
def __iter__(self):
""" Lazy corpus iterator """
self._build()
return self
def __next__(self):
""" returns the corpus lazy """
row = next(self.cursor, None)
if row is None or row[0] is None:
# maybe someone wants to access the results again
raise StopIteration
rating = None
if self.with_rating:
rating = row[1]
if self.with_synonyms:
if row[0] is None:
# filter invalid entries
logger.debug("invalid row %s", row)
return self.__next__()
if row[0] == self.keyword or len(self.synonyms) == 0:
rating = row[1]
else:
rating = self.synonym_influence * \
row[1] * sqrt(len(self.synonyms))
if rating is None:
return (row[0],)
else:
return (row[0], rating)
def random_keyword(self):
valid_keywords = [k for k in self.list_keywords() if k[1] >= self.threshold]
self.keyword = weighted_choice(valid_keywords)
logger.debug("keyword selected: %s", self.keyword)
def list_keywords(self):
""" lists all available keywords """
self._execute_and_log(("SELECT keyword, count(*) as count FROM horoscopes "
"WHERE length(keyword) > 0 GROUP BY keyword ORDER BY count desc"))
return self.cursor.fetchall()
def select_keyword_range(self, min_val=0, max_val=1, val=1):
""" evenly maps a val with min and max to the keywords
As the mapping is surjective the remaining slice is then weighted_chosen"""
kws = [k for k in self.list_keywords() if k[1] >= self.threshold]
val_range = max_val - min_val
factor = int(len(kws) / val_range)
upper_end = val * factor
if upper_end < factor:
upper_end = factor
lower_end = upper_end - factor
self.keyword = weighted_choice(kws[lower_end:upper_end])
logger.debug("moon keyword selected: %s", self.keyword)
def _get_synonyms(self, keyword):
""" Queries Wordnik for synonyms """
client = swagger.ApiClient(self.wordnik_api_key, self.wordnik_api_url)
word_api = WordApi.WordApi(client)
words = word_api.getRelatedWords(keyword, relationshipTypes='synonym')
if words is None or len(words) == 0:
return []
else:
return words[0].words
def get_present_synonyms(self):
""" Compares Wordnik result with present synonyms in DB
Returns:
List of synonyms occurring in the database
"""
self.synonyms = self._get_synonyms(self.keyword)
logger.debug("found %d synonyms", len(self.synonyms))
if len(self.synonyms) > 0:
self._execute_and_log('SELECT keyword FROM horoscopes WHERE keyword IN (%s) GROUP BY keyword' %
','.join('?' for _ in self.synonyms), tuple(self.synonyms))
return [row[0] for row in self.cursor if row is not None]
else:
return []
| 0
| 0
| 0
| 8,801
| 0
| 0
| 0
| 25
| 111
|
f49d473cc0d800aa406361c6dce4b00757bccf54
| 2,906
|
py
|
Python
|
remedy/remedy.py
|
4n6ir/remedy-delete-default-vpcs
|
ce1250140c7f3c967646c9be5ef09dde88c191b0
|
[
"Apache-2.0"
] | null | null | null |
remedy/remedy.py
|
4n6ir/remedy-delete-default-vpcs
|
ce1250140c7f3c967646c9be5ef09dde88c191b0
|
[
"Apache-2.0"
] | null | null | null |
remedy/remedy.py
|
4n6ir/remedy-delete-default-vpcs
|
ce1250140c7f3c967646c9be5ef09dde88c191b0
|
[
"Apache-2.0"
] | null | null | null |
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
| 44.707692
| 93
| 0.399862
|
import boto3
import json
import logging
import os
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def handler(event, context):
client = boto3.client('ec2')
regions = client.describe_regions()
for region in regions['Regions']:
ec2_client = boto3.client('ec2', region_name=region['RegionName'])
paginator = ec2_client.get_paginator('describe_vpcs')
response_iterator = paginator.paginate()
for page in response_iterator:
if len(page['Vpcs']) > 0:
for item in page['Vpcs']:
if item['IsDefault'] is True:
paginator2 = ec2_client.get_paginator('describe_internet_gateways')
response_iterator2 = paginator2.paginate()
for page2 in response_iterator2:
for item2 in page2['InternetGateways']:
if len(page2['InternetGateways']) > 0:
if item2['Attachments'][0]['VpcId'] == item['VpcId']:
try:
ec2_client.detach_internet_gateway(
InternetGatewayId=item2['InternetGatewayId'],
VpcId=item['VpcId']
)
ec2_client.delete_internet_gateway(
InternetGatewayId=item2['InternetGatewayId']
)
except:
logger.info('USED '+str(item2))
paginator3 = ec2_client.get_paginator('describe_subnets')
response_iterator3 = paginator3.paginate()
for page3 in response_iterator3:
for item3 in page3['Subnets']:
if len(page3['Subnets']) > 0:
if item3['VpcId'] == item['VpcId']:
try:
ec2_client.delete_subnet(
SubnetId=item3['SubnetId']
)
except:
logger.info('USED '+str(item3))
try:
ec2_client.delete_vpc(
VpcId=item['VpcId']
)
except:
logger.info('USED '+str(item))
pass
return {
'statusCode': 200,
'body': json.dumps('Delete Default VPCs')
}
| 0
| 0
| 0
| 0
| 0
| 2,773
| 0
| -31
| 89
|
39869b293ee78812692ef8612d361826ac2281dd
| 5,815
|
py
|
Python
|
papers/BLS/BLSBasic/BLS.py
|
mindspore-ai/contrib
|
85dccac7a2ba6e962092ecd51aefd962d7f2aeac
|
[
"Apache-2.0"
] | 2
|
2021-11-10T06:16:55.000Z
|
2022-02-22T11:30:04.000Z
|
papers/BLS/BLSBasic/BLS.py
|
mindspore-ai/contrib
|
85dccac7a2ba6e962092ecd51aefd962d7f2aeac
|
[
"Apache-2.0"
] | null | null | null |
papers/BLS/BLSBasic/BLS.py
|
mindspore-ai/contrib
|
85dccac7a2ba6e962092ecd51aefd962d7f2aeac
|
[
"Apache-2.0"
] | 1
|
2022-03-22T06:03:15.000Z
|
2022-03-22T06:03:15.000Z
|
import mindspore.context as context
import mindspore.ops as ops
| 44.389313
| 115
| 0.692863
|
import numpy
import numpy as np
from mindspore import Tensor, dtype
from mindspore.train.serialization import export, save_checkpoint
import mindspore.dataset as ds
import mindspore.context as context
import mindspore.ops as ops
import mindspore.nn as N
import mindspore.numpy as mnp
class BLSBasicTrain(N.Cell):
def __init__(self) -> None:
super(BLSBasicTrain, self).__init__()
self.s = 0.8
self.c = 2 ** -15
self.n1 = 10
self.n2 = 10
self.n3 = 20
self.y_max = 1
self.y_min = 0
self.iterations = 2
# 用于训练结果输出
self.argmax_op = ops.Argmax()
self.sign_op = ops.Sign()
self.select_op = ops.Select()
self.accuracy_op = N.Accuracy('classification')
def construct(self, _train_data, _train_label):
output, weight, _, _, _, _, _ = self.train(_train_data, _train_label)
return output, weight
def train(self, x, y):
standardized_data = self.standardize_input(x)
feature, mapped_features, _, _ = self.generate_mapped_features(standardized_data)
feature_with_bias = self.enhance_layer_input(feature)
enhance_layer_weight = self.generate_random_weight_of_enhance_layer()
enhance_layer_output, shrink_parameter = self.enhance_layer_output(feature_with_bias, enhance_layer_weight)
output, output_weight = self.final_output(feature, enhance_layer_output, y)
return output, output_weight, mapped_features, _, _, enhance_layer_weight, shrink_parameter
def generate_mapped_features(self, standardized_train_x):
feature = self.input_features(standardized_train_x)
output = []
weight = mnp.full((self.n2, feature.shape[1], self.n1), 0.0)
max_list = mnp.full((self.n2, self.n1), 0.0)
min_list = mnp.full((self.n2, self.n1), 0.0)
for i in range(self.n2):
# 生成随机权重
weight_of_each_window = self.generate_random_weight_of_window(standardized_train_x, i)
# 生成窗口特征
temp_feature_of_each_window = mnp.matmul(feature, weight_of_each_window)
# 压缩
feature_of_each_window, _, _ = self.mapminmax(temp_feature_of_each_window, -1.0, 1.0)
# 通过稀疏化计算，生成最终权重
beta = self.sparse_bls(feature_of_each_window, feature)
# 计算窗口输出 T1
output_of_each_window_next = self.window_output(feature, beta)
# 压缩
output_of_each_window_next, max_list, min_list = self.mapminmax(output_of_each_window_next, 0.0, 1.0)
# 拼接窗口输出
output = self.concat_window_output(output, output_of_each_window_next)
# 更新输出的权重
weight[i] = beta
max_list[i] = max_list
min_list[i] = min_list
output = self.stack_window_output(output)
return output, weight, max_list, min_list
def generate_random_weight_of_enhance_layer(self):
weight = []
uniform = ops.UniformReal(seed=2)
rand = uniform((self.n2 * self.n1 + 1, self.n3), 0.0, 1.0)
weight.append(self.orthonormalize(2 * rand - mnp.full(rand.shape, 1.0)))
return mnp.stack(weight, axis=1)
def final_output(self, _output_of_feature_mapping_layer, _output_of_enhance_layer, _train_label):
# 拼接T2和y, 生成T3
concat = mnp.concatenate((_output_of_feature_mapping_layer, _output_of_enhance_layer), axis=1)
weight = self.pseudo_inverse(concat, _train_label)
# 生成训练输出
output = self.output_layer(concat, weight)
return output, weight
def generate_random_weight_of_window(self, standardized_x, i):
uniform = ops.UniformReal(seed=2)
weight = 2.0 * uniform((standardized_x.shape[1] + 1, self.n1)) - 1.0 # 生成每个窗口的权重系数，最后一行为偏差
return weight
def input_features(self, standardized_train_x):
ones = mnp.full((standardized_train_x.shape[0], 1), 0.1)
feature_of_input_data_with_bias = mnp.concatenate((standardized_train_x, ones), axis=1)
return feature_of_input_data_with_bias
def window_output(self, feature_of_input_data_with_bias, beta):
output_of_each_window = mnp.matmul(feature_of_input_data_with_bias, beta)
return output_of_each_window
def concat_window_output(self, output_of_feature_mapping_layer, t1):
output_of_feature_mapping_layer.append(t1)
return output_of_feature_mapping_layer
def stack_window_output(self, output_of_feature_mapping_layer):
res = mnp.stack(output_of_feature_mapping_layer, axis=1)
res = mnp.reshape(res, (res.shape[0], -1))
return res
def enhance_layer_input(self, mapped_feature):
data_concat_second = mnp.full((mapped_feature.shape[0], 1), 0.1)
res = mnp.concatenate((mapped_feature, data_concat_second), axis=1)
return res
def enhance_layer_output(self, _input_of_enhance_layer_with_bias, _weight_of_enhance_layer):
res_squeeze_input0 = mnp.squeeze(_input_of_enhance_layer_with_bias)
res_squeeze_input1 = mnp.squeeze(_weight_of_enhance_layer)
res_matmul = mnp.matmul(res_squeeze_input0, res_squeeze_input1)
res_reduce_max = mnp.amax(res_matmul)
shrink_parameter = self.s * mnp.full(res_reduce_max.shape, 1.0) / res_reduce_max
res_tanh = mnp.tanh(res_matmul * shrink_parameter)
return res_tanh, shrink_parameter
def pseudo_inverse(self, _concatenate_of_two_layer, _train_y):
pseudo_inverse = self.pinv(_concatenate_of_two_layer, self.c)
new_output_weight = mnp.matmul(pseudo_inverse, _train_y)
return new_output_weight
def output_layer(self, concatenate_of_two_layer, output_weight):
output_of_result = mnp.matmul(concatenate_of_two_layer, output_weight)
return output_of_result
| 261
| 0
| 0
| 5,420
| 0
| 0
| 0
| 66
| 177
|
ced8f7377ff6afeb026e4db4b64351647f6b9de4
| 8,333
|
py
|
Python
|
tests/zquantum/core/wip/circuits/_gates_test.py
|
bartubisgin/z-quantum-core
|
b61aef12cc86f0a8234229b9b26b21cde950d6f1
|
[
"Apache-2.0"
] | null | null | null |
tests/zquantum/core/wip/circuits/_gates_test.py
|
bartubisgin/z-quantum-core
|
b61aef12cc86f0a8234229b9b26b21cde950d6f1
|
[
"Apache-2.0"
] | null | null | null |
tests/zquantum/core/wip/circuits/_gates_test.py
|
bartubisgin/z-quantum-core
|
b61aef12cc86f0a8234229b9b26b21cde950d6f1
|
[
"Apache-2.0"
] | 1
|
2022-03-19T02:23:53.000Z
|
2022-03-19T02:23:53.000Z
|
"""Test cases for _gates module."""
import sympy
from zquantum.core.wip.circuits import _builtin_gates
GATES_REPRESENTATIVES = [
_builtin_gates.X,
_builtin_gates.Y,
_builtin_gates.Z,
_builtin_gates.T,
_builtin_gates.H,
_builtin_gates.I,
_builtin_gates.RX(sympy.Symbol("theta")),
_builtin_gates.RY(0.5),
_builtin_gates.RZ(0),
_builtin_gates.PHASE(sympy.pi / 5),
_builtin_gates.CZ,
_builtin_gates.CNOT,
_builtin_gates.SWAP,
_builtin_gates.ISWAP,
_builtin_gates.XX(sympy.cos(sympy.Symbol("phi"))),
_builtin_gates.YY(sympy.pi),
_builtin_gates.ZZ(sympy.Symbol("x") + sympy.Symbol("y")),
_builtin_gates.CPHASE(1.5),
]
| 37.200893
| 88
| 0.677667
|
"""Test cases for _gates module."""
from unittest.mock import Mock
import pytest
import sympy
from zquantum.core.wip.circuits import _builtin_gates
from zquantum.core.wip.circuits._gates import GateOperation, MatrixFactoryGate
GATES_REPRESENTATIVES = [
_builtin_gates.X,
_builtin_gates.Y,
_builtin_gates.Z,
_builtin_gates.T,
_builtin_gates.H,
_builtin_gates.I,
_builtin_gates.RX(sympy.Symbol("theta")),
_builtin_gates.RY(0.5),
_builtin_gates.RZ(0),
_builtin_gates.PHASE(sympy.pi / 5),
_builtin_gates.CZ,
_builtin_gates.CNOT,
_builtin_gates.SWAP,
_builtin_gates.ISWAP,
_builtin_gates.XX(sympy.cos(sympy.Symbol("phi"))),
_builtin_gates.YY(sympy.pi),
_builtin_gates.ZZ(sympy.Symbol("x") + sympy.Symbol("y")),
_builtin_gates.CPHASE(1.5),
]
def example_one_qubit_matrix_factory(a, b):
return sympy.Matrix([[a, b], [b, a]])
def example_two_qubit_matrix_factory(a, b, c):
return sympy.Matrix([[a, 0, 0, 0], [0, b, 0, 0], [0, 0, c, 0], [0, 0, 0, 1]])
class TestMatrixFactoryGate:
@pytest.mark.parametrize(
"params, factory, num_qubits",
[
((0.5, sympy.Symbol("theta")), example_one_qubit_matrix_factory, 1),
(
(sympy.Symbol("alpha"), sympy.Symbol("beta"), 1),
example_two_qubit_matrix_factory,
2,
),
],
)
def test_constructs_its_matrix_by_calling_factory_with_bound_parameter(
self, params, factory, num_qubits
):
wrapped_factory = Mock(wraps=factory)
gate = MatrixFactoryGate("U", wrapped_factory, params, num_qubits)
assert gate.matrix == factory(*params)
wrapped_factory.assert_called_once_with(*params)
def test_binding_parameters_creates_new_instance_with_substituted_free_params(self):
gamma, theta, x, y = sympy.symbols("gamma, theta, x, y")
params = (theta, x + y)
gate = MatrixFactoryGate("U", example_one_qubit_matrix_factory, params, 1)
new_gate = gate.bind({theta: 0.5, x: gamma, y: 3})
assert new_gate.name == gate.name
assert new_gate.matrix_factory == gate.matrix_factory
assert new_gate.num_qubits == gate.num_qubits
assert new_gate.params == (0.5, gamma + 3)
def test_binding_parameters_with_symbol_outside_of_free_symbols_does_not_raise(
self,
):
gamma, theta = sympy.symbols("gamma, theta")
params = (theta, 2 * theta)
gate = MatrixFactoryGate("U", example_one_qubit_matrix_factory, params, 1)
new_gate = gate.bind({gamma: 0.5, theta: 1})
assert new_gate.params == (1, 2)
def test_binding_parameters_does_not_change_parameters_without_free_symbols(self):
theta = sympy.Symbol("theta")
gate = MatrixFactoryGate("V", example_one_qubit_matrix_factory, (1, 2), 1)
new_gate = gate.bind({theta: 5.0})
assert new_gate.params == (1, 2)
def test_replace_parameters_correctly_gives_instance_with_correctly_set_parameters(
self,
):
theta = sympy.Symbol("theta")
gate = MatrixFactoryGate("V", example_one_qubit_matrix_factory, (1, 2), 1)
new_gate = gate.replace_params((theta, 0.5))
assert new_gate == MatrixFactoryGate(
"V", example_one_qubit_matrix_factory, (theta, 0.5), 1
)
def test_daggers_matrix_is_adjoint_of_original_gates_matrix(self):
gate = MatrixFactoryGate("V", example_one_qubit_matrix_factory, (1, 2), 1)
assert gate.dagger.matrix == gate.matrix.adjoint()
def test_dagger_has_the_same_params_and_num_qubits_as_wrapped_gate(self):
gate = MatrixFactoryGate(
"U", example_two_qubit_matrix_factory, (0.5, 0.1, sympy.Symbol("a")), 2
)
assert gate.dagger.num_qubits == gate.num_qubits
assert gate.dagger.params == gate.params
def test_dagger_of_hermitian_gate_is_the_same_gate(self):
gate = MatrixFactoryGate(
"V", example_one_qubit_matrix_factory, (1, 0), 1, is_hermitian=True
)
assert gate.dagger is gate
def test_binding_gates_in_dagger_is_propagated_to_wrapped_gate(self):
theta = sympy.Symbol("theta")
gate = MatrixFactoryGate("V", example_one_qubit_matrix_factory, (theta, 0), 1)
assert gate.dagger.bind({theta: 0.5}) == gate.bind({theta: 0.5}).dagger
def test_dagger_of_dagger_is_the_same_as_original_gate(self):
gate = MatrixFactoryGate("V", example_one_qubit_matrix_factory, (1, 0), 1)
assert gate.dagger.dagger is gate
def test_applying_dagger_and_replacing_parameters_commutes(self):
gate = MatrixFactoryGate("V", example_one_qubit_matrix_factory, (1, 0), 1)
new_params = (sympy.Symbol("theta"), 4.2)
assert (
gate.dagger.replace_params(new_params)
== gate.replace_params(new_params).dagger
)
def test_applying_gate_returns_operation_with_correct_gate_and_indices(self):
theta = sympy.Symbol("theta")
gamma = sympy.Symbol("gamma")
gate = MatrixFactoryGate(
"A", example_two_qubit_matrix_factory, (theta, gamma, 42), 2
)
operation = gate(4, 1)
assert operation.gate == gate
assert operation.qubit_indices == (4, 1)
@pytest.mark.parametrize("gate", GATES_REPRESENTATIVES)
class TestControlledGate:
def test_num_qubits_equal_to_wrapped_gates_num_qubits_plus_num_controlled_qubits(
self, gate
):
assert gate.controlled(3).num_qubits == gate.num_qubits + 3
def test_has_matrix_with_eye_and_wrapped_gates_matrix_as_bottom_left_block(
self, gate
):
controlled_gate = gate.controlled(2)
n = gate.matrix.shape[0]
assert gate.matrix.shape[1] == n
assert controlled_gate.matrix[0:-n, 0:-n] == sympy.eye(
2 ** controlled_gate.num_qubits - n
)
assert controlled_gate.matrix[-n:, -n:] == gate.matrix
def test_controlled_of_controlled_gate_has_summed_number_of_control_qubits(
self, gate
):
controlled_gate = gate.controlled(2)
double_controlled_gate = controlled_gate.controlled(3)
assert double_controlled_gate.wrapped_gate == gate
assert double_controlled_gate.num_qubits == gate.num_qubits + 2 + 3
assert double_controlled_gate.num_control_qubits == 2 + 3
assert double_controlled_gate.matrix.shape == 2 * (
2 ** (gate.num_qubits + 2 + 3),
)
def test_has_the_same_parameters_as_wrapped_gate(self, gate):
controlled_gate = gate.controlled(4)
assert controlled_gate.params == gate.params
def test_dagger_of_controlled_gate_is_controlled_gate_wrapping_dagger(self, gate):
controlled_gate = gate.controlled(4)
assert controlled_gate.dagger == gate.dagger.controlled(4)
def test_binding_parameters_in_control_gate_is_propagated_to_wrapped_gate(
self, gate
):
controlled_gate = gate.controlled(2)
symbols_map = {sympy.Symbol("theta"): 0.5, sympy.Symbol("x"): 3}
assert controlled_gate.bind(symbols_map) == gate.bind(symbols_map).controlled(2)
def test_constructing_controlled_gate_and_replacing_parameters_commute(self, gate):
controlled_gate = gate.controlled(2)
new_params = tuple(3 * param for param in controlled_gate.params)
assert controlled_gate.replace_params(new_params) == gate.replace_params(
new_params
).controlled(2)
@pytest.mark.parametrize("gate", GATES_REPRESENTATIVES)
class TestGateOperation:
def test_bound_symbols_are_not_present_in_gate_parameters(self, gate):
op = GateOperation(gate, tuple(range(gate.num_qubits)))
symbols_map = {sympy.Symbol("phi"): 0.5, sympy.Symbol("y"): 1.1}
assert all(
symbol not in sympy.sympify(param).atoms(sympy.Symbol)
for symbol in symbols_map
for param in op.bind(symbols_map).params
)
def test_replacing_parameters_constructs_operation_of_gate_with_new_parameters(
self, gate
):
op = GateOperation(gate, tuple(range(gate.num_qubits)))
new_params = tuple(-1 * param for param in op.params)
assert op.replace_params(new_params).params == new_params
| 0
| 3,628
| 0
| 3,604
| 0
| 171
| 0
| 58
| 182
|
5a4509ded52d8b17265869fdb30826814374cffa
| 2,138
|
py
|
Python
|
8-puzzle-game/manhattan.py
|
itepifanio/jogo-dos-8-numeros
|
692b84f9b48fef5d8da9afba67adb1a1f9c13b28
|
[
"MIT"
] | null | null | null |
8-puzzle-game/manhattan.py
|
itepifanio/jogo-dos-8-numeros
|
692b84f9b48fef5d8da9afba67adb1a1f9c13b28
|
[
"MIT"
] | null | null | null |
8-puzzle-game/manhattan.py
|
itepifanio/jogo-dos-8-numeros
|
692b84f9b48fef5d8da9afba67adb1a1f9c13b28
|
[
"MIT"
] | null | null | null |
import time
if __name__ == '__main__':
algorithm = Manhanttan()
algorithm.run()
| 30.542857
| 89
| 0.563143
|
from game.game import Game
from heapq import heappush, heappop
import time
import itertools
import time
class Manhanttan(Game):
def heuristic(self, a):
result = 0
node = list(itertools.chain(*a))
for current, target in enumerate(node):
currentRow = int(current/3)
currentColumn = current%3
targetRow = int(target/3)
targetColumn = target%3
result += abs(currentRow-targetRow) + abs(currentColumn-targetColumn)
return result
def run(self):
self.nodeList.append(self.startNode)
self.visitedList.append(self.startNode)
t0 = time.time()
found = False
while (not found and len(self.nodeList) != 0):
fList = []
for node in self.nodeList:
h = self.heuristic(node)
g = len(node)
f = g+h
heappush(fList, (f, node))
currentNode = self.nodeList.pop(
self.nodeList.index(
heappop(fList)[1]
)
)
blankIndex = self.getBlankIndexes(currentNode)
if self.board.canMoveTop(blankIndex):
topNode = self.board.top(currentNode, blankIndex[0], blankIndex[1])
found = self.checkFinal(topNode)
if self.board.canMoveLeft(blankIndex) and found == False:
leftNode = self.board.left(currentNode, blankIndex[0], blankIndex[1])
found = self.checkFinal(leftNode)
if self.board.canMoveRight(blankIndex) and found == False:
rightNode = self.board.right(currentNode, blankIndex[0], blankIndex[1])
found = self.checkFinal(rightNode)
if self.board.canMoveBottom(blankIndex) and found == False:
bottomNode = self.board.bottom(currentNode, blankIndex[0], blankIndex[1])
found = self.checkFinal(bottomNode)
t1 = time.time()
print('Time:', t1-t0)
print('------')
if __name__ == '__main__':
algorithm = Manhanttan()
algorithm.run()
| 0
| 0
| 0
| 1,925
| 0
| 0
| 0
| 4
| 115
|
8747cd2d839a44ad5113082afaedeed9aa0cc8f8
| 9,839
|
py
|
Python
|
benchmark_utils.py
|
neyudin/AcceleratedGNMethodEquations
|
b5606f98221d72ff3e9ebb8ce0709981db08cefd
|
[
"BSD-3-Clause"
] | null | null | null |
benchmark_utils.py
|
neyudin/AcceleratedGNMethodEquations
|
b5606f98221d72ff3e9ebb8ce0709981db08cefd
|
[
"BSD-3-Clause"
] | null | null | null |
benchmark_utils.py
|
neyudin/AcceleratedGNMethodEquations
|
b5606f98221d72ff3e9ebb8ce0709981db08cefd
|
[
"BSD-3-Clause"
] | null | null | null |
import gc
import time
def experiment_runner(args, x_0_dict):
"""
Runner routine which performs the whole experiment set.
Parameters
----------
args : populated namespace object from ArgumentParser
The system of equations evaluated at point x.
x_0_dict : dict
The dictionary of initial points x.
Returns
-------
dict
Aggregated experiment data.
"""
gc.enable()
gc.collect()
exp_res_dict = dict()
if args.verbose:
print("Started DetGNM!")
exp_res_dict['DetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['DetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['DetGNM'][name][n] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _ = DetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None)
start = time.time() - start
exp_res_dict['DetGNM'][name][n][i] = {'f_vals': f_vals, 'nabla_f_2_norm_vals': nabla_f_2_norm_vals, 'avg_time_s': start / len(f_vals), 'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, start
gc.collect()
if args.verbose:
print("Started ArmijoAccDetGNM!")
exp_res_dict['ArmijoAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['ArmijoAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['ArmijoAccDetGNM'][name][n] = dict()
for pair_num, (c1, c2) in enumerate(zip(args.c1_list, args.c2_list)):
if args.verbose:
print(' c1 = {:.4f}, c2 = {:.4f}:'.format(c1, c2))
exp_res_dict['ArmijoAccDetGNM'][name][n][pair_num] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _, local_steps_list, spec_steps_list = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Armijo", c1=c1, c2=c2)
start = time.time() - start
exp_res_dict['ArmijoAccDetGNM'][name][n][pair_num][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'local_steps_list': local_steps_list,
'spec_steps_list': spec_steps_list,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, local_steps_list, spec_steps_list, start
gc.collect()
if args.verbose:
print("Started ExtrapolationAccDetGNM!")
exp_res_dict['ExtrapolationAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['ExtrapolationAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['ExtrapolationAccDetGNM'][name][n] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _, n_iter_list = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Extrapolation")
start = time.time() - start
exp_res_dict['ExtrapolationAccDetGNM'][name][n][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'n_iter_list': n_iter_list,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, n_iter_list, start
gc.collect()
if args.verbose:
print("Started InterpolationAccDetGNM!")
exp_res_dict['InterpolationAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['InterpolationAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['InterpolationAccDetGNM'][name][n] = dict()
for n_points in args.n_points_list:
if args.verbose:
print(' n_points:', n_points)
exp_res_dict['InterpolationAccDetGNM'][name][n][n_points] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _ = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Interpolation")
start = time.time() - start
exp_res_dict['InterpolationAccDetGNM'][name][n][n_points][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, start
gc.collect()
if args.verbose:
print("Started SamplingAccDetGNM!")
exp_res_dict['SamplingAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['SamplingAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['SamplingAccDetGNM'][name][n] = dict()
for n_points in args.n_points_list:
if args.verbose:
print(' n_points:', n_points)
exp_res_dict['SamplingAccDetGNM'][name][n][n_points] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _ = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Sampling")
start = time.time() - start
exp_res_dict['SamplingAccDetGNM'][name][n][n_points][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, start
gc.collect()
if args.verbose:
print("Started GoldenRatioAccDetGNM!")
exp_res_dict['GoldenRatioAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['GoldenRatioAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['GoldenRatioAccDetGNM'][name][n] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _, n_iter_list = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "GoldenRatio")
start = time.time() - start
exp_res_dict['GoldenRatioAccDetGNM'][name][n][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'n_iter_list': n_iter_list,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, n_iter_list, start
gc.collect()
return exp_res_dict
| 53.472826
| 195
| 0.476979
|
from optimizers import *
import gc
import time
def experiment_runner(args, x_0_dict):
"""
Runner routine which performs the whole experiment set.
Parameters
----------
args : populated namespace object from ArgumentParser
The system of equations evaluated at point x.
x_0_dict : dict
The dictionary of initial points x.
Returns
-------
dict
Aggregated experiment data.
"""
gc.enable()
gc.collect()
exp_res_dict = dict()
if args.verbose:
print("Started DetGNM!")
exp_res_dict['DetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['DetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['DetGNM'][name][n] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _ = DetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None)
start = time.time() - start
exp_res_dict['DetGNM'][name][n][i] = {'f_vals': f_vals, 'nabla_f_2_norm_vals': nabla_f_2_norm_vals, 'avg_time_s': start / len(f_vals), 'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, start
gc.collect()
if args.verbose:
print("Started ArmijoAccDetGNM!")
exp_res_dict['ArmijoAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['ArmijoAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['ArmijoAccDetGNM'][name][n] = dict()
for pair_num, (c1, c2) in enumerate(zip(args.c1_list, args.c2_list)):
if args.verbose:
print(' c1 = {:.4f}, c2 = {:.4f}:'.format(c1, c2))
exp_res_dict['ArmijoAccDetGNM'][name][n][pair_num] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _, local_steps_list, spec_steps_list = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Armijo", c1=c1, c2=c2)
start = time.time() - start
exp_res_dict['ArmijoAccDetGNM'][name][n][pair_num][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'local_steps_list': local_steps_list,
'spec_steps_list': spec_steps_list,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, local_steps_list, spec_steps_list, start
gc.collect()
if args.verbose:
print("Started ExtrapolationAccDetGNM!")
exp_res_dict['ExtrapolationAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['ExtrapolationAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['ExtrapolationAccDetGNM'][name][n] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _, n_iter_list = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Extrapolation")
start = time.time() - start
exp_res_dict['ExtrapolationAccDetGNM'][name][n][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'n_iter_list': n_iter_list,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, n_iter_list, start
gc.collect()
if args.verbose:
print("Started InterpolationAccDetGNM!")
exp_res_dict['InterpolationAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['InterpolationAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['InterpolationAccDetGNM'][name][n] = dict()
for n_points in args.n_points_list:
if args.verbose:
print(' n_points:', n_points)
exp_res_dict['InterpolationAccDetGNM'][name][n][n_points] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _ = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Interpolation")
start = time.time() - start
exp_res_dict['InterpolationAccDetGNM'][name][n][n_points][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, start
gc.collect()
if args.verbose:
print("Started SamplingAccDetGNM!")
exp_res_dict['SamplingAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['SamplingAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['SamplingAccDetGNM'][name][n] = dict()
for n_points in args.n_points_list:
if args.verbose:
print(' n_points:', n_points)
exp_res_dict['SamplingAccDetGNM'][name][n][n_points] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _ = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "Sampling")
start = time.time() - start
exp_res_dict['SamplingAccDetGNM'][name][n][n_points][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, start
gc.collect()
if args.verbose:
print("Started GoldenRatioAccDetGNM!")
exp_res_dict['GoldenRatioAccDetGNM'] = dict()
for oracle_class, name in [(NesterovSkokovOracle, 'Nesterov-Skokov'), (HatOracle, 'Hat'), (PLOracle, 'PL')]:
if args.verbose:
print('Oracle:', name)
exp_res_dict['GoldenRatioAccDetGNM'][name] = dict()
for n in args.n_dims:
if args.verbose:
print(' n:', n)
exp_res_dict['GoldenRatioAccDetGNM'][name][n] = dict()
for i in range(args.n_starts):
if args.verbose:
print(' start #:', i + 1)
start = time.time()
_, f_vals, nabla_f_2_norm_vals, _, _, n_iter_list = AccDetGNM(oracle_class(n), args.N_iter, x_0_dict[n][i], args.L_0, True, None, "GoldenRatio")
start = time.time() - start
exp_res_dict['GoldenRatioAccDetGNM'][name][n][i] = {'f_vals': f_vals,
'nabla_f_2_norm_vals': nabla_f_2_norm_vals,
'n_iter_list': n_iter_list,
'avg_time_s': start / len(f_vals),
'time_s': start}
del _, f_vals, nabla_f_2_norm_vals, n_iter_list, start
gc.collect()
return exp_res_dict
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 3
| 22
|
51311d98800bb3f9984f334fadd5802ed4789387
| 509
|
py
|
Python
|
docs/components_page/components/button/usage.py
|
glsdown/dash-bootstrap-components
|
0ebea4f7de43975f6e3a2958359c4480ae1d4927
|
[
"Apache-2.0"
] | 776
|
2019-02-07T19:36:59.000Z
|
2022-03-31T05:53:04.000Z
|
docs/components_page/components/button/usage.py
|
glsdown/dash-bootstrap-components
|
0ebea4f7de43975f6e3a2958359c4480ae1d4927
|
[
"Apache-2.0"
] | 350
|
2019-02-05T10:42:19.000Z
|
2022-03-31T19:23:35.000Z
|
docs/components_page/components/button/usage.py
|
glsdown/dash-bootstrap-components
|
0ebea4f7de43975f6e3a2958359c4480ae1d4927
|
[
"Apache-2.0"
] | 219
|
2019-02-10T13:46:25.000Z
|
2022-03-23T17:03:39.000Z
|
import dash_bootstrap_components as dbc
from dash import html
button = html.Div(
[
dbc.Button(
"Click me", id="example-button", className="me-2", n_clicks=0
),
html.Span(id="example-output", style={"verticalAlign": "middle"}),
]
)
| 23.136364
| 79
| 0.607073
|
import dash_bootstrap_components as dbc
from dash import Input, Output, html
button = html.Div(
[
dbc.Button(
"Click me", id="example-button", className="me-2", n_clicks=0
),
html.Span(id="example-output", style={"verticalAlign": "middle"}),
]
)
@app.callback(
Output("example-output", "children"), [Input("example-button", "n_clicks")]
)
def on_button_click(n):
if n is None:
return "Not clicked."
else:
return f"Clicked {n} times."
| 0
| 194
| 0
| 0
| 0
| 0
| 0
| 15
| 23
|
4fef830c6935b201ebe3270607f09b48471f7f26
| 4,094
|
py
|
Python
|
python/functions/calculateInterest.py
|
Z88897050/emulateSamples
|
57a519422e0e15aadc301470ac6a8397848d93c3
|
[
"Apache-2.0"
] | null | null | null |
python/functions/calculateInterest.py
|
Z88897050/emulateSamples
|
57a519422e0e15aadc301470ac6a8397848d93c3
|
[
"Apache-2.0"
] | 1
|
2021-09-21T14:38:33.000Z
|
2021-09-21T14:38:33.000Z
|
python/functions/calculateInterest.py
|
Z88897050/emulateSamples
|
57a519422e0e15aadc301470ac6a8397848d93c3
|
[
"Apache-2.0"
] | 1
|
2019-05-17T11:31:47.000Z
|
2019-05-17T11:31:47.000Z
|
#
area = float(input('(m^2): '))
unit_price = float(input('(): '))
year_interest = float(input('(%): '))
# '''
#
# 1,5700.79()
# 2,5691.69()
# 3,5682.59()
# 4,5673.5()
# ......
#
# '''
#
#
# #
# def get_average_capital():
# file_path = input(': ')
# average_capital_data = []
# file_object = open(file_path, 'r')
# try:
# for line in file_object:
# average_capital_data.append(float(line[line.find(',') + 1:line.find('(')]))
# finally:
# file_object.close()
# print(average_capital_data)
# return average_capital_data
'''
:
A= B= C= D= E=
==AC
==AB
:(-)=(A-AC)B
:(A-2AC)B
...
C:(A-(C-1)AC)B
E=B(AC-[A+2A+3A+...+(C-1)A]C)=B(AC-A(C-1)2)=AB(C+1)2
n:D=+n=AC+(A-(n-1)AC)B=(1+B(C-n+1))AC
'''
# 30
'''
A= B= C= D= E=
AB
[-]ADABBABD1B^1D
ADABDABD1B^1DBABD1B^2D
ABD1B^3D
.....
CABD1B^C1D
EABD[1B^0+1B^1+...+1B^C1]+CD=ABD1B^C1BCD
ECDAE
:DAB1B^C1B^C1
:ECD-A=CAB1B^C1B^C1A
:E+A=CAB1B^C1B^C1
:a^bab
'''
# 30
total = unit_price * area
loan = int((total - total * 0.3) / 10000) * 10000
first_pay = total - loan
print(':', total, ':', loan, ':', first_pay)
print("===============(+)================")
for j in range(1, 31):
# print(first_pay + prepayment_average_capital(j), first_pay + prepayment_average_capital_plus_interest(j), total)
print(first_pay + prepayment_average_capital(j))
print("===============(+)=================")
for k in range(1, 31):
print(first_pay + prepayment_average_capital_plus_interest(k))
print("===============-======")
for m in range(1, 31):
print(prepayment_average_capital_plus_interest(m) - prepayment_average_capital(m))
print("=====================")
for n in range(1, 31):
print(loan - (n / 30) * loan)
print("================================")
for l in range(1, 31):
print(total)
print("=====================")
for o in range(1, 31):
t1 = first_pay + prepayment_average_capital(o)
t2 = loan - (o / 30) * loan
print(t1 + t2)
print("=====================")
for p in range(1, 31):
t1 = first_pay + prepayment_average_capital_plus_interest(p)
t2 = loan - (p / 30) * loan
print(t1 + t2)
# # 15()
# prepayment_average_capital_data = prepayment_average_capital()
# # 15()
# prepayment_average_capital_plus_interest_data = prepayment_average_capital_plus_interest()
#
# print('', first_pay + prepayment_average_capital_data)
# print('', first_pay + prepayment_average_capital_plus_interest_data)
# print('', prepayment_average_capital_plus_interest_data - prepayment_average_capital_data)
| 27.662162
| 118
| 0.610405
|
# 等额本金和等额本息贷款计算
import math
area = float(input('请输入房屋面积(m^2): '))
unit_price = float(input('请输入单价(元): '))
year_interest = float(input('请输入贷款利率(%): '))
# '''
# 等额本金的每月还款情况文件，格式如下：
# 1月,5700.79(元)
# 2月,5691.69(元)
# 3月,5682.59(元)
# 4月,5673.5(元)
# ......
#
# '''
#
#
# # 读取文件内容，即每个月的还款数据
# def get_average_capital():
# file_path = input('请输入等额本金文件路径: ')
# average_capital_data = []
# file_object = open(file_path, 'r')
# try:
# for line in file_object:
# average_capital_data.append(float(line[line.find(',') + 1:line.find('(')]))
# finally:
# file_object.close()
# print(average_capital_data)
# return average_capital_data
'''
等额本金还款法:
设：A=贷款额 B=贷款月利率 C=贷款月数 D=每月还款额 E=还款利息总和
每月应还本金=贷款额÷还款月数=A÷C
第一月还款利息=剩余本金×月利率=A×B
第二月还款利息:(贷款额-第一月已归还本金额)×月利率=(A-A÷C)×B
第三月还款利息:(A-2×A÷C)×B
...
第C月还款利息:(A-(C-1)×A÷C)×B
求以上和得E=B(AC-[A+2A+3A+...+(C-1)A]÷C)=B(AC-A(C-1)÷2)=A×B×(C+1)÷2
每n月还款:D=本金+第n月利息=A÷C+(A-(n-1)×A÷C)×B=(1+B(C-n+1))×A÷C
'''
def get_average_capital():
average_capital_data = []
for i in range(0, 360):
average_capital_data.append((loan / 360) + ((loan - loan * i / 360) * (year_interest / (12 * 100))))
return average_capital_data
# 等额本金计算，默认30年
def prepayment_average_capital(year=30):
arr_float = get_average_capital()
count = 0
pay = 0
# pay = loan - (year / 30) * loan
for i in arr_float:
pay += i
count += 1
if count == year * 12:
break
return pay
'''
等额本息还款法：
设：A=贷款额 B=贷款月利率 C=贷款月数 D=每月还款额 E=还款利息总和
第一月还款利息为【贷款额×月利率】：A×B
第二月还款利息为【[贷款额-第一个月已还本金]×月利率】：〔A－（D－A×B）〕×B＝（A×B－D）×（1＋B）^1＋D
第三月还款利息为：｛A－（D－A×B）－〔D－（A×B－D）×（1＋B）^1－D〕｝×B＝（A×B－D）×（1＋B）^2＋D
第四月还款利息为：＝（A×B－D）×（1＋B）^3＋D
.....
第C月还款利息为：＝（A×B－D）×（1＋B）^（C－1）＋D
求以上和为：E＝（A×B－D）[（1＋B）^0+（1＋B）^1+...+（1＋B）^（C－1）]+C×D=（A×B－D）×〔（1＋B）^C－1〕÷B＋C×D
而利息总和E＝C×D－A，两项E值相等求得
月均还款:D＝A×B×（1＋B）^C÷〔（1＋B）^C－1〕
支付利息总和:E＝C×D-A=C×A×B×（1＋B）^C÷〔（1＋B）^C－1〕－A
还款总额:E+A=C×A×B×（1＋B）^C÷〔（1＋B）^C－1〕
附注:a^b表示a的b次方。
'''
# 等额本息计算，默认30年
def prepayment_average_capital_plus_interest(year=30):
# pay = loan - (year / 30) * loan
pay = 0
month_interest = year_interest / (12 * 100)
month_pay = (loan * month_interest * math.pow((1 + month_interest), 360)) / (
math.pow((1 + month_interest), 360) - 1)
for i in range(1, year * 12 + 1):
pay += month_pay
return pay
total = unit_price * area
loan = int((total - total * 0.3) / 10000) * 10000
first_pay = total - loan
print('房屋总价:', total, '贷款额:', loan, '首付款:', first_pay)
print("===============等额本金已还款总额(本金+利息)================")
for j in range(1, 31):
# print(first_pay + prepayment_average_capital(j), first_pay + prepayment_average_capital_plus_interest(j), total)
print(first_pay + prepayment_average_capital(j))
print("===============等额本息已还款总额(本金+利息)=================")
for k in range(1, 31):
print(first_pay + prepayment_average_capital_plus_interest(k))
print("===============等额本息已还款总额-等额本金已还款总额======")
for m in range(1, 31):
print(prepayment_average_capital_plus_interest(m) - prepayment_average_capital(m))
print("===============一次性还清剩余贷款本金======")
for n in range(1, 31):
print(loan - (n / 30) * loan)
print("===============总房价=================")
for l in range(1, 31):
print(total)
print("===============等额本金总还款额======")
for o in range(1, 31):
t1 = first_pay + prepayment_average_capital(o)
t2 = loan - (o / 30) * loan
print(t1 + t2)
print("===============等额本息总还款额======")
for p in range(1, 31):
t1 = first_pay + prepayment_average_capital_plus_interest(p)
t2 = loan - (p / 30) * loan
print(t1 + t2)
# # 输入年份计算等额本金已经付给银行的钱，默认15年(提前还)
# prepayment_average_capital_data = prepayment_average_capital()
# # 输入年份计算等额本息已经付给银行的钱，默认15年(提前还)
# prepayment_average_capital_plus_interest_data = prepayment_average_capital_plus_interest()
#
# print('等额本金总耗资：', first_pay + prepayment_average_capital_data)
# print('等额本息总耗资：', first_pay + prepayment_average_capital_plus_interest_data)
# print('少还利息：', prepayment_average_capital_plus_interest_data - prepayment_average_capital_data)
| 2,007
| 0
| 0
| 0
| 0
| 789
| 0
| -10
| 91
|
10f7ff6712dc6b5dcc4a16b9b8b4446dac1019fe
| 4,949
|
py
|
Python
|
cmsplugin_blog_categories/migrations/0001_initial.py
|
bitmazk/cmsplugin-blog-categories
|
05e2fa3d50a8501f3f3f9cab784269838079cc37
|
[
"MIT"
] | null | null | null |
cmsplugin_blog_categories/migrations/0001_initial.py
|
bitmazk/cmsplugin-blog-categories
|
05e2fa3d50a8501f3f3f9cab784269838079cc37
|
[
"MIT"
] | 3
|
2020-02-11T22:01:45.000Z
|
2021-06-10T17:38:13.000Z
|
cmsplugin_blog_categories/migrations/0001_initial.py
|
bitmazk/cmsplugin-blog-categories
|
05e2fa3d50a8501f3f3f9cab784269838079cc37
|
[
"MIT"
] | null | null | null |
# flake8: noqa
# -*- coding: utf-8 -*-
| 53.793478
| 137
| 0.62255
|
# flake8: noqa
# -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'Category'
db.create_table('cmsplugin_blog_categories_category', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('creation_date', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),
('slug', self.gf('django.db.models.fields.SlugField')(max_length=512)),
))
db.send_create_signal('cmsplugin_blog_categories', ['Category'])
# Adding model 'CategoryTitle'
db.create_table('cmsplugin_blog_categories_categorytitle', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('title', self.gf('django.db.models.fields.CharField')(max_length=256)),
('category', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['cmsplugin_blog_categories.Category'])),
('language', self.gf('django.db.models.fields.CharField')(max_length=5)),
))
db.send_create_signal('cmsplugin_blog_categories', ['CategoryTitle'])
# Adding model 'EntryCategory'
db.create_table('cmsplugin_blog_categories_entrycategory', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('entry', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['cmsplugin_blog.Entry'])),
('category', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['cmsplugin_blog_categories.Category'])),
))
db.send_create_signal('cmsplugin_blog_categories', ['EntryCategory'])
# Adding unique constraint on 'EntryCategory', fields ['entry', 'category']
db.create_unique('cmsplugin_blog_categories_entrycategory', ['entry_id', 'category_id'])
def backwards(self, orm):
# Removing unique constraint on 'EntryCategory', fields ['entry', 'category']
db.delete_unique('cmsplugin_blog_categories_entrycategory', ['entry_id', 'category_id'])
# Deleting model 'Category'
db.delete_table('cmsplugin_blog_categories_category')
# Deleting model 'CategoryTitle'
db.delete_table('cmsplugin_blog_categories_categorytitle')
# Deleting model 'EntryCategory'
db.delete_table('cmsplugin_blog_categories_entrycategory')
models = {
'cms.placeholder': {
'Meta': {'object_name': 'Placeholder'},
'default_width': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'slot': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'})
},
'cmsplugin_blog.entry': {
'Meta': {'ordering': "('-pub_date',)", 'object_name': 'Entry'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_published': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'placeholders': ('djangocms_utils.fields.M2MPlaceholderField', [], {'to': "orm['cms.Placeholder']", 'symmetrical': 'False'}),
'pub_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'tags': ('tagging.fields.TagField', [], {})
},
'cmsplugin_blog_categories.category': {
'Meta': {'object_name': 'Category'},
'creation_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '512'})
},
'cmsplugin_blog_categories.categorytitle': {
'Meta': {'object_name': 'CategoryTitle'},
'category': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cmsplugin_blog_categories.Category']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'language': ('django.db.models.fields.CharField', [], {'max_length': '5'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '256'})
},
'cmsplugin_blog_categories.entrycategory': {
'Meta': {'unique_together': "(('entry', 'category'),)", 'object_name': 'EntryCategory'},
'category': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cmsplugin_blog_categories.Category']"}),
'entry': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cmsplugin_blog.Entry']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'})
}
}
complete_apps = ['cmsplugin_blog_categories']
| 0
| 0
| 0
| 4,780
| 0
| 0
| 0
| 18
| 111
|
715bfc9c37739cc5dc1ee47ee653d0ae23d340ef
| 5,326
|
py
|
Python
|
src/analyze/track/analyze_race.py
|
kishorekolli/deep_racer_guru
|
0a0a56103f395f958e8177ee0bd5ae1481f93d98
|
[
"MIT"
] | 9
|
2020-07-31T03:04:24.000Z
|
2021-11-02T13:44:11.000Z
|
src/analyze/track/analyze_race.py
|
kishorekolli/deep_racer_guru
|
0a0a56103f395f958e8177ee0bd5ae1481f93d98
|
[
"MIT"
] | 137
|
2020-08-04T08:04:11.000Z
|
2021-11-10T10:35:58.000Z
|
src/analyze/track/analyze_race.py
|
kishorekolli/deep_racer_guru
|
0a0a56103f395f958e8177ee0bd5ae1481f93d98
|
[
"MIT"
] | 11
|
2020-06-24T23:38:38.000Z
|
2021-11-02T14:42:31.000Z
|
#
# DeepRacer Guru
#
# Version 3.0 onwards
#
# Copyright (c) 2021 dmh23
#
import src.utils.geometry as geometry
| 37.507042
| 111
| 0.665039
|
#
# DeepRacer Guru
#
# Version 3.0 onwards
#
# Copyright (c) 2021 dmh23
#
import threading
import time
import src.utils.geometry as geometry
import tkinter as tk
from src.analyze.track.track_analyzer import TrackAnalyzer
from src.episode.episode import Episode
from src.graphics.track_graphics import TrackGraphics
from src.analyze.core.controls import VideoControls, LapTimeControl
from src.personalize.configuration.appearance import RACE_COLOURS
class AnalyzeRace(TrackAnalyzer):
def __init__(self, guru_parent_redraw, track_graphics :TrackGraphics, control_frame :tk.Frame):
super().__init__(guru_parent_redraw, track_graphics, control_frame)
self._video_controls = VideoControls(self._button_pressed, control_frame)
self._display_lap_time = LapTimeControl(control_frame)
self._timer = AnalyzeRace.Timer(self._draw)
# self._race_episode = self.all_episodes[0]
def build_control_frame(self, control_frame):
self._display_lap_time.add_to_control_frame()
self._video_controls.add_to_control_frame()
def _button_pressed(self, button_type):
if button_type == VideoControls.STOP:
self._timer.stop()
elif button_type == VideoControls.RESET:
self._timer.reset()
elif button_type == VideoControls.PLAY:
self._timer.play()
def redraw(self):
self._timer.redraw()
def warning_filtered_episodes_changed(self):
self._timer.reset(False)
def warning_lost_control(self):
self._timer.stop()
def _draw(self, simulation_time):
self._display_lap_time.show_time(simulation_time)
self.track_graphics.prepare_to_remove_old_cars()
all_done = True
if self.filtered_episodes:
for i, episode in enumerate(self.filtered_episodes[0:len(RACE_COLOURS)]):
self._draw_episode_car(episode, simulation_time, RACE_COLOURS[i])
if simulation_time < episode.time_taken:
all_done = False
self.track_graphics.remove_cars()
if all_done:
self._timer.soft_stop()
def _draw_episode_car(self, episode: Episode, simulation_time: float, colour: str):
event_index = episode.get_latest_event_index_on_or_before(simulation_time)
before_event = episode.events[event_index]
if event_index == len(episode.events) - 1:
self.track_graphics.draw_car(before_event.x, before_event.y, colour, before_event.heading)
else:
after_event = episode.events[event_index + 1]
event_x_gap = after_event.x - before_event.x
event_y_gap = after_event.y - before_event.y
event_time_gap = after_event.time_elapsed - before_event.time_elapsed
event_heading_gap = geometry.get_turn_between_directions(before_event.heading, after_event.heading)
ratio = (simulation_time - before_event.time_elapsed) / event_time_gap
x = before_event.x + ratio * event_x_gap
y = before_event.y + ratio * event_y_gap
heading = geometry.get_angle_in_proper_range(before_event.heading + ratio * event_heading_gap)
self.track_graphics.draw_car(x, y, colour, heading)
class Timer:
def __init__(self, redraw_callback: callable):
self._machine_start_time = 0.0
self._simulation_start_time = 0.0
self._simulation_stop_time = 0.0
self._keep_running = False
self._is_still_running = False
self._thread = None
self._redraw_callback = redraw_callback
def stop(self):
if self._keep_running:
self._keep_running = False
if self._is_still_running:
self._thread.join(0.2)
def soft_stop(self):
self._keep_running = False
def play(self):
if not self._keep_running and not self._is_still_running:
self._keep_running = True
self._thread = threading.Thread(target=self._run_until_stopped)
self._thread.daemon = True # Set as daemon so thread is killed if main GUI is closed
self._thread.start()
def reset(self, redraw=True):
self.stop()
self._simulation_stop_time = 0.0
self._simulation_start_time = 0.0
if redraw:
self._redraw_callback(0.0)
def redraw(self):
if self._is_still_running:
self._redraw_callback(self.get_current_simulation_time())
else:
self._redraw_callback(self._simulation_stop_time)
def _run_until_stopped(self):
self._is_still_running = True
self._simulation_start_time = self._simulation_stop_time
self._machine_start_time = time.time()
while self._keep_running:
simulation_time = self.get_current_simulation_time()
self._redraw_callback(simulation_time)
time.sleep(0.02)
self._simulation_stop_time = self.get_current_simulation_time()
self._is_still_running = False
def get_current_simulation_time(self):
return time.time() - self._machine_start_time + self._simulation_start_time
| 0
| 0
| 0
| 4,849
| 0
| 0
| 0
| 161
| 203
|
f0e86e901437c0295d3035b8e8488571c49c3943
| 1,821
|
py
|
Python
|
algospot/lec11/[cutz]gamecover.py
|
cutz-j/AlgorithmStudy
|
de0f81220e29bd5e109d174800f507b12a3bee36
|
[
"MIT"
] | 3
|
2019-11-26T14:31:01.000Z
|
2020-01-10T18:19:46.000Z
|
algospot/lec11/[cutz]gamecover.py
|
cutz-j/AlgorithmStudy
|
de0f81220e29bd5e109d174800f507b12a3bee36
|
[
"MIT"
] | null | null | null |
algospot/lec11/[cutz]gamecover.py
|
cutz-j/AlgorithmStudy
|
de0f81220e29bd5e109d174800f507b12a3bee36
|
[
"MIT"
] | null | null | null |
cover = [[[0, 0], [1, 0], [0, 1]],
[[0, 0], [0, 1], [1, 1]],
[[0, 0], [1, 0], [1, 1]],
[[0, 0], [1, 0], [1, -1]]]
#rl = lambda: sys.stdin.readline()
rl = input
C = int(rl())
for _ in range(C):
H, W = map(int, rl().split())
block_list = []
white, black = 0, 0
for __ in range(H):
block_row = rl()
tmp_list = []
for b in block_row:
if b == '#':
black += 1
tmp_list.append(1)
else:
white += 1
tmp_list.append(0)
block_list.append(tmp_list)
if white % 3 != 0:
print(0)
continue
if black == (W*H):
print(1)
continue
print(cover_block(block_list))
| 21.939759
| 62
| 0.409665
|
import sys
cover = [[[0, 0], [1, 0], [0, 1]],
[[0, 0], [0, 1], [1, 1]],
[[0, 0], [1, 0], [1, 1]],
[[0, 0], [1, 0], [1, -1]]]
def set_block(board, y, x, cover_type, delta):
# delta 1 --> cover // -1 --> clear
ok = True
for i in range(3):
new_x = x + cover[cover_type][i][1]
new_y = y + cover[cover_type][i][0]
# 게임판을 넘어가는 경우
if new_y < 0 or new_y >= H or new_x < 0 or new_x >= W:
ok = False
# 중복으로 덮은 경우
else:
board[new_y][new_x] += delta
if board[new_y][new_x] > 1:
ok = False
return ok
def cover_block(board):
x, y = -1, -1
for i in range(H):
for j in range(W):
if board[i][j] == 0:
x = j
y = i
break
if y != -1:
break
# basis
if y == -1:
return 1
ret = 0
# 4가지 방법 모두 실행
for t in range(4):
# block 넣기
if set_block(board, y, x, t, 1) == True:
ret += cover_block(board)
# block 치우기
set_block(board, y, x, t, -1)
return ret
#rl = lambda: sys.stdin.readline()
rl = input
C = int(rl())
for _ in range(C):
H, W = map(int, rl().split())
block_list = []
white, black = 0, 0
for __ in range(H):
block_row = rl()
tmp_list = []
for b in block_row:
if b == '#':
black += 1
tmp_list.append(1)
else:
white += 1
tmp_list.append(0)
block_list.append(tmp_list)
if white % 3 != 0:
print(0)
continue
if black == (W*H):
print(1)
continue
print(cover_block(block_list))
| 93
| 0
| 0
| 0
| 0
| 933
| 0
| -11
| 68
|
64c67a4ed2acbb4051af77291436c1f297af4078
| 1,167
|
py
|
Python
|
cmsplugin_soundcloud/cms_plugins.py
|
misli/cmsplugin-soundcloud
|
d50e6fd1fd95916eb5a396fd9a437483d96626b3
|
[
"BSD-3-Clause"
] | null | null | null |
cmsplugin_soundcloud/cms_plugins.py
|
misli/cmsplugin-soundcloud
|
d50e6fd1fd95916eb5a396fd9a437483d96626b3
|
[
"BSD-3-Clause"
] | 1
|
2021-05-13T15:23:09.000Z
|
2021-06-29T09:25:36.000Z
|
cmsplugin_soundcloud/cms_plugins.py
|
misli/cmsplugin-soundcloud
|
d50e6fd1fd95916eb5a396fd9a437483d96626b3
|
[
"BSD-3-Clause"
] | 1
|
2017-06-01T15:46:46.000Z
|
2017-06-01T15:46:46.000Z
|
from cms.plugin_pool import plugin_pool
from django.conf import settings
# use CMSPLUGIN_SOUNDCLOUD_PARAMS to override PARAMS
PARAMS = getattr(settings, 'CMSPLUGIN_SOUNDCLOUD_PARAMS',
'width="100%" height="166" scrolling="no" frameborder="no"')
plugin_pool.register_plugin(SoundCloudPlugin)
| 30.710526
| 78
| 0.709512
|
from cms.plugin_base import CMSPluginBase
from cms.plugin_pool import plugin_pool
from .models import SoundCloud, COLORS
from django.conf import settings
from django.utils.translation import ugettext as _
from django.utils.safestring import mark_safe
# use CMSPLUGIN_SOUNDCLOUD_PARAMS to override PARAMS
PARAMS = getattr(settings, 'CMSPLUGIN_SOUNDCLOUD_PARAMS',
'width="100%" height="166" scrolling="no" frameborder="no"')
class SoundCloudPlugin(CMSPluginBase):
model = SoundCloud
name = _('SoundCloud')
text_enabled = True
render_template = 'cmsplugin_soundcloud/cmsplugin_soundcloud.html'
def render(self, context, instance, placeholder):
context.update({'plugin_soundcloud': instance})
return context
def icon_src(self, instance):
return instance.thumbnail_url
#def get_form(self, request, obj=None, **kwargs):
# if obj:
# kwargs['exclude'] = ['url']
# else:
# kwargs['exclude'] = ['title', 'description', 'thumbnail_url']
# return super(SoundCloudPlugin, self).get_form(request, obj, **kwargs)
plugin_pool.register_plugin(SoundCloudPlugin)
| 0
| 0
| 0
| 651
| 0
| 0
| 0
| 90
| 112
|
43cbe788101c59dea633374ac2936b60b77131a3
| 408
|
py
|
Python
|
1/find_inimum_in_rotated_sorted_array.py
|
IronCore864/leetcode
|
a62a4cdde9814ae48997176debcaad537f7ad01f
|
[
"Apache-2.0"
] | 4
|
2018-03-07T02:56:03.000Z
|
2021-06-15T05:43:31.000Z
|
1/find_inimum_in_rotated_sorted_array.py
|
IronCore864/leetcode
|
a62a4cdde9814ae48997176debcaad537f7ad01f
|
[
"Apache-2.0"
] | null | null | null |
1/find_inimum_in_rotated_sorted_array.py
|
IronCore864/leetcode
|
a62a4cdde9814ae48997176debcaad537f7ad01f
|
[
"Apache-2.0"
] | 1
|
2021-09-02T12:05:15.000Z
|
2021-09-02T12:05:15.000Z
|
s = Solution()
print s.findMin([4, 5, 5, 6, 7, 0, 1, 2, 2, 2])
| 20.4
| 47
| 0.360294
|
class Solution(object):
def findMin(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
i = 0
j = len(nums) - 1
while i < j:
m = i + (j - i) / 2
if nums[m] > nums[j]:
i = m + 1
else:
j = m
return nums[i]
s = Solution()
print s.findMin([4, 5, 5, 6, 7, 0, 1, 2, 2, 2])
| 0
| 0
| 0
| 321
| 0
| 0
| 0
| 0
| 22
|
e965a8d33c910c1887e79ac768c1271d3fda827c
| 3,711
|
py
|
Python
|
qiling/qiling/os/posix/syscall/fcntl.py
|
mrTavas/owasp-fstm-auto
|
6e9ff36e46d885701c7419db3eca15f12063a7f3
|
[
"CC0-1.0"
] | 2
|
2021-05-05T12:03:01.000Z
|
2021-06-04T14:27:15.000Z
|
qiling/qiling/os/posix/syscall/fcntl.py
|
mrTavas/owasp-fstm-auto
|
6e9ff36e46d885701c7419db3eca15f12063a7f3
|
[
"CC0-1.0"
] | null | null | null |
qiling/qiling/os/posix/syscall/fcntl.py
|
mrTavas/owasp-fstm-auto
|
6e9ff36e46d885701c7419db3eca15f12063a7f3
|
[
"CC0-1.0"
] | 2
|
2021-05-05T12:03:09.000Z
|
2021-06-04T14:27:21.000Z
|
#!/usr/bin/env python3
#
# Cross Platform and Multi Architecture Advanced Binary Emulation Framework
#
| 27.488889
| 118
| 0.61924
|
#!/usr/bin/env python3
#
# Cross Platform and Multi Architecture Advanced Binary Emulation Framework
#
from qiling.const import *
from qiling.os.linux.thread import *
from qiling.const import *
from qiling.os.posix.filestruct import *
from qiling.os.filestruct import *
from qiling.os.posix.const_mapping import *
from qiling.exception import *
def ql_syscall_open(ql, filename, flags, mode, *args, **kw):
path = ql.mem.string(filename)
real_path = ql.os.path.transform_to_real_path(path)
relative_path = ql.os.path.transform_to_relative_path(path)
flags = flags & 0xffffffff
mode = mode & 0xffffffff
for i in range(256):
if ql.os.fd[i] == 0:
idx = i
break
else:
idx = -1
if idx == -1:
# errno ENOMEM Insufficient kernel memory was available.
regreturn = -12
else:
try:
if ql.archtype== QL_ARCH.ARM:
mode = 0
flags = ql_open_flag_mapping(ql, flags)
ql.os.fd[idx] = ql.os.fs_mapper.open_ql_file(path, flags, mode)
regreturn = idx
except QlSyscallError as e:
regreturn = - e.errno
ql.log.debug("open(%s, %s, 0o%o) = %d" % (relative_path, open_flags_mapping(flags, ql.archtype), mode, regreturn))
if regreturn >= 0 and regreturn != 2:
ql.log.debug("File Found: %s" % real_path)
else:
ql.log.debug("File Not Found %s" % real_path)
return regreturn
def ql_syscall_openat(ql, openat_fd, openat_path, openat_flags, openat_mode, *args, **kw):
openat_fd = ql.unpacks(ql.pack(openat_fd))
openat_path = ql.mem.string(openat_path)
real_path = ql.os.path.transform_to_real_path(openat_path)
relative_path = ql.os.path.transform_to_relative_path(openat_path)
openat_flags = openat_flags & 0xffffffff
openat_mode = openat_mode & 0xffffffff
for i in range(256):
if ql.os.fd[i] == 0:
idx = i
break
else:
idx = -1
if idx == -1:
regreturn = -1
else:
try:
if ql.archtype== QL_ARCH.ARM:
mode = 0
openat_flags = ql_open_flag_mapping(ql, openat_flags)
ql.os.fd[idx] = ql.os.fs_mapper.open_ql_file(openat_path, openat_flags, openat_mode)
regreturn = idx
except QlSyscallError:
regreturn = -1
ql.log.debug("openat(%d, %s, %s, 0o%o) = %d" % (
openat_fd, relative_path, open_flags_mapping(openat_flags, ql.archtype), openat_mode, regreturn))
if regreturn >= 0 and regreturn != 2:
ql.log.debug("File Found: %s" % real_path)
else:
ql.log.debug("File Not Found %s" % real_path)
return regreturn
def ql_syscall_fcntl(ql, fcntl_fd, fcntl_cmd, *args, **kw):
F_SETFD = 2
F_GETFL = 3
F_SETFL = 4
regreturn = 0
if fcntl_cmd == F_SETFD:
regreturn = 0
elif fcntl_cmd == F_GETFL:
regreturn = 2
elif fcntl_cmd == F_SETFL:
regreturn = 0
return regreturn
def ql_syscall_fcntl64(ql, fcntl_fd, fcntl_cmd, fcntl_arg, *args, **kw):
F_GETFD = 1
F_SETFD = 2
F_GETFL = 3
F_SETFL = 4
if fcntl_cmd == F_GETFL:
regreturn = 2
elif fcntl_cmd == F_SETFL:
if isinstance(ql.os.fd[fcntl_fd], ql_socket):
ql.os.fd[fcntl_fd].fcntl(fcntl_cmd, fcntl_arg)
regreturn = 0
elif fcntl_cmd == F_GETFD:
regreturn = 2
elif fcntl_cmd == F_SETFD:
regreturn = 0
else:
regreturn = 0
return regreturn
def ql_syscall_flock(ql, flock_fd, flock_operation, *args, **kw):
# Should always return 0, we don't need a actual file lock
regreturn = 0
return regreturn
| 0
| 0
| 0
| 0
| 0
| 3,245
| 0
| 88
| 270
|
4e431cabe02f617dd38da093c80ad9c5404af08a
| 2,904
|
py
|
Python
|
test/unit/mysql_rep_admin/add_miss_slaves.py
|
mjpernot/mysql-rep-admin
|
08b8f5daf28bc3e462bd72968842f2c44161c084
|
[
"MIT"
] | null | null | null |
test/unit/mysql_rep_admin/add_miss_slaves.py
|
mjpernot/mysql-rep-admin
|
08b8f5daf28bc3e462bd72968842f2c44161c084
|
[
"MIT"
] | null | null | null |
test/unit/mysql_rep_admin/add_miss_slaves.py
|
mjpernot/mysql-rep-admin
|
08b8f5daf28bc3e462bd72968842f2c44161c084
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
# Classification (U)
"""Program: add_miss_slaves.py
Description: Unit testing of add_miss_slaves in mysql_rep_admin.py.
Usage:
test/unit/mysql_rep_admin/add_miss_slaves.py
Arguments:
"""
# Libraries and Global Variables
# Standard
import sys
import os
if sys.version_info < (2, 7):
import unittest2 as unittest
else:
import unittest
# Third-party
# Local
sys.path.append(os.getcwd())
import version
__version__ = version.__version__
if __name__ == "__main__":
unittest.main()
| 20.892086
| 75
| 0.539945
|
#!/usr/bin/python
# Classification (U)
"""Program: add_miss_slaves.py
Description: Unit testing of add_miss_slaves in mysql_rep_admin.py.
Usage:
test/unit/mysql_rep_admin/add_miss_slaves.py
Arguments:
"""
# Libraries and Global Variables
# Standard
import sys
import os
if sys.version_info < (2, 7):
import unittest2 as unittest
else:
import unittest
# Third-party
# Local
sys.path.append(os.getcwd())
import mysql_rep_admin
import version
__version__ = version.__version__
class MasterRep(object):
"""Class: MasterRep
Description: Class stub holder for mysql_class.MasterRep class.
Methods:
__init__
show_slv_hosts
"""
def __init__(self):
"""Method: __init__
Description: Class initialization.
Arguments:
"""
self.name = "Master_Name"
self.slv_hosts = [{"Host": "slave1"}, {"Host": "slave2"},
{"Host": "slave3"}]
def show_slv_hosts(self):
"""Method: show_slv_hosts
Description: Stub method holder for SlaveRep.get_err_stat.
Arguments:
"""
return self.slv_hosts
class UnitTest(unittest.TestCase):
"""Class: UnitTest
Description: Class which is a representation of a unit testing.
Methods:
setUp
test_no_slv_miss
test_one_slv_miss
"""
def setUp(self):
"""Function: setUp
Description: Initialization for unit testing.
Arguments:
"""
self.master = MasterRep()
self.outdata = {"Slaves": [{"Name": "slave1", "LagTime": None},
{"Name": "slave2", "LagTime": None}]}
self.outdata2 = {"Slaves": [{"Name": "slave1", "LagTime": None},
{"Name": "slave2", "LagTime": None},
{"Name": "slave3", "LagTime": None}]}
self.final_list = {"Slaves": [{"Name": "slave1", "LagTime": None},
{"Name": "slave2", "LagTime": None},
{"Name": "slave3", "LagTime": None}]}
def test_no_slv_miss(self):
"""Function: test_no_slv_miss
Description: Test with no slave missing.
Arguments:
"""
self.assertEqual(mysql_rep_admin.add_miss_slaves(self.master,
self.outdata2),
self.final_list)
def test_one_slv_miss(self):
"""Function: test_one_slv_miss
Description: Test with one slave missing.
Arguments:
"""
self.assertEqual(mysql_rep_admin.add_miss_slaves(self.master,
self.outdata),
self.final_list)
if __name__ == "__main__":
unittest.main()
| 0
| 0
| 0
| 2,292
| 0
| 0
| 0
| 1
| 68
|
99017e06c8585e5388a22a35b7a3f448afce5ddd
| 9,116
|
py
|
Python
|
SUnCNN_DC2.py
|
BehnoodRasti/SUnCNN
|
f19245cd7fc0e142bfc66c9a809444d22ed7da36
|
[
"Apache-2.0"
] | 3
|
2021-11-17T09:34:15.000Z
|
2022-03-26T11:51:48.000Z
|
SUnCNN_DC2.py
|
BehnoodRasti/SUnCNN
|
f19245cd7fc0e142bfc66c9a809444d22ed7da36
|
[
"Apache-2.0"
] | null | null | null |
SUnCNN_DC2.py
|
BehnoodRasti/SUnCNN
|
f19245cd7fc0e142bfc66c9a809444d22ed7da36
|
[
"Apache-2.0"
] | 2
|
2022-02-24T12:41:02.000Z
|
2022-03-30T12:26:21.000Z
|
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 4 17:53:50 2020
@author: behnood
"""
from __future__ import print_function
#%matplotlib inline
#os.environ['CUDA_VISIBLE_DEVICES'] = '3'
import numpy as np
import torch
import torch.optim
from numpy import linalg as LA
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark =True
dtype = torch.cuda.FloatTensor
PLOT = False
import scipy.io
#%%
#%%
fname2 = "Data/DC2/Y_clean.mat"
mat2 = scipy.io.loadmat(fname2)
img_np_gt = mat2["Y_clean"]
img_np_gt = img_np_gt.transpose(2,0,1)
[p1, nr1, nc1] = img_np_gt.shape
#%%
fname3 = "Data/DC2/XT.mat"
mat3 = scipy.io.loadmat(fname3)
A_true_np = mat3["XT"]
#%%
fname4 = "Data/DC2/EE.mat"
mat4 = scipy.io.loadmat(fname4)
EE = mat4["EE"]
#%%
LibS=EE.shape[1]
#%%
npar=np.zeros((1,3))
npar[0,0]=14.7
npar[0,1]=46.5
npar[0,2]=147
#npar[0,3]=367
tol1=npar.shape[1]
tol2=1
save_result=False
from tqdm import tqdm
rmax=9
for fi in tqdm(range(tol1)):
for fj in tqdm(range(tol2)):
#%%
img_np_gt=np.clip(img_np_gt, 0, 1)
img_noisy_np = add_noise(img_np_gt, 1/npar[0,fi])#11.55 20 dB, 36.7 30 dB, 116.5 40 dB
print(compare_snr(img_np_gt, img_noisy_np))
img_resh=np.reshape(img_noisy_np,(p1,nr1*nc1))
V, SS, U = scipy.linalg.svd(img_resh, full_matrices=False)
PC=np.diag(SS)@U
img_resh_DN=V[:,:rmax]@PC[:rmax,:]
img_noisy_np=np.reshape(np.clip(img_resh_DN, 0, 1),(p1,nr1,nc1))
INPUT = 'noise' # 'meshgrid'
pad = 'reflection'
need_bias=True
OPT_OVER = 'net' # 'net,input'
#
reg_noise_std = 0.0
LR1 = 0.001
OPTIMIZER1='adam'# 'RMSprop'#'adam' # 'LBFGS'
show_every = 500
exp_weight=0.99
if fi==0:
num_iter1 = 4000
elif fi==1:
num_iter1 = 8000
elif fi==2:
num_iter1 = 12000
input_depth =img_noisy_np.shape[0]
net1 = CAE_AbEst()
net1.cuda()
print(net1)
# Compute number of parameters
s = sum([np.prod(list(p11.size())) for p11 in net1.parameters()]);
print ('Number of params: %d' % s)
# Loss
mse = torch.nn.MSELoss().type(dtype)
img_noisy_torch = np_to_torch(img_noisy_np).type(dtype)
# if fk==0:
net_input1 = get_noise(input_depth, INPUT,
(img_noisy_np.shape[1], img_noisy_np.shape[2])).type(dtype).detach()
# net_input1 = img_noisy_torch
E_torch = np_to_torch(EE).type(dtype)
#%%
net_input_saved = net_input1.detach().clone()
noise = net_input1.detach().clone()
out_avg = None
out_HR_avg= None
last_net = None
RMSE_LR_last = 0
loss=np.zeros((num_iter1,1))
AE=np.zeros((num_iter1,1))
i = 0
p11 = get_params(OPT_OVER, net1, net_input1)
optimize(OPTIMIZER1, p11, closure1, LR1, num_iter1)
if 1:
out_LR_np = out_LR.detach().cpu().squeeze().numpy()
out_avg_np = out_avg.detach().cpu().squeeze().numpy()
MAE_LR_avg= 100*np.mean(abs(A_true_np.astype(np.float32)- np.clip(out_avg_np, 0, 1)))
MAE_LR= 100*np.mean(abs(A_true_np.astype(np.float32)- np.clip(out_LR_np, 0, 1)))
SRE=10*np.log10(LA.norm(A_true_np.astype(np.float32).reshape((EE.shape[1],nr1*nc1)),'fro')/LA.norm((A_true_np.astype(np.float32)- np.clip(out_LR_np, 0, 1)).reshape((EE.shape[1],nr1*nc1)),'fro'))
SRE_avg=10*np.log10(LA.norm(A_true_np.astype(np.float32).reshape((EE.shape[1],nr1*nc1)),'fro')/LA.norm((A_true_np.astype(np.float32)- np.clip(out_avg_np, 0, 1)).reshape((EE.shape[1],nr1*nc1)),'fro'))
print ('Iteration %05d MAE_LR: %f MAE_LR_avg: %f SRE: %f SRE_avg: %f ' % (i, MAE_LR, MAE_LR_avg, SRE, SRE_avg), '\r', end='')
# if save_result is True:
# scipy.io.savemat("C:/Users/behnood/Desktop/Sparse Unmixing/Results/Sim2/demo1/10runs/out_avg_np%01d%01d.mat" % (fi+2,fj+1),
# {'out_avg_np%01d%01d' % (fi+2, fj+1):out_avg_np.transpose(1,2,0)})
# scipy.io.savemat("C:/Users/behnood/Desktop/Sparse Unmixing/Results/Sim2/demo1/10runs/out_LR_np%01d%01d.mat" % (fi+2,fj+1),
# {'out_LR_np%01d%01d' % (fi+2, fj+1):out_LR_np.transpose(1,2,0)})
#%%
| 41.625571
| 213
| 0.554739
|
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 4 17:53:50 2020
@author: behnood
"""
from __future__ import print_function
import matplotlib.pyplot as plt
#%matplotlib inline
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '3'
import numpy as np
from models import *
import torch
import torch.optim
from skimage.measure import compare_psnr
from skimage.measure import compare_mse
from utils.denoising_utils import *
from skimage._shared import *
from skimage.util import *
from skimage.metrics.simple_metrics import _as_floats
from skimage.metrics.simple_metrics import mean_squared_error
from UtilityMine import *
from utils.sr_utils import tv_loss
from numpy import linalg as LA
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark =True
dtype = torch.cuda.FloatTensor
PLOT = False
import scipy.io
#%%
#%%
fname2 = "Data/DC2/Y_clean.mat"
mat2 = scipy.io.loadmat(fname2)
img_np_gt = mat2["Y_clean"]
img_np_gt = img_np_gt.transpose(2,0,1)
[p1, nr1, nc1] = img_np_gt.shape
#%%
fname3 = "Data/DC2/XT.mat"
mat3 = scipy.io.loadmat(fname3)
A_true_np = mat3["XT"]
#%%
fname4 = "Data/DC2/EE.mat"
mat4 = scipy.io.loadmat(fname4)
EE = mat4["EE"]
#%%
LibS=EE.shape[1]
#%%
npar=np.zeros((1,3))
npar[0,0]=14.7
npar[0,1]=46.5
npar[0,2]=147
#npar[0,3]=367
tol1=npar.shape[1]
tol2=1
save_result=False
import time
from tqdm import tqdm
rmax=9
for fi in tqdm(range(tol1)):
for fj in tqdm(range(tol2)):
#%%
img_np_gt=np.clip(img_np_gt, 0, 1)
img_noisy_np = add_noise(img_np_gt, 1/npar[0,fi])#11.55 20 dB, 36.7 30 dB, 116.5 40 dB
print(compare_snr(img_np_gt, img_noisy_np))
img_resh=np.reshape(img_noisy_np,(p1,nr1*nc1))
V, SS, U = scipy.linalg.svd(img_resh, full_matrices=False)
PC=np.diag(SS)@U
img_resh_DN=V[:,:rmax]@PC[:rmax,:]
img_noisy_np=np.reshape(np.clip(img_resh_DN, 0, 1),(p1,nr1,nc1))
INPUT = 'noise' # 'meshgrid'
pad = 'reflection'
need_bias=True
OPT_OVER = 'net' # 'net,input'
#
reg_noise_std = 0.0
LR1 = 0.001
OPTIMIZER1='adam'# 'RMSprop'#'adam' # 'LBFGS'
show_every = 500
exp_weight=0.99
if fi==0:
num_iter1 = 4000
elif fi==1:
num_iter1 = 8000
elif fi==2:
num_iter1 = 12000
input_depth =img_noisy_np.shape[0]
class CAE_AbEst(nn.Module):
def __init__(self):
super(CAE_AbEst, self).__init__()
self.conv1 = nn.Sequential(
UnmixArch(
input_depth, EE.shape[1],
# num_channels_down = [8, 16, 32, 64, 128],
# num_channels_up = [8, 16, 32, 64, 128],
# num_channels_skip = [4, 4, 4, 4, 4],
num_channels_down = [ 256],
num_channels_up = [ 256],
num_channels_skip = [ 4],
filter_size_up = 3,filter_size_down = 3, filter_skip_size=1,
upsample_mode='bilinear', # downsample_mode='avg',
need1x1_up=True,
need_sigmoid=True, need_bias=True, pad=pad, act_fun='ReLU').type(dtype)
)
def forward(self, x):
x = self.conv1(x)
return x
net1 = CAE_AbEst()
net1.cuda()
print(net1)
# Compute number of parameters
s = sum([np.prod(list(p11.size())) for p11 in net1.parameters()]);
print ('Number of params: %d' % s)
# Loss
mse = torch.nn.MSELoss().type(dtype)
img_noisy_torch = np_to_torch(img_noisy_np).type(dtype)
# if fk==0:
net_input1 = get_noise(input_depth, INPUT,
(img_noisy_np.shape[1], img_noisy_np.shape[2])).type(dtype).detach()
# net_input1 = img_noisy_torch
E_torch = np_to_torch(EE).type(dtype)
#%%
net_input_saved = net_input1.detach().clone()
noise = net_input1.detach().clone()
out_avg = None
out_HR_avg= None
last_net = None
RMSE_LR_last = 0
loss=np.zeros((num_iter1,1))
AE=np.zeros((num_iter1,1))
i = 0
def closure1():
global i, RMSE_LR, RMSE_LR_ave, RMSE_HR, out_LR_np, out_avg_np, out_LR\
, out_avg,out_HR_np, out_HR_avg, out_HR_avg_np, RMSE_LR_last, last_net\
, net_input,RMSE_LR_avg,RMSE_HR_avg,RE_HR_avg, RE_HR, Eest,loss,AE\
, MAE_LR,MAE_LR_avg,MAE_HR,MAE_HR_avg
if reg_noise_std > 0:
net_input = net_input_saved + (noise.normal_() * reg_noise_std)
out_LR = net1(net_input1)
out_HR=torch.mm(E_torch.view(p1,LibS),out_LR.view(LibS,nr1*nc1))
# Smoothing
if out_avg is None:
out_avg = out_LR.detach()
out_HR_avg = out_HR.detach()
else:
out_avg = out_avg * exp_weight + out_LR.detach() * (1 - exp_weight)
out_HR_avg = out_HR_avg * exp_weight + out_HR.detach() * (1 - exp_weight)
#%%
out_HR=out_HR.view((1,p1,nr1,nc1))
total_loss = mse(img_noisy_torch, out_HR)
total_loss.backward()
if 1:
out_LR_np = out_LR.detach().cpu().squeeze().numpy()
out_avg_np = out_avg.detach().cpu().squeeze().numpy()
SRE=10*np.log10(LA.norm(A_true_np.astype(np.float32).reshape((EE.shape[1],nr1*nc1)),'fro')/LA.norm((A_true_np.astype(np.float32)- np.clip(out_LR_np, 0, 1)).reshape((EE.shape[1],nr1*nc1)),'fro'))
SRE_avg=10*np.log10(LA.norm(A_true_np.astype(np.float32).reshape((EE.shape[1],nr1*nc1)),'fro')/LA.norm((A_true_np.astype(np.float32)- np.clip(out_avg_np, 0, 1)).reshape((EE.shape[1],nr1*nc1)),'fro'))
MAE_LR= 100*np.mean(abs(A_true_np.astype(np.float32)- np.clip(out_LR_np, 0, 1)))
MAE_LR_avg= 100*np.mean(abs(A_true_np.astype(np.float32)- np.clip(out_avg_np, 0, 1)))
print ('Iteration %05d Loss %f MAE_LR: %f MAE_LR_avg: %f SRE: %f SRE_avg: %f' % (i, total_loss.item(), MAE_LR, MAE_LR_avg, SRE, SRE_avg), '\r', end='')
if PLOT and i % show_every == 0:
out_LR_np = torch_to_np(out_LR)
out_avg_np = torch_to_np(out_avg)
# plot_image_grid([np.clip(out_np, 0, 1),
# np.clip(torch_to_np(out_avg), 0, 1)], factor=figsize, nrow=1)
# out_LR_np = np.clip(out_LR_np, 0, 1)
# out_avg_np = np.clip(out_avg_np, 0, 1)
# f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(10,10))
# ax1.imshow(np.stack((out_LR_np[2,:,:],out_LR_np[1,:,:],out_LR_np[0,:,:]),2))
# ax2.imshow(np.stack((out_avg_np[2,:,:],out_avg_np[1,:,:],out_avg_np[0,:,:]),2))
# ax3.imshow(np.stack((A_true_np[2,:,:],A_true_np[1,:,:],A_true_np[0,:,:]),2))
# plt.show()
plt.plot(out_LR_np.reshape(LibS,nr1*nc1))
loss[i]=total_loss.item()
i += 1
return total_loss
p11 = get_params(OPT_OVER, net1, net_input1)
optimize(OPTIMIZER1, p11, closure1, LR1, num_iter1)
if 1:
out_LR_np = out_LR.detach().cpu().squeeze().numpy()
out_avg_np = out_avg.detach().cpu().squeeze().numpy()
MAE_LR_avg= 100*np.mean(abs(A_true_np.astype(np.float32)- np.clip(out_avg_np, 0, 1)))
MAE_LR= 100*np.mean(abs(A_true_np.astype(np.float32)- np.clip(out_LR_np, 0, 1)))
SRE=10*np.log10(LA.norm(A_true_np.astype(np.float32).reshape((EE.shape[1],nr1*nc1)),'fro')/LA.norm((A_true_np.astype(np.float32)- np.clip(out_LR_np, 0, 1)).reshape((EE.shape[1],nr1*nc1)),'fro'))
SRE_avg=10*np.log10(LA.norm(A_true_np.astype(np.float32).reshape((EE.shape[1],nr1*nc1)),'fro')/LA.norm((A_true_np.astype(np.float32)- np.clip(out_avg_np, 0, 1)).reshape((EE.shape[1],nr1*nc1)),'fro'))
print ('Iteration %05d MAE_LR: %f MAE_LR_avg: %f SRE: %f SRE_avg: %f ' % (i, MAE_LR, MAE_LR_avg, SRE, SRE_avg), '\r', end='')
# if save_result is True:
# scipy.io.savemat("C:/Users/behnood/Desktop/Sparse Unmixing/Results/Sim2/demo1/10runs/out_avg_np%01d%01d.mat" % (fi+2,fj+1),
# {'out_avg_np%01d%01d' % (fi+2, fj+1):out_avg_np.transpose(1,2,0)})
# scipy.io.savemat("C:/Users/behnood/Desktop/Sparse Unmixing/Results/Sim2/demo1/10runs/out_LR_np%01d%01d.mat" % (fi+2,fj+1),
# {'out_LR_np%01d%01d' % (fi+2, fj+1):out_LR_np.transpose(1,2,0)})
#%%
| 0
| 0
| 0
| 1,055
| 0
| 3,057
| 0
| 140
| 369
|
75b9590a65adb6b1e657e5a4109bd8c90e601615
| 379
|
py
|
Python
|
Curso de Python/numeros_primos.py
|
Cazcode/Curso_python
|
cbaaedacec8801da2d62da4c3aadce052f04bd64
|
[
"MIT"
] | null | null | null |
Curso de Python/numeros_primos.py
|
Cazcode/Curso_python
|
cbaaedacec8801da2d62da4c3aadce052f04bd64
|
[
"MIT"
] | null | null | null |
Curso de Python/numeros_primos.py
|
Cazcode/Curso_python
|
cbaaedacec8801da2d62da4c3aadce052f04bd64
|
[
"MIT"
] | null | null | null |
if __name__ == '__main__':
run()
| 18.047619
| 46
| 0.522427
|
def es_primo(number):
if number < 2 or number % 2 == 0:
return False
for i in range(3, number):
if number % i == 0:
return False
return True
def run():
number = int(input('Ingrese un número: '))
if es_primo(number):
print('Es Primo')
else:
print('No es primo')
if __name__ == '__main__':
run()
| 2
| 0
| 0
| 0
| 0
| 294
| 0
| 0
| 45
|
813f1d0c8d016d91369c44e1d9a6e6b88e01a527
| 23,140
|
py
|
Python
|
fdc/fdc.py
|
alexandreday/Fast_Density_Clustering
|
91e7ee0ccf2b297b40747823302a21cd70d59dc9
|
[
"BSD-3-Clause"
] | 7
|
2019-11-02T02:07:17.000Z
|
2022-01-25T10:50:09.000Z
|
fdc/fdc.py
|
alexandreday/Fast_Density_Clustering
|
91e7ee0ccf2b297b40747823302a21cd70d59dc9
|
[
"BSD-3-Clause"
] | 1
|
2021-09-21T16:47:37.000Z
|
2022-01-30T00:58:11.000Z
|
fdc/fdc.py
|
alexandreday/Fast_Density_Clustering
|
91e7ee0ccf2b297b40747823302a21cd70d59dc9
|
[
"BSD-3-Clause"
] | 7
|
2017-12-05T03:18:08.000Z
|
2021-12-20T19:10:49.000Z
|
'''
Created : Jan 16, 2017
Last major update : June 29, 2017
@author: Alexandre Day
Purpose:
Fast density clustering
'''
import numpy as np
import sys, os
#####################################################
#####################################################
############ utility functions below ################
#####################################################
#####################################################
def check_cluster_stability(self, X, threshold):
"""
Given the identified cluster centers, performs a more rigourous
neighborhood search (based on some noise threshold) for points with higher densities.
This is vaguely similar to a watershed cuts in image segmentation and basically
makes sure we haven't identified spurious cluster centers w.r.t to some noise threshold (false positive).
This has bad memory complexity, needs improvement if we want to run on N>10^5 data points.
"""
density_graph = self.density_graph
nn_delta = self.nn_delta
delta = self.delta
rho = self.rho
nn_list = self.nn_list
idx_centers = self.idx_centers_unmerged
cluster_label = self.cluster_label
n_false_pos = 0
idx_true_centers = []
for idx in idx_centers:
rho_center = rho[idx]
delta_rho = rho_center - threshold
if threshold < 1e-3: # just check nn_list ...
NH=nn_list[idx][1:self.search_size]
else:
NH = self.find_NH_tree_search(idx, delta_rho, cluster_label)
#print(len(NH))
label_centers_nn = np.unique(self.cluster_label[NH])#[cluster_label[ni] for ni in NH])
idx_max = idx_centers[ label_centers_nn[np.argmax(rho[idx_centers[label_centers_nn]])] ]
rho_current = rho[idx]
if ( rho_current < rho[idx_max] ) & ( idx != idx_max ) :
nn_delta[idx] = idx_max
delta[idx] = np.linalg.norm(X[idx_max]-X[idx])
density_graph[idx_max].append(idx)
n_false_pos+=1
else:
idx_true_centers.append(idx)
return np.array(idx_true_centers,dtype=np.int), n_false_pos
def assign_cluster(idx_centers, nn_delta, density_graph):
"""
Given the cluster centers and the local gradients (nn_delta) assign to every
point a cluster label
"""
n_center = idx_centers.shape[0]
n_sample = nn_delta.shape[0]
cluster_label = -1*np.ones(n_sample,dtype=np.int) # reinitialized every time.
for c, label in zip(idx_centers, range(n_center) ):
cluster_label[c] = label
assign_cluster_deep(density_graph[c], cluster_label, density_graph, label)
return cluster_label
def assign_cluster_deep(root,cluster_label,density_graph,label):
"""
Recursive function for assigning labels for a tree graph.
Stopping condition is met when the root is empty (i.e. a leaf has been reached)
"""
if not root: # then must be a leaf !
return
else:
for child in root:
cluster_label[child]=label
assign_cluster_deep(density_graph[child],cluster_label,density_graph,label)
def index_greater(array, prec=1e-8):
"""
Purpose:
Function for finding first item in an array that has a value greater than the first element in that array
If no element is found, returns None
Precision:
1e-8
Return:
int or None
"""
item=array[0]
for idx, val in np.ndenumerate(array): # slow ! : could be cythonized
if val > (item + prec):
return idx[0]
def blockPrint():
"""Blocks printing to screen"""
sys.stdout = open(os.devnull, 'w')
def enablePrint():
"""Enables printing to screen"""
sys.stdout = sys.__stdout__
| 34.485842
| 158
| 0.591357
|
'''
Created : Jan 16, 2017
Last major update : June 29, 2017
@author: Alexandre Day
Purpose:
Fast density clustering
'''
import numpy as np
import time
from numpy.random import random
import sys, os
from .density_estimation import KDE
import pickle
from collections import OrderedDict as OD
from sklearn.neighbors import NearestNeighbors
import multiprocessing
class FDC:
""" Fast Density Clustering via kernel density modelling for low-dimensional data (D <~ 8)
Parameters
----------
nh_size : int, optional (default = 'auto')
Neighborhood size. This is the scale used for identifying the initial modes in the density distribution, regardless
of the covariance. If a point has the maximum density among it's nh_size neighbors, it is marked as
a potential cluster center. 'auto' means that the nh_size is scaled with number of samples. We
use nh_size = 100 for 10000 samples. The minimum neighborhood size is set to 10.
eta : float, optional (default = 0.4)
Noise threshold used to merge clusters. This is done by quenching directly to the specified noise threshold
(as opposed to progressively coarse-graining). The noise threshold determines the extended
neighborhood of cluster centers. Points that have a relative density difference of less than
"noise_threshold" and that are density-reachable, are part of the extended neighborhood.
random_state: int, optional (default = 0)
Random number for seeding random number generator. By default, the
method generates the same results. This random is used to seed
the cross-validation (set partitions) which will in turn affect the bandwitdth value
test_ratio_size: float, optional (default = 0.8)
Ratio size of the test set used when performing maximum likehood estimation.
In order to have smooth density estimations (prevent overfitting), it is recommended to
use a large test_ratio_size (closer to 1.0) rather than a small one.
verbose: int, optional (default = 1)
Set to 0 if you don't want to see print to screen.
bandwidth: float, optional (default = None)
If you want the bandwidth for kernel density to be set automatically or want to set it yourself.
By default it is set automatically.
merge: bool, optinal (default = True)
Optional merging at zero noise threshold, merges overlapping minimal clusters
atol: float, optional (default = 0.000005)
kernel density estimate precision parameter. determines the precision used for kde.
smaller values leads to slower execution but better precision
rtol: float, optional (default = 0.00005)
kernel density estimate precision parameter. determines the precision used for kde.
smaller values leads to slower execution but better precision
xtol: float, optional (default = 0.01)
precision parameter for optimizing the bandwidth using maximum likelihood on a test set
search_size: int, optional (default = 20)
when performing search over neighborhoods, size of each local neighborhood to check when
expanding. This drastically slows the coarse-graining if chosen to be too big !
kernel: str, optional (default='gaussian')
Type of Kernel to use for density estimates. Other options are {'epanechnikov'|'linear','tophat'}.
"""
def __init__(self, nh_size='auto', eta=0.5,
random_state=0, test_ratio_size=0.8, verbose=1, bandwidth=None,
merge=True,
atol=0.01,
rtol=0.0001,
xtol=0.01,
search_size = 20,
n_cluster_init = None,
kernel = 'gaussian',
n_job='auto'
):
self.test_ratio_size = test_ratio_size
self.random_state = random_state
self.verbose = verbose
self.nh_size = nh_size
self.bandwidth = bandwidth
self.eta = eta
self.merge = merge
self.atol = atol
self.rtol = rtol
self.xtol = xtol
self.cluster_label = None
self.search_size = search_size
self.n_cluster_init = n_cluster_init
self.kernel = kernel
self.nbrs= None
self.nn_dist= None
self.nn_list= None
self.density_model = None
if n_job == 'auto':
self.n_job=multiprocessing.cpu_count()
else:
if n_job > multiprocessing.cpu_count():
self.n_job=multiprocessing.cpu_count()
else:
self.n_job=n_job
def fit(self, X):
""" Performs density clustering on given data set
Parameters
----------
X : array, (n_sample, n_feature)
Data to cluster.
Returns
----------
self : fdc object
To obtain new cluster labels use self.cluster_label
"""
t = time.time()
self.X = X # shallow copy
self.n_sample = X.shape[0]
if self.n_sample < 10:
assert False, "Too few samples for computing densities !"
if self.nh_size is 'auto':
self.nh_size = max([int(25*np.log10(self.n_sample)), 10])
if self.search_size > self.nh_size:
self.search_size = self.nh_size
if self.verbose == 0:
blockPrint()
self.display_main_parameters()
print("[fdc] Starting clustering with n=%i samples..." % X.shape[0])
start = time.time()
print("[fdc] Fitting kernel model for density estimation ...")
self.fit_density(X)
#print("here")
print("[fdc] Finding centers ...")
self.compute_delta(X, self.rho)
print("[fdc] Found %i potential centers ..." % self.idx_centers_unmerged.shape[0])
# temporary idx for the centers :
self.idx_centers = self.idx_centers_unmerged
self.cluster_label = assign_cluster(self.idx_centers_unmerged, self.nn_delta, self.density_graph)
if self.merge: # usually by default one should perform this minimal merging ..
print("[fdc] Merging overlapping minimal clusters ...")
self.check_cluster_stability_fast(X, 0.) # given
if self.eta >= 1e-3 :
print("[fdc] Iterating up to specified noise threshold ...")
self.check_cluster_stability_fast(X, self.eta) # merging 'unstable' clusters
print("[fdc] Done in %.3f s" % (time.time()-start))
enablePrint()
return self
def save(self, name=None):
""" Saves current model to specified path 'name' """
if name is None:
fname = self.make_file_name()
else:
fname = name
fopen = open(fname,'wb')
pickle.dump(self,fopen)
fopen.close()
return fname
def load(self, name=None):
if name is None:
name = self.make_file_name()
self.__dict__.update(pickle.load(open(name,'rb')).__dict__)
return self
def fit_density(self, X):
# nearest neighbors class
self.nbrs = NearestNeighbors(n_neighbors = self.nh_size, algorithm='kd_tree').fit(X)
# get k-NN
self.nn_dist, self.nn_list = self.nbrs.kneighbors(X)
# density model class
self.density_model = KDE(bandwidth=self.bandwidth, test_ratio_size=self.test_ratio_size,
atol=self.atol, rtol=self.rtol, xtol=self.xtol, nn_dist = self.nn_dist, kernel=self.kernel)
# fit density model to data
self.density_model.fit(X)
# save bandwidth
self.bandwidth = self.density_model.bandwidth
# compute density map based on kernel density model
if (self.n_sample > 30000) & (self.n_job !=1) :
print("[fdc] Computing density with %i threads..."%self.n_job)
p = multiprocessing.Pool(self.n_job)
size_split = X.shape[0]//self.n_job
results =[]
idx_split = chunkIt(len(X), self.n_job) # find the index to split the array in approx. n_job equal parts.
for i in range(self.n_job):
results.append(p.apply_async(self.f_tmp, [X[idx_split[i][0]:idx_split[i][1]], i]))
results = [res.get() for res in results]
asort = np.argsort([results[i][0] for i in range(self.n_job)]) # reordering
#print(asort)
self.rho=np.hstack([results[a][1] for a in asort])
else:
print("[fdc] Computing density with 1 thread...")
self.rho = self.density_model.evaluate_density(X)
return self
def f_tmp(self, X_, i_):
"""evaluating density and keeping track of threading order"""
return (i_, self.density_model.evaluate_density(X_))
#@profile
def coarse_grain(self, noise_iterable):
"""Started from an initial noise scale, progressively merges clusters.
If specified, saves the cluster assignments at every level of the coarse graining.
Parameters
-----------
noise_iterable : iterable of floats
Should be an iterable containg noise values at which to perform coarse graining. Usually
one should start from 0 and go to larger values by small increments. The whole clustering
information is stored in self.hierarchy
Return
------
self
"""
if self.verbose == 0:
blockPrint()
print("[fdc] Coarse graining until desired noise threshold ...")
noise_range = [n for n in noise_iterable]
#hierarchy = []
self.max_noise = -1
n_cluster = 0
# note to self, if no merger is done, no need to store hierarchy ... just work with noise_range dict ...
for nt in noise_range:
if self.n_cluster_init is not None:
if len(self.idx_centers) <= self.n_cluster_init:
print("[fdc.py] Reached number of specified clusters [= %i] (or close to), n_cluster = %i"%(self.n_cluster_init,len(self.idx_centers)))
break
self.check_cluster_stability_fast(self.X, eta = nt)
#hierarchy.append(OD({'idx_centers': self.idx_centers, 'cluster_labels': self.cluster_label})) # -> the only required information <-
if len(self.idx_centers) != n_cluster:
n_cluster = len(self.idx_centers)
self.max_noise = nt
#self.hierarchy = hierarchy
self.noise_range = noise_range
self.noise_threshold = noise_range[-1]
enablePrint()
return self
#@profile
def compute_delta(self, X, rho = None):
"""
Purpose:
Computes distance to nearest-neighbor with higher density
Return:
delta,nn_delta,idx_centers,density_graph
:delta: distance to n.n. with higher density (within some neighborhood cutoff)
:nn_delta: index of n.n. with ...
:idx_centers: list of points that have the largest density in their neigborhood cutoff
:density_graph: for every point, list of points are incoming (via the density gradient)
"""
if rho is None:
rho = self.rho
n_sample, n_feature = X.shape
maxdist = np.linalg.norm([np.max(X[:,i])-np.min(X[:,i]) for i in range(n_feature)])
delta = maxdist*np.ones(n_sample, dtype=np.float)
nn_delta = np.ones(n_sample, dtype=np.int)
density_graph = [[] for i in range(n_sample)] # store incoming leaves
### ----------->
nn_list = self.nn_list # restricted over neighborhood (nh_size)
### ----------->
for i in range(n_sample):
idx = index_greater(rho[nn_list[i]])
if idx:
density_graph[nn_list[i,idx]].append(i)
nn_delta[i] = nn_list[i,idx]
delta[i] = self.nn_dist[i,idx]
else:
nn_delta[i]=-1
idx_centers=np.array(range(n_sample))[delta > 0.999*maxdist]
self.delta = delta
self.nn_delta = nn_delta
self.idx_centers_unmerged = idx_centers
self.density_graph = density_graph
return self
def estimate_eta(self):
""" Based on the density distribution, computes a scale for eta
Need more experimenting, this is not quite working ...
"""
from matplotlib import pyplot as plt
idx = int(self.n_sample/10.)
idx = np.argsort(self.rho)[:-5*idx]#[2:idx:4*idx]
drho = []
for i in idx:
rho_init = self.rho[i]
nn_i = self.nn_delta[i]
while nn_i != -1:
rho_c = self.rho[nn_i]
nn_i = self.nn_delta[nn_i]
drho.append(rho_c- rho_init)
""" plt.his(drho,bins=60)
plt.show()
exit() """
eta = np.mean(drho)#+0.5*np.std(drho)
self.cout("Using std eta of %.3f"%eta)
return eta
def check_cluster_stability_fast(self, X, eta = None): # given
if self.verbose == 0:
blockPrint()
if eta is None:
eta = self.eta
while True: # iterates untill number of cluster does not change ...
self.cluster_label = assign_cluster(self.idx_centers_unmerged, self.nn_delta, self.density_graph) # first approximation of assignments
self.idx_centers, n_false_pos = check_cluster_stability(self, X, eta)
self.idx_centers_unmerged = self.idx_centers
if n_false_pos == 0:
print(" # of stable clusters with noise %.6f : %i" % (eta, self.idx_centers.shape[0]))
break
enablePrint()
""" def get_cluster_info(self, eta = None):
if eta is None:
return self.cluster_label, self.idx_centers
else:
pos = np.argmin(np.abs(np.array(self.noise_range)-eta))
#delta_ = self.noise_range[pos]
#idx_centers = self.hierarchy[pos]['idx_centers']
cluster_label = self.hierarchy[pos]['cluster_labels']
idx_center = self.hierarchy[pos]['idx_centers']
return cluster_label, idx_center """
""" def update_labels(self, idx_centers, cluster_label):
self.idx_centers = idx_centers
self.cluster_label = cluster_label """
#@profile
def find_NH_tree_search(self, idx, eta, cluster_label):
"""
Function for searching for nearest neighbors within some density threshold.
NH should be an empty set for the inital function call.
Note to myself : lots of optimization, this is pretty time/memory consumming !
Parameters
-----------
idx : int
index of the cluster centroid to start from
eta : float
maximum density you can spill over (this is "density_center - eta")
cluster_label: array of int
cluster label for every datapoint.
Returns
-----------
List of points in the neighborhood of point idx : 1D array
"""
rho = self.rho
zero_array = np.zeros(len(self.nn_list),dtype=bool)
nn_list = self.nn_list
zero_array[nn_list[idx, :self.search_size]] = True
new_leaves = zero_array
is_NH = (rho > eta) & (new_leaves)
current_label = cluster_label[idx]
# This could probably be improved, but at least it's fully vectorized and scalable (NlogN in time and N in memory)
while True:
update = False
leaves=np.copy(new_leaves)
#y_leave = cluster_label[leaves]
leaves_cluster = (leaves) & (cluster_label == current_label)
new_leaves=np.zeros(len(self.nn_list), dtype=bool)
nn_leaf = np.unique(nn_list[leaves_cluster][:self.search_size].flatten())
res = nn_leaf[is_NH[nn_leaf]==False]
pos = np.where(rho[res] > eta)[0]
if len(pos) > 0: update=True
is_NH[res[pos]] = True
new_leaves[res[pos]] = True
if update is False:
break
return np.where(is_NH)[0]
def find_NH_tree_search_v1(self, idx, eta, cluster_label):
"""
Function for searching for nearest neighbors within
some density threshold.
NH should be an empty set for the inital function call.
Note to myself : lots of optimization, this is pretty time consumming !
Returns
-----------
List of points in the neighborhood of point idx : 1D array
"""
rho = self.rho
nn_list = self.nn_list
new_leaves=nn_list[idx][:self.search_size]
is_NH = np.zeros(len(self.nn_list),dtype=np.int)
is_NH[new_leaves[rho[new_leaves] > eta]] = 1
current_label = cluster_label[idx]
# ideally here we cythonize what's below... this is highly ineficient ...
while True:
update = False
leaves=np.hstack(new_leaves)
new_leaves=[]
y_leave = cluster_label[leaves]
leaves_cluster = leaves[y_leave == current_label]
nn_leaf = nn_list[leaves_cluster]
for i in range(1, self.search_size):
res = nn_leaf[is_NH[nn_leaf[:,i]] == 0, i]
pos = np.where(rho[res] > eta)[0]
if len(pos) > 0: update=True
is_NH[res[pos]] = 1
new_leaves.append(res[pos])
if update is False:
break
return np.where(is_NH == 1)[0]
def cout(self, s):
print('[fdc] '+s)
def make_file_name(self):
t_name = "fdc_nhSize=%i_eta=%.3f_ratio=%.2f.pkl"
return t_name%(self.nh_size, self.eta, self.test_ratio_size)
""" def compute_coarse_grain_graph(self):
graph = {}
for idx in self.idx_centers: # at some scale
NH = self.find_NH_tree_search(idx, eta, cluster_label)
label_centers_nn = np.unique([cluster_label[ni] for ni in NH]) """
def display_main_parameters(self):
if self.eta is not 'auto':
eta = "%.3f"%self.eta
else:
eta = self.eta
out = [
"[fdc] {0:<20s}{1:<4s}{2:<6d}".format("nh_size",":",self.nh_size),
"[fdc] {0:<20s}{1:<4s}{2:<6s}".format("eta",":",eta),
"[fdc] {0:<20s}{1:<4s}{2:<6s}".format("merge",":",str(self.merge)),
"[fdc] {0:<20s}{1:<4s}{2:<6d}".format("search_size",":",self.search_size),
"[fdc] {0:<20s}{1:<4s}{2:<6.3f}".format("test_size_ratio",":",self.test_ratio_size)
]
for o in out:
print(o)
def reset(self):
self.bandwidth = None
#####################################################
#####################################################
############ utility functions below ################
#####################################################
#####################################################
def check_cluster_stability(self, X, threshold):
"""
Given the identified cluster centers, performs a more rigourous
neighborhood search (based on some noise threshold) for points with higher densities.
This is vaguely similar to a watershed cuts in image segmentation and basically
makes sure we haven't identified spurious cluster centers w.r.t to some noise threshold (false positive).
This has bad memory complexity, needs improvement if we want to run on N>10^5 data points.
"""
density_graph = self.density_graph
nn_delta = self.nn_delta
delta = self.delta
rho = self.rho
nn_list = self.nn_list
idx_centers = self.idx_centers_unmerged
cluster_label = self.cluster_label
n_false_pos = 0
idx_true_centers = []
for idx in idx_centers:
rho_center = rho[idx]
delta_rho = rho_center - threshold
if threshold < 1e-3: # just check nn_list ...
NH=nn_list[idx][1:self.search_size]
else:
NH = self.find_NH_tree_search(idx, delta_rho, cluster_label)
#print(len(NH))
label_centers_nn = np.unique(self.cluster_label[NH])#[cluster_label[ni] for ni in NH])
idx_max = idx_centers[ label_centers_nn[np.argmax(rho[idx_centers[label_centers_nn]])] ]
rho_current = rho[idx]
if ( rho_current < rho[idx_max] ) & ( idx != idx_max ) :
nn_delta[idx] = idx_max
delta[idx] = np.linalg.norm(X[idx_max]-X[idx])
density_graph[idx_max].append(idx)
n_false_pos+=1
else:
idx_true_centers.append(idx)
return np.array(idx_true_centers,dtype=np.int), n_false_pos
def assign_cluster(idx_centers, nn_delta, density_graph):
"""
Given the cluster centers and the local gradients (nn_delta) assign to every
point a cluster label
"""
n_center = idx_centers.shape[0]
n_sample = nn_delta.shape[0]
cluster_label = -1*np.ones(n_sample,dtype=np.int) # reinitialized every time.
for c, label in zip(idx_centers, range(n_center) ):
cluster_label[c] = label
assign_cluster_deep(density_graph[c], cluster_label, density_graph, label)
return cluster_label
def assign_cluster_deep(root,cluster_label,density_graph,label):
"""
Recursive function for assigning labels for a tree graph.
Stopping condition is met when the root is empty (i.e. a leaf has been reached)
"""
if not root: # then must be a leaf !
return
else:
for child in root:
cluster_label[child]=label
assign_cluster_deep(density_graph[child],cluster_label,density_graph,label)
def index_greater(array, prec=1e-8):
"""
Purpose:
Function for finding first item in an array that has a value greater than the first element in that array
If no element is found, returns None
Precision:
1e-8
Return:
int or None
"""
item=array[0]
for idx, val in np.ndenumerate(array): # slow ! : could be cythonized
if val > (item + prec):
return idx[0]
def blockPrint():
"""Blocks printing to screen"""
sys.stdout = open(os.devnull, 'w')
def enablePrint():
"""Enables printing to screen"""
sys.stdout = sys.__stdout__
def chunkIt(length_seq, num):
avg = length_seq / float(num)
out = []
last = 0.0
idx_list = []
while last < length_seq:
idx_list.append([int(last),int(last + avg)])
last += avg
if len(idx_list) > num:
idx_list.pop()
idx_list[-1] = [idx_list[-1][0], length_seq]
return idx_list
| 0
| 0
| 0
| 18,788
| 0
| 321
| 0
| 52
| 204
|
f3302cd3e06d5ffb2be99f117180800962602ad7
| 1,021
|
py
|
Python
|
src/start_stop_timer.py
|
MarioMey/OBS-Studio-Python-Scripting-Cheatsheet-obspython-Examples-of-API
|
7f1f2a00c2731c558f6b9fd75edb697fc0719a7c
|
[
"MIT"
] | 4
|
2021-03-23T05:25:24.000Z
|
2021-12-29T16:46:01.000Z
|
src/start_stop_timer.py
|
MarioMey/OBS-Studio-Python-Scripting-Cheatsheet-obspython-Examples-of-API
|
7f1f2a00c2731c558f6b9fd75edb697fc0719a7c
|
[
"MIT"
] | null | null | null |
src/start_stop_timer.py
|
MarioMey/OBS-Studio-Python-Scripting-Cheatsheet-obspython-Examples-of-API
|
7f1f2a00c2731c558f6b9fd75edb697fc0719a7c
|
[
"MIT"
] | null | null | null |
FLAG = True
INTERVAL = 100
eg = Example() # class created ,obs part starts
| 22.688889
| 76
| 0.624878
|
import obspython as obs
from random import choices
FLAG = True
INTERVAL = 100
class Example:
def __init__(self):
self.lock = True
def random_numbers(self):
print(choices(range(1,10),k=3))
def ticker(self):
""" how fast update.One callback at time with lock"""
if self.lock:
self.random_numbers()
if not self.lock:
obs.remove_current_callback()
eg = Example() # class created ,obs part starts
def stop_pressed(props, prop):
global FLAG
FLAG = True
eg.lock = False
def start_pressed(props, prop):
global FLAG # to keep only one timer callback
if FLAG:
obs.timer_add(eg.ticker, INTERVAL)
eg.lock = True
FLAG = False
def script_properties(): # ui
props = obs.obs_properties_create()
obs.obs_properties_add_button(props, "button", "Stop", stop_pressed)
obs.obs_properties_add_button(props, "button2", "Start", start_pressed)
return props
| 0
| 0
| 0
| 334
| 0
| 441
| 0
| 7
| 146
|
6f8866f4896b6cf29e177021809a4addfceb41c6
| 3,582
|
py
|
Python
|
esp32/sensor_identifikation/ntc.py
|
phofmeier/wifi_temp_sensor
|
93ebc466b6913842cac6eb9385272c79651d2479
|
[
"MIT"
] | null | null | null |
esp32/sensor_identifikation/ntc.py
|
phofmeier/wifi_temp_sensor
|
93ebc466b6913842cac6eb9385272c79651d2479
|
[
"MIT"
] | null | null | null |
esp32/sensor_identifikation/ntc.py
|
phofmeier/wifi_temp_sensor
|
93ebc466b6913842cac6eb9385272c79651d2479
|
[
"MIT"
] | null | null | null |
import numpy as np
import matplotlib.pyplot as plt
import casadi as cas
# define functions
# Check if Sensor is a NTC and find out the Resistance
R_measured = [103e3, 120e3, 70e3, 15.2e3]
T_measured_C = [25, 20, 33, 74]
R_N = 103e3
T_N = 298.15
U_ges = 3.3
T_range_C = [0, 200]
# Kelvin from Temp
T_range = [i + 273.15 for i in T_range_C]
T_measured = [i + 273.15 for i in T_measured_C]
# Fit B
B_sym = cas.SX.sym("B")
f = 0
for R, T in zip(R_measured, T_measured):
f += (R-R_ntc(R_N, B_sym, T, T_N))**2
nlp = {'x': B_sym, 'f': f}
S = cas.nlpsol('S', 'ipopt', nlp)
res = S()
B = res['x'].full()[0]
# Search for the Value of R_1 which minimizes the deviation of Measured Voltage at a specific Temperature
T_Nenn_C = 65
T_Nenn = T_Nenn_C + 273.15
R_1 = cas.SX.sym("R_1")
T_sym = cas.SX.sym("T")
U = U_meas(U_ges, R_1, R_ntc(R_N, B, T_sym, T_N))
jac = cas.Function("dudT", [R_1, T_sym], [cas.jacobian(U, T_sym)])
lbx = [0]
ubx = [cas.inf]
nlp = {'x': R_1, 'f': jac(R_1, T_Nenn)}
S = cas.nlpsol('S', 'ipopt', nlp)
res = S(lbx=lbx, ubx=ubx)
R_1 = res['x'].full()[0]
# plot
T = np.linspace(T_range[0], T_range[1], num=200)
T_C = np.linspace(T_range_C[0], T_range_C[1], num=200)
print("R_1: %e" % R_1)
print("B: %e" % B)
plt.figure()
plt.title("Fitted and Measured Resistance of the NTC")
plt.plot(T_C, R_ntc(R_N, B, T, T_N), label="R_fitted")
plt.plot(T_measured_C, R_measured, "x", label="R_measured")
plt.xlabel("Temp [Grad]")
plt.ylabel("R [Ohm]")
plt.legend()
U_range = U_meas(U_ges, R_1, R_ntc(R_N, B, T, T_N))
plt.figure()
plt.title("Voltage vs. Temperature with optimal Resistance. Vertical line shows the optimal Temperature.")
plt.plot(T_C, U_range, label="R/NTC")
plt.vlines(T_Nenn_C, U_range.min(), U_range.max())
plt.xlabel("Temp [Grad]")
plt.ylabel("U [V]")
plt.legend()
# Calibrate Sensor from ADC measurements
# measurements
adc_voltage_measured = [2.21634, 2.17343, 1.59904, 1.49781, 0.84637, 0.71773, 0.59042, 0.67618, 0.18674, 0.22936,0.18058,0.16265, 0.31080, 0.35350, 0.51420, 0.54871, 0.69035, 0.74026, 0.93252, 0.97848, 1.14037, 1.22280, 1.62269, 1.66503, 2.68944, 2.70243]
T_measured = [42, 43, 60, 65, 92, 99, 106, 101, 156, 146, 158, 161, 133, 128, 112, 109, 100, 97, 87, 85, 78, 75, 61, 59, 24, 23]
# Fit Model
R1 = 24.9e3
B_sym = cas.SX.sym("B")
Rn_sym = cas.SX.sym("Rn")
Tn_sym = cas.SX.sym("Tn")
syms = cas.vertcat(B_sym, Rn_sym, Tn_sym)
x0 = [B, 103e3, T_N]
f = 0
for T, adc_voltage in zip(T_measured, adc_voltage_measured):
f += (T-Temp(adc_voltage, R1, U_ges, B_sym, Rn_sym, Tn_sym))**2
f += (Rn_sym-103e3)**2+(Tn_sym-T_N)**2
nlp = {'x': syms, 'f': f}
S = cas.nlpsol('S', 'ipopt', nlp)
res = S(x0=x0)
B = res['x'].full()[0]
Rn = res['x'].full()[1]
Tn = res['x'].full()[2]
print("B:", B, "R1:", R1, "Rn:", Rn, "Tn:", Tn)
plt.figure()
plt.title("Fitted Model for measured Voltage vs. Temperature")
adc_range = np.linspace(min(adc_voltage_measured), max(adc_voltage_measured))
plt.plot(adc_range, Temp(adc_range, R1, U_ges, B, Rn, Tn), label="Fitted Model")
plt.plot(adc_voltage_measured, T_measured, 'x', label="Measured")
plt.xlabel("ADC voltage [V]")
plt.ylabel("Temp [Grad]")
plt.legend()
plt.show()
| 27.343511
| 255
| 0.650475
|
import numpy as np
import matplotlib.pyplot as plt
import casadi as cas
# define functions
def R_ntc(R_N, B, T, T_N):
return R_N * np.exp(B*(1/T-1/T_N))
def U_meas(U_ges, R_1, R_2):
return U_ges/(R_1+R_2) * R_2
def Temp(U_meas, R_1, U_ges, B, R_N, T_N):
R_NTC = U_meas * R_1 / (U_ges - U_meas)
T_kelvin = 1 / (np.log(R_NTC / R_N) / B + 1 / T_N)
return T_kelvin - 273.15
def Temp_adc(adc_value, R_1, U_ges, B, R_N, T_N):
return Temp(U_ges/4096 * adc_value, R_1, U_ges, B, R_N, T_N)
# Check if Sensor is a NTC and find out the Resistance
R_measured = [103e3, 120e3, 70e3, 15.2e3]
T_measured_C = [25, 20, 33, 74]
R_N = 103e3
T_N = 298.15
U_ges = 3.3
T_range_C = [0, 200]
# Kelvin from Temp
T_range = [i + 273.15 for i in T_range_C]
T_measured = [i + 273.15 for i in T_measured_C]
# Fit B
B_sym = cas.SX.sym("B")
f = 0
for R, T in zip(R_measured, T_measured):
f += (R-R_ntc(R_N, B_sym, T, T_N))**2
nlp = {'x': B_sym, 'f': f}
S = cas.nlpsol('S', 'ipopt', nlp)
res = S()
B = res['x'].full()[0]
# Search for the Value of R_1 which minimizes the deviation of Measured Voltage at a specific Temperature
T_Nenn_C = 65
T_Nenn = T_Nenn_C + 273.15
R_1 = cas.SX.sym("R_1")
T_sym = cas.SX.sym("T")
U = U_meas(U_ges, R_1, R_ntc(R_N, B, T_sym, T_N))
jac = cas.Function("dudT", [R_1, T_sym], [cas.jacobian(U, T_sym)])
lbx = [0]
ubx = [cas.inf]
nlp = {'x': R_1, 'f': jac(R_1, T_Nenn)}
S = cas.nlpsol('S', 'ipopt', nlp)
res = S(lbx=lbx, ubx=ubx)
R_1 = res['x'].full()[0]
# plot
T = np.linspace(T_range[0], T_range[1], num=200)
T_C = np.linspace(T_range_C[0], T_range_C[1], num=200)
print("R_1: %e" % R_1)
print("B: %e" % B)
plt.figure()
plt.title("Fitted and Measured Resistance of the NTC")
plt.plot(T_C, R_ntc(R_N, B, T, T_N), label="R_fitted")
plt.plot(T_measured_C, R_measured, "x", label="R_measured")
plt.xlabel("Temp [Grad]")
plt.ylabel("R [Ohm]")
plt.legend()
U_range = U_meas(U_ges, R_1, R_ntc(R_N, B, T, T_N))
plt.figure()
plt.title("Voltage vs. Temperature with optimal Resistance. Vertical line shows the optimal Temperature.")
plt.plot(T_C, U_range, label="R/NTC")
plt.vlines(T_Nenn_C, U_range.min(), U_range.max())
plt.xlabel("Temp [Grad]")
plt.ylabel("U [V]")
plt.legend()
# Calibrate Sensor from ADC measurements
# measurements
adc_voltage_measured = [2.21634, 2.17343, 1.59904, 1.49781, 0.84637, 0.71773, 0.59042, 0.67618, 0.18674, 0.22936,0.18058,0.16265, 0.31080, 0.35350, 0.51420, 0.54871, 0.69035, 0.74026, 0.93252, 0.97848, 1.14037, 1.22280, 1.62269, 1.66503, 2.68944, 2.70243]
T_measured = [42, 43, 60, 65, 92, 99, 106, 101, 156, 146, 158, 161, 133, 128, 112, 109, 100, 97, 87, 85, 78, 75, 61, 59, 24, 23]
# Fit Model
R1 = 24.9e3
B_sym = cas.SX.sym("B")
Rn_sym = cas.SX.sym("Rn")
Tn_sym = cas.SX.sym("Tn")
syms = cas.vertcat(B_sym, Rn_sym, Tn_sym)
x0 = [B, 103e3, T_N]
f = 0
for T, adc_voltage in zip(T_measured, adc_voltage_measured):
f += (T-Temp(adc_voltage, R1, U_ges, B_sym, Rn_sym, Tn_sym))**2
f += (Rn_sym-103e3)**2+(Tn_sym-T_N)**2
nlp = {'x': syms, 'f': f}
S = cas.nlpsol('S', 'ipopt', nlp)
res = S(x0=x0)
B = res['x'].full()[0]
Rn = res['x'].full()[1]
Tn = res['x'].full()[2]
print("B:", B, "R1:", R1, "Rn:", Rn, "Tn:", Tn)
plt.figure()
plt.title("Fitted Model for measured Voltage vs. Temperature")
adc_range = np.linspace(min(adc_voltage_measured), max(adc_voltage_measured))
plt.plot(adc_range, Temp(adc_range, R1, U_ges, B, Rn, Tn), label="Fitted Model")
plt.plot(adc_voltage_measured, T_measured, 'x', label="Measured")
plt.xlabel("ADC voltage [V]")
plt.ylabel("Temp [Grad]")
plt.legend()
plt.show()
| 0
| 0
| 0
| 0
| 0
| 327
| 0
| 0
| 91
|
11b074486878ba17beb24bb3f56ff683fc015de8
| 2,698
|
py
|
Python
|
tools/diff_filter.py
|
caizhanjin/deepseg
|
5e91a387683ad73075b51b49da8957d8f4bb6b7f
|
[
"Apache-2.0"
] | null | null | null |
tools/diff_filter.py
|
caizhanjin/deepseg
|
5e91a387683ad73075b51b49da8957d8f4bb6b7f
|
[
"Apache-2.0"
] | null | null | null |
tools/diff_filter.py
|
caizhanjin/deepseg
|
5e91a387683ad73075b51b49da8957d8f4bb6b7f
|
[
"Apache-2.0"
] | null | null | null |
if __name__ == "__main__":
filters = [SingleChineseFilter()]
diff_file = "C:\\Users\\allen.luo\\Desktop\\diff_filter\\test.diff.txt"
differ = DiffFilter(diff_file)
differ.filter(filters)
| 32.506024
| 92
| 0.490734
|
import os
import re
class Filter(object):
def accept(self, line):
raise NotImplementedError()
class SingleChineseFilter(Filter):
def __init__(self):
self.pattern = re.compile("[\u4e00-\u9fa5]+")
self.key_words = ['号', '层', '幢', '与', '栋', '旁', '室', '楼']
def accept(self, line):
words = line.split(" ")
acc = False
for w in words:
if len(w) > 1:
continue
if w in self.key_words:
continue
m = self.pattern.findall(w)
if not m:
continue
acc = True
break
return acc
class DiffFilter(object):
def __init__(self, diff_file):
self.diff_file = diff_file
def filter(self, filters):
filepath = str(self.diff_file).split(os.sep)[0:-1]
skipped_file = os.path.join(os.sep.join(filepath), "skipped.txt")
selected_file = os.path.join(os.sep.join(filepath), "selected.txt")
editable = os.path.join(os.sep.join(filepath), "editable.txt")
with open(self.diff_file, mode="rt", encoding="utf8", buffering=8192) as f, \
open(skipped_file, mode="wt", encoding="utf8", buffering=8192) as skip, \
open(selected_file, mode="wt", encoding="utf8", buffering=8192) as select, \
open(editable, mode="wt", encoding="utf8", buffering=8192) as ed:
while True:
line = f.readline()
if not line:
break
# empty = f.readline()
jieba = f.readline().replace("STD_label:", "").strip("\n")
model = f.readline().replace("SEG_label:", "").strip("\n")
print(jieba)
print(model)
print()
if not jieba or not model:
continue
skip_line = True
for flt in filters:
if flt.accept(model):
skip_line = False
break
if not skip_line:
select.write("jieba: %s\n" % jieba)
select.write("model: %s\n" % model)
select.write("\n")
ed.write(model + "\n")
ed.write("\n\n")
else:
skip.write("jieba: %s\n" % jieba)
skip.write("model: %s\n" % model)
skip.write("\n")
if __name__ == "__main__":
filters = [SingleChineseFilter()]
diff_file = "C:\\Users\\allen.luo\\Desktop\\diff_filter\\test.diff.txt"
differ = DiffFilter(diff_file)
differ.filter(filters)
| 24
| 0
| 0
| 2,393
| 0
| 0
| 0
| -24
| 113
|
8b67b98d8ea2d3b839bcd5bef3e02df81aed5aca
| 1,142
|
py
|
Python
|
instances/apps.py
|
glzjin/webvirtcloud
|
ecaf11e02aeb57654257ed502d3da6fd8405f21b
|
[
"Apache-2.0"
] | null | null | null |
instances/apps.py
|
glzjin/webvirtcloud
|
ecaf11e02aeb57654257ed502d3da6fd8405f21b
|
[
"Apache-2.0"
] | null | null | null |
instances/apps.py
|
glzjin/webvirtcloud
|
ecaf11e02aeb57654257ed502d3da6fd8405f21b
|
[
"Apache-2.0"
] | null | null | null |
def migrate_can_clone_instances(sender, **kwargs):
'''
Migrate can clone instances user attribute to permission
'''
from django.contrib.auth.models import User, Permission
plan = kwargs['plan']
for migration, rolled_back in plan:
if migration.app_label == 'instances' and migration.name == '0002_permissionset' and not rolled_back:
users = User.objects.all()
permission = Permission.objects.get(codename='clone_instances')
print('\033[92mMigrating can_clone_instaces user attribute to permission\033[0m')
for user in users:
if user.userattributes:
if user.userattributes.can_clone_instances:
user.user_permissions.add(permission)
break
| 35.6875
| 109
| 0.683888
|
from django.apps import AppConfig
from django.db.models.signals import post_migrate
def migrate_can_clone_instances(sender, **kwargs):
'''
Migrate can clone instances user attribute to permission
'''
from django.conf import settings
from django.contrib.auth.models import User, Permission
from accounts.models import UserAttributes
plan = kwargs['plan']
for migration, rolled_back in plan:
if migration.app_label == 'instances' and migration.name == '0002_permissionset' and not rolled_back:
users = User.objects.all()
permission = Permission.objects.get(codename='clone_instances')
print('\033[92mMigrating can_clone_instaces user attribute to permission\033[0m')
for user in users:
if user.userattributes:
if user.userattributes.can_clone_instances:
user.user_permissions.add(permission)
break
class InstancesConfig(AppConfig):
name = 'instances'
verbose_name = 'instances'
def ready(self):
post_migrate.connect(migrate_can_clone_instances, sender=self)
| 0
| 0
| 0
| 159
| 0
| 0
| 0
| 72
| 119
|
db9b52b59816ce2444968110f44841a8d37eb201
| 9,759
|
py
|
Python
|
tests/opc/test_rel.py
|
revvsales/python-docx-1
|
5b3ff2b828cc30f1567cb1682a8cb399143732d7
|
[
"MIT"
] | 3,031
|
2015-01-02T11:11:24.000Z
|
2022-03-30T00:57:17.000Z
|
tests/opc/test_rel.py
|
revvsales/python-docx-1
|
5b3ff2b828cc30f1567cb1682a8cb399143732d7
|
[
"MIT"
] | 934
|
2015-01-06T20:53:56.000Z
|
2022-03-28T10:08:03.000Z
|
tests/opc/test_rel.py
|
revvsales/python-docx-1
|
5b3ff2b828cc30f1567cb1682a8cb399143732d7
|
[
"MIT"
] | 901
|
2015-01-07T18:22:07.000Z
|
2022-03-31T18:38:51.000Z
|
# encoding: utf-8
"""
Unit test suite for the docx.opc.rel module
"""
from __future__ import (
absolute_import, division, print_function, unicode_literals
)
| 34.242105
| 77
| 0.630905
|
# encoding: utf-8
"""
Unit test suite for the docx.opc.rel module
"""
from __future__ import (
absolute_import, division, print_function, unicode_literals
)
import pytest
from docx.opc.oxml import CT_Relationships
from docx.opc.packuri import PackURI
from docx.opc.part import Part
from docx.opc.rel import _Relationship, Relationships
from ..unitutil.mock import (
call, class_mock, instance_mock, Mock, patch, PropertyMock
)
class Describe_Relationship(object):
def it_remembers_construction_values(self):
# test data --------------------
rId = 'rId9'
reltype = 'reltype'
target = Mock(name='target_part')
external = False
# exercise ---------------------
rel = _Relationship(rId, reltype, target, None, external)
# verify -----------------------
assert rel.rId == rId
assert rel.reltype == reltype
assert rel.target_part == target
assert rel.is_external == external
def it_should_raise_on_target_part_access_on_external_rel(self):
rel = _Relationship(None, None, None, None, external=True)
with pytest.raises(ValueError):
rel.target_part
def it_should_have_target_ref_for_external_rel(self):
rel = _Relationship(None, None, 'target', None, external=True)
assert rel.target_ref == 'target'
def it_should_have_relative_ref_for_internal_rel(self):
"""
Internal relationships (TargetMode == 'Internal' in the XML) should
have a relative ref, e.g. '../slideLayouts/slideLayout1.xml', for
the target_ref attribute.
"""
part = Mock(name='part', partname=PackURI('/ppt/media/image1.png'))
baseURI = '/ppt/slides'
rel = _Relationship(None, None, part, baseURI) # external=False
assert rel.target_ref == '../media/image1.png'
class DescribeRelationships(object):
def it_can_add_a_relationship(self, _Relationship_):
baseURI, rId, reltype, target, external = (
'baseURI', 'rId9', 'reltype', 'target', False
)
rels = Relationships(baseURI)
rel = rels.add_relationship(reltype, target, rId, external)
_Relationship_.assert_called_once_with(
rId, reltype, target, baseURI, external
)
assert rels[rId] == rel
assert rel == _Relationship_.return_value
def it_can_add_an_external_relationship(self, add_ext_rel_fixture_):
rels, reltype, url = add_ext_rel_fixture_
rId = rels.get_or_add_ext_rel(reltype, url)
rel = rels[rId]
assert rel.is_external
assert rel.target_ref == url
assert rel.reltype == reltype
def it_can_find_a_relationship_by_rId(self):
rel = Mock(name='rel', rId='foobar')
rels = Relationships(None)
rels['foobar'] = rel
assert rels['foobar'] == rel
def it_can_find_or_add_a_relationship(
self, rels_with_matching_rel_, rels_with_missing_rel_):
rels, reltype, part, matching_rel = rels_with_matching_rel_
assert rels.get_or_add(reltype, part) == matching_rel
rels, reltype, part, new_rel = rels_with_missing_rel_
assert rels.get_or_add(reltype, part) == new_rel
def it_can_find_or_add_an_external_relationship(
self, add_matching_ext_rel_fixture_):
rels, reltype, url, rId = add_matching_ext_rel_fixture_
_rId = rels.get_or_add_ext_rel(reltype, url)
assert _rId == rId
assert len(rels) == 1
def it_can_find_a_related_part_by_rId(self, rels_with_known_target_part):
rels, rId, known_target_part = rels_with_known_target_part
part = rels.related_parts[rId]
assert part is known_target_part
def it_raises_on_related_part_not_found(self, rels):
with pytest.raises(KeyError):
rels.related_parts['rId666']
def it_can_find_a_related_part_by_reltype(
self, rels_with_target_known_by_reltype):
rels, reltype, known_target_part = rels_with_target_known_by_reltype
part = rels.part_with_reltype(reltype)
assert part is known_target_part
def it_can_compose_rels_xml(self, rels, rels_elm):
# exercise ---------------------
rels.xml
# verify -----------------------
rels_elm.assert_has_calls(
[
call.add_rel(
'rId1', 'http://rt-hyperlink', 'http://some/link', True
),
call.add_rel(
'rId2', 'http://rt-image', '../media/image1.png', False
),
call.xml()
],
any_order=True
)
def it_knows_the_next_available_rId_to_help(self, rels_with_rId_gap):
rels, expected_next_rId = rels_with_rId_gap
next_rId = rels._next_rId
assert next_rId == expected_next_rId
# fixtures ---------------------------------------------
@pytest.fixture
def add_ext_rel_fixture_(self, reltype, url):
rels = Relationships(None)
return rels, reltype, url
@pytest.fixture
def add_matching_ext_rel_fixture_(self, request, reltype, url):
rId = 'rId369'
rels = Relationships(None)
rels.add_relationship(reltype, url, rId, is_external=True)
return rels, reltype, url, rId
# fixture components -----------------------------------
@pytest.fixture
def _baseURI(self):
return '/baseURI'
@pytest.fixture
def _Relationship_(self, request):
return class_mock(request, 'docx.opc.rel._Relationship')
@pytest.fixture
def _rel_with_target_known_by_reltype(
self, _rId, reltype, _target_part, _baseURI):
rel = _Relationship(_rId, reltype, _target_part, _baseURI)
return rel, reltype, _target_part
@pytest.fixture
def rels(self):
"""
Populated Relationships instance that will exercise the rels.xml
property.
"""
rels = Relationships('/baseURI')
rels.add_relationship(
reltype='http://rt-hyperlink', target='http://some/link',
rId='rId1', is_external=True
)
part = Mock(name='part')
part.partname.relative_ref.return_value = '../media/image1.png'
rels.add_relationship(reltype='http://rt-image', target=part,
rId='rId2')
return rels
@pytest.fixture
def rels_elm(self, request):
"""
Return a rels_elm mock that will be returned from
CT_Relationships.new()
"""
# create rels_elm mock with a .xml property
rels_elm = Mock(name='rels_elm')
xml = PropertyMock(name='xml')
type(rels_elm).xml = xml
rels_elm.attach_mock(xml, 'xml')
rels_elm.reset_mock() # to clear attach_mock call
# patch CT_Relationships to return that rels_elm
patch_ = patch.object(CT_Relationships, 'new', return_value=rels_elm)
patch_.start()
request.addfinalizer(patch_.stop)
return rels_elm
@pytest.fixture
def _rel_with_known_target_part(
self, _rId, reltype, _target_part, _baseURI):
rel = _Relationship(_rId, reltype, _target_part, _baseURI)
return rel, _rId, _target_part
@pytest.fixture
def rels_with_known_target_part(self, rels, _rel_with_known_target_part):
rel, rId, target_part = _rel_with_known_target_part
rels.add_relationship(None, target_part, rId)
return rels, rId, target_part
@pytest.fixture
def rels_with_matching_rel_(self, request, rels):
matching_reltype_ = instance_mock(
request, str, name='matching_reltype_'
)
matching_part_ = instance_mock(
request, Part, name='matching_part_'
)
matching_rel_ = instance_mock(
request, _Relationship, name='matching_rel_',
reltype=matching_reltype_, target_part=matching_part_,
is_external=False
)
rels[1] = matching_rel_
return rels, matching_reltype_, matching_part_, matching_rel_
@pytest.fixture
def rels_with_missing_rel_(self, request, rels, _Relationship_):
missing_reltype_ = instance_mock(
request, str, name='missing_reltype_'
)
missing_part_ = instance_mock(
request, Part, name='missing_part_'
)
new_rel_ = instance_mock(
request, _Relationship, name='new_rel_',
reltype=missing_reltype_, target_part=missing_part_,
is_external=False
)
_Relationship_.return_value = new_rel_
return rels, missing_reltype_, missing_part_, new_rel_
@pytest.fixture
def rels_with_rId_gap(self, request):
rels = Relationships(None)
rel_with_rId1 = instance_mock(
request, _Relationship, name='rel_with_rId1', rId='rId1'
)
rel_with_rId3 = instance_mock(
request, _Relationship, name='rel_with_rId3', rId='rId3'
)
rels['rId1'] = rel_with_rId1
rels['rId3'] = rel_with_rId3
return rels, 'rId2'
@pytest.fixture
def rels_with_target_known_by_reltype(
self, rels, _rel_with_target_known_by_reltype):
rel, reltype, target_part = _rel_with_target_known_by_reltype
rels[1] = rel
return rels, reltype, target_part
@pytest.fixture
def reltype(self):
return 'http://rel/type'
@pytest.fixture
def _rId(self):
return 'rId6'
@pytest.fixture
def _target_part(self, request):
return instance_mock(request, Part)
@pytest.fixture
def url(self):
return 'https://github.com/scanny/python-docx'
| 0
| 4,282
| 0
| 4,989
| 0
| 0
| 0
| 142
| 181
|
5d363713f0c567c72f60e40b049c1d6047d88f04
| 4,873
|
py
|
Python
|
python/plugins/broker/mqtt/tests/test_params.py
|
ulen2000/sinetstream
|
efbd1688be0754c38b0ea88f0f253f91b44689be
|
[
"Apache-2.0"
] | 1
|
2020-03-24T15:29:23.000Z
|
2020-03-24T15:29:23.000Z
|
python/plugins/broker/mqtt/tests/test_params.py
|
ulen2000/sinetstream
|
efbd1688be0754c38b0ea88f0f253f91b44689be
|
[
"Apache-2.0"
] | null | null | null |
python/plugins/broker/mqtt/tests/test_params.py
|
ulen2000/sinetstream
|
efbd1688be0754c38b0ea88f0f253f91b44689be
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/local/bin/python3.6
# vim: expandtab shiftwidth=4
# Copyright (C) 2020 National Institute of Informatics
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
import pytest
from conftest import (TOPIC, CACERT_PATH)
pytestmark = pytest.mark.usefixtures('setup_config')
wss_params = {
'transport': 'websockets',
'tls': {
'ca_certs': str(CACERT_PATH),
},
}
ws_set_options_params = {
'transport': 'websockets',
'ws_set_options': {
'path': '/mqtt',
},
}
will_set_params = {
'will_set': {
'topic': TOPIC,
'payload': 'XXX',
'qos': 1,
'retain': True,
}
}
reconnect_delay_params = {
'reconnect_delay_set': {
'min_delay': 1,
'max_delay': 120,
}
}
| 26.058824
| 77
| 0.682742
|
#!/usr/local/bin/python3.6
# vim: expandtab shiftwidth=4
# Copyright (C) 2020 National Institute of Informatics
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
import pytest
from sinetstream import MessageReader, MessageWriter, InvalidArgumentError
from conftest import (
SERVICE, TOPIC, BROKER, WS_BROKER, WSS_BROKER, CACERT_PATH,
)
from itertools import product
pytestmark = pytest.mark.usefixtures('setup_config')
@pytest.mark.parametrize("io,config_params", product(
[MessageReader, MessageWriter],
[{'protocol': x} for x in ['MQTTv31', 'MQTTv311', 'MQTTv5']],
))
def test_protocol(io):
with io(SERVICE) as f:
pass
@pytest.mark.parametrize("io,config_params", [
(MessageReader, {'protocol': 'xxx'}),
(MessageWriter, {'protocol': 'xxx'}),
])
def test_bad_protocol(io):
with pytest.raises(InvalidArgumentError):
with io(SERVICE) as f:
pass
@pytest.mark.parametrize("io,config_params", [
(MessageReader, {'transport': 'tcp'}),
(MessageWriter, {'transport': 'tcp'}),
])
def test_transport_tcp(io):
with io(SERVICE) as f:
pass
@pytest.mark.skipif(WS_BROKER is None, reason='MQTT_WS_BROKER is not set.')
@pytest.mark.parametrize("io,config_params,config_brokers", [
(MessageReader, {'transport': 'websockets'}, WS_BROKER),
(MessageWriter, {'transport': 'websockets'}, WS_BROKER),
])
def test_transport_ws(io):
with io(SERVICE) as f:
pass
wss_params = {
'transport': 'websockets',
'tls': {
'ca_certs': str(CACERT_PATH),
},
}
@pytest.mark.skipif(WSS_BROKER is None, reason='MQTT_WSS_BROKER is not set.')
@pytest.mark.parametrize("io,config_params,config_brokers", [
(MessageReader, wss_params, WSS_BROKER),
(MessageWriter, wss_params, WSS_BROKER),
])
def test_transport_wss(io):
with io(SERVICE) as f:
pass
@pytest.mark.parametrize("io,config_params", [
(MessageReader, {'transport': 'xxx'}),
(MessageWriter, {'transport': 'xxx'}),
])
def test_transport_bad_value(io):
with pytest.raises(InvalidArgumentError):
with io(SERVICE) as f:
pass
@pytest.mark.parametrize("io,config_params", product(
[MessageReader, MessageWriter],
[{'clean_session': x} for x in [True, False]],
))
def test_clean_session(io):
with io(SERVICE) as f:
pass
@pytest.mark.parametrize("io,config_params", product(
[MessageReader, MessageWriter],
[{'max_inflight_messages_set': {'inflight': x}} for x in [20, 40]],
))
def test_max_inflight_messages(io):
with io(SERVICE) as f:
pass
@pytest.mark.parametrize("io,config_params", product(
[MessageReader, MessageWriter],
[{'max_queued_messages_set': {'queue_size': x}} for x in [0, 10]],
))
def test_max_queued_messages(io):
with io(SERVICE) as f:
pass
@pytest.mark.parametrize("io,config_params", product(
[MessageReader, MessageWriter],
[{'message_retry_set': {'retry': x}} for x in [5, 10]],
))
def test_message_retry(io):
with io(SERVICE) as f:
pass
ws_set_options_params = {
'transport': 'websockets',
'ws_set_options': {
'path': '/mqtt',
},
}
@pytest.mark.skipif(WS_BROKER is None, reason='MQTT_WS_BROKER is not set.')
@pytest.mark.parametrize("io,config_params,config_brokers", [
(MessageReader, ws_set_options_params, WS_BROKER),
(MessageWriter, ws_set_options_params, WS_BROKER),
])
def test_ws_set_options(io):
with io(SERVICE) as f:
pass
will_set_params = {
'will_set': {
'topic': TOPIC,
'payload': 'XXX',
'qos': 1,
'retain': True,
}
}
@pytest.mark.parametrize("io,config_params", [
(MessageReader, will_set_params),
(MessageWriter, will_set_params),
])
def test_will(io):
with io(SERVICE) as f:
pass
reconnect_delay_params = {
'reconnect_delay_set': {
'min_delay': 1,
'max_delay': 120,
}
}
@pytest.mark.parametrize("io,config_params", [
(MessageReader, reconnect_delay_params),
(MessageWriter, reconnect_delay_params),
])
def test_reconnect_delay(io):
with io(SERVICE) as f:
pass
| 0
| 2,917
| 0
| 0
| 0
| 0
| 0
| 108
| 343
|
3d601cf4bfe04df1b7cde903f2017afd1f54a428
| 407
|
py
|
Python
|
test/backup_setup.py
|
jinyiabc/china_stock_lib
|
d580b9f2a3f20ca9f87c7a4d42aeedccfa450f4f
|
[
"MIT"
] | null | null | null |
test/backup_setup.py
|
jinyiabc/china_stock_lib
|
d580b9f2a3f20ca9f87c7a4d42aeedccfa450f4f
|
[
"MIT"
] | null | null | null |
test/backup_setup.py
|
jinyiabc/china_stock_lib
|
d580b9f2a3f20ca9f87c7a4d42aeedccfa450f4f
|
[
"MIT"
] | 2
|
2021-12-30T23:53:55.000Z
|
2022-02-01T18:10:42.000Z
|
# from distutils.core import setup
# setup(name='helper',
# version='0.1.1',
# py_modules=['upload_github'],
# data_files=[('config', ['mysql.cfg'])],
# )
# from setuptools import setup
#
# setup(
# name='mypackage',
# version='0.0.1',
# packages=['mypackage'],
# install_requires=[
# 'requests',
# 'importlib; python_version == "2.6"',
# ],
# )
| 22.611111
| 47
| 0.538084
|
# from distutils.core import setup
# setup(name='helper',
# version='0.1.1',
# py_modules=['upload_github'],
# data_files=[('config', ['mysql.cfg'])],
# )
# from setuptools import setup
#
# setup(
# name='mypackage',
# version='0.0.1',
# packages=['mypackage'],
# install_requires=[
# 'requests',
# 'importlib; python_version == "2.6"',
# ],
# )
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
79225c470cde861cd7490495560ca1243733a6b2
| 4,223
|
py
|
Python
|
tests/unit_tests/test_units/test_core.py
|
radical-project/radical.dreamer
|
74bb2a9a705fc90b0dc773963f2bfd48af6e1b84
|
[
"MIT"
] | 4
|
2021-04-30T04:25:12.000Z
|
2021-12-16T19:53:37.000Z
|
tests/unit_tests/test_units/test_core.py
|
radical-project/radical.dreamer
|
74bb2a9a705fc90b0dc773963f2bfd48af6e1b84
|
[
"MIT"
] | 1
|
2021-04-20T22:08:24.000Z
|
2021-04-20T22:08:24.000Z
|
tests/unit_tests/test_units/test_core.py
|
radical-project/radical.dreamer
|
74bb2a9a705fc90b0dc773963f2bfd48af6e1b84
|
[
"MIT"
] | 1
|
2021-01-10T20:09:19.000Z
|
2021-01-10T20:09:19.000Z
|
__copyright__ = 'Copyright 2021, The RADICAL-Cybertools Team'
__license__ = 'MIT'
TEST_CASES_PATH = 'tests/unit_tests/test_units/test_cases/core.*.json'
| 35.487395
| 76
| 0.600047
|
__copyright__ = 'Copyright 2021, The RADICAL-Cybertools Team'
__license__ = 'MIT'
import glob
import radical.utils as ru
from radical.dreamer.units import Core, Task, CORE_STATE
from unittest import TestCase
TEST_CASES_PATH = 'tests/unit_tests/test_units/test_cases/core.*.json'
class CoreTestClass(TestCase):
@classmethod
def setUpClass(cls):
cls._test_cases = []
for f in glob.glob(TEST_CASES_PATH):
cls._test_cases.extend(ru.read_json(f))
def test_init(self):
# default attributes
c = Core()
self.assertTrue(c.uid.startswith('core.'))
self.assertEqual(c.perf, 1.)
self.assertEqual(c.perf_dynamic, 1.)
self.assertFalse(c.perf_history)
self.assertIsInstance(c.perf_history, list)
self.assertEqual(c.io_rate, 0.)
self.assertEqual(c.io_time, 0.)
self.assertEqual(c.acquire_time, 0.)
self.assertEqual(c.release_time, 0.)
self.assertEqual(c.planned_compute_time, 0.)
self.assertEqual(c.planned_release_time, 0.)
self.assertEqual(c.state, CORE_STATE.Idle)
# with input data
for test_case in self._test_cases:
c = Core(**test_case['input'])
result = dict(test_case['input'])
for k, v in Core._defaults.items():
if k not in result:
result[k] = v
if result.get('uid'):
self.assertEqual(c.uid, result['uid'])
self.assertEqual(c.perf, result['perf'])
self.assertEqual(c.perf_dynamic, result['perf_dynamic'])
self.assertEqual(c.perf_history, result['perf_history'])
self.assertEqual(c.io_rate, result['io_rate'])
self.assertEqual(c.io_time, result['io_time'])
self.assertEqual(c.acquire_time, result['acquire_time'])
self.assertEqual(c.release_time, result['release_time'])
self.assertEqual(c.planned_compute_time,
result['planned_compute_time'])
self.assertEqual(c.planned_release_time,
result['planned_release_time'])
self.assertEqual(c.state, result['state'])
def test_execute(self):
for test_case in self._test_cases:
if 'task' not in test_case or 'result_execute' not in test_case:
continue
t = Task(**test_case['task'])
c = Core(**test_case['input'])
previous_release_time = c.release_time
ret_c = c.execute(task=t)
result = test_case['result_execute']
self.assertIsInstance(ret_c, Core)
self.assertEqual(ret_c.io_time, result['io_time'])
self.assertEqual(ret_c.acquire_time, previous_release_time)
self.assertEqual(ret_c.release_time, result['release_time'])
self.assertEqual(ret_c.planned_compute_time,
result['planned_compute_time'])
self.assertEqual(ret_c.planned_release_time,
result['planned_release_time'])
self.assertEqual(ret_c.planned_compute_time,
ret_c.planned_release_time
- ret_c.acquire_time
- ret_c.io_time)
self.assertTrue(ret_c.is_busy)
self.assertEqual(t.start_time, ret_c.acquire_time)
self.assertEqual(t.end_time, ret_c.release_time)
def test_release(self):
for test_case in self._test_cases:
if 'task' not in test_case:
continue
c = Core(**test_case['input'])
t = Task(**test_case['task'])
c.execute(task=t)
perf_history_len = len(c.perf_history)
self.assertEqual(c.state, CORE_STATE.Busy)
ret_c = c.release()
self.assertIsInstance(ret_c, Core)
self.assertEqual(ret_c.perf_dynamic, ret_c.perf_history[-1])
self.assertEqual(len(ret_c.perf_history), perf_history_len + 1)
self.assertFalse(ret_c.is_busy)
self.assertFalse(ret_c.io_time)
self.assertFalse(ret_c.planned_compute_time)
| 0
| 142
| 0
| 3,770
| 0
| 0
| 0
| 38
| 115
|
bcf1f57f33b1cf0327c51d531ce1989a4096bd93
| 24,349
|
py
|
Python
|
LivSim Processing/event.py
|
kbui1993/LivSim-Codes
|
5317fa6ea773d5967871dfb67dec1a0118ec2f5e
|
[
"MIT"
] | 2
|
2018-05-07T03:31:54.000Z
|
2019-07-02T18:30:41.000Z
|
LivSim Processing/event.py
|
kbui1993/LivSim-Codes
|
5317fa6ea773d5967871dfb67dec1a0118ec2f5e
|
[
"MIT"
] | null | null | null |
LivSim Processing/event.py
|
kbui1993/LivSim-Codes
|
5317fa6ea773d5967871dfb67dec1a0118ec2f5e
|
[
"MIT"
] | 1
|
2019-01-19T19:56:43.000Z
|
2019-01-19T19:56:43.000Z
|
import entity, allocate
import numpy as nump
import datetime
from copy import deepcopy
ndsa = 58
#######################################################################Event Processes####################################################################################
def Arrival(arrivalinfo, Sim, Stat, OPTN):
"""
This function simulates the arrival of patients. It computes the MELD score of the patient and adds him to the corresponding DSA waiting list.
Input:
@arrivalinfo: all the patient's information
@Sim: class object that contains variables relevant to the simulation
@Stat: class object that containts statistical info of the simulation
@OPTN: complete patient data
Output:
@Sim: updated Sim
@Stat: updated Stat
@OPTN updated OPTN
"""
#Create Patient Entity
newpatient = entity.Patient(arrivalinfo[1].astype(int),arrivalinfo[3].astype(int),Sim.clock)
#Assign Patient Characteristics
newpatient.Status1 = arrivalinfo[9].astype(int)
newpatient.ABO = arrivalinfo[5].astype(int)
newpatient.HCC = arrivalinfo[8].astype(int)
newpatient.Na = arrivalinfo[10].astype(int)
newpatient.lMELD = arrivalinfo[7].astype(int)
newpatient.MELD = arrivalinfo[6].astype(int)
newpatient.Inactive = arrivalinfo[11].astype(int)
#Assign Allocation MELD based on policy
if Sim.sodium ==1: #if sodium policy is selected
#bound the sodium score
effective_na = newpatient.Na
if effective_na <125:
effective_na = 125
elif effective_na > 137:
effective_na = 137
#compute the allocation MELD score
if nump.rint(newpatient.lMELD + 1.32*(137-effective_na)-(0.033*newpatient.lMELD*(137-effective_na))) <6:
newpatient.MELD =6
elif nump.rint(newpatient.lMELD + 1.32*(137-effective_na)-(0.033*newpatient.lMELD*(137-effective_na))) > 40:
newpatient.MELD =40
else:
newpatient.MELD = nump.rint(newpatient.lMELD + 1.32*(137-effective_na)-(0.033*newpatient.lMELD*(137-effective_na)))
else: #if sodium policy not selected
#bound the sodium score
if newpatient.MELD <6:
newpatient.MELD =6
elif newpatient.MELD >40:
newpatient.MELD = 40
#Apply Status1 and HCC exceptions (if applicable)
if newpatient.Status1 ==1:
newpatient.MELD =41
elif newpatient.HCC ==1 and Sim.capanddelay ==1:
newpatient.MELD = min(newpatient.lMELD,28)
#Place Patient in DSA List
if newpatient.DSA >= 0:
OPTN[newpatient.DSA].append(newpatient)
#Update Stats
Stat.yarrivals[newpatient.DSA] = Stat.yarrivals[newpatient.DSA] + 1
Stat.numcandidates[newpatient.DSA] = Stat.numcandidates[newpatient.DSA] + 1
#return updated Sim, Stat, and OPTN
return Sim, Stat, OPTN
#Diagnostic Info
#here
def Progression(proginfo, Sim, Stat, OPTN, reps):
"""
This function searches for a particular patient from the OPTN data structure and update the patient's characteristics
Input:
@proginfo: all of the information of the patient being searched up
@Sim: class object containing relevant information of the patient being searched up
@Stat: class object containing statistical information of the simulation
@OPTN: complete patient data
@reps: current replication number
Output:
@Sim: updated Sim
@Stat: updated Stat
@OPTN: updated OPTN
"""
progdsa = proginfo[9].astype(int) #obtain DSA
#test =0
#run if we have an actual DSA
if progdsa >=0:
#search for patient in the OPTN data structure
for i, patient in enumerate(OPTN[progdsa]):
#check if the id matches
if patient.id == proginfo[1].astype(int):
#Update Patient
if patient.Relist ==1:
#Ignore updates for relisted patients
break
elif proginfo[3].astype(int) == 1:
#Patient dies, remove and update stats
Stat.ydeaths[progdsa] = Stat.ydeaths[progdsa] + 1 #increment the number of deaths by one
Stat.numcandidates[progdsa] = Stat.numcandidates[progdsa] - 1 #decrement the number of candidates waiting by one
del OPTN[progdsa][i] #delete the patient object since patient died
break
elif proginfo[4].astype(int) ==1:
#Patient removed, remove and update stats
Stat.yremoved[progdsa] = Stat.yremoved[progdsa] + 1 #increment the number of removals by one
Stat.numcandidates[progdsa] = Stat.numcandidates[progdsa] - 1 #decrement the number of candidates waiting by one
#record as follows (time of removal, repetition, patient id, patient allocation MELD, patient lab MELD)
oidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,patient.MELD, patient.lMELD]
Sim.record_removals = nump.vstack((Sim.record_removals, oidreport)) #concatenate the row oidreport to the record of removals
del OPTN[progdsa][i] #delete patient object since patient is removed
break
else:
#Update candidate
patient.lMELD = proginfo[6].astype(int)
patient.Na = proginfo[7].astype(int)
patient.MELD = proginfo[5].astype(int)
patient.Inactive = proginfo[10].astype(int)
#set bound on MELD score
if patient.MELD <6:
patient.MELD =6
elif patient.MELD >40:
patient.MELD = 40
#Update Allocation MELD based on policy (if applicable)
if Sim.sodium ==1 and patient.Status1 != 1 and patient.HCC != 1:
#if sodium score policy is selected, then update the meld score for non-status1, non-HCC patient
#set bound on sodium score
effective_na = patient.Na
if effective_na <125:
effective_na = 125
elif effective_na > 137:
effective_na = 137
#compute MELD score
if nump.rint(patient.lMELD + 1.32*(137-effective_na)-(0.033*patient.lMELD*(137-effective_na))) <6:
patient.MELD =6
elif nump.rint(patient.lMELD + 1.32*(137-effective_na)-(0.033*patient.lMELD*(137-effective_na))) > 40:
patient.MELD =40
else:
patient.MELD = nump.rint(patient.lMELD + 1.32*(137-effective_na)-(0.033*patient.lMELD*(137-effective_na)))
elif Sim.capanddelay ==1 and patient.Status1 != 1 and patient.HCC == 1:
#if cap and delay policy is selected, update meld score for status1, HCC patient
#compute MELD score
if Sim.clock - patient.create_time <= .5:
patient.MELD = max(28,patient.MELD)
elif (Sim.clock - patient.create_time) > .5 and (Sim.clock - patient.create_time <= .75):
patient.MELD = max(29,patient.MELD)
elif (Sim.clock - patient.create_time) > .75 and (Sim.clock - patient.create_time <= 1):
patient.MELD = max(31,patient.MELD)
elif (Sim.clock - patient.create_time) > 1 and (Sim.clock - patient.create_time <= 1.25):
patient.MELD = max(33,patient.MELD)
elif (Sim.clock - patient.create_time) > 1.25 and (Sim.clock - patient.create_time <= 1.5):
patient.MELD = max(34,patient.MELD)
else:
patient.MELD = min(patient.MELD+1,40)
break
#return updated Sim, Stat, and OPTN
return Sim, Stat, OPTN
def OrganArrival(organinfo, Sim, Stat, OPTN, Regions, SharingPartners, Patients_Accept, Donor_Accept, DSA_Avg_Times, AcceptanceModelS1, AcceptanceModel, Relist, reps):
"""
This function simulates the organ arrival. It tries to match the organ to a patient from the corresponding DSA waitlist.
Input:
@organinfo: information on the organ
@Sim: class object containing relevant variables for simulation
@Stat: class object containing statistical information of simulation
@OPTN: complete patient data
@Regions: neighbhorhood map for regions, districts, or neighbhorhoods
@SharingPartners: neighborhood map adding sharing partners to existing geographic relationships among OPOs
@Patients_Accept: coefficients regarding donor's characteristics for acceptance model
@Donor_Accept: coefficients regarding donor's characteristics for acceptance model
@DSA_Avg_Times: data on average transport times between DSAs
@AcceptanceModelS1: coefficients regarding patient's characteristics for status-1 acceptance model
@AccpetanceModel: coefficients regarding patient's characteristics for non-status-1 acceptance model
@Relist: values regarding the probability that a transplanted patient will relist
@reps: replication number
Output:
@Sim: updated Sim
@Stat: updated Stat
@OPTN: updated OPTN
"""
#Create Organ
neworgan = entity.Organ(int(organinfo[2]))
neworgan.organid = Sim.oid
Sim.oid = Sim.oid + 1
#Assign Organ Attributes
neworgan.ABO = organinfo[4].astype(int)
#Allocate Organ
#disposition is tuple of organ status (accept/reject) , transplanting DSA, and patient id if accepted
disposition = allocate.Allocate(neworgan, OPTN, Sim, Regions, SharingPartners, Patients_Accept, Donor_Accept, DSA_Avg_Times, AcceptanceModelS1, AcceptanceModel)
if disposition[0] == 1: #organ is transplanted
#Remove transplanted patient from waiting list and update statistics
for i, patient in enumerate(OPTN[disposition[1]]):
#search for the patient in the OPTN data structure
if patient.id == disposition[2]:
#Determine if patient will relist and assign special attributes if necesary
willrelist = 0
if patient.Relist ==0:
#if patient has not been relisted
#Determine if patient will be relisted if was not already
r1 =nump.random.uniform(Relist[0],Relist[1],1)
r2 =nump.random.uniform(0,1,1)
if r2 < r1:
willrelist =1
#Determine when current graft will fail
r3 = nump.random.uniform(0,1,1)
if r3 < .4:
patient.RelistTxTime = Sim.clock + 5
elif r3 >= .4 and r3 < .6:
patient.RelistTxTime = Sim.clock + 2
elif r3 >= 6 and r3 < .8:
patient.RelistTxTime = Sim.clock + 1
else:
patient.RelistTxTime = Sim.clock
#Update relist statistics
Stat.yrelists[disposition[1]] = Stat.yrelists[disposition[1]] +1
#record the floor of current time, reptitiion, current time, patient id, patient meld score, and relist tx time
relistidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,patient.MELD,patient.RelistTxTime]
#concatenate the relistidreport to the record of relists
Sim.record_relists = nump.vstack((Sim.record_relists, relistidreport))
#Update Stats for Transplants
#Number of Transplants and Regrafts
Stat.ytransplants[disposition[1]] = Stat.ytransplants[disposition[1]] +1
Sim.record_txDSA[neworgan.DSA,disposition[1]] = Sim.record_txDSA[neworgan.DSA,disposition[1]] +1
if patient.Relist ==1:
#if patient is relisted
Stat.yregrafts[disposition[1]] = Stat.yregrafts[disposition[1]] +1 #increase number of retransplanted patients by 1
#Compute waiting Time
if patient.Relist ==0:
#if patient is not relisted
Stat.ywait[disposition[1]] = Stat.ywait[disposition[1]] + (Sim.clock - patient.create_time)
else:
#if patient is relisted
Stat.ywait[disposition[1]] = Stat.ywait[disposition[1]] + (Sim.clock - patient.RelistTxTime)
#Waiting List Sizes
if willrelist ==0:
Stat.numcandidates[disposition[1]] = Stat.numcandidates[disposition[1]] -1 #decrease the number of waitling list candidates for the DSA by 1
#1st Transplant MELD
if patient.Status1 ==0 and patient.Relist ==0:
#Tx-MELD at measure assumed to exclude re-grafts
Stat.yMELD[disposition[1]] = Stat.yMELD[disposition[1]] + patient.MELD #increase the MELD score the DSA
Stat.ymedMELD[disposition[1]].append(patient.MELD) #record the patient MELD score
#Output for Posttransplant processing for those who were not ever relisted or will be
if willrelist ==0 and patient.Relist ==0:
regtx =0
nattx = 0
if patient.DSA != neworgan.DSA and (Regions[neworgan.DSA,patient.DSA] ==1 or SharingPartners[neworgan.DSA, patient.DSA] == 1):
regtx =1 #patient had regional transplant
elif patient.DSA != neworgan.DSA:
nattx=1 #patient had national transplant
#record the floor of the current time, repetition, current time, patient id, indicator for regional transplant, indicator for national
#transplant
txidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,regtx,nattx]
Sim.record_txID = nump.vstack((Sim.record_txID, txidreport)) #add new record to list of transplant records
#record as follows (time of removal, repetition, patient id, patient allocation MELD, patient lab MElD)
oidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,Sim.oid]
#add to list of transplant records
Sim.record_doID = nump.vstack((Sim.record_doID, oidreport))
#Out for Posttransplant proceesing for regrafts
if patient.Relist ==1:
regtx =0 #indicator for regional transplant
nattx = 0 #indicator for national transplant
if patient.DSA != neworgan.DSA and (Regions[neworgan.DSA,patient.DSA] ==1 or SharingPartners[neworgan.DSA, patient.DSA] == 1):
regtx =1 #patient had regional transplant
elif patient.DSA != neworgan.DSA:
nattx=1 #patient had national transplant
#record the floor of the current time, repetition, current time, patient id, indicator for regional transplant, indicator for national transplant
txidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,regtx,nattx]
#add to list of relisted transplants
Sim.record_txIDregraft = nump.vstack((Sim.record_txIDregraft, txidreport))
#record as follows (time of removal, repetition, patient id, patient allocation MELD, patient lab MELD)
oidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,Sim.oid]
#add to list of retransplant records
Sim.record_doIDregraft = nump.vstack((Sim.record_doIDregraft, oidreport))
if willrelist ==1:
#if patient will relist, update relist status and MELD score
OPTN[disposition[1]][i].Relist =1
OPTN[disposition[1]][i].MELD = 32
OPTN[disposition[1]][i].lMELD = 32
else:
#remove transplanted patient if will not be relisted
del OPTN[disposition[1]][i]
break
else: #organ is discarded; update statistics (optional)
pass
#return updated Sim, Stat, and OPTN
return Sim, Stat, OPTN
def Year(Sim, Stat, reps):
"""
This function updates the statistics of the simulation per year
Input:
@Sim: class object that contains relevant variables for the simulation
@Stat: class object that contains statistical information of the simulation
@reps: current replication number
Output:
@Sim: updated Sim
@Stat: updated Stat
"""
#Annual Disparity Statistics
mr_1 = nump.zeros(shape=(ndsa,1),dtype=float)
tr_1 = nump.zeros(shape=(ndsa,1),dtype=float)
wt_1 = nump.zeros(shape=(ndsa,1),dtype=float)
meld_l = nump.zeros(shape=(ndsa,1),dtype=float)
for i in range(0,ndsa):
if Stat.ytransplants[i] > 0:
wt_1[i] = Stat.ywait[i] / Stat.ytransplants[i] #compute the total waiting list/total # of transplant per DSA
meld_l[i] = Stat.yMELD[i] / Stat.ytransplants[i] #compute the total MELD score/total # of transplant per DSA
else:
#write nan if no values available
wt_1[i] = nump.nan
meld_l[i] = nump.nan
if (Stat.yarrivals[i] + Stat.ycandidates[i]) == 0:
#write nan if no values available
mr_1[i] = nump.nan
tr_1[i] = nump.nan
else:
mr_1[i] = Stat.ydeaths[i] / (Stat.yarrivals[i] + Stat.ycandidates[i]) #compute mortality rate (number of deaths/number of waiting candidates)
tr_1[i] = Stat.ytransplants[i] / (Stat.yarrivals[i] + Stat.ycandidates[i]) #compute transplant rate (number of transplants/number of waiting candidates)
#compute the median MELD score
medianmelds = nump.zeros(shape=(ndsa,1),dtype=float)
for i in range(0,ndsa):
if Stat.ymedMELD[i] != []:
medianmelds[i] = nump.nanmedian(Stat.ymedMELD[i])
else:
medianmelds[i] = nump.nan
#Intermediate Data Outputs
#nump.savetxt("Output_check.txt", Stat.numcandidates, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check2.txt", Stat.yremoved, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check3.txt", Stat.yarrivals, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check4.txt", Stat.ydeaths, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check5.txt", Stat.ytransplants, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check6.txt", mr_1, fmt='%1.4e', delimiter='\t', newline='\n')
mr_numdeaths = [nump.sum(Stat.ydeaths),nump.floor(Sim.clock),reps] #record the total number of deaths along with its current time and current repetition
#mr_disparity = [nump.linalg.norm(mr_1,ord=1),nump.floor(Sim.clock),reps]
#tx_disparity = [nump.linalg.norm(tr_1,ord=1),nump.floor(Sim.clock),reps]
#wt_disparity = [nump.linalg.norm(wt_1,ord=1),nump.floor(Sim.clock),reps]
mr_disparity_mean = [nump.nanmean(mr_1),nump.floor(Sim.clock),reps] #record the mean mortality rate along with its current time and current repetition
mr_disparity_std = [nump.nanstd(mr_1),nump.floor(Sim.clock),reps] #record the standard deviation of mortality rate along withs current time and current repetition
meld_disparity_mean = [nump.nanmean(meld_l),nump.floor(Sim.clock),reps] #record the mean MELD score along with current time and current repetition
meld_disparity_std = [nump.nanstd(meld_l),nump.floor(Sim.clock),reps] #record the standard deviation of the MELD score along with current time and current repetition
medmeld_mean = [nump.nanmean(medianmelds),nump.floor(Sim.clock),reps] #record the mean median MELD score along with current time and current repetition
medmeld_std = [nump.nanstd(medianmelds),nump.floor(Sim.clock),reps] #record the standard deviation of the median MELD score along with current time and current repetition
#print(tx_disparity)
#add the records to the list of yearly statistics
Sim.record_deaths = nump.vstack((Sim.record_deaths, mr_numdeaths))
Sim.record_mr_disparity_mean = nump.vstack((Sim.record_mr_disparity_mean, mr_disparity_mean))
Sim.record_mr_disparity_std = nump.vstack((Sim.record_mr_disparity_std, mr_disparity_std))
Sim.record_meld_disparity_mean = nump.vstack((Sim.record_meld_disparity_mean, meld_disparity_mean))
Sim.record_meld_disparity_std = nump.vstack((Sim.record_meld_disparity_std, meld_disparity_std))
Sim.record_medMELDmean = nump.vstack((Sim.record_medMELDmean, medmeld_mean))
Sim.record_medMELDstd = nump.vstack((Sim.record_medMELDstd, medmeld_std))
Sim.record_txDSAoutput = nump.vstack((Sim.record_txDSAoutput, Sim.record_txDSA))
#create array that records the current time and repetition
recindex =nump.ndarray(shape=(1,3))
recindex[0,0] = nump.floor(Sim.clock)
recindex[0,1] = reps
#add DSA vector regarding deaths, transplants, etc. to the list of records
Sim.record_ydeaths =nump.concatenate((Sim.record_ydeaths,nump.concatenate((recindex,nump.transpose(Stat.ydeaths)),axis=1)),axis=0)
Sim.record_ytransplants = nump.concatenate((Sim.record_ytransplants,nump.concatenate((recindex,nump.transpose(Stat.ytransplants)),axis=1)),axis=0)
Sim.record_yarrivals = nump.concatenate((Sim.record_yarrivals,nump.concatenate((recindex,nump.transpose(Stat.yarrivals)),axis=1)),axis=0)
Sim.record_ycandidates = nump.concatenate((Sim.record_ycandidates,nump.concatenate((recindex,nump.transpose(Stat.ycandidates)),axis=1)),axis=0)
Sim.record_yremoved =nump.concatenate((Sim.record_yremoved,nump.concatenate((recindex,nump.transpose(Stat.yremoved)),axis=1)),axis=0)
Sim.record_ywait = nump.concatenate((Sim.record_ywait,nump.concatenate((recindex,nump.transpose(Stat.ywait)),axis=1)),axis=0)
Sim.record_yMELD = nump.concatenate((Sim.record_yMELD,nump.concatenate((recindex,nump.transpose(Stat.yMELD)),axis=1)),axis=0)
Sim.record_yrelists =nump.concatenate((Sim.record_yrelists,nump.concatenate((recindex,nump.transpose(Stat.yrelists)),axis=1)),axis=0)
Sim.record_yregrafts =nump.concatenate((Sim.record_yregrafts,nump.concatenate((recindex,nump.transpose(Stat.yregrafts)),axis=1)),axis=0)
#Reset Statistics for Following Year
Stat.yarrivals = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ydeaths = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.yremoved = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ytransplants = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ycandidates = deepcopy(Stat.numcandidates)
Stat.ywait = nump.zeros(shape=(ndsa,1),dtype=float)
Stat.yMELD = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ymedMELD = [[] for i in range(0,ndsa)]
Stat.yrelists = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.yregrafts = nump.zeros(shape=(ndsa,1),dtype=int)
#return updated Sim, Stat
return Sim, Stat
def EndRep():
"""
Prints a message saying that a replication ended.
"""
print("Ending replication, time is: ", datetime.datetime.now().time())
#print(Sim.clock)
#####################################################################################################################################################################################################################
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| 0.611278
|
import entity, allocate
import numpy as nump
import datetime
from copy import deepcopy
ndsa = 58
#######################################################################Event Processes####################################################################################
def Arrival(arrivalinfo, Sim, Stat, OPTN):
"""
This function simulates the arrival of patients. It computes the MELD score of the patient and adds him to the corresponding DSA waiting list.
Input:
@arrivalinfo: all the patient's information
@Sim: class object that contains variables relevant to the simulation
@Stat: class object that containts statistical info of the simulation
@OPTN: complete patient data
Output:
@Sim: updated Sim
@Stat: updated Stat
@OPTN updated OPTN
"""
#Create Patient Entity
newpatient = entity.Patient(arrivalinfo[1].astype(int),arrivalinfo[3].astype(int),Sim.clock)
#Assign Patient Characteristics
newpatient.Status1 = arrivalinfo[9].astype(int)
newpatient.ABO = arrivalinfo[5].astype(int)
newpatient.HCC = arrivalinfo[8].astype(int)
newpatient.Na = arrivalinfo[10].astype(int)
newpatient.lMELD = arrivalinfo[7].astype(int)
newpatient.MELD = arrivalinfo[6].astype(int)
newpatient.Inactive = arrivalinfo[11].astype(int)
#Assign Allocation MELD based on policy
if Sim.sodium ==1: #if sodium policy is selected
#bound the sodium score
effective_na = newpatient.Na
if effective_na <125:
effective_na = 125
elif effective_na > 137:
effective_na = 137
#compute the allocation MELD score
if nump.rint(newpatient.lMELD + 1.32*(137-effective_na)-(0.033*newpatient.lMELD*(137-effective_na))) <6:
newpatient.MELD =6
elif nump.rint(newpatient.lMELD + 1.32*(137-effective_na)-(0.033*newpatient.lMELD*(137-effective_na))) > 40:
newpatient.MELD =40
else:
newpatient.MELD = nump.rint(newpatient.lMELD + 1.32*(137-effective_na)-(0.033*newpatient.lMELD*(137-effective_na)))
else: #if sodium policy not selected
#bound the sodium score
if newpatient.MELD <6:
newpatient.MELD =6
elif newpatient.MELD >40:
newpatient.MELD = 40
#Apply Status1 and HCC exceptions (if applicable)
if newpatient.Status1 ==1:
newpatient.MELD =41
elif newpatient.HCC ==1 and Sim.capanddelay ==1:
newpatient.MELD = min(newpatient.lMELD,28)
#Place Patient in DSA List
if newpatient.DSA >= 0:
OPTN[newpatient.DSA].append(newpatient)
#Update Stats
Stat.yarrivals[newpatient.DSA] = Stat.yarrivals[newpatient.DSA] + 1
Stat.numcandidates[newpatient.DSA] = Stat.numcandidates[newpatient.DSA] + 1
#return updated Sim, Stat, and OPTN
return Sim, Stat, OPTN
#Diagnostic Info
#here
def Progression(proginfo, Sim, Stat, OPTN, reps):
"""
This function searches for a particular patient from the OPTN data structure and update the patient's characteristics
Input:
@proginfo: all of the information of the patient being searched up
@Sim: class object containing relevant information of the patient being searched up
@Stat: class object containing statistical information of the simulation
@OPTN: complete patient data
@reps: current replication number
Output:
@Sim: updated Sim
@Stat: updated Stat
@OPTN: updated OPTN
"""
progdsa = proginfo[9].astype(int) #obtain DSA
#test =0
#run if we have an actual DSA
if progdsa >=0:
#search for patient in the OPTN data structure
for i, patient in enumerate(OPTN[progdsa]):
#check if the id matches
if patient.id == proginfo[1].astype(int):
#Update Patient
if patient.Relist ==1:
#Ignore updates for relisted patients
break
elif proginfo[3].astype(int) == 1:
#Patient dies, remove and update stats
Stat.ydeaths[progdsa] = Stat.ydeaths[progdsa] + 1 #increment the number of deaths by one
Stat.numcandidates[progdsa] = Stat.numcandidates[progdsa] - 1 #decrement the number of candidates waiting by one
del OPTN[progdsa][i] #delete the patient object since patient died
break
elif proginfo[4].astype(int) ==1:
#Patient removed, remove and update stats
Stat.yremoved[progdsa] = Stat.yremoved[progdsa] + 1 #increment the number of removals by one
Stat.numcandidates[progdsa] = Stat.numcandidates[progdsa] - 1 #decrement the number of candidates waiting by one
#record as follows (time of removal, repetition, patient id, patient allocation MELD, patient lab MELD)
oidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,patient.MELD, patient.lMELD]
Sim.record_removals = nump.vstack((Sim.record_removals, oidreport)) #concatenate the row oidreport to the record of removals
del OPTN[progdsa][i] #delete patient object since patient is removed
break
else:
#Update candidate
patient.lMELD = proginfo[6].astype(int)
patient.Na = proginfo[7].astype(int)
patient.MELD = proginfo[5].astype(int)
patient.Inactive = proginfo[10].astype(int)
#set bound on MELD score
if patient.MELD <6:
patient.MELD =6
elif patient.MELD >40:
patient.MELD = 40
#Update Allocation MELD based on policy (if applicable)
if Sim.sodium ==1 and patient.Status1 != 1 and patient.HCC != 1:
#if sodium score policy is selected, then update the meld score for non-status1, non-HCC patient
#set bound on sodium score
effective_na = patient.Na
if effective_na <125:
effective_na = 125
elif effective_na > 137:
effective_na = 137
#compute MELD score
if nump.rint(patient.lMELD + 1.32*(137-effective_na)-(0.033*patient.lMELD*(137-effective_na))) <6:
patient.MELD =6
elif nump.rint(patient.lMELD + 1.32*(137-effective_na)-(0.033*patient.lMELD*(137-effective_na))) > 40:
patient.MELD =40
else:
patient.MELD = nump.rint(patient.lMELD + 1.32*(137-effective_na)-(0.033*patient.lMELD*(137-effective_na)))
elif Sim.capanddelay ==1 and patient.Status1 != 1 and patient.HCC == 1:
#if cap and delay policy is selected, update meld score for status1, HCC patient
#compute MELD score
if Sim.clock - patient.create_time <= .5:
patient.MELD = max(28,patient.MELD)
elif (Sim.clock - patient.create_time) > .5 and (Sim.clock - patient.create_time <= .75):
patient.MELD = max(29,patient.MELD)
elif (Sim.clock - patient.create_time) > .75 and (Sim.clock - patient.create_time <= 1):
patient.MELD = max(31,patient.MELD)
elif (Sim.clock - patient.create_time) > 1 and (Sim.clock - patient.create_time <= 1.25):
patient.MELD = max(33,patient.MELD)
elif (Sim.clock - patient.create_time) > 1.25 and (Sim.clock - patient.create_time <= 1.5):
patient.MELD = max(34,patient.MELD)
else:
patient.MELD = min(patient.MELD+1,40)
break
#return updated Sim, Stat, and OPTN
return Sim, Stat, OPTN
def OrganArrival(organinfo, Sim, Stat, OPTN, Regions, SharingPartners, Patients_Accept, Donor_Accept, DSA_Avg_Times, AcceptanceModelS1, AcceptanceModel, Relist, reps):
"""
This function simulates the organ arrival. It tries to match the organ to a patient from the corresponding DSA waitlist.
Input:
@organinfo: information on the organ
@Sim: class object containing relevant variables for simulation
@Stat: class object containing statistical information of simulation
@OPTN: complete patient data
@Regions: neighbhorhood map for regions, districts, or neighbhorhoods
@SharingPartners: neighborhood map adding sharing partners to existing geographic relationships among OPOs
@Patients_Accept: coefficients regarding donor's characteristics for acceptance model
@Donor_Accept: coefficients regarding donor's characteristics for acceptance model
@DSA_Avg_Times: data on average transport times between DSAs
@AcceptanceModelS1: coefficients regarding patient's characteristics for status-1 acceptance model
@AccpetanceModel: coefficients regarding patient's characteristics for non-status-1 acceptance model
@Relist: values regarding the probability that a transplanted patient will relist
@reps: replication number
Output:
@Sim: updated Sim
@Stat: updated Stat
@OPTN: updated OPTN
"""
#Create Organ
neworgan = entity.Organ(int(organinfo[2]))
neworgan.organid = Sim.oid
Sim.oid = Sim.oid + 1
#Assign Organ Attributes
neworgan.ABO = organinfo[4].astype(int)
#Allocate Organ
#disposition is tuple of organ status (accept/reject) , transplanting DSA, and patient id if accepted
disposition = allocate.Allocate(neworgan, OPTN, Sim, Regions, SharingPartners, Patients_Accept, Donor_Accept, DSA_Avg_Times, AcceptanceModelS1, AcceptanceModel)
if disposition[0] == 1: #organ is transplanted
#Remove transplanted patient from waiting list and update statistics
for i, patient in enumerate(OPTN[disposition[1]]):
#search for the patient in the OPTN data structure
if patient.id == disposition[2]:
#Determine if patient will relist and assign special attributes if necesary
willrelist = 0
if patient.Relist ==0:
#if patient has not been relisted
#Determine if patient will be relisted if was not already
r1 =nump.random.uniform(Relist[0],Relist[1],1)
r2 =nump.random.uniform(0,1,1)
if r2 < r1:
willrelist =1
#Determine when current graft will fail
r3 = nump.random.uniform(0,1,1)
if r3 < .4:
patient.RelistTxTime = Sim.clock + 5
elif r3 >= .4 and r3 < .6:
patient.RelistTxTime = Sim.clock + 2
elif r3 >= 6 and r3 < .8:
patient.RelistTxTime = Sim.clock + 1
else:
patient.RelistTxTime = Sim.clock
#Update relist statistics
Stat.yrelists[disposition[1]] = Stat.yrelists[disposition[1]] +1
#record the floor of current time, reptitiion, current time, patient id, patient meld score, and relist tx time
relistidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,patient.MELD,patient.RelistTxTime]
#concatenate the relistidreport to the record of relists
Sim.record_relists = nump.vstack((Sim.record_relists, relistidreport))
#Update Stats for Transplants
#Number of Transplants and Regrafts
Stat.ytransplants[disposition[1]] = Stat.ytransplants[disposition[1]] +1
Sim.record_txDSA[neworgan.DSA,disposition[1]] = Sim.record_txDSA[neworgan.DSA,disposition[1]] +1
if patient.Relist ==1:
#if patient is relisted
Stat.yregrafts[disposition[1]] = Stat.yregrafts[disposition[1]] +1 #increase number of retransplanted patients by 1
#Compute waiting Time
if patient.Relist ==0:
#if patient is not relisted
Stat.ywait[disposition[1]] = Stat.ywait[disposition[1]] + (Sim.clock - patient.create_time)
else:
#if patient is relisted
Stat.ywait[disposition[1]] = Stat.ywait[disposition[1]] + (Sim.clock - patient.RelistTxTime)
#Waiting List Sizes
if willrelist ==0:
Stat.numcandidates[disposition[1]] = Stat.numcandidates[disposition[1]] -1 #decrease the number of waitling list candidates for the DSA by 1
#1st Transplant MELD
if patient.Status1 ==0 and patient.Relist ==0:
#Tx-MELD at measure assumed to exclude re-grafts
Stat.yMELD[disposition[1]] = Stat.yMELD[disposition[1]] + patient.MELD #increase the MELD score the DSA
Stat.ymedMELD[disposition[1]].append(patient.MELD) #record the patient MELD score
#Output for Posttransplant processing for those who were not ever relisted or will be
if willrelist ==0 and patient.Relist ==0:
regtx =0
nattx = 0
if patient.DSA != neworgan.DSA and (Regions[neworgan.DSA,patient.DSA] ==1 or SharingPartners[neworgan.DSA, patient.DSA] == 1):
regtx =1 #patient had regional transplant
elif patient.DSA != neworgan.DSA:
nattx=1 #patient had national transplant
#record the floor of the current time, repetition, current time, patient id, indicator for regional transplant, indicator for national
#transplant
txidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,regtx,nattx]
Sim.record_txID = nump.vstack((Sim.record_txID, txidreport)) #add new record to list of transplant records
#record as follows (time of removal, repetition, patient id, patient allocation MELD, patient lab MElD)
oidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,Sim.oid]
#add to list of transplant records
Sim.record_doID = nump.vstack((Sim.record_doID, oidreport))
#Out for Posttransplant proceesing for regrafts
if patient.Relist ==1:
regtx =0 #indicator for regional transplant
nattx = 0 #indicator for national transplant
if patient.DSA != neworgan.DSA and (Regions[neworgan.DSA,patient.DSA] ==1 or SharingPartners[neworgan.DSA, patient.DSA] == 1):
regtx =1 #patient had regional transplant
elif patient.DSA != neworgan.DSA:
nattx=1 #patient had national transplant
#record the floor of the current time, repetition, current time, patient id, indicator for regional transplant, indicator for national transplant
txidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,regtx,nattx]
#add to list of relisted transplants
Sim.record_txIDregraft = nump.vstack((Sim.record_txIDregraft, txidreport))
#record as follows (time of removal, repetition, patient id, patient allocation MELD, patient lab MELD)
oidreport = [nump.floor(Sim.clock), reps, Sim.clock, patient.id,Sim.oid]
#add to list of retransplant records
Sim.record_doIDregraft = nump.vstack((Sim.record_doIDregraft, oidreport))
if willrelist ==1:
#if patient will relist, update relist status and MELD score
OPTN[disposition[1]][i].Relist =1
OPTN[disposition[1]][i].MELD = 32
OPTN[disposition[1]][i].lMELD = 32
else:
#remove transplanted patient if will not be relisted
del OPTN[disposition[1]][i]
break
else: #organ is discarded; update statistics (optional)
pass
#return updated Sim, Stat, and OPTN
return Sim, Stat, OPTN
def Year(Sim, Stat, reps):
"""
This function updates the statistics of the simulation per year
Input:
@Sim: class object that contains relevant variables for the simulation
@Stat: class object that contains statistical information of the simulation
@reps: current replication number
Output:
@Sim: updated Sim
@Stat: updated Stat
"""
#Annual Disparity Statistics
mr_1 = nump.zeros(shape=(ndsa,1),dtype=float)
tr_1 = nump.zeros(shape=(ndsa,1),dtype=float)
wt_1 = nump.zeros(shape=(ndsa,1),dtype=float)
meld_l = nump.zeros(shape=(ndsa,1),dtype=float)
for i in range(0,ndsa):
if Stat.ytransplants[i] > 0:
wt_1[i] = Stat.ywait[i] / Stat.ytransplants[i] #compute the total waiting list/total # of transplant per DSA
meld_l[i] = Stat.yMELD[i] / Stat.ytransplants[i] #compute the total MELD score/total # of transplant per DSA
else:
#write nan if no values available
wt_1[i] = nump.nan
meld_l[i] = nump.nan
if (Stat.yarrivals[i] + Stat.ycandidates[i]) == 0:
#write nan if no values available
mr_1[i] = nump.nan
tr_1[i] = nump.nan
else:
mr_1[i] = Stat.ydeaths[i] / (Stat.yarrivals[i] + Stat.ycandidates[i]) #compute mortality rate (number of deaths/number of waiting candidates)
tr_1[i] = Stat.ytransplants[i] / (Stat.yarrivals[i] + Stat.ycandidates[i]) #compute transplant rate (number of transplants/number of waiting candidates)
#compute the median MELD score
medianmelds = nump.zeros(shape=(ndsa,1),dtype=float)
for i in range(0,ndsa):
if Stat.ymedMELD[i] != []:
medianmelds[i] = nump.nanmedian(Stat.ymedMELD[i])
else:
medianmelds[i] = nump.nan
#Intermediate Data Outputs
#nump.savetxt("Output_check.txt", Stat.numcandidates, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check2.txt", Stat.yremoved, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check3.txt", Stat.yarrivals, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check4.txt", Stat.ydeaths, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check5.txt", Stat.ytransplants, fmt='%1.4e', delimiter='\t', newline='\n')
#nump.savetxt("Output_check6.txt", mr_1, fmt='%1.4e', delimiter='\t', newline='\n')
mr_numdeaths = [nump.sum(Stat.ydeaths),nump.floor(Sim.clock),reps] #record the total number of deaths along with its current time and current repetition
#mr_disparity = [nump.linalg.norm(mr_1,ord=1),nump.floor(Sim.clock),reps]
#tx_disparity = [nump.linalg.norm(tr_1,ord=1),nump.floor(Sim.clock),reps]
#wt_disparity = [nump.linalg.norm(wt_1,ord=1),nump.floor(Sim.clock),reps]
mr_disparity_mean = [nump.nanmean(mr_1),nump.floor(Sim.clock),reps] #record the mean mortality rate along with its current time and current repetition
mr_disparity_std = [nump.nanstd(mr_1),nump.floor(Sim.clock),reps] #record the standard deviation of mortality rate along withs current time and current repetition
meld_disparity_mean = [nump.nanmean(meld_l),nump.floor(Sim.clock),reps] #record the mean MELD score along with current time and current repetition
meld_disparity_std = [nump.nanstd(meld_l),nump.floor(Sim.clock),reps] #record the standard deviation of the MELD score along with current time and current repetition
medmeld_mean = [nump.nanmean(medianmelds),nump.floor(Sim.clock),reps] #record the mean median MELD score along with current time and current repetition
medmeld_std = [nump.nanstd(medianmelds),nump.floor(Sim.clock),reps] #record the standard deviation of the median MELD score along with current time and current repetition
#print(tx_disparity)
#add the records to the list of yearly statistics
Sim.record_deaths = nump.vstack((Sim.record_deaths, mr_numdeaths))
Sim.record_mr_disparity_mean = nump.vstack((Sim.record_mr_disparity_mean, mr_disparity_mean))
Sim.record_mr_disparity_std = nump.vstack((Sim.record_mr_disparity_std, mr_disparity_std))
Sim.record_meld_disparity_mean = nump.vstack((Sim.record_meld_disparity_mean, meld_disparity_mean))
Sim.record_meld_disparity_std = nump.vstack((Sim.record_meld_disparity_std, meld_disparity_std))
Sim.record_medMELDmean = nump.vstack((Sim.record_medMELDmean, medmeld_mean))
Sim.record_medMELDstd = nump.vstack((Sim.record_medMELDstd, medmeld_std))
Sim.record_txDSAoutput = nump.vstack((Sim.record_txDSAoutput, Sim.record_txDSA))
#create array that records the current time and repetition
recindex =nump.ndarray(shape=(1,3))
recindex[0,0] = nump.floor(Sim.clock)
recindex[0,1] = reps
#add DSA vector regarding deaths, transplants, etc. to the list of records
Sim.record_ydeaths =nump.concatenate((Sim.record_ydeaths,nump.concatenate((recindex,nump.transpose(Stat.ydeaths)),axis=1)),axis=0)
Sim.record_ytransplants = nump.concatenate((Sim.record_ytransplants,nump.concatenate((recindex,nump.transpose(Stat.ytransplants)),axis=1)),axis=0)
Sim.record_yarrivals = nump.concatenate((Sim.record_yarrivals,nump.concatenate((recindex,nump.transpose(Stat.yarrivals)),axis=1)),axis=0)
Sim.record_ycandidates = nump.concatenate((Sim.record_ycandidates,nump.concatenate((recindex,nump.transpose(Stat.ycandidates)),axis=1)),axis=0)
Sim.record_yremoved =nump.concatenate((Sim.record_yremoved,nump.concatenate((recindex,nump.transpose(Stat.yremoved)),axis=1)),axis=0)
Sim.record_ywait = nump.concatenate((Sim.record_ywait,nump.concatenate((recindex,nump.transpose(Stat.ywait)),axis=1)),axis=0)
Sim.record_yMELD = nump.concatenate((Sim.record_yMELD,nump.concatenate((recindex,nump.transpose(Stat.yMELD)),axis=1)),axis=0)
Sim.record_yrelists =nump.concatenate((Sim.record_yrelists,nump.concatenate((recindex,nump.transpose(Stat.yrelists)),axis=1)),axis=0)
Sim.record_yregrafts =nump.concatenate((Sim.record_yregrafts,nump.concatenate((recindex,nump.transpose(Stat.yregrafts)),axis=1)),axis=0)
#Reset Statistics for Following Year
Stat.yarrivals = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ydeaths = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.yremoved = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ytransplants = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ycandidates = deepcopy(Stat.numcandidates)
Stat.ywait = nump.zeros(shape=(ndsa,1),dtype=float)
Stat.yMELD = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.ymedMELD = [[] for i in range(0,ndsa)]
Stat.yrelists = nump.zeros(shape=(ndsa,1),dtype=int)
Stat.yregrafts = nump.zeros(shape=(ndsa,1),dtype=int)
#return updated Sim, Stat
return Sim, Stat
def EndRep():
"""
Prints a message saying that a replication ended.
"""
print("Ending replication, time is: ", datetime.datetime.now().time())
#print(Sim.clock)
#####################################################################################################################################################################################################################
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
89b6a5aacdae170197bc3ceae867aa296d074173
| 594
|
py
|
Python
|
Algorithms/Searching & Sorting/Counting Sort/counting_sort.py
|
sol4ik/interview-techdev-guide
|
2f1c755df3f34125850a2d6322edc24dc097c1fb
|
[
"MIT"
] | null | null | null |
Algorithms/Searching & Sorting/Counting Sort/counting_sort.py
|
sol4ik/interview-techdev-guide
|
2f1c755df3f34125850a2d6322edc24dc097c1fb
|
[
"MIT"
] | null | null | null |
Algorithms/Searching & Sorting/Counting Sort/counting_sort.py
|
sol4ik/interview-techdev-guide
|
2f1c755df3f34125850a2d6322edc24dc097c1fb
|
[
"MIT"
] | null | null | null |
def countSort(array):
"""
Counting Sort algorithm implementation on Python.
:param array: array that need to be sorted
:return: resulting sorted array
"""
output = [0 for i in range(256)]
count = [0 for i in range(256)]
result = ["" for element in array]
for i in array:
result[ord(i)] += 1
for i in range(256):
result[i] += result[i - 1]
for i in range(len(array)):
output[result[ord(array[i])] - 1] = array[i]
count[ord(array[i])] -= 1
for i in range(len(array)):
result[i] = output[i]
return result
| 29.7
| 53
| 0.574074
|
def countSort(array):
"""
Counting Sort algorithm implementation on Python.
:param array: array that need to be sorted
:return: resulting sorted array
"""
output = [0 for i in range(256)]
count = [0 for i in range(256)]
result = ["" for element in array]
for i in array:
result[ord(i)] += 1
for i in range(256):
result[i] += result[i - 1]
for i in range(len(array)):
output[result[ord(array[i])] - 1] = array[i]
count[ord(array[i])] -= 1
for i in range(len(array)):
result[i] = output[i]
return result
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
159c632f19d51c0cb382b2c9ba57cbb71f415051
| 96
|
py
|
Python
|
samtranslator/public/models.py
|
eugeniosu/serverless-application-model
|
d93e15232a1921fa51667389d83aeabbf1ff72d3
|
[
"Apache-2.0"
] | 6
|
2019-03-29T02:56:59.000Z
|
2021-03-28T22:07:02.000Z
|
samtranslator/public/models.py
|
eugeniosu/serverless-application-model
|
d93e15232a1921fa51667389d83aeabbf1ff72d3
|
[
"Apache-2.0"
] | 18
|
2019-10-09T23:27:48.000Z
|
2021-06-25T15:18:24.000Z
|
samtranslator/public/models.py
|
Mattlk13/serverless-application-model
|
27b5934de46c42d47ba1484d5432310cac694b25
|
[
"Apache-2.0"
] | 15
|
2019-05-27T01:04:30.000Z
|
2021-10-01T05:54:45.000Z
|
# flake8: noqa
| 24
| 79
| 0.854167
|
# flake8: noqa
from samtranslator.model.function_policies import FunctionPolicies, PolicyTypes
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 58
| 23
|
43c0f69870ce566bd19cef135e12fb3a3b95c79c
| 32,952
|
py
|
Python
|
src/shared_gui.py
|
NicholasPSnow/99-CapstoneProject-201920
|
ca3baa3b3c53ae6c5af70cd93b3af450a1da41ad
|
[
"MIT"
] | null | null | null |
src/shared_gui.py
|
NicholasPSnow/99-CapstoneProject-201920
|
ca3baa3b3c53ae6c5af70cd93b3af450a1da41ad
|
[
"MIT"
] | null | null | null |
src/shared_gui.py
|
NicholasPSnow/99-CapstoneProject-201920
|
ca3baa3b3c53ae6c5af70cd93b3af450a1da41ad
|
[
"MIT"
] | null | null | null |
"""
Capstone Project. Code to run on a LAPTOP (NOT the robot).
Constructs and returns Frame objects for the basics:
-- teleoperation
-- arm movement
-- stopping the robot program
This code is SHARED by all team members. It contains both:
-- High-level, general-purpose methods for a Snatch3r EV3 robot.
-- Lower-level code to interact with the EV3 robot library.
Author: Your professors (for the framework and lower-level code)
and Nicholas Snow, Katana College, and Zach Kelly.
Winter term, 2018-2019.
"""
import tkinter
from tkinter import ttk
def get_teleoperation_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Entry and Button objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Teleoperation")
left_speed_label = ttk.Label(frame, text="Left wheel speed (0 to 100)")
right_speed_label = ttk.Label(frame, text="Right wheel speed (0 to 100)")
left_speed_entry = ttk.Entry(frame, width=8)
left_speed_entry.insert(0, "100")
right_speed_entry = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
right_speed_entry.insert(0, "100")
forward_button = ttk.Button(frame, text="Forward")
backward_button = ttk.Button(frame, text="Backward")
left_button = ttk.Button(frame, text="Left")
right_button = ttk.Button(frame, text="Right")
stop_button = ttk.Button(frame, text="Stop")
# Grid the widgets:
frame_label.grid(row=0, column=1)
left_speed_label.grid(row=1, column=0)
right_speed_label.grid(row=1, column=2)
left_speed_entry.grid(row=2, column=0)
right_speed_entry.grid(row=2, column=2)
forward_button.grid(row=3, column=1)
left_button.grid(row=4, column=0)
stop_button.grid(row=4, column=1)
right_button.grid(row=4, column=2)
backward_button.grid(row=5, column=1)
# Set the button callbacks:
forward_button["command"] = lambda: handle_forward(
left_speed_entry, right_speed_entry, mqtt_sender)
backward_button["command"] = lambda: handle_backward(
left_speed_entry, right_speed_entry, mqtt_sender)
left_button["command"] = lambda: handle_left(
left_speed_entry, right_speed_entry, mqtt_sender)
right_button["command"] = lambda: handle_right(
left_speed_entry, right_speed_entry, mqtt_sender)
stop_button["command"] = lambda: handle_stop(mqtt_sender)
return frame
def get_arm_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Entry and Button objects that control the EV3 robot's Arm
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Arm and Claw")
position_label = ttk.Label(frame, text="Desired arm position:")
position_entry = ttk.Entry(frame, width=8)
raise_arm_button = ttk.Button(frame, text="Raise arm")
lower_arm_button = ttk.Button(frame, text="Lower arm")
calibrate_arm_button = ttk.Button(frame, text="Calibrate arm")
move_arm_button = ttk.Button(frame,
text="Move arm to position (0 to 5112)")
blank_label = ttk.Label(frame, text="")
# Grid the widgets:
frame_label.grid(row=0, column=1)
position_label.grid(row=1, column=0)
position_entry.grid(row=1, column=1)
position_entry.insert(0, "0")
move_arm_button.grid(row=1, column=2)
blank_label.grid(row=2, column=1)
raise_arm_button.grid(row=3, column=0)
lower_arm_button.grid(row=3, column=1)
calibrate_arm_button.grid(row=3, column=2)
# Set the Button callbacks:
raise_arm_button["command"] = lambda: handle_raise_arm(mqtt_sender)
lower_arm_button["command"] = lambda: handle_lower_arm(mqtt_sender)
calibrate_arm_button["command"] = lambda: handle_calibrate_arm(mqtt_sender)
move_arm_button["command"] = lambda: handle_move_arm_to_position(position_entry.get(), mqtt_sender)
return frame
def get_control_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame has
Button objects to exit this program and/or the robot's program (via MQTT).
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Control")
quit_robot_button = ttk.Button(frame, text="Stop the robot's program")
exit_button = ttk.Button(frame, text="Stop this and the robot's program")
# Grid the widgets:
frame_label.grid(row=0, column=1)
quit_robot_button.grid(row=1, column=0)
exit_button.grid(row=1, column=2)
# Set the Button callbacks:
quit_robot_button["command"] = lambda: handle_quit(mqtt_sender)
exit_button["command"] = lambda: handle_exit(mqtt_sender)
return frame
###############################################################################
###############################################################################
# The following specifies, for each Button,
# what should happen when the Button is pressed.
###############################################################################
###############################################################################
###############################################################################
# Handlers for Buttons in the Teleoperation frame.
###############################################################################
def handle_forward(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
with the speeds used as given.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('forward', left_entry_box.get(), right_entry_box.get())
mqtt_sender.send_message('movement', [left_entry_box.get(), right_entry_box.get()])
def handle_backward(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
but using the negatives of the speeds in the entry boxes.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('backward', left_entry_box.get(), right_entry_box.get())
left = -int(left_entry_box.get())
right = -int(right_entry_box.get())
mqtt_sender.send_message('movement', [str(left), str(right)])
def handle_left(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
but using the negative of the speed in the left entry box.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('left', left_entry_box.get(), right_entry_box.get())
left = -int(left_entry_box.get())
right = int(right_entry_box.get())
mqtt_sender.send_message('movement', [str(left), str(right)])
def handle_right(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
but using the negative of the speed in the right entry box.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('right', left_entry_box.get(), right_entry_box.get())
left = int(left_entry_box.get())
right = -int(right_entry_box.get())
mqtt_sender.send_message('movement', [str(left), str(right)])
def handle_stop(mqtt_sender):
"""
Tells the robot to stop.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('stop')
print("Stop")
###############################################################################
# Handlers for Buttons in the ArmAndClaw frame.
###############################################################################
def handle_raise_arm(mqtt_sender):
"""
Tells the robot to raise its Arm until its touch sensor is pressed.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('up')
print("Move Arm Up")
def handle_lower_arm(mqtt_sender):
"""
Tells the robot to lower its Arm until it is all the way down.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('down')
print("Move Arm Down")
def handle_calibrate_arm(mqtt_sender):
"""
Tells the robot to calibrate its Arm, that is, first to raise its Arm
until its touch sensor is pressed, then to lower its Arm until it is
all the way down, and then to mark taht position as position 0.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('calibrate')
print("Calibrate")
def handle_move_arm_to_position(position_entry, mqtt_sender):
"""
Tells the robot to move its Arm to the position in the given Entry box.
The robot must have previously calibrated its Arm.
:type arm_position_entry ttk.Entry
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('move_to_pos', [str(position_entry)])
print("Move to Position:",position_entry)
###############################################################################
# Handlers for Buttons in the Control frame.
###############################################################################
def handle_quit(mqtt_sender):
"""
Tell the robot's program to stop its loop (and hence quit).
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('command', ['quit'])
print('########')
print('# Quit #')
print('########')
def handle_exit(mqtt_sender):
"""
Tell the robot's program to stop its loop (and hence quit).
Then exit this program.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('command', ['exit'])
print('########')
print('# Exit #')
print('########')
def get_Sprint_1_Drive_System_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Special objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Secondary Drive System")
wheel_speed_label = ttk.Label(frame, text="Wheel Speed (0 to 100)")
time_label = ttk.Label(frame, text="Movement Time (0 to INF)")
inches_label = ttk.Label(frame, text="Movement Distance (0 to INF)")
wheel_speed_entry = ttk.Entry(frame, width=8)
wheel_speed_entry.insert(0, "100")
time_entry = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
time_entry.insert(0, "10")
inches_entry = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
inches_entry.insert(0, "10")
forward_time_button = ttk.Button(frame, text="Forward for Seconds")
forward_time_inches_button = ttk.Button(frame, text="Forward for Inches(time)")
forward_inches_button = ttk.Button(frame, text="Forward for Inches(Encoder)")
# Grid the widgets:
frame_label.grid(row=0, column=1)
wheel_speed_label.grid(row=1, column=0)
time_label.grid(row=1, column=1)
inches_label.grid(row=1, column=2)
wheel_speed_entry.grid(row=2, column=0)
time_entry.grid(row=2, column=1)
inches_entry.grid(row=2, column=2)
forward_time_button.grid(row=3, column=0)
forward_time_inches_button.grid(row=3, column=1)
forward_inches_button.grid(row=3, column=2)
# Set the button callbacks:
forward_time_button["command"] = lambda: handle_forward_time_button(wheel_speed_entry.get(), time_entry.get(), mqtt_sender)
forward_time_inches_button["command"] = lambda: handle_forward_time_inches_button(wheel_speed_entry.get(), inches_entry.get(), mqtt_sender)
forward_inches_button["command"] = lambda: handle_forward_inches_button(wheel_speed_entry.get(), inches_entry.get(), mqtt_sender)
return frame
def get_Sprint_1_Beeper_System_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Sound System")
number_of_beeps_label = ttk.Label(frame, text="Number of Beeps")
tone_duration_label = ttk.Label(frame, text="Duration of Tone")
tone_frequency_label = ttk.Label(frame, text="Tone Frequency")
speak_text_label = ttk.Label(frame, text="Text to Speech")
number_of_beeps= ttk.Entry(frame, width=8)
number_of_beeps.insert(0, "10")
tone_duration = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
tone_duration.insert(0, "10")
tone_frequency = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
tone_frequency.insert(0, "10")
speak_text = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
speak_text.insert(0, "Type Here")
beep_button = ttk.Button(frame, text="Play Beeps")
tone_button = ttk.Button(frame, text="Play Tone")
speak_button = ttk.Button(frame, text="Read Text")
# Grid the widgets:
frame_label.grid(row=0, column=1)
number_of_beeps_label.grid(row=1, column=0)
tone_duration_label.grid(row=1, column=1)
tone_frequency_label.grid(row=1, column=2)
speak_text_label.grid(row=1, column=3)
number_of_beeps.grid(row=2, column=0)
tone_duration.grid(row=2, column=1)
tone_frequency.grid(row=2, column=2)
speak_text.grid(row=2, column=3)
beep_button.grid(row=3, column=0)
tone_button.grid(row=3, column=1)
speak_button.grid(row=3, column=3)
# Set the button callbacks:
beep_button["command"] = lambda: handle_beep_button(number_of_beeps.get(), mqtt_sender)
tone_button["command"] = lambda: handle_tone_button(tone_duration.get(), tone_frequency.get(), mqtt_sender)
speak_button["command"] = lambda: handle_speak_button(speak_text.get(), mqtt_sender)
return frame
def get_Katana_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Katana's System")
obtain_with_sensor_label = ttk.Label(frame, text="Get Object with Proximity")
obtain_with_sensor_button = ttk.Button(frame, text="Get")
wheel_speed_label = ttk.Label(frame, text="Move Speed")
rate_of_frequency_label = ttk.Label(frame, text="Frequency Rate (Increasing)")
rate_of_frequency = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
rate_of_frequency.insert(0, "10")
initial_frequency_label = ttk.Label(frame, text="Initial Frequency")
initial_frequency = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
initial_frequency.insert(0, "5")
obtain_with_camera_label = ttk.Label(frame, text="Get Object with Camera")
obtain_with_camera_button = ttk.Button(frame, text="Get")
wheel_speed_entry = ttk.Entry(frame, width=8)
wheel_speed_entry.insert(0, "100")
spin_speed_label = ttk.Label(frame, text="Spinning Speed")
spin_speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
spin_speed.insert(0, "10")
spin_direction_label = ttk.Label(frame, text="Spinning Direction")
spin_direction = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
spin_direction.insert(0, "Counter Clockwise")
# Grid the widgets:
frame_label.grid(row=0, column=2)
obtain_with_sensor_label.grid(row=1, column=1)
obtain_with_sensor_button.grid(row=2, column=1)
rate_of_frequency_label.grid(row=1,column=2)
rate_of_frequency.grid(row=2, column=2)
initial_frequency_label.grid(row=1, column=3)
initial_frequency.grid(row=2, column=3)
wheel_speed_label.grid(row=3, column=2)
wheel_speed_entry.grid(row=4, column=2)
obtain_with_camera_label.grid(row=5, column=1)
obtain_with_camera_button.grid(row=6, column=1)
spin_speed_label.grid(row=5, column=2)
spin_speed.grid(row=6, column=2)
spin_direction_label.grid(row=5, column=3)
spin_direction.grid(row=6, column=3)
# Set the button callbacks:
obtain_with_sensor_button["command"] = lambda: handle_obtain_with_sensor_button(wheel_speed_entry.get(),rate_of_frequency.get(),
initial_frequency.get(),
mqtt_sender)
obtain_with_camera_button["command"] = lambda: handle_obtain_with_camera_button(wheel_speed_entry.get(),
spin_speed.get(),
spin_direction.get(),
rate_of_frequency.get(),
initial_frequency.get(),mqtt_sender)
return frame
def get_Nick_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Nick's Sprint 2 System")
proximity_label = ttk.Label(frame, text="Go to and Pick up Object (Proximity)")
proximity_button = ttk.Button(frame, text="Run Proximity Grab")
camera_label = ttk.Label(frame, text="Go to and Pick up Object (Camera)")
camera_button = ttk.Button(frame, text="Run Camera Grab")
line_follower_label = ttk.Label(frame, text="Line Follower (Bang Bang Method)")
line_button = ttk.Button(frame, text="Follow Line")
rate_of_beeps_label = ttk.Label(frame, text="Beep Rate Increase")
rate_of_beeps= ttk.Entry(frame, width=8, justify=tkinter.LEFT)
rate_of_beeps.insert(0, "10")
initial_beeps_label = ttk.Label(frame, text="Initial Beep Rate")
initial_beeps = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
initial_beeps.insert(0, "5")
speed_label = ttk.Label(frame, text="Turning Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "100")
direction_label = ttk.Label(frame, text="Turning Direction, Clockwise or Counter-Clockwise")
direction = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
direction.insert(0, "Clockwise")
starting_side_label = ttk.Label(frame, text="Turning Direction, Right or Left")
starting_side = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
starting_side.insert(0, "Right")
# Grid the widgets:
frame_label.grid(row=0, column=1)
proximity_label.grid(row=1, column=0)
proximity_button.grid(row=1, column=1)
rate_of_beeps_label.grid(row=2, column=0)
rate_of_beeps.grid(row=2, column=1)
initial_beeps_label.grid(row=3, column=0)
initial_beeps.grid(row=3, column=1)
camera_label.grid(row=4, column=0)
camera_button.grid(row=4, column=1)
speed_label.grid(row=5, column=0)
speed.grid(row=5, column=1)
direction_label.grid(row=6, column=0)
direction.grid(row=6, column=1)
line_follower_label.grid(row=7, column=0)
line_button.grid(row=7, column=1)
starting_side_label.grid(row=8, column=0)
starting_side.grid(row=8, column=1)
# Set the button callbacks:
proximity_button["command"] = lambda: handle_proximity_button(rate_of_beeps.get(), initial_beeps.get(), mqtt_sender)
camera_button["command"] = lambda: handle_camera_button(speed.get(),direction.get(),rate_of_beeps.get(), initial_beeps.get(), mqtt_sender)
line_button["command"] = lambda: handle_line_button(starting_side.get(), mqtt_sender)
return frame
##COLOR FRAMING
def get_Sprint_2_Color_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Color Sensor")
intensity_less_label = ttk.Label(frame, text="Go Until Intensity is Less Than")
intensity_less_button = ttk.Button(frame, text="Run Less than Intensity")
intensity_greater_label = ttk.Label(frame, text="Go Until Intensity is Greater Than")
intensity_greater_button = ttk.Button(frame, text="Run Greater than Intensity")
until_color_label = ttk.Label(frame, text="Go Until Color")
until_color_button = ttk.Button(frame, text="Run Go Until Color")
until_not_color_label = ttk.Label(frame, text="Go Until Not Color")
until_not_color_button = ttk.Button(frame, text="Run Go Until Not Color")
color_label = ttk.Label(frame, text="Color")
color= ttk.Entry(frame, width=8, justify=tkinter.LEFT)
color.insert(0, "Red")
speed_label = ttk.Label(frame, text="Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "50")
intensity_label = ttk.Label(frame, text="Light Intensity")
intensity = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
intensity.insert(0, "50")
# Grid the widgets:
frame_label.grid(row=0, column=1)
color_label.grid(row=1, column=0)
color.grid(row=2, column=0)
speed_label.grid(row=1, column=1)
speed.grid(row=2, column=1)
intensity_label.grid(row=1, column=2)
intensity.grid(row=2, column=2)
intensity_less_label.grid(row=3, column=0)
intensity_less_button.grid(row=4, column=0)
intensity_greater_label.grid(row=5, column=0)
intensity_greater_button.grid(row=6, column=0)
until_color_label.grid(row=3, column=2)
until_color_button.grid(row=4, column=2)
until_not_color_label.grid(row=5, column=2)
until_not_color_button.grid(row=6, column=2)
# Set the button callbacks:
intensity_less_button["command"] = lambda: handle_intensity_less_button(speed.get(), intensity.get(), mqtt_sender)
intensity_greater_button["command"] = lambda: handle_intensity_greater_button(speed.get(),intensity.get(), mqtt_sender)
until_color_button["command"] = lambda: handle_until_color_button(speed.get(),color.get(), mqtt_sender)
until_not_color_button["command"] = lambda: handle_until_not_color_button(speed.get(),color.get(), mqtt_sender)
return frame
##PROXIMITY SENSOR
def get_Sprint_2_Proximity_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Proximity Sensor")
distance_less_label = ttk.Label(frame, text="Go Until Distance is Less Than")
distance_less_button = ttk.Button(frame, text="Run Less than Distance")
distance_greater_label = ttk.Label(frame, text="Go Until Distance is Greater Than")
distance_greater_button = ttk.Button(frame, text="Run Greater than Distance")
until_distance_label = ttk.Label(frame, text="Go Until Distance Within")
until_distance_button = ttk.Button(frame, text="Run Go Until Distance Within")
inches_label = ttk.Label(frame, text="Inches")
inches = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
inches.insert(0, "10")
speed_label = ttk.Label(frame, text="Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "100")
delta_label = ttk.Label(frame, text="Delta Distance")
delta = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
delta.insert(0, "50")
# Grid the widgets:
frame_label.grid(row=0, column=1)
distance_less_label.grid(row=3, column=0)
distance_less_button.grid(row=4, column=0)
distance_greater_label.grid(row=3, column=1)
distance_greater_button.grid(row=4, column=1)
until_distance_label.grid(row=3, column=2)
until_distance_button.grid(row=4, column=2)
delta_label.grid(row=1, column=0)
delta.grid(row=2, column=0)
speed_label.grid(row=1, column=1)
speed.grid(row=2, column=1)
inches_label.grid(row=1, column=2)
inches.grid(row=2, column=2)
# Set the button callbacks:
distance_greater_button["command"] = lambda: handle_distance_greater_button(speed.get(), inches.get(), mqtt_sender)
distance_less_button["command"] = lambda: handle_distance_less_button(speed.get(), inches.get(),mqtt_sender)
until_distance_button["command"] = lambda: handle_until_distance_button(speed.get(), inches.get(), delta.get(), mqtt_sender)
return frame
##Camera SENSOR
def get_Sprint_2_Camera_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Camera Sensor")
counter_clockwise_label = ttk.Label(frame, text="Search Counterclockwise")
counter_clockwise_button = ttk.Button(frame, text="Run CCW Search")
clockwise_label = ttk.Label(frame, text="Search Counterclockwise")
clockwise_button = ttk.Button(frame, text="Run CW Search")
speed_label = ttk.Label(frame, text="Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "100")
area_label = ttk.Label(frame, text="Area Size")
area = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
area.insert(0, "10")
# Grid the widgets:
frame_label.grid(row=0, column=1)
counter_clockwise_label.grid(row=1, column=0)
counter_clockwise_button.grid(row=2, column=0)
clockwise_label.grid(row=1, column=2)
clockwise_button.grid(row=2, column=2)
area_label.grid(row=1, column=1)
area.grid(row=2, column=1)
speed_label.grid(row=3, column=1)
speed.grid(row=4, column=1)
# Set the button callbacks:
counter_clockwise_button["command"] = lambda: handle_counter_clockwise_button(speed.get(), area.get(), mqtt_sender)
clockwise_button["command"] = lambda: handle_clockwise_button(speed.get(), area.get(), mqtt_sender)
return frame
| 41.711392
| 160
| 0.689306
|
"""
Capstone Project. Code to run on a LAPTOP (NOT the robot).
Constructs and returns Frame objects for the basics:
-- teleoperation
-- arm movement
-- stopping the robot program
This code is SHARED by all team members. It contains both:
-- High-level, general-purpose methods for a Snatch3r EV3 robot.
-- Lower-level code to interact with the EV3 robot library.
Author: Your professors (for the framework and lower-level code)
and Nicholas Snow, Katana College, and Zach Kelly.
Winter term, 2018-2019.
"""
import tkinter
from tkinter import ttk
import time
def get_teleoperation_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Entry and Button objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Teleoperation")
left_speed_label = ttk.Label(frame, text="Left wheel speed (0 to 100)")
right_speed_label = ttk.Label(frame, text="Right wheel speed (0 to 100)")
left_speed_entry = ttk.Entry(frame, width=8)
left_speed_entry.insert(0, "100")
right_speed_entry = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
right_speed_entry.insert(0, "100")
forward_button = ttk.Button(frame, text="Forward")
backward_button = ttk.Button(frame, text="Backward")
left_button = ttk.Button(frame, text="Left")
right_button = ttk.Button(frame, text="Right")
stop_button = ttk.Button(frame, text="Stop")
# Grid the widgets:
frame_label.grid(row=0, column=1)
left_speed_label.grid(row=1, column=0)
right_speed_label.grid(row=1, column=2)
left_speed_entry.grid(row=2, column=0)
right_speed_entry.grid(row=2, column=2)
forward_button.grid(row=3, column=1)
left_button.grid(row=4, column=0)
stop_button.grid(row=4, column=1)
right_button.grid(row=4, column=2)
backward_button.grid(row=5, column=1)
# Set the button callbacks:
forward_button["command"] = lambda: handle_forward(
left_speed_entry, right_speed_entry, mqtt_sender)
backward_button["command"] = lambda: handle_backward(
left_speed_entry, right_speed_entry, mqtt_sender)
left_button["command"] = lambda: handle_left(
left_speed_entry, right_speed_entry, mqtt_sender)
right_button["command"] = lambda: handle_right(
left_speed_entry, right_speed_entry, mqtt_sender)
stop_button["command"] = lambda: handle_stop(mqtt_sender)
return frame
def get_arm_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Entry and Button objects that control the EV3 robot's Arm
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Arm and Claw")
position_label = ttk.Label(frame, text="Desired arm position:")
position_entry = ttk.Entry(frame, width=8)
raise_arm_button = ttk.Button(frame, text="Raise arm")
lower_arm_button = ttk.Button(frame, text="Lower arm")
calibrate_arm_button = ttk.Button(frame, text="Calibrate arm")
move_arm_button = ttk.Button(frame,
text="Move arm to position (0 to 5112)")
blank_label = ttk.Label(frame, text="")
# Grid the widgets:
frame_label.grid(row=0, column=1)
position_label.grid(row=1, column=0)
position_entry.grid(row=1, column=1)
position_entry.insert(0, "0")
move_arm_button.grid(row=1, column=2)
blank_label.grid(row=2, column=1)
raise_arm_button.grid(row=3, column=0)
lower_arm_button.grid(row=3, column=1)
calibrate_arm_button.grid(row=3, column=2)
# Set the Button callbacks:
raise_arm_button["command"] = lambda: handle_raise_arm(mqtt_sender)
lower_arm_button["command"] = lambda: handle_lower_arm(mqtt_sender)
calibrate_arm_button["command"] = lambda: handle_calibrate_arm(mqtt_sender)
move_arm_button["command"] = lambda: handle_move_arm_to_position(position_entry.get(), mqtt_sender)
return frame
def get_control_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame has
Button objects to exit this program and/or the robot's program (via MQTT).
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Control")
quit_robot_button = ttk.Button(frame, text="Stop the robot's program")
exit_button = ttk.Button(frame, text="Stop this and the robot's program")
# Grid the widgets:
frame_label.grid(row=0, column=1)
quit_robot_button.grid(row=1, column=0)
exit_button.grid(row=1, column=2)
# Set the Button callbacks:
quit_robot_button["command"] = lambda: handle_quit(mqtt_sender)
exit_button["command"] = lambda: handle_exit(mqtt_sender)
return frame
###############################################################################
###############################################################################
# The following specifies, for each Button,
# what should happen when the Button is pressed.
###############################################################################
###############################################################################
###############################################################################
# Handlers for Buttons in the Teleoperation frame.
###############################################################################
def handle_forward(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
with the speeds used as given.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('forward', left_entry_box.get(), right_entry_box.get())
mqtt_sender.send_message('movement', [left_entry_box.get(), right_entry_box.get()])
def handle_backward(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
but using the negatives of the speeds in the entry boxes.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('backward', left_entry_box.get(), right_entry_box.get())
left = -int(left_entry_box.get())
right = -int(right_entry_box.get())
mqtt_sender.send_message('movement', [str(left), str(right)])
def handle_left(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
but using the negative of the speed in the left entry box.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('left', left_entry_box.get(), right_entry_box.get())
left = -int(left_entry_box.get())
right = int(right_entry_box.get())
mqtt_sender.send_message('movement', [str(left), str(right)])
def handle_right(left_entry_box, right_entry_box, mqtt_sender):
"""
Tells the robot to move using the speeds in the given entry boxes,
but using the negative of the speed in the right entry box.
:type left_entry_box: ttk.Entry
:type right_entry_box: ttk.Entry
:type mqtt_sender: com.MqttClient
"""
print('right', left_entry_box.get(), right_entry_box.get())
left = int(left_entry_box.get())
right = -int(right_entry_box.get())
mqtt_sender.send_message('movement', [str(left), str(right)])
def handle_stop(mqtt_sender):
"""
Tells the robot to stop.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('stop')
print("Stop")
###############################################################################
# Handlers for Buttons in the ArmAndClaw frame.
###############################################################################
def handle_raise_arm(mqtt_sender):
"""
Tells the robot to raise its Arm until its touch sensor is pressed.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('up')
print("Move Arm Up")
def handle_lower_arm(mqtt_sender):
"""
Tells the robot to lower its Arm until it is all the way down.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('down')
print("Move Arm Down")
def handle_calibrate_arm(mqtt_sender):
"""
Tells the robot to calibrate its Arm, that is, first to raise its Arm
until its touch sensor is pressed, then to lower its Arm until it is
all the way down, and then to mark taht position as position 0.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('calibrate')
print("Calibrate")
def handle_move_arm_to_position(position_entry, mqtt_sender):
"""
Tells the robot to move its Arm to the position in the given Entry box.
The robot must have previously calibrated its Arm.
:type arm_position_entry ttk.Entry
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('move_to_pos', [str(position_entry)])
print("Move to Position:",position_entry)
###############################################################################
# Handlers for Buttons in the Control frame.
###############################################################################
def handle_quit(mqtt_sender):
"""
Tell the robot's program to stop its loop (and hence quit).
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('command', ['quit'])
print('########')
print('# Quit #')
print('########')
def handle_exit(mqtt_sender):
"""
Tell the robot's program to stop its loop (and hence quit).
Then exit this program.
:type mqtt_sender: com.MqttClient
"""
mqtt_sender.send_message('command', ['exit'])
print('########')
print('# Exit #')
print('########')
def get_Sprint_1_Drive_System_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Special objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Secondary Drive System")
wheel_speed_label = ttk.Label(frame, text="Wheel Speed (0 to 100)")
time_label = ttk.Label(frame, text="Movement Time (0 to INF)")
inches_label = ttk.Label(frame, text="Movement Distance (0 to INF)")
wheel_speed_entry = ttk.Entry(frame, width=8)
wheel_speed_entry.insert(0, "100")
time_entry = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
time_entry.insert(0, "10")
inches_entry = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
inches_entry.insert(0, "10")
forward_time_button = ttk.Button(frame, text="Forward for Seconds")
forward_time_inches_button = ttk.Button(frame, text="Forward for Inches(time)")
forward_inches_button = ttk.Button(frame, text="Forward for Inches(Encoder)")
# Grid the widgets:
frame_label.grid(row=0, column=1)
wheel_speed_label.grid(row=1, column=0)
time_label.grid(row=1, column=1)
inches_label.grid(row=1, column=2)
wheel_speed_entry.grid(row=2, column=0)
time_entry.grid(row=2, column=1)
inches_entry.grid(row=2, column=2)
forward_time_button.grid(row=3, column=0)
forward_time_inches_button.grid(row=3, column=1)
forward_inches_button.grid(row=3, column=2)
# Set the button callbacks:
forward_time_button["command"] = lambda: handle_forward_time_button(wheel_speed_entry.get(), time_entry.get(), mqtt_sender)
forward_time_inches_button["command"] = lambda: handle_forward_time_inches_button(wheel_speed_entry.get(), inches_entry.get(), mqtt_sender)
forward_inches_button["command"] = lambda: handle_forward_inches_button(wheel_speed_entry.get(), inches_entry.get(), mqtt_sender)
return frame
def handle_forward_time_button(speed,time,mqtt_sender):
mqtt_sender.send_message('Forward_Time', [str(speed),str(time)])
print('Forward_Time',speed,time)
def handle_forward_time_inches_button(speed,inches,mqtt_sender):
mqtt_sender.send_message('Forward_Time_Inches', [str(speed), str(inches)])
print('Forward_Time_Inches', speed, inches)
def handle_forward_inches_button(speed,inches,mqtt_sender):
mqtt_sender.send_message('Forward_Inches', [str(speed), str(inches)])
print('Forward_Inches', speed, inches)
def get_Sprint_1_Beeper_System_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Sound System")
number_of_beeps_label = ttk.Label(frame, text="Number of Beeps")
tone_duration_label = ttk.Label(frame, text="Duration of Tone")
tone_frequency_label = ttk.Label(frame, text="Tone Frequency")
speak_text_label = ttk.Label(frame, text="Text to Speech")
number_of_beeps= ttk.Entry(frame, width=8)
number_of_beeps.insert(0, "10")
tone_duration = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
tone_duration.insert(0, "10")
tone_frequency = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
tone_frequency.insert(0, "10")
speak_text = ttk.Entry(frame, width=8, justify=tkinter.RIGHT)
speak_text.insert(0, "Type Here")
beep_button = ttk.Button(frame, text="Play Beeps")
tone_button = ttk.Button(frame, text="Play Tone")
speak_button = ttk.Button(frame, text="Read Text")
# Grid the widgets:
frame_label.grid(row=0, column=1)
number_of_beeps_label.grid(row=1, column=0)
tone_duration_label.grid(row=1, column=1)
tone_frequency_label.grid(row=1, column=2)
speak_text_label.grid(row=1, column=3)
number_of_beeps.grid(row=2, column=0)
tone_duration.grid(row=2, column=1)
tone_frequency.grid(row=2, column=2)
speak_text.grid(row=2, column=3)
beep_button.grid(row=3, column=0)
tone_button.grid(row=3, column=1)
speak_button.grid(row=3, column=3)
# Set the button callbacks:
beep_button["command"] = lambda: handle_beep_button(number_of_beeps.get(), mqtt_sender)
tone_button["command"] = lambda: handle_tone_button(tone_duration.get(), tone_frequency.get(), mqtt_sender)
speak_button["command"] = lambda: handle_speak_button(speak_text.get(), mqtt_sender)
return frame
def handle_beep_button(numberofbeeps,mqtt_sender):
mqtt_sender.send_message('beep_button', [str(numberofbeeps)])
print('beep_button',numberofbeeps)
def handle_tone_button(duration,frequency,mqtt_sender):
mqtt_sender.send_message('tone_button', [str(duration), str(frequency)])
print('tone_button', duration, frequency)
def handle_speak_button(text,mqtt_sender):
mqtt_sender.send_message('speak_button', [text])
print('speak_button', text)
def get_Katana_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Katana's System")
obtain_with_sensor_label = ttk.Label(frame, text="Get Object with Proximity")
obtain_with_sensor_button = ttk.Button(frame, text="Get")
wheel_speed_label = ttk.Label(frame, text="Move Speed")
rate_of_frequency_label = ttk.Label(frame, text="Frequency Rate (Increasing)")
rate_of_frequency = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
rate_of_frequency.insert(0, "10")
initial_frequency_label = ttk.Label(frame, text="Initial Frequency")
initial_frequency = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
initial_frequency.insert(0, "5")
obtain_with_camera_label = ttk.Label(frame, text="Get Object with Camera")
obtain_with_camera_button = ttk.Button(frame, text="Get")
wheel_speed_entry = ttk.Entry(frame, width=8)
wheel_speed_entry.insert(0, "100")
spin_speed_label = ttk.Label(frame, text="Spinning Speed")
spin_speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
spin_speed.insert(0, "10")
spin_direction_label = ttk.Label(frame, text="Spinning Direction")
spin_direction = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
spin_direction.insert(0, "Counter Clockwise")
# Grid the widgets:
frame_label.grid(row=0, column=2)
obtain_with_sensor_label.grid(row=1, column=1)
obtain_with_sensor_button.grid(row=2, column=1)
rate_of_frequency_label.grid(row=1,column=2)
rate_of_frequency.grid(row=2, column=2)
initial_frequency_label.grid(row=1, column=3)
initial_frequency.grid(row=2, column=3)
wheel_speed_label.grid(row=3, column=2)
wheel_speed_entry.grid(row=4, column=2)
obtain_with_camera_label.grid(row=5, column=1)
obtain_with_camera_button.grid(row=6, column=1)
spin_speed_label.grid(row=5, column=2)
spin_speed.grid(row=6, column=2)
spin_direction_label.grid(row=5, column=3)
spin_direction.grid(row=6, column=3)
# Set the button callbacks:
obtain_with_sensor_button["command"] = lambda: handle_obtain_with_sensor_button(wheel_speed_entry.get(),rate_of_frequency.get(),
initial_frequency.get(),
mqtt_sender)
obtain_with_camera_button["command"] = lambda: handle_obtain_with_camera_button(wheel_speed_entry.get(),
spin_speed.get(),
spin_direction.get(),
rate_of_frequency.get(),
initial_frequency.get(),mqtt_sender)
return frame
def handle_obtain_with_sensor_button(wheel_speed,rate_of_frequency, initial_frequency, mqtt_sender):
print('handler')
mqtt_sender.send_message('obtain_with_sensor_button', [str(wheel_speed),str(rate_of_frequency),str(initial_frequency)])
print('obtain_with_sensor',wheel_speed, rate_of_frequency, initial_frequency)
def handle_obtain_with_camera_button(wheel_speed, spin_speed, spin_direction, rate_of_frequency, initial_frequency, mqtt_sender):
mqtt_sender.send_message('obtain_with_camera_button', [str(wheel_speed),str(spin_speed), str(spin_direction),str(rate_of_frequency),str(initial_frequency)])
print('obtain_with_camera', wheel_speed, spin_speed, spin_direction, rate_of_frequency, initial_frequency)
def get_Nick_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Nick's Sprint 2 System")
proximity_label = ttk.Label(frame, text="Go to and Pick up Object (Proximity)")
proximity_button = ttk.Button(frame, text="Run Proximity Grab")
camera_label = ttk.Label(frame, text="Go to and Pick up Object (Camera)")
camera_button = ttk.Button(frame, text="Run Camera Grab")
line_follower_label = ttk.Label(frame, text="Line Follower (Bang Bang Method)")
line_button = ttk.Button(frame, text="Follow Line")
rate_of_beeps_label = ttk.Label(frame, text="Beep Rate Increase")
rate_of_beeps= ttk.Entry(frame, width=8, justify=tkinter.LEFT)
rate_of_beeps.insert(0, "10")
initial_beeps_label = ttk.Label(frame, text="Initial Beep Rate")
initial_beeps = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
initial_beeps.insert(0, "5")
speed_label = ttk.Label(frame, text="Turning Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "100")
direction_label = ttk.Label(frame, text="Turning Direction, Clockwise or Counter-Clockwise")
direction = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
direction.insert(0, "Clockwise")
starting_side_label = ttk.Label(frame, text="Turning Direction, Right or Left")
starting_side = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
starting_side.insert(0, "Right")
# Grid the widgets:
frame_label.grid(row=0, column=1)
proximity_label.grid(row=1, column=0)
proximity_button.grid(row=1, column=1)
rate_of_beeps_label.grid(row=2, column=0)
rate_of_beeps.grid(row=2, column=1)
initial_beeps_label.grid(row=3, column=0)
initial_beeps.grid(row=3, column=1)
camera_label.grid(row=4, column=0)
camera_button.grid(row=4, column=1)
speed_label.grid(row=5, column=0)
speed.grid(row=5, column=1)
direction_label.grid(row=6, column=0)
direction.grid(row=6, column=1)
line_follower_label.grid(row=7, column=0)
line_button.grid(row=7, column=1)
starting_side_label.grid(row=8, column=0)
starting_side.grid(row=8, column=1)
# Set the button callbacks:
proximity_button["command"] = lambda: handle_proximity_button(rate_of_beeps.get(), initial_beeps.get(), mqtt_sender)
camera_button["command"] = lambda: handle_camera_button(speed.get(),direction.get(),rate_of_beeps.get(), initial_beeps.get(), mqtt_sender)
line_button["command"] = lambda: handle_line_button(starting_side.get(), mqtt_sender)
return frame
def handle_proximity_button(rate_of_beeps, initial_beeps, mqtt_sender):
mqtt_sender.send_message('m1proximity_button', [str(rate_of_beeps),str(initial_beeps)])
print('proximity',rate_of_beeps, initial_beeps)
def handle_camera_button(speed,direction,rate_of_beeps, initial_beeps, mqtt_sender):
mqtt_sender.send_message('m1camera_button', [str(speed), str(direction),str(rate_of_beeps),str(initial_beeps)])
print('camera', speed, direction, rate_of_beeps, initial_beeps)
def handle_line_button(starting_side, mqtt_sender):
mqtt_sender.send_message('m1line_button', [str(starting_side)])
print('line', starting_side)
##COLOR FRAMING
def get_Sprint_2_Color_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Color Sensor")
intensity_less_label = ttk.Label(frame, text="Go Until Intensity is Less Than")
intensity_less_button = ttk.Button(frame, text="Run Less than Intensity")
intensity_greater_label = ttk.Label(frame, text="Go Until Intensity is Greater Than")
intensity_greater_button = ttk.Button(frame, text="Run Greater than Intensity")
until_color_label = ttk.Label(frame, text="Go Until Color")
until_color_button = ttk.Button(frame, text="Run Go Until Color")
until_not_color_label = ttk.Label(frame, text="Go Until Not Color")
until_not_color_button = ttk.Button(frame, text="Run Go Until Not Color")
color_label = ttk.Label(frame, text="Color")
color= ttk.Entry(frame, width=8, justify=tkinter.LEFT)
color.insert(0, "Red")
speed_label = ttk.Label(frame, text="Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "50")
intensity_label = ttk.Label(frame, text="Light Intensity")
intensity = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
intensity.insert(0, "50")
# Grid the widgets:
frame_label.grid(row=0, column=1)
color_label.grid(row=1, column=0)
color.grid(row=2, column=0)
speed_label.grid(row=1, column=1)
speed.grid(row=2, column=1)
intensity_label.grid(row=1, column=2)
intensity.grid(row=2, column=2)
intensity_less_label.grid(row=3, column=0)
intensity_less_button.grid(row=4, column=0)
intensity_greater_label.grid(row=5, column=0)
intensity_greater_button.grid(row=6, column=0)
until_color_label.grid(row=3, column=2)
until_color_button.grid(row=4, column=2)
until_not_color_label.grid(row=5, column=2)
until_not_color_button.grid(row=6, column=2)
# Set the button callbacks:
intensity_less_button["command"] = lambda: handle_intensity_less_button(speed.get(), intensity.get(), mqtt_sender)
intensity_greater_button["command"] = lambda: handle_intensity_greater_button(speed.get(),intensity.get(), mqtt_sender)
until_color_button["command"] = lambda: handle_until_color_button(speed.get(),color.get(), mqtt_sender)
until_not_color_button["command"] = lambda: handle_until_not_color_button(speed.get(),color.get(), mqtt_sender)
return frame
def handle_intensity_less_button(speed, intensity, mqtt_sender):
mqtt_sender.send_message('intensity_less_button', [str(speed),str(intensity)])
print('intensity_less_button',speed, intensity)
def handle_intensity_greater_button(speed, intensity, mqtt_sender):
mqtt_sender.send_message('intensity_greater_button', [str(speed), str(intensity)])
print('intensity_greater_button', speed, intensity)
def handle_until_color_button(speed,color, mqtt_sender):
mqtt_sender.send_message('until_color_button', [str(speed),str(color)])
print('until_color_button', speed,color)
def handle_until_not_color_button(speed,color, mqtt_sender):
mqtt_sender.send_message('until_not_color_button', [str(speed),str(color)])
print('until_not_color_button', speed,color)
##PROXIMITY SENSOR
def get_Sprint_2_Proximity_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Proximity Sensor")
distance_less_label = ttk.Label(frame, text="Go Until Distance is Less Than")
distance_less_button = ttk.Button(frame, text="Run Less than Distance")
distance_greater_label = ttk.Label(frame, text="Go Until Distance is Greater Than")
distance_greater_button = ttk.Button(frame, text="Run Greater than Distance")
until_distance_label = ttk.Label(frame, text="Go Until Distance Within")
until_distance_button = ttk.Button(frame, text="Run Go Until Distance Within")
inches_label = ttk.Label(frame, text="Inches")
inches = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
inches.insert(0, "10")
speed_label = ttk.Label(frame, text="Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "100")
delta_label = ttk.Label(frame, text="Delta Distance")
delta = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
delta.insert(0, "50")
# Grid the widgets:
frame_label.grid(row=0, column=1)
distance_less_label.grid(row=3, column=0)
distance_less_button.grid(row=4, column=0)
distance_greater_label.grid(row=3, column=1)
distance_greater_button.grid(row=4, column=1)
until_distance_label.grid(row=3, column=2)
until_distance_button.grid(row=4, column=2)
delta_label.grid(row=1, column=0)
delta.grid(row=2, column=0)
speed_label.grid(row=1, column=1)
speed.grid(row=2, column=1)
inches_label.grid(row=1, column=2)
inches.grid(row=2, column=2)
# Set the button callbacks:
distance_greater_button["command"] = lambda: handle_distance_greater_button(speed.get(), inches.get(), mqtt_sender)
distance_less_button["command"] = lambda: handle_distance_less_button(speed.get(), inches.get(),mqtt_sender)
until_distance_button["command"] = lambda: handle_until_distance_button(speed.get(), inches.get(), delta.get(), mqtt_sender)
return frame
def handle_distance_greater_button(speed, inches, mqtt_sender):
mqtt_sender.send_message('distance_greater_button', [str(speed), str(inches)])
print('distance_greater_button', speed, inches)
def handle_distance_less_button(speed, inches,mqtt_sender):
mqtt_sender.send_message('distance_less_button', [str(speed), str(inches)])
print('distance_less_button', speed, inches)
def handle_until_distance_button(speed, inches, delta, mqtt_sender):
mqtt_sender.send_message('until_distance_button', [str(speed), str(inches),str(delta)])
print('until_distance_button', speed, inches, delta)
##Camera SENSOR
def get_Sprint_2_Camera_frame(window, mqtt_sender):
"""
Constructs and returns a frame on the given window, where the frame
has Beeper objects that control the EV3 robot's motion
by passing messages using the given MQTT Sender.
:type window: ttk.Frame | ttk.Toplevel
:type mqtt_sender: com.MqttClient
"""
# Construct the frame to return:
frame = ttk.Frame(window, padding=10, borderwidth=5, relief="ridge")
frame.grid()
# Construct the widgets on the frame:
frame_label = ttk.Label(frame, text="Camera Sensor")
counter_clockwise_label = ttk.Label(frame, text="Search Counterclockwise")
counter_clockwise_button = ttk.Button(frame, text="Run CCW Search")
clockwise_label = ttk.Label(frame, text="Search Counterclockwise")
clockwise_button = ttk.Button(frame, text="Run CW Search")
speed_label = ttk.Label(frame, text="Speed")
speed = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
speed.insert(0, "100")
area_label = ttk.Label(frame, text="Area Size")
area = ttk.Entry(frame, width=8, justify=tkinter.LEFT)
area.insert(0, "10")
# Grid the widgets:
frame_label.grid(row=0, column=1)
counter_clockwise_label.grid(row=1, column=0)
counter_clockwise_button.grid(row=2, column=0)
clockwise_label.grid(row=1, column=2)
clockwise_button.grid(row=2, column=2)
area_label.grid(row=1, column=1)
area.grid(row=2, column=1)
speed_label.grid(row=3, column=1)
speed.grid(row=4, column=1)
# Set the button callbacks:
counter_clockwise_button["command"] = lambda: handle_counter_clockwise_button(speed.get(), area.get(), mqtt_sender)
clockwise_button["command"] = lambda: handle_clockwise_button(speed.get(), area.get(), mqtt_sender)
return frame
def handle_counter_clockwise_button(speed, area, mqtt_sender):
mqtt_sender.send_message('camera_counter_clockwise_button', [str(speed), str(area)])
print('camera_counter_clockwise_button', speed, area)
def handle_clockwise_button(speed, area, mqtt_sender):
mqtt_sender.send_message('camera_clockwise_button', [str(speed), str(area)])
print('camera_clockwise_button', speed, area)
| 0
| 0
| 0
| 0
| 0
| 3,703
| 0
| -10
| 474
|
1825f4b23a74762f04636ac05fc56fa9252aa0dc
| 1,765
|
py
|
Python
|
segmentation_models/encoders/__init__.py
|
jmerkow/segmentation_models.pytorch
|
d33fb5ea4a66da1ed0006eaca4dbfa88aa986925
|
[
"MIT"
] | null | null | null |
segmentation_models/encoders/__init__.py
|
jmerkow/segmentation_models.pytorch
|
d33fb5ea4a66da1ed0006eaca4dbfa88aa986925
|
[
"MIT"
] | null | null | null |
segmentation_models/encoders/__init__.py
|
jmerkow/segmentation_models.pytorch
|
d33fb5ea4a66da1ed0006eaca4dbfa88aa986925
|
[
"MIT"
] | 2
|
2019-07-25T16:52:29.000Z
|
2019-08-19T17:44:46.000Z
|
import torch.utils.model_zoo as model_zoo
from .densenet import densenet_encoders
from .dpn import dpn_encoders
from .efficientnet import efficientnet_encoders
from .inceptionresnetv2 import inception_encoders
from .resnet import resnet_encoders
from .senet import senet_encoders
from .vgg import vgg_encoders
from .xception import xception_encoders
encoders = {}
encoders.update(resnet_encoders)
encoders.update(dpn_encoders)
encoders.update(vgg_encoders)
encoders.update(senet_encoders)
encoders.update(densenet_encoders)
encoders.update(inception_encoders)
encoders.update(xception_encoders)
encoders.update(efficientnet_encoders)
| 32.685185
| 116
| 0.782436
|
import functools
import torch.utils.model_zoo as model_zoo
from ._preprocessing import preprocess_input
from .densenet import densenet_encoders
from .dpn import dpn_encoders
from .efficientnet import efficientnet_encoders
from .inceptionresnetv2 import inception_encoders
from .resnet import resnet_encoders
from .senet import senet_encoders
from .vgg import vgg_encoders
from .xception import xception_encoders
encoders = {}
encoders.update(resnet_encoders)
encoders.update(dpn_encoders)
encoders.update(vgg_encoders)
encoders.update(senet_encoders)
encoders.update(densenet_encoders)
encoders.update(inception_encoders)
encoders.update(xception_encoders)
encoders.update(efficientnet_encoders)
def get_encoder(name, encoder_weights=None, model_dir=None):
Encoder = encoders[name]['encoder']
encoder = Encoder(**encoders[name]['params'])
encoder.out_shapes = encoders[name]['out_shapes']
if encoder_weights is not None:
settings = encoders[name]['pretrained_settings'][encoder_weights]
encoder.load_state_dict(model_zoo.load_url(settings['url'], model_dir=model_dir))
return encoder
def get_encoder_names():
return list(encoders.keys())
def get_preprocessing_fn(encoder_name, pretrained='imagenet'):
settings = encoders[encoder_name]['pretrained_settings']
if pretrained not in settings.keys():
raise ValueError('Avaliable pretrained options {}'.format(settings.keys()))
input_space = settings[pretrained].get('input_space')
input_range = settings[pretrained].get('input_range')
mean = settings[pretrained].get('mean')
std = settings[pretrained].get('std')
return functools.partial(preprocess_input, mean=mean, std=std, input_space=input_space, input_range=input_range)
| 0
| 0
| 0
| 0
| 0
| 994
| 0
| 18
| 114
|
eebfc13ced21d7e77e7aebf9405251558b6a72e0
| 284
|
py
|
Python
|
exercicios/vestibular.py
|
IgoPereiraBarros/maratona-data-science-brasil
|
cc07476579134a2764f00d229d415657555dcdd1
|
[
"MIT"
] | null | null | null |
exercicios/vestibular.py
|
IgoPereiraBarros/maratona-data-science-brasil
|
cc07476579134a2764f00d229d415657555dcdd1
|
[
"MIT"
] | null | null | null |
exercicios/vestibular.py
|
IgoPereiraBarros/maratona-data-science-brasil
|
cc07476579134a2764f00d229d415657555dcdd1
|
[
"MIT"
] | null | null | null |
N = int(input())
gabarito_prova = input().split()
gabarito_aluno = input().split()
list_aluno = []
list_prova = []
count = 0
for i in range(gabarito_aluno):
for j in range(gabarito_prova):
if gabarito_aluno[i] == gabarito_prova[j]:
count += 1
print(count)
| 17.75
| 50
| 0.637324
|
N = int(input())
gabarito_prova = input().split()
gabarito_aluno = input().split()
list_aluno = []
list_prova = []
count = 0
for i in range(gabarito_aluno):
for j in range(gabarito_prova):
if gabarito_aluno[i] == gabarito_prova[j]:
count += 1
print(count)
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
616305a77276e725923fc24a8acb3ff30c156348
| 194
|
py
|
Python
|
apps/contact/urls.py
|
Kpaubert/onlineweb4
|
9ac79f163bc3a816db57ffa8477ea88770d97807
|
[
"MIT"
] | 32
|
2017-02-22T13:38:38.000Z
|
2022-03-31T23:29:54.000Z
|
apps/contact/urls.py
|
Kpaubert/onlineweb4
|
9ac79f163bc3a816db57ffa8477ea88770d97807
|
[
"MIT"
] | 694
|
2017-02-15T23:09:52.000Z
|
2022-03-31T23:16:07.000Z
|
apps/contact/urls.py
|
Kpaubert/onlineweb4
|
9ac79f163bc3a816db57ffa8477ea88770d97807
|
[
"MIT"
] | 35
|
2017-09-02T21:13:09.000Z
|
2022-02-21T11:30:30.000Z
|
from django.conf.urls import url
from apps.contact import views
urlpatterns = [
url(r"^$", views.index, name="contact_index"),
url(r"^submit/", views.contact_submit, name="submit"),
]
| 21.555556
| 58
| 0.690722
|
from django.conf.urls import url
from apps.contact import views
urlpatterns = [
url(r"^$", views.index, name="contact_index"),
url(r"^submit/", views.contact_submit, name="submit"),
]
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
60b9f934da6221160ed9c0661e0ab963f52e38f1
| 2,993
|
py
|
Python
|
src/plugins/nonebot_plugin_picsearcher/iqdb.py
|
ltyec/Kiba
|
1c7db0939151aaa46ef865638b8b347ceebb71a1
|
[
"MIT"
] | null | null | null |
src/plugins/nonebot_plugin_picsearcher/iqdb.py
|
ltyec/Kiba
|
1c7db0939151aaa46ef865638b8b347ceebb71a1
|
[
"MIT"
] | null | null | null |
src/plugins/nonebot_plugin_picsearcher/iqdb.py
|
ltyec/Kiba
|
1c7db0939151aaa46ef865638b8b347ceebb71a1
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
headers = {
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',
'Accept-Encoding': 'gzip, deflate', 'Accept-Language': 'zh-CN,zh;q=0.9', 'Cache-Control': 'max-age=0',
'Connection': 'keep-alive',
'Content-Type': 'multipart/form-data; boundary=----WebKitFormBoundaryuwjSiBcpPag4k159',
'Cookie': 'Hm_lvt_765ecde8c11b85f1ac5f168fa6e6821f=1602471368; Hm_lpvt_765ecde8c11b85f1ac5f168fa6e6821f=1602472300',
'Host': 'iqdb.org', 'Origin': 'http://iqdb.org', 'Referer': 'http://iqdb.org/', 'Upgrade-Insecure-Requests': '1',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36'}
| 37.4125
| 141
| 0.625459
|
# -*- coding: utf-8 -*-
import asyncio
from typing import List, Tuple
import io
from urllib.parse import urljoin
from lxml.html import fromstring
import aiohttp
from nonebot.adapters.cqhttp import MessageSegment
from .formdata import FormData
headers = {
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',
'Accept-Encoding': 'gzip, deflate', 'Accept-Language': 'zh-CN,zh;q=0.9', 'Cache-Control': 'max-age=0',
'Connection': 'keep-alive',
'Content-Type': 'multipart/form-data; boundary=----WebKitFormBoundaryuwjSiBcpPag4k159',
'Cookie': 'Hm_lvt_765ecde8c11b85f1ac5f168fa6e6821f=1602471368; Hm_lpvt_765ecde8c11b85f1ac5f168fa6e6821f=1602472300',
'Host': 'iqdb.org', 'Origin': 'http://iqdb.org', 'Referer': 'http://iqdb.org/', 'Upgrade-Insecure-Requests': '1',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36'}
def parse_html(html: str):
selector = fromstring(html)
for tag in selector.xpath('//div[@id="pages"]/div[position()>1]/table'):
# 第一个是bestmatch
if pic_url := tag.xpath('./tr[2]/td/a/img/@src'):
pic_url = urljoin("http://iqdb.org/", pic_url[0]) # 缩略图
else:
pic_url = "没有最相似的"
similarity = tag.xpath('./tr[last()]/td/text()')[0] # 相似度
href: List[str] = tag.xpath('./tr/td/a/@href') # 第一个href
href.extend(tag.xpath('./tr/td/span/a/@href')) # 第二个 可能是空
href = list(map(lambda x: "https:" + x if not x.startswith("https") else x, href))
yield pic_url, similarity, href
pass
async def get_pic_from_url(url: str):
"""
返回信息元祖
:param url:
:return:
"""
async with aiohttp.ClientSession() as session:
async with session.get(url) as resp:
content = io.BytesIO(await resp.read())
data = FormData(boundary="----WebKitFormBoundaryuwjSiBcpPag4k159")
data.add_field(name="MAX_FILE_SIZE", value="")
for i in range(1, 7):
data.add_field(name="service[]", value=str(i))
data.add_field(name="service[]", value="11")
data.add_field(name="service[]", value="13")
data.add_field(name="file", value=content, content_type="application/octet-stream", filename="0.jpg")
data.add_field(name="url", value="")
async with session.post("http://iqdb.org/", data=data, headers=headers) as res:
html = await res.text()
return [i for i in parse_html(html)]
pass
async def get_des(url: str):
"""
返回详细简介 cq码转义
:param url:
:return:
"""
image_data: List[Tuple] = await get_pic_from_url(url)
if not image_data:
msg: str = "找不到高相似度的"
yield msg
return
for pic in image_data:
msg = MessageSegment.image(file=pic[0]) + f"\n{pic[1]}\n"
for i in pic[2]:
msg = msg + f"{i}\n"
yield msg
| 147
| 0
| 1,238
| 0
| 626
| 0
| 0
| 43
| 247
|
4cecb8040d2df98220d60255b7529141552cd38a
| 2,703
|
py
|
Python
|
explorer/api/inner_api.py
|
AthenaExplorer/xm_s_explorer_v2
|
203f7b5d129552f5b7c977c4247d2060956f8add
|
[
"MIT"
] | null | null | null |
explorer/api/inner_api.py
|
AthenaExplorer/xm_s_explorer_v2
|
203f7b5d129552f5b7c977c4247d2060956f8add
|
[
"MIT"
] | null | null | null |
explorer/api/inner_api.py
|
AthenaExplorer/xm_s_explorer_v2
|
203f7b5d129552f5b7c977c4247d2060956f8add
|
[
"MIT"
] | 1
|
2022-03-02T19:20:52.000Z
|
2022-03-02T19:20:52.000Z
|
import datetime
from flask import request
from explorer.services.message import MessageService
from explorer.services.miner import MinerService
from explorer.services.wallets import WalletsService
from explorer.services.blocks import BlocksService
from base.utils.fil import datetime_to_height
from base.response import response_json
def get_message_list_by_height():
"""
Send
:return:
"""
height = request.form.get('height')
result = MessageService.get_message_list_by_height(height)
return response_json(result)
def get_miner_info_by_miner_no():
"""
miner_no
:return:
"""
miner_no = request.form.get('miner_no')
date_str = request.form.get('date_str')
result = MinerService.get_miner_info_by_miner_no(miner_no, date_str)
return response_json(result)
def get_wallet_address_change():
"""
:return:
"""
wallet_address = request.form.get('wallet_address')
balance_value = request.form.get('balance_value')
height = int(request.form.get('height'))
result = WalletsService.get_wallet_address_change(wallet_address, balance_value, height)
return response_json(result)
def get_is_all_wallet():
"""
:return:
"""
address = request.form.get("address")
if not address:
return response_json(False)
result = WalletsService.get_is_all_wallet(address)
if result:
return response_json(result["value"])
return response_json(False)
def get_miner_day_list():
"""
miner
:return:
"""
miner_no = request.form.get("miner_no")
date = request.form.get("date")
data = MinerService.get_miner_day_list(miner_no, date)
return response_json(data)
def get_init_value():
"""
miner
:return:
"""
miner_no = request.form.get("miner_no")
fields = request.form.get("fields")
end_time = request.form.get("end_time")
data = MinerService.get_init_value(miner_no, fields, end_time)
return response_json(data)
def get_block_count():
"""
:return:
"""
date = request.form.get('date')
height = datetime_to_height(date)
count = BlocksService.get_tipset_block_count(start_height=height)
return response_json({"count": count})
def get_miner_increment():
"""
:return:
"""
miner_no = request.form.get('miner_no')
date = request.form.get('date')
key = request.form.get('key')
if not date:
date = str(datetime.date.today() - datetime.timedelta(days=1))
result = MinerService.get_miner_increment(miner_no, date, key)
return response_json(result)
| 25.027778
| 92
| 0.699593
|
import datetime
from flask import request
from explorer.services.message import MessageService
from explorer.services.miner import MinerService
from explorer.services.wallets import WalletsService
from explorer.services.blocks import BlocksService
from base.utils.fil import datetime_to_height
from base.response import response_json
def get_message_list_by_height():
"""
获取Send消息列表根据高度
:return:
"""
height = request.form.get('height')
result = MessageService.get_message_list_by_height(height)
return response_json(result)
def get_miner_info_by_miner_no():
"""
获取指定信息列表miner_no
:return:
"""
miner_no = request.form.get('miner_no')
date_str = request.form.get('date_str')
result = MinerService.get_miner_info_by_miner_no(miner_no, date_str)
return response_json(result)
def get_wallet_address_change():
"""
获取钱包指点数据大小的变化量
:return:
"""
wallet_address = request.form.get('wallet_address')
balance_value = request.form.get('balance_value')
height = int(request.form.get('height'))
result = WalletsService.get_wallet_address_change(wallet_address, balance_value, height)
return response_json(result)
def get_is_all_wallet():
"""
查询是否是钱包
:return:
"""
address = request.form.get("address")
if not address:
return response_json(False)
result = WalletsService.get_is_all_wallet(address)
if result:
return response_json(result["value"])
return response_json(False)
def get_miner_day_list():
"""
存储提供者每天的miner数据
:return:
"""
miner_no = request.form.get("miner_no")
date = request.form.get("date")
data = MinerService.get_miner_day_list(miner_no, date)
return response_json(data)
def get_init_value():
"""
存储提供者每天的miner数据
:return:
"""
miner_no = request.form.get("miner_no")
fields = request.form.get("fields")
end_time = request.form.get("end_time")
data = MinerService.get_init_value(miner_no, fields, end_time)
return response_json(data)
def get_block_count():
"""
查询指定时间后是否还有新的区块
:return:
"""
date = request.form.get('date')
height = datetime_to_height(date)
count = BlocksService.get_tipset_block_count(start_height=height)
return response_json({"count": count})
def get_miner_increment():
"""
查询指定时间后是否还有新的区块
:return:
"""
miner_no = request.form.get('miner_no')
date = request.form.get('date')
key = request.form.get('key')
if not date:
date = str(datetime.date.today() - datetime.timedelta(days=1))
result = MinerService.get_miner_increment(miner_no, date, key)
return response_json(result)
| 267
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
d2832ef568cd65320be37ac0297b38c2de6deae9
| 3,743
|
py
|
Python
|
starter_code/student_utils.py
|
ykhiari/Patient-Selection-for-Diabetes-Drug-Testing
|
ce8e698bff4cbf5a9319607404edada539c5c099
|
[
"MIT"
] | null | null | null |
starter_code/student_utils.py
|
ykhiari/Patient-Selection-for-Diabetes-Drug-Testing
|
ce8e698bff4cbf5a9319607404edada539c5c099
|
[
"MIT"
] | null | null | null |
starter_code/student_utils.py
|
ykhiari/Patient-Selection-for-Diabetes-Drug-Testing
|
ce8e698bff4cbf5a9319607404edada539c5c099
|
[
"MIT"
] | null | null | null |
import numpy as np
import os
import tensorflow as tf
from functools import partial
####### STUDENTS FILL THIS OUT ######
#Question 3
def reduce_dimension_ndc(df, ndc_code_df):
'''
df: pandas dataframe, input dataset
ndc_df: pandas dataframe, drug code dataset used for mapping in generic names
return:
df: pandas dataframe, output dataframe with joined generic drug name
'''
mapping = dict(ndc_code_df[['NDC_Code', 'Non-proprietary Name']].values)
mapping['nan'] = np.nan
df['generic_drug_name'] = df['ndc_code'].astype(str).apply(lambda x : mapping[x])
return df
#Question 4
def select_first_encounter(df):
'''
df: pandas dataframe, dataframe with all encounters
return:
- first_encounter_df: pandas dataframe, dataframe with only the first encounter for a given patient
'''
df.sort_values(by = 'encounter_id')
first_encounters = df.groupby('patient_nbr')['encounter_id'].first().values
first_encounter_df = df[df['encounter_id'].isin(first_encounters)]
return first_encounter_df
#Question 6
#Question 7
def create_tf_categorical_feature_cols(categorical_col_list,
vocab_dir='./diabetes_vocab/'):
'''
categorical_col_list: list, categorical field list that will be transformed with TF feature column
vocab_dir: string, the path where the vocabulary text files are located
return:
output_tf_list: list of TF feature columns
'''
output_tf_list = []
for c in categorical_col_list:
vocab_file_path = os.path.join(vocab_dir, c + "_vocab.txt")
vocab = tf.feature_column.categorical_column_with_vocabulary_file(key=c,
vocabulary_file = vocab_file_path,
num_oov_buckets=1)
tf_categorical_feature_column = tf.feature_column.indicator_column(vocab)
output_tf_list.append(tf_categorical_feature_column)
return output_tf_list
#Question 8
def normalize_numeric_with_zscore(col, mean, std):
'''
This function can be used in conjunction with the tf feature column for normalization
'''
return (col - mean)/std
def create_tf_numeric_feature(col, MEAN, STD, default_value=0):
'''
col: string, input numerical column name
MEAN: the mean for the column in the training data
STD: the standard deviation for the column in the training data
default_value: the value that will be used for imputing the field
return:
tf_numeric_feature: tf feature column representation of the input field
'''
normalizer_fn = lambda col, m, s : (col - m) / s
normalizer = partial(normalizer_fn, m = MEAN, s = STD)
tf_numeric_feature = tf.feature_column.numeric_column(col, normalizer_fn = normalizer, dtype = tf.float64,
default_value = default_value)
return tf_numeric_feature
#Question 9
def get_mean_std_from_preds(diabetes_yhat):
'''
diabetes_yhat: TF Probability prediction object
'''
m = diabetes_yhat.mean()
s = diabetes_yhat.stddev()
return m, s
# Question 10
def get_student_binary_prediction(df, col):
'''
df: pandas dataframe prediction output dataframe
col: str, probability mean prediction field
return:
student_binary_prediction: pandas dataframe converting input to flattened numpy array and binary labels
'''
student_binary_prediction = df[col].apply(lambda x : 1 if x >= 5 else 0)
return student_binary_prediction
| 36.339806
| 111
| 0.678333
|
import pandas as pd
import numpy as np
import os
import tensorflow as tf
from functools import partial
####### STUDENTS FILL THIS OUT ######
#Question 3
def reduce_dimension_ndc(df, ndc_code_df):
'''
df: pandas dataframe, input dataset
ndc_df: pandas dataframe, drug code dataset used for mapping in generic names
return:
df: pandas dataframe, output dataframe with joined generic drug name
'''
mapping = dict(ndc_code_df[['NDC_Code', 'Non-proprietary Name']].values)
mapping['nan'] = np.nan
df['generic_drug_name'] = df['ndc_code'].astype(str).apply(lambda x : mapping[x])
return df
#Question 4
def select_first_encounter(df):
'''
df: pandas dataframe, dataframe with all encounters
return:
- first_encounter_df: pandas dataframe, dataframe with only the first encounter for a given patient
'''
df.sort_values(by = 'encounter_id')
first_encounters = df.groupby('patient_nbr')['encounter_id'].first().values
first_encounter_df = df[df['encounter_id'].isin(first_encounters)]
return first_encounter_df
#Question 6
def patient_dataset_splitter(df, PREDICTOR_FIELD, patient_key='patient_nbr'):
pass
#Question 7
def create_tf_categorical_feature_cols(categorical_col_list,
vocab_dir='./diabetes_vocab/'):
'''
categorical_col_list: list, categorical field list that will be transformed with TF feature column
vocab_dir: string, the path where the vocabulary text files are located
return:
output_tf_list: list of TF feature columns
'''
output_tf_list = []
for c in categorical_col_list:
vocab_file_path = os.path.join(vocab_dir, c + "_vocab.txt")
vocab = tf.feature_column.categorical_column_with_vocabulary_file(key=c,
vocabulary_file = vocab_file_path,
num_oov_buckets=1)
tf_categorical_feature_column = tf.feature_column.indicator_column(vocab)
output_tf_list.append(tf_categorical_feature_column)
return output_tf_list
#Question 8
def normalize_numeric_with_zscore(col, mean, std):
'''
This function can be used in conjunction with the tf feature column for normalization
'''
return (col - mean)/std
def create_tf_numeric_feature(col, MEAN, STD, default_value=0):
'''
col: string, input numerical column name
MEAN: the mean for the column in the training data
STD: the standard deviation for the column in the training data
default_value: the value that will be used for imputing the field
return:
tf_numeric_feature: tf feature column representation of the input field
'''
normalizer_fn = lambda col, m, s : (col - m) / s
normalizer = partial(normalizer_fn, m = MEAN, s = STD)
tf_numeric_feature = tf.feature_column.numeric_column(col, normalizer_fn = normalizer, dtype = tf.float64,
default_value = default_value)
return tf_numeric_feature
#Question 9
def get_mean_std_from_preds(diabetes_yhat):
'''
diabetes_yhat: TF Probability prediction object
'''
m = diabetes_yhat.mean()
s = diabetes_yhat.stddev()
return m, s
# Question 10
def get_student_binary_prediction(df, col):
'''
df: pandas dataframe prediction output dataframe
col: str, probability mean prediction field
return:
student_binary_prediction: pandas dataframe converting input to flattened numpy array and binary labels
'''
student_binary_prediction = df[col].apply(lambda x : 1 if x >= 5 else 0)
return student_binary_prediction
| 0
| 0
| 0
| 0
| 0
| 65
| 0
| -2
| 44
|
455bb28e06d1af0c76eed9c4216798bb512e32a8
| 1,115
|
py
|
Python
|
snake.py
|
junio-firmino/snake_game
|
fd78695406a55d057c2db54a126f46d552e77865
|
[
"MIT"
] | null | null | null |
snake.py
|
junio-firmino/snake_game
|
fd78695406a55d057c2db54a126f46d552e77865
|
[
"MIT"
] | null | null | null |
snake.py
|
junio-firmino/snake_game
|
fd78695406a55d057c2db54a126f46d552e77865
|
[
"MIT"
] | null | null | null |
from turtle import Screen
import time
from food import Food
from snake_shape import Snake
from snake_scoreboard import Scoreboard
screen = Screen()
screen.setup(width=600, height=600)
screen.bgcolor('black')
screen.title('My snake game for desktop.')
screen.tracer(0)
snake = Snake()
food = Food()
scoreboard = Scoreboard()
screen.listen()
screen.onkey(snake.up, "Up")
screen.onkey(snake.down, "Down")
screen.onkey(snake.left, "Left")
screen.onkey(snake.right, "Right")
game_is_on = True
while game_is_on:
screen.update()
time.sleep(0.1)
snake.move()
if snake.segment[0].distance(food) < 15:
food.refresh()
snake.extend()
scoreboard.increase_score()
if snake.segment[0].xcor() > 280 or snake.segment[0].xcor() < -280 or snake.segment[0].ycor() > 255 or snake.segment[0].ycor() < -280:
scoreboard.game_over()
game_is_on = False
for segment in snake.segment[1:]:
if snake.segment[0].distance(segment) < 10:
game_is_on = False
scoreboard.game_over()
screen.exitonclick()
| 25.340909
| 139
| 0.650224
|
from turtle import Screen
import time
from food import Food
from snake_shape import Snake
from snake_scoreboard import Scoreboard
screen = Screen()
screen.setup(width=600, height=600)
screen.bgcolor('black')
screen.title('My snake game for desktop.')
screen.tracer(0)
snake = Snake()
food = Food()
scoreboard = Scoreboard()
screen.listen()
screen.onkey(snake.up, "Up")
screen.onkey(snake.down, "Down")
screen.onkey(snake.left, "Left")
screen.onkey(snake.right, "Right")
game_is_on = True
while game_is_on:
screen.update()
time.sleep(0.1)
snake.move()
if snake.segment[0].distance(food) < 15:
food.refresh()
snake.extend()
scoreboard.increase_score()
if snake.segment[0].xcor() > 280 or snake.segment[0].xcor() < -280 or snake.segment[0].ycor() > 255 or snake.segment[0].ycor() < -280:
scoreboard.game_over()
game_is_on = False
for segment in snake.segment[1:]:
if snake.segment[0].distance(segment) < 10:
game_is_on = False
scoreboard.game_over()
screen.exitonclick()
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
8db4e0a2b4356561beec71bf632f2ecd064965c3
| 5,060
|
py
|
Python
|
tests/test_nelson_siegel_svensson.py
|
luphord/nelson_siegel_svensson
|
e2437a9bf924d6cd54181de018ed8af8214a6055
|
[
"MIT"
] | 55
|
2019-02-13T21:23:43.000Z
|
2022-03-19T13:15:35.000Z
|
tests/test_nelson_siegel_svensson.py
|
musacan1300/nelson_siegel_svensson
|
b5c652f5f6d134457571467055fa12cd7df57213
|
[
"MIT"
] | 10
|
2019-04-06T12:46:33.000Z
|
2022-03-30T13:00:27.000Z
|
tests/test_nelson_siegel_svensson.py
|
musacan1300/nelson_siegel_svensson
|
b5c652f5f6d134457571467055fa12cd7df57213
|
[
"MIT"
] | 33
|
2019-05-20T22:42:22.000Z
|
2022-02-03T12:20:26.000Z
|
# -*- coding: utf-8 -*-
| 43.62069
| 79
| 0.538933
|
# -*- coding: utf-8 -*-
import unittest
import os
import json
from dataclasses import asdict
import numpy as np
import click
from click.testing import CliRunner
from nelson_siegel_svensson import cli, NelsonSiegelCurve, \
NelsonSiegelSvenssonCurve
class TestNelson_siegel_svensson(unittest.TestCase):
'''Tests for `nelson_siegel_svensson` CLI.'''
def setUp(self):
self.y1 = NelsonSiegelCurve(0.017, -0.023, 0.24, 2.2)
self.y2 = NelsonSiegelSvenssonCurve(0.017, -0.023, 0.24, 0.1, 2.2, 3.1)
self.t = [0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0,
10.0, 15.0, 20.0, 25.0, 30.0]
self.y = [0.01, 0.011, 0.013, 0.016, 0.019, 0.021,
0.026, 0.03, 0.035, 0.037, 0.038, 0.04]
self.runner = CliRunner()
def test_command_line_interface(self):
'''Test the CLI.'''
result = self.runner.invoke(cli.cli_main)
self.assertEqual(0, result.exit_code)
help_result = self.runner.invoke(cli.cli_main, ['--help'])
self.assertEqual(0, help_result.exit_code)
self.assertIn('--help Show this message and exit.',
help_result.output)
def test_cli_evaluate(self):
'''Test evaluate CLI.'''
param = ['evaluate', '-c',
'{"beta0": 0.017, "beta1": -0.023, "beta2": 0.24, "tau": 2}',
'-t', '[1,2,3]']
result = self.runner.invoke(cli.cli_main, param)
self.assertEqual(0, result.exit_code)
self.assertIn('0.0758359', result.output)
def test_cli_calibrate(self):
'''Test calibrate CLI.'''
param = ['calibrate', '-t', json.dumps(self.t),
'-y', json.dumps(self.y)]
result = self.runner.invoke(cli.cli_main, param)
self.assertEqual(0, result.exit_code)
self.assertIn('0.0451', result.output)
first_output = result.output
result = self.runner.invoke(cli.cli_main,
param + ['--nelson-siegel-svensson'])
self.assertEqual(0, result.exit_code)
self.assertEqual(first_output, result.output)
result = self.runner.invoke(cli.cli_main,
param + ['--nelson-siegel'])
self.assertEqual(0, result.exit_code)
self.assertIn('0.0425', result.output)
result = self.runner.invoke(cli.cli_main,
param + ['--nelson-siegel',
'--initial-tau1', '1.234'])
self.assertEqual(0, result.exit_code)
self.assertIn('0.04179', result.output)
def test_cli_plot(self):
'''Test plot CLI.'''
fname = 'output.png'
param = ['plot', '-o', fname, '-c',
'{"beta0": 0.017, "beta1": -0.023, "beta2": 0.24, "tau": 2}']
with self.runner.isolated_filesystem():
result = self.runner.invoke(cli.cli_main, param)
self.assertEqual(0, result.exit_code)
self.assertTrue(os.path.exists(fname), fname + ' missing')
result = self.runner.invoke(cli.cli_main,
param + ['-f', '10', '-t', '20'])
self.assertEqual(0, result.exit_code)
self.assertTrue(os.path.exists(fname), fname + ' missing')
def test_curve_parameters(self):
'''Test curve parameter.'''
param = cli.Curve()
self.assertRaises(click.BadParameter, param.convert,
value='', param=None, ctx=None)
self.assertRaises(click.BadParameter, param.convert,
value='{}', param=None, ctx=None)
missing_tau = '{"beta0": 0.017, "beta1": -0.023, "beta2": 0.24}'
self.assertRaises(click.BadParameter, param.convert,
value=missing_tau, param=None, ctx=None)
self.assertEqual(self.y1,
param.convert(json.dumps(asdict(self.y1)),
None, None))
self.assertEqual(self.y2,
param.convert(json.dumps(asdict(self.y2)),
None, None))
def test_float_array_parameters(self):
'''Test float array parameter.'''
param = cli.FloatArray()
self.assertRaises(click.BadParameter, param.convert,
value='', param=None, ctx=None)
self.assertRaises(click.BadParameter, param.convert,
value='{"a": 1}', param=None, ctx=None)
self.assertRaises(click.BadParameter, param.convert,
value='["a"]', param=None, ctx=None)
self.assertEqual(np.array([1.0]),
param.convert('[1.0]', None, None))
self.assertEqual(np.array([1.0]),
param.convert('[1]', None, None))
self.assertEqual([],
param.convert('[]', None, None).tolist())
self.assertTrue((np.array([1.0, 2.0, 3.0]) ==
param.convert('[1, 2,3.0]', None, None)).all())
| 0
| 0
| 0
| 4,781
| 0
| 0
| 0
| 52
| 202
|
05b83b0f032b96f78decc11085191dfb8bd3a7f2
| 2,114
|
py
|
Python
|
Data Management Tools/reproject_shp.py
|
MBoustani/Geothon
|
07a499d4ac0bb767677cd59b301022ad2ab16136
|
[
"Apache-2.0"
] | 60
|
2015-01-12T08:36:19.000Z
|
2021-12-20T11:06:25.000Z
|
Data Management Tools/reproject_shp.py
|
MBoustani/Geothon
|
07a499d4ac0bb767677cd59b301022ad2ab16136
|
[
"Apache-2.0"
] | null | null | null |
Data Management Tools/reproject_shp.py
|
MBoustani/Geothon
|
07a499d4ac0bb767677cd59b301022ad2ab16136
|
[
"Apache-2.0"
] | 20
|
2015-02-20T03:05:17.000Z
|
2021-12-27T16:18:45.000Z
|
#!/usr/bin/env python
'''
Project: Geothon (https://github.com/MBoustani/Geothon)
File: Vector/reproject_shp.py
Description: This code reprojects Shapefile.
Author: Maziyar Boustani (github.com/MBoustani)
'''
import os
try:
import ogr
except ImportError:
from osgeo import ogr
try:
import osr
except ImportError:
from osgeo import osr
#an example input shapefile file.
in_shp_file = '../static_files/shapefile/rivers_lake_centerlines/ne_50m_rivers_lake_centerlines.shp'
#set the driver to ESRI Shapefile
driver = ogr.GetDriverByName('ESRI Shapefile')
#open input shapefile
in_shp_datasource = driver.Open(in_shp_file)
#get input shapefile layer
in_layer = in_shp_datasource.GetLayerByIndex(0)
#get input shapefile geometry
in_geom_type = in_layer.GetGeomType()
#get input shapefile spatial reference
source = in_layer.GetSpatialRef()
#create spatial reference for output shapefile
target = osr.SpatialReference()
#in this case NAD83(HARN) / California zone 4
target.ImportFromEPSG(2873)
#create a trasnform from source to target
transform = osr.CoordinateTransformation(source, target)
#output shapefile name
out_shp = 'reprojected.shp'
#output shapefile layer name
out_layer_name = 'shp_layer'
#create output shapefile data_source(file)
if os.path.exists(out_shp):
driver.DeleteDataSource(out_shp)
data_source = driver.CreateDataSource(out_shp)
#define output shapefile layer
out_layer = data_source.CreateLayer(out_layer_name, target, in_geom_type)
#get input shapefile layer definition
in_layer_defn = in_layer.GetLayerDefn()
num_field_col = in_layer_defn.GetFieldCount()
for each in range(num_field_col):
field = in_layer_defn.GetFieldDefn(each)
out_layer.CreateField(field)
#get input shapefile number of features
in_num_feature = in_layer.GetFeatureCount()
for feature in range(in_num_feature):
in_feature = in_layer.GetFeature(feature)
in_geom = in_feature.GetGeometryRef()
in_geom.Transform(transform)
feature = ogr.Feature(out_layer.GetLayerDefn())
feature.SetGeometry(in_geom)
out_layer.CreateFeature(feature)
| 27.102564
| 100
| 0.784295
|
#!/usr/bin/env python
'''
Project: Geothon (https://github.com/MBoustani/Geothon)
File: Vector/reproject_shp.py
Description: This code reprojects Shapefile.
Author: Maziyar Boustani (github.com/MBoustani)
'''
import os
try:
import ogr
except ImportError:
from osgeo import ogr
try:
import osr
except ImportError:
from osgeo import osr
#an example input shapefile file.
in_shp_file = '../static_files/shapefile/rivers_lake_centerlines/ne_50m_rivers_lake_centerlines.shp'
#set the driver to ESRI Shapefile
driver = ogr.GetDriverByName('ESRI Shapefile')
#open input shapefile
in_shp_datasource = driver.Open(in_shp_file)
#get input shapefile layer
in_layer = in_shp_datasource.GetLayerByIndex(0)
#get input shapefile geometry
in_geom_type = in_layer.GetGeomType()
#get input shapefile spatial reference
source = in_layer.GetSpatialRef()
#create spatial reference for output shapefile
target = osr.SpatialReference()
#in this case NAD83(HARN) / California zone 4
target.ImportFromEPSG(2873)
#create a trasnform from source to target
transform = osr.CoordinateTransformation(source, target)
#output shapefile name
out_shp = 'reprojected.shp'
#output shapefile layer name
out_layer_name = 'shp_layer'
#create output shapefile data_source(file)
if os.path.exists(out_shp):
driver.DeleteDataSource(out_shp)
data_source = driver.CreateDataSource(out_shp)
#define output shapefile layer
out_layer = data_source.CreateLayer(out_layer_name, target, in_geom_type)
#get input shapefile layer definition
in_layer_defn = in_layer.GetLayerDefn()
num_field_col = in_layer_defn.GetFieldCount()
for each in range(num_field_col):
field = in_layer_defn.GetFieldDefn(each)
out_layer.CreateField(field)
#get input shapefile number of features
in_num_feature = in_layer.GetFeatureCount()
for feature in range(in_num_feature):
in_feature = in_layer.GetFeature(feature)
in_geom = in_feature.GetGeometryRef()
in_geom.Transform(transform)
feature = ogr.Feature(out_layer.GetLayerDefn())
feature.SetGeometry(in_geom)
out_layer.CreateFeature(feature)
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
13e23dc0a6662a0f1c60b6e0b4542b80784914e5
| 275
|
py
|
Python
|
proj02/proj02_01.py
|
scienceman44/SAVY
|
5a9781a9d08288c40f883602dc3b6bab4d6c63c1
|
[
"MIT"
] | null | null | null |
proj02/proj02_01.py
|
scienceman44/SAVY
|
5a9781a9d08288c40f883602dc3b6bab4d6c63c1
|
[
"MIT"
] | null | null | null |
proj02/proj02_01.py
|
scienceman44/SAVY
|
5a9781a9d08288c40f883602dc3b6bab4d6c63c1
|
[
"MIT"
] | null | null | null |
# Name:
# Date:
result = 0
loop_control = True
while loop_control == True:
b = int(raw_input('Enter a number to add, or 0 to indicate you are finished:'))
if b == 0:
loop_control = False
result = result + b
print 'your result is:'
print result
| 18.333333
| 83
| 0.621818
|
# Name:
# Date:
result = 0
loop_control = True
while loop_control == True:
b = int(raw_input('Enter a number to add, or 0 to indicate you are finished:'))
if b == 0:
loop_control = False
result = result + b
print 'your result is:'
print result
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
04b3ca56fafcb17962d19c797e31c3700c6e41b8
| 9,753
|
py
|
Python
|
generate_qumulo_cloudformation_template_test.py
|
Qumulo/Cloud-Deployment-Samples
|
a1ed1850b80ec14ea7520c5829421209efce8382
|
[
"MIT"
] | 4
|
2019-12-25T22:09:37.000Z
|
2022-02-07T19:46:03.000Z
|
generate_qumulo_cloudformation_template_test.py
|
Qumulo/Cloud-Deployment-Samples
|
a1ed1850b80ec14ea7520c5829421209efce8382
|
[
"MIT"
] | 1
|
2020-07-10T22:24:53.000Z
|
2020-07-10T22:24:53.000Z
|
generate_qumulo_cloudformation_template_test.py
|
Qumulo/cloud-samples
|
a1ed1850b80ec14ea7520c5829421209efce8382
|
[
"MIT"
] | 3
|
2020-07-10T22:05:14.000Z
|
2022-02-07T19:46:04.000Z
|
import unittest
if __name__ == '__main__':
unittest.main()
| 34.708185
| 86
| 0.58177
|
import os
import json
import unittest
from troposphere import ec2, Template
from generate_qumulo_cloudformation_template import *
class ChassisSpecTest(unittest.TestCase):
def test_init(self) -> None:
spec = ChassisSpec(
volume_count=20,
pairing_ratio=4,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 5},
)
self.assertEqual(spec.working_volume_count, 4)
self.assertEqual(spec.backing_volume_count, 16)
def test_init_no_backing_spec(self) -> None:
spec = ChassisSpec(
volume_count=5,
pairing_ratio=0,
working_spec={'VolumeSize': 1},
backing_spec=None,
)
self.assertEqual(spec.working_volume_count, 5)
self.assertEqual(spec.backing_volume_count, 0)
def test_init_too_many_volumes(self) -> None:
with self.assertRaisesRegex(AssertionError, 'Too many volumes specified'):
ChassisSpec(
volume_count=26,
pairing_ratio=0,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 5},
)
def test_init_bad_pairing_ratio(self) -> None:
with self.assertRaisesRegex(AssertionError, 'Not all volumes can be used'):
ChassisSpec(
volume_count=10,
pairing_ratio=3,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 3},
)
def test_init_need_backing_spec(self) -> None:
with self.assertRaisesRegex(AssertionError, 'Backing volumes require'):
ChassisSpec(
volume_count=10,
pairing_ratio=1,
working_spec={'VolumeSize': 1},
backing_spec=None,
)
def test_from_json(self) -> None:
json_spec = {
'slot_count': 12,
'pairing_ratio': 2,
'working_spec': {'VolumeSize': 1},
'backing_spec': {'VolumeSize': 5}
}
spec = ChassisSpec.from_json(json_spec)
self.assertEqual(spec.working_volume_count, 4)
self.assertEqual(spec.backing_volume_count, 8)
def test_get_block_device_mappings(self) -> None:
spec = ChassisSpec(
volume_count=2,
pairing_ratio=1,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 5},
)
mappings = spec.get_block_device_mappings()
self.assertEqual(len(mappings), 3)
devices = [mapping.to_dict()['DeviceName'] for mapping in mappings]
self.assertEqual(devices, ['/dev/sda1', '/dev/xvdb', '/dev/xvdc'])
def test_get_slot_specs(self) -> None:
spec = ChassisSpec(
volume_count=2,
pairing_ratio=1,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 5},
)
slot_specs = spec.get_slot_specs()
expected_specs = [
{
'drive_bay': '/dev/xvdb',
'disk_role': 'working',
'disk_size': 1073741824,
},
{
'drive_bay': '/dev/xvdc',
'disk_role': 'backing',
'disk_size': 5368709120,
}
]
self.assertEqual(slot_specs['slot_specs'], expected_specs)
class TemplateTest(unittest.TestCase):
def test_add_conditions(self) -> None:
template = Template()
add_conditions(template)
self.assertEqual(
list(template.conditions.keys()),
['HasEncryptionKey', 'HasIamInstanceProfile', 'HasInstanceRecoveryTopic']
)
def test_add_params_with_ingress_cidr_param(self) -> None:
template = Template()
add_params(template, True)
expected_parameters = [
'ClusterName', 'KeyName', 'InstanceType', 'VpcId', 'SubnetId', 'SgCidr',
'VolumesEncryptionKey', 'IamInstanceProfile', 'InstanceRecoveryTopic'
]
self.assertEqual(list(template.parameters.keys()), expected_parameters)
def test_add_params_without_ingress_cidr_param(self) -> None:
template = Template()
add_params(template, False)
expected_parameters = [
'ClusterName', 'KeyName', 'InstanceType', 'VpcId', 'SubnetId',
'VolumesEncryptionKey', 'IamInstanceProfile', 'InstanceRecoveryTopic'
]
self.assertEqual(list(template.parameters.keys()), expected_parameters)
def test_add_ami_map(self) -> None:
template = Template()
add_ami_map(template, 'ami-1234')
expected_mapping = {
'us-east-1': {'AMI': 'ami-1234'},
'us-east-2': {'AMI': 'ami-1234'},
'us-west-1': {'AMI': 'ami-1234'},
'us-west-2': {'AMI': 'ami-1234'},
'ca-central-1': {'AMI': 'ami-1234'},
'eu-central-1': {'AMI': 'ami-1234'},
'eu-west-1': {'AMI': 'ami-1234'},
'eu-west-2': {'AMI': 'ami-1234'},
'eu-west-3': {'AMI': 'ami-1234'},
}
self.assertEqual(template.mappings['RegionMap'], expected_mapping)
def test_add_security_group(self) -> None:
template = Template()
add_security_group(template)
self.assertEqual(
list(template.resources.keys()),
['QumuloSecurityGroup', 'QumuloSecurityGroupNodeRule']
)
class GenerateUserDataTest(unittest.TestCase):
def test_generate_node1_user_data(self) -> None:
instances = [ec2.Instance('t1'), ec2.Instance('t2')]
spec = ChassisSpec(
volume_count=2,
pairing_ratio=1,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 5},
)
user_data = generate_node1_user_data(
instances, spec, get_ip_ref=lambda x: x, cluster_name_ref='nameref'
)
self.assertIn('t2', user_data)
self.assertIn('nameref', user_data)
self.assertIn('"spec_info": ', user_data)
self.assertIn(' "slot_specs": [', user_data)
def test_generate_other_nodes_user_data(self) -> None:
spec = ChassisSpec(
volume_count=2,
pairing_ratio=1,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 5},
)
user_data = generate_other_nodes_user_data(spec)
self.assertIn('"spec_info": ', user_data)
self.assertIn(' "slot_specs": [', user_data)
class AddNodesTest(unittest.TestCase):
def setUp(self) -> None:
self.spec = ChassisSpec(
volume_count=2,
pairing_ratio=1,
working_spec={'VolumeSize': 1},
backing_spec={'VolumeSize': 5},
)
self.expected_resources = [
'testEni1',
'testEni2',
'testNode1',
'testNode2',
'CWRecoveryAlarmtestNode1',
'CWRecoveryAlarmtestNode2'
]
self.expected_outputs = [
'ClusterInstanceIDs',
'ClusterPrivateIPs',
'SecurityGroup',
'TemporaryPassword',
'LinkToManagement',
'QumuloKnowledgeBase'
]
def test_nodes_no_secondary_ips(self) -> None:
template = Template()
add_nodes(template, 2, 'test', self.spec, 0, 'sg-9')
self.assertEqual(list(template.resources.keys()), self.expected_resources)
self.assertEqual(list(template.outputs.keys()), self.expected_outputs)
def test_nodes_has_secondary_ips(self) -> None:
template = Template()
add_nodes(template, 2, 'test', self.spec, 1, 'sg-9')
self.assertEqual(list(template.resources.keys()), self.expected_resources)
self.expected_outputs.insert(2, 'ClusterSecondaryPrivateIPs')
self.assertEqual(list(template.outputs.keys()), self.expected_outputs)
class GenerateQumuloCloudformationTemplateTest(unittest.TestCase):
def setUp(self) -> None:
self.file_path = os.path.join(os.getcwd(), 'config_file.json')
def tearDown(self) -> None:
if os.path.exists(self.file_path):
os.remove(self.file_path)
def test_generate_qcft_with_override(self) -> None:
config = {
'slot_count': 12,
'pairing_ratio': 2,
'working_spec': {'VolumeSize': 1},
'backing_spec': {'VolumeSize': 5}
}
json_config = json.dumps(config, indent = 4)
with open(self.file_path, 'w+') as config_file:
config_file.write(json_config)
template = generate_qcft(2, self.file_path, 'st1', 'ami-123')
self.assertIsNotNone(template)
def test_generate_qcft_no_override(self) -> None:
config = {
'slot_count': 12,
'pairing_ratio': 2,
'working_spec': {'VolumeSize': 1},
'backing_spec': {'VolumeSize': 5}
}
json_config = json.dumps(config, indent = 4)
with open(self.file_path, 'w+') as config_file:
config_file.write(json_config)
template = generate_qcft(2, self.file_path, None, 'ami-123')
self.assertIsNotNone(template)
def test_generate_qcft_bad_override(self) -> None:
config = {
'slot_count': 12,
'pairing_ratio': 2,
'working_spec': {'VolumeSize': 1},
}
json_config = json.dumps(config, indent = 4)
with open(self.file_path, 'w+') as config_file:
config_file.write(json_config)
with self.assertRaisesRegex(NoBackingVolumesException, 'The backing volumes'):
generate_qcft(2, self.file_path, 'st1', 'ami-123')
if __name__ == '__main__':
unittest.main()
| 0
| 0
| 0
| 9,458
| 0
| 0
| 0
| 26
| 205
|
86d20c2cd0960aaa6593f882e80973ad808bffa1
| 23,886
|
py
|
Python
|
sdk/python/pulumi_azure_native/notificationhubs/v20160301/notification_hub.py
|
sebtelko/pulumi-azure-native
|
711ec021b5c73da05611c56c8a35adb0ce3244e4
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/notificationhubs/v20160301/notification_hub.py
|
sebtelko/pulumi-azure-native
|
711ec021b5c73da05611c56c8a35adb0ce3244e4
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/notificationhubs/v20160301/notification_hub.py
|
sebtelko/pulumi-azure-native
|
711ec021b5c73da05611c56c8a35adb0ce3244e4
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import pulumi
from ... import _utilities
__all__ = ['NotificationHubArgs', 'NotificationHub']
| 46.111969
| 590
| 0.668174
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['NotificationHubArgs', 'NotificationHub']
@pulumi.input_type
class NotificationHubArgs:
def __init__(__self__, *,
namespace_name: pulumi.Input[str],
resource_group_name: pulumi.Input[str],
adm_credential: Optional[pulumi.Input['AdmCredentialArgs']] = None,
apns_credential: Optional[pulumi.Input['ApnsCredentialArgs']] = None,
authorization_rules: Optional[pulumi.Input[Sequence[pulumi.Input['SharedAccessAuthorizationRulePropertiesArgs']]]] = None,
baidu_credential: Optional[pulumi.Input['BaiduCredentialArgs']] = None,
gcm_credential: Optional[pulumi.Input['GcmCredentialArgs']] = None,
location: Optional[pulumi.Input[str]] = None,
mpns_credential: Optional[pulumi.Input['MpnsCredentialArgs']] = None,
name: Optional[pulumi.Input[str]] = None,
notification_hub_name: Optional[pulumi.Input[str]] = None,
registration_ttl: Optional[pulumi.Input[str]] = None,
sku: Optional[pulumi.Input['SkuArgs']] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
wns_credential: Optional[pulumi.Input['WnsCredentialArgs']] = None):
"""
The set of arguments for constructing a NotificationHub resource.
:param pulumi.Input[str] namespace_name: The namespace name.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input['AdmCredentialArgs'] adm_credential: The AdmCredential of the created NotificationHub
:param pulumi.Input['ApnsCredentialArgs'] apns_credential: The ApnsCredential of the created NotificationHub
:param pulumi.Input[Sequence[pulumi.Input['SharedAccessAuthorizationRulePropertiesArgs']]] authorization_rules: The AuthorizationRules of the created NotificationHub
:param pulumi.Input['BaiduCredentialArgs'] baidu_credential: The BaiduCredential of the created NotificationHub
:param pulumi.Input['GcmCredentialArgs'] gcm_credential: The GcmCredential of the created NotificationHub
:param pulumi.Input[str] location: Resource location
:param pulumi.Input['MpnsCredentialArgs'] mpns_credential: The MpnsCredential of the created NotificationHub
:param pulumi.Input[str] name: The NotificationHub name.
:param pulumi.Input[str] notification_hub_name: The notification hub name.
:param pulumi.Input[str] registration_ttl: The RegistrationTtl of the created NotificationHub
:param pulumi.Input['SkuArgs'] sku: The sku of the created namespace
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags
:param pulumi.Input['WnsCredentialArgs'] wns_credential: The WnsCredential of the created NotificationHub
"""
pulumi.set(__self__, "namespace_name", namespace_name)
pulumi.set(__self__, "resource_group_name", resource_group_name)
if adm_credential is not None:
pulumi.set(__self__, "adm_credential", adm_credential)
if apns_credential is not None:
pulumi.set(__self__, "apns_credential", apns_credential)
if authorization_rules is not None:
pulumi.set(__self__, "authorization_rules", authorization_rules)
if baidu_credential is not None:
pulumi.set(__self__, "baidu_credential", baidu_credential)
if gcm_credential is not None:
pulumi.set(__self__, "gcm_credential", gcm_credential)
if location is not None:
pulumi.set(__self__, "location", location)
if mpns_credential is not None:
pulumi.set(__self__, "mpns_credential", mpns_credential)
if name is not None:
pulumi.set(__self__, "name", name)
if notification_hub_name is not None:
pulumi.set(__self__, "notification_hub_name", notification_hub_name)
if registration_ttl is not None:
pulumi.set(__self__, "registration_ttl", registration_ttl)
if sku is not None:
pulumi.set(__self__, "sku", sku)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if wns_credential is not None:
pulumi.set(__self__, "wns_credential", wns_credential)
@property
@pulumi.getter(name="namespaceName")
def namespace_name(self) -> pulumi.Input[str]:
"""
The namespace name.
"""
return pulumi.get(self, "namespace_name")
@namespace_name.setter
def namespace_name(self, value: pulumi.Input[str]):
pulumi.set(self, "namespace_name", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
"""
The name of the resource group.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter(name="admCredential")
def adm_credential(self) -> Optional[pulumi.Input['AdmCredentialArgs']]:
"""
The AdmCredential of the created NotificationHub
"""
return pulumi.get(self, "adm_credential")
@adm_credential.setter
def adm_credential(self, value: Optional[pulumi.Input['AdmCredentialArgs']]):
pulumi.set(self, "adm_credential", value)
@property
@pulumi.getter(name="apnsCredential")
def apns_credential(self) -> Optional[pulumi.Input['ApnsCredentialArgs']]:
"""
The ApnsCredential of the created NotificationHub
"""
return pulumi.get(self, "apns_credential")
@apns_credential.setter
def apns_credential(self, value: Optional[pulumi.Input['ApnsCredentialArgs']]):
pulumi.set(self, "apns_credential", value)
@property
@pulumi.getter(name="authorizationRules")
def authorization_rules(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SharedAccessAuthorizationRulePropertiesArgs']]]]:
"""
The AuthorizationRules of the created NotificationHub
"""
return pulumi.get(self, "authorization_rules")
@authorization_rules.setter
def authorization_rules(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['SharedAccessAuthorizationRulePropertiesArgs']]]]):
pulumi.set(self, "authorization_rules", value)
@property
@pulumi.getter(name="baiduCredential")
def baidu_credential(self) -> Optional[pulumi.Input['BaiduCredentialArgs']]:
"""
The BaiduCredential of the created NotificationHub
"""
return pulumi.get(self, "baidu_credential")
@baidu_credential.setter
def baidu_credential(self, value: Optional[pulumi.Input['BaiduCredentialArgs']]):
pulumi.set(self, "baidu_credential", value)
@property
@pulumi.getter(name="gcmCredential")
def gcm_credential(self) -> Optional[pulumi.Input['GcmCredentialArgs']]:
"""
The GcmCredential of the created NotificationHub
"""
return pulumi.get(self, "gcm_credential")
@gcm_credential.setter
def gcm_credential(self, value: Optional[pulumi.Input['GcmCredentialArgs']]):
pulumi.set(self, "gcm_credential", value)
@property
@pulumi.getter
def location(self) -> Optional[pulumi.Input[str]]:
"""
Resource location
"""
return pulumi.get(self, "location")
@location.setter
def location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "location", value)
@property
@pulumi.getter(name="mpnsCredential")
def mpns_credential(self) -> Optional[pulumi.Input['MpnsCredentialArgs']]:
"""
The MpnsCredential of the created NotificationHub
"""
return pulumi.get(self, "mpns_credential")
@mpns_credential.setter
def mpns_credential(self, value: Optional[pulumi.Input['MpnsCredentialArgs']]):
pulumi.set(self, "mpns_credential", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The NotificationHub name.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="notificationHubName")
def notification_hub_name(self) -> Optional[pulumi.Input[str]]:
"""
The notification hub name.
"""
return pulumi.get(self, "notification_hub_name")
@notification_hub_name.setter
def notification_hub_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "notification_hub_name", value)
@property
@pulumi.getter(name="registrationTtl")
def registration_ttl(self) -> Optional[pulumi.Input[str]]:
"""
The RegistrationTtl of the created NotificationHub
"""
return pulumi.get(self, "registration_ttl")
@registration_ttl.setter
def registration_ttl(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "registration_ttl", value)
@property
@pulumi.getter
def sku(self) -> Optional[pulumi.Input['SkuArgs']]:
"""
The sku of the created namespace
"""
return pulumi.get(self, "sku")
@sku.setter
def sku(self, value: Optional[pulumi.Input['SkuArgs']]):
pulumi.set(self, "sku", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
Resource tags
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="wnsCredential")
def wns_credential(self) -> Optional[pulumi.Input['WnsCredentialArgs']]:
"""
The WnsCredential of the created NotificationHub
"""
return pulumi.get(self, "wns_credential")
@wns_credential.setter
def wns_credential(self, value: Optional[pulumi.Input['WnsCredentialArgs']]):
pulumi.set(self, "wns_credential", value)
class NotificationHub(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
adm_credential: Optional[pulumi.Input[pulumi.InputType['AdmCredentialArgs']]] = None,
apns_credential: Optional[pulumi.Input[pulumi.InputType['ApnsCredentialArgs']]] = None,
authorization_rules: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SharedAccessAuthorizationRulePropertiesArgs']]]]] = None,
baidu_credential: Optional[pulumi.Input[pulumi.InputType['BaiduCredentialArgs']]] = None,
gcm_credential: Optional[pulumi.Input[pulumi.InputType['GcmCredentialArgs']]] = None,
location: Optional[pulumi.Input[str]] = None,
mpns_credential: Optional[pulumi.Input[pulumi.InputType['MpnsCredentialArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace_name: Optional[pulumi.Input[str]] = None,
notification_hub_name: Optional[pulumi.Input[str]] = None,
registration_ttl: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
sku: Optional[pulumi.Input[pulumi.InputType['SkuArgs']]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
wns_credential: Optional[pulumi.Input[pulumi.InputType['WnsCredentialArgs']]] = None,
__props__=None):
"""
Description of a NotificationHub Resource.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['AdmCredentialArgs']] adm_credential: The AdmCredential of the created NotificationHub
:param pulumi.Input[pulumi.InputType['ApnsCredentialArgs']] apns_credential: The ApnsCredential of the created NotificationHub
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SharedAccessAuthorizationRulePropertiesArgs']]]] authorization_rules: The AuthorizationRules of the created NotificationHub
:param pulumi.Input[pulumi.InputType['BaiduCredentialArgs']] baidu_credential: The BaiduCredential of the created NotificationHub
:param pulumi.Input[pulumi.InputType['GcmCredentialArgs']] gcm_credential: The GcmCredential of the created NotificationHub
:param pulumi.Input[str] location: Resource location
:param pulumi.Input[pulumi.InputType['MpnsCredentialArgs']] mpns_credential: The MpnsCredential of the created NotificationHub
:param pulumi.Input[str] name: The NotificationHub name.
:param pulumi.Input[str] namespace_name: The namespace name.
:param pulumi.Input[str] notification_hub_name: The notification hub name.
:param pulumi.Input[str] registration_ttl: The RegistrationTtl of the created NotificationHub
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[pulumi.InputType['SkuArgs']] sku: The sku of the created namespace
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags
:param pulumi.Input[pulumi.InputType['WnsCredentialArgs']] wns_credential: The WnsCredential of the created NotificationHub
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: NotificationHubArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Description of a NotificationHub Resource.
:param str resource_name: The name of the resource.
:param NotificationHubArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(NotificationHubArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
adm_credential: Optional[pulumi.Input[pulumi.InputType['AdmCredentialArgs']]] = None,
apns_credential: Optional[pulumi.Input[pulumi.InputType['ApnsCredentialArgs']]] = None,
authorization_rules: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SharedAccessAuthorizationRulePropertiesArgs']]]]] = None,
baidu_credential: Optional[pulumi.Input[pulumi.InputType['BaiduCredentialArgs']]] = None,
gcm_credential: Optional[pulumi.Input[pulumi.InputType['GcmCredentialArgs']]] = None,
location: Optional[pulumi.Input[str]] = None,
mpns_credential: Optional[pulumi.Input[pulumi.InputType['MpnsCredentialArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace_name: Optional[pulumi.Input[str]] = None,
notification_hub_name: Optional[pulumi.Input[str]] = None,
registration_ttl: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
sku: Optional[pulumi.Input[pulumi.InputType['SkuArgs']]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
wns_credential: Optional[pulumi.Input[pulumi.InputType['WnsCredentialArgs']]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = NotificationHubArgs.__new__(NotificationHubArgs)
__props__.__dict__["adm_credential"] = adm_credential
__props__.__dict__["apns_credential"] = apns_credential
__props__.__dict__["authorization_rules"] = authorization_rules
__props__.__dict__["baidu_credential"] = baidu_credential
__props__.__dict__["gcm_credential"] = gcm_credential
__props__.__dict__["location"] = location
__props__.__dict__["mpns_credential"] = mpns_credential
__props__.__dict__["name"] = name
if namespace_name is None and not opts.urn:
raise TypeError("Missing required property 'namespace_name'")
__props__.__dict__["namespace_name"] = namespace_name
__props__.__dict__["notification_hub_name"] = notification_hub_name
__props__.__dict__["registration_ttl"] = registration_ttl
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["sku"] = sku
__props__.__dict__["tags"] = tags
__props__.__dict__["wns_credential"] = wns_credential
__props__.__dict__["type"] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:notificationhubs/v20160301:NotificationHub"), pulumi.Alias(type_="azure-native:notificationhubs:NotificationHub"), pulumi.Alias(type_="azure-nextgen:notificationhubs:NotificationHub"), pulumi.Alias(type_="azure-native:notificationhubs/v20140901:NotificationHub"), pulumi.Alias(type_="azure-nextgen:notificationhubs/v20140901:NotificationHub"), pulumi.Alias(type_="azure-native:notificationhubs/v20170401:NotificationHub"), pulumi.Alias(type_="azure-nextgen:notificationhubs/v20170401:NotificationHub")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(NotificationHub, __self__).__init__(
'azure-native:notificationhubs/v20160301:NotificationHub',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'NotificationHub':
"""
Get an existing NotificationHub resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = NotificationHubArgs.__new__(NotificationHubArgs)
__props__.__dict__["adm_credential"] = None
__props__.__dict__["apns_credential"] = None
__props__.__dict__["authorization_rules"] = None
__props__.__dict__["baidu_credential"] = None
__props__.__dict__["gcm_credential"] = None
__props__.__dict__["location"] = None
__props__.__dict__["mpns_credential"] = None
__props__.__dict__["name"] = None
__props__.__dict__["registration_ttl"] = None
__props__.__dict__["sku"] = None
__props__.__dict__["tags"] = None
__props__.__dict__["type"] = None
__props__.__dict__["wns_credential"] = None
return NotificationHub(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="admCredential")
def adm_credential(self) -> pulumi.Output[Optional['outputs.AdmCredentialResponse']]:
"""
The AdmCredential of the created NotificationHub
"""
return pulumi.get(self, "adm_credential")
@property
@pulumi.getter(name="apnsCredential")
def apns_credential(self) -> pulumi.Output[Optional['outputs.ApnsCredentialResponse']]:
"""
The ApnsCredential of the created NotificationHub
"""
return pulumi.get(self, "apns_credential")
@property
@pulumi.getter(name="authorizationRules")
def authorization_rules(self) -> pulumi.Output[Optional[Sequence['outputs.SharedAccessAuthorizationRulePropertiesResponse']]]:
"""
The AuthorizationRules of the created NotificationHub
"""
return pulumi.get(self, "authorization_rules")
@property
@pulumi.getter(name="baiduCredential")
def baidu_credential(self) -> pulumi.Output[Optional['outputs.BaiduCredentialResponse']]:
"""
The BaiduCredential of the created NotificationHub
"""
return pulumi.get(self, "baidu_credential")
@property
@pulumi.getter(name="gcmCredential")
def gcm_credential(self) -> pulumi.Output[Optional['outputs.GcmCredentialResponse']]:
"""
The GcmCredential of the created NotificationHub
"""
return pulumi.get(self, "gcm_credential")
@property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
"""
Resource location
"""
return pulumi.get(self, "location")
@property
@pulumi.getter(name="mpnsCredential")
def mpns_credential(self) -> pulumi.Output[Optional['outputs.MpnsCredentialResponse']]:
"""
The MpnsCredential of the created NotificationHub
"""
return pulumi.get(self, "mpns_credential")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Resource name
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="registrationTtl")
def registration_ttl(self) -> pulumi.Output[Optional[str]]:
"""
The RegistrationTtl of the created NotificationHub
"""
return pulumi.get(self, "registration_ttl")
@property
@pulumi.getter
def sku(self) -> pulumi.Output[Optional['outputs.SkuResponse']]:
"""
The sku of the created namespace
"""
return pulumi.get(self, "sku")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Resource tags
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Resource type
"""
return pulumi.get(self, "type")
@property
@pulumi.getter(name="wnsCredential")
def wns_credential(self) -> pulumi.Output[Optional['outputs.WnsCredentialResponse']]:
"""
The WnsCredential of the created NotificationHub
"""
return pulumi.get(self, "wns_credential")
| 0
| 18,313
| 0
| 5,087
| 0
| 0
| 0
| 42
| 179
|
ff0414b799e8f6504673a326a43404552c0e74fc
| 12,480
|
py
|
Python
|
sts_auth/stsauth.py
|
cshamrick/stsauth
|
f30cc37ed1d5e18cb47a250bf4c67a5d6332478e
|
[
"MIT"
] | 20
|
2018-05-21T21:21:47.000Z
|
2022-02-09T04:11:06.000Z
|
sts_auth/stsauth.py
|
cshamrick/stsauth
|
f30cc37ed1d5e18cb47a250bf4c67a5d6332478e
|
[
"MIT"
] | 15
|
2018-07-04T02:57:14.000Z
|
2021-08-09T18:03:12.000Z
|
sts_auth/stsauth.py
|
cshamrick/stsauth
|
f30cc37ed1d5e18cb47a250bf4c67a5d6332478e
|
[
"MIT"
] | 6
|
2018-09-04T05:07:09.000Z
|
2021-06-30T19:49:21.000Z
|
import sys
from typing import Optional, Mapping
import boto3 # type: ignore[import]
import click # type: ignore[import]
from botocore.exceptions import ProfileNotFound, ClientError # type: ignore[import]
def fetch_aws_sts_token(
role_arn: str,
principal_arn: str,
assertion: str,
duration_seconds: Optional[int] = 3600,
aws_profile: Optional[str] = None,
) -> Mapping[str, str]:
"""Use the assertion to get an AWS STS token using `assume_role_with_saml`"""
sts = sts_client(aws_profile)
token = sts.assume_role_with_saml(
RoleArn=role_arn,
PrincipalArn=principal_arn,
SAMLAssertion=assertion,
DurationSeconds=duration_seconds,
)
return token
def fetch_aws_sts_token_assume_role(
role_arn: str,
role_session_name: str,
aws_profile: str,
duration_seconds: Optional[int] = 3600,
) -> Mapping[str, str]:
"""Use the assertion to get an AWS STS token using `assume_role_with_saml`"""
sts = sts_client(aws_profile)
try:
token = sts.assume_role(
RoleArn=role_arn,
RoleSessionName=role_session_name,
DurationSeconds=duration_seconds,
)
except ClientError as e:
click.secho(str(e), fg="red")
sys.exit(1)
return token
def sts_client(aws_profile: Optional[str]) -> boto3.Session.client:
"""Generate a boto3 sts client."""
try:
session = boto3.Session(profile_name=aws_profile)
sts = session.client("sts")
except ProfileNotFound as e:
click.secho(str(e), fg="red")
sys.exit(1)
except Exception as e:
# TODO: Proper exception and message
raise e
return sts
| 43.034483
| 110
| 0.634936
|
import os
import re
import sys
from datetime import datetime
from typing import Optional, Mapping
from urllib.parse import urlparse, urlunparse
import boto3 # type: ignore[import]
import click # type: ignore[import]
import requests
from requests_ntlm import HttpNtlmAuth # type: ignore[import]
from bs4 import BeautifulSoup # type: ignore[import]
from botocore.exceptions import ProfileNotFound, ClientError # type: ignore[import]
from sts_auth import utils
from sts_auth.okta import Okta
from sts_auth.config import Config
from sts_auth.utils import logger
class STSAuth(object):
"""Initializes an STS Authenticator.
:param username: Username to authenticate with (required).
:param password: Password to authenticate with (required).
:param credentialsfile: A path to an AWS Credentials file (required).
See https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html
for more details.
:param idpentryurl: URL to the IDP Entrypoint.
:param profile: Name of an AWS Profile to automatically fetch credentials for.
:param okta_org: Name of the Okta organization, ex: `my-company`.
:param domain: Domain which your username resides if required.
:param region: Region for AWS to authenticate in.
:param output: Output format, one of: `json`, `text`, `table`.
"""
def __init__(
self,
username: str,
password: str,
credentialsfile: str,
idpentryurl: Optional[str] = None,
profile: Optional[str] = None,
okta_org: Optional[str] = None,
okta_shared_secret: Optional[str] = None,
domain: Optional[str] = None,
region: Optional[str] = None,
output: Optional[str] = None,
vip_access_security_code: Optional[str] = None,
force: Optional[bool] = False,
):
self.credentialsfile = os.path.expanduser(credentialsfile)
self.vip_access_security_code = vip_access_security_code
self.config = Config(
self.credentialsfile,
username=username, # type: ignore[arg-type]
password=password, # type: ignore[arg-type]
domain=domain, # type: ignore[arg-type]
idpentryurl=idpentryurl, # type: ignore[arg-type]
region=region, # type: ignore[arg-type]
output=output, # type: ignore[arg-type]
okta_org=okta_org, # type: ignore[arg-type]
okta_shared_secret=okta_shared_secret, # type: ignore[arg-type]
profile=profile, # type: ignore[arg-type]
)
self.config.load()
self.profile = self.config.profile
self.session = requests.Session()
self.session.headers.update({"content-type": "application/json"})
self.session.auth = HttpNtlmAuth(self.config.domain_user, self.config.password)
def get_saml_response(self, response: Optional[requests.Response] = None) -> requests.Response:
if not response:
logger.debug("No response provided. Fetching IDP Entry URL...")
response = self.session.get(self.config.idpentryurl)
response.soup = BeautifulSoup(response.text, "lxml") # type: ignore[attr-defined]
assertion_pattern = re.compile(r"name=\"SAMLResponse\" value=\"(.*)\"\s*/><noscript>")
assertion = re.search(assertion_pattern, response.text)
if assertion:
# If there is already an assertion in the response body,
# we can attach the parsed assertion to the response object and
# return the whole response for use later.
# return account_map, assertion.group(1)
response.assertion = assertion.group(1) # type: ignore[attr-defined]
return response
logger.debug("No SAML assertion found in response. Attempting to log in...")
login_form = response.soup.find(id="loginForm") # type: ignore[attr-defined]
okta_login = response.soup.find(id="okta-login-container") # type: ignore[attr-defined]
if okta_login:
state_token = utils.get_state_token_from_response(response.text)
if state_token is None:
click.secho("No State Token found in response. Exiting...", fg="red")
sys.exit(1)
okta_client = Okta(
session=self.session,
state_token=state_token,
okta_org=self.config.okta_org,
okta_shared_secret=self.config.okta_shared_secret,
)
okta_response = okta_client.handle_okta_verification(response)
return self.get_saml_response(response=okta_response)
if login_form:
# If there is no assertion, it is possible the user is attempting
# to authenticate from outside the network, so we check for a login
# form in their response.
form_response = self.authenticate_to_adfs_portal(response)
return self.get_saml_response(response=form_response)
else:
msg = "Response did not contain a valid SAML assertion, a valid login form, or request MFA."
click.secho(msg, fg="red")
sys.exit(1)
def generate_payload_from_login_page(self, response: requests.Response) -> Mapping[str, str]:
login_page = BeautifulSoup(response.text, "html.parser")
payload = {}
for input_tag in login_page.find_all(re.compile("(INPUT|input)")):
name = input_tag.get("name", "")
value = input_tag.get("value", "")
logger.debug("Adding value for {!r} to Login Form payload.".format(name))
if "user" in name.lower():
payload[name] = self.config.domain_user
elif "email" in name.lower():
payload[name] = self.config.domain_user
elif "pass" in name.lower():
payload[name] = self.config.password
elif "security_code" in name.lower():
payload[name] = self.vip_access_security_code # type: ignore[assignment]
else:
payload[name] = value
return payload
def build_idp_auth_url(self, response: requests.Response) -> str:
idp_auth_form_submit_url = response.url
login_page = BeautifulSoup(response.text, "html.parser")
for form in login_page.find_all(re.compile("(FORM|form)")):
action = form.get("action")
if action:
parsed_action = urlparse(action)
parsed_idp_url = urlparse(self.config.idpentryurl)
# Fallback to the IDP Entry URL from the config file if the
# form action does not contain a fully defined URL.
# i.e. action='/path/to/something' vs action='http://test.com/path/to/something'
scheme = parsed_action.scheme if parsed_action.scheme else parsed_idp_url.scheme
netloc = parsed_action.netloc if parsed_action.netloc else parsed_idp_url.netloc
url_parts = (
scheme,
netloc,
parsed_action.path,
None,
parsed_action.query,
None,
)
idp_auth_form_submit_url = urlunparse(url_parts)
return idp_auth_form_submit_url
def authenticate_to_adfs_portal(self, response: requests.Response) -> requests.Response:
payload = self.generate_payload_from_login_page(response)
idp_auth_form_submit_url = self.build_idp_auth_url(response)
logger.debug("Posting login data to URL: {}".format(idp_auth_form_submit_url))
login_response = self.session.post(idp_auth_form_submit_url, data=payload, verify=True)
login_response_page = BeautifulSoup(login_response.text, "html.parser")
# Checks for errorText id on page to indicate any errors
login_error_message = login_response_page.find(id="errorText")
# Checks for specific text in a paragraph element to indicate any errors
vip_login_error_message = login_response_page.find(
lambda tag: tag.name == "p" and "Authentication failed" in tag.text
)
if (login_error_message and len(login_error_message.string) > 0) or (
vip_login_error_message and len(vip_login_error_message) > 0
):
msg = "Login page returned the following message. Please resolve this issue before continuing:"
click.secho(msg, fg="red")
error_msg = login_error_message if login_error_message else vip_login_error_message
click.secho(error_msg.string, fg="red")
sys.exit(1)
return login_response
def fetch_aws_account_names(self, response: requests.Response) -> Optional[requests.Response]:
"""Posts ADFS form to get account list response"""
hiddenform = response.soup.find("form", {"name": "hiddenform"}) # type: ignore[attr-defined]
headers = {
"Referer": response.url,
"Content-Type": "application/x-www-form-urlencoded",
}
selectors = ",".join("{}[name]".format(i) for i in ("input", "button", "textarea", "select"))
data = [(tag.get("name"), tag.get("value")) for tag in hiddenform.select(selectors)]
url = hiddenform.attrs.get("action")
try:
adfs_response = self.session.post(url, data=data, headers=headers, timeout=5)
except requests.exceptions.ConnectionError as e:
msg_fmt = "Could not fetch account aliases from {} due to an exception. Using cached values!\n {}"
click.secho(msg_fmt.format(url, str(e)), fg="red")
return None
adfs_response.soup = BeautifulSoup(adfs_response.text, "lxml") # type: ignore[attr-defined]
return adfs_response
def generate_login_url(self, token: Mapping[str, Mapping[str, str]]) -> str:
federation_base_url = "https://signin.aws.amazon.com/federation"
request_params = {
"Action": "getSigninToken",
"SessionDuration": "43200",
"Session": str(
{
"sessionId": token["Credentials"]["AccessKeyId"],
"sessionKey": token["Credentials"]["SecretAccessKey"],
"sessionToken": token["Credentials"]["SessionToken"],
}
),
}
r = self.session.get(federation_base_url, params=request_params)
signin_token = r.json()
login_params = {
"Action": "login",
"Destination": "https://console.aws.amazon.com/",
"SigninToken": signin_token["SigninToken"],
}
request_parameters = requests.compat.urlencode(login_params) # type: ignore[attr-defined]
request_url = "{base_url}?{request_parameters}".format(
base_url=federation_base_url, request_parameters=request_parameters
)
return request_url
def fetch_aws_sts_token(
role_arn: str,
principal_arn: str,
assertion: str,
duration_seconds: Optional[int] = 3600,
aws_profile: Optional[str] = None,
) -> Mapping[str, str]:
"""Use the assertion to get an AWS STS token using `assume_role_with_saml`"""
sts = sts_client(aws_profile)
token = sts.assume_role_with_saml(
RoleArn=role_arn,
PrincipalArn=principal_arn,
SAMLAssertion=assertion,
DurationSeconds=duration_seconds,
)
return token
def fetch_aws_sts_token_assume_role(
role_arn: str,
role_session_name: str,
aws_profile: str,
duration_seconds: Optional[int] = 3600,
) -> Mapping[str, str]:
"""Use the assertion to get an AWS STS token using `assume_role_with_saml`"""
sts = sts_client(aws_profile)
try:
token = sts.assume_role(
RoleArn=role_arn,
RoleSessionName=role_session_name,
DurationSeconds=duration_seconds,
)
except ClientError as e:
click.secho(str(e), fg="red")
sys.exit(1)
return token
def sts_client(aws_profile: Optional[str]) -> boto3.Session.client:
"""Generate a boto3 sts client."""
try:
session = boto3.Session(profile_name=aws_profile)
sts = session.client("sts")
except ProfileNotFound as e:
click.secho(str(e), fg="red")
sys.exit(1)
except Exception as e:
# TODO: Proper exception and message
raise e
return sts
| 0
| 0
| 0
| 10,399
| 0
| 0
| 0
| 66
| 314
|
f8b6c498aceb5c6cd20a8bd5c69808fe4c7a6870
| 561
|
py
|
Python
|
src/unittest/python/rds_utils_tests.py
|
ImmobilienScout24/rds_log_dog
|
f8df42b78f24856358db15b20675f0a155a02f19
|
[
"MIT"
] | 1
|
2018-01-11T18:36:28.000Z
|
2018-01-11T18:36:28.000Z
|
src/unittest/python/rds_utils_tests.py
|
ImmobilienScout24/rds_log_dog
|
f8df42b78f24856358db15b20675f0a155a02f19
|
[
"MIT"
] | 2
|
2017-01-05T15:45:47.000Z
|
2018-04-16T11:12:01.000Z
|
src/unittest/python/rds_utils_tests.py
|
ImmobilienScout24/rds_log_dog
|
f8df42b78f24856358db15b20675f0a155a02f19
|
[
"MIT"
] | 5
|
2017-01-05T15:39:02.000Z
|
2018-04-16T10:37:44.000Z
|
from __future__ import print_function, absolute_import, division
| 33
| 65
| 0.741533
|
from __future__ import print_function, absolute_import, division
import unittest2 as unittest
from mock import patch
from rds_log_dog.rds_utils import get_size
class Test(unittest.TestCase):
@patch('rds_log_dog.rds_utils.describe_logfiles_of_instance')
def test_get_size(self, describe_logfiles_of_instance):
describe_logfiles_of_instance.return_value = [
dict(LogFileName='foo', LastWritten='bar', Size=42),
dict(LogFileName='foo2', LastWritten='bar', Size=23)]
self.assertEqual(42, get_size('foo', 'foo'))
| 0
| 339
| 0
| 9
| 0
| 0
| 0
| 29
| 118
|
32833472f753245d568128df8a1f79f7efbe1fb7
| 549
|
py
|
Python
|
Scripts/hello_world.py
|
hasauino/Python
|
c94ea3a15f8310c95b7aaabaaa82f59dbf748a85
|
[
"MIT"
] | null | null | null |
Scripts/hello_world.py
|
hasauino/Python
|
c94ea3a15f8310c95b7aaabaaa82f59dbf748a85
|
[
"MIT"
] | null | null | null |
Scripts/hello_world.py
|
hasauino/Python
|
c94ea3a15f8310c95b7aaabaaa82f59dbf748a85
|
[
"MIT"
] | null | null | null |
print "Salam Alaikom !"
import sys
# if statement -------------------------------
if False:
print "hi"
print "Hello Wolrd!"
if True:
print "1"
print "2"
# array --------------------------------------
days = ["Monday", 'Tuesday', 'Wednesday',
'Thursday', 'Friday']
print days[0]; a=1; b=2
# --------------------------------------------
print a,b,'\n'
if len(sys.argv)>1:
print 'passed arguments are: ',str(sys.argv)
if len(sys.argv)==1:
print 'passed argument is: ',str(sys.argv[0])
raw_input("\n\nPress the enter key to exit")
| 14.837838
| 46
| 0.500911
|
print "Salam Alaikom !"
import sys
# if statement -------------------------------
if False:
print "hi"
print "Hello Wolrd!"
if True:
print "1"
print "2"
# array --------------------------------------
days = ["Monday", 'Tuesday', 'Wednesday',
'Thursday', 'Friday']
print days[0]; a=1; b=2
# --------------------------------------------
print a,b,'\n'
if len(sys.argv)>1:
print 'passed arguments are: ',str(sys.argv)
if len(sys.argv)==1:
print 'passed argument is: ',str(sys.argv[0])
raw_input("\n\nPress the enter key to exit")
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
a365a4252c5d814005389ed06e5f4dcda0f48f17
| 504
|
py
|
Python
|
tests/test.py
|
rose-okoth/News-API
|
1c19fe74903105599a40c53e68de61eccd993a46
|
[
"Unlicense",
"MIT"
] | null | null | null |
tests/test.py
|
rose-okoth/News-API
|
1c19fe74903105599a40c53e68de61eccd993a46
|
[
"Unlicense",
"MIT"
] | null | null | null |
tests/test.py
|
rose-okoth/News-API
|
1c19fe74903105599a40c53e68de61eccd993a46
|
[
"Unlicense",
"MIT"
] | null | null | null |
import unittest
from app.models import News
News=news.News
if __name__ == '__main__':
unittest.main()
| 26.526316
| 180
| 0.68254
|
import unittest
from app.models import News
News=news.News
class NewsTest(unittest.TestCase):
'''
Test class to test the behaviour of the news class
'''
def setUp(self):
self.new_news = News('CNN','CNN','Surviving in a pandemic','Effects of Covid 19 in the past year',2021-2-22,'https://cnn.com','https://image.tmdb.org/t/p/w500/khsjha27hbs')
def test_instance(self):
self.assertTrue(isinstance(self.new_news,News))
if __name__ == '__main__':
unittest.main()
| 0
| 0
| 0
| 373
| 0
| 0
| 0
| 0
| 23
|
b155700b84e2bb0d3b9d155501dd2186f8955f11
| 2,095
|
py
|
Python
|
pygraph/functions/searching/astar.py
|
schlpbch/pygraph
|
037bb2f32503fecb60d62921f9766d54109f15e2
|
[
"MIT"
] | 62
|
2015-08-24T14:51:17.000Z
|
2021-08-03T03:19:15.000Z
|
pygraph/functions/searching/astar.py
|
schlpbch/pygraph
|
037bb2f32503fecb60d62921f9766d54109f15e2
|
[
"MIT"
] | 3
|
2017-10-31T16:41:51.000Z
|
2020-04-13T20:49:39.000Z
|
pygraph/functions/searching/astar.py
|
schlpbch/pygraph
|
037bb2f32503fecb60d62921f9766d54109f15e2
|
[
"MIT"
] | 30
|
2017-04-18T01:42:56.000Z
|
2021-09-06T09:49:07.000Z
|
"""Implements A* Search functionality."""
from ...helpers import PriorityQueue
from ...exceptions import NonexistentNodeError
def a_star_search(graph, start, goal):
"""Runs an A* search on the specified graph to find a path from the ''start'' node to the ''goal'' node.
Returns a list of nodes specifying a minimal path between the two nodes.
If no path exists (disconnected components), returns an empty list.
"""
all_nodes = graph.get_all_node_ids()
if start not in all_nodes:
raise NonexistentNodeError(start)
if goal not in all_nodes:
raise NonexistentNodeError(goal)
came_from, cost_so_far, goal_reached = _a_star_search_internal(graph, start, goal)
if goal_reached:
path = reconstruct_path(came_from, start, goal)
path.reverse()
return path
else:
return []
# A* Search Helpers
def _a_star_search_internal(graph, start, goal):
"""Performs an A* search, returning information about whether the goal node was reached
and path cost information that can be used to reconstruct the path.
"""
frontier = PriorityQueue()
frontier.put(start, 0)
came_from = {start: None}
cost_so_far = {start: 0}
goal_reached = False
while not frontier.empty():
current = frontier.get()
if current == goal:
goal_reached = True
break
for next_node in graph.neighbors(current):
new_cost = cost_so_far[current] + graph.edge_cost(current, next_node)
if next_node not in cost_so_far or new_cost < cost_so_far[next_node]:
cost_so_far[next_node] = new_cost
priority = new_cost + heuristic(goal, next_node)
frontier.put(next_node, priority)
came_from[next_node] = current
return came_from, cost_so_far, goal_reached
| 31.742424
| 108
| 0.663962
|
"""Implements A* Search functionality."""
from ...helpers import PriorityQueue
from ...exceptions import NonexistentNodeError
def a_star_search(graph, start, goal):
"""Runs an A* search on the specified graph to find a path from the ''start'' node to the ''goal'' node.
Returns a list of nodes specifying a minimal path between the two nodes.
If no path exists (disconnected components), returns an empty list.
"""
all_nodes = graph.get_all_node_ids()
if start not in all_nodes:
raise NonexistentNodeError(start)
if goal not in all_nodes:
raise NonexistentNodeError(goal)
came_from, cost_so_far, goal_reached = _a_star_search_internal(graph, start, goal)
if goal_reached:
path = reconstruct_path(came_from, start, goal)
path.reverse()
return path
else:
return []
# A* Search Helpers
def heuristic(a, b):
return 1
def _a_star_search_internal(graph, start, goal):
"""Performs an A* search, returning information about whether the goal node was reached
and path cost information that can be used to reconstruct the path.
"""
frontier = PriorityQueue()
frontier.put(start, 0)
came_from = {start: None}
cost_so_far = {start: 0}
goal_reached = False
while not frontier.empty():
current = frontier.get()
if current == goal:
goal_reached = True
break
for next_node in graph.neighbors(current):
new_cost = cost_so_far[current] + graph.edge_cost(current, next_node)
if next_node not in cost_so_far or new_cost < cost_so_far[next_node]:
cost_so_far[next_node] = new_cost
priority = new_cost + heuristic(goal, next_node)
frontier.put(next_node, priority)
came_from[next_node] = current
return came_from, cost_so_far, goal_reached
def reconstruct_path(came_from, start, goal):
current = goal
path = [current]
while current != start:
current = came_from[current]
path.append(current)
return path
| 0
| 0
| 0
| 0
| 0
| 186
| 0
| 0
| 45
|
e1d9d2d9a20f6919f3eaa0b19980f164fdc47b5f
| 30,254
|
py
|
Python
|
addons/survey/models/survey_question.py
|
SHIVJITH/Odoo_Machine_Test
|
310497a9872db7844b521e6dab5f7a9f61d365a4
|
[
"Apache-2.0"
] | null | null | null |
addons/survey/models/survey_question.py
|
SHIVJITH/Odoo_Machine_Test
|
310497a9872db7844b521e6dab5f7a9f61d365a4
|
[
"Apache-2.0"
] | null | null | null |
addons/survey/models/survey_question.py
|
SHIVJITH/Odoo_Machine_Test
|
310497a9872db7844b521e6dab5f7a9f61d365a4
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
| 53.928699
| 162
| 0.658888
|
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
import collections
import json
import itertools
import operator
from odoo import api, fields, models, tools, _
from odoo.exceptions import ValidationError
class SurveyQuestion(models.Model):
""" Questions that will be asked in a survey.
Each question can have one of more suggested answers (eg. in case of
multi-answer checkboxes, radio buttons...).
Technical note:
survey.question is also the model used for the survey's pages (with the "is_page" field set to True).
A page corresponds to a "section" in the interface, and the fact that it separates the survey in
actual pages in the interface depends on the "questions_layout" parameter on the survey.survey model.
Pages are also used when randomizing questions. The randomization can happen within a "page".
Using the same model for questions and pages allows to put all the pages and questions together in a o2m field
(see survey.survey.question_and_page_ids) on the view side and easily reorganize your survey by dragging the
items around.
It also removes on level of encoding by directly having 'Add a page' and 'Add a question'
links on the tree view of questions, enabling a faster encoding.
However, this has the downside of making the code reading a little bit more complicated.
Efforts were made at the model level to create computed fields so that the use of these models
still seems somewhat logical. That means:
- A survey still has "page_ids" (question_and_page_ids filtered on is_page = True)
- These "page_ids" still have question_ids (questions located between this page and the next)
- These "question_ids" still have a "page_id"
That makes the use and display of these information at view and controller levels easier to understand.
"""
_name = 'survey.question'
_description = 'Survey Question'
_rec_name = 'title'
_order = 'sequence,id'
@api.model
def default_get(self, fields):
defaults = super(SurveyQuestion, self).default_get(fields)
if (not fields or 'question_type' in fields):
defaults['question_type'] = False if defaults.get('is_page') == True else 'text_box'
return defaults
# question generic data
title = fields.Char('Title', required=True, translate=True)
description = fields.Html(
'Description', translate=True, sanitize=False, # TDE TODO: sanitize but find a way to keep youtube iframe media stuff
help="Use this field to add additional explanations about your question or to illustrate it with pictures or a video")
survey_id = fields.Many2one('survey.survey', string='Survey', ondelete='cascade')
scoring_type = fields.Selection(related='survey_id.scoring_type', string='Scoring Type', readonly=True)
sequence = fields.Integer('Sequence', default=10)
# page specific
is_page = fields.Boolean('Is a page?')
question_ids = fields.One2many('survey.question', string='Questions', compute="_compute_question_ids")
questions_selection = fields.Selection(
related='survey_id.questions_selection', readonly=True,
help="If randomized is selected, add the number of random questions next to the section.")
random_questions_count = fields.Integer(
'Random questions count', default=1,
help="Used on randomized sections to take X random questions from all the questions of that section.")
# question specific
page_id = fields.Many2one('survey.question', string='Page', compute="_compute_page_id", store=True)
question_type = fields.Selection([
('text_box', 'Multiple Lines Text Box'),
('char_box', 'Single Line Text Box'),
('numerical_box', 'Numerical Value'),
('date', 'Date'),
('datetime', 'Datetime'),
('simple_choice', 'Multiple choice: only one answer'),
('multiple_choice', 'Multiple choice: multiple answers allowed'),
('matrix', 'Matrix')], string='Question Type',
compute='_compute_question_type', readonly=False, store=True)
is_scored_question = fields.Boolean(
'Scored', compute='_compute_is_scored_question',
readonly=False, store=True, copy=True,
help="Include this question as part of quiz scoring. Requires an answer and answer score to be taken into account.")
# -- scoreable/answerable simple answer_types: numerical_box / date / datetime
answer_numerical_box = fields.Float('Correct numerical answer', help="Correct number answer for this question.")
answer_date = fields.Date('Correct date answer', help="Correct date answer for this question.")
answer_datetime = fields.Datetime('Correct datetime answer', help="Correct date and time answer for this question.")
answer_score = fields.Float('Score', help="Score value for a correct answer to this question.")
# -- char_box
save_as_email = fields.Boolean(
"Save as user email", compute='_compute_save_as_email', readonly=False, store=True, copy=True,
help="If checked, this option will save the user's answer as its email address.")
save_as_nickname = fields.Boolean(
"Save as user nickname", compute='_compute_save_as_nickname', readonly=False, store=True, copy=True,
help="If checked, this option will save the user's answer as its nickname.")
# -- simple choice / multiple choice / matrix
suggested_answer_ids = fields.One2many(
'survey.question.answer', 'question_id', string='Types of answers', copy=True,
help='Labels used for proposed choices: simple choice, multiple choice and columns of matrix')
allow_value_image = fields.Boolean('Images on answers', help='Display images in addition to answer label. Valid only for simple / multiple choice questions.')
# -- matrix
matrix_subtype = fields.Selection([
('simple', 'One choice per row'),
('multiple', 'Multiple choices per row')], string='Matrix Type', default='simple')
matrix_row_ids = fields.One2many(
'survey.question.answer', 'matrix_question_id', string='Matrix Rows', copy=True,
help='Labels used for proposed choices: rows of matrix')
# -- display & timing options
column_nb = fields.Selection([
('12', '1'), ('6', '2'), ('4', '3'), ('3', '4'), ('2', '6')],
string='Number of columns', default='12',
help='These options refer to col-xx-[12|6|4|3|2] classes in Bootstrap for dropdown-based simple and multiple choice questions.')
is_time_limited = fields.Boolean("The question is limited in time",
help="Currently only supported for live sessions.")
time_limit = fields.Integer("Time limit (seconds)")
# -- comments (simple choice, multiple choice, matrix (without count as an answer))
comments_allowed = fields.Boolean('Show Comments Field')
comments_message = fields.Char('Comment Message', translate=True, default=lambda self: _("If other, please specify:"))
comment_count_as_answer = fields.Boolean('Comment Field is an Answer Choice')
# question validation
validation_required = fields.Boolean('Validate entry')
validation_email = fields.Boolean('Input must be an email')
validation_length_min = fields.Integer('Minimum Text Length', default=0)
validation_length_max = fields.Integer('Maximum Text Length', default=0)
validation_min_float_value = fields.Float('Minimum value', default=0.0)
validation_max_float_value = fields.Float('Maximum value', default=0.0)
validation_min_date = fields.Date('Minimum Date')
validation_max_date = fields.Date('Maximum Date')
validation_min_datetime = fields.Datetime('Minimum Datetime')
validation_max_datetime = fields.Datetime('Maximum Datetime')
validation_error_msg = fields.Char('Validation Error message', translate=True, default=lambda self: _("The answer you entered is not valid."))
constr_mandatory = fields.Boolean('Mandatory Answer')
constr_error_msg = fields.Char('Error message', translate=True, default=lambda self: _("This question requires an answer."))
# answers
user_input_line_ids = fields.One2many(
'survey.user_input.line', 'question_id', string='Answers',
domain=[('skipped', '=', False)], groups='survey.group_survey_user')
# Conditional display
is_conditional = fields.Boolean(
string='Conditional Display', copy=False, help="""If checked, this question will be displayed only
if the specified conditional answer have been selected in a previous question""")
triggering_question_id = fields.Many2one(
'survey.question', string="Triggering Question", copy=False, compute="_compute_triggering_question_id",
store=True, readonly=False, help="Question containing the triggering answer to display the current question.",
domain="""[('survey_id', '=', survey_id),
'&', ('question_type', 'in', ['simple_choice', 'multiple_choice']),
'|',
('sequence', '<', sequence),
'&', ('sequence', '=', sequence), ('id', '<', id)]""")
triggering_answer_id = fields.Many2one(
'survey.question.answer', string="Triggering Answer", copy=False, compute="_compute_triggering_answer_id",
store=True, readonly=False, help="Answer that will trigger the display of the current question.",
domain="[('question_id', '=', triggering_question_id)]")
_sql_constraints = [
('positive_len_min', 'CHECK (validation_length_min >= 0)', 'A length must be positive!'),
('positive_len_max', 'CHECK (validation_length_max >= 0)', 'A length must be positive!'),
('validation_length', 'CHECK (validation_length_min <= validation_length_max)', 'Max length cannot be smaller than min length!'),
('validation_float', 'CHECK (validation_min_float_value <= validation_max_float_value)', 'Max value cannot be smaller than min value!'),
('validation_date', 'CHECK (validation_min_date <= validation_max_date)', 'Max date cannot be smaller than min date!'),
('validation_datetime', 'CHECK (validation_min_datetime <= validation_max_datetime)', 'Max datetime cannot be smaller than min datetime!'),
('positive_answer_score', 'CHECK (answer_score >= 0)', 'An answer score for a non-multiple choice question cannot be negative!'),
('scored_datetime_have_answers', "CHECK (is_scored_question != True OR question_type != 'datetime' OR answer_datetime is not null)",
'All "Is a scored question = True" and "Question Type: Datetime" questions need an answer'),
('scored_date_have_answers', "CHECK (is_scored_question != True OR question_type != 'date' OR answer_date is not null)",
'All "Is a scored question = True" and "Question Type: Date" questions need an answer')
]
@api.depends('is_page')
def _compute_question_type(self):
for question in self:
if not question.question_type or question.is_page:
question.question_type = False
@api.depends('survey_id.question_and_page_ids.is_page', 'survey_id.question_and_page_ids.sequence')
def _compute_question_ids(self):
"""Will take all questions of the survey for which the index is higher than the index of this page
and lower than the index of the next page."""
for question in self:
if question.is_page:
next_page_index = False
for page in question.survey_id.page_ids:
if page._index() > question._index():
next_page_index = page._index()
break
question.question_ids = question.survey_id.question_ids.filtered(
lambda q: q._index() > question._index() and (not next_page_index or q._index() < next_page_index)
)
else:
question.question_ids = self.env['survey.question']
@api.depends('survey_id.question_and_page_ids.is_page', 'survey_id.question_and_page_ids.sequence')
def _compute_page_id(self):
"""Will find the page to which this question belongs to by looking inside the corresponding survey"""
for question in self:
if question.is_page:
question.page_id = None
else:
page = None
for q in question.survey_id.question_and_page_ids.sorted():
if q == question:
break
if q.is_page:
page = q
question.page_id = page
@api.depends('question_type', 'validation_email')
def _compute_save_as_email(self):
for question in self:
if question.question_type != 'char_box' or not question.validation_email:
question.save_as_email = False
@api.depends('question_type')
def _compute_save_as_nickname(self):
for question in self:
if question.question_type != 'char_box':
question.save_as_nickname = False
@api.depends('is_conditional')
def _compute_triggering_question_id(self):
""" Used as an 'onchange' : Reset the triggering question if user uncheck 'Conditional Display'
Avoid CacheMiss : set the value to False if the value is not set yet."""
for question in self:
if not question.is_conditional or question.triggering_question_id is None:
question.triggering_question_id = False
@api.depends('triggering_question_id')
def _compute_triggering_answer_id(self):
""" Used as an 'onchange' : Reset the triggering answer if user unset or change the triggering question
or uncheck 'Conditional Display'.
Avoid CacheMiss : set the value to False if the value is not set yet."""
for question in self:
if not question.triggering_question_id \
or question.triggering_question_id != question.triggering_answer_id.question_id\
or question.triggering_answer_id is None:
question.triggering_answer_id = False
@api.depends('question_type', 'scoring_type', 'answer_date', 'answer_datetime', 'answer_numerical_box')
def _compute_is_scored_question(self):
""" Computes whether a question "is scored" or not. Handles following cases:
- inconsistent Boolean=None edge case that breaks tests => False
- survey is not scored => False
- 'date'/'datetime'/'numerical_box' question types w/correct answer => True
(implied without user having to activate, except for numerical whose correct value is 0.0)
- 'simple_choice / multiple_choice': set to True even if logic is a bit different (coming from answers)
- question_type isn't scoreable (note: choice questions scoring logic handled separately) => False
"""
for question in self:
if question.is_scored_question is None or question.scoring_type == 'no_scoring':
question.is_scored_question = False
elif question.question_type == 'date':
question.is_scored_question = bool(question.answer_date)
elif question.question_type == 'datetime':
question.is_scored_question = bool(question.answer_datetime)
elif question.question_type == 'numerical_box' and question.answer_numerical_box:
question.is_scored_question = True
elif question.question_type in ['simple_choice', 'multiple_choice']:
question.is_scored_question = True
else:
question.is_scored_question = False
# ------------------------------------------------------------
# VALIDATION
# ------------------------------------------------------------
def validate_question(self, answer, comment=None):
""" Validate question, depending on question type and parameters
for simple choice, text, date and number, answer is simply the answer of the question.
For other multiple choices questions, answer is a list of answers (the selected choices
or a list of selected answers per question -for matrix type-):
- Simple answer : answer = 'example' or 2 or question_answer_id or 2019/10/10
- Multiple choice : answer = [question_answer_id1, question_answer_id2, question_answer_id3]
- Matrix: answer = { 'rowId1' : [colId1, colId2,...], 'rowId2' : [colId1, colId3, ...] }
return dict {question.id (int): error (str)} -> empty dict if no validation error.
"""
self.ensure_one()
if isinstance(answer, str):
answer = answer.strip()
# Empty answer to mandatory question
if self.constr_mandatory and not answer and self.question_type not in ['simple_choice', 'multiple_choice']:
return {self.id: self.constr_error_msg}
# because in choices question types, comment can count as answer
if answer or self.question_type in ['simple_choice', 'multiple_choice']:
if self.question_type == 'char_box':
return self._validate_char_box(answer)
elif self.question_type == 'numerical_box':
return self._validate_numerical_box(answer)
elif self.question_type in ['date', 'datetime']:
return self._validate_date(answer)
elif self.question_type in ['simple_choice', 'multiple_choice']:
return self._validate_choice(answer, comment)
elif self.question_type == 'matrix':
return self._validate_matrix(answer)
return {}
def _validate_char_box(self, answer):
# Email format validation
# all the strings of the form "<something>@<anything>.<extension>" will be accepted
if self.validation_email:
if not tools.email_normalize(answer):
return {self.id: _('This answer must be an email address')}
# Answer validation (if properly defined)
# Length of the answer must be in a range
if self.validation_required:
if not (self.validation_length_min <= len(answer) <= self.validation_length_max):
return {self.id: self.validation_error_msg}
return {}
def _validate_numerical_box(self, answer):
try:
floatanswer = float(answer)
except ValueError:
return {self.id: _('This is not a number')}
if self.validation_required:
# Answer is not in the right range
with tools.ignore(Exception):
if not (self.validation_min_float_value <= floatanswer <= self.validation_max_float_value):
return {self.id: self.validation_error_msg}
return {}
def _validate_date(self, answer):
isDatetime = self.question_type == 'datetime'
# Checks if user input is a date
try:
dateanswer = fields.Datetime.from_string(answer) if isDatetime else fields.Date.from_string(answer)
except ValueError:
return {self.id: _('This is not a date')}
if self.validation_required:
# Check if answer is in the right range
if isDatetime:
min_date = fields.Datetime.from_string(self.validation_min_datetime)
max_date = fields.Datetime.from_string(self.validation_max_datetime)
dateanswer = fields.Datetime.from_string(answer)
else:
min_date = fields.Date.from_string(self.validation_min_date)
max_date = fields.Date.from_string(self.validation_max_date)
dateanswer = fields.Date.from_string(answer)
if (min_date and max_date and not (min_date <= dateanswer <= max_date))\
or (min_date and not min_date <= dateanswer)\
or (max_date and not dateanswer <= max_date):
return {self.id: self.validation_error_msg}
return {}
def _validate_choice(self, answer, comment):
# Empty comment
if self.constr_mandatory \
and not answer \
and not (self.comments_allowed and self.comment_count_as_answer and comment):
return {self.id: self.constr_error_msg}
return {}
def _validate_matrix(self, answers):
# Validate that each line has been answered
if self.constr_mandatory and len(self.matrix_row_ids) != len(answers):
return {self.id: self.constr_error_msg}
return {}
def _index(self):
"""We would normally just use the 'sequence' field of questions BUT, if the pages and questions are
created without ever moving records around, the sequence field can be set to 0 for all the questions.
However, the order of the recordset is always correct so we can rely on the index method."""
self.ensure_one()
return list(self.survey_id.question_and_page_ids).index(self)
# ------------------------------------------------------------
# STATISTICS / REPORTING
# ------------------------------------------------------------
def _prepare_statistics(self, user_input_lines):
""" Compute statistical data for questions by counting number of vote per choice on basis of filter """
all_questions_data = []
for question in self:
question_data = {'question': question, 'is_page': question.is_page}
if question.is_page:
all_questions_data.append(question_data)
continue
# fetch answer lines, separate comments from real answers
all_lines = user_input_lines.filtered(lambda line: line.question_id == question)
if question.question_type in ['simple_choice', 'multiple_choice', 'matrix']:
answer_lines = all_lines.filtered(
lambda line: line.answer_type == 'suggestion' or (
line.answer_type == 'char_box' and question.comment_count_as_answer)
)
comment_line_ids = all_lines.filtered(lambda line: line.answer_type == 'char_box')
else:
answer_lines = all_lines
comment_line_ids = self.env['survey.user_input.line']
skipped_lines = answer_lines.filtered(lambda line: line.skipped)
done_lines = answer_lines - skipped_lines
question_data.update(
answer_line_ids=answer_lines,
answer_line_done_ids=done_lines,
answer_input_done_ids=done_lines.mapped('user_input_id'),
answer_input_skipped_ids=skipped_lines.mapped('user_input_id'),
comment_line_ids=comment_line_ids)
question_data.update(question._get_stats_summary_data(answer_lines))
# prepare table and graph data
table_data, graph_data = question._get_stats_data(answer_lines)
question_data['table_data'] = table_data
question_data['graph_data'] = json.dumps(graph_data)
all_questions_data.append(question_data)
return all_questions_data
def _get_stats_data(self, user_input_lines):
if self.question_type == 'simple_choice':
return self._get_stats_data_answers(user_input_lines)
elif self.question_type == 'multiple_choice':
table_data, graph_data = self._get_stats_data_answers(user_input_lines)
return table_data, [{'key': self.title, 'values': graph_data}]
elif self.question_type == 'matrix':
return self._get_stats_graph_data_matrix(user_input_lines)
return [line for line in user_input_lines], []
def _get_stats_data_answers(self, user_input_lines):
""" Statistics for question.answer based questions (simple choice, multiple
choice.). A corner case with a void record survey.question.answer is added
to count comments that should be considered as valid answers. This small hack
allow to have everything available in the same standard structure. """
suggested_answers = [answer for answer in self.mapped('suggested_answer_ids')]
if self.comment_count_as_answer:
suggested_answers += [self.env['survey.question.answer']]
count_data = dict.fromkeys(suggested_answers, 0)
for line in user_input_lines:
if line.suggested_answer_id or (line.value_char_box and self.comment_count_as_answer):
count_data[line.suggested_answer_id] += 1
table_data = [{
'value': _('Other (see comments)') if not sug_answer else sug_answer.value,
'suggested_answer': sug_answer,
'count': count_data[sug_answer]
}
for sug_answer in suggested_answers]
graph_data = [{
'text': _('Other (see comments)') if not sug_answer else sug_answer.value,
'count': count_data[sug_answer]
}
for sug_answer in suggested_answers]
return table_data, graph_data
def _get_stats_graph_data_matrix(self, user_input_lines):
suggested_answers = self.mapped('suggested_answer_ids')
matrix_rows = self.mapped('matrix_row_ids')
count_data = dict.fromkeys(itertools.product(matrix_rows, suggested_answers), 0)
for line in user_input_lines:
if line.matrix_row_id and line.suggested_answer_id:
count_data[(line.matrix_row_id, line.suggested_answer_id)] += 1
table_data = [{
'row': row,
'columns': [{
'suggested_answer': sug_answer,
'count': count_data[(row, sug_answer)]
} for sug_answer in suggested_answers],
} for row in matrix_rows]
graph_data = [{
'key': sug_answer.value,
'values': [{
'text': row.value,
'count': count_data[(row, sug_answer)]
}
for row in matrix_rows
]
} for sug_answer in suggested_answers]
return table_data, graph_data
def _get_stats_summary_data(self, user_input_lines):
stats = {}
if self.question_type in ['simple_choice', 'multiple_choice']:
stats.update(self._get_stats_summary_data_choice(user_input_lines))
elif self.question_type == 'numerical_box':
stats.update(self._get_stats_summary_data_numerical(user_input_lines))
if self.question_type in ['numerical_box', 'date', 'datetime']:
stats.update(self._get_stats_summary_data_scored(user_input_lines))
return stats
def _get_stats_summary_data_choice(self, user_input_lines):
right_inputs, partial_inputs = self.env['survey.user_input'], self.env['survey.user_input']
right_answers = self.suggested_answer_ids.filtered(lambda label: label.is_correct)
if self.question_type == 'multiple_choice':
for user_input, lines in tools.groupby(user_input_lines, operator.itemgetter('user_input_id')):
user_input_answers = self.env['survey.user_input.line'].concat(*lines).filtered(lambda l: l.answer_is_correct).mapped('suggested_answer_id')
if user_input_answers and user_input_answers < right_answers:
partial_inputs += user_input
elif user_input_answers:
right_inputs += user_input
else:
right_inputs = user_input_lines.filtered(lambda line: line.answer_is_correct).mapped('user_input_id')
return {
'right_answers': right_answers,
'right_inputs_count': len(right_inputs),
'partial_inputs_count': len(partial_inputs),
}
def _get_stats_summary_data_numerical(self, user_input_lines):
all_values = user_input_lines.filtered(lambda line: not line.skipped).mapped('value_numerical_box')
lines_sum = sum(all_values)
return {
'numerical_max': max(all_values, default=0),
'numerical_min': min(all_values, default=0),
'numerical_average': round(lines_sum / (len(all_values) or 1), 2),
}
def _get_stats_summary_data_scored(self, user_input_lines):
return {
'common_lines': collections.Counter(
user_input_lines.filtered(lambda line: not line.skipped).mapped('value_%s' % self.question_type)
).most_common(5) if self.question_type != 'datetime' else [],
'right_inputs_count': len(user_input_lines.filtered(lambda line: line.answer_is_correct).mapped('user_input_id'))
}
class SurveyQuestionAnswer(models.Model):
""" A preconfigured answer for a question. This model stores values used
for
* simple choice, multiple choice: proposed values for the selection /
radio;
* matrix: row and column values;
"""
_name = 'survey.question.answer'
_rec_name = 'value'
_order = 'sequence, id'
_description = 'Survey Label'
question_id = fields.Many2one('survey.question', string='Question', ondelete='cascade')
matrix_question_id = fields.Many2one('survey.question', string='Question (as matrix row)', ondelete='cascade')
sequence = fields.Integer('Label Sequence order', default=10)
value = fields.Char('Suggested value', translate=True, required=True)
value_image = fields.Image('Image', max_width=256, max_height=256)
is_correct = fields.Boolean('Is a correct answer')
answer_score = fields.Float('Score for this choice', help="A positive score indicates a correct choice; a negative or null score indicates a wrong answer")
@api.constrains('question_id', 'matrix_question_id')
def _check_question_not_empty(self):
"""Ensure that field question_id XOR field matrix_question_id is not null"""
for label in self:
if not bool(label.question_id) != bool(label.matrix_question_id):
raise ValidationError(_("A label must be attached to only one question."))
| 0
| 5,268
| 0
| 24,682
| 0
| 0
| 0
| 23
| 180
|
64bcde3e3778f24206da1450da29a5bf8fcaeb50
| 1,448
|
py
|
Python
|
gcloud_dataproc/submit.py
|
macarthur-lab/hail-elasticsearch-pipelines
|
7082681fd125e4f23a512aeff49853c5fc0f3136
|
[
"MIT"
] | 15
|
2017-11-22T14:48:04.000Z
|
2020-10-05T18:22:24.000Z
|
gcloud_dataproc/submit.py
|
macarthur-lab/hail-elasticsearch-pipelines
|
7082681fd125e4f23a512aeff49853c5fc0f3136
|
[
"MIT"
] | 86
|
2017-12-14T23:45:29.000Z
|
2020-10-13T18:15:54.000Z
|
gcloud_dataproc/submit.py
|
macarthur-lab/hail-elasticsearch-pipelines
|
7082681fd125e4f23a512aeff49853c5fc0f3136
|
[
"MIT"
] | 7
|
2019-01-29T09:08:10.000Z
|
2020-02-25T16:22:57.000Z
|
#!/usr/bin/env python
import argparse
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument("-c", "--cluster", default="no-vep")
p.add_argument("script")
args, unparsed_args = p.parse_known_args()
submit(args.script, unparsed_args, cluster=args.cluster)
| 33.674419
| 148
| 0.674724
|
#!/usr/bin/env python
import argparse
import os
import subprocess
import tempfile
def submit(script, script_args_list, cluster='no-vep', wait_for_job=True, use_existing_scripts_zip=False, region=None, spark_env=None, job_id=None):
script_args = " ".join(['"%s"' % arg for arg in script_args_list])
hail_scripts_zip = os.path.join(tempfile.gettempdir(), 'hail_scripts.zip')
os.chdir(os.path.join(os.path.dirname(__file__), ".."))
if use_existing_scripts_zip and os.path.exists(hail_scripts_zip):
print('Using existing scripts zip file')
else:
os.system(
"zip -r %(hail_scripts_zip)s hail_scripts kubernetes sv_pipeline download_and_create_reference_datasets/v02/hail_scripts" % locals())
command = f"""gcloud dataproc jobs submit pyspark \
--cluster={cluster} \
--py-files={hail_scripts_zip} \
{f'--properties="spark.executorEnv.{spark_env}"' if spark_env else ''} \
{f'--region={region}' if region else ''} \
{f'--id={job_id}' if job_id else ''} \
{'' if wait_for_job else '--async'} \
"{script}" -- {script_args}
"""
print(command)
subprocess.check_call(command, shell=True)
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument("-c", "--cluster", default="no-vep")
p.add_argument("script")
args, unparsed_args = p.parse_known_args()
submit(args.script, unparsed_args, cluster=args.cluster)
| 0
| 0
| 0
| 0
| 0
| 1,083
| 0
| -22
| 89
|
309813fa77ab295af0296cf9deb86684b7f31eaa
| 1,827
|
py
|
Python
|
wavelet/compression/compressor_magnitude.py
|
AP-Atul/wavelets-ext
|
00ced22462c369584ebd32f9b5f357f092de0142
|
[
"MIT"
] | 4
|
2021-02-01T07:43:10.000Z
|
2021-04-27T06:58:54.000Z
|
wavelet/compression/compressor_magnitude.py
|
AP-Atul/wavelets-ext
|
00ced22462c369584ebd32f9b5f357f092de0142
|
[
"MIT"
] | null | null | null |
wavelet/compression/compressor_magnitude.py
|
AP-Atul/wavelets-ext
|
00ced22462c369584ebd32f9b5f357f092de0142
|
[
"MIT"
] | null | null | null |
""" Compression using the average of signal as a magnitude """
| 25.375
| 84
| 0.605911
|
""" Compression using the average of signal as a magnitude """
import numpy as np
from .compressor import Compressor
class CompressorMagnitude:
"""
The average of the signal is used to perform the compression on the input
data signal. Check the Compressor class on how the thresholding is done
with the magnitude
Attributes
----------
__magnitude: float
magnitude calculated using the average of the signal
__compressor: Compressor
object to call the compress function using the magnitude calculated
"""
def __init__(self):
self.__magnitude = 0.
self.__compressor = Compressor()
def compress(self, data):
"""
Apply thresholding techniques to remove the signal below the magnitude
Parameters
----------
data: array_like
input data signal, mostly coefficients output of the decompose
Returns
-------
array_like
thresholded data/ coefficients
"""
self.__magnitude = np.sum(data, axis=0)
lenD = len(data)
return self.__compressor.compress(data, (self.__magnitude / lenD))
def getCompressionRate(self, data):
"""
Run the compression calculation on the data to check how well the compressor
performed
Parameters
----------
data: array_like
input data from the final step of re-construction
Returns
-------
float
percentage of the compression
"""
return self.__compressor.calculateCompressionRate(data)
def getMagnitude(self):
"""
Returns the calculated magnitude
Returns
-------
float
calculated magnitude
"""
return self.__magnitude
| 0
| 0
| 0
| 1,684
| 0
| 0
| 0
| 10
| 69
|
96186744d6789db434bb6817cc7c93a3fee17ace
| 2,246
|
py
|
Python
|
mkcloud.py
|
JunhoYeo/mkcloud
|
2a4629fdbbf954a55283f8d3ba5e430a221e4b94
|
[
"MIT"
] | 1
|
2018-08-13T23:51:57.000Z
|
2018-08-13T23:51:57.000Z
|
mkcloud.py
|
JunhoYeo/mkcloud
|
2a4629fdbbf954a55283f8d3ba5e430a221e4b94
|
[
"MIT"
] | null | null | null |
mkcloud.py
|
JunhoYeo/mkcloud
|
2a4629fdbbf954a55283f8d3ba5e430a221e4b94
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
if __name__ == '__main__':
import multidict as multidict
text = getFrequencyDictForText(open('lorem-ipsum.txt').read())
mkcloud('lorem-ipsum.png', text, '#FFF2CC', '#F030A8')
| 35.09375
| 94
| 0.601514
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from wordcloud import WordCloud, ImageColorGenerator
from PIL import Image
import numpy as np
import random
def mkcloud(filename, dic, theme_start, theme_end):
def linear_gradient(n=10):
''' returns a gradient list of (n) colors between
two hex colors. start_hex and finish_hex
should be the full six-digit color string,
inlcuding the number sign ("#FFFFFF") '''
# https://gist.github.com/RoboDonut/83ec5909621a6037f275799032e97563
start = tuple(int(theme_start.lstrip('#')[i:i+2], 16) for i in (0, 2 ,4))
end = tuple(int(theme_end.lstrip('#')[i:i+2], 16) for i in (0, 2 ,4))
RGB_list = [start]
for t in range(1, n):
curr_vector = tuple([
int(start[j] + (float(t)/(n-1))*(end[j]-start[j]))
for j in range(3)
])
RGB_list.append(curr_vector)
return ['#%02x%02x%02x'%i for i in RGB_list]
colors = linear_gradient()
def color_from_theme(word, font_size, position, orientation, random_state=None, **kwargs):
return random.choice(colors)
font_path = './NotoSansCJKkr-Regular.otf'
x, y = np.ogrid[:800, :800]
mask = (x - 400) ** 2 + (y - 400) ** 2 > 360 ** 2
mask = 255 * mask.astype(int)
wc = WordCloud(
color_func=color_from_theme,
font_path=font_path,
background_color="rgba(255, 255, 255, 0)",
mode="RGBA", mask=mask, width=800, height=800
)
wc.generate_from_frequencies(dic)
wc.to_file(filename)
if __name__ == '__main__':
import re
import multidict as multidict
def getFrequencyDictForText(sentence):
fullTermsDict = multidict.MultiDict()
tmpDict = {}
for text in sentence.split(" "):
if re.match("a|the|an|the|to|in|for|of|or|by|with|is|on|that|be", text):
continue
val = tmpDict.get(text, 0)
tmpDict[text.lower()] = val + 1
for key in tmpDict:
fullTermsDict.add(key, tmpDict[key])
return dict(fullTermsDict)
text = getFrequencyDictForText(open('lorem-ipsum.txt').read())
mkcloud('lorem-ipsum.png', text, '#FFF2CC', '#F030A8')
| 0
| 0
| 0
| 0
| 0
| 1,833
| 0
| 8
| 165
|
8119d973649690caaf8fc15259c7a1f12bccb05a
| 289
|
py
|
Python
|
fe_cipher/constants/areas.py
|
uakihir0/fecipher
|
dd5c2a0d47d2701570b43f4906bdec993f4d571c
|
[
"MIT"
] | 3
|
2020-01-16T10:53:26.000Z
|
2020-01-16T10:53:40.000Z
|
fe_cipher/constants/areas.py
|
uakihir0/fecipher
|
dd5c2a0d47d2701570b43f4906bdec993f4d571c
|
[
"MIT"
] | null | null | null |
fe_cipher/constants/areas.py
|
uakihir0/fecipher
|
dd5c2a0d47d2701570b43f4906bdec993f4d571c
|
[
"MIT"
] | 1
|
2020-01-16T11:08:15.000Z
|
2020-01-16T11:08:15.000Z
|
# -*- coding: utf-8 -*-
#
| 11.56
| 23
| 0.435986
|
# -*- coding: utf-8 -*-
from enum import Enum
# エリア定義
class Areas(Enum):
# デッキ
Deck = 1
# 手札
Hand = 2
# 絆
Bond = 3
# 前衛
FrontLine = 4
# 後衛
BackLine = 5
# 退避
Retreat = 6
# 支援
Support = 7
# オーブ
Orb = 8
# 無限
Mugen = 9
| 72
| 0
| 0
| 192
| 0
| 0
| 0
| 0
| 44
|
395b142680f5cde9c72f9f6f5f6119c63915dae1
| 4,052
|
py
|
Python
|
attention.py
|
dsj96/TITS
|
c84f04bdb9f1f0af5f6cca1e6a10151e4dae162d
|
[
"MIT"
] | 2
|
2021-12-05T16:34:05.000Z
|
2021-12-30T00:25:29.000Z
|
attention.py
|
dsj96/CTVI-master
|
ea9c4dc812ff871c7ccb2e3748e35d3b634920d0
|
[
"MIT"
] | null | null | null |
attention.py
|
dsj96/CTVI-master
|
ea9c4dc812ff871c7ccb2e3748e35d3b634920d0
|
[
"MIT"
] | null | null | null |
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
if __name__ == "__main__":
'''self-attention'''
input_data = torch.tensor(np.arange(24.).reshape(2,3,4),dtype=torch.float32)
attention = Multi_Head_SelfAttention(num_head=3,num_vocab=3,input_dim=4,hidden_dim=5,out_dim=5)
output_data = attention(input_data)
print("output_data: \n",output_data)
'''position encoder'''
# input_data = torch.tensor([[[1,0,1,0],[0,2,0,2],[1,1,1,1]], [[1,0,1,0],[0,2,0,2],[1,1,1,1]] ],dtype=torch.float32)
# print("input_data:\n",input_data)
# positional_encoder = PositionalEncoder(d_model=4) #
# output_data = positional_encoder(input_data)
# print("output_data:\n", output_data)
| 37.174312
| 121
| 0.628825
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import softmax
import torch.optim as optim
from torch.autograd import Variable
import math
import numpy as np
from utils import write_pkl, read_pkl
class SelfAttention(nn.Module):
def __init__(self, num_vocab, input_dim, hidden_dim):
super(SelfAttention, self).__init__()
self.num_vocab = num_vocab
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.query_W = nn.Linear(input_dim, hidden_dim)
self.key_W = nn.Linear(input_dim, hidden_dim)
self.value_W = nn.Linear(input_dim, hidden_dim)
self.position_encoder = PositionalEncoder(d_model=hidden_dim)
self.init()
def init(self):
stdv = 1. / math.sqrt(self.query_W.weight.size(1))
self.query_W.weight.data.uniform_(-stdv, stdv)
stdv = 1. / math.sqrt(self.query_W.weight.size(1))
self.key_W.weight.data.uniform_(-stdv, stdv)
stdv = 1. / math.sqrt(self.query_W.weight.size(1))
self.key_W.weight.data.uniform_(-stdv, stdv)
def forward(self, input_data):
query_M = self.query_W(input_data)
key_M = self.key_W(input_data)
value_M = self.value_W(input_data)
attn_scores = query_M @ key_M.transpose(-1,-2)
attn_scores = attn_scores / math.sqrt(self.hidden_dim)
attn_scores_softmax = softmax(attn_scores, dim=-1)
outputs = self.position_encoder(attn_scores_softmax.bmm(value_M))
return outputs
class Multi_Head_SelfAttention(nn.Module):
def __init__(self, num_head, num_vocab, input_dim, hidden_dim, out_dim):
super(Multi_Head_SelfAttention, self).__init__()
self.num_head = num_head
self.num_vocab = num_vocab
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.fn = nn.Linear(num_head*hidden_dim, out_dim)
self.at_block_list = []
for i in range(self.num_head):
self.at_block_list.append(SelfAttention(self.num_vocab, self.input_dim, self.hidden_dim))
def forward(self, input_data):
output_list = []
for i in range(self.num_head):
cur_output = self.at_block_list[i](input_data)
output_list.append(cur_output)
output_M = torch.cat(output_list,dim=-1)
outputs = self.fn(output_M)
return outputs
class PositionalEncoder(nn.Module):
def __init__(self, d_model, max_seq_len = 30):
super(PositionalEncoder, self).__init__()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.d_model = d_model
self.max_seq_len = max_seq_len
self.pe = torch.zeros((self.max_seq_len, self.d_model), requires_grad=False)
for pos in range(self.max_seq_len):
for i in range(0, self.d_model, 2):
self.pe[pos, i] = \
math.sin(pos / (10000 ** ((2 * i)/self.d_model)))
if i+1< self.d_model:
self.pe[pos, i + 1] = \
math.cos(pos / (10000 ** ((2 * (i + 1))/self.d_model)))
def forward(self, x):
seq_len = x.shape[1]
x = x + self.pe[:seq_len,:self.d_model]
return x
if __name__ == "__main__":
'''self-attention'''
input_data = torch.tensor(np.arange(24.).reshape(2,3,4),dtype=torch.float32)
attention = Multi_Head_SelfAttention(num_head=3,num_vocab=3,input_dim=4,hidden_dim=5,out_dim=5)
output_data = attention(input_data)
print("output_data: \n",output_data)
'''position encoder'''
# input_data = torch.tensor([[[1,0,1,0],[0,2,0,2],[1,1,1,1]], [[1,0,1,0],[0,2,0,2],[1,1,1,1]] ],dtype=torch.float32)
# print("input_data:\n",input_data)
# positional_encoder = PositionalEncoder(d_model=4) # 该参数代表输出的嵌入维度，因为需要和输入的
# output_data = positional_encoder(input_data)
# print("output_data:\n", output_data)
| 63
| 0
| 0
| 3,024
| 0
| 0
| 0
| 48
| 196
|
f2f782820b6e0361ff854c10c04a57c67e53f14a
| 3,489
|
py
|
Python
|
bot.py
|
victor-tsai/discord-bot
|
44ef9bcaa69c5ea682be56efe88df091dc0784c3
|
[
"MIT"
] | null | null | null |
bot.py
|
victor-tsai/discord-bot
|
44ef9bcaa69c5ea682be56efe88df091dc0784c3
|
[
"MIT"
] | null | null | null |
bot.py
|
victor-tsai/discord-bot
|
44ef9bcaa69c5ea682be56efe88df091dc0784c3
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
from discord.ext import commands
TOKEN = "Your Discord token here"
OWNER_ID = 0 # Your user ID here
RTT_USERNAME = "Realtime Trains API username"
RTT_PASSWORD = "Realtime Trains API password"
## BOT SETUP
bot = commands.Bot(command_prefix = ">")
# Comment to respond to messages from anyone
## UTILITY FUNCTIONS
## COMMANDS
bot.run(TOKEN)
| 37.117021
| 109
| 0.518487
|
#!/usr/bin/python3
import requests
from discord.ext import commands
TOKEN = "Your Discord token here"
OWNER_ID = 0 # Your user ID here
RTT_USERNAME = "Realtime Trains API username"
RTT_PASSWORD = "Realtime Trains API password"
## BOT SETUP
bot = commands.Bot(command_prefix = ">")
# Comment to respond to messages from anyone
@bot.check
def isOwner(ctx):
async def predicate(ctx):
return ctx.author.id == OWNER_ID
return commands.check(predicate)
## UTILITY FUNCTIONS
def rttDepartures(station):
rttData = requests.get("https://api.rtt.io/api/v1/json/search/"+station,
auth = (RTT_USERNAME, RTT_PASSWORD))
rttJson = rttData.json()
rttColour = 0xFF0000
try:
rttTitle = ("Departures from **%s**, powered by **Realtime Trains**"
% rttJson["location"]["name"])
rttDescription = (" ID | Time | Live | Plat | Destination\n" +
"-" * 41 + "\n")
if rttJson["services"] == None:
rttDescription = "No services at the moment."
else:
for service in rttJson["services"]:
if len(rttDescription)<1800:
try:
trainID = service["runningIdentity"]
except KeyError:
trainID = service["trainIdentity"]
depTime = service["locationDetail"]["gbttBookedDeparture"]
depTimeFormatted = depTime[:2] + ":" + depTime[2:]
try:
liveTime = service["locationDetail"]["realtimeDeparture"]
liveTimeFormatted = liveTime[:2] + ":" + liveTime[2:]
except KeyError:
liveTimeFormatted = " N/A "
try:
# | 10A* |
# | 12B |
# | 13 |
# | 6 |
MAX_PLAT_CHARS = 4
platform = service["locationDetail"]["platform"]
if service["locationDetail"]["platformChanged"]:
platform += "*"
if len(platform) < MAX_PLAT_CHARS:
platform += " " * (MAX_PLAT_CHARS - len(platform))
except KeyError:
platform = "----"
destination = service["locationDetail"]["destination"][0]["description"]
rttDescription += ("%s | %s | %s | %s | %s\n" %
(trainID, depTimeFormatted, liveTimeFormatted, platform, destination))
except KeyError:
rttTitle = "Please give me a valid NRS (3 letters) or TIPLOC (7 characters) code."
rttDescription = ("It appears that you took a wrong route back at " +
"Croxley Green Jn, as the rails to this station don't appear to exist " +
"anymore. Just in case there was meant to be a station here, we've told " +
"the permanent way team and they'll have a look into it.")
rttMessage = rttTitle + "\n```" + rttDescription + "```"
return rttMessage
## COMMANDS
@bot.command()
async def ping(ctx):
await ctx.send("pong")
@bot.command()
async def logout(ctx):
await ctx.bot.logout()
@bot.command()
async def trains(ctx, station):
await ctx.send(rttDepartures(station))
bot.run(TOKEN)
| 0
| 267
| 0
| 0
| 0
| 2,724
| 0
| -6
| 138
|
42e8e2aaaeb8d0c7280bc8c0f0d4326303a7a369
| 558
|
py
|
Python
|
Mundo_1/aula07.py
|
GabrielProdi/Python_CursoEmVideo
|
8bfff1b9a73d09c8f42dc3778f895525f1bc946d
|
[
"MIT"
] | null | null | null |
Mundo_1/aula07.py
|
GabrielProdi/Python_CursoEmVideo
|
8bfff1b9a73d09c8f42dc3778f895525f1bc946d
|
[
"MIT"
] | null | null | null |
Mundo_1/aula07.py
|
GabrielProdi/Python_CursoEmVideo
|
8bfff1b9a73d09c8f42dc3778f895525f1bc946d
|
[
"MIT"
] | null | null | null |
#Operadores Aritmeticos
n1 = int( input('Digite um nmero: ' ) )
n2 = int( input('Digite outro nmero: ' ) )
soma = n1 + n2
prod = n1 * n2
div = n1 / n2
divint = n1 // n2
exp = n1 ** n2
resto = n1 % n2
# {: } os dois pontos formatam o valor da mascara conforme as diretrizes
#quebra de linha \n
# end= escrever o conteudo no final do print
print( 'A soma {}, \no produto {} \ne a diviso {:.2}. '.format( soma, prod, div ), end='' )
print( 'A diviso inteira {}, \na potenciao {} \ne o resto da diviso {}'.format( divint, exp, resto ) )
| 32.823529
| 112
| 0.629032
|
#Operadores Aritmeticos
n1 = int( input('Digite um número: ' ) )
n2 = int( input('Digite outro número: ' ) )
soma = n1 + n2
prod = n1 * n2
div = n1 / n2
divint = n1 // n2
exp = n1 ** n2
resto = n1 % n2
# {: } os dois pontos formatam o valor da mascara conforme as diretrizes
#quebra de linha é \n
# end= escrever o conteudo no final do print
print( 'A soma é {}, \no produto é {} \ne a divisão é {:.2}. '.format( soma, prod, div ), end='' )
print( 'A divisão inteira é {}, \na potenciação é {} \ne o resto da divisão é {}'.format( divint, exp, resto ) )
| 28
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
596d64b56e4d8d8991ebc5da5fab0164bcddc3e1
| 3,156
|
py
|
Python
|
regression/gallery/contour9.py
|
b8raoult/magics
|
eb2c86ec6e392e89c90044128dc671f22283d6ad
|
[
"ECL-2.0",
"Apache-2.0"
] | 41
|
2018-12-07T23:10:50.000Z
|
2022-02-19T03:01:49.000Z
|
regression/gallery/contour9.py
|
b8raoult/magics
|
eb2c86ec6e392e89c90044128dc671f22283d6ad
|
[
"ECL-2.0",
"Apache-2.0"
] | 59
|
2019-01-04T15:43:30.000Z
|
2022-03-31T09:48:15.000Z
|
regression/gallery/contour9.py
|
b8raoult/magics
|
eb2c86ec6e392e89c90044128dc671f22283d6ad
|
[
"ECL-2.0",
"Apache-2.0"
] | 13
|
2019-01-07T14:36:33.000Z
|
2021-09-06T14:48:36.000Z
|
# (C) Copyright 1996-2016 ECMWF.
#
# This software is licensed under the terms of the Apache Licence Version 2.0
# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
# In applying this licence, ECMWF does not waive the privileges and immunities
# granted to it by virtue of its status as an intergovernmental organisation nor
# does it submit to any jurisdiction.
#Loading GRIB file
data = mgrib(grib_input_file_name='2m_temperature.grib',grib_field_position=1)
#Setting output
output = output(
output_formats = ['png'],
output_name = 'contour9',
output_name_first_page_number = 'off')
#Setting the geographical area
area = mmap(
subpage_lower_left_latitude = 34.5,
subpage_lower_left_longitude = -10.67,
subpage_map_projection = 'cylindrical',
subpage_upper_right_latitude = 71.05,
subpage_upper_right_longitude = 31.55,
)
#Setting the coastlines
background = mcoast(
map_coastline_land_shade = 'on',
map_coastline_land_shade_colour = 'cream')
foreground = mcoast(
map_coastline_land_shade = 'off',
map_grid_line_style = 'dash',
map_grid_colour = 'grey',
map_label = 'on',
map_coastline_colour = 'black')
#Defining the contour
contour = mcont(
contour = 'off',
contour_highlight_frequency = 100,
contour_highlight_thickness = 5,
contour_hilo = 'off',
contour_interval = 2.0,
contour_label = 'off',
contour_level_selection_type = 'interval',
contour_line_thickness = 3,
contour_shade = 'on',
contour_shade_colour_list = ['rgb(0.3,0.3,0.3)', 'rgb(0.4,0.4,0.4)', 'rgb(0.5,0.5,0.5)', 'rgb(0.6,0.6,0.6)', 'rgb(0.7,0.7,0.7)', 'rgb(0.8,0.8,0.8)', 'rgb(0.35,0,0.6)', 'rgb(0.5,0,0.9)', 'rgb(0.6,0.2,1)', 'rgb(0.75,0.4,1)', 'rgb(0.85,0.6,1)', 'rgb(0,0,0.75)', 'rgb(0,0,1)', 'rgb(0.2,0.4,1)', 'rgb(0.4,0.7,1)', 'rgb(0.6,0.9,1)', 'rgb(0,0.55,0.19)', 'rgb(0.15,0.75,0.1)', 'rgb(0.5,0.85,0)', 'rgb(0.65,0.95,0)', 'rgb(0.8,1,0.2)', 'rgb(0.65,0.65,0)', 'rgb(0.8,0.8,0)', 'rgb(0.92,0.92,0)', 'rgb(1,1,0)', 'rgb(1,1,0.6)', 'rgb(0.85,0.45,0)', 'rgb(1,0.5,0)', 'rgb(1,0.62,0)', 'rgb(1,0.74,0)', 'rgb(1,0.85,0)', 'rgb(0.6,0,0)', 'rgb(0.8,0,0)', 'rgb(1,0,0)', 'rgb(1,0.4,0.4)', 'rgb(1,0.6,0.6)', 'rgb(1,0.75,0.75)'],
contour_shade_colour_method = 'list',
contour_shade_max_level = 42.0,
contour_shade_method = 'area_fill',
contour_shade_min_level = -32.0,
legend = 'on',
)
#Picking the grib metadata
title = mtext(
text_lines = ['<font size="1">2m temperature (Range: -32 .. 42)</font>','<magics_title/>'],
text_justification = 'left',
text_font_size = 0.6,
text_colour = 'charcoal')
#Plotting
plot(output,area,background,data,title,contour,foreground)
#For documentation only
tofortran('contour9',output,area,data,background,contour,foreground,title)
tomv4('contour9',contour)
tohtml('contour9',data,contour)
| 43.232877
| 709
| 0.606781
|
# (C) Copyright 1996-2016 ECMWF.
#
# This software is licensed under the terms of the Apache Licence Version 2.0
# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
# In applying this licence, ECMWF does not waive the privileges and immunities
# granted to it by virtue of its status as an intergovernmental organisation nor
# does it submit to any jurisdiction.
from Magics.macro import *
#Loading GRIB file
data = mgrib(grib_input_file_name='2m_temperature.grib',grib_field_position=1)
#Setting output
output = output(
output_formats = ['png'],
output_name = 'contour9',
output_name_first_page_number = 'off')
#Setting the geographical area
area = mmap(
subpage_lower_left_latitude = 34.5,
subpage_lower_left_longitude = -10.67,
subpage_map_projection = 'cylindrical',
subpage_upper_right_latitude = 71.05,
subpage_upper_right_longitude = 31.55,
)
#Setting the coastlines
background = mcoast(
map_coastline_land_shade = 'on',
map_coastline_land_shade_colour = 'cream')
foreground = mcoast(
map_coastline_land_shade = 'off',
map_grid_line_style = 'dash',
map_grid_colour = 'grey',
map_label = 'on',
map_coastline_colour = 'black')
#Defining the contour
contour = mcont(
contour = 'off',
contour_highlight_frequency = 100,
contour_highlight_thickness = 5,
contour_hilo = 'off',
contour_interval = 2.0,
contour_label = 'off',
contour_level_selection_type = 'interval',
contour_line_thickness = 3,
contour_shade = 'on',
contour_shade_colour_list = ['rgb(0.3,0.3,0.3)', 'rgb(0.4,0.4,0.4)', 'rgb(0.5,0.5,0.5)', 'rgb(0.6,0.6,0.6)', 'rgb(0.7,0.7,0.7)', 'rgb(0.8,0.8,0.8)', 'rgb(0.35,0,0.6)', 'rgb(0.5,0,0.9)', 'rgb(0.6,0.2,1)', 'rgb(0.75,0.4,1)', 'rgb(0.85,0.6,1)', 'rgb(0,0,0.75)', 'rgb(0,0,1)', 'rgb(0.2,0.4,1)', 'rgb(0.4,0.7,1)', 'rgb(0.6,0.9,1)', 'rgb(0,0.55,0.19)', 'rgb(0.15,0.75,0.1)', 'rgb(0.5,0.85,0)', 'rgb(0.65,0.95,0)', 'rgb(0.8,1,0.2)', 'rgb(0.65,0.65,0)', 'rgb(0.8,0.8,0)', 'rgb(0.92,0.92,0)', 'rgb(1,1,0)', 'rgb(1,1,0.6)', 'rgb(0.85,0.45,0)', 'rgb(1,0.5,0)', 'rgb(1,0.62,0)', 'rgb(1,0.74,0)', 'rgb(1,0.85,0)', 'rgb(0.6,0,0)', 'rgb(0.8,0,0)', 'rgb(1,0,0)', 'rgb(1,0.4,0.4)', 'rgb(1,0.6,0.6)', 'rgb(1,0.75,0.75)'],
contour_shade_colour_method = 'list',
contour_shade_max_level = 42.0,
contour_shade_method = 'area_fill',
contour_shade_min_level = -32.0,
legend = 'on',
)
#Picking the grib metadata
title = mtext(
text_lines = ['<font size="1">2m temperature (Range: -32 .. 42)</font>','<magics_title/>'],
text_justification = 'left',
text_font_size = 0.6,
text_colour = 'charcoal')
#Plotting
plot(output,area,background,data,title,contour,foreground)
#For documentation only
tofortran('contour9',output,area,data,background,contour,foreground,title)
tomv4('contour9',contour)
tohtml('contour9',data,contour)
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 5
| 23
|
2a71651e70b4a7528bd63355ddd1957a197426fa
| 154,019
|
py
|
Python
|
ui/psychsim_rc.py
|
richardyang/psychsim
|
191497d72077fe95cde94a2004a8be6e926c121f
|
[
"MIT"
] | 1
|
2016-07-19T16:56:46.000Z
|
2016-07-19T16:56:46.000Z
|
ui/psychsim_rc.py
|
richardyang/psychsim
|
191497d72077fe95cde94a2004a8be6e926c121f
|
[
"MIT"
] | 1
|
2018-08-27T22:31:16.000Z
|
2018-08-27T22:31:16.000Z
|
ui/psychsim_rc.py
|
richardyang/psychsim
|
191497d72077fe95cde94a2004a8be6e926c121f
|
[
"MIT"
] | 1
|
2019-09-06T03:05:35.000Z
|
2019-09-06T03:05:35.000Z
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created: Mon Dec 22 15:59:06 2014
# by: The Resource Compiler for PyQt (Qt v4.8.6)
#
# WARNING! All changes made in this file will be lost!
qt_resource_data = "\
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"
qt_resource_name = "\
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qt_resource_struct = "\
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qInitResources()
| 65.317642
| 96
| 0.727274
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created: Mon Dec 22 15:59:06 2014
# by: The Resource Compiler for PyQt (Qt v4.8.6)
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore
qt_resource_data = "\
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qt_resource_name = "\
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qt_resource_struct = "\
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"
def qInitResources():
QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 0
| 0
| 0
| 0
| 0
| 195
| 0
| 3
| 69
|
ec4441541b2c6be31fc394effb8172bbc4f5104c
| 457
|
py
|
Python
|
moreover/base/config.py
|
moriW/moreover
|
330b49f4888211d2502208c13c3fff49a24124ad
|
[
"MIT"
] | null | null | null |
moreover/base/config.py
|
moriW/moreover
|
330b49f4888211d2502208c13c3fff49a24124ad
|
[
"MIT"
] | null | null | null |
moreover/base/config.py
|
moriW/moreover
|
330b49f4888211d2502208c13c3fff49a24124ad
|
[
"MIT"
] | null | null | null |
#! /bin/python
# config store & parse
#
# @file: config
# @time: 2022/02/05
# @author: Mori
#
from tornado.util import ObjectDict
global_config = ObjectDict()
| 19.041667
| 50
| 0.691466
|
#! /bin/python
# config store & parse
#
# @file: config
# @time: 2022/02/05
# @author: Mori
#
import json
from tornado.util import ObjectDict
global_config = ObjectDict()
def define(config: str, default_value: str):
if config in global_config:
raise KeyError(f"{config} already exists")
global_config[config] = default_value
def parse_config_file(file: str):
with open(file, "r") as _f:
global_config.update(json.load(_f))
| 0
| 0
| 0
| 0
| 0
| 236
| 0
| -10
| 69
|
536db5165bdc70a8820e97cc1b002c271c3566b3
| 4,824
|
py
|
Python
|
noolite/noolite/noolite.py
|
andvikt/ha_addons
|
98343e6465516b821008abd58c38f5f4306e0d41
|
[
"Apache-2.0"
] | null | null | null |
noolite/noolite/noolite.py
|
andvikt/ha_addons
|
98343e6465516b821008abd58c38f5f4306e0d41
|
[
"Apache-2.0"
] | null | null | null |
noolite/noolite/noolite.py
|
andvikt/ha_addons
|
98343e6465516b821008abd58c38f5f4306e0d41
|
[
"Apache-2.0"
] | null | null | null |
import serial
from logger import root_logger
lg = root_logger.getChild('noolite')
def _get_tty(tty_name) -> serial.Serial:
"""
:param tty_name:
:return:
"""
serial_port = serial.Serial(tty_name, 9600, timeout=2)
if not serial_port.is_open:
serial_port.open()
serial_port.flushInput()
serial_port.flushOutput()
return serial_port
| 33.041096
| 119
| 0.583126
|
import asyncio
from datetime import datetime
import serial
from logger import root_logger
from . import const
from .typing import NooliteCommand, BaseNooliteRemote, MotionSensor
from typing import Dict, Callable
import typing
lg = root_logger.getChild('noolite')
class NotApprovedError(Exception):
pass
class Noolite:
def __init__(
self,
tty_name: str,
loop: typing.Optional[asyncio.AbstractEventLoop],
):
self.callbacks: Dict[int, Callable] = {}
self.global_cbs = []
self._cmd_log: typing.Dict[int, datetime] = {}
self.event_que: asyncio.Queue[BaseNooliteRemote] = asyncio.Queue()
self.tty = _get_tty(tty_name)
self.loop = loop
self.loop.add_reader(self.tty.fd, self._handle_tty)
self.wait_ftr: typing.Optional[asyncio.Future] = None
self.wait_cmd: typing.Optional[NooliteCommand] = None
self.send_lck = asyncio.Lock()
def _handle_tty(self):
"""
Хендлер входящих данных от адаптера
:return:
"""
while self.tty.in_waiting >= 17:
in_bytes = self.tty.read(17)
resp = NooliteCommand(*(x for x in in_bytes))
lg.debug(f'< %s', list(in_bytes))
if self._cancel_waiting(resp):
return
asyncio.create_task(self.handle_command(resp))
def _cancel_waiting(self, msg: NooliteCommand):
"""
Отменяет ожидание подвтерждения, возвращает истину если ожидание было, ложь, если нет
:return:
"""
if isinstance(self.wait_ftr, asyncio.Future) \
and msg.ch == self.wait_cmd.ch \
and msg.mode == self.wait_cmd.mode:
self.wait_ftr.set_result(True)
lg.debug(f'{"Approved:".rjust(20, " ")} {self.wait_cmd}')
return True
else:
return False
@property
async def in_commands(self):
"""
Возвращает пришедшие команды в бесконечном цикле
:return:
"""
while True:
yield await self.event_que.get()
async def handle_command(self, resp: NooliteCommand):
"""
При приеме входящего сообщения нужно вызвать этот метод
:param resp:
:return:
"""
try:
dispatcher, name = const.dispatchers.get(resp.cmd, (None, None))
if name:
lg.debug(f'dispatching {name}')
if dispatcher is None:
dispatcher = BaseNooliteRemote
remote = dispatcher(resp)
if isinstance(remote, MotionSensor):
l = self._cmd_log.get(remote.channel)
self._cmd_log[remote.channel] = datetime.now()
if l is not None:
td = (datetime.now() - l).total_seconds()
if td < const.MOTION_JITTER:
lg.debug(f'anti-jitter: {resp}')
return
await self.event_que.put(remote)
except Exception:
lg.exception(f'handling {resp}')
raise
async def send_command(self, command: typing.Union[NooliteCommand, bytearray]):
"""
Отправляет команды, асинхронно, ждет подтверждения уже отправленной команды
:param command:
:return:
"""
# отправляем только одну команду до получения подтверждения
async with self.send_lck:
if isinstance(command, NooliteCommand):
cmd = command.as_tuple()
else:
cmd = command
lg.debug(f'> {cmd}')
self.tty.write(bytearray(cmd))
# отправляем команду и ждем секунду, если придет ответ, то ожидание будет отменено с ошибкой CancelledError
# - значит от модуля пришел ответ о подтверждении команды, в противном случае поднимаем ошибку о том что
# команда не подтверждена
if command.commit is None:
return True
self.wait_ftr = self.loop.create_future()
self.wait_cmd = command
try:
await asyncio.wait_for(self.wait_ftr, command.commit)
await asyncio.sleep(0.05)
except asyncio.CancelledError:
return True
except asyncio.TimeoutError:
raise NotApprovedError(command)
finally:
self.wait_ftr = None
self.wait_cmd = None
def _get_tty(tty_name) -> serial.Serial:
"""
Подключение к последовательному порту
:param tty_name: имя порта
:return:
"""
serial_port = serial.Serial(tty_name, 9600, timeout=2)
if not serial_port.is_open:
serial_port.open()
serial_port.flushInput()
serial_port.flushOutput()
return serial_port
| 1,066
| 141
| 1,969
| 1,564
| 0
| 0
| 0
| 49
| 178
|
07baeeac2fe71021abc2ba135d5e09a36888cf1c
| 2,416
|
py
|
Python
|
jom/freq.py
|
JoM-Lab/JoM
|
cd03fd401ebbd8133c8b4816115bb87e2fd24690
|
[
"MIT"
] | 1
|
2016-03-10T04:52:56.000Z
|
2016-03-10T04:52:56.000Z
|
jom/freq.py
|
JoM-Lab/JoM
|
cd03fd401ebbd8133c8b4816115bb87e2fd24690
|
[
"MIT"
] | null | null | null |
jom/freq.py
|
JoM-Lab/JoM
|
cd03fd401ebbd8133c8b4816115bb87e2fd24690
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# coding: utf-8
from __future__ import unicode_literals, print_function
from collections import Counter
from .db import new_session, Tweet
import string
import re
zh = re.compile(r"[\u4e00-\u9fa5]+")
other = re.compile(r"[a-zA-Z0-9_]+")
bad = ("_ 1 2 3 4 5 I O RT The a and are be bit co com for gt http https html in "
"is it jpg ly me media my not of on org p pbs png r s status t that the this to "
"twimg via www you").split()
alphanum = set(string.ascii_letters + string.digits)
def freq(texts, limit=400, p=12):
'''Find the most frequent words and their frequencies in a set of texts.
:param texts: a list of strings
:type texts: List[str]
:param int limit: a soft limit to cut off some words
:param int p: longest word length
:return: list of words and frequencies
:rtype: List[(str, int)]
'''
cnt = Counter()
cntE = Counter()
for l in texts:
# find all Chinese
for w in zh.findall(l):
saw = set()
for ln in range(2, p+1): # all length
for i in range(len(w)-(ln-1)): # all start pos
saw.add(w[i:i+ln].strip('').lstrip(''))
for v in saw:
cnt[v] += 1
# English words and digits
for w in other.findall(l):
cntE[w] += 1
for w in bad:
cntE[w] = 0 # remove
# find top `limit` ones
freqs = cnt.most_common(limit)
# initialize results as top 10 English words & digits
results = list(cntE.most_common(10))
# already in results
filt = Counter()
# from longest to shortest
for ln in range(p, 1, -1):
# find all with this length but a part of longer ones
cur = [(k, v) for k, v in freqs if len(k) == ln]
# filter some with `filt`
cur = [(k, v) for k, v in cur if k not in filt or filt[k] * 2 < v]
cur.sort(key=lambda t: t[1], reverse=True)
results.extend(cur)
# put all parts into `filt`
for w, v in cur:
for l in range(2, ln):
for i in range(len(w)-(l-1)):
filt[w[i:i+l]] += v
# print(results)
return results
if __name__ == '__main__':
session = new_session()
texts = [i.text for i in session.query(Tweet.text).filter(Tweet.sender == 'masked')]
cnt = freq(texts)
print(sorted([(v, k) for k, v in cnt], reverse=True))
| 34.514286
| 88
| 0.579056
|
#!/usr/bin/env python3
# coding: utf-8
from __future__ import unicode_literals, print_function
from collections import Counter
from .db import new_session, Tweet
import string
import re
zh = re.compile(r"[\u4e00-\u9fa5]+")
other = re.compile(r"[a-zA-Z0-9_]+")
bad = ("_ 1 2 3 4 5 I O RT The a and are be bit co com for gt http https html in "
"is it jpg ly me media my not of on org p pbs png r s status t that the this to "
"twimg via www you").split()
alphanum = set(string.ascii_letters + string.digits)
def freq(texts, limit=400, p=12):
'''Find the most frequent words and their frequencies in a set of texts.
:param texts: a list of strings
:type texts: List[str]
:param int limit: a soft limit to cut off some words
:param int p: longest word length
:return: list of words and frequencies
:rtype: List[(str, int)]
'''
cnt = Counter()
cntE = Counter()
for l in texts:
# find all Chinese
for w in zh.findall(l):
saw = set()
for ln in range(2, p+1): # all length
for i in range(len(w)-(ln-1)): # all start pos
saw.add(w[i:i+ln].strip('的').lstrip('了'))
for v in saw:
cnt[v] += 1
# English words and digits
for w in other.findall(l):
cntE[w] += 1
for w in bad:
cntE[w] = 0 # remove
# find top `limit` ones
freqs = cnt.most_common(limit)
# initialize results as top 10 English words & digits
results = list(cntE.most_common(10))
# already in results
filt = Counter()
# from longest to shortest
for ln in range(p, 1, -1):
# find all with this length but a part of longer ones
cur = [(k, v) for k, v in freqs if len(k) == ln]
# filter some with `filt`
cur = [(k, v) for k, v in cur if k not in filt or filt[k] * 2 < v]
cur.sort(key=lambda t: t[1], reverse=True)
results.extend(cur)
# put all parts into `filt`
for w, v in cur:
for l in range(2, ln):
for i in range(len(w)-(l-1)):
filt[w[i:i+l]] += v
# print(results)
return results
if __name__ == '__main__':
session = new_session()
texts = [i.text for i in session.query(Tweet.text).filter(Tweet.sender == 'masked')]
cnt = freq(texts)
print(sorted([(v, k) for k, v in cnt], reverse=True))
| 6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
6fdd4da2f419ff8d134e23b97de8a08353bf1a3a
| 429
|
py
|
Python
|
utils/sms.py
|
ljygit/12306
|
3de32a7046a2cfef35c1a2793172607237bc7ef7
|
[
"MIT"
] | 1
|
2020-06-22T10:18:09.000Z
|
2020-06-22T10:18:09.000Z
|
utils/sms.py
|
ljygit/12306
|
3de32a7046a2cfef35c1a2793172607237bc7ef7
|
[
"MIT"
] | 8
|
2021-03-19T03:19:50.000Z
|
2022-03-11T23:58:15.000Z
|
utils/sms.py
|
Mr-Mei/12306-
|
ff6f027497962c34c8ad6df0138e617a7dc8be71
|
[
"MIT"
] | 1
|
2019-11-18T23:17:07.000Z
|
2019-11-18T23:17:07.000Z
|
if __name__ == '__main__':
from configure import ACCOUNT_SID, AUTO_TOKEN, FROM_NUM, TO_NUM
msg = ''
send_sms(ACCOUNT_SID,AUTO_TOKEN,FROM_NUM,TO_NUM,msg)
| 30.642857
| 64
| 0.72028
|
from twilio.rest import Client
def send_sms(account_sid,auth_token,from_man,to_man,msg):
client = Client(account_sid, auth_token)
message = client.messages.create(
to=to_man,
from_=from_man,
body=msg)
return message.sid
if __name__ == '__main__':
from configure import ACCOUNT_SID,AUTO_TOKEN,FROM_NUM,TO_NUM
msg = '测试一下，亲爱的路人'
send_sms(ACCOUNT_SID,AUTO_TOKEN,FROM_NUM,TO_NUM,msg)
| 30
| 0
| 0
| 0
| 0
| 203
| 0
| 6
| 45
|
a8b0a4469467b3bf1e3dba6386f211a25e5876c2
| 1,513
|
py
|
Python
|
revolve.py
|
safsaf150/revolve
|
ff12ac6992c99116b9d571dc277165362d8d5602
|
[
"Apache-2.0"
] | null | null | null |
revolve.py
|
safsaf150/revolve
|
ff12ac6992c99116b9d571dc277165362d8d5602
|
[
"Apache-2.0"
] | null | null | null |
revolve.py
|
safsaf150/revolve
|
ff12ac6992c99116b9d571dc277165362d8d5602
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python3
import os
import sys
from pyrevolve import parser
here = os.path.dirname(os.path.abspath(__file__))
rvpath = os.path.abspath(os.path.join(here, '..', 'revolve'))
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
if __name__ == "__main__":
settings = parser.parse_args()
manager_settings = sys.argv[1:]
supervisor = OnlineEvolutionSupervisor(
manager_cmd=settings.manager,
manager_args=manager_settings,
world_file=settings.world,
simulator_cmd=settings.simulator_cmd,
simulator_args=["--verbose"],
plugins_dir_path=os.path.join(rvpath, 'build', 'lib'),
models_dir_path=os.path.join(rvpath, 'models')
)
if settings.manager is None:
ret = supervisor.launch_simulator()
else:
ret = supervisor.launch()
sys.exit(ret)
| 27.509091
| 72
| 0.636484
|
#!/usr/bin/env python3
import os
import sys
from pyrevolve import parser
from pyrevolve.util import Supervisor
here = os.path.dirname(os.path.abspath(__file__))
rvpath = os.path.abspath(os.path.join(here, '..', 'revolve'))
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
class OnlineEvolutionSupervisor(Supervisor):
"""
Supervisor class that adds some output filtering for ODE errors
"""
def __init__(self, *args, **kwargs):
super(OnlineEvolutionSupervisor, self).__init__(*args, **kwargs)
self.ode_errors = 0
def write_stderr(self, data):
"""
:param data:
:return:
"""
if 'ODE Message 3' in data:
self.ode_errors += 1
elif data.strip():
sys.stderr.write(data)
if self.ode_errors >= 100:
self.ode_errors = 0
sys.stderr.write('ODE Message 3 (100)\n')
if __name__ == "__main__":
settings = parser.parse_args()
manager_settings = sys.argv[1:]
supervisor = OnlineEvolutionSupervisor(
manager_cmd=settings.manager,
manager_args=manager_settings,
world_file=settings.world,
simulator_cmd=settings.simulator_cmd,
simulator_args=["--verbose"],
plugins_dir_path=os.path.join(rvpath, 'build', 'lib'),
models_dir_path=os.path.join(rvpath, 'models')
)
if settings.manager is None:
ret = supervisor.launch_simulator()
else:
ret = supervisor.launch()
sys.exit(ret)
| 0
| 0
| 0
| 600
| 0
| 0
| 0
| 16
| 45
|
5a6c067c4c899f822dcfbcf61b4d8154e18f0052
| 509
|
py
|
Python
|
src/jvm/io/fsq/twofishes/scripts/refresh-store.py
|
jglesner/fsqio
|
436dd3a7667fd23f638bf96bdcd9ec83266a2319
|
[
"Apache-2.0"
] | 252
|
2016-01-08T23:12:13.000Z
|
2022-01-17T16:31:49.000Z
|
src/jvm/io/fsq/twofishes/scripts/refresh-store.py
|
jglesner/fsqio
|
436dd3a7667fd23f638bf96bdcd9ec83266a2319
|
[
"Apache-2.0"
] | 67
|
2016-01-13T17:34:12.000Z
|
2021-08-04T18:50:24.000Z
|
src/jvm/io/fsq/twofishes/scripts/refresh-store.py
|
jglesner/fsqio
|
436dd3a7667fd23f638bf96bdcd9ec83266a2319
|
[
"Apache-2.0"
] | 59
|
2016-03-25T20:49:03.000Z
|
2021-08-04T05:36:38.000Z
|
#!/usr/bin/python
import urllib2
import json
import sys
from optparse import OptionParser
usage = "usage: %prog [options] host"
parser = OptionParser(usage = usage)
parser.add_option("-t", "--token", dest="token", default="", type='string',
help="token")
(options, args) = parser.parse_args()
if len(args) != 1:
parser.print_usage()
sys.exit(1)
host = args[0]
data = { "token": options.token }
dataStr = json.dumps(data)
print urllib2.urlopen('http://%s/private/refreshStore' % host, dataStr).read()
| 24.238095
| 78
| 0.695481
|
#!/usr/bin/python
import urllib2
import json
import sys
from optparse import OptionParser
usage = "usage: %prog [options] host"
parser = OptionParser(usage = usage)
parser.add_option("-t", "--token", dest="token", default="", type='string',
help="token")
(options, args) = parser.parse_args()
if len(args) != 1:
parser.print_usage()
sys.exit(1)
host = args[0]
data = { "token": options.token }
dataStr = json.dumps(data)
print urllib2.urlopen('http://%s/private/refreshStore' % host, dataStr).read()
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
9dbd5ad7e1294e12a9fb2519e92fbb06c8562dc1
| 4,723
|
py
|
Python
|
eplist/web_sources/anidb.py
|
djt5019/episode_renamer
|
84d3dda24b54c70a489c2a6bcb9c7c2c2dadfba9
|
[
"Unlicense"
] | null | null | null |
eplist/web_sources/anidb.py
|
djt5019/episode_renamer
|
84d3dda24b54c70a489c2a6bcb9c7c2c2dadfba9
|
[
"Unlicense"
] | null | null | null |
eplist/web_sources/anidb.py
|
djt5019/episode_renamer
|
84d3dda24b54c70a489c2a6bcb9c7c2c2dadfba9
|
[
"Unlicense"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import re
import difflib
import logging
import functools
from string import punctuation as punct
from eplist import utils
from eplist.episode import Episode
try:
from bs4 import BeautifulSoup
Soup = functools.partial(BeautifulSoup)
except ImportError:
try:
from BeautifulSoup import BeautifulStoneSoup as Soup
except ImportError:
logging.critical("Error: BeautifulSoup was not found, unable to parse AniDB")
priority = 3
anidb_client = 'eprenamer'
anidb_version = '1'
anidb_proto = '1'
anidb_http_url = 'http://api.anidb.net:9001/httpapi?{}'
anidb_request = "request=anime&client={}&clientver={}&protover={}&aid={{}}"
anidb_request = anidb_request.format(anidb_client, anidb_version, anidb_proto)
anidb_http_url = anidb_http_url.format(anidb_request)
def _parse_local(title):
"""
Try to find the anime ID (aid) in the dump file provided by AniDB
"""
if not utils.file_exists_in_resources('animetitles.dat'):
logging.warning("AniDB database file not found, unable to poll AniDB at this time")
logging.warning("Try using the --update-db option to download an copy of it")
return -1
logging.info("Searching the AniDB database file")
title = title.lower()
regex = re.compile(r'(?P<aid>\d+)\|(?P<type>\d)\|(?P<lang>.+)\|(?P<title>.*)')
sequence = difflib.SequenceMatcher(lambda x: x in punct, title.lower())
guesses = []
with utils.open_file_in_resources('animetitles.dat') as f:
for line in f:
res = regex.search(line)
if not res:
continue
original_title = utils.encode(res.group('title').lower())
clean_title = utils.prepare_title(utils.encode(res.group('title'))).lower()
if utils.prepare_title(title) in (original_title, clean_title):
return int(res.group('aid'))
sequence.set_seq2(clean_title.lower())
ratio = sequence.quick_ratio()
if ratio > .80:
d = dict(ratio=ratio, aid=res.group('aid'), title=original_title)
guesses.append(d)
if guesses:
logging.info("{} possibilities".format(len(guesses)))
guesses = sorted(guesses, key=lambda x: x['ratio'])
aid = guesses[0]['aid']
name = guesses[0]['title']
logging.error("Closest show to '{}' on AniDB is '{}'' with id {}".format(title, name, aid))
for guess in guesses[1:]:
logging.info("Similar show {} [{}] also found".format(guess['title'], guess['aid']))
return -1
def _connect_HTTP(aid):
"""
Connect to AniDB using the public HTTP Api, this is used as an alternative to the UDP connection function
"""
url = anidb_http_url.format(aid)
resp = utils.get_url_descriptor(url)
if resp is None:
return utils.show_not_found
soup = Soup(resp.content)
if soup.find('error'):
logging.error("Temporally banned from AniDB, most likely due to flooding")
return []
episodes = soup.findAll('episode')
if not episodes:
return utils.show_not_found
eplist = []
for e in episodes:
# 1 is a normal episode, 2 is a special
# with beautifulsoup3 it returns a list of attribute dictionaries
# rather than just a single dictionary like in bs4
if 'type' in e.epno.attrs:
ep_type = e.epno.attrs['type']
else:
ep_type = e.epno.attrs[0][1]
if ep_type not in ('1', '2'):
continue
title = e.find('title', {'xml:lang': 'en'})
title = title.getText()
if ep_type == '1':
epNum = int(e.epno.getText())
type_ = "Episode"
else:
epNum = int(e.epno.getText()[1:])
type_ = "OVA"
e = Episode(title=title, number=epNum, count=epNum, type=type_)
eplist.append(e)
return eplist
| 27.619883
| 109
| 0.629261
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import re
import difflib
import logging
import functools
from string import punctuation as punct
from eplist import utils
from eplist.episode import Episode
from eplist.settings import Settings
try:
from bs4 import BeautifulSoup
Soup = functools.partial(BeautifulSoup)
except ImportError:
try:
from BeautifulSoup import BeautifulStoneSoup as Soup
except ImportError:
logging.critical("Error: BeautifulSoup was not found, unable to parse AniDB")
priority = 3
anidb_client = 'eprenamer'
anidb_version = '1'
anidb_proto = '1'
anidb_http_url = 'http://api.anidb.net:9001/httpapi?{}'
anidb_request = "request=anime&client={}&clientver={}&protover={}&aid={{}}"
anidb_request = anidb_request.format(anidb_client, anidb_version, anidb_proto)
anidb_http_url = anidb_http_url.format(anidb_request)
def _parse_local(title):
"""
Try to find the anime ID (aid) in the dump file provided by AniDB
"""
if not utils.file_exists_in_resources('animetitles.dat'):
logging.warning("AniDB database file not found, unable to poll AniDB at this time")
logging.warning("Try using the --update-db option to download an copy of it")
return -1
logging.info("Searching the AniDB database file")
title = title.lower()
regex = re.compile(r'(?P<aid>\d+)\|(?P<type>\d)\|(?P<lang>.+)\|(?P<title>.*)')
sequence = difflib.SequenceMatcher(lambda x: x in punct, title.lower())
guesses = []
with utils.open_file_in_resources('animetitles.dat') as f:
for line in f:
res = regex.search(line)
if not res:
continue
original_title = utils.encode(res.group('title').lower())
clean_title = utils.prepare_title(utils.encode(res.group('title'))).lower()
if utils.prepare_title(title) in (original_title, clean_title):
return int(res.group('aid'))
sequence.set_seq2(clean_title.lower())
ratio = sequence.quick_ratio()
if ratio > .80:
d = dict(ratio=ratio, aid=res.group('aid'), title=original_title)
guesses.append(d)
if guesses:
logging.info("{} possibilities".format(len(guesses)))
guesses = sorted(guesses, key=lambda x: x['ratio'])
aid = guesses[0]['aid']
name = guesses[0]['title']
logging.error("Closest show to '{}' on AniDB is '{}'' with id {}".format(title, name, aid))
for guess in guesses[1:]:
logging.info("Similar show {} [{}] also found".format(guess['title'], guess['aid']))
return -1
def _connect_UDP(aid):
## Todo: make this work so we stop relying on the http protocol
raise NotImplementedError("I will get to this later... promise.")
if not Settings.anidb_username or not Settings.anidb_password:
raise ValueError("Username/Password required to poll AniDB")
def _connect_HTTP(aid):
"""
Connect to AniDB using the public HTTP Api, this is used as an alternative to the UDP connection function
"""
url = anidb_http_url.format(aid)
resp = utils.get_url_descriptor(url)
if resp is None:
return utils.show_not_found
soup = Soup(resp.content)
if soup.find('error'):
logging.error("Temporally banned from AniDB, most likely due to flooding")
return []
episodes = soup.findAll('episode')
if not episodes:
return utils.show_not_found
eplist = []
for e in episodes:
# 1 is a normal episode, 2 is a special
# with beautifulsoup3 it returns a list of attribute dictionaries
# rather than just a single dictionary like in bs4
if 'type' in e.epno.attrs:
ep_type = e.epno.attrs['type']
else:
ep_type = e.epno.attrs[0][1]
if ep_type not in ('1', '2'):
continue
title = e.find('title', {'xml:lang': 'en'})
title = title.getText()
if ep_type == '1':
epNum = int(e.epno.getText())
type_ = "Episode"
else:
epNum = int(e.epno.getText()[1:])
type_ = "OVA"
e = Episode(title=title, number=epNum, count=epNum, type=type_)
eplist.append(e)
return eplist
def poll(title=""):
try:
Soup
except NameError:
return utils.show_not_found
aid = _parse_local(title.lower())
if aid < 0:
return utils.show_not_found
logging.info("Found AID: {}".format(aid))
if utils.able_to_poll('AniDB'):
episodes = _connect_HTTP(aid)
if episodes:
return episodes
return utils.show_not_found
| 0
| 0
| 0
| 0
| 0
| 650
| 0
| 15
| 68
|
d2fdf77b2d8c1bb4d3bf7464c2a44161dda68f02
| 1,284
|
py
|
Python
|
replot.py
|
MQSchleich/dylightful
|
6abbb690c8387c522c9bff21c72b5c66aab77ede
|
[
"MIT"
] | null | null | null |
replot.py
|
MQSchleich/dylightful
|
6abbb690c8387c522c9bff21c72b5c66aab77ede
|
[
"MIT"
] | 5
|
2022-02-05T12:47:42.000Z
|
2022-03-16T11:42:20.000Z
|
replot.py
|
MQSchleich/dylightful
|
6abbb690c8387c522c9bff21c72b5c66aab77ede
|
[
"MIT"
] | null | null | null |
import os
import numpy as np
from dylightful.utilities import load_parsed_dyno, get_dir
from dylightful.bar_plot import make_barplot
dirname = os.path.dirname(__file__)
save_dir = "/media/julian/INTENSO/ZIKV/"
name = "ZIKV-Pro-427-1_dynophore"
traj_path = save_dir + name + ".json"
prefix = "ligand"
pml = save_dir + name + ".pml"
ligand_path = save_dir + name + "_time_series.json"
topology = save_dir + "topo0.pdb"
trajectory = save_dir + "trajectory.dcd"
traj_path = os.path.join(dirname, traj_path)
time_ser, num_obs = load_parsed_dyno(traj_path=ligand_path)
time_ser = time_ser
print(type(time_ser))
print(time_ser.shape)
print(time_ser[0:2, :])
# traj_path = os.path.join(dirname, traj_path)
# env_partners = load_env_partners_mixed(json_path=traj_path)
# env_partner_arr = []
# for partner in env_partners.keys():
# env_partner_arr.append(env_partners[partner])
# env_partner_arr = np.array(env_partner_arr)
# time_ser = env_partner_arr.T
# print(type(time_ser))
# print(time_ser.shape)
# print(time_ser[0:2,:])
save_path = get_dir(traj_path)
base = save_dir
make_barplot(
time_ser=time_ser,
ylabel="Ligand Perspective",
yticks=np.arange(time_ser.shape[1]),
prefix=prefix,
save_path=save_path,
)
| 29.181818
| 61
| 0.760125
|
import os
import numpy as np
from dylightful.utilities import load_parsed_dyno, get_dir
from dylightful.parser import load_env_partners_mixed
from dylightful.bar_plot import make_barplot
dirname = os.path.dirname(__file__)
save_dir = "/media/julian/INTENSO/ZIKV/"
name = "ZIKV-Pro-427-1_dynophore"
traj_path = save_dir + name + ".json"
prefix = "ligand"
pml = save_dir + name + ".pml"
ligand_path = save_dir + name + "_time_series.json"
topology = save_dir + "topo0.pdb"
trajectory = save_dir + "trajectory.dcd"
traj_path = os.path.join(dirname, traj_path)
time_ser, num_obs = load_parsed_dyno(traj_path=ligand_path)
time_ser = time_ser
print(type(time_ser))
print(time_ser.shape)
print(time_ser[0:2, :])
# traj_path = os.path.join(dirname, traj_path)
# env_partners = load_env_partners_mixed(json_path=traj_path)
# env_partner_arr = []
# for partner in env_partners.keys():
# env_partner_arr.append(env_partners[partner])
# env_partner_arr = np.array(env_partner_arr)
# time_ser = env_partner_arr.T
# print(type(time_ser))
# print(time_ser.shape)
# print(time_ser[0:2,:])
save_path = get_dir(traj_path)
base = save_dir
make_barplot(
time_ser=time_ser,
ylabel="Ligand Perspective",
yticks=np.arange(time_ser.shape[1]),
prefix=prefix,
save_path=save_path,
)
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 32
| 22
|
05c583400c5f51e6d3818158d9d2a62630174a12
| 1,286
|
py
|
Python
|
codereviewr/urls.py
|
percyperez/codereviewr
|
d7bc927455559389354f615cf7c130e7122e04ef
|
[
"MIT"
] | 2
|
2015-12-08T13:40:32.000Z
|
2016-05-08T06:22:14.000Z
|
codereviewr/urls.py
|
na/codereviewr
|
c1045f2e9a16d64398306dbf9124b4f59ef2fdbb
|
[
"MIT"
] | null | null | null |
codereviewr/urls.py
|
na/codereviewr
|
c1045f2e9a16d64398306dbf9124b4f59ef2fdbb
|
[
"MIT"
] | null | null | null |
from django.contrib.comments.feeds import LatestCommentsFeed
from django.views.generic.simple import direct_to_template
from codereviewr.settings import PROJECT_PATH, DEBUG
#feeds dictionary
feeds = {
'code': CodeFeed,
'comments': LatestCommentsFeed,
'language': LanguageFeed,
'latest': LatestFeed,
'user': UserFeed,
}
urlpatterns = patterns('',
(r'^code/', include('codereviewr.code.urls')),
# Admin
(r'^admin/code/language/refresh/$', 'code.views.refresh_languages'),
(r'^admin/', include('django.contrib.admin.urls')),
# OpenID
(r'^openid/$', 'openid_cr.views.begin', {'redirect_to': '/openid/complete/'}),
(r'^openid/complete/$', 'openid_cr.views.complete'),
(r'^openid/signout/$', 'openid_cr.views.signout'),
# account registration
(r'^accounts/', include('registration.urls')),
#for homepage - testing
(r'^$', direct_to_template, {'template': 'homepage.html'}),
#Feeds
(r'^feeds/(?P<url>.*)/$', 'django.contrib.syndication.views.feed', {'feed_dict': feeds}),
)
if DEBUG:
urlpatterns += patterns('',
(r'^media/(.*)$', 'django.views.static.serve', {'document_root': '%s/../media' % (PROJECT_PATH)})
)
| 30.619048
| 105
| 0.662519
|
from django.conf.urls.defaults import *
from django.contrib.comments.feeds import LatestCommentsFeed
from django.views.generic.simple import direct_to_template, redirect_to
from codereviewr.settings import PROJECT_PATH, DEBUG
from codereviewr.feeds import *
#feeds dictionary
feeds = {
'code': CodeFeed,
'comments': LatestCommentsFeed,
'language': LanguageFeed,
'latest': LatestFeed,
'user': UserFeed,
}
urlpatterns = patterns('',
(r'^code/', include('codereviewr.code.urls')),
# Admin
(r'^admin/code/language/refresh/$', 'code.views.refresh_languages'),
(r'^admin/', include('django.contrib.admin.urls')),
# OpenID
(r'^openid/$', 'openid_cr.views.begin', {'redirect_to': '/openid/complete/'}),
(r'^openid/complete/$', 'openid_cr.views.complete'),
(r'^openid/signout/$', 'openid_cr.views.signout'),
# account registration
(r'^accounts/', include('registration.urls')),
#for homepage - testing
(r'^$', direct_to_template, {'template': 'homepage.html'}),
#Feeds
(r'^feeds/(?P<url>.*)/$', 'django.contrib.syndication.views.feed', {'feed_dict': feeds}),
)
if DEBUG:
urlpatterns += patterns('',
(r'^media/(.*)$', 'django.views.static.serve', {'document_root': '%s/../media' % (PROJECT_PATH)})
)
| 3
| 0
| 0
| 0
| 0
| 0
| 0
| 41
| 44
|
41c0d02a489f00ea9b52cab70a55f753870dba52
| 2,483
|
py
|
Python
|
save_preds.py
|
somepago/dbViz
|
aeea84c42a32efafebbb29c3d944204aa74963cf
|
[
"Apache-2.0"
] | 22
|
2022-03-16T10:04:43.000Z
|
2022-03-24T22:57:23.000Z
|
save_preds.py
|
somepago/dbViz
|
aeea84c42a32efafebbb29c3d944204aa74963cf
|
[
"Apache-2.0"
] | null | null | null |
save_preds.py
|
somepago/dbViz
|
aeea84c42a32efafebbb29c3d944204aa74963cf
|
[
"Apache-2.0"
] | 2
|
2022-03-19T07:13:39.000Z
|
2022-03-29T06:09:53.000Z
|
'''Train CIFAR10 with PyTorch.'''
import torch
import random
import os
from model import get_model
from data import get_data, make_planeloader
from utils import get_random_images
from evaluation import decision_boundary
from options import options
'''
This module calculates and saves prediction arrays for different saved models.
E.g. if you have saved models with the structure:
/path/to/saved/models
- model_1.pth
- model_2.pth
- model_3.pth
then this script will make a new folder /path/to/saved/models/predictions which
will save the following prediction arrays:
/path/to/save/models/predictions
- model_1_preds.pth
- model_2_preds.pth
- model_3_preds.pth
Note: the original model paths should be of the form:
ResNet18.pth
...
i.e. the name of the model used should be the same as the model in model.py
'''
args = options().parse_args()
print(args)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.manual_seed(args.set_data_seed)
trainloader, testloader = get_data(args)
paths = [os.path.join(args.load_net, p) for p in os.listdir(args.load_net) if 'pred' not in p]
for path in paths:
os.makedirs(os.path.join(args.load_net, path, 'predictions'), exist_ok=True)
for p in sorted(os.listdir(path)):
if 'pred' not in p:
if 'wide' in path.lower():
args.net = 'WideResNet'
# Here the net path is saved like '/path/to/saved/models/WideResNet_10.pth'
args.widen_factor = int(path.split('/')[-1].split('.')[0].split('_')[1])
else:
args.net = path.split('/')[-1].split('.')[0]
net = get_model(args, device)
temp_path = os.path.join(path,p)
net.load_state_dict(torch.load(temp_path))
pred_arr = []
for run in range(args.epochs):
random.seed(a=(args.set_data_seed+run), version=2)
images, labels, image_ids = get_random_images(testloader.dataset)
planeloader = make_planeloader(images, args)
preds = decision_boundary(args, net, planeloader, device)
pred_arr.append(torch.stack(preds).argmax(1).cpu())
torch.save(pred_arr, os.path.join(args.load_net, path, 'predictions') + '/' + temp_path.split('/')[-1].split('.pth')[0] + '_preds.pth')
| 37.059701
| 147
| 0.664921
|
'''Train CIFAR10 with PyTorch.'''
import torch
import random
import os
import argparse
from model import get_model
from data import get_data, make_planeloader
from utils import get_loss_function, get_scheduler, get_random_images, produce_plot, get_noisy_images, AttackPGD
from evaluation import decision_boundary
from options import options
from utils import simple_lapsed_time
'''
This module calculates and saves prediction arrays for different saved models.
E.g. if you have saved models with the structure:
/path/to/saved/models
- model_1.pth
- model_2.pth
- model_3.pth
then this script will make a new folder /path/to/saved/models/predictions which
will save the following prediction arrays:
/path/to/save/models/predictions
- model_1_preds.pth
- model_2_preds.pth
- model_3_preds.pth
Note: the original model paths should be of the form:
ResNet18.pth
...
i.e. the name of the model used should be the same as the model in model.py
'''
args = options().parse_args()
print(args)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.manual_seed(args.set_data_seed)
trainloader, testloader = get_data(args)
paths = [os.path.join(args.load_net, p) for p in os.listdir(args.load_net) if 'pred' not in p]
for path in paths:
os.makedirs(os.path.join(args.load_net, path, 'predictions'), exist_ok=True)
for p in sorted(os.listdir(path)):
if 'pred' not in p:
if 'wide' in path.lower():
args.net = 'WideResNet'
# Here the net path is saved like '/path/to/saved/models/WideResNet_10.pth'
args.widen_factor = int(path.split('/')[-1].split('.')[0].split('_')[1])
else:
args.net = path.split('/')[-1].split('.')[0]
net = get_model(args, device)
temp_path = os.path.join(path,p)
net.load_state_dict(torch.load(temp_path))
pred_arr = []
for run in range(args.epochs):
random.seed(a=(args.set_data_seed+run), version=2)
images, labels, image_ids = get_random_images(testloader.dataset)
planeloader = make_planeloader(images, args)
preds = decision_boundary(args, net, planeloader, device)
pred_arr.append(torch.stack(preds).argmax(1).cpu())
torch.save(pred_arr, os.path.join(args.load_net, path, 'predictions') + '/' + temp_path.split('/')[-1].split('.pth')[0] + '_preds.pth')
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 86
| 44
|
aa2541d0b8aba2e6c7ed95ea73a4a7d1006eb805
| 7,820
|
py
|
Python
|
data_preprocessing/flatten.py
|
billylegota/ECE-380L-Term-Project
|
9838449e7e4b40e4444fdcb0f7e23cf43e87e0f1
|
[
"MIT"
] | null | null | null |
data_preprocessing/flatten.py
|
billylegota/ECE-380L-Term-Project
|
9838449e7e4b40e4444fdcb0f7e23cf43e87e0f1
|
[
"MIT"
] | null | null | null |
data_preprocessing/flatten.py
|
billylegota/ECE-380L-Term-Project
|
9838449e7e4b40e4444fdcb0f7e23cf43e87e0f1
|
[
"MIT"
] | null | null | null |
"""flatten.py -- Flattens HDF5 dataset.
"""
import glob
import math
import os
import h5py
import numpy as np
import scipy.io
def flatten(source: str, dest: str, packets_per_chunk: int = 1000, synthetic: bool = False,
silent: bool = True, constant_features_path: str = 'constant_features.mat') -> (int, int):
"""Flatten an HDF5 dataset.
:param source: path to original dataset.
:param dest: path to store flattened dataset.
:param packets_per_chunk: number of packets to process at a time. Ideally the number of symbols per chunk is < 1000.
:param synthetic: flag the source dataset as being synthetic (changes data input format).
:param silent: suppress output.
:param constant_features_path: path to constant features.
:return: tuple containing the number of (symbols, packets) converted.
"""
data = h5py.File(source, 'r')
num_packets = len(data['rxLltfFftOut1'])
# Load constant features. This contains the indices for data and pilot tones.
constant_features = scipy.io.loadmat(constant_features_path, squeeze_me=True)
constant_features = constant_features['constant']
# Values in `constant_features.mat` are one-indexed so we subtract one for zero-indexed.
he_ltf_data_indices = constant_features['iMDataTone_Heltf'][()].astype(np.int32) - 1
he_ltf_pilot_indices = constant_features['iMPilotTone_Heltf'][()].astype(np.int32) - 1
data_indices = constant_features['iMDataTone_HePpdu'][()].astype(np.int32) - 1
pilot_indices = constant_features['iMPilotTone_HePpdu'][()].astype(np.int32) - 1
# Create the file to write the data to.
output = h5py.File(dest, 'w')
for chunk in range(math.ceil(num_packets / packets_per_chunk)):
chunk_indices = np.arange(chunk * packets_per_chunk, min((chunk + 1) * packets_per_chunk, num_packets))
# Fixes bug with h5py version on TACC.
chunk_indices = list(chunk_indices)
# Load L-LTF fields.
rx_l_ltf_1 = np.array(data['rxLltfFftOut1'][chunk_indices])
rx_l_ltf_2 = np.array(data['rxLltfFftOut2'][chunk_indices])
# Load HE-LTF and split into data and pilot.
rx_he_ltf = np.array(data['rxHeltfFftOut'][chunk_indices])
rx_he_ltf_data = rx_he_ltf[:, he_ltf_data_indices]
rx_he_ltf_pilot = rx_he_ltf[:, he_ltf_pilot_indices]
# Load RX and TX data symbols and split into data and pilot, keeping track of the corresponding LTF fields.
# FIXME: This is very slow. On the other hand, I can't be bothered to make it faster since it only runs once.
ltf_indices = []
rx_symbols = []
rx_ref_data = []
tx_symbols = []
if synthetic:
for i, j in enumerate(chunk_indices):
iterator = (data['rxHePpduFftOut'][j], data['rxHePpdutoneMappedFreq'][j], data['txConstellation'][j])
for rx_symbol, rx_ref_symbol, tx_symbol in zip(*iterator):
ltf_indices.append(i)
rx_symbols.append(rx_symbol)
rx_ref_data.append(rx_ref_symbol)
tx_symbols.append(tx_symbol)
else:
for i, j in enumerate(chunk_indices):
iterator = (data[f'rxHePpduFftOut{j}'], data[f'rxHePpdutoneMappedFreq{j}'], data[f'txConstellation{j}'])
for rx_symbol, rx_ref_symbol, tx_symbol in zip(*iterator):
ltf_indices.append(i)
rx_symbols.append(rx_symbol)
rx_ref_data.append(rx_ref_symbol)
tx_symbols.append(tx_symbol)
num_symbols = len(ltf_indices)
ltf_indices = np.array(ltf_indices)
rx_symbols = np.array(rx_symbols)
rx_ref_data = np.array(rx_ref_data)
tx_symbols = np.array(tx_symbols)
rx_data = rx_symbols[:, data_indices]
rx_pilot = rx_symbols[:, pilot_indices]
tx_data = tx_symbols[:, data_indices]
tx_pilot = tx_symbols[:, pilot_indices]
# Duplicate the entries in the LTF fields so that we have `num_symbols` entries.
rx_l_ltf_1 = np.array([rx_l_ltf_1[ltf_indices[i]] for i in range(num_symbols)])
rx_l_ltf_2 = np.array([rx_l_ltf_2[ltf_indices[i]] for i in range(num_symbols)])
rx_he_ltf_data = np.array([rx_he_ltf_data[ltf_indices[i]] for i in range(num_symbols)])
rx_he_ltf_pilot = np.array([rx_he_ltf_pilot[ltf_indices[i]] for i in range(num_symbols)])
# Lump the arrays together and save them.
arrays = {
'rx_l_ltf_1': rx_l_ltf_1,
'rx_l_ltf_2': rx_l_ltf_2,
'rx_he_ltf_data': rx_he_ltf_data,
'rx_he_ltf_pilot': rx_he_ltf_pilot,
'rx_data': rx_data,
'rx_pilot': rx_pilot,
'rx_ref_data': rx_ref_data,
'tx_data': tx_data,
'tx_pilot': tx_pilot
}
for key, value in arrays.items():
if key not in output:
output.create_dataset(name=key, data=value, maxshape=(None, *value.shape[1:]), chunks=True)
else:
output[key].resize(output[key].shape[0] + value.shape[0], axis=0)
output[key][-value.shape[0]:] = value
if not silent:
print(f'Wrote {num_symbols} symbols in {len(chunk_indices)} packets.')
if not silent:
print(f'TOTAL: {output["rx_l_ltf_1"].shape[0]} symbols in {num_packets} packets.')
return output['rx_l_ltf_1'].shape[0], num_packets
def flatten_dir(source_dir: str, dest_dir: str, packets_per_chunk: int = 1000, synthetic: bool = False,
silent: bool = True, force: bool = False,
constant_features_path: str = 'constant_features.mat') -> (int, int):
"""Flatten all HDF5 files in a directory and place them in another.
:param source_dir: source directory.
:param dest_dir: destination directory.
:param packets_per_chunk: number of packets to process at a time.
:param synthetic: flag the source dataset as being synthetic (changes data input format).
:param silent: suppress output.
:param force: force reprocessing of already-processed files.
:param constant_features_path: path to constant features.
:return: tuple containing the number of (symbols, packets) converted.
"""
total_symbols = 0
total_packets = 0
for source in glob.glob(f'{source_dir}/*.h5'):
if 'flat' in source:
if not silent:
print(f'{source} is already flattened. Skipping.')
print()
continue
dest = f'{dest_dir}/{os.path.splitext(os.path.basename(source))[0]}_flat.h5'
if not force and os.path.exists(dest):
if not silent:
print(f'{dest_dir} exists. Skipping.')
print()
continue
num_symbols, num_packets = flatten(source, dest, packets_per_chunk, synthetic=synthetic, silent=silent,
constant_features_path=constant_features_path)
total_symbols += num_symbols
total_packets += num_packets
if not silent:
print()
if not silent:
print(f'TOTAL: {total_symbols} symbols in {total_packets} packets.')
if __name__ == '__main__':
main()
| 42.27027
| 120
| 0.649105
|
"""flatten.py -- Flattens HDF5 dataset.
"""
import glob
import math
import os
import h5py
import numpy as np
import scipy.io
def flatten(source: str, dest: str, packets_per_chunk: int = 1000, synthetic: bool = False,
silent: bool = True, constant_features_path: str = 'constant_features.mat') -> (int, int):
"""Flatten an HDF5 dataset.
:param source: path to original dataset.
:param dest: path to store flattened dataset.
:param packets_per_chunk: number of packets to process at a time. Ideally the number of symbols per chunk is < 1000.
:param synthetic: flag the source dataset as being synthetic (changes data input format).
:param silent: suppress output.
:param constant_features_path: path to constant features.
:return: tuple containing the number of (symbols, packets) converted.
"""
data = h5py.File(source, 'r')
num_packets = len(data['rxLltfFftOut1'])
# Load constant features. This contains the indices for data and pilot tones.
constant_features = scipy.io.loadmat(constant_features_path, squeeze_me=True)
constant_features = constant_features['constant']
# Values in `constant_features.mat` are one-indexed so we subtract one for zero-indexed.
he_ltf_data_indices = constant_features['iMDataTone_Heltf'][()].astype(np.int32) - 1
he_ltf_pilot_indices = constant_features['iMPilotTone_Heltf'][()].astype(np.int32) - 1
data_indices = constant_features['iMDataTone_HePpdu'][()].astype(np.int32) - 1
pilot_indices = constant_features['iMPilotTone_HePpdu'][()].astype(np.int32) - 1
# Create the file to write the data to.
output = h5py.File(dest, 'w')
for chunk in range(math.ceil(num_packets / packets_per_chunk)):
chunk_indices = np.arange(chunk * packets_per_chunk, min((chunk + 1) * packets_per_chunk, num_packets))
# Fixes bug with h5py version on TACC.
chunk_indices = list(chunk_indices)
# Load L-LTF fields.
rx_l_ltf_1 = np.array(data['rxLltfFftOut1'][chunk_indices])
rx_l_ltf_2 = np.array(data['rxLltfFftOut2'][chunk_indices])
# Load HE-LTF and split into data and pilot.
rx_he_ltf = np.array(data['rxHeltfFftOut'][chunk_indices])
rx_he_ltf_data = rx_he_ltf[:, he_ltf_data_indices]
rx_he_ltf_pilot = rx_he_ltf[:, he_ltf_pilot_indices]
# Load RX and TX data symbols and split into data and pilot, keeping track of the corresponding LTF fields.
# FIXME: This is very slow. On the other hand, I can't be bothered to make it faster since it only runs once.
ltf_indices = []
rx_symbols = []
rx_ref_data = []
tx_symbols = []
if synthetic:
for i, j in enumerate(chunk_indices):
iterator = (data['rxHePpduFftOut'][j], data['rxHePpdutoneMappedFreq'][j], data['txConstellation'][j])
for rx_symbol, rx_ref_symbol, tx_symbol in zip(*iterator):
ltf_indices.append(i)
rx_symbols.append(rx_symbol)
rx_ref_data.append(rx_ref_symbol)
tx_symbols.append(tx_symbol)
else:
for i, j in enumerate(chunk_indices):
iterator = (data[f'rxHePpduFftOut{j}'], data[f'rxHePpdutoneMappedFreq{j}'], data[f'txConstellation{j}'])
for rx_symbol, rx_ref_symbol, tx_symbol in zip(*iterator):
ltf_indices.append(i)
rx_symbols.append(rx_symbol)
rx_ref_data.append(rx_ref_symbol)
tx_symbols.append(tx_symbol)
num_symbols = len(ltf_indices)
ltf_indices = np.array(ltf_indices)
rx_symbols = np.array(rx_symbols)
rx_ref_data = np.array(rx_ref_data)
tx_symbols = np.array(tx_symbols)
rx_data = rx_symbols[:, data_indices]
rx_pilot = rx_symbols[:, pilot_indices]
tx_data = tx_symbols[:, data_indices]
tx_pilot = tx_symbols[:, pilot_indices]
# Duplicate the entries in the LTF fields so that we have `num_symbols` entries.
rx_l_ltf_1 = np.array([rx_l_ltf_1[ltf_indices[i]] for i in range(num_symbols)])
rx_l_ltf_2 = np.array([rx_l_ltf_2[ltf_indices[i]] for i in range(num_symbols)])
rx_he_ltf_data = np.array([rx_he_ltf_data[ltf_indices[i]] for i in range(num_symbols)])
rx_he_ltf_pilot = np.array([rx_he_ltf_pilot[ltf_indices[i]] for i in range(num_symbols)])
# Lump the arrays together and save them.
arrays = {
'rx_l_ltf_1': rx_l_ltf_1,
'rx_l_ltf_2': rx_l_ltf_2,
'rx_he_ltf_data': rx_he_ltf_data,
'rx_he_ltf_pilot': rx_he_ltf_pilot,
'rx_data': rx_data,
'rx_pilot': rx_pilot,
'rx_ref_data': rx_ref_data,
'tx_data': tx_data,
'tx_pilot': tx_pilot
}
for key, value in arrays.items():
if key not in output:
output.create_dataset(name=key, data=value, maxshape=(None, *value.shape[1:]), chunks=True)
else:
output[key].resize(output[key].shape[0] + value.shape[0], axis=0)
output[key][-value.shape[0]:] = value
if not silent:
print(f'Wrote {num_symbols} symbols in {len(chunk_indices)} packets.')
if not silent:
print(f'TOTAL: {output["rx_l_ltf_1"].shape[0]} symbols in {num_packets} packets.')
return output['rx_l_ltf_1'].shape[0], num_packets
def flatten_dir(source_dir: str, dest_dir: str, packets_per_chunk: int = 1000, synthetic: bool = False,
silent: bool = True, force: bool = False,
constant_features_path: str = 'constant_features.mat') -> (int, int):
"""Flatten all HDF5 files in a directory and place them in another.
:param source_dir: source directory.
:param dest_dir: destination directory.
:param packets_per_chunk: number of packets to process at a time.
:param synthetic: flag the source dataset as being synthetic (changes data input format).
:param silent: suppress output.
:param force: force reprocessing of already-processed files.
:param constant_features_path: path to constant features.
:return: tuple containing the number of (symbols, packets) converted.
"""
total_symbols = 0
total_packets = 0
for source in glob.glob(f'{source_dir}/*.h5'):
if 'flat' in source:
if not silent:
print(f'{source} is already flattened. Skipping.')
print()
continue
dest = f'{dest_dir}/{os.path.splitext(os.path.basename(source))[0]}_flat.h5'
if not force and os.path.exists(dest):
if not silent:
print(f'{dest_dir} exists. Skipping.')
print()
continue
num_symbols, num_packets = flatten(source, dest, packets_per_chunk, synthetic=synthetic, silent=silent,
constant_features_path=constant_features_path)
total_symbols += num_symbols
total_packets += num_packets
if not silent:
print()
if not silent:
print(f'TOTAL: {total_symbols} symbols in {total_packets} packets.')
def main():
import argparse
# noinspection PyTypeChecker
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('source', help='directory to flatten', type=str)
parser.add_argument('dest', help='directory to store result', type=str)
parser.add_argument('--synthetic', help='flag the source dataset as being synthetic', action='store_true')
args = parser.parse_args()
flatten_dir(args.source, args.dest, synthetic=args.synthetic, silent=False)
if __name__ == '__main__':
main()
| 0
| 0
| 0
| 0
| 0
| 509
| 0
| 0
| 23
|
79f8e6aa646486ac4177d50c3694c1c7632c0a4a
| 461
|
py
|
Python
|
lpbm/lib/slugify.py
|
fmichea/lpbm
|
172772d562e2f1aa4aba72599150f95f89bdf6ce
|
[
"BSD-3-Clause"
] | 1
|
2015-11-09T11:30:41.000Z
|
2015-11-09T11:30:41.000Z
|
lpbm/lib/slugify.py
|
fmichea/lpbm
|
172772d562e2f1aa4aba72599150f95f89bdf6ce
|
[
"BSD-3-Clause"
] | 1
|
2015-04-28T07:02:21.000Z
|
2016-01-23T19:12:11.000Z
|
lpbm/lib/slugify.py
|
fmichea/lpbm
|
172772d562e2f1aa4aba72599150f95f89bdf6ce
|
[
"BSD-3-Clause"
] | 2
|
2016-01-11T17:55:42.000Z
|
2018-03-19T19:03:15.000Z
|
import string
import unicodedata
SLUG_CHARS_DISPLAY = '[a-z0-9-]'
_SLUG_CHARS = string.ascii_lowercase + string.digits + '-'
_SLUG_SIZE = 50
def slugify(text):
'''Returns the slug of a string (that can be used in an URL for example.'''
slug = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore')
slug = slug.decode('utf-8').lower().replace(' ', '-')
slug = ''.join(c for c in slug if c in _SLUG_CHARS)
return slug[:_SLUG_SIZE]
| 27.117647
| 79
| 0.668113
|
import string
import unicodedata
SLUG_CHARS_DISPLAY = '[a-z0-9-]'
_SLUG_CHARS = string.ascii_lowercase + string.digits + '-'
_SLUG_SIZE = 50
def slugify(text):
'''Returns the slug of a string (that can be used in an URL for example.'''
slug = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore')
slug = slug.decode('utf-8').lower().replace(' ', '-')
slug = ''.join(c for c in slug if c in _SLUG_CHARS)
return slug[:_SLUG_SIZE]
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
5427b466a12fe41de8c9198002e141c9edef8f1f
| 610
|
py
|
Python
|
Chapter09/wifi-sniff.py
|
PacktPublishing/Python-Penetration-Testing-Cookbook
|
74eba54a5d9921db0c4679cb4374a742118d4be8
|
[
"MIT"
] | 48
|
2017-12-08T16:38:09.000Z
|
2022-03-20T12:49:31.000Z
|
Chapter09/wifi-sniff.py
|
shamiul94/Python-Penetration-Testing-Cookbook
|
a152f14bf7eb1fde2612808f47d7609b58d48980
|
[
"MIT"
] | null | null | null |
Chapter09/wifi-sniff.py
|
shamiul94/Python-Penetration-Testing-Cookbook
|
a152f14bf7eb1fde2612808f47d7609b58d48980
|
[
"MIT"
] | 34
|
2017-12-28T14:01:10.000Z
|
2022-03-16T15:54:30.000Z
|
ap_list = []
sniff(iface='en0', prn=ssid, count=10, timeout=3, store=0)
# for packet in sniff(offline='sample.pcap', prn=changePacketParameters):
# packets.append(packet)
# for packet in packets:
# if packet.haslayer(TCP):
# writeToPcapFile(packet)
# print(packet.show())
| 30.5
| 73
| 0.595082
|
from scapy.all import *
ap_list = []
def ssid(pkt):
# print(pkt.show())
if pkt.haslayer(Dot11):
print(pkt.show())
if pkt.type == 0 and pkt.subtype == 8:
if pkt.addr2 not in ap_list:
ap_list.append(pkt.addr2)
print("AP: %s SSID: %s" % (pkt.addr2, pkt.info))
sniff(iface='en0', prn=ssid, count=10, timeout=3, store=0)
# for packet in sniff(offline='sample.pcap', prn=changePacketParameters):
# packets.append(packet)
# for packet in packets:
# if packet.haslayer(TCP):
# writeToPcapFile(packet)
# print(packet.show())
| 0
| 0
| 0
| 0
| 0
| 266
| 0
| 2
| 44
|
072d438eba683fe36f066d77044cc9fbcf982237
| 6,298
|
py
|
Python
|
python/rain/client/data.py
|
aimof/rain
|
86b77666e1310888ad483c7976c2c99f3d5bfeaa
|
[
"MIT"
] | null | null | null |
python/rain/client/data.py
|
aimof/rain
|
86b77666e1310888ad483c7976c2c99f3d5bfeaa
|
[
"MIT"
] | null | null | null |
python/rain/client/data.py
|
aimof/rain
|
86b77666e1310888ad483c7976c2c99f3d5bfeaa
|
[
"MIT"
] | null | null | null |
from ..common import RainException
from ..common.content_type import check_content_type, encode_value
def blob(value, label="const", content_type=None, encode=None):
"""
Create a constant data object with accompanying data.
Given `value` may be either `bytes` or any object to be encoded with
`encoding` content type. Strings are encoded with utf-8 by default.
Specify at most one of `content_type` and `encode`.
"""
if content_type is not None:
if encode is not None:
raise RainException("Specify only one of content_type and encode")
if not isinstance(value, bytes):
raise RainException("content_type only allowed for `bytes`")
if encode is None and isinstance(value, str):
encode = "text:utf-8"
if content_type is not None:
raise RainException("content_type not allowed for `str`, use `encode=...`")
if encode is not None:
check_content_type(encode)
value = encode_value(value, content_type=encode)
content_type = encode
if not isinstance(value, bytes):
raise RainException(
"Invalid blob type (only str or bytes are allowed without `encode`)")
dataobj = DataObject(label, content_type=content_type)
dataobj.data = value
return dataobj
def pickled(val, label="pickle"):
"""
Create a data object with pickled `val`.
A shorthand for `blob(val, ancode='pickle')`.
The default label is "pickle".
"""
return blob(val, encode='pickle', label=label)
def to_data(obj):
"""Convert an object to DataObject/DataObjectPart"""
if isinstance(obj, DataObject):
return obj
from .task import Task
if isinstance(obj, Task):
if len(obj.outputs) == 1:
return obj.outputs[0]
if len(obj.outputs) == 0:
raise RainException("{} does not have any output".format(obj))
else:
raise RainException("{} returns multiple outputs".format(obj))
if isinstance(obj, str) or isinstance(obj, bytes):
raise RainException(
"Instance of {!r} cannot be used as a data object.\n"
"Hint: Wrap it with `blob` to use it as data object."
.format(type(obj)))
raise RainException(
"Instance of {!r} cannot be used as a data object.\n"
"Hint: Wrap it with `pickled` or `blob(encode=...)` to use it as a data object."
.format(type(obj)))
| 31.49
| 93
| 0.618292
|
import capnp
import tarfile
import io
from .session import get_active_session
from ..common import RainException, ids, ID
from ..common.attributes import attributes_to_capnp
from ..common.content_type import check_content_type, encode_value
from ..common import DataType
class DataObject:
id = None
# Flag if data object should be kept on server
_keep = False
# State of object
# None = Not submitted
state = None
# Value of data object (value can be filled by client if it is constant,
# or by fetching from server)
data = None
def __init__(self, label=None, session=None, data_type=DataType.BLOB, content_type=None):
assert isinstance(data_type, DataType)
if session is None:
session = get_active_session()
self.session = session
self.label = label
self.id = session._register_dataobj(self)
assert isinstance(self.id, ID)
self.attributes = {
"spec": {"content_type": content_type}
}
self.data_type = data_type
@property
def content_type(self):
return self.attributes["spec"]["content_type"]
def _free(self):
"""Set flag that object is not available on the server """
self._keep = False
def unkeep(self):
"""Remove data object from the server"""
self.session.unkeep((self,))
def keep(self):
"""Set flag that is object should be kept on the server"""
if self.state is not None:
raise RainException("Cannot keep submitted task")
self._keep = True
def is_kept(self):
"""Returns the value of self._keep"""
return self._keep
def to_capnp(self, out):
ids.id_to_capnp(self.id, out.id)
out.keep = self._keep
if self.label:
out.label = self.label
out.dataType = self.data_type.to_capnp()
if self.data is not None:
out.data = self.data
out.hasData = True
else:
out.hasData = False
attributes_to_capnp(self.attributes, out.attributes)
def wait(self):
self.session.wait((self,))
def fetch(self):
"""
Fetch the object data and update its state.
Returns:
DataInstance
"""
return self.session.fetch(self)
def update(self):
self.session.update((self,))
def __del__(self):
if self.state is not None and self._keep:
try:
self.session.client._unkeep((self,))
except capnp.lib.capnp.KjException:
# Ignore capnp exception, since this constructor may be
# called when connection is closed
pass
def __reduce__(self):
"""Speciaization to replace with subworker.unpickle_input_object
in Python task args while (cloud)pickling."""
from . import pycode
from ..subworker import subworker
if pycode._global_pickle_inputs is None:
# call normal __reduce__
return super().__reduce__()
base_name, counter, inputs, input_proto = pycode._global_pickle_inputs
input_name = "{}{{{}}}".format(base_name, counter)
pycode._global_pickle_inputs[1] += 1
inputs.append((input_name, self))
return (subworker.unpickle_input_object,
(input_name, len(inputs) - 1,
input_proto.load, input_proto.content_type))
def __repr__(self):
t = " [D]" if self.data_type == DataType.DIRECTORY else ""
return "<DObj {}{} id={} {}>".format(
self.label, t, self.id, self.attributes)
def blob(value, label="const", content_type=None, encode=None):
"""
Create a constant data object with accompanying data.
Given `value` may be either `bytes` or any object to be encoded with
`encoding` content type. Strings are encoded with utf-8 by default.
Specify at most one of `content_type` and `encode`.
"""
if content_type is not None:
if encode is not None:
raise RainException("Specify only one of content_type and encode")
if not isinstance(value, bytes):
raise RainException("content_type only allowed for `bytes`")
if encode is None and isinstance(value, str):
encode = "text:utf-8"
if content_type is not None:
raise RainException("content_type not allowed for `str`, use `encode=...`")
if encode is not None:
check_content_type(encode)
value = encode_value(value, content_type=encode)
content_type = encode
if not isinstance(value, bytes):
raise RainException(
"Invalid blob type (only str or bytes are allowed without `encode`)")
dataobj = DataObject(label, content_type=content_type)
dataobj.data = value
return dataobj
def pickled(val, label="pickle"):
"""
Create a data object with pickled `val`.
A shorthand for `blob(val, ancode='pickle')`.
The default label is "pickle".
"""
return blob(val, encode='pickle', label=label)
def directory(path=None, label="const_dir"):
f = io.BytesIO()
tf = tarfile.open(fileobj=f, mode="w")
tf.add(path, ".")
tf.close()
data = f.getvalue()
dataobj = DataObject(label, data_type=DataType.DIRECTORY)
dataobj.data = data
return dataobj
def to_data(obj):
"""Convert an object to DataObject/DataObjectPart"""
if isinstance(obj, DataObject):
return obj
from .task import Task
if isinstance(obj, Task):
if len(obj.outputs) == 1:
return obj.outputs[0]
if len(obj.outputs) == 0:
raise RainException("{} does not have any output".format(obj))
else:
raise RainException("{} returns multiple outputs".format(obj))
if isinstance(obj, str) or isinstance(obj, bytes):
raise RainException(
"Instance of {!r} cannot be used as a data object.\n"
"Hint: Wrap it with `blob` to use it as data object."
.format(type(obj)))
raise RainException(
"Instance of {!r} cannot be used as a data object.\n"
"Hint: Wrap it with `pickled` or `blob(encode=...)` to use it as a data object."
.format(type(obj)))
| 0
| 71
| 0
| 3,290
| 0
| 253
| 0
| 37
| 179
|
0e8a820f45484929c8266b2b20f11189f4b1fe60
| 3,211
|
py
|
Python
|
legate/pandas/core/bitmask.py
|
marcinz/legate.pandas
|
94c21c436f59c06cfba454c6569e9f5d7109d839
|
[
"Apache-2.0"
] | 67
|
2021-04-12T18:06:55.000Z
|
2022-03-28T06:51:05.000Z
|
legate/pandas/core/bitmask.py
|
marcinz/legate.pandas
|
94c21c436f59c06cfba454c6569e9f5d7109d839
|
[
"Apache-2.0"
] | 2
|
2021-06-22T00:30:36.000Z
|
2021-07-01T22:12:43.000Z
|
legate/pandas/core/bitmask.py
|
marcinz/legate.pandas
|
94c21c436f59c06cfba454c6569e9f5d7109d839
|
[
"Apache-2.0"
] | 6
|
2021-04-14T21:28:00.000Z
|
2022-03-22T09:45:25.000Z
|
# Copyright 2021 NVIDIA Corporation
#
# 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.
#
| 28.669643
| 74
| 0.678293
|
# Copyright 2021 NVIDIA Corporation
#
# 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.
#
from legate.pandas.common import types as ty
from legate.pandas.config import OpCode
from legate.pandas.core.pattern import Map, Projection, ScalarMap
class Bitmask(object):
alloc_type = ty.uint8
def __init__(self, runtime, storage):
self._runtime = runtime
self._storage = storage
self._compact_bitmask = None
@property
def region(self):
return self._storage.region
@property
def field_id(self):
return self._storage.field_id
@property
def dtype(self):
return self._storage.dtype
@property
def num_pieces(self):
return self._storage.num_pieces
@property
def launch_domain(self):
return self._storage.launch_domain
@property
def storage(self):
return self._storage.storage
@property
def ispace(self):
return self._storage.ispace
@property
def primary_ipart(self):
return self._storage.primary_ipart
@property
def fspace(self):
return self._storage.fspace
@property
def compact_bitmask(self):
if self._compact_bitmask is None:
result = self._storage.storage.create_new_field(self.dtype)
result.set_primary_ipart(self.primary_ipart)
plan = Map(self._runtime, OpCode.TO_BITMASK)
proj = Projection(self._storage.primary_ipart)
plan.add_output_only(result)
plan.add_scalar_arg(False, ty.bool)
plan.add_scalar_arg(0, ty.uint32)
plan.add_input(self._storage, proj)
plan.add_scalar_arg(False, ty.bool)
plan.add_scalar_arg(0, ty.uint32)
plan.execute(self.launch_domain)
self._compact_bitmask = result
return self._compact_bitmask
def clone(self):
return Bitmask(self._runtime, self._storage.clone())
def set_primary_ipart(self, ipart):
self._storage.set_primary_ipart(ipart)
def get_view(self, ipart):
return self._runtime.create_region_partition(self.region, ipart)
# XXX: This method should be used with caution as it gets blocked
# on a future internally
def has_nulls(self):
return self.count_nulls() > 0
def count_nulls(self):
from .column import Column
plan = ScalarMap(self._runtime, OpCode.COUNT_NULLS, ty.uint64)
boolmask = Column(self._runtime, self._storage)
boolmask.add_to_plan(plan, True)
counts = plan.execute(self.launch_domain)
return counts.sum().get_value()
def to_raw_address(self):
return self._storage.to_raw_address()
| 0
| 1,167
| 0
| 1,284
| 0
| 0
| 0
| 85
| 90
|
0a2586d7e1874e8ca79ba94674abca86141dfa90
| 5,066
|
py
|
Python
|
backend/config/settings/base.py
|
code-for-canada/django-nginx-reactjs-docker
|
12b2f79872273bb0ac4736d709b8e0904bc54258
|
[
"MIT"
] | 3
|
2019-01-04T10:53:03.000Z
|
2020-01-29T16:20:38.000Z
|
backend/config/settings/base.py
|
code-for-canada/django-nginx-reactjs-docker
|
12b2f79872273bb0ac4736d709b8e0904bc54258
|
[
"MIT"
] | 215
|
2019-01-04T11:34:03.000Z
|
2019-07-22T13:36:18.000Z
|
backend/config/settings/base.py
|
code-for-canada/django-nginx-reactjs-docker
|
12b2f79872273bb0ac4736d709b8e0904bc54258
|
[
"MIT"
] | 8
|
2019-01-08T22:45:11.000Z
|
2020-01-29T16:20:40.000Z
|
"""
Django settings for backend project.
Generated by 'django-admin startproject' using Django 2.1.4.
Since the start of the project, we have upgraded the version to 2.1.7
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
import datetime
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = "%qviowlmp*kitbai+y!%1y=jdl_o3_#7+_ud6l9uwn$$=bxt1y"
ALLOWED_HOSTS = "*"
CORS_ORIGIN_ALLOW_ALL = True
# Application definition
INSTALLED_APPS = [
# Personal,
"custom_models",
# BASE
"django.contrib.admin",
"django.contrib.auth",
"django.contrib.contenttypes",
"django.contrib.sessions",
"django.contrib.messages",
"django.contrib.staticfiles",
# Requirements
"corsheaders",
"rest_framework",
"djoser",
"rest_framework_swagger",
"rest_framework.authtoken",
]
MIDDLEWARE = [
"django.middleware.security.SecurityMiddleware",
"django.contrib.sessions.middleware.SessionMiddleware",
"django.middleware.common.CommonMiddleware",
"django.middleware.csrf.CsrfViewMiddleware",
"django.contrib.auth.middleware.AuthenticationMiddleware",
"django.contrib.messages.middleware.MessageMiddleware",
"django.middleware.clickjacking.XFrameOptionsMiddleware",
"corsheaders.middleware.CorsMiddleware",
]
ROOT_URLCONF = "config.urls"
WSGI_APPLICATION = "config.wsgi.application"
TEMPLATES = [
{
"BACKEND": "django.template.backends.django.DjangoTemplates",
"DIRS": [],
"APP_DIRS": True,
"OPTIONS": {
"context_processors": [
"django.template.context_processors.debug",
"django.template.context_processors.request",
"django.contrib.auth.context_processors.auth",
"django.contrib.messages.context_processors.messages",
]
},
}
]
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
"NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator"
},
{"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator"},
{"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator"},
{"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator"},
]
REST_FRAMEWORK = {
"DEFAULT_PERMISSION_CLASSES": (
"rest_framework.permissions.IsAuthenticatedOrReadOnly",
),
"DEFAULT_AUTHENTICATION_CLASSES": (
"rest_framework.authentication.SessionAuthentication",
"rest_framework.authentication.BasicAuthentication",
"rest_framework_jwt.authentication.JSONWebTokenAuthentication",
),
}
LANGUAGE_CODE = "en-us"
TIME_ZONE = "Canada/Eastern"
USE_I18N = True
USE_L10N = True
USE_TZ = True
CORS_ALLOW_HEADERS = [
"accept",
"accept-encoding",
"authorization",
"content-disposition",
"content-type",
"origin",
"user-agent",
"x-csrftoken",
"x-requested-with",
]
# Allow the user to log in by email or username
AUTH_USER_MODEL = "custom_models.User"
# JWT settings for authentication
JWT_AUTH = {
"JWT_EXPIRATION_DELTA": datetime.timedelta(hours=1),
"JWT_ALLOW_REFRESH": True,
}
SIMPLE_JWT = {"AUTH_HEADER_TYPES": ("JWT",)}
# Swagger settings for documentation
SWAGGER_SETTINGS = {
"LOGIN_URL": "rest_framework:login",
"LOGOUT_URL": "rest_framework:logout",
}
# Djoser settings for Rest Login (https://djoser.readthedocs.io/en/latest/settings.html)
DJOSER = {
"SET_PASSWORD_RETYPE": True,
"SEND_ACTIVATION_EMAIL": False,
"PERMISSIONS": {
# Admin Only
"activation": ["rest_framework.permissions.IsAdminUser"], #
"set_username": ["rest_framework.permissions.IsAdminUser"],
"user_delete": ["rest_framework.permissions.IsAdminUser"],
"user_list": ["rest_framework.permissions.IsAdminUser"],
"password_reset": ["rest_framework.permissions.IsAdminUser"],
# Authenticated
"token_destroy": ["rest_framework.permissions.IsAuthenticated"],
# Current User or Admin
"user": ["djoser.permissions.CurrentUserOrAdmin"],
"set_password": ["djoser.permissions.CurrentUserOrAdmin"],
# Any
"password_reset_confirm": ["rest_framework.permissions.AllowAny"],
"user_create": ["rest_framework.permissions.AllowAny"],
"token_create": ["rest_framework.permissions.AllowAny"],
},
}
| 32.267516
| 91
| 0.681603
|
"""
Django settings for backend project.
Generated by 'django-admin startproject' using Django 2.1.4.
Since the start of the project, we have upgraded the version to 2.1.7
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
import datetime
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = "%qviowlmp*kitbai+y!%1y=jdl_o3_#7+_ud6l9uwn$$=bxt1y"
ALLOWED_HOSTS = "*"
CORS_ORIGIN_ALLOW_ALL = True
# Application definition
INSTALLED_APPS = [
# Personal,
"custom_models",
# BASE
"django.contrib.admin",
"django.contrib.auth",
"django.contrib.contenttypes",
"django.contrib.sessions",
"django.contrib.messages",
"django.contrib.staticfiles",
# Requirements
"corsheaders",
"rest_framework",
"djoser",
"rest_framework_swagger",
"rest_framework.authtoken",
]
MIDDLEWARE = [
"django.middleware.security.SecurityMiddleware",
"django.contrib.sessions.middleware.SessionMiddleware",
"django.middleware.common.CommonMiddleware",
"django.middleware.csrf.CsrfViewMiddleware",
"django.contrib.auth.middleware.AuthenticationMiddleware",
"django.contrib.messages.middleware.MessageMiddleware",
"django.middleware.clickjacking.XFrameOptionsMiddleware",
"corsheaders.middleware.CorsMiddleware",
]
ROOT_URLCONF = "config.urls"
WSGI_APPLICATION = "config.wsgi.application"
TEMPLATES = [
{
"BACKEND": "django.template.backends.django.DjangoTemplates",
"DIRS": [],
"APP_DIRS": True,
"OPTIONS": {
"context_processors": [
"django.template.context_processors.debug",
"django.template.context_processors.request",
"django.contrib.auth.context_processors.auth",
"django.contrib.messages.context_processors.messages",
]
},
}
]
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
"NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator"
},
{"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator"},
{"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator"},
{"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator"},
]
REST_FRAMEWORK = {
"DEFAULT_PERMISSION_CLASSES": (
"rest_framework.permissions.IsAuthenticatedOrReadOnly",
),
"DEFAULT_AUTHENTICATION_CLASSES": (
"rest_framework.authentication.SessionAuthentication",
"rest_framework.authentication.BasicAuthentication",
"rest_framework_jwt.authentication.JSONWebTokenAuthentication",
),
}
LANGUAGE_CODE = "en-us"
TIME_ZONE = "Canada/Eastern"
USE_I18N = True
USE_L10N = True
USE_TZ = True
CORS_ALLOW_HEADERS = [
"accept",
"accept-encoding",
"authorization",
"content-disposition",
"content-type",
"origin",
"user-agent",
"x-csrftoken",
"x-requested-with",
]
# Allow the user to log in by email or username
AUTH_USER_MODEL = "custom_models.User"
# JWT settings for authentication
JWT_AUTH = {
"JWT_EXPIRATION_DELTA": datetime.timedelta(hours=1),
"JWT_ALLOW_REFRESH": True,
}
SIMPLE_JWT = {"AUTH_HEADER_TYPES": ("JWT",)}
# Swagger settings for documentation
SWAGGER_SETTINGS = {
"LOGIN_URL": "rest_framework:login",
"LOGOUT_URL": "rest_framework:logout",
}
# Djoser settings for Rest Login (https://djoser.readthedocs.io/en/latest/settings.html)
DJOSER = {
"SET_PASSWORD_RETYPE": True,
"SEND_ACTIVATION_EMAIL": False,
"PERMISSIONS": {
# Admin Only
"activation": ["rest_framework.permissions.IsAdminUser"], #
"set_username": ["rest_framework.permissions.IsAdminUser"],
"user_delete": ["rest_framework.permissions.IsAdminUser"],
"user_list": ["rest_framework.permissions.IsAdminUser"],
"password_reset": ["rest_framework.permissions.IsAdminUser"],
# Authenticated
"token_destroy": ["rest_framework.permissions.IsAuthenticated"],
# Current User or Admin
"user": ["djoser.permissions.CurrentUserOrAdmin"],
"set_password": ["djoser.permissions.CurrentUserOrAdmin"],
# Any
"password_reset_confirm": ["rest_framework.permissions.AllowAny"],
"user_create": ["rest_framework.permissions.AllowAny"],
"token_create": ["rest_framework.permissions.AllowAny"],
},
}
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
708f01c2e192ca13b47544b19668cfceb08b35f7
| 18,306
|
py
|
Python
|
project/src/main/python/backTest/factorAnalysis/updateStockPool.py
|
daifengqi/big-data-hft
|
013747ca3c2ca984eeac723fd5d8f8e3458b840c
|
[
"MIT"
] | 1
|
2022-03-07T09:32:40.000Z
|
2022-03-07T09:32:40.000Z
|
project/src/main/python/backTest/factorAnalysis/updateStockPool.py
|
daifengqi/big-data-hft
|
013747ca3c2ca984eeac723fd5d8f8e3458b840c
|
[
"MIT"
] | null | null | null |
project/src/main/python/backTest/factorAnalysis/updateStockPool.py
|
daifengqi/big-data-hft
|
013747ca3c2ca984eeac723fd5d8f8e3458b840c
|
[
"MIT"
] | 1
|
2022-03-03T16:22:37.000Z
|
2022-03-03T16:22:37.000Z
|
if __name__ == '__main__':
config_path = r"D:\HX_proj\factorAnalysis\updateStockPoolConfig.yaml"
updateSP = UpdateStockPool(config_path)
updateSP.update_stock_info()
updateSP.update_stock_pool_basic()
updateSP.update_stock_HS300()
updateSP.update_stock_ZZ500()
updateSP.update_stock_ZZ800()
stock_pool_name = 'STOCK_POOL_basic'
stock_pool_path = f"E:\\data\\interday\\{stock_pool_name}.pkl"
update_benchmark_return(stock_pool_path=stock_pool_path,stock_pool_name=stock_pool_name,benchmark_ret_save_dir = r"E:\data\interday")
| 54.159763
| 149
| 0.689282
|
import copy
import itertools
import os
import time
import numpy as np
import pandas as pd
import scipy.io as scio
import yaml
import factorAnalysisIOTools as IOTools
from factorAnalysisCalTools import prepare_RET_dict, time_horizon_dict
def save_df(this: pd.DataFrame,config: dict,file_name):
file_path = os.path.join(config['utility_data_dir'],config[file_name]+".pkl")
if os.path.exists(file_path):
that = pd.read_pickle(file_path)
this = that.append(this)
this.drop_duplicates(subset=['date','code'],inplace=True,keep='last')
this.to_pickle(file_path)
def loadmat2df_interday(path):
temp = scio.loadmat(path)
this = pd.DataFrame(temp['SCORE'],
index=temp['TRADE_DT'].squeeze().tolist(),
columns=[x[0] for x in temp['STOCK_CODE'].squeeze()])
return this
class LoadStockInfo():
def __init__(self,config):
self.config = config
temp = scio.loadmat(os.path.join(config['utility_data_dir'],config['SIZE_file_name']+".mat"))
self.all_date_mat = temp['TRADE_DT'].squeeze()
self.all_code_mat = [x[0] for x in temp['STOCK_CODE'].squeeze()]
self.start_date = self.all_date_mat[self.all_date_mat>=config['start_date']][0]
self.end_date = self.all_date_mat[self.all_date_mat<=config['end_date']][-1]
self.start_indexer = np.where(self.all_date_mat==self.start_date)[0][0]
self.end_indexer = np.where(self.all_date_mat==self.end_date)[0][0]
self.date_list = self.all_date_mat[self.start_indexer:self.end_indexer+1]
self.date_code_product = np.array(list(itertools.product(self.date_list,self.all_code_mat)))
# self.date_code_product = np.array(list(itertools.product(self.date_list,self.all_code_mat)),dtype=[('date','int'),('code','object')])
def load_arr(self,file_name,start_back_days=0,end_forward_days=0):
path = os.path.join(
self.config['utility_data_dir'],
self.config[file_name]+".mat"
)
# log--20210804：对于ZX_1的数据，他们的STOCK_CODE的顺序和数量和其他的可能不一样
# 查找self.all_code_mat的code在stkcd里面的位置，对应的score里面取出这些地方的元素，相当于reindex一下
# update: 直接读取df，然后reindex，然后取value
score = loadmat2df_interday(path).reindex(index=self.all_date_mat,columns=self.all_code_mat).values
print(file_name,score.shape)
start = max(self.start_indexer-start_back_days,0)
end = min(self.end_indexer+end_forward_days+1,len(score))
data = score[start:end]
pad_width_axis0 = (
max(start_back_days-self.start_indexer,0),
max(self.end_indexer+end_forward_days+1-len(score),0)
)
if pad_width_axis0 != (0,0):
data = np.pad(data, pad_width=(pad_width_axis0,(0,0)),mode='edge')
return data
def update_limit_300_688(self,start_back_days=0,end_forward_days=0):
code_300 = np.array([x.startswith("300") for x in self.all_code_mat])
code_688 = np.array([x.startswith("688") for x in self.all_code_mat])
chg_date_300 = np.array([x>=20200824 for x in self.all_date_mat])
code_300_score = np.tile(code_300,(len(chg_date_300),1))
code_688_score = np.tile(code_688,(len(chg_date_300),1))
chg_date_300_score = np.tile(chg_date_300.reshape(-1,1),(1,len(code_300)))
assert code_300_score.shape == (len(self.all_date_mat),len(self.all_code_mat)), "code_300 shape wrong"
start = max(self.start_indexer-start_back_days,0)
end = min(self.end_indexer+end_forward_days+1,len(code_300_score))
code_300_data = code_300_score[start:end]
code_688_data = code_688_score[start:end]
chg_date_data = chg_date_300_score[start:end]
return code_300_data,code_688_data,chg_date_data
class UpdateStockPool():
def __init__(self,config_path):
with open(config_path) as f:
self.config = yaml.load(f.read(),Loader=yaml.FullLoader)
self.config['STOCK_INFO_file_path'] = os.path.join(self.config['utility_data_dir'],self.config['STOCK_INFO_file_name']+".pkl")
def update_stock_info(self):
config = copy.deepcopy(self.config)
stock_info_loader = LoadStockInfo(config)
t = time.time()
# ADJFACTOR_2d_forward1 = stock_info_loader.load_arr('ADJFACTOR_file_name',end_forward_days=1)[1:]
# OPEN_2d_forward1 = stock_info_loader.load_arr('ADJFACTOR_file_name',end_forward_days=1)[1:]
# ADJOPEN_2d_forward1 = ADJFACTOR_2d_forward1*OPEN_2d_forward1
FF_CAPITAL_2d = stock_info_loader.load_arr('FF_CAPITAL_file_name')
PRECLOSE_2d_forward1 = stock_info_loader.load_arr('PRECLOSE_file_name',end_forward_days=1)[1:]
# RETURN_TWAP_Q1_2d = stock_info_loader.load_arr('RETURN_TWAP_Q1_file_name',end_forward_days=1)
FCT_HF_TWAP_H1_forward1 = stock_info_loader.load_arr('FCT_HF_TWAP_H1_file_name',end_forward_days=1)[1:]
RETURN_2d = stock_info_loader.load_arr('RETURN_file_name',end_forward_days=1)
# 20210917: 更新计算涨跌停的方法，算涨跌停的时候，20200824开始创业板涨幅停调整到了20%
# 创业板通过stock_code前3位等于300识别； 还有科创板，前3位是688； 科创板自上市以来就是20%涨跌幅，创业板是20200824开始改20%的
code_300_data,code_688_data,chg_date_data = stock_info_loader.update_limit_300_688()
lmt1_t0 = (abs(RETURN_2d)>0.195)[:-1]
lmt2_t0 = (abs(RETURN_2d)>0.095)[:-1] & (code_688_data==False) & (chg_date_data==False)
lmt3_t0 = (abs(RETURN_2d)>0.095)[:-1] & (code_688_data==False) & (code_300_data==False)
LIMIT_UD_t0_1d = (lmt1_t0 | lmt2_t0 | lmt3_t0).reshape(-1,)
# 20211007: ADJOPEN*limit_ratio*5/6=LIMIT_PRICE_THRESHOLD_H1
lmt1_t1 = (abs(FCT_HF_TWAP_H1_forward1/PRECLOSE_2d_forward1-1)>0.195*5/6)
lmt2_t1 = (abs(FCT_HF_TWAP_H1_forward1/PRECLOSE_2d_forward1-1)>0.095*5/6) & (code_688_data==False) & (chg_date_data==False)
lmt3_t1 = (abs(FCT_HF_TWAP_H1_forward1/PRECLOSE_2d_forward1-1)>0.095*5/6) & (code_688_data==False) & (code_300_data==False)
LIMIT_UD_t1_1d = (lmt1_t1 | lmt2_t1 | lmt3_t1).reshape(-1,)
LIMIT_UD_filter_t0_t1_1d = ((LIMIT_UD_t0_1d + LIMIT_UD_t1_1d) == 0)
# LIMIT_UD_t0_1d = ((abs(RETURN_TWAP_Q1_2d)>0.095)[:-1]).reshape(-1,)
# LIMIT_UD_t1_1d = ((abs(RETURN_TWAP_Q1_2d)>0.095)[1:]).reshape(-1,)
# LIMIT_UD_filter_t0_t1_1d = (LIMIT_UD_t0_1d + LIMIT_UD_t1_1d) == 0
ADJFACTOR_2d = stock_info_loader.load_arr('ADJFACTOR_file_name')
VWAP_2d = stock_info_loader.load_arr('VWAP_file_name')
CLOSE_2d = stock_info_loader.load_arr('CLOSE_file_name')
AMOUNT_1d = stock_info_loader.load_arr('AMOUNT_file_name').reshape(-1,)
ADJVWAP_1d = (ADJFACTOR_2d*VWAP_2d).reshape(-1,)
ADJCLOSE_1d = (ADJFACTOR_2d*CLOSE_2d).reshape(-1,)
CLOSE_1d = CLOSE_2d.reshape(-1,)
FF_SIZE_1d = (FF_CAPITAL_2d*VWAP_2d).reshape(-1,)
ADJFACTOR_2d_back19 = stock_info_loader.load_arr('ADJFACTOR_file_name',start_back_days=19)
VOLUME_2d_back19 = stock_info_loader.load_arr('VOLUME_file_name',start_back_days=19)
ADJVOLUME_2d_back19 = VOLUME_2d_back19/ADJFACTOR_2d_back19
ADJVOLUME_ma20_2d = pd.DataFrame(ADJVOLUME_2d_back19).rolling(20).mean().values[19:]
ADJVOLUME_ma20_1d = ADJVOLUME_ma20_2d.reshape(-1,)
ADJVOLUME_ma20_q20_1d = np.nanquantile(ADJVOLUME_ma20_2d, q=0.2,axis=1,keepdims=False)
ADJVOLUME_ma20_q20_1d = np.repeat(ADJVOLUME_ma20_q20_1d, repeats=ADJVOLUME_ma20_2d.shape[1])
FF_CAPITAL_ma20_2d = stock_info_loader.load_arr('FF_CAPITAL_file_name',start_back_days=19)
FF_CAPITAL_ma20_2d = pd.DataFrame(FF_CAPITAL_ma20_2d).rolling(20).mean().values[19:]
FF_CAPITAL_ma20_1d = FF_CAPITAL_ma20_2d.reshape(-1,)
FF_CAPITAL_ma20_q20_1d = np.nanquantile(FF_CAPITAL_ma20_2d, q=0.2,axis=1,keepdims=False)
FF_CAPITAL_ma20_q20_1d = np.repeat(FF_CAPITAL_ma20_q20_1d, repeats=FF_CAPITAL_ma20_2d.shape[1])
TOTAL_TRADEDAYS_1d = stock_info_loader.load_arr('TOTAL_TRADEDAYS_file_name').reshape(-1,)
HS300_member_1d = stock_info_loader.load_arr('HS300_member_file_name').reshape(-1,)
ZZ500_member_1d = stock_info_loader.load_arr('ZZ500_member_file_name').reshape(-1,)
ISST_1d = stock_info_loader.load_arr('ISST_file_name').reshape(-1,)
ISTRADEDAY_1d = stock_info_loader.load_arr('ISTRADEDAY_file_name').reshape(-1,)
ZX_1_1d = stock_info_loader.load_arr('ZX_1_file_name').reshape(-1,)
SIZE_1d = stock_info_loader.load_arr('SIZE_file_name').reshape(-1,)
LIQUIDTY_1d = stock_info_loader.load_arr('LIQUIDTY_file_name').reshape(-1,)
MOMENTUM_1d = stock_info_loader.load_arr('MOMENTUM_file_name').reshape(-1,)
RESVOL_1d = stock_info_loader.load_arr('RESVOL_file_name').reshape(-1,)
SIZENL_1d = stock_info_loader.load_arr('SIZENL_file_name').reshape(-1,)
SRISK_1d = stock_info_loader.load_arr('SRISK_file_name').reshape(-1,)
ADP_1d = stock_info_loader.load_arr('ADP_file_name').reshape(-1,)
BETA_1d = stock_info_loader.load_arr('BETA_file_name').reshape(-1,)
BTOP_1d = stock_info_loader.load_arr('BTOP_file_name').reshape(-1,)
EARNYILD_1d = stock_info_loader.load_arr('EARNYILD_file_name').reshape(-1,)
GROWTH_1d = stock_info_loader.load_arr('GROWTH_file_name').reshape(-1,)
LEVERAGE_1d = stock_info_loader.load_arr('LEVERAGE_file_name').reshape(-1,)
print("IO time",time.time()-t)
t = time.time()
STOCK_INFO_2d = np.stack(
[
SIZE_1d,ZX_1_1d,ADJVWAP_1d,ADJCLOSE_1d,CLOSE_1d,AMOUNT_1d,FF_SIZE_1d,
HS300_member_1d,ZZ500_member_1d,ISST_1d,
ISTRADEDAY_1d,TOTAL_TRADEDAYS_1d,LIMIT_UD_t0_1d,LIMIT_UD_t1_1d,LIMIT_UD_filter_t0_t1_1d,
ADJVOLUME_ma20_1d,ADJVOLUME_ma20_q20_1d,FF_CAPITAL_ma20_1d,FF_CAPITAL_ma20_q20_1d,
LIQUIDTY_1d,MOMENTUM_1d,RESVOL_1d,SIZENL_1d,SRISK_1d,
ADP_1d,BETA_1d,BTOP_1d,EARNYILD_1d,GROWTH_1d,LEVERAGE_1d
],
axis=1
)
print("concate time",time.time()-t)
t = time.time()
date_code_product_2d = stock_info_loader.date_code_product
STOCK_INFO_cols_name = [
'SIZE','ZX_1','ADJVWAP','ADJCLOSE','CLOSE','AMOUNT','FF_SIZE','HS300_member','ZZ500_member',
'ISST','ISTRADEDAY','TOTAL_TRADEDAYS','LIMIT_UD_t0','LIMIT_UD_t1','LIMIT_UD_filter_t0_t1_1d',
'ADJVOLUME_ma20','ADJVOLUME_ma20_q20','FF_CAPITAL_ma20','FF_CAPITAL_ma20_q20',
'LIQUIDTY','MOMENTUM','RESVOL','SIZENL','SRISK',
'ADP','BETA','BTOP','EARNYILD','GROWTH','LEVERAGE',
'date','code'
]
STOCK_INFO_df = pd.DataFrame(STOCK_INFO_2d.astype('float32'),columns=STOCK_INFO_cols_name[:-2])
STOCK_INFO_df['date'] = date_code_product_2d[:,0].astype('int')
STOCK_INFO_df['code'] = date_code_product_2d[:,1]
print("form df time: ",time.time()-t)
t = time.time()
save_df(STOCK_INFO_df,self.config,'STOCK_INFO_file_name')
self.STOCK_INFO = STOCK_INFO_df
print("save time",time.time()-t)
def update_stock_pool_basic(self,STOCK_INFO_file_path=None):
'''
T0当天结束计算的factor，这个SP用于分组，后续可能在真的计算分组return的时候，需要再筛选一个
能否在第二天买入的SP_T1，（对于分析因子的影响？？？）
SP_basic：
- 全市场
# - T0当天没有涨跌停（RETURN的abs大于0.095）
# - T1当天没有涨跌停
- 不是ST
- 是TRADEDAY
- 上市满一年（STOCK_INFO.datetime-STOCK_INFO.list_date > 365）
'''
if hasattr(self, 'STOCK_INFO'):
STOCK_INFO = self.STOCK_INFO
else:
if STOCK_INFO_file_path is None:
STOCK_INFO_file_path=self.config['STOCK_INFO_file_path']
STOCK_INFO = pd.read_pickle(STOCK_INFO_file_path)
STOCK_INFO = STOCK_INFO[(STOCK_INFO.date>=self.config['start_date'])&(STOCK_INFO.date<=self.config['end_date'])]
filter_basic = (STOCK_INFO['ISTRADEDAY']==1.0) & (STOCK_INFO['TOTAL_TRADEDAYS']>250) & (STOCK_INFO['ISST']==0.0)
STOCK_POOL_basic = STOCK_INFO.loc[filter_basic,self.config['STOCK_POOL_cols']]
save_df(STOCK_POOL_basic,self.config,'STOCK_POOL_basic_file_name')
def update_stock_pool_cap_vol_drop20(self,STOCK_INFO_file_path=None):
'''
T0当天结束计算的factor，这个SP用于分组，后续可能在真的计算分组return的时候，需要再筛选一个
能否在第二天买入的SP_T1，（对于分析因子的影响？？？）
SP_cap_vol_drop20：
- 全市场
- 去除流动性后20%（过去20天的voluma ma大于截面的q20）
- 去除流通市值后20%
- T0当天没有涨跌停（RETURN的abs大于0.095）
- T1当天没有涨跌停
- 不是ST
- 是TRADEDAY
- 上市满一年（STOCK_INFO.datetime-STOCK_INFO.list_date > 365）
'''
if hasattr(self, 'STOCK_INFO'):
STOCK_INFO = self.STOCK_INFO
else:
if STOCK_INFO_file_path is None:
STOCK_INFO_file_path=self.config['STOCK_INFO_file_path']
STOCK_INFO = pd.read_pickle(STOCK_INFO_file_path)
STOCK_INFO = STOCK_INFO[(STOCK_INFO.date>=self.config['start_date'])&(STOCK_INFO.date<=self.config['end_date'])]
filter_basic = ( STOCK_INFO['ADJVOLUME_ma20']>STOCK_INFO['ADJVOLUME_ma20_q20'])&\
(STOCK_INFO['FF_CAPITAL_ma20']>STOCK_INFO['FF_CAPITAL_ma20_q20'])&\
(STOCK_INFO['ISTRADEDAY']==1.0) & (STOCK_INFO['TOTAL_TRADEDAYS']>250) & (STOCK_INFO['ISST']==0.0)
STOCK_POOL_basic = STOCK_INFO.loc[filter_basic,self.config['STOCK_POOL_cols']]
save_df(STOCK_POOL_basic,self.config,'STOCK_POOL_cap_vol_drop20_file_name')
def update_stock_HS300(self,STOCK_INFO_file_path=None):
'''
满足basic的要求且是HS300里面的成分股
'''
if hasattr(self, 'STOCK_INFO'):
STOCK_INFO = self.STOCK_INFO
else:
if STOCK_INFO_file_path is None:
STOCK_INFO_file_path=self.config['STOCK_INFO_file_path']
STOCK_INFO = pd.read_pickle(STOCK_INFO_file_path)
STOCK_INFO = STOCK_INFO[(STOCK_INFO.date>=self.config['start_date'])&(STOCK_INFO.date<=self.config['end_date'])]
filter_HS300 = (STOCK_INFO['HS300_member']==1.0) & (STOCK_INFO['LIMIT_UD_t0'] == 0.0) &\
(STOCK_INFO['ISTRADEDAY']==1.0) & (STOCK_INFO['TOTAL_TRADEDAYS']>250) & (STOCK_INFO['ISST']==0.0)
STOCK_POOL_HS300 = STOCK_INFO.loc[filter_HS300,self.config['STOCK_POOL_cols']]
save_df(STOCK_POOL_HS300,self.config,'STOCK_POOL_HS300_file_name')
def update_stock_ZZ500(self,STOCK_INFO_file_path=None):
if hasattr(self, 'STOCK_INFO'):
STOCK_INFO = self.STOCK_INFO
else:
if STOCK_INFO_file_path is None:
STOCK_INFO_file_path=self.config['STOCK_INFO_file_path']
STOCK_INFO = pd.read_pickle(STOCK_INFO_file_path)
STOCK_INFO = STOCK_INFO[(STOCK_INFO.date>=self.config['start_date'])&(STOCK_INFO.date<=self.config['end_date'])]
filter_ZZ500 = (STOCK_INFO['ZZ500_member']==1.0) &\
(STOCK_INFO['ISTRADEDAY']==1.0) & (STOCK_INFO['TOTAL_TRADEDAYS']>250) & (STOCK_INFO['ISST']==0.0)
STOCK_POOL_ZZ500 = STOCK_INFO.loc[filter_ZZ500,self.config['STOCK_POOL_cols']]
save_df(STOCK_POOL_ZZ500,self.config,'STOCK_POOL_ZZ500_file_name')
def update_stock_ZZ800(self,STOCK_INFO_file_path=None):
if hasattr(self, 'STOCK_INFO'):
STOCK_INFO = self.STOCK_INFO
else:
if STOCK_INFO_file_path is None:
STOCK_INFO_file_path=self.config['STOCK_INFO_file_path']
STOCK_INFO = pd.read_pickle(STOCK_INFO_file_path)
STOCK_INFO = STOCK_INFO[(STOCK_INFO.date>=self.config['start_date'])&(STOCK_INFO.date<=self.config['end_date'])]
filter_ZZ500 = ((STOCK_INFO['ZZ500_member']==1.0) | (STOCK_INFO['HS300_member']==1.0))&\
(STOCK_INFO['ISTRADEDAY']==1.0) & (STOCK_INFO['TOTAL_TRADEDAYS']>250) & (STOCK_INFO['ISST']==0.0)
STOCK_POOL_ZZ500 = STOCK_INFO.loc[filter_ZZ500,self.config['STOCK_POOL_cols']]
save_df(STOCK_POOL_ZZ500,self.config,'STOCK_POOL_ZZ800_file_name')
def update_stock_800(self,STOCK_INFO_file_path=None):
if hasattr(self, 'STOCK_INFO'):
STOCK_INFO = self.STOCK_INFO
else:
if STOCK_INFO_file_path is None:
STOCK_INFO_file_path=self.config['STOCK_INFO_file_path']
STOCK_INFO = pd.read_pickle(STOCK_INFO_file_path)
STOCK_INFO = STOCK_INFO[(STOCK_INFO.date>=self.config['start_date'])&(STOCK_INFO.date<=self.config['end_date'])]
filter_ZZ500 = ((STOCK_INFO['ZZ500_member']==1.0) | (STOCK_INFO['HS300_member']==1.0))&\
(STOCK_INFO['ISTRADEDAY']==1.0) & (STOCK_INFO['TOTAL_TRADEDAYS']>250) & (STOCK_INFO['ISST']==0.0)
STOCK_POOL_ZZ500 = STOCK_INFO.loc[filter_ZZ500,self.config['STOCK_POOL_cols']]
save_df(STOCK_POOL_ZZ500,self.config,'STOCK_POOL_800_file_name')
def update_benchmark_return(stock_pool=None,stock_pool_path=None,stock_pool_name=None,benchmark_ret_save_dir=None):
if stock_pool is None:
stock_pool = pd.read_pickle(stock_pool_path)
stock_pool_adjvwap = stock_pool.pivot(index='date',columns='code',values='ADJVWAP')
stock_pool_adjclose = stock_pool.pivot(index='date',columns='code',values='ADJCLOSE')
RET_dict = prepare_RET_dict(stock_pool_adjvwap, stock_pool_adjclose)
benchmark_ret_list = []
for RET_key, RET_tn in RET_dict.items():
RET_tn = RET_tn/time_horizon_dict[RET_key]
benchmark_ret_list.append(pd.DataFrame(RET_tn.mean(axis=1),columns=[RET_key]))
IOTools.save_this(pd.concat(benchmark_ret_list,axis=1), benchmark_ret_save_dir,f'BENCHMARK_RETURN_{stock_pool_name.replace("STOCK_POOL_","")}')
if __name__ == '__main__':
config_path = r"D:\HX_proj\factorAnalysis\updateStockPoolConfig.yaml"
updateSP = UpdateStockPool(config_path)
updateSP.update_stock_info()
updateSP.update_stock_pool_basic()
updateSP.update_stock_HS300()
updateSP.update_stock_ZZ500()
updateSP.update_stock_ZZ800()
stock_pool_name = 'STOCK_POOL_basic'
stock_pool_path = f"E:\\data\\interday\\{stock_pool_name}.pkl"
update_benchmark_return(stock_pool_path=stock_pool_path,stock_pool_name=stock_pool_name,benchmark_ret_save_dir = r"E:\data\interday")
| 1,098
| 0
| 0
| 15,653
| 0
| 1,352
| 0
| 17
| 342
|
5dd0ee9d6513012c54587e1dc2268e195652a1f7
| 2,585
|
py
|
Python
|
examples/PyGame/orientation_control_osc.py
|
elsuizo/abr_control
|
d7d47a1c152dfcb8d1a3093083d53f19cc4922d6
|
[
"BSD-3-Clause"
] | 1
|
2021-07-07T13:26:38.000Z
|
2021-07-07T13:26:38.000Z
|
examples/PyGame/orientation_control_osc.py
|
elsuizo/abr_control
|
d7d47a1c152dfcb8d1a3093083d53f19cc4922d6
|
[
"BSD-3-Clause"
] | null | null | null |
examples/PyGame/orientation_control_osc.py
|
elsuizo/abr_control
|
d7d47a1c152dfcb8d1a3093083d53f19cc4922d6
|
[
"BSD-3-Clause"
] | null | null | null |
"""
Running operational space control with the PyGame display. The arm will
move the end-effector to a target orientation, which can be changed by
pressing the left/right arrow keys.
"""
import numpy as np
from os import environ
environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
from abr_control.arms import threejoint as arm
# from abr_control.arms import twojoint as arm
from abr_control.interfaces import PyGame
from abr_control.controllers import OSC, Damping
# initialize our robot config
robot_config = arm.Config(use_cython=True)
# create our arm simulation
arm_sim = arm.ArmSim(robot_config)
# damp the movements of the arm
damping = Damping(robot_config, kv=10)
# create operational space controller
ctrlr = OSC(robot_config, kp=50, null_controllers=[damping],
# control (gamma) out of [x, y, z, alpha, beta, gamma]
ctrlr_dof = [False, False, False, False, False, True])
# create our interface
interface = PyGame(robot_config, arm_sim, dt=.001,
on_keypress=on_keypress)
interface.connect()
interface.theta = -3 * np.pi / 4
feedback = interface.get_feedback()
R = robot_config.R('EE', feedback['q'])
interface.on_keypress(interface, None)
try:
print('\nSimulation starting...')
print('Press left or right arrow to change target orientation angle.\n')
count = 0
while 1:
# get arm feedback
feedback = interface.get_feedback()
hand_xyz = robot_config.Tx('EE', feedback['q'])
target = np.hstack([np.zeros(3), interface.target_angles])
u = ctrlr.generate(
q=feedback['q'],
dq=feedback['dq'],
target=target,
)
# apply the control signal, step the sim forward
interface.send_forces(
u, update_display=True if count % 20 == 0 else False)
count += 1
finally:
# stop and reset the simulation
interface.disconnect()
print('Simulation terminated...')
| 30.05814
| 76
| 0.670019
|
"""
Running operational space control with the PyGame display. The arm will
move the end-effector to a target orientation, which can be changed by
pressing the left/right arrow keys.
"""
import numpy as np
from os import environ
environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
import pygame
from abr_control.arms import threejoint as arm
# from abr_control.arms import twojoint as arm
from abr_control.interfaces import PyGame
from abr_control.utils import transformations
from abr_control.controllers import OSC, Damping
# initialize our robot config
robot_config = arm.Config(use_cython=True)
# create our arm simulation
arm_sim = arm.ArmSim(robot_config)
# damp the movements of the arm
damping = Damping(robot_config, kv=10)
# create operational space controller
ctrlr = OSC(robot_config, kp=50, null_controllers=[damping],
# control (gamma) out of [x, y, z, alpha, beta, gamma]
ctrlr_dof = [False, False, False, False, False, True])
def on_keypress(self, key):
if key == pygame.K_LEFT:
self.theta += np.pi / 10
if key == pygame.K_RIGHT:
self.theta -= np.pi / 10
print('theta: ', self.theta)
# set the target orientation to be the initial EE
# orientation rotated by theta
R_theta = np.array([
[np.cos(interface.theta), -np.sin(interface.theta), 0],
[np.sin(interface.theta), np.cos(interface.theta), 0],
[0, 0, 1]])
R_target = np.dot(R_theta, R)
self.target_angles = transformations.euler_from_matrix(
R_target, axes='sxyz')
# create our interface
interface = PyGame(robot_config, arm_sim, dt=.001,
on_keypress=on_keypress)
interface.connect()
interface.theta = -3 * np.pi / 4
feedback = interface.get_feedback()
R = robot_config.R('EE', feedback['q'])
interface.on_keypress(interface, None)
try:
print('\nSimulation starting...')
print('Press left or right arrow to change target orientation angle.\n')
count = 0
while 1:
# get arm feedback
feedback = interface.get_feedback()
hand_xyz = robot_config.Tx('EE', feedback['q'])
target = np.hstack([np.zeros(3), interface.target_angles])
u = ctrlr.generate(
q=feedback['q'],
dq=feedback['dq'],
target=target,
)
# apply the control signal, step the sim forward
interface.send_forces(
u, update_display=True if count % 20 == 0 else False)
count += 1
finally:
# stop and reset the simulation
interface.disconnect()
print('Simulation terminated...')
| 0
| 0
| 0
| 0
| 0
| 551
| 0
| 16
| 67
|
ec3070556999b4b313c77a9f09b9213babe68567
| 419
|
py
|
Python
|
test/test_utils/test_next_version/test_get_current_calver_tags.py
|
132nd-etcher/epab
|
5226d3a36580f8cc50cf5dcac426adb1350a2c9b
|
[
"MIT"
] | 2
|
2018-12-13T06:49:10.000Z
|
2018-12-13T07:37:49.000Z
|
test/test_utils/test_next_version/test_get_current_calver_tags.py
|
etcher-be/epab
|
5226d3a36580f8cc50cf5dcac426adb1350a2c9b
|
[
"MIT"
] | 109
|
2018-08-22T04:25:56.000Z
|
2019-10-17T05:10:21.000Z
|
test/test_utils/test_next_version/test_get_current_calver_tags.py
|
etcher-be/epab
|
5226d3a36580f8cc50cf5dcac426adb1350a2c9b
|
[
"MIT"
] | 1
|
2018-02-25T05:53:18.000Z
|
2018-02-25T05:53:18.000Z
|
# coding=utf-8
| 26.1875
| 56
| 0.72315
|
# coding=utf-8
import pytest
import epab.utils
from epab.utils import _next_version as nv
@pytest.mark.long
def test_get_current_calver_tags(repo: epab.utils.Repo):
calver = '2018.1.1'
assert len(nv._get_current_calver_tags(calver)) == 0
repo.tag(f'{calver}.1')
assert len(nv._get_current_calver_tags(calver)) == 1
repo.tag(f'{calver}.2')
assert len(nv._get_current_calver_tags(calver)) == 2
| 0
| 304
| 0
| 0
| 0
| 0
| 0
| 9
| 90
|
ff5902120c035db34bdcd8cb1814c2eb99ed8af1
| 3,843
|
py
|
Python
|
irods/rule.py
|
MaastrichtUniversity/python-irodsclient
|
e6e8b37205edeb94c57f3d30786b56cccd91d985
|
[
"Xnet",
"X11"
] | null | null | null |
irods/rule.py
|
MaastrichtUniversity/python-irodsclient
|
e6e8b37205edeb94c57f3d30786b56cccd91d985
|
[
"Xnet",
"X11"
] | null | null | null |
irods/rule.py
|
MaastrichtUniversity/python-irodsclient
|
e6e8b37205edeb94c57f3d30786b56cccd91d985
|
[
"Xnet",
"X11"
] | null | null | null |
from __future__ import absolute_import
| 38.049505
| 148
| 0.577934
|
from __future__ import absolute_import
from irods.message import iRODSMessage, StringStringMap, RodsHostAddress, STR_PI, MsParam, MsParamArray, RuleExecutionRequest
from irods.api_number import api_number
from io import open as io_open
from irods.message import Message, StringProperty
class RemoveRuleMessage(Message):
#define RULE_EXEC_DEL_INP_PI "str ruleExecId[NAME_LEN];"
_name = 'RULE_EXEC_DEL_INP_PI'
ruleExecId = StringProperty()
def __init__(self,id_):
super(RemoveRuleMessage,self).__init__()
self.ruleExecId = str(id_)
class Rule(object):
def __init__(self, session, rule_file=None, body='', params=None, output=''):
self.session = session
self.params = {}
self.output = ''
if rule_file:
self.load(rule_file)
else:
self.body = '@external\n' + body
# overwrite params and output if received arguments
if params is not None:
self.params = params
if output != '':
self.output = output
def remove_by_id(self,*ids):
with self.session.pool.get_connection() as conn:
for id_ in ids:
request = iRODSMessage("RODS_API_REQ", msg=RemoveRuleMessage(id_),
int_info=api_number['RULE_EXEC_DEL_AN'])
conn.send(request)
response = conn.recv()
if response.int_info != 0:
raise RuntimeError("Error removing rule {id_}".format(**locals()))
def load(self, rule_file, encoding = 'utf-8'):
self.body = '@external\n'
# parse rule file
with io_open(rule_file, encoding = encoding) as f:
for line in f:
# parse input line
if line.strip().lower().startswith('input'):
input_header, input_line = line.split(None, 1)
# sanity check
if input_header.lower() != 'input':
raise ValueError
# parse *param0="value0",*param1="value1",...
for pair in input_line.split(','):
label, value = pair.split('=')
self.params[label.strip()] = value.strip()
# parse output line
elif line.strip().lower().startswith('output'):
output_header, output_line = line.split(None, 1)
# sanity check
if output_header.lower() != 'output':
raise ValueError
# use line as is
self.output = output_line.strip()
# parse rule
else:
self.body += line
def execute(self, session_clean_up=False):
# rule input
param_array = []
for label, value in self.params.items():
inOutStruct = STR_PI(myStr=value)
param_array.append(MsParam(label=label, type='STR_PI', inOutStruct=inOutStruct))
inpParamArray = MsParamArray(paramLen=len(param_array), oprType=0, MsParam_PI=param_array)
# rule body
addr = RodsHostAddress(hostAddr='', rodsZone='', port=0, dummyInt=0)
condInput = StringStringMap({})
message_body = RuleExecutionRequest(myRule=self.body, addr=addr, condInput=condInput, outParamDesc=self.output, inpParamArray=inpParamArray)
request = iRODSMessage("RODS_API_REQ", msg=message_body, int_info=api_number['EXEC_MY_RULE_AN'])
with self.session.pool.get_connection() as conn:
conn.send(request)
response = conn.recv()
out_param_array = response.get_main_message(MsParamArray)
if session_clean_up:
self.session.cleanup()
return out_param_array
| 0
| 0
| 0
| 3,511
| 0
| 0
| 0
| 159
| 134
|
8a090a8ec15b5e802b163d342a26be6e4d9b7783
| 786
|
py
|
Python
|
guitab_knn/midi.py
|
bestvibes/guitab
|
b3491bab9bc7390eb3722ada44e0284731abf521
|
[
"MIT"
] | 1
|
2022-03-02T09:36:28.000Z
|
2022-03-02T09:36:28.000Z
|
guitab_container/webapp/webapp/midi.py
|
bestvibes/guitab
|
b3491bab9bc7390eb3722ada44e0284731abf521
|
[
"MIT"
] | 3
|
2020-02-12T00:40:58.000Z
|
2021-06-10T20:15:45.000Z
|
guitab_container/webapp/webapp/midi.py
|
bestvibes/guitab
|
b3491bab9bc7390eb3722ada44e0284731abf521
|
[
"MIT"
] | 2
|
2020-09-23T16:26:46.000Z
|
2022-02-16T23:57:39.000Z
|
# Array showing guitar string's relative pitches, in semi-tones, with "0" being low E
# ["Low E", "A", "D", "G", "B", "High E"]
strings = [40, 45, 50, 55, 60, 65];
| 30.230769
| 93
| 0.577608
|
# Array showing guitar string's relative pitches, in semi-tones, with "0" being low E
# ["Low E", "A", "D", "G", "B", "High E"]
strings = [40, 45, 50, 55, 60, 65];
def midi_to_label(midi):
if (midi < strings[0]):
raise ValueError("Note " + note + " is not playable on a guitar in standard tuning.")
idealString = 0
for string, string_midi in enumerate(strings):
if (midi < string_midi):
break
idealString = string
label = [-1, -1, -1, -1, -1, -1]
label[idealString] = midi - strings[idealString];
return label
def label_to_midi(label):
outp = []
for string, fret in enumerate(label):
if (fret != -1):
outp.append(fret + strings[string])
else:
outp.append(-1)
return outp
| 0
| 0
| 0
| 0
| 0
| 577
| 0
| 0
| 46
|
9a8f0064512347b712e42bdfed88fe249599ba83
| 6,616
|
py
|
Python
|
Lib/turtledemo/nim.py
|
sireliah/polish-python
|
605df4944c2d3bc25f8bf6964b274c0a0d297cc3
|
[
"PSF-2.0"
] | 1
|
2018-06-21T18:21:24.000Z
|
2018-06-21T18:21:24.000Z
|
Lib/turtledemo/nim.py
|
sireliah/polish-python
|
605df4944c2d3bc25f8bf6964b274c0a0d297cc3
|
[
"PSF-2.0"
] | null | null | null |
Lib/turtledemo/nim.py
|
sireliah/polish-python
|
605df4944c2d3bc25f8bf6964b274c0a0d297cc3
|
[
"PSF-2.0"
] | null | null | null |
""" turtle-example-suite:
tdemo_nim.py
Play nim against the computer. The player
who takes the last stick jest the winner.
Implements the model-view-controller
design pattern.
"""
zaimportuj turtle
zaimportuj random
zaimportuj time
SCREENWIDTH = 640
SCREENHEIGHT = 480
MINSTICKS = 7
MAXSTICKS = 31
HUNIT = SCREENHEIGHT // 12
WUNIT = SCREENWIDTH // ((MAXSTICKS // 5) * 11 + (MAXSTICKS % 5) * 2)
SCOLOR = (63, 63, 31)
HCOLOR = (255, 204, 204)
COLOR = (204, 204, 255)
dla z w range(3):
s = state[z] ^ xored
jeeli s <= state[z]:
move = (z, s)
zwr move
def randommove(state):
m = max(state)
dopki Prawda:
z = random.randint(0,2)
jeeli state[z] > (m > 1):
przerwij
rand = random.randint(m > 1, state[z]-1)
zwr z, rand
klasa NimModel(object):
albo_inaczej self.player == 0:
self.player = 1
row, col = computerzug(self.sticks)
self.move(row, col)
self.player = 0
def game_over(self):
zwr self.sticks == [0, 0, 0]
def notify_move(self, row, col):
jeeli self.sticks[row] <= col:
zwr
self.move(row, col)
klasa Stick(turtle.Turtle):
self.game.controller.notify_move(self.row, self.col)
klasa NimView(object):
self.display("... a moment please ...")
self.screen.tracer(Prawda)
def display(self, msg1, msg2=Nic):
self.screen.tracer(Nieprawda)
self.writer.clear()
jeeli msg2 jest nie Nic:
self.writer.goto(0, - SCREENHEIGHT // 2 + 48)
self.writer.pencolor("red")
self.writer.write(msg2, align="center", font=("Courier",18,"bold"))
self.writer.goto(0, - SCREENHEIGHT // 2 + 20)
self.writer.pencolor("black")
self.writer.write(msg1, align="center", font=("Courier",14,"bold"))
self.screen.tracer(Prawda)
def setup(self):
self.screen.tracer(Nieprawda)
dla row w range(3):
dla col w range(self.model.sticks[row]):
self.sticks[(row, col)].color(SCOLOR)
dla row w range(3):
dla col w range(self.model.sticks[row], MAXSTICKS):
self.sticks[(row, col)].color("white")
self.display("Your turn! Click leftmost stick to remove.")
self.screen.tracer(Prawda)
def notify_move(self, row, col, maxspalte, player):
jeeli player == 0:
farbe = HCOLOR
dla s w range(col, maxspalte):
self.sticks[(row, s)].color(farbe)
inaczej:
self.display(" ... thinking ... ")
time.sleep(0.5)
self.display(" ... thinking ... aaah ...")
farbe = COLOR
dla s w range(maxspalte-1, col-1, -1):
time.sleep(0.2)
self.sticks[(row, s)].color(farbe)
self.display("Your turn! Click leftmost stick to remove.")
def notify_over(self):
jeeli self.game.model.winner == 0:
msg2 = "Congrats. You're the winner!!!"
inaczej:
msg2 = "Sorry, the computer jest the winner."
self.display("To play again press space bar. To leave press ESC.", msg2)
def clear(self):
jeeli self.game.state == Nim.OVER:
self.screen.clear()
klasa NimController(object):
self.game.screen.onkey(self.game.model.setup, "space")
self.game.screen.onkey(self.game.view.clear, "Escape")
self.game.view.display("Press space bar to start game")
self.game.screen.listen()
def notify_move(self, row, col):
jeeli self.BUSY:
zwr
self.BUSY = Prawda
self.game.model.notify_move(row, col)
self.BUSY = Nieprawda
klasa Nim(object):
CREATED = 0
RUNNING = 1
OVER = 2
def main():
mainscreen = turtle.Screen()
mainscreen.mode("standard")
mainscreen.setup(SCREENWIDTH, SCREENHEIGHT)
nim = Nim(mainscreen)
zwr "EVENTLOOP"
jeeli __name__ == "__main__":
main()
turtle.mainloop()
| 29.145374
| 83
| 0.57104
|
""" turtle-example-suite:
tdemo_nim.py
Play nim against the computer. The player
who takes the last stick jest the winner.
Implements the model-view-controller
design pattern.
"""
zaimportuj turtle
zaimportuj random
zaimportuj time
SCREENWIDTH = 640
SCREENHEIGHT = 480
MINSTICKS = 7
MAXSTICKS = 31
HUNIT = SCREENHEIGHT // 12
WUNIT = SCREENWIDTH // ((MAXSTICKS // 5) * 11 + (MAXSTICKS % 5) * 2)
SCOLOR = (63, 63, 31)
HCOLOR = (255, 204, 204)
COLOR = (204, 204, 255)
def randomrow():
zwróć random.randint(MINSTICKS, MAXSTICKS)
def computerzug(state):
xored = state[0] ^ state[1] ^ state[2]
jeżeli xored == 0:
zwróć randommove(state)
dla z w range(3):
s = state[z] ^ xored
jeżeli s <= state[z]:
move = (z, s)
zwróć move
def randommove(state):
m = max(state)
dopóki Prawda:
z = random.randint(0,2)
jeżeli state[z] > (m > 1):
przerwij
rand = random.randint(m > 1, state[z]-1)
zwróć z, rand
klasa NimModel(object):
def __init__(self, game):
self.game = game
def setup(self):
jeżeli self.game.state nie w [Nim.CREATED, Nim.OVER]:
zwróć
self.sticks = [randomrow(), randomrow(), randomrow()]
self.player = 0
self.winner = Nic
self.game.view.setup()
self.game.state = Nim.RUNNING
def move(self, row, col):
maxspalte = self.sticks[row]
self.sticks[row] = col
self.game.view.notify_move(row, col, maxspalte, self.player)
jeżeli self.game_over():
self.game.state = Nim.OVER
self.winner = self.player
self.game.view.notify_over()
albo_inaczej self.player == 0:
self.player = 1
row, col = computerzug(self.sticks)
self.move(row, col)
self.player = 0
def game_over(self):
zwróć self.sticks == [0, 0, 0]
def notify_move(self, row, col):
jeżeli self.sticks[row] <= col:
zwróć
self.move(row, col)
klasa Stick(turtle.Turtle):
def __init__(self, row, col, game):
turtle.Turtle.__init__(self, visible=Nieprawda)
self.row = row
self.col = col
self.game = game
x, y = self.coords(row, col)
self.shape("square")
self.shapesize(HUNIT/10.0, WUNIT/20.0)
self.speed(0)
self.pu()
self.goto(x,y)
self.color("white")
self.showturtle()
def coords(self, row, col):
packet, remainder = divmod(col, 5)
x = (3 + 11 * packet + 2 * remainder) * WUNIT
y = (2 + 3 * row) * HUNIT
zwróć x - SCREENWIDTH // 2 + WUNIT // 2, SCREENHEIGHT // 2 - y - HUNIT // 2
def makemove(self, x, y):
jeżeli self.game.state != Nim.RUNNING:
zwróć
self.game.controller.notify_move(self.row, self.col)
klasa NimView(object):
def __init__(self, game):
self.game = game
self.screen = game.screen
self.mousuń = game.model
self.screen.colormode(255)
self.screen.tracer(Nieprawda)
self.screen.bgcolor((240, 240, 255))
self.writer = turtle.Turtle(visible=Nieprawda)
self.writer.pu()
self.writer.speed(0)
self.sticks = {}
dla row w range(3):
dla col w range(MAXSTICKS):
self.sticks[(row, col)] = Stick(row, col, game)
self.display("... a moment please ...")
self.screen.tracer(Prawda)
def display(self, msg1, msg2=Nic):
self.screen.tracer(Nieprawda)
self.writer.clear()
jeżeli msg2 jest nie Nic:
self.writer.goto(0, - SCREENHEIGHT // 2 + 48)
self.writer.pencolor("red")
self.writer.write(msg2, align="center", font=("Courier",18,"bold"))
self.writer.goto(0, - SCREENHEIGHT // 2 + 20)
self.writer.pencolor("black")
self.writer.write(msg1, align="center", font=("Courier",14,"bold"))
self.screen.tracer(Prawda)
def setup(self):
self.screen.tracer(Nieprawda)
dla row w range(3):
dla col w range(self.model.sticks[row]):
self.sticks[(row, col)].color(SCOLOR)
dla row w range(3):
dla col w range(self.model.sticks[row], MAXSTICKS):
self.sticks[(row, col)].color("white")
self.display("Your turn! Click leftmost stick to remove.")
self.screen.tracer(Prawda)
def notify_move(self, row, col, maxspalte, player):
jeżeli player == 0:
farbe = HCOLOR
dla s w range(col, maxspalte):
self.sticks[(row, s)].color(farbe)
inaczej:
self.display(" ... thinking ... ")
time.sleep(0.5)
self.display(" ... thinking ... aaah ...")
farbe = COLOR
dla s w range(maxspalte-1, col-1, -1):
time.sleep(0.2)
self.sticks[(row, s)].color(farbe)
self.display("Your turn! Click leftmost stick to remove.")
def notify_over(self):
jeżeli self.game.model.winner == 0:
msg2 = "Congrats. You're the winner!!!"
inaczej:
msg2 = "Sorry, the computer jest the winner."
self.display("To play again press space bar. To leave press ESC.", msg2)
def clear(self):
jeżeli self.game.state == Nim.OVER:
self.screen.clear()
klasa NimController(object):
def __init__(self, game):
self.game = game
self.sticks = game.view.sticks
self.BUSY = Nieprawda
dla stick w self.sticks.values():
stick.onclick(stick.makemove)
self.game.screen.onkey(self.game.model.setup, "space")
self.game.screen.onkey(self.game.view.clear, "Escape")
self.game.view.display("Press space bar to start game")
self.game.screen.listen()
def notify_move(self, row, col):
jeżeli self.BUSY:
zwróć
self.BUSY = Prawda
self.game.model.notify_move(row, col)
self.BUSY = Nieprawda
klasa Nim(object):
CREATED = 0
RUNNING = 1
OVER = 2
def __init__(self, screen):
self.state = Nim.CREATED
self.screen = screen
self.mousuń = NimModel(self)
self.view = NimView(self)
self.controller = NimController(self)
def main():
mainscreen = turtle.Screen()
mainscreen.mode("standard")
mainscreen.setup(SCREENWIDTH, SCREENHEIGHT)
nim = Nim(mainscreen)
zwróć "EVENTLOOP"
jeżeli __name__ == "__main__":
main()
turtle.mainloop()
| 76
| 0
| 0
| 0
| 0
| 2,211
| 0
| 0
| 285
|
528cf1b59e68183f561c087b20046483ff243394
| 918
|
py
|
Python
|
port_scanner.py
|
nemzyxt/port-scanner
|
52378c2d5e5c6ff7f7b74018a27a6dee1f566c54
|
[
"MIT"
] | null | null | null |
port_scanner.py
|
nemzyxt/port-scanner
|
52378c2d5e5c6ff7f7b74018a27a6dee1f566c54
|
[
"MIT"
] | null | null | null |
port_scanner.py
|
nemzyxt/port-scanner
|
52378c2d5e5c6ff7f7b74018a27a6dee1f566c54
|
[
"MIT"
] | null | null | null |
#Author : Nemuel Wainaina
import socket
from colorama import init, Fore
#some color, haha
init()
GREEN = Fore.GREEN
BLUE = Fore.BLUE
RED = Fore.RED
GRAY = Fore.LIGHTBLACK_EX
RESET = Fore.RESET
def is_port_open(target, port):
'''
This function simply attempts to create a connection
to the target machine on the specified port and
based on whether or not the connection is successful,
prints to the user whether or not the port is open
'''
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((target, port))
print(f"{GREEN}[+] Port {port} is Open {RESET}")
return True
except:
#Comment out the line in order to exclude the results for closed ports
print(f"{GRAY}[-] Port {port} is Closed {RESET}")
return False
finally:
s.close()
| 24.810811
| 79
| 0.611111
|
#Author : Nemuel Wainaina
import socket
from colorama import init, Fore
#some color, haha
init()
GREEN = Fore.GREEN
BLUE = Fore.BLUE
RED = Fore.RED
GRAY = Fore.LIGHTBLACK_EX
RESET = Fore.RESET
def is_port_open(target, port):
'''
This function simply attempts to create a connection
to the target machine on the specified port and
based on whether or not the connection is successful,
prints to the user whether or not the port is open
'''
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((target, port))
print(f"{GREEN}[+] Port {port} is Open {RESET}")
return True
except:
#Comment out the line in order to exclude the results for closed ports
print(f"{GRAY}[-] Port {port} is Closed {RESET}")
return False
finally:
s.close()
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
a83c3aaee2d1d702fdd7e9f3c5ea7dd431a4b156
| 2,290
|
py
|
Python
|
generate_split.py
|
ruoyunz/caltech-ee148-spring2020-hw02
|
671b08cd914134f06db2c1eeec3fa4d4d3e0cd5f
|
[
"MIT"
] | null | null | null |
generate_split.py
|
ruoyunz/caltech-ee148-spring2020-hw02
|
671b08cd914134f06db2c1eeec3fa4d4d3e0cd5f
|
[
"MIT"
] | null | null | null |
generate_split.py
|
ruoyunz/caltech-ee148-spring2020-hw02
|
671b08cd914134f06db2c1eeec3fa4d4d3e0cd5f
|
[
"MIT"
] | null | null | null |
import numpy as np
import os
import json
np.random.seed(2020) # to ensure you always get the same train/test split
data_path = '../data/RedLights2011_Medium'
gts_path = '../data/hw02_annotations'
split_path = '../data/hw02_splits'
os.makedirs(split_path, exist_ok=True) # create directory if needed
split_test = True # set to True and run when annotations are available
train_frac = 0.85
# get sorted list of files:
file_names = sorted(os.listdir(data_path))
# remove any non-JPEG files:
file_names = [f for f in file_names if '.jpg' in f]
# split file names into train and test
file_names_train = []
file_names_test = []
np.random.shuffle(file_names)
file_names_train = file_names[0:(int(train_frac * len(file_names)))]
file_names_test = file_names[(int(train_frac * len(file_names))):]
assert (len(file_names_train) + len(file_names_test)) == len(file_names)
assert len(np.intersect1d(file_names_train,file_names_test)) == 0
np.save(os.path.join(split_path,'file_names_train.npy'),file_names_train)
np.save(os.path.join(split_path,'file_names_test.npy'),file_names_test)
# Function for viewing annotations one by one if needed
if split_test:
with open(os.path.join(gts_path, 'formatted_annotations_students.json'),'r') as f:
gts = json.load(f)
# Use file_names_train and file_names_test to apply the split to the
# annotations
gts_train = {}
gts_test = {}
for i in range(len(file_names_train)):
gts_train[file_names_train[i]] = gts[file_names_train[i]]
for i in range(len(file_names_test)):
gts_test[file_names_test[i]] = gts[file_names_test[i]]
with open(os.path.join(gts_path, 'annotations_train.json'),'w') as f:
json.dump(gts_train,f)
with open(os.path.join(gts_path, 'annotations_test.json'),'w') as f:
json.dump(gts_test,f)
| 30.945946
| 86
| 0.676419
|
import numpy as np
import os
import json
import cv2
import sys
np.random.seed(2020) # to ensure you always get the same train/test split
data_path = '../data/RedLights2011_Medium'
gts_path = '../data/hw02_annotations'
split_path = '../data/hw02_splits'
os.makedirs(split_path, exist_ok=True) # create directory if needed
split_test = True # set to True and run when annotations are available
train_frac = 0.85
# get sorted list of files:
file_names = sorted(os.listdir(data_path))
# remove any non-JPEG files:
file_names = [f for f in file_names if '.jpg' in f]
# split file names into train and test
file_names_train = []
file_names_test = []
np.random.shuffle(file_names)
file_names_train = file_names[0:(int(train_frac * len(file_names)))]
file_names_test = file_names[(int(train_frac * len(file_names))):]
assert (len(file_names_train) + len(file_names_test)) == len(file_names)
assert len(np.intersect1d(file_names_train,file_names_test)) == 0
np.save(os.path.join(split_path,'file_names_train.npy'),file_names_train)
np.save(os.path.join(split_path,'file_names_test.npy'),file_names_test)
# Function for viewing annotations one by one if needed
def view_annotation(fs):
for i in range(len(fs)):
img = cv2.imread(os.path.join(data_path,fs[i]),cv2.IMREAD_COLOR)
preds = gts_stud[fs[i]]
for p in preds2:
(r, c, r2, c2) = p
cv2.rectangle(img, (int(c), int(r)), (int(c2), int(r2)), (0, 255, 0), 1)
cv2.imshow('image',img)
ch = cv2.waitKey(0)
if ch == 27:
exit()
if split_test:
with open(os.path.join(gts_path, 'formatted_annotations_students.json'),'r') as f:
gts = json.load(f)
# Use file_names_train and file_names_test to apply the split to the
# annotations
gts_train = {}
gts_test = {}
for i in range(len(file_names_train)):
gts_train[file_names_train[i]] = gts[file_names_train[i]]
for i in range(len(file_names_test)):
gts_test[file_names_test[i]] = gts[file_names_test[i]]
with open(os.path.join(gts_path, 'annotations_train.json'),'w') as f:
json.dump(gts_train,f)
with open(os.path.join(gts_path, 'annotations_test.json'),'w') as f:
json.dump(gts_test,f)
| 0
| 0
| 0
| 0
| 0
| 403
| 0
| -22
| 67
|
1431c36367740490b243f3436653ce18816364f6
| 2,356
|
py
|
Python
|
src/ygopro/urldecode.py
|
o7878x/pytoolk
|
eb5edfbd5a8907b9705c4042013bf7c828e2fdf3
|
[
"MIT"
] | null | null | null |
src/ygopro/urldecode.py
|
o7878x/pytoolk
|
eb5edfbd5a8907b9705c4042013bf7c828e2fdf3
|
[
"MIT"
] | null | null | null |
src/ygopro/urldecode.py
|
o7878x/pytoolk
|
eb5edfbd5a8907b9705c4042013bf7c828e2fdf3
|
[
"MIT"
] | null | null | null |
import time
import fire
YGO_SCHEME = 'ygo'
YGO_NETLOC = 'deck'
DEFAULT_HEADER = '#created by ...\n'
CARD_SEP_SYM = '_'
CARD_MULTI_SYM = '*'
DEFAULT_SYM = '#'
NO_SYM = '!'
MAIN_HEADER = 'main\n'
EXTRA_HEADER = 'extra\n'
SIDE_HEADER = 'side\n'
DEFAULT_YDK_NAME: str = "default" + "_" + str(int(time.time()))
if __name__ == '__main__':
fire.Fire(save_file)
| 23.79798
| 91
| 0.64983
|
import logging
import time
import typing
from urllib.parse import urlparse, parse_qs
import fire
YGO_SCHEME = 'ygo'
YGO_NETLOC = 'deck'
DEFAULT_HEADER = '#created by ...\n'
CARD_SEP_SYM = '_'
CARD_MULTI_SYM = '*'
DEFAULT_SYM = '#'
NO_SYM = '!'
MAIN_HEADER = 'main\n'
EXTRA_HEADER = 'extra\n'
SIDE_HEADER = 'side\n'
DEFAULT_YDK_NAME: str = "default" + "_" + str(int(time.time()))
def decode_url(url: str) -> str:
res = urlparse(url)
if not res:
logging.info('can not parse url')
return ''
if res.scheme != YGO_SCHEME or res.netloc != YGO_NETLOC:
logging.info('it is not a YGO link')
return ''
qs = parse_qs(res.query)
if not qs:
logging.info('can not parse query')
return ''
return get_deck_str(qs)
def get_code_arr(part_str: str) -> typing.List:
res = []
if not part_str:
return res
parts = str.split(part_str, CARD_SEP_SYM)
for counted_part in parts:
code_count_pair = str.split(counted_part, CARD_MULTI_SYM)
pair_len = len(code_count_pair)
if pair_len == 1:
res.append(counted_part)
elif pair_len == 2:
res += [code_count_pair[0]] * int(code_count_pair[1])
return res
def get_target_field_str(target_dict: typing.Dict, field_name: str, index: int = 0) -> str:
res = ""
try:
res = target_dict[field_name][index]
except KeyError:
logging.info("key error")
finally:
return res
def get_deck_str(query_dict: typing.Dict) -> str:
main_deck_arr = get_code_arr(get_target_field_str(query_dict, 'main'))
print(main_deck_arr)
extra_deck_arr = get_code_arr(get_target_field_str(query_dict, 'extra'))
side_deck_arr = get_code_arr(get_target_field_str(query_dict, 'side'))
target_str = DEFAULT_HEADER
target_str += (DEFAULT_SYM + MAIN_HEADER + '\n'.join(main_deck_arr))
target_str += '\n'
target_str += (DEFAULT_SYM + EXTRA_HEADER + '\n'.join(extra_deck_arr))
target_str += '\n'
target_str += (NO_SYM + SIDE_HEADER + '\n'.join(side_deck_arr))
return target_str
def save_file(origin_url: str, file_name: str = DEFAULT_YDK_NAME):
res_str = decode_url(origin_url)
with open('.'.join([file_name, 'ydk']), 'w') as f:
f.write(res_str)
if __name__ == '__main__':
fire.Fire(save_file)
| 0
| 0
| 0
| 0
| 0
| 1,797
| 0
| 7
| 181
|
7b1edf412d6711b1edca18fb5738889669261b6e
| 21,465
|
py
|
Python
|
tests/test_page.py
|
crempp/mdweb
|
ce3c26e4b7b2dfba0ac793534a06581a8c214570
|
[
"MIT"
] | 12
|
2015-07-31T07:53:57.000Z
|
2021-08-09T18:05:55.000Z
|
tests/test_page.py
|
crempp/mdweb
|
ce3c26e4b7b2dfba0ac793534a06581a8c214570
|
[
"MIT"
] | 59
|
2015-07-31T07:44:16.000Z
|
2020-08-20T19:54:22.000Z
|
tests/test_page.py
|
crempp/mdweb
|
ce3c26e4b7b2dfba0ac793534a06581a8c214570
|
[
"MIT"
] | 1
|
2016-02-22T22:57:17.000Z
|
2016-02-22T22:57:17.000Z
|
# -*- coding: utf-8 -*-
"""Tests for the MDWeb Navigation parser.
Tests to write
* Handle symlinks
* File already open
* Non supported extension (.xls)
* Permissions
Maybe test?
* atime, mtime
* large file
"""
try:
# Python >= 3.3
from unittest import mock
except ImportError:
# Python < 3.3
| 29.283765
| 259
| 0.646261
|
# -*- coding: utf-8 -*-
"""Tests for the MDWeb Navigation parser.
Tests to write
* Handle symlinks
* File already open
* Non supported extension (.xls)
* Permissions
Maybe test?
* atime, mtime
* large file
"""
import datetime
from pyfakefs import fake_filesystem_unittest
from unittest import skip
try:
# Python >= 3.3
from unittest import mock
except ImportError:
# Python < 3.3
import mock
from mdweb.Page import PageMetaInf, Page, load_page
from mdweb.Exceptions import (
PageMetaInfFieldException,
PageParseException,
ContentException,
)
class TestPageMeta(fake_filesystem_unittest.TestCase):
"""PageMetaInf object tests."""
def setUp(self):
"""Create fake filesystem."""
self.setUpPyfakefs()
def test_parse_empty_string(self):
"""Empty string should parse successfully."""
file_string = u""
meta_inf = PageMetaInf(file_string)
self.assertIsNone(meta_inf.author)
self.assertIsNone(meta_inf.date)
self.assertIsNone(meta_inf.description)
self.assertIsNone(meta_inf.nav_name)
self.assertEqual(meta_inf.order, 0)
self.assertIsNone(meta_inf.template)
self.assertIsNone(meta_inf.title)
def test_parse_some_fields(self):
"""A few defined fields should parse successfully."""
file_string = u"""Title: MDWeb
Description: The minimalistic markdown NaCMS
Date: February 1st, 2016
"""
meta_inf = PageMetaInf(file_string)
self.assertIsNone(meta_inf.author)
self.assertEqual(meta_inf.date, datetime.datetime(2016, 2, 1, 0, 0))
self.assertEqual(meta_inf.description,
u'The minimalistic markdown NaCMS')
self.assertEqual(meta_inf.order, 0)
self.assertIsNone(meta_inf.template)
self.assertEqual(meta_inf.title, u'MDWeb')
def test_parse_all_fields(self):
"""All available fields should parse successfully."""
file_string = u"""Title: MDWeb
Description: The minimalistic markdown NaCMS
Author: Chad Rempp
Date: February 1st, 2016
Order: 1
Template: page_home.html
Nav Name: Home Page
Sitemap Changefreq: Monthly
Sitemap Priority: 0.5
Teaser: This is a teaser paragraph that will be availble to pages
Teaser Image: /contentassets/home/00041_thumb.jpg
"""
meta_inf = PageMetaInf(file_string)
self.assertEqual(meta_inf.author, u'Chad Rempp')
self.assertEqual(meta_inf.date, datetime.datetime(2016, 2, 1, 0, 0))
self.assertEqual(meta_inf.description,
u'The minimalistic markdown NaCMS')
self.assertEqual(meta_inf.order, 1)
self.assertEqual(meta_inf.template, u'page_home.html')
self.assertEqual(meta_inf.title, u'MDWeb')
self.assertEqual(meta_inf.nav_name, u'Home Page')
self.assertEqual(meta_inf.sitemap_changefreq, u'Monthly')
self.assertEqual(meta_inf.sitemap_priority, u'0.5')
self.assertEqual(meta_inf.teaser, u'This is a teaser paragraph that will be availble to pages')
self.assertEqual(meta_inf.teaser_image, u'/contentassets/home/00041_thumb.jpg')
def test_metainf_spacing(self):
"""Spacing should not matter in parsing."""
file_string = u"""Title: MDWeb
Description: The minimalistic markdown NaCMS
Author: Chad Rempp
Date : February 1st, 2016
Order: 1
Template: page_home.html
"""
meta_inf = PageMetaInf(file_string)
self.assertEqual(meta_inf.author, u'Chad Rempp')
self.assertEqual(meta_inf.date, datetime.datetime(2016, 2, 1, 0, 0))
self.assertEqual(meta_inf.description,
u'The minimalistic markdown NaCMS')
self.assertEqual(meta_inf.order, 1)
self.assertEqual(meta_inf.template, u'page_home.html')
self.assertEqual(meta_inf.title, u'MDWeb')
def test_comments(self):
"""Comments should be skipped during parsing."""
file_string = u"""# This is a comment
Title: MDWeb
Description: The minimalistic markdown NaCMS
#Author: Chad Rempp
#Date: February 1st, 2016
Order: 1
Template: page_home.html
#
# Nothing to see here
"""
meta_inf = PageMetaInf(file_string)
self.assertIsNone(meta_inf.author)
self.assertIsNone(meta_inf.date)
self.assertEqual(meta_inf.description,
u'The minimalistic markdown NaCMS')
self.assertEqual(meta_inf.order, 1)
self.assertEqual(meta_inf.template, u'page_home.html')
self.assertEqual(meta_inf.title, u'MDWeb')
def test_unicode(self):
"""Parser should handle unicode."""
file_string = u"""Title: советских
Description: ᚠᛇᚻ᛫ᛒᛦᚦ᛫ᚠᚱᚩᚠᚢᚱ᛫ᚠᛁᚱᚪ᛫ᚷᛖᚻᚹᛦᛚᚳᚢᛗ
Author: Οδυσσέα Ελύτη
Date: February 1st, 2016
Order: 1
Template: ღმერთსი.html
"""
meta_inf = PageMetaInf(file_string)
self.assertEqual(meta_inf.author, u'Οδυσσέα Ελύτη')
self.assertEqual(meta_inf.date, datetime.datetime(2016, 2, 1, 0, 0))
self.assertEqual(meta_inf.description, u'ᚠᛇᚻ᛫ᛒᛦᚦ᛫ᚠᚱᚩᚠᚢᚱ᛫ᚠᛁᚱᚪ᛫ᚷᛖᚻᚹᛦᛚᚳᚢᛗ')
self.assertEqual(meta_inf.order, 1)
self.assertEqual(meta_inf.template, u'ღმერთსი.html')
self.assertEqual(meta_inf.title, u'советских')
def test_parse_multiline_field(self):
"""Multiline fields should parse successfully."""
file_string = u"""Title: MDWeb
Description: The minimalistic markdown NaCMS
Author: Chad Rempp
Date: February 1st, 2016
Order: 1
Template: page_home.html
Nav Name: Home Page
Sitemap Changefreq: Monthly
Sitemap Priority: 0.5
Teaser: This is a teaser paragraph that will be available to pages
and the teaser may
span multiple lines indented with whitespace even if the line looks
like a metainf field
Not A Field: This won't get parsed as a field
Teaser Image: /contentassets/home/00041_thumb.jpg
"""
meta_inf = PageMetaInf(file_string)
self.assertEqual(meta_inf.teaser, u'This is a teaser paragraph that will be available to pages and the teaser may span multiple lines indented with whitespace even if the line looks like a metainf field Not A Field: This won\'t get parsed as a field')
def test_parse_custom_field(self):
"""Multiline fields should parse successfully."""
file_string = u"""Title: MDWeb
Description: The minimalistic markdown NaCMS
Author: Chad Rempp
Summary Image: blah.jpg
"""
meta_inf = PageMetaInf(file_string)
self.assertEqual(meta_inf.custom_summary_image, u'blah.jpg')
@skip
def test_unpublished_page(self):
"""Unpublished pages should have the correct attr value."""
self.assertEqual(1, 2)
class TestPage(fake_filesystem_unittest.TestCase):
"""Page object tests."""
def setUp(self):
"""Create fake filesystem."""
self.setUpPyfakefs()
def test_page_instantiation(self):
"""A page should be instantiated with appropriate attributes."""
file_string = u"This is a page"
self.fs.create_file('/my/content/about/history.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/about/history.md'))
self.assertEqual(page.page_path, '/my/content/about/history.md')
self.assertEqual(page.url_path, 'about/history')
self.fs.create_file('/my/content/super/deep/url/path/index.md',
contents=file_string)
page = Page(*load_page('/my/content',
'/my/content/super/deep/url/path/index.md'))
self.assertEqual(page.page_path,
'/my/content/super/deep/url/path/index.md')
self.assertEqual(page.url_path, 'super/deep/url/path')
def test_unparsable_path(self):
"""An unparsable page path should raise PageParseException."""
file_string = u""
self.fs.create_file('/my/content/index',
contents=file_string)
# Not an MD file
self.assertRaises(PageParseException, load_page, '/my/content',
'/my/content/index')
@mock.patch('mdweb.Page.PageMetaInf')
def test_repr(self, mock_page_meta_inf):
"""A Page object should return the proper repr."""
file_string = u""
self.fs.create_file('/my/content/index.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/index.md'))
self.assertEqual(str(page), '/my/content/index.md')
@mock.patch('mdweb.Page.PageMetaInf')
def test_parse_empty_file(self, mock_page_meta_inf):
"""An empty file should parse properly."""
file_string = u""
self.fs.create_file('/my/content/index.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/index.md'))
mock_page_meta_inf.assert_called_once_with('')
self.assertEqual(page.markdown_str, '')
self.assertEqual(page.page_html, '')
def test_no_file(self):
"""If the path has no file a ContentException should be raised."""
self.assertRaises(ContentException, load_page, '/my/content',
'/my/content/index.md')
@mock.patch('mdweb.Page.PageMetaInf')
def test_no_meta_inf(self, mock_page_meta_inf):
"""A page with no meta information should parse."""
# pylint: disable=C0301
# pylint: disable=E501
file_string = u"""Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
The quick brown fox jumped over the lazy
dog's back."""
self.fs.create_file('/my/content/index.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/index.md'))
mock_page_meta_inf.assert_called_once_with('')
self.assertEqual(page.markdown_str, '''Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
The quick brown fox jumped over the lazy
dog's back.''')
self.assertEqual(page.page_html, '''<p>Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.</p>
<p>The quick brown fox jumped over the lazy
dog's back.</p>''')
@mock.patch('mdweb.Page.PageMetaInf')
def test_page_with_meta_inf(self, mock_page_meta_inf):
"""A page with meta info should parse the content and meta inf."""
file_string = '''```metainf
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
```
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
The quick brown fox jumped over the lazy
dog's back.'''
self.fs.create_file('/my/content/index.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/index.md'))
mock_page_meta_inf.assert_called_once_with('''
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
''')
self.assertEqual(page.markdown_str, '''
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
The quick brown fox jumped over the lazy
dog's back.''')
self.assertEqual(page.page_html,
'''<p>Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.</p>
<p>The quick brown fox jumped over the lazy
dog's back.</p>''')
@mock.patch('mdweb.Page.PageMetaInf')
def test_page_with_slash_star_slash(self, mock_page_meta_inf):
"""A page with /*/ in the content should parse corectly.
Regression test for https://github.com/crempp/mdweb/issues/47"""
file_string = '''```metainf
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
```
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
This is a link that blows up the parser
[asdf](https://web.archive.org/web/*/http://site.com)
The quick brown fox jumped over the lazy
dog's back.'''
self.fs.create_file('/my/content/index.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/index.md'))
mock_page_meta_inf.assert_called_once_with('''
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
''')
self.assertEqual(page.markdown_str, '''
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
This is a link that blows up the parser
[asdf](https://web.archive.org/web/*/http://site.com)
The quick brown fox jumped over the lazy
dog's back.''')
self.assertEqual(page.page_html,
'''<p>Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.</p>
<p>This is a link that blows up the parser
<a href="https://web.archive.org/web/*/http://site.com">asdf</a></p>
<p>The quick brown fox jumped over the lazy
dog's back.</p>''')
@mock.patch('mdweb.Page.PageMetaInf')
def test_page_with_second_metainf(self, mock_page_meta_inf):
"""A page with a second metainf should only parse the first"""
file_string = '''```metainf
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
```
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
Here is another metainf block
```metainf
Title: Fake Block
```
The quick brown fox jumped over the lazy
dog's back.'''
self.fs.create_file('/my/content/index.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/index.md'))
mock_page_meta_inf.assert_called_once_with('''
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
''')
self.assertEqual(page.markdown_str, '''
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
Here is another metainf block
```metainf
Title: Fake Block
```
The quick brown fox jumped over the lazy
dog's back.''')
self.assertEqual(page.page_html,
'''<p>Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.</p>
<p>Here is another metainf block
<code>metainf
Title: Fake Block</code></p>
<p>The quick brown fox jumped over the lazy
dog's back.</p>''')
@mock.patch('mdweb.Page.PageMetaInf')
def test_markdown_formatting(self, mock_page_meta_inf):
"""Markdown should parse correctly.
We won't test this extensively as we should trust the markdown
libraries to test themselves.
"""
file_string = '''```metainf
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
```
Examples taken from https://daringfireball.net/projects/markdown/basics
A First Level Header
====================
A Second Level Header
---------------------
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
The quick brown fox jumped over the lazy
dog's back.
### Header 3
> This is a blockquote.
>
> This is the second paragraph in the blockquote.
>
> ## This is an H2 in a blockquote
---------------------------------------
Some of these words *are emphasized*.
Some of these words _are emphasized also_.
Use two asterisks for **strong emphasis**.
Or, if you prefer, __use two underscores instead__.
---------------------------------------
* Candy.
* Gum.
* Booze.
---------------------------------------
1. Red
2. Green
3. Blue
---------------------------------------
* A list item.
With multiple paragraphs.
* Another item in the list.
---------------------------------------
This is an [example link](http://example.com/).
This is an [example link](http://example.com/ "With a Title").
---------------------------------------
I get 10 times more traffic from [Google][1] than from
[Yahoo][2] or [MSN][3].
[1]: http://google.com/ "Google"
[2]: http://search.yahoo.com/ "Yahoo Search"
[3]: http://search.msn.com/ "MSN Search"
I start my morning with a cup of coffee and
[The New York Times][NY Times].
[ny times]: http://www.nytimes.com/
---------------------------------------
![alt text][id]
[id]: /path/to/img.jpg "Title"
---------------------------------------
I strongly recommend against using any `<blink>` tags.
I wish SmartyPants used named entities like `—`
instead of decimal-encoded entites like `—`.
If you want your page to validate under XHTML 1.0 Strict,
you've got to put paragraph tags in your blockquotes:
<blockquote>
<p>For example.</p>
</blockquote>
---------------------------------------'''
self.fs.create_file('/my/content/index.md',
contents=file_string)
page = Page(*load_page('/my/content', '/my/content/index.md'))
mock_page_meta_inf.assert_called_once_with('''
Title: MDWeb Examples
Description: Examples of how to use MDWeb
Order: 4
''')
self.assertEqual(page.markdown_str, '''
Examples taken from https://daringfireball.net/projects/markdown/basics
A First Level Header
====================
A Second Level Header
---------------------
Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.
The quick brown fox jumped over the lazy
dog's back.
### Header 3
> This is a blockquote.
>
> This is the second paragraph in the blockquote.
>
> ## This is an H2 in a blockquote
---------------------------------------
Some of these words *are emphasized*.
Some of these words _are emphasized also_.
Use two asterisks for **strong emphasis**.
Or, if you prefer, __use two underscores instead__.
---------------------------------------
* Candy.
* Gum.
* Booze.
---------------------------------------
1. Red
2. Green
3. Blue
---------------------------------------
* A list item.
With multiple paragraphs.
* Another item in the list.
---------------------------------------
This is an [example link](http://example.com/).
This is an [example link](http://example.com/ "With a Title").
---------------------------------------
I get 10 times more traffic from [Google][1] than from
[Yahoo][2] or [MSN][3].
[1]: http://google.com/ "Google"
[2]: http://search.yahoo.com/ "Yahoo Search"
[3]: http://search.msn.com/ "MSN Search"
I start my morning with a cup of coffee and
[The New York Times][NY Times].
[ny times]: http://www.nytimes.com/
---------------------------------------
![alt text][id]
[id]: /path/to/img.jpg "Title"
---------------------------------------
I strongly recommend against using any `<blink>` tags.
I wish SmartyPants used named entities like `—`
instead of decimal-encoded entites like `—`.
If you want your page to validate under XHTML 1.0 Strict,
you've got to put paragraph tags in your blockquotes:
<blockquote>
<p>For example.</p>
</blockquote>
---------------------------------------''')
# pylint: disable=C0301
# pylint: disable=E501
self.assertEqual(page.page_html,
'''<p>Examples taken from https://daringfireball.net/projects/markdown/basics</p>
<h1>A First Level Header</h1>
<h2>A Second Level Header</h2>
<p>Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.</p>
<p>The quick brown fox jumped over the lazy
dog's back.</p>
<h3>Header 3</h3>
<blockquote>
<p>This is a blockquote.</p>
<p>This is the second paragraph in the blockquote.</p>
<h2>This is an H2 in a blockquote</h2>
</blockquote>
<hr />
<p>Some of these words <em>are emphasized</em>.
Some of these words <em>are emphasized also</em>.</p>
<p>Use two asterisks for <strong>strong emphasis</strong>.
Or, if you prefer, <strong>use two underscores instead</strong>.</p>
<hr />
<ul>
<li>Candy.</li>
<li>Gum.</li>
<li>Booze.</li>
</ul>
<hr />
<ol>
<li>Red</li>
<li>Green</li>
<li>Blue</li>
</ol>
<hr />
<ul>
<li>
<p>A list item.</p>
<p>With multiple paragraphs.</p>
</li>
<li>
<p>Another item in the list.</p>
</li>
</ul>
<hr />
<p>This is an <a href="http://example.com/">example link</a>.</p>
<p>This is an <a href="http://example.com/" title="With a Title">example link</a>.</p>
<hr />
<p>I get 10 times more traffic from <a href="http://google.com/" title="Google">Google</a> than from
<a href="http://search.yahoo.com/" title="Yahoo Search">Yahoo</a> or <a href="http://search.msn.com/" title="MSN Search">MSN</a>.</p>
<p>I start my morning with a cup of coffee and
<a href="http://www.nytimes.com/">The New York Times</a>.</p>
<hr />
<p><img alt="alt text" src="/path/to/img.jpg" title="Title" /></p>
<p><img alt="alt text" src="/path/to/img.jpg" title="Title" /></p>
<hr />
<p>I strongly recommend against using any <code><blink></code> tags.</p>
<p>I wish SmartyPants used named entities like <code>&mdash;</code>
instead of decimal-encoded entites like <code>&#8212;</code>.</p>
<p>If you want your page to validate under XHTML 1.0 Strict,
you've got to put paragraph tags in your blockquotes:</p>
<pre><code><blockquote>
<p>For example.</p>
</blockquote>
</code></pre>
<hr />''')
| 300
| 13,100
| 0
| 7,619
| 0
| 0
| 0
| 130
| 183
|
2d857a8f033972ecd5bd933956e549a665614872
| 1,080
|
py
|
Python
|
day13/shuttlebus2.py
|
fpeterek/aoc-2020
|
ae08a96c2213e94d204fc11051e2a9f535d62973
|
[
"MIT"
] | null | null | null |
day13/shuttlebus2.py
|
fpeterek/aoc-2020
|
ae08a96c2213e94d204fc11051e2a9f535d62973
|
[
"MIT"
] | null | null | null |
day13/shuttlebus2.py
|
fpeterek/aoc-2020
|
ae08a96c2213e94d204fc11051e2a9f535d62973
|
[
"MIT"
] | null | null | null |
if __name__ == '__main__':
run()
| 26.341463
| 96
| 0.591667
|
class Bus:
def __init__(self, number: int, offset: int, begin: int = 0):
self.number = number
self.offset = offset
self.begin = begin
def load_file(filename: str) -> tuple[int, list[int]]:
with open(filename) as f:
timestamp = int(f.readline())
buses = f.readline().split(',')
return [Bus(int(bus), offset) for offset, bus in enumerate(buses) if bus and bus != 'x']
def lcm(x: int, y: int, begin: int = 0, offset: int = 0) -> int:
delta = x
multiply = begin if begin else delta
while (multiply < offset) or ((multiply + offset) % y):
multiply += delta
return multiply
def find(b1: Bus, b2: Bus) -> Bus:
period = lcm(b1.number, b2.number)
t1 = lcm(x=b1.number, y=b2.number, begin=b1.begin, offset=b2.offset)
return Bus(number=period, offset=0, begin=t1)
def run() -> None:
buses = load_file('in.txt')
while len(buses) > 1:
b1, b2 = buses.pop(0), buses.pop(0)
buses.insert(0, find(b1, b2))
print(buses[0].begin)
if __name__ == '__main__':
run()
| 0
| 0
| 0
| 140
| 0
| 780
| 0
| 0
| 116
|
2467cce351ee3950d6051b279111c2511b02732a
| 76
|
py
|
Python
|
pytest_harvest/tests_raw/conftest.py
|
keszybz/python-pytest-harvest
|
ef11d3addeae51168ab892b7806c2b4c270e2a82
|
[
"BSD-3-Clause"
] | 36
|
2018-11-07T19:32:08.000Z
|
2022-03-19T10:24:48.000Z
|
pytest_harvest/tests_raw/conftest.py
|
keszybz/python-pytest-harvest
|
ef11d3addeae51168ab892b7806c2b4c270e2a82
|
[
"BSD-3-Clause"
] | 55
|
2018-11-13T10:58:30.000Z
|
2022-01-06T10:32:53.000Z
|
pytest_harvest/tests_raw/conftest.py
|
keszybz/python-pytest-harvest
|
ef11d3addeae51168ab892b7806c2b4c270e2a82
|
[
"BSD-3-Clause"
] | 4
|
2019-10-05T09:50:09.000Z
|
2021-03-31T20:33:16.000Z
|
# This is actually not even needed apparently
# pytest_plugins = ["harvest"]
| 38
| 45
| 0.763158
|
# This is actually not even needed apparently
# pytest_plugins = ["harvest"]
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0cab310479ab3cc6d73dc8671b51f36e59625a28
| 456
|
py
|
Python
|
fly/ModelCreate.py
|
cheburakshu/fly
|
d452af4b83e4cb0f8d0094bf1e0c1b407d39bdf5
|
[
"Apache-2.0"
] | null | null | null |
fly/ModelCreate.py
|
cheburakshu/fly
|
d452af4b83e4cb0f8d0094bf1e0c1b407d39bdf5
|
[
"Apache-2.0"
] | null | null | null |
fly/ModelCreate.py
|
cheburakshu/fly
|
d452af4b83e4cb0f8d0094bf1e0c1b407d39bdf5
|
[
"Apache-2.0"
] | null | null | null |
#fly
| 26.823529
| 81
| 0.64693
|
#fly
from .ModelIO import ModelIO
from .ModelConfig import ModelConfig
class ModelCreate(object):
def __init__(self,*args,**kwargs):
self._ModelConfig = ModelConfig(kwargs.get('filename')) #*args,**kwargs)
self._modelNames = self._ModelConfig.getModels()
self._model = None
def create(self,*args,**kwargs):
self._model = ModelIO(*args,**kwargs)
def getModel(self):
return self._model
| 0
| 0
| 0
| 355
| 0
| 0
| 0
| 22
| 71
|
d4aeef5e3912b8f3cd0610d8270c456d1f413716
| 17,328
|
py
|
Python
|
pedal/core/feedback.py
|
acbart/python-analysis
|
3cd2cc22d50a414ae6b62c74d2643be4742238d4
|
[
"MIT"
] | 14
|
2019-08-22T03:40:23.000Z
|
2022-03-13T00:30:53.000Z
|
pedal/core/feedback.py
|
pedal-edu/pedal
|
3cd2cc22d50a414ae6b62c74d2643be4742238d4
|
[
"MIT"
] | 74
|
2019-09-12T04:35:56.000Z
|
2022-01-26T19:21:32.000Z
|
pedal/core/feedback.py
|
acbart/python-analysis
|
3cd2cc22d50a414ae6b62c74d2643be4742238d4
|
[
"MIT"
] | 2
|
2018-09-16T22:39:15.000Z
|
2018-09-17T12:53:28.000Z
|
"""
Simple data classes for storing feedback to present to learners.
"""
__all__ = ['Feedback', 'FeedbackKind', 'FeedbackCategory',
"CompositeFeedbackFunction",
"FeedbackResponse"]
from pedal.core.feedback_category import FeedbackKind, FeedbackCategory
PEDAL_DEVELOPERS = ["Austin Cory Bart <[email protected]>",
"Luke Gusukuma <[email protected]>"]
def CompositeFeedbackFunction(*functions):
"""
Decorator for functions that return multiple types of feedback functions.
Args:
functions (callable): A list of callable functions.
Returns:
callable: The decorated function.
"""
def CompositeFeedbackFunction_with_attrs(function):
"""
Args:
function:
Returns:
"""
CompositeFeedbackFunction_with_attrs.functions = functions
return function
return CompositeFeedbackFunction_with_attrs
DEFAULT_CATEGORY_PRIORITY = [
"highest",
# Static
Feedback.CATEGORIES.SYNTAX,
Feedback.CATEGORIES.MISTAKES,
Feedback.CATEGORIES.INSTRUCTOR,
Feedback.CATEGORIES.ALGORITHMIC,
# Dynamic
Feedback.CATEGORIES.RUNTIME,
Feedback.CATEGORIES.STUDENT,
Feedback.CATEGORIES.SPECIFICATION,
Feedback.CATEGORIES.POSITIVE,
Feedback.CATEGORIES.INSTRUCTIONS,
Feedback.CATEGORIES.UNKNOWN,
"lowest"
]
| 39.381818
| 118
| 0.627539
|
"""
Simple data classes for storing feedback to present to learners.
"""
__all__ = ['Feedback', 'FeedbackKind', 'FeedbackCategory',
"CompositeFeedbackFunction",
"FeedbackResponse"]
from pedal.core.formatting import FeedbackFieldWrapper
from pedal.core.location import Location
from pedal.core.report import MAIN_REPORT
from pedal.core.feedback_category import FeedbackKind, FeedbackCategory, FeedbackStatus
PEDAL_DEVELOPERS = ["Austin Cory Bart <[email protected]>",
"Luke Gusukuma <[email protected]>"]
class Feedback:
"""
A class for storing raw feedback.
Attributes:
label (str): An internal name for this specific piece of feedback. The
label should be an underscore-separated string following the same
conventions as names in Python. They do not have to be globally
unique, but labels should be as unique as possible (especially
within a category).
tool (str, optional): An internal name for indicating the tool that created
this feedback. Should be taken directly from the Tool itself. If `None`, then
this was not created by a tool but directly by the control script.
category (str): A human-presentable name showable to the learner, indicating what
sort of feedback this falls into (e.g., "runtime", "syntax", "algorithm").
More than one feedback will be in a category, but a feedback cannot be in
more than one category.
kind (str): The pedagogical role of this feedback, e.g., "misconception", "mistake",
"hint", "constraint". Usually, a piece of Feedback is pointing out a mistake,
but feedback can also be used for various other purposes.
justification (str): An instructor-facing string briefly describing why this
feedback was selected. Serves as a "TL;DR" for this feedback category, useful
for debugging why a piece of feedback appeared.
priority (str): An indication of how important this feedback is relative to other types
of feedback in the same category. Might be "high/medium/low". Exactly how this
gets used is up to the resolver, but typicaly it helps break ties.
valence (int): Indicates whether this is negative, positive, or neutral feedback.
Either 1, -1, or 0.
title (str, optional): A formal, student-facing title for this feedback. If None, indicates
that the :py:attr:`~pedal.core.feedback.Feedback.label` should be used instead.
message (str): A markdown-formatted message (aka also supporting HTML) that could be rendered
to the user.
message_template (str): A markdown-formatted message template that will
be used if a ``message`` is None. Any ``fields`` will be injected
into the template IF the ``condition`` is met.
fields (Dict[str,Any]): The raw data that was used to interpolate the
template to produce the message.
location (:py:attr:`~pedal.core.location.Location` or int): Information
about specific locations relevant to this message.
score (int): A numeric score to modify the students' total score, indicating their overall performance.
It is ultimately up to the Resolver to decide how to combine all the different scores; a typical
strategy would be to add all the scores together for any non-muted feedback.
correct (bool): Indicates that the entire submission should be considered correct (success) and that the
task is now finished.
muted (bool): Whether this piece of feedback is something that should be shown to a student. There are
various use cases for muted feedback: they can serve as flags for later conditionals, suppressed
default kinds of feedback, or perhaps feedback that is interesting for analysis but not pedagogically
helpful to give to the student. They will still contribute to overall score, but not to the correcntess
of the submission.
unscored (bool): Whether or not this piece of feedback contributes to
the score/correctness.
else_message (str): A string to render as a message when a
NEGATIVE valence feedback is NOT triggered, or a POSITIVE valence
feedback IS triggered.
TODO: Should we also have an else_message_template? Probably.
activate (bool): Used for default feedback objects without a custom
condition, to indicate whether they should be considered triggered.
Defaults to True; setting this to False means that the feedback
object will be deactivated. Note that most inheriting Feedback
Functions will not respect this parameter.
author (List[str]): A list of names/emails that indicate who created this piece of feedback. They can be
either names, emails, or combinations in the style of `"Cory Bart <[email protected]>"`.
version (str): A version string in the style of Semantic Version (semvar) such as `"0.0.1"`. The last (third)
digit should be incremented for small bug fixes/changes. The middle (second) digit should be used for more
serious and intense changes. The first digit should be incremented when changes are made on exposure to
learners or some other evidence-based motivation.
tags (list[:py:class:`~pedal.core.tag.Tag`]): Any tags that you want to
attach to this feedback.
parent (int, str, or `pedal.core.feedback.Feedback`): Information about
what logical grouping within the submission this belongs to.
Various tools can chunk up a submission (e.g., by section), they
can use this field to keep track of how that decision was made.
Resolvers can also use this information to organize feedback or to
report multiple categories.
report (:py:class:`~pedal.core.report.Report`): The Report object to
attach this feedback to. Defaults to MAIN_REPORT. Unspecified fields
will be filled in by inspecting the current Feedback Function
context.
"""
POSITIVE_VALENCE = 1
NEUTRAL_VALENCE = 0
NEGATIVE_VALENCE = -1
CATEGORIES = FeedbackCategory
KINDS = FeedbackKind
label = None
category = None
justification = None
constant_fields = None
fields = None
field_names = None
kind = None
title = None
message = None
message_template = None
else_message = None
priority = None
valence = None
location = None
score = None
correct = None
muted = None
unscored = None
tool = None
version = '1.0.0'
author = PEDAL_DEVELOPERS
tags = None
parent = None
activate: bool
_status: str
_exception: Exception or None
_met_condition: bool
_stored_args: tuple
_stored_kwargs: dict
#MAIN_REPORT
def __init__(self, *args, label=None,
category=None, justification=None,
fields=None, field_names=None,
kind=None, title=None,
message=None, message_template=None,
else_message=None,
priority=None, valence=None,
location=None, score=None, correct=None,
muted=None, unscored=None,
tool=None, version=None, author=None,
tags=None, parent=None, report=MAIN_REPORT,
delay_condition=False, activate=True,
**kwargs):
# Internal data
self.report = report
# Metadata
if label is not None:
self.label = label
else:
self.label = self.__class__.__name__
# Condition
if category is not None:
self.category = category
if justification is not None:
self.justification = justification
self.activate = activate
# Response
if kind is not None:
self.kind = kind
if priority is not None:
self.priority = priority
if valence is not None:
self.valence = valence
# Presentation
if fields is not None:
self.fields = fields
else:
self.fields = {}
if self.constant_fields is not None:
self.fields.update(self.constant_fields)
if field_names is not None:
self.field_names = field_names
if title is not None:
self.title = title
elif self.title is None:
self.title = label
if message is not None:
self.message = message
if message_template is not None:
self.message_template = message_template
if else_message is not None:
self.else_message = else_message
# Locations
if isinstance(location, int):
location = Location(location)
# TODO: Handle tuples (Line, Col) and (Filename, Line, Col), and
# possibly lists thereof
if location is not None:
self.location = location
# Result
if score is not None:
self.score = score
if correct is not None:
self.correct = correct
if muted is not None:
self.muted = muted
if unscored is not None:
self.unscored = unscored
# Metadata
if tool is not None:
self.tool = tool
if version is not None:
self.version = version
if author is not None:
self.author = author
if tags is not None:
self.tags = tags
# Organizational
if parent is not None:
self.parent = parent
if self.parent is None:
# Might inherit from Report's current group
self.parent = self.report.get_current_group()
if self.field_names is not None:
for field_name in self.field_names:
self.fields[field_name] = kwargs.get(field_name)
for key, value in kwargs.items():
self.fields[key] = value
if 'location' not in self.fields and self.location is not None:
self.fields['location'] = self.location
# Potentially delay the condition check
self._stored_args = args
self._stored_kwargs = kwargs
if delay_condition:
self._met_condition = False
self._status = FeedbackStatus.DELAYED
else:
self._handle_condition()
def _handle_condition(self):
""" Actually handle the condition check, updating message and report. """
# Self-attach to a given report?
self._exception = None
try:
self._met_condition = self.condition(*self._stored_args, **self._stored_kwargs)
# Generate the message field as needed
if self._met_condition:
self.message = self._get_message()
self._status = FeedbackStatus.ACTIVE
else:
self._status = FeedbackStatus.INACTIVE
except Exception as e:
self._met_condition = False
self._exception = e
self._status = FeedbackStatus.ERROR
if self.report is not None:
if self._met_condition:
self.report.add_feedback(self)
else:
self.report.add_ignored_feedback(self)
# Free up these temporary fields after condition is handled
# del self._stored_args
# del self._stored_kwargs
if self._exception is not None:
raise self._exception
def condition(self, *args, **kwargs):
"""
Detect if this feedback is present in the code.
Defaults to true through the `activate` parameter.
Returns:
bool: Whether or not this feedback's condition was detected.
"""
return self.activate
def _get_message(self):
"""
Determines the appropriate value for the message. It will attempt
to use this instance's message, but if it's not available then it will
try to generate one from the message_template. Then, it returns a
generic message.
You can override this to create a truly dynamic message, if you want.
Returns:
str: The message for this feedback.
"""
if self.message is not None:
return self.message
if self.message_template is not None:
fields = {field: FeedbackFieldWrapper(field, value, self.report.format)
for field, value in self.fields.items()}
return self.message_template.format(**fields)
return "No feedback message provided"
def _get_child_feedback(self, feedback, active):
""" Callback function that Reports will call when a new piece of
feedback is being considered. By default, does nothing. This is useful
for :py:class:`pedal.core.group.FeedbackGroup`, most other feedbacks
will just not care.
The ``active`` parameter controls whether or not the condition for the
feedback was met. """
def __xor__(self, other):
if isinstance(other, Feedback):
self.muted = bool(self) and not bool(other)
self.unscored = self.muted
other.muted = bool(other) and not bool(self)
other.unscored = other.muted
if isinstance(other, bool):
self.muted = bool(self) and not bool(other)
self.unscored = self.muted
def __rxor__(self, other):
return self.__xor__(other)
def __bool__(self):
return bool(self._met_condition)
def __str__(self):
"""
Create a string representation of this piece of Feedback for quick, dev purposes.
Returns:
str: String representation
"""
return "<Feedback ({},{})>".format(self.category, self.label)
def __repr__(self):
"""
Create a string representation of this piece of Feedback that displays considerably more information.
Returns:
str: String representation with more data
"""
metadata = ""
if self.tool is not None:
metadata += ", tool=" + self.tool
if self.category is not None:
metadata += ", category=" + self.category
if self.priority is not None:
metadata += ", priority=" + self.priority
if self.parent is not None:
metadata += ", parent=" + str(self.parent.label)
return "Feedback({}{})".format(self.label, metadata)
def update_location(self, location):
""" Updates both the fields and location attribute.
TODO: Handle less information intelligently. """
if isinstance(location, int):
location = Location(location)
self.location = location
self.fields['location'] = location
def _fields_to_json(self):
return self.fields.copy()
def to_json(self):
return {
'correct': self.correct,
'score': self.score,
'title': self.title,
'message': self.message,
'label': self.label,
'active': bool(self),
'muted': self.muted,
'unscored': self.unscored,
'category': self.category,
'kind': self.kind,
'valence': self.valence,
'version': self.version,
'fields': self._fields_to_json(),
'justification': self.justification,
'priority': self.priority,
'location': self.location.to_json() if self.location is not None else None
}
class FeedbackResponse(Feedback):
"""
An extension of :py:class:`~pedal.core.feedback.Feedback` that is meant
to indicate that the class should not have any condition behind its
response.
"""
def CompositeFeedbackFunction(*functions):
"""
Decorator for functions that return multiple types of feedback functions.
Args:
functions (callable): A list of callable functions.
Returns:
callable: The decorated function.
"""
def CompositeFeedbackFunction_with_attrs(function):
"""
Args:
function:
Returns:
"""
CompositeFeedbackFunction_with_attrs.functions = functions
return function
return CompositeFeedbackFunction_with_attrs
class FeedbackGroup(Feedback):
"""
An extension of :py:class:`~pedal.core.feedback.Feedback` that is meant
to indicate that this class will start a new Group context within the report
and do something interesting with any children it gets.
"""
DEFAULT_CATEGORY_PRIORITY = [
"highest",
# Static
Feedback.CATEGORIES.SYNTAX,
Feedback.CATEGORIES.MISTAKES,
Feedback.CATEGORIES.INSTRUCTOR,
Feedback.CATEGORIES.ALGORITHMIC,
# Dynamic
Feedback.CATEGORIES.RUNTIME,
Feedback.CATEGORIES.STUDENT,
Feedback.CATEGORIES.SPECIFICATION,
Feedback.CATEGORIES.POSITIVE,
Feedback.CATEGORIES.INSTRUCTIONS,
Feedback.CATEGORIES.UNKNOWN,
"lowest"
]
| 0
| 0
| 0
| 15,732
| 0
| 0
| 0
| 88
| 136
|
25d93ebd57650e95d922e302d78dcc9e2626544e
| 1,501
|
py
|
Python
|
oops_fhir/r4/code_system/precision_estimate_type.py
|
Mikuana/oops_fhir
|
77963315d123756b7d21ae881f433778096a1d25
|
[
"MIT"
] | null | null | null |
oops_fhir/r4/code_system/precision_estimate_type.py
|
Mikuana/oops_fhir
|
77963315d123756b7d21ae881f433778096a1d25
|
[
"MIT"
] | null | null | null |
oops_fhir/r4/code_system/precision_estimate_type.py
|
Mikuana/oops_fhir
|
77963315d123756b7d21ae881f433778096a1d25
|
[
"MIT"
] | null | null | null |
from pathlib import Path
from fhir.resources.codesystem import CodeSystem
__all__ = ["PrecisionEstimateType"]
_resource = CodeSystem.parse_file(Path(__file__).with_suffix(".json"))
| 19.493506
| 84
| 0.588274
|
from pathlib import Path
from fhir.resources.codesystem import CodeSystem
from oops_fhir.utils import CodeSystemConcept
__all__ = ["PrecisionEstimateType"]
_resource = CodeSystem.parse_file(Path(__file__).with_suffix(".json"))
class PrecisionEstimateType:
"""
PrecisionEstimateType
Method of reporting variability of estimates, such as confidence
intervals, interquartile range or standard deviation.
Status: draft - Version: 4.0.1
Copyright None
http://terminology.hl7.org/CodeSystem/precision-estimate-type
"""
ci = CodeSystemConcept(
{
"code": "CI",
"definition": "confidence interval.",
"display": "confidence interval",
}
)
"""
confidence interval
confidence interval.
"""
iqr = CodeSystemConcept(
{
"code": "IQR",
"definition": "interquartile range.",
"display": "interquartile range",
}
)
"""
interquartile range
interquartile range.
"""
sd = CodeSystemConcept(
{
"code": "SD",
"definition": "standard deviation.",
"display": "standard deviation",
}
)
"""
standard deviation
standard deviation.
"""
se = CodeSystemConcept(
{"code": "SE", "definition": "standard error.", "display": "standard error"}
)
"""
standard error
standard error.
"""
class Meta:
resource = _resource
| 0
| 0
| 0
| 1,245
| 0
| 0
| 0
| 24
| 46
|
b9dac44e185c9b124e9894a09b40bf74c9e25e50
| 43,059
|
py
|
Python
|
onezone/zone.py
|
aemerick/onezone
|
3a3c9a6542d0d8b9de7d18b94f866205dd3210b6
|
[
"MIT"
] | null | null | null |
onezone/zone.py
|
aemerick/onezone
|
3a3c9a6542d0d8b9de7d18b94f866205dd3210b6
|
[
"MIT"
] | null | null | null |
onezone/zone.py
|
aemerick/onezone
|
3a3c9a6542d0d8b9de7d18b94f866205dd3210b6
|
[
"MIT"
] | 1
|
2021-05-10T20:15:16.000Z
|
2021-05-10T20:15:16.000Z
|
"""
Author : A. Emerick
Date : May 2016
Purpose:
"""
__author__ = "aemerick <[email protected]>"
# external
#from collections import OrderedDict
try:
import dill as pickle
except:
print("WARNING: Dill unavailable, attempting to use pickle, which may crash")
try:
import pickle as pickle
except:
print("WARNING: cPickle unavailable, using pickle - data dumps will be slow")
import pickle
# internal
from . import imf as imf
#from . import star as star
from . import config as config
def restart(filename):
"""
Restart evolution from chosen picked output file
"""
zone = pickle.load( open(filename, 'r'))
zone.evolve()
return
| 37.837434
| 167
| 0.564853
|
"""
Author : A. Emerick
Date : May 2016
Purpose:
"""
__author__ = "aemerick <[email protected]>"
# external
import numpy as np
#from collections import OrderedDict
import os, h5py
from scipy.interpolate import interp1d
try:
import dill as pickle
except:
print("WARNING: Dill unavailable, attempting to use pickle, which may crash")
try:
import pickle as pickle
except:
print("WARNING: cPickle unavailable, using pickle - data dumps will be slow")
import pickle
# internal
from . import imf as imf
#from . import star as star
from onezone.cython_ext import cython_star as star
from . import config as config
from .constants import CONST as const
from . import performance_tools as perf
def restart(filename):
"""
Restart evolution from chosen picked output file
"""
zone = pickle.load( open(filename, 'r'))
zone.evolve()
return
class Zone:
"""
Zone Class
Single zone gas reservoir attached to star formation and
chemical enrichment models. Parameters for a given simulation
can be set using config. Zone initializes a simulation using
initial conditions parameters set in config.
Once initialized and abundances are set, simulation can be
evolved:
>>> sim = Zone()
>>> sim.set_initial_abundances(list_of_element_names)
>>> sim.evolve()
I/O controlled by config parameters, but can be done manually
with a full (pickle) dump or an output of summary statistics:
>>> sim.write_full_pickle()
>>> sim.write_summary_output()
"""
def __init__(self):
#if config.global_values.profile_performance:
config.global_values.profiler =\
perf.PerformanceTimer(config.global_values.profile_performance)
#
# important values and stars
#
self.M_gas = config.zone.initial_gas_mass
self.M_DM = config.zone.initial_dark_matter_mass
self.all_stars = star.StarList()
self.Z = config.zone.initial_metallicity
self._M_sf_reservoir = 0.0
self._SFR_initialized = False # only for SF method 4
self._mass_loading_initialized = False # only for mass_outflow_method 2
self.initial_abundances = config.zone.initial_abundances
self.species_masses = {} # OrderedDict()
self.halo_masses = {} # add other phase models?
#
# some private things
#
self._t_last_dump = 0.0
self._t_last_summary = 0.0
self._t_last_pickle = 0.0
self._cycle_number = 0
self._cycle_last_dump = 0
self._cycle_last_summary = 0
self.pickle_output_number = 0
self._summary_output_number = 0
self._output_number = 0
self.t = config.zone.t_o
self.dt = config.zone.dt
self._summary_data = {}
self.Mdot_ej = 0.0
self.Mdot_DM = 0.0
self.Mdot_out = 0.0
self.Mdot_out_species = {} # OrderedDict()
self.Mdot_ej_masses = {} # OrderedDict()
self.SN_ej_masses = {} # OrderedDict()
self.N_SNIa = 0
self.N_SNII = 0
#
# Create stars if starting with initial cluster
#
for e in config.zone.species_to_track:
self.species_masses[e] = 0.0
self.Mdot_out_species[e] = 0.0
self.halo_masses[e] = 0.0
if (config.zone.initial_stellar_mass > 0.0):
self._make_new_stars( M_sf = config.zone.initial_stellar_mass )
self._compute_dt()
self._update_globals()
return
def set_initial_abundances(self, elements, abundances = None):
"""
Set initial abundances using a list of desired elements and
associated abundances. If no abundances are provided, all are
set to zero except H and He. Abundances dict does not have to be
complete
"""
self.initial_abundances = {} # OrderedDict()
if abundances == None:
abundances = {'empty' : 0.0}
for e in elements:
if e in abundances.keys():
self.initial_abundances[e] = abundances[e]
elif e == 'H':
self.initial_abundances[e] = 0.75*(1.0 - self.Z)
elif e == 'He':
self.initial_abundances[e] = 0.25*(1.0 - self.Z)
elif e == 'm_metal':
self.initial_abundances[e] = self.Z
elif e == 'm_tot':
self.initial_abundances[e] = 1.0
else:
self.initial_abundances[e] = 0.0
for e in self.initial_abundances.keys():
self.species_masses[e] = self.M_gas * self.initial_abundances[e]
self.halo_masses[e] = 0.0
#
# One day, set this as list with second list of conditionals
# so one can (at runtime) arbitrarily track on any condition
#
self.special_mass_accumulator = {} # OrderedDict()
self.special_mass_accumulator['m_massive'] = 0.0
return None
def _accumulate_new_sn(self):
"""
Looks through all stars, checking to see if any new SN or SNIa
occured in current timestep. Adds these to the counters
"""
star_type = list(self.all_stars.property_asarray('type'))
#if np.size(star_type) > 1:
self.N_SNIa += star_type.count('new_SNIa_remnant')
self.N_SNII += star_type.count('new_remnant')
return
def evolve(self):
"""
Evolves the system until the end time assigned in config,
using a constant timestep. This handles the formation of new
stars, gas inflow and outflow, enrichment from stars, and
outputs.
"""
config.global_values.profiler.start_timer("total_time",True)
while self.t <= config.zone.t_final:
config.global_values.profiler.start_timer('compute_dt')
self._compute_dt()
config.global_values.profiler.end_timer('compute_dt')
#
# Check if output conditions are met
#
config.global_values.profiler.start_timer('check_output')
self._check_output()
config.global_values.profiler.end_timer('check_output')
#
# I) Evolve stars, computing ejecta rates and abundances
#
config.global_values.profiler.start_timer('evolve_stars')
self._evolve_stars()
config.global_values.profiler.end_timer('evolve_stars')
#
# II) Sum up and store number of supernovae
#
config.global_values.profiler.start_timer('accumulate_sn')
self._accumulate_new_sn()
config.global_values.profiler.end_timer('accumulate_sn')
#
# III) Compute SFR and make new stars
#
config.global_values.profiler.start_timer('compute_sfr')
self._compute_sfr()
config.global_values.profiler.end_timer('compute_sfr')
config.global_values.profiler.start_timer('make_stars')
self.M_sf = self._make_new_stars()
config.global_values.profiler.end_timer('make_stars')
#
# IV) Compute inflow and outflow
#
config.global_values.profiler.start_timer('outflow_inflow_mdot')
self._compute_outflow()
self._compute_inflow()
self._compute_mdot_dm()
config.global_values.profiler.end_timer('outflow_inflow_mdot')
#
# V) Add/remove gas from zone due to inflow,
# outflow, SF, and stellar ejecta
#
abundances = self.abundances
temp1 = abundances['m_metal'] * 1.0
new_gas_mass = self.M_gas + (self.Mdot_in + self.Mdot_ej_masses['m_tot'] -\
self.Mdot_out) * self.dt - self.M_sf +\
self.SN_ej_masses['m_tot']
#
# VI) Check if reservoir is empty
#
if self.M_gas <= 0:
self.M_gas = 0.0
_my_print("Gas in zone depleted. Ending simulation")
break
#
# VII) Compute increase / decrease of individual abundances
#
for e in self.species_masses.keys():
self.species_masses[e] = self.species_masses[e] + (self.Mdot_in * self.Mdot_in_abundances(e) +\
self.Mdot_ej_masses[e] -\
self.Mdot_out_species[e]) * self.dt -\
self.M_sf * abundances[e] + self.SN_ej_masses[e]
self.halo_masses[e] = self.halo_masses[e] + self.Mdot_out_species[e] * self.dt # no halo accretion for now.
self.M_gas = new_gas_mass
self.M_DM = self.M_DM + self.Mdot_DM * self.dt
#
# VII) i) ensure metallicity is consistent with new abundances
#
self._update_metallicity()
#
# VIII) End of evolution, increment counters
#
config.global_values.profiler.start_timer('update_globals')
self.t += self.dt
self._cycle_number += 1
self._update_globals()
config.global_values.profiler.end_timer('update_globals')
outstr = "======================================================\n" +\
"===== Cycle = %00005i time = %5.5E =======\n"%(self._cycle_number,self.t) +\
"======================================================\n"
config.global_values.profiler.write_performance(outstr=outstr)
#
# At end of simulation, force summary and dump
# outputs
#
config.global_values.profiler.end_timer("total_time")
config.global_values.profiler.write_performance(outstr=outstr)
self._check_output(force=True)
self._clean_up()
return
def _clean_up(self):
# delete / close things that need closing here. Call other
# clean-up routines
config.io._clean_up()
return
def _update_globals(self):
config.global_values.time = self.t
return
@property
def current_redshift(self):
t_h = config.units.hubble_time
z = (2.0 * t_h / (3.0*self.t))**(2.0/3.0) - 1.0
return z
def _update_metallicity(self):
if config.zone.constant_metallicity:
return
self.Z = self.species_masses['m_metal'] / self.M_gas
return
@property
def abundances(self):
"""
Returns dictionary of abundances
"""
abund = {}
for x in self.species_masses.keys():
abund[x] = self.species_masses[x] / self.M_gas
return abund
@property
def halo_abundances(self):
abund={}
for x in self.species_masses.keys():
abund[x] = self.halo_masses[x] / self.halo_masses['m_tot']
return abund
def Mdot_in_abundances(self, e):
"""
Inflow abundances
"""
if e == 'H':
abund = 0.75
elif e == 'He':
abund = 0.25
elif e == 'm_tot':
abund = 1.0
else:
abund = 0.0
return abund
@property
def N_stars(self):
return self.all_stars.N_stars()
@property
def M_stars(self):
return np.sum(self.all_stars.M())
def _compute_dt(self):
if config.zone.adaptive_timestep:
lifetimes = np.asarray(self.all_stars.property_asarray('lifetime','star'))
if np.size(lifetimes) > 1:
max_dt = config.zone.max_dt * const.Myr / config.units.time
min_lifetime = np.min( lifetimes ) / (config.units.time)
self.dt = np.min( [min_lifetime / (1.0 * config.zone.timestep_safety_factor), max_dt] )
return
def _evolve_stars(self):
"""
Evolve stars using star list methods, and compute the total
amount of ejecta from winds and supernova
"""
#
# zero mass accumulators before evolving
#
for key in self.species_masses.keys():
self.Mdot_ej_masses[key] = 0.0
self.SN_ej_masses[key] = 0.0
#
# advance each star one timestep
# optional mass arguments mean stars add
# to ejecta bins during evolution (winds and SN)
# to limit number of loops through star list
#
self.all_stars.evolve(self.t, self.dt, ej_masses = self.Mdot_ej_masses,
sn_masses = self.SN_ej_masses,
special_accumulator = self.special_mass_accumulator)
self.Mdot_ej = self.Mdot_ej_masses['m_tot'] * config.units.time
for e in self.species_masses.keys():
self.Mdot_ej_masses[e] *= config.units.time
return
#
# set total dM/dt from all stellar winds
#
# self.Mdot_ej = 0.0
# mass_loss_rate = self.all_stars.property_asarray('Mdot_ej') * config.units.time
# self.Mdot_ej = np.sum( mass_loss_rate )
#
# set dM/dt for all species
# add up SN ejecta
#
# i = 0
# for e in self.species_masses.iterkeys():
#
# add wind ejecta abundaces from "stars" and stars that may have formed SN
# but were alive for part of timestep.
#
# self.Mdot_ej_masses[e] = np.sum(self.all_stars.species_asarray('Mdot_ej_' + e, 'star') * self.all_stars.property_asarray('Mdot_ej','star'))
# self.Mdot_ej_masses[e] += np.sum(self.all_stars.species_asarray('Mdot_ej_' + e, 'new_WD') * self.all_stars.property_asarray('Mdot_ej', 'new_WD'))
# self.Mdot_ej_masses[e] += np.sum(self.all_stars.species_asarray('Mdot_ej_' + e, 'new_remnant') * self.all_stars.property_asarray('Mdot_ej','new_remnant'))
# self.Mdot_ej_masses[e] *= config.units.time
#
# self.SN_ej_masses[e] = np.sum(self.all_stars.species_asarray('SN_ej_' + e, 'new_SNIa_remnant'))
# self.SN_ej_masses[e] += np.sum(self.all_stars.species_asarray('SN_ej_' + e, 'new_remnant'))
# i = i + 1
#
#
#
# return
def _make_new_stars(self, M_sf = -1):
"""
Sample IMF to make new stars. Includes methods to
handle low SFR's, i.e. when SFR * dt < maximum star mass
"""
#
# compute the amount of gas to convert
# into stars this timestep. Otherwise, make fixed
# mass of stars
#
if (M_sf < 0.0):
M_sf = self.dt * self.Mdot_sf
if config.zone.use_SF_mass_reservoir and M_sf > 0.0:
#
# Accumulate mass into "reservoir" and wait until
# this is surpassed to form stars
#
self._M_sf_reservoir += M_sf
if (self._M_sf_reservoir > config.zone.SF_mass_reservoir_size):
# sample from IMF until M_sf is reached
M_sf = self._M_sf_reservoir
self._M_sf_reservoir = 0.0 # reset counter
else:
M_sf = 0.0
elif (config.zone.use_stochastic_mass_sampling and\
M_sf < config.zone.stochastic_sample_mass) and M_sf > 0.0:
#
# Prevent undersampling the IMF by requiring minimum mass
# threshold. Allow SF to happen stochastically when M_sf is
# below this threshold
#
probability_of_sf = M_sf / config.zone.stochastic_sample_mass
if (probability_of_sf > np.random.random()):
M_sf = config.zone.stochastic_sample_mass
else:
M_sf = 0.0
#
# Make new stars if M_gas->star > 0
#
if M_sf > 0.0:
# sample from IMF and sum sampled stars
# to get actual star formation mass
config.global_values.profiler.start_timer('make_stars-imf',True)
star_masses = config.zone.imf.sample(M = M_sf)
config.global_values.profiler.end_timer('make_stars-imf')
M_sf = np.sum(star_masses)
if config.zone.minimum_star_particle_mass > 0:
select = star_masses > config.zone.minimum_star_particle_mass
i_unresolved = 0
if np.size(star_masses[star_masses<=config.zone.minimum_star_particle_mass]) > 0:
i_unresolved = 1
# add each new star to the star list
ids = np.zeros(np.size(star_masses[select]) + i_unresolved)
if np.size(ids) == 1:
ids = [ids]
i = 0
config.global_values.profiler.start_timer('make_stars-add',True)
for i,m in enumerate(star_masses[select]):
ids[i] = self._assign_particle_id()
self.all_stars.add_new_star( star.Star(M=m,Z=self.Z,
abundances=self.abundances,
tform=self.t,id=ids[i]))
if i_unresolved > 0:
if i > 0:
i = i + 1
ids[i] = self._assign_particle_id()
M_unresolved = np.sum( star_masses[star_masses<=config.zone.minimum_star_particle_mass])
self.all_stars.add_new_star( star.Star(M=M_unresolved,Z=self.Z,
abundances=self.abundances,tform=self.t,
id= ids[i], star_type = "unresolved_star"))
config.global_values.profiler.end_timer('make_stars-add')
else:
# add each new star to the star list
ids = np.zeros(np.size(star_masses))
for i,m in enumerate(star_masses):
ids[i] = self._assign_particle_id()
self.all_stars.add_new_star( star.Star(M=m, Z=self.Z,
abundances=self.abundances,
tform=self.t,id=ids[i]))
return M_sf
def _assign_particle_id(self):
"""
Generates unique, consecutive ID for particle
"""
if (not hasattr(self, '_global_id_counter')):
self._global_id_counter = 0
num = self._global_id_counter
self._global_id_counter += 1
return num
def _compute_mdot_dm(self):
"""
For cosmological simulations, computes growth of DM halo
"""
self.Mdot_DM = 0.0
if not config.zone.cosmological_evolution:
return
self.Mdot_DM = 46.1 * ( self.M_DM / 1.0E12 )**(1.1) *\
(1.0 + 1.11 * self.current_redshift) *\
np.sqrt( config.units.omega_matter * (1.0 + self.current_redshift)**3 + config.units.omega_lambda)
self.Mdot_DM *= const.yr_to_s
self.Mdot_DM /= config.units.time
return
def _compute_inflow(self):
"""
Compute inflow rate, as a function of outflow
"""
self.Mdot_in = config.zone.inflow_factor * self.Mdot_out
return
def _compute_outflow(self):
"""
Compute outflow rate, as a function of SFR
"""
# If either of the discrete SF sampling methods are used,
# outflow should be determined by mass of stars formed, not
# rate
factor = config.zone.mass_loading_factor
if config.zone.cosmological_evolution:
factor = factor * (1.0 + self.current_redshift)**(-config.zone.mass_loading_index/2.0)
elif config.zone.mass_outflow_method == 1:
# mass outflow is controlled by a mass loading factor parameter
if config.zone.use_SF_mass_reservoir or config.zone.use_stochastic_mass_sampling:
self.Mdot_out = config.zone.mass_loading_factor * self.M_sf / self.dt
else:
self.Mdot_out = config.zone.mass_loading_factor * self.Mdot_sf
elif config.zone.mass_outflow_method == 4:
self.Mdot_out = self._interpolate_tabulated_outflow('m_tot') * config.zone.outflow_factor * self.Mdot_sf * self.M_gas
for e in self.Mdot_out_species.keys():
self.Mdot_out_species[e] = self.Mdot_ej_masses[e] * config.zone.wind_ejection_fraction +\
(self.SN_ej_masses[e] * config.zone.sn_ejection_fraction / self.dt)
for e in ['H','He']: # throw out ambient
self.Mdot_out_species[e] = self.Mdot_out * self.abundances[e]
elif config.zone.mass_outflow_method == 2 or config.zone.mass_outflow_method == 3:
# these are fractional outflow rates:
self.Mdot_out = self._interpolate_tabulated_outflow('m_tot') # get total outflow rate
for e in self.Mdot_out_species.keys():
self.Mdot_out_species[e] = self._interpolate_tabulated_outflow(e) # for each species
if config.zone.mass_outflow_method == 2: # outflow depends on sfr
# multiply by SFR and current total amount of each species
self.Mdot_out = self.Mdot_out * self.Mdot_sf * self.M_gas
for e in self.Mdot_out_species.keys():
self.Mdot_out_species[e] = self.Mdot_out_species[e] * self.Mdot_sf * (self.M_gas * self.abundances[e])
else: # outflow is a fixed fraction of injection - use mass loading factor for total, H, and He
self.Mdot_out = config.zone.mass_loading_factor * (self.M_sf / self.dt)
for e in self.abundances.keys():
self.Mdot_out_species[e] = (self.Mdot_ej_masses[e] + self.SN_ej_masses[e]) / self.dt # converted to a rate for consistency
#if 'H' in self.Mdot_out_species.keys():
self.Mdot_out_species['H'] = self.Mdot_out * self.abundances['H']
#if 'He' in self.Mdot_out_species.keys():
self.Mdot_out_species['He'] = self.Mdot_out * self.abundances['He']
return
@property
def t_dyn(self):
if not config.zone.cosmological_evolution:
_my_print("Error: Cannot compute cosmological dynamical time with non-cosmological simulation")
raise NotImplementedError
return 0.1 * config.units.hubble_time * (1.0 + self.current_redshift)**(-3.0/2.0)
def _compute_sfr(self):
"""
Compute SFR using method set in config
"""
if config.zone.star_formation_method == 0:
# no star formation
self.Mdot_sf = 0.0
elif config.zone.star_formation_method == 1:
# constant, user supplied SFR
self.Mdot_sf = config.zone.constant_SFR
elif config.zone.star_formation_method == 2 :
self.Mdot_sf = config.zone.SFR_efficiency * self.M_gas
elif config.zone.star_formation_method == 3:
self.Mdot_sf = config.zone.SFR_dyn_efficiency * self.M_gas / self.t_dyn
elif config.zone.star_formation_method == 4 :
# interpolate SFR from tabulated SFH
self.Mdot_sf = self._interpolate_SFR()
return
def _interpolate_tabulated_outflow(self, species):
if not self._mass_loading_initialized:
self._initialize_tabulated_mass_outflow()
t = self.t + self.dt*0.5
if np.size(self._tabulated_outflow_t) > 1: # allow constant
if t < self._tabulated_outflow_t[0]:
_my_print("Current time below minimum time in tabulated outflow rates %3.3E %3.3E"%(t, self._tabulated_outflow_t[0]))
raise ValueError
if t > self._tabulated_outflow_t[-1]:
_my_print("Current time above maximum time in tabulated outflow %3.3E %3.3E"%(t, self._tabulated_outflow_t[-1]))
_my_print("Assuming this is expected behavior. Saving and exiting.")
self._check_output(force = True)
raise ValueError
return self._outflow_interpolation_generator(species)(t)
def _interpolate_SFR(self):
if not self._SFR_initialized:
self._initialize_tabulated_sfr()
t = self.t + self.dt*0.5
if t < self._tabulated_SFR_t[0]:
_my_print("Current time below minimum time in tabulated SFR %3.3E %3.3E"%(t, self._tabulated_SFR_t[0]))
raise ValueError
if t > self._tabulated_SFR_t[-1]:
if t - 0.5*self.dt <= self._tabulated_SFR_t[-1]:
# likely on final time step
t = self.t
else:
_my_print("Current time above maximum time in tabulated SFR %3.3E %3.3E"%(t, self._tabulated_SFR_t[-1]))
_my_print("Assuming this is expected behavior. Saving and exiting.")
self._check_output(force = True)
raise ValueError
return self._SFR_interpolation_function(t)
def _initialize_tabulated_sfr(self):
if not (config.zone.SFR_filename is None):
if not os.path.isfile(config.zone.SFR_filename):
_my_print(config.zone.SFR_filename + " does not exist. Must set to use tabulated SFR")
raise ValueError
else:
_my_print("Must set config.zone.SFR_file to use tabulated SFR")
raise ValueError
data = np.genfromtxt(config.zone.SFR_filename, names = True)
self._tabulated_SFR_t = data['t'] * const.Myr / config.units.time # in Myr
self._tabulated_SFR = data['SFR'] / const.yr_to_s * config.units.time
self._SFR_interpolation_function = interp1d(self._tabulated_SFR_t,
self._tabulated_SFR, kind = 'linear')
self._SFR_initialized = True
return
def _initialize_tabulated_mass_outflow(self):
if not (config.zone.outflow_filename is None):
if not os.path.isfile(config.zone.outflow_filename):
_my_print(config.zone.outflow_filename + " does not exist. Must set properly to use tabulated outflow rates")
raise ValueError
else:
_my_print("Must set config.zone.outflow_filename to use tabulated outflow rates")
raise ValueError
# skip header assuming this is generated by galaxy_analysis generation utilities
data = np.genfromtxt(config.zone.outflow_filename, names = True, skip_header = 5)
self._tabulated_outflow_t = data['t'] * const.Myr / config.units.time
species = [x for x in data.dtype.names if x is not 't']
self._tabulated_outflow = {}
for e in species:
self._tabulated_outflow[e] = data[e]
for e in ['H','He']:
# if not provided, take H and He outflows as the same as the ambient / total
# mass outflow. Fine if majority of H/He ejected is ambient ISM (likely).
# this is o.k. to do since outflow rates are scaled fractional
if not (e in self._tabulated_outflow.keys()):
self._tabulated_outflow[e] = 1.0*self._tabulated_outflow['m_tot']
if np.size(data['t']) == 1: # for constant values (no time evolution)
self._outflow_interpolation_generator = lambda _e : lambda t : self._tabulated_outflow[_e]
else:
self._outflow_interpolation_generator = lambda _e : interp1d(self._tabulated_outflow_t,
self._tabulated_outflow[_e],
kind = 'linear')
self.Mdot_out_species = {} # set up dictionary to store outflow rates
for e in species + ['H','He']:
self.Mdot_out_species[e] = 0.0
self._mass_loading_initialized = True
return
def _check_output(self, force = False):
"""
Checks output conditions, output if any are met
"""
if force:
self.write_output()
self.write_full_pickle()
self.write_summary_output()
return
#
# Otherwise output based on user supplied conditions
#
#
# check for full write out
#
if( (self.t - self._t_last_dump) >= config.io.dt_dump and\
config.io.dt_dump > 0 ):
self._t_last_dump = self.t
self.write_output()
if( self._cycle_number == 0 or\
((self._cycle_number - self._cycle_last_dump) >= config.io.cycle_dump )\
and config.io.cycle_dump > 0 ):
self._cycle_last_dump = self._cycle_number
self.write_output()
if( (self.t - self._t_last_pickle) >= config.io.dt_pickle and\
config.io.dt_pickle > 0 ):
self._t_last_pickle = self.t
self.write_full_pickle()
if( self._cycle_number == 0 or\
((self._cycle_number - self._cycle_last_pickle) >= config.io.cycle_pickle )\
and config.io.cycle_pickle > 0 ):
self._cycle_last_pickle = self._cycle_number
self.write_full_pickle()
#
# now check for partial (summary) writes
#
if( self._cycle_number == 0 or\
(self._cycle_number - self._cycle_last_summary >= config.io.cycle_summary)\
and config.io.cycle_summary > 0):
self._cycle_last_summary = self._cycle_number
self.write_summary_output()
if( ((self.t - self._t_last_summary) > config.io.dt_summary) and
config.io.dt_summary > 0 ):
self._t_last_summary = self.t
self.write_summary_output()
return
def write_output(self):
"""
Output the full information needed to reconstruct the simulation. This
is the mass, metallicity, birth mass, age, abundance, and type for each star,
along with the current gas mass, dark matter mass, sfr, and gas abundances.
"""
# make an HDF5 file to write out to
name = config.io.dump_output_basename + "_%0004i"%(self._output_number) + '.h5'
hf = h5py.File(name, 'w')
zone_grp = hf.create_group('zone')
star_grp = hf.create_group('star')
params = hf.create_group('parameters')
# save parameters
for param_list, grpname in [ (config.units,'units'),\
(config.zone,'zone'),\
(config.stars,'stars'),\
(config.io,'io'), (config.data,'data_table')]:
subgrp = params.create_group(grpname)
for p in dir(param_list):
# There may be a better way to do this, but I don't want to have
# to explicityly state any params, just print all. Need to skip
# all objects or functions though
if ('__' in p) or\
callable( getattr(param_list, p))\
or (p[0] == '_') or p == 'imf':
continue
#print p, getattr(param_list,p)
val = getattr(param_list,p)
if val is None:
val = "None"
subgrp.attrs[p] = val
#
# save meta-data as attributes
#
self._accumulate_summary_data()
hf.attrs['current_time'] = self.t
#
# Save zone parameters. This is anything to do with gas or DM
# properties
zone_grp.attrs['M_gas'] = self.M_gas
zone_grp.attrs['M_DM'] = self.M_DM
zone_grp.attrs['M_star'] = self._summary_data['M_star']
zone_grp.attrs['M_star_o'] = self._summary_data['M_star_o']
zone_grp.attrs['Z_gas'] = self.Z
for e in self.abundances.keys():
zone_grp.attrs[e] = self.abundances[e]
zone_grp.attrs[e + '_mass'] = self.species_masses[e]
#
# Save star parameters as lists of values
#
star_dict = {} # OrderedDict()
_gather_properties = { 'mass' : 'mass', 'birth_mass' : 'birth_mass',
'metallicity' : 'metallicity', 'age' : 'age'}
for k in _gather_properties:
star_dict[k] = np.asarray(self.all_stars.property_asarray( _gather_properties[k]))
star_dict['type'] = np.asarray(self.all_stars.property_asarray('type')).astype('S')
Nstars = np.size(star_dict['mass'])
if Nstars > 1:
# there has to be a better way to do this... but various iterations
# of the below did not work b/c of the different variable types...
# doing this the slow way......
# star_data = [None]*Nstars
# for i in np.arange(Nstars):
# star_data[i] = (star_dict['id'][i], star_dict['type'][i],
# star_dict['initial_mass'][i],
# star_dict['masses'][i], star_dict['age'][i],
# star_dict['metallicity'][i])
# star_data = np.array(star_data, dtype = [ ('id',star_dict['id'].dtype),
# ('type',star_dict['type'].dtype),
# ('birth_mass',star_dict['initial_mass'].dtype),
# ('mass',star_dict['masses'].dtype),
# ('age',star_dict['age'].dtype),
# ('metallicity',star_dict['metallicity'].dtype)])
# star_data = np.column_stack(
# (star_dict['masses'].astype('float64'), star_dict['initial_mass'].astype('float64'),
# star_dict['metallicity'].astype('float64'), star_dict['id'].astype('int64'),
# star_dict['type'].astype('str'),
# star_dict['age'].astype('float64'))).ravel().view([ ('mass', np.dtype('float64')),
# ('initial_mass', np.dtype('float64')),
# ('metallicity', np.dtype('float64')),
# ('id', np.dtype('int64')),
# ('type', np.dtype('str')),
# ('age', np.dtype('float64'))])
for k in star_dict.keys():
star_grp.create_dataset(k, data=star_dict[k])
else:
Nstars = 0
star_data = np.zeros(1)
for k in star_dict.keys():
star_grp.create_dataset(k, data = star_data)
star_grp.attrs['number_of_stars'] = Nstars
hf.close()
self._output_number += 1
return
def write_full_pickle(self):
"""
Pickle current simulation
"""
name = str(config.io.pickle_output_basename + "_%00004i"%(self.pickle_output_number))
_my_print("Writing full dump output as " + name + " at time t = %4.4f"%(self.t))
pickle.dump( self , open(name, "wb"), -1)
self.pickle_output_number += 1
return
def _accumulate_summary_data(self):
"""
Set up list of all of the data to output. Sum over particle properties
to do this.
"""
self._summary_data = {} # OrderedDict()
self._summary_data['t'] = self.t
self._summary_data['M_gas'] = self.M_gas
self._summary_data['M_DM'] = self.M_DM
self._summary_data['M_star'] = np.sum(self.all_stars.M())
self._summary_data['M_star_o'] = np.sum( self.all_stars.M_o())
self._summary_data['Z_gas'] = self.Z
self._summary_data['Z_star'] = np.average( self.all_stars.Z() )
self._summary_data['N_star'] = self.N_stars
self._summary_data['N_SNIa'] = self.N_SNIa
self._summary_data['N_SNII'] = self.N_SNII
sum_names = ['Mdot_ej', 'L_FUV', 'L_LW', 'Q0', 'Q1']
for n in sum_names:
self._summary_data[n] = np.sum(np.asarray(self.all_stars.property_asarray(n)))
self._summary_data['L_bol'] = np.sum(np.asarray(self.all_stars.property_asarray('luminosity')))
self._summary_data['L_wind'] = np.sum(np.asarray(self.all_stars.property_asarray('mechanical_luminosity')))
self._summary_data['L_Q0'] = np.sum(self.all_stars.property_asarray('Q0') * self.all_stars.property_asarray('E0'))
self._summary_data['L_Q1'] = np.sum(self.all_stars.property_asarray('Q1') * self.all_stars.property_asarray('E1'))
# now do all of the abundances
for e in self.abundances.keys():
self._summary_data[e] = self.abundances[e]
self._summary_data[e + '_mass'] = self.species_masses[e]
self._summary_data[e + '_halo_mass'] = self.halo_masses[e]
for key in self.special_mass_accumulator.keys():
self._summary_data[key] = self.special_mass_accumulator[key]
if config.io.radiation_binned_output:
condition_1 = {'mass': lambda x : (x.M_o >= 1.0) * (x.M_o < 8.0) * (x.properties['type'] == 'star')}
condition_2 = {'mass': lambda x : (x.M_o >= 8.0) * (x.M_o < 16.0) * (x.properties['type'] == 'star')}
condition_3 = {'mass': lambda x : (x.M_o >= 16.0) * (x.M_o < 24.0) * (x.properties['type'] == 'star')}
condition_4 = {'mass': lambda x : (x.M_o >= 24.0) * (x.M_o < 1000.0) * (x.properties['type'] == 'star')}
self._summary_data['low_mass_LQ0'] = np.sum(self.all_stars.property_asarray('Q0', subset_condition = condition_1) *\
self.all_stars.property_asarray('E0', subset_condition = condition_1))
self._summary_data['int_mass_LQ0'] = np.sum(self.all_stars.property_asarray('Q0', subset_condition = condition_2) *\
self.all_stars.property_asarray('E0', subset_condition = condition_2))
self._summary_data['high_mass_LQ0'] = np.sum(self.all_stars.property_asarray('Q0', subset_condition = condition_3) *\
self.all_stars.property_asarray('E0', subset_condition = condition_3))
self._summary_data['vhigh_mass_LQ0'] = np.sum(self.all_stars.property_asarray('Q0', subset_condition = condition_4) *\
self.all_stars.property_asarray('E0', subset_condition = condition_4))
self._summary_data['low_mass_LQ1'] = np.sum(self.all_stars.property_asarray('Q1', subset_condition = condition_1) *\
self.all_stars.property_asarray('E1', subset_condition = condition_1))
self._summary_data['int_mass_LQ1'] = np.sum(self.all_stars.property_asarray('Q1', subset_condition = condition_2) *\
self.all_stars.property_asarray('E1', subset_condition = condition_2))
self._summary_data['high_mass_LQ1'] = np.sum(self.all_stars.property_asarray('Q1', subset_condition = condition_3) *\
self.all_stars.property_asarray('E1', subset_condition = condition_3))
self._summary_data['vhigh_mass_LQ1'] = np.sum(self.all_stars.property_asarray('Q1', subset_condition = condition_4) *\
self.all_stars.property_asarray('E1', subset_condition = condition_4))
FUV_1 = self.all_stars.property_asarray('L_FUV', subset_condition = condition_1)
FUV_2 = self.all_stars.property_asarray('L_FUV', subset_condition = condition_2)
FUV_3 = self.all_stars.property_asarray('L_FUV', subset_condition = condition_3)
FUV_4 = self.all_stars.property_asarray('L_FUV', subset_condition = condition_4)
self._summary_data['low_mass_LFUV'] = np.sum(FUV_1)
self._summary_data['int_mass_LFUV'] = np.sum(FUV_2)
self._summary_data['high_mass_LFUV'] = np.sum(FUV_3)
self._summary_data['vhigh_mass_LFUV'] = np.sum(FUV_4)
LW_1 = self.all_stars.property_asarray('L_LW', subset_condition = condition_1)
LW_2 = self.all_stars.property_asarray('L_LW', subset_condition = condition_2)
LW_3 = self.all_stars.property_asarray('L_LW', subset_condition = condition_3)
LW_4 = self.all_stars.property_asarray('L_LW', subset_condition = condition_4)
self._summary_data['low_mass_LLW'] = np.sum(LW_1)
self._summary_data['int_mass_LLW'] = np.sum(LW_2)
self._summary_data['high_mass_LLW'] = np.sum(LW_3)
self._summary_data['vhigh_mass_LLW'] = np.sum(LW_4)
self._summary_data['low_mass_count'] = np.size(FUV_1)
self._summary_data['int_mass_count'] = np.size(FUV_2)
self._summary_data['high_mass_count'] = np.size(FUV_3)
self._summary_data['vhigh_mass_count'] = np.size(FUV_4)
return
def write_summary_output(self):
"""
Write out summary output by appending to ASCII file. Filename is overwritten
at first write out, so be careful.
"""
self._accumulate_summary_data()
ncol = np.size(self._summary_data.keys())
if self._summary_output_number == 0: # print the header only once
header = " " + " ".join(list(self._summary_data.keys())) + "\n"
f = open(config.io.summary_output_filename,'w')
f.write(header)
else:
f = open(config.io.summary_output_filename, 'a')
fmt = "%5.5E "*ncol + "\n"
# output_val = list(self._summary_data.values())
# f.write(fmt% ())
#print(self._summary_data)
#print(self._summary_data.values())
f.writelines("%5.5E "% x for x in self._summary_data.values() )
f.write("\n")
self._summary_output_number += 1
self._summary_data.clear()
f.close()
def _my_print(string):
print('[Zone]: ' + string)
| 0
| 937
| 0
| 41,112
| 0
| 32
| 0
| 71
| 178
|
5613da50aa1d3dd12fedcbd4b7b022564dee7bea
| 5,965
|
py
|
Python
|
graph.py
|
cbsteh/PySawit
|
fcec4f19270b9ddef0530c3e22f41aea2dd0153e
|
[
"MIT"
] | 15
|
2017-10-20T23:57:57.000Z
|
2021-09-04T00:34:27.000Z
|
graph.py
|
cbsteh/PySawit
|
fcec4f19270b9ddef0530c3e22f41aea2dd0153e
|
[
"MIT"
] | 3
|
2017-10-23T14:47:26.000Z
|
2019-02-22T00:37:20.000Z
|
graph.py
|
cbsteh/PySawit
|
fcec4f19270b9ddef0530c3e22f41aea2dd0153e
|
[
"MIT"
] | 6
|
2019-01-13T02:20:26.000Z
|
2021-11-02T15:55:09.000Z
|
"""
Graphing the program flow module.
This module aids in understanding the flow of program by creating a visual map (network graph or
map) of the program flow path. The graph map is in DOT and GML (XML) types.
!!! warning "Required installation"
* pycallgraph (run `pip install pycallgraph`)
* [Graphviz](http://www.graphviz.org) visualization software
# Author - Christopher Teh Boon Sung
------------------------------------
"""
| 29.529703
| 99
| 0.555407
|
"""
Graphing the program flow module.
This module aids in understanding the flow of program by creating a visual map (network graph or
map) of the program flow path. The graph map is in DOT and GML (XML) types.
!!! warning "Required installation"
* pycallgraph (run `pip install pycallgraph`)
* [Graphviz](http://www.graphviz.org) visualization software
# Author - Christopher Teh Boon Sung
------------------------------------
"""
import os
import re
import pycallgraph
from pycallgraph.output import GraphvizOutput
class Graph(GraphvizOutput):
"""
Graph class.
Creates a network map by showing/tracing the program flow. DOT and GML (XML) files will be
created to show the flow. Use a dedicated Graph Editor like [yEd](
http://www.yworks.com/products/yed) to view the network map.
Inherits the `pycallgraph.output.GraphvizOutput` class to modify certain default
output/behavior. Override the following base methods
```text
prepare_graph_attributes
node
edge
done
update
```
# METHODS
trace: traces the program flow and creates (.dot) DOT and (.gml) GML files
"""
def __init__(self, **kwargs):
"""
Create and initialize the Graph object.
# Arguments
kwargs (dict): change any attributes or initialize new attributes with
this dictionary
"""
self.graph_attributes = None
GraphvizOutput.__init__(self, **kwargs)
def prepare_graph_attributes(self):
"""
Override to change the default attributes for graph, nodes, and edges.
# Returns
None:
"""
# change the font name and size and to have arrows for edges
self.graph_attributes = {
'graph': {
'overlap': 'scalexy',
'fontname': 'Arial',
'fontsize': 9,
'fontcolor': '#000000ff', # black
},
'node': {
'fontname': 'Arial',
'fontsize': 9,
'fontcolor': '#000000ff',
'style': 'filled',
'shape': 'rect',
},
'edge': {
'arrowhead': 'normal',
'fontname': 'Arial',
'fontsize': 9,
'fontcolor': '#000000ff',
}
}
def node(self, key, attr):
"""
Override to delete newlines and number and length of function calls in node labels.
# Returns
str: node label
"""
for k in attr.keys():
attr[k] = re.sub(r'\\n.*', '', attr[k])
return '"{}" [{}];'.format(key, self.attrs_from_dict(attr))
def edge(self, edge, attr):
"""
Override to delete edge labels.
# Returns
str: edge label
"""
return '"{0.src_func}" -> "{0.dst_func}"'.format(edge, self.attrs_from_dict(attr))
def done(self):
"""
Override to avoid creating the graph picture file.
# Returns
None:
"""
pass # do nothing (bypass the parent's done function)
def update(self):
"""
Override to avoid warning message to implement all abstract methods.
Does nothing.
# Returns
None:
"""
pass
@staticmethod
def _colorize(txt, cls_dict):
"""
Change the color of nodes depending on class.
!!! note
`_colorize` is a static method.
# Arguments
txt (str): DOT text
cls_dict (dict): node colors (`None` by default)
# Returns
str: color text
"""
# cls is the class name; clr is its node color (in hexadecimal, must be 8 letters long)
for cls, clr in cls_dict.items():
fmt = cls + r'\..*color = "#.{8}"'
for match in re.finditer(fmt, txt):
nend = match.end()
txt = txt[:nend - 9] + clr + txt[nend - 1:] # substitute for the desired color
return txt
def trace(self, fn, fname, exclude_from_trace=None, strings_to_delete=None, class_colors=None):
"""
Trace the program flow and creates DOT (*.dot) and GML (*.gml) files.
# Arguments
fn (object): function to call for the entry point to trace the program flow
fname (str): filename for graph files (do not supply file extension)
exclude_from_trace (list): names (`str`) to exclude from trace (`None` by default)
strings_to_delete (list): text (`str`) to remove from graph files (`None` by default)
class_colors (dict): node colors (`None` by default)
# Returns
None:
"""
# 1. filter out some unwanted functions and classes:
config = pycallgraph.Config()
if exclude_from_trace is not None:
config.trace_filter = pycallgraph.GlobbingFilter(exclude=exclude_from_trace)
# 2. trace the program flow:
with pycallgraph.PyCallGraph(output=self, config=config):
fn() # entry point for tracing the program flow
dotstr = self.generate()
# 3. customize the output:
# 3a. delete strings
if strings_to_delete is not None:
for rx in strings_to_delete:
dotstr = rx.sub('', dotstr)
# 3b. customization: change color for class nodes
if class_colors is not None:
dotstr = Graph._colorize(dotstr, class_colors)
# 4. create the DOT file
fname_dot = fname + '.dot'
with open(fname_dot, 'wt') as fdot:
fdot.write(dotstr)
# 5. create the GML (XML) file
fname_gml = fname + '.gml'
cmd = 'dot ' + fname_dot + ' | gv2gml >' + fname_gml
os.system(cmd) # use the dot.exe to convert the DOT file into GML file
| 0
| 712
| 0
| 4,697
| 0
| 0
| 0
| -3
| 113
|
ef2e3c6b09aa1fe3a4d3f5db2221c153a8e6b8df
| 5,728
|
py
|
Python
|
main.py
|
HJaen/dad-bot
|
7ccfa7cf84f0eb5e6c42e8a4cbef56aeb11520b0
|
[
"MIT"
] | null | null | null |
main.py
|
HJaen/dad-bot
|
7ccfa7cf84f0eb5e6c42e8a4cbef56aeb11520b0
|
[
"MIT"
] | null | null | null |
main.py
|
HJaen/dad-bot
|
7ccfa7cf84f0eb5e6c42e8a4cbef56aeb11520b0
|
[
"MIT"
] | null | null | null |
# A Discord bot that does typical dad things
# By Jason Saini and James Nguyen
# Created: January 31, 2021
# Modified: January 20, 2022
import discord
from os import getenv
from requests import Session
from pyowm import OWM
from dotenv import load_dotenv
# setup client and tokens
load_dotenv('token.env')
client = discord.Client()
DISCORD_TOKEN = getenv('DISCORD_TOKEN')
WEATHER_TOKEN = getenv('WEATHER_TOKEN')
COIN_TOKEN = getenv('COIN_TOKEN')
OWNER_ID = getenv('OWNER_ID')
owm = OWM(WEATHER_TOKEN)
mgr = owm.weather_manager()
cmc = Session()
cmc.headers.update({'Accepts': 'application/json', 'X-CMC_PRO_API_KEY': COIN_TOKEN})
cmc_url = 'https://pro-api.coinmarketcap.com'
client.run(DISCORD_TOKEN)
| 30.306878
| 181
| 0.678247
|
# A Discord bot that does typical dad things
# By Jason Saini and James Nguyen
# Created: January 31, 2021
# Modified: January 20, 2022
import discord
from os import getenv
from requests import get, Session
from dadjokes import Dadjoke
from pyowm import OWM
from dotenv import load_dotenv
import yfinance as yf
# setup client and tokens
load_dotenv('token.env')
client = discord.Client()
DISCORD_TOKEN = getenv('DISCORD_TOKEN')
WEATHER_TOKEN = getenv('WEATHER_TOKEN')
COIN_TOKEN = getenv('COIN_TOKEN')
OWNER_ID = getenv('OWNER_ID')
owm = OWM(WEATHER_TOKEN)
mgr = owm.weather_manager()
cmc = Session()
cmc.headers.update({'Accepts': 'application/json', 'X-CMC_PRO_API_KEY': COIN_TOKEN})
cmc_url = 'https://pro-api.coinmarketcap.com'
def get_quote():
response = get('https://zenquotes.io/api/random')
json_data = response.json()
quote = 'You know, ' + json_data[0]['a'] + ' said "' + json_data[0]['q'] + '" I think that\'ll help.'
return quote
def get_i_am_joke(message):
words = message.content.split(' ')
try:
if 'i\'m' in message.content:
while (words[0] != 'i\'m'): words.pop(0)
words.pop(0)
else:
while (words[0] != 'i' and words[1] != 'am'): words.pop(0)
words.pop(0)
words.pop(0)
except:
words = message.author.name
joke = ' '.join(words)
return joke
def get_weather(message):
location = ''
words = message.content.split(' ')
try:
# parse for location in message
location = words[words.index('in') + 1].capitalize()
if location[-1] == '?': location = location[:-1]
observation = mgr.weather_at_place(location)
except:
# if we have an invalid location, use Hollywood (real-life Dimmsdale)
print('Invalid location : ' + location)
observation = mgr.weather_at_place('Hollywood')
# get weather
w = observation.weather
weather = w.detailed_status.capitalize()
weather_dict = w.temperature('fahrenheit')
temperature = weather_dict.get('temp')
# return weather as dict
return {'location': location,
'temperature': temperature,
'weather': weather}
def get_stock(message):
words = message.content.split(' ')
try:
# ticker after the word 'stock'
stock_ticker = words[words.index('stock') + 1].upper()
stock = yf.Ticker(stock_ticker)
# determine if the ticket exists in domestic US market
stock.info['symbol']
except:
print('Invalid stock : ' + stock_ticker)
stock = yf.Ticker('MSFT')
# return dict of stock info
return stock.info
def get_crypto(message):
words = message.content.split(' ')
try:
crypto_symbol = words[words.index('crypto') + 1].upper()
except:
print('Invalid crypto :')
[print(word) for word in words]
crypto_symbol = 'BTC'
response = cmc.get('https://pro-api.coinmarketcap.com/v1/cryptocurrency/quotes/latest', params={'symbol': crypto_symbol})
crypto_price = response.json()['data'][crypto_symbol]['quote']['USD']['price']
return {'symbol': crypto_symbol,
'price': crypto_price}
@client.event
async def on_ready():
print('We have logged in as {0.user}!'.format(client))
return
async def shutdown(self,ctx):
if ctx.message.author.id == OWNER_ID: #replace OWNERID with your user id
print("shutdown")
try:
await self.bot.logout()
except:
print("EnvironmentError")
self.bot.clear()
else:
await ctx.send("You do not own this bot!")
@client.event
async def on_message(message):
if message.author == client.user:
return
message.content = message.content.lower()
#greets user
if (message.content.startswith('hello') or message.content.startswith('hi')) and 'dad' in message.content:
await message.channel.send('Hi, Timmy! Oh you\'re not my Timmy, sorry there, friend!')
return
#tells a random dad joke
if 'joke' in message.content and 'dad' in message.content:
dadjoke = Dadjoke()
await message.channel.send(dadjoke.joke)
return
# tells 'hi [], i'm dad' joke
if 'i\'m' in message.content or 'i am' in message.content:
joke = get_i_am_joke(message)
await message.channel.send(f'Hi, {joke.capitalize()}! I\'m Dad Bot!')
return
# tells weather
if 'weather' in message.content:
weather_dict = get_weather(message)
await message.channel.send('In {0}, the weather\'s {1} and it\'s {2}\xb0F today!'.format(weather_dict['location'], weather_dict['weather'].lower(), weather_dict['temperature']))
return
# sends help information to user
if 'help' in message.content:
await message.channel.send('Say hi to Your Dad Bot by saying Hi or Hello Dad\n'
'Ask for the weather. (Like \"How\'s the weather like in Orlando?\"\n'
'Ask me to tell a joke!\n'
'Ask me for quotes!\n'
'Start a sentence with \'I\'m\'\n'
'Ask me about stocks Like \"How\'s stock AAPL doing?\"')
return
# tells a random quote
if 'quote' in message.content and 'dad' in message.content:
quote = get_quote()
await message.channel.send(quote)
return
# tells stock trading price
if 'stock' in message.content:
stock_dict = get_stock(message)
await message.channel.send('Looks like {0} is trading for ${1:.2f}.'.format(stock_dict['symbol'], stock_dict['regularMarketOpen']))
return
# tells crypto trading price
if 'crypto' in message.content:
crypto_dict = get_crypto(message)
await message.channel.send('Looks like {0} is trading for ${1:.6f}.'.format(crypto_dict['symbol'], crypto_dict['price']))
return
if ('thanks' in message.content or 'thank you' in message.content) and 'dad' in message.content:
await message.channel.send('You\'re welcome, Timmy, even if you\'re not my Timmy.')
return
else:
return
client.run(DISCORD_TOKEN)
| 0
| 2,363
| 266
| 0
| 0
| 2,153
| 0
| 12
| 228
|
85051de1f6a5e87b6148a3d81c6e5f3a6fea82ef
| 3,160
|
py
|
Python
|
dogbot/bot/listeners/material.py
|
moondropx/dogbot
|
4883b558e92c8fdcdbef97240cfa8252ec343eb4
|
[
"Apache-2.0"
] | 6
|
2017-08-01T09:06:59.000Z
|
2018-06-23T11:28:51.000Z
|
dogbot/bot/listeners/material.py
|
moondropx/dogbot
|
4883b558e92c8fdcdbef97240cfa8252ec343eb4
|
[
"Apache-2.0"
] | null | null | null |
dogbot/bot/listeners/material.py
|
moondropx/dogbot
|
4883b558e92c8fdcdbef97240cfa8252ec343eb4
|
[
"Apache-2.0"
] | 2
|
2017-08-14T04:35:57.000Z
|
2019-05-26T05:57:58.000Z
|
import getopt
import shlex
from config import config
from dogbot.cqsdk.utils import reply
def material(bot, message):
""": -material [-h] [-r [-o]]
-h :
-r :
-o :
: , . cc. .
:
-matrial
-matrial -r
-matrial -r -o
"""
try:
cmd, *args = shlex.split(message.text)
except ValueError:
return False
if not cmd[0] in config['trigger']:
return False
if not cmd[1:] == 'material':
return False
try:
options, args = getopt.gnu_getopt(args, 'hro')
except getopt.GetoptError:
#
reply(bot, message, material.__doc__)
return True
#
is_reverse = False
is_orb = False
for o, a in options:
if o == '-r':
#
is_reverse = True
elif o == '-o':
#
is_orb = True
elif o == '-h':
#
reply(bot, message, material.__doc__)
return True
#
if len(args) < 1:
reply(bot, message, material.__doc__)
return True
target_name = args[0]
target = Class.objects(Q(name=target_name) | Q(translate=target_name) | Q(nickname=target_name)).first()
if not target:
target = Unit.objects(Q(name=target_name) | Q(nickname=target_name)).first()
if target:
target = target.class_
if not target:
reply(bot, message, '/...')
return True
msg = '{}({}):\n'.format(target.name, target.translate)
if not is_reverse:
#
if target.cc_material:
msg += '\nCC: '
for mat in target.cc_material:
msg += '[{}] '.format(mat.translate)
else:
msg += '\nCC'
if target.awake_material:
msg += '\n: '
for mat in target.awake_material:
msg += '[{}] '.format(mat.translate)
msg += '\n: '
for mat in target.awake_orb:
msg += '({}) '.format(mat.translate)
else:
msg += '\n'
else:
#
if not is_orb:
cc_usage = Class.objects(cc_material=target)
if cc_usage:
msg += '\nCC: '
for usage in cc_usage:
msg += '[{}] '.format(usage.translate)
else:
msg += '\nCC'
awake_usage = Class.objects(awake_material=target)
if awake_usage:
msg += '\n: '
for usage in awake_usage:
msg += '[{}] '.format(usage.translate)
else:
msg += '\n'
else:
orb_usage = Class.objects(awake_orb=target)
if orb_usage:
msg += '\n: '
for usage in orb_usage:
msg += '[{}] '.format(usage.translate)
else:
msg += '\n'
reply(bot, message, msg)
return True
| 25.691057
| 108
| 0.496203
|
import getopt
import shlex
from config import config
from dogbot.cqsdk.utils import reply
from dogbot.models import *
def material(bot, message):
"""用法: -material [-h] [-r [-o]] 职业
-h : 打印本帮助
-r : 逆查
-o : 珠子逆查
职业 : 职业名, 可以是日文原文或者黑话. 原文一概采用未cc的初始职业名. 也接受单位的日文原文或者黑话.
例:
-matrial 士兵
-matrial -r 月影
-matrial -r -o アルケミスト
"""
try:
cmd, *args = shlex.split(message.text)
except ValueError:
return False
if not cmd[0] in config['trigger']:
return False
if not cmd[1:] == 'material':
return False
try:
options, args = getopt.gnu_getopt(args, 'hro')
except getopt.GetoptError:
# 格式不对
reply(bot, message, material.__doc__)
return True
# 拆参数
is_reverse = False
is_orb = False
for o, a in options:
if o == '-r':
# 反查
is_reverse = True
elif o == '-o':
# 珠子反查
is_orb = True
elif o == '-h':
# 帮助
reply(bot, message, material.__doc__)
return True
# 必须要有职业参数
if len(args) < 1:
reply(bot, message, material.__doc__)
return True
target_name = args[0]
target = Class.objects(Q(name=target_name) | Q(translate=target_name) | Q(nickname=target_name)).first()
if not target:
target = Unit.objects(Q(name=target_name) | Q(nickname=target_name)).first()
if target:
target = target.class_
if not target:
reply(bot, message, '没找到这个职业/单位...')
return True
msg = '{}({}):\n'.format(target.name, target.translate)
if not is_reverse:
# 查询素材
if target.cc_material:
msg += '\nCC素材: '
for mat in target.cc_material:
msg += '[{}] '.format(mat.translate)
else:
msg += '\n该职业没有CC'
if target.awake_material:
msg += '\n觉醒素材: '
for mat in target.awake_material:
msg += '[{}] '.format(mat.translate)
msg += '\n珠子: '
for mat in target.awake_orb:
msg += '({}) '.format(mat.translate)
else:
msg += '\n该职业没有觉醒'
else:
# 反查
if not is_orb:
cc_usage = Class.objects(cc_material=target)
if cc_usage:
msg += '\nCC所用: '
for usage in cc_usage:
msg += '[{}] '.format(usage.translate)
else:
msg += '\n该职业不会被CC所使用'
awake_usage = Class.objects(awake_material=target)
if awake_usage:
msg += '\n觉醒所用: '
for usage in awake_usage:
msg += '[{}] '.format(usage.translate)
else:
msg += '\n该职业不会被觉醒所使用'
else:
orb_usage = Class.objects(awake_orb=target)
if orb_usage:
msg += '\n珠子所用: '
for usage in orb_usage:
msg += '[{}] '.format(usage.translate)
else:
msg += '\n该职业并不是珠子'
reply(bot, message, msg)
return True
| 495
| 0
| 0
| 0
| 0
| 0
| 0
| 6
| 22
|
74dd66fe68b737ebf12160e8d8200121aab64bd4
| 7,981
|
py
|
Python
|
controlimcap/decoders/cfattention.py
|
SikandarBakht/asg2cap
|
d8a6360eaccdb8c3add5f9c4f6fd72764e47e762
|
[
"MIT"
] | 169
|
2020-03-15T08:41:39.000Z
|
2022-03-30T09:36:17.000Z
|
controlimcap/decoders/cfattention.py
|
wtr850/asg2cap
|
97a1d866d4a2b86c1f474bb168518f97eb2f8b96
|
[
"MIT"
] | 25
|
2020-05-23T15:14:00.000Z
|
2022-03-10T06:20:31.000Z
|
controlimcap/decoders/cfattention.py
|
wtr850/asg2cap
|
97a1d866d4a2b86c1f474bb168518f97eb2f8b96
|
[
"MIT"
] | 25
|
2020-04-02T10:08:01.000Z
|
2021-12-09T12:10:10.000Z
|
import torch.nn as nn
import caption.utils.inference
| 40.719388
| 109
| 0.712943
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import caption.utils.inference
import caption.decoders.vanilla
from framework.modules.embeddings import Embedding
from framework.modules.global_attention import GlobalAttention
class ContentFlowAttentionDecoder(caption.decoders.attention.BUTDAttnDecoder):
def __init__(self, config):
super().__init__(config)
memory_size = self.config.attn_size if self.config.memory_same_key_value else self.config.attn_input_size
self.address_layer = nn.Sequential(
nn.Linear(self.config.hidden_size + memory_size, memory_size),
nn.ReLU(),
nn.Linear(memory_size, 1 + 3))
def forward(self, inputs, enc_globals, enc_memories, enc_masks, flow_edges, return_attn=False):
'''
Args:
inputs: (batch, dec_seq_len)
enc_globals: (batch, hidden_size)
enc_memories: (batch, enc_seq_len, attn_input_size)
enc_masks: (batch, enc_seq_len)
Returns:
logits: (batch*seq_len, num_words)
'''
batch_size, max_attn_len = enc_masks.size()
device = inputs.device
states = self.init_dec_state(batch_size) # zero init state
# initialize content attention
memory_keys, memory_values = self.gen_memory_key_value(enc_memories)
# initialize location attention score: (batch, max_attn_len)
prev_attn_score = torch.zeros((batch_size, max_attn_len)).to(device)
prev_attn_score[:, 0] = 1
step_outs, step_attns = [], []
for t in range(inputs.size(1)):
wordids = inputs[:, t]
if t > 0 and self.config.schedule_sampling:
sample_rate = torch.rand(wordids.size(0)).to(wordids.device)
sample_mask = sample_rate < self.config.ss_rate
prob = self.softmax(step_outs[-1]).detach() # detach grad
sampled_wordids = torch.multinomial(prob, 1).view(-1)
wordids.masked_scatter_(sample_mask, sampled_wordids)
embed = self.embedding(wordids)
h_attn_lstm, c_attn_lstm = self.attn_lstm(
torch.cat([states[0][1], enc_globals, embed], dim=1),
(states[0][0], states[1][0]))
prev_memory = torch.sum(prev_attn_score.unsqueeze(2) * memory_values, 1)
address_params = self.address_layer(torch.cat([h_attn_lstm, prev_memory], 1))
interpolate_gate = torch.sigmoid(address_params[:, :1])
flow_gate = torch.softmax(address_params[:, 1:], dim=1)
# content_attn_score: (batch, max_attn_len)
content_attn_score, content_attn_memory = self.attn(h_attn_lstm,
memory_keys, memory_values, enc_masks)
# location attention flow: (batch, max_attn_len)
flow_attn_score_1 = torch.einsum('bts,bs->bt', flow_edges, prev_attn_score)
flow_attn_score_2 = torch.einsum('bts,bs->bt',flow_edges, flow_attn_score_1)
# (batch, max_attn_len, 3)
flow_attn_score = torch.stack([x.view(batch_size, max_attn_len) \
for x in [prev_attn_score, flow_attn_score_1, flow_attn_score_2]], 2)
flow_attn_score = torch.sum(flow_gate.unsqueeze(1) * flow_attn_score, 2)
# content + location interpolation
attn_score = interpolate_gate * content_attn_score + (1 - interpolate_gate) * flow_attn_score
# final attention
step_attns.append(attn_score)
prev_attn_score = attn_score
attn_memory = torch.sum(attn_score.unsqueeze(2) * memory_values, 1)
# next layer with attended context
h_lang_lstm, c_lang_lstm = self.lang_lstm(
torch.cat([h_attn_lstm, attn_memory], dim=1),
(states[0][1], states[1][1]))
outs = h_lang_lstm
logit = self.calc_logits_with_rnn_outs(outs)
step_outs.append(logit)
states = (torch.stack([h_attn_lstm, h_lang_lstm], dim=0),
torch.stack([c_attn_lstm, c_lang_lstm], dim=0))
logits = torch.stack(step_outs, 1)
logits = logits.view(-1, self.config.num_words)
if return_attn:
return logits, step_attns
return logits
def step_fn(self, words, step, **kwargs):
states = kwargs['states']
enc_globals = kwargs['enc_globals']
memory_keys = kwargs['memory_keys']
memory_values = kwargs['memory_values']
memory_masks = kwargs['memory_masks']
prev_attn_score = kwargs['prev_attn_score']
flow_edges = kwargs['flow_edges']
batch_size, max_attn_len = memory_masks.size()
embed = self.embedding(words.squeeze(1))
h_attn_lstm, c_attn_lstm = self.attn_lstm(
torch.cat([states[0][1], enc_globals, embed], dim=1),
(states[0][0], states[1][0]))
prev_memory = torch.sum(prev_attn_score.unsqueeze(2) * memory_values, 1)
address_params = self.address_layer(torch.cat([h_attn_lstm, prev_memory], 1))
interpolate_gate = torch.sigmoid(address_params[:, :1])
flow_gate = torch.softmax(address_params[:, 1:], dim=1)
# content_attn_score: (batch, max_attn_len)
content_attn_score, content_attn_memory = self.attn(h_attn_lstm,
memory_keys, memory_values, memory_masks)
# location attention flow: (batch, max_attn_len)
flow_attn_score_1 = torch.einsum('bts,bs->bt', flow_edges, prev_attn_score)
flow_attn_score_2 = torch.einsum('bts,bs->bt', flow_edges, flow_attn_score_1)
flow_attn_score = torch.stack([x.view(batch_size, max_attn_len) \
for x in [prev_attn_score, flow_attn_score_1, flow_attn_score_2]], 2)
flow_attn_score = torch.sum(flow_gate.unsqueeze(1) * flow_attn_score, 2)
# content + location interpolation
attn_score = interpolate_gate * content_attn_score + (1 - interpolate_gate) * flow_attn_score
# final attention
attn_memory = torch.sum(attn_score.unsqueeze(2) * memory_values, 1)
h_lang_lstm, c_lang_lstm = self.lang_lstm(
torch.cat([h_attn_lstm, attn_memory], dim=1),
(states[0][1], states[1][1]))
logits = self.calc_logits_with_rnn_outs(h_lang_lstm)
logprobs = self.log_softmax(logits)
states = (torch.stack([h_attn_lstm, h_lang_lstm], dim=0),
torch.stack([c_attn_lstm, c_lang_lstm], dim=0))
kwargs['prev_attn_score'] = attn_score
kwargs['states'] = states
return logprobs, kwargs
def sample_decode(self, words, enc_globals, enc_memories, enc_masks, flow_edges, greedy=True):
'''Args:
words: (batch, )
enc_globals: (batch, hidden_size)
enc_memories: (batch, enc_seq_len, attn_input_size)
enc_masks: (batch, enc_seq_len)
flow_edges: sparse matrix, (batch*max_attn_len, batch*max_attn_len)
'''
batch_size, max_attn_len = enc_masks.size()
device = enc_masks.device
states = self.init_dec_state(batch_size)
memory_keys, memory_values = self.gen_memory_key_value(enc_memories)
prev_attn_score = torch.zeros((batch_size, max_attn_len)).to(device)
prev_attn_score[:, 0] = 1
seq_words, seq_word_logprobs = caption.utils.inference.sample_decode(
words, self.step_fn, self.config.max_words_in_sent,
greedy=greedy, states=states, enc_globals=enc_globals,
memory_keys=memory_keys, memory_values=memory_values, memory_masks=enc_masks,
prev_attn_score=prev_attn_score, flow_edges=flow_edges)
return seq_words, seq_word_logprobs
def beam_search_decode(self, words, enc_globals, enc_memories, enc_masks, flow_edges):
batch_size, max_attn_len = enc_masks.size()
device = enc_masks.device
states = self.init_dec_state(batch_size)
memory_keys, memory_values = self.gen_memory_key_value(enc_memories)
prev_attn_score = torch.zeros((batch_size, max_attn_len)).to(device)
prev_attn_score[:, 0] = 1
sent_pool = caption.utils.inference.beam_search_decode(words, self.step_fn,
self.config.max_words_in_sent, beam_width=self.config.beam_width,
sent_pool_size=self.config.sent_pool_size,
expand_fn=self.expand_fn, select_fn=self.select_fn,
memory_keys=memory_keys, memory_values=memory_values, memory_masks=enc_masks,
states=states, enc_globals=enc_globals,
prev_attn_score=prev_attn_score, flow_edges=flow_edges)
return sent_pool
| 0
| 0
| 0
| 7,711
| 0
| 0
| 0
| 81
| 134
|
35dd4db4651fde50d5a6898decf9c51ba241f80d
| 713
|
py
|
Python
|
xml/parse_xml.py
|
bruce-webber/python-intro
|
78881eb65951e2c4451df1d62da1b7d79f5b1970
|
[
"CC-BY-4.0"
] | null | null | null |
xml/parse_xml.py
|
bruce-webber/python-intro
|
78881eb65951e2c4451df1d62da1b7d79f5b1970
|
[
"CC-BY-4.0"
] | null | null | null |
xml/parse_xml.py
|
bruce-webber/python-intro
|
78881eb65951e2c4451df1d62da1b7d79f5b1970
|
[
"CC-BY-4.0"
] | null | null | null |
"""
This is a demonstration of xml.etree (part of the standard library), which is used
to parse, modify and create XML files.
"""
import xml.etree.ElementTree as ET
tree = ET.parse('country_data.xml')
root = tree.getroot()
# The top level tag.
print(root.tag)
# The tag of the first sub-element.
print(root[0].tag)
# The attributes of the first sub-element.
print(root[0].attrib)
print()
# The names of the countries and their neighbors.
for country in root:
if 'name' in country.attrib:
print(country.attrib['name'])
for element in country:
if element.tag == 'neighbor':
if 'name' in element.attrib:
print(' ' + element.attrib['name'])
| 25.464286
| 82
| 0.650771
|
"""
This is a demonstration of xml.etree (part of the standard library), which is used
to parse, modify and create XML files.
"""
import xml.etree.ElementTree as ET
tree = ET.parse('country_data.xml')
root = tree.getroot()
# The top level tag.
print(root.tag)
# The tag of the first sub-element.
print(root[0].tag)
# The attributes of the first sub-element.
print(root[0].attrib)
print()
# The names of the countries and their neighbors.
for country in root:
if 'name' in country.attrib:
print(country.attrib['name'])
for element in country:
if element.tag == 'neighbor':
if 'name' in element.attrib:
print(' ' + element.attrib['name'])
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|