code
stringlengths 22
1.05M
| apis
listlengths 1
3.31k
| extract_api
stringlengths 75
3.25M
|
|---|---|---|
from pygameImporter import pygame
from Frame.baseFunctions import *
from Frame.gui.Gui import Gui
class ProgressBar(Gui):
def __init__(self, fillPercentage, fillColor, *args, **kwargs):
super().__init__(*args, **kwargs)
output("Progress Bar: Creating " + self.text + " progress bar...", "debug")
self.progress = fillPercentage
self.fillColor = fillColor
self.touchable = False
def setProgress(self, progress):
output("Progress Bar: Setting progress to " + str(progress) + "%...", "debug")
if progress > 100:
progress = 100
self.progress = progress
def render(self):
super().render(False)
output("Progress Bar: Getting points for drawing...", "complete")
points = [[self.coords[0] + 1, self.coords[1] + 1], [self.coords[0] + self.coords[2] * self.progress / 100 - 1, self.coords[1] + 1],
[self.coords[0] + self.coords[2] * self.progress / 100 - 1, self.coords[1] + self.coords[3] - 1],
[self.coords[0] + 1, self.coords[1] + self.coords[3] - 1]]
output("Progress Bar: Drawing...", "complete")
pygame.draw.polygon(self.window.surface, self.fillColor, points)
output("Progress Bar: Rendering text...", "complete")
try:
if not self.writable and self.text == "":
self.renderObj.text(self.fontFile, int(self.textSize + self.height - self.startCoords[3]), self.enterText, self.antialias, self.textColor, None,
self.window.surface, width = self.width, height = self.height, addX = self.x, addY = self.y)
except AttributeError:
self.renderObj.text(self.fontFile, int(self.textSize + self.height - self.startCoords[3]), self.text, self.antialias, self.textColor, None,
self.window.surface, width = self.width, height = self.height, addX = self.x, addY = self.y)
|
[
"pygameImporter.pygame.draw.polygon"
] |
[((1171, 1235), 'pygameImporter.pygame.draw.polygon', 'pygame.draw.polygon', (['self.window.surface', 'self.fillColor', 'points'], {}), '(self.window.surface, self.fillColor, points)\n', (1190, 1235), False, 'from pygameImporter import pygame\n')]
|
from enums.enums import MediusEnum, CallbackStatus
from utils import utils
from medius.mediuspackets.disbandclanresponse import DisbandClanResponseSerializer
class DisbandClanSerializer:
data_dict = [
{'name': 'mediusid', 'n_bytes': 2, 'cast': None},
{'name': 'message_id', 'n_bytes': MediusEnum.MESSAGEID_MAXLEN, 'cast': None},
{'name': 'session_key', 'n_bytes': MediusEnum.SESSIONKEY_MAXLEN, 'cast': None},
{'name': 'buf', 'n_bytes': 2, 'cast': None},
{'name': 'clan_id', 'n_bytes': 4, 'cast': utils.bytes_to_int_little},
]
class DisbandClanHandler:
def process(self, serialized, monolith, con):
client_manager = monolith.get_client_manager()
client_manager.disband_clan(serialized['clan_id'])
return [DisbandClanResponseSerializer.build(
serialized['message_id'],
CallbackStatus.SUCCESS
)]
|
[
"medius.mediuspackets.disbandclanresponse.DisbandClanResponseSerializer.build"
] |
[((784, 873), 'medius.mediuspackets.disbandclanresponse.DisbandClanResponseSerializer.build', 'DisbandClanResponseSerializer.build', (["serialized['message_id']", 'CallbackStatus.SUCCESS'], {}), "(serialized['message_id'],\n CallbackStatus.SUCCESS)\n", (819, 873), False, 'from medius.mediuspackets.disbandclanresponse import DisbandClanResponseSerializer\n')]
|
# coding=utf-8
from bs4 import BeautifulSoup
import copy
import time
import json
import LOGIN
import MENU
class PlannedCourseInfo:
def __init__(self, main_num=None, name=None, code=None, margin=None, detail=None, url=None, course_dic=None):
if course_dic is None:
self.num = str(main_num)
self.name = str(name)
self.code = str(code)
self.margin = str(margin)
self.url = url
self.detail = copy.deepcopy(detail)
else:
self.num = course_dic["num"]
self.name = course_dic["name"]
self.code = course_dic["code"]
self.margin = course_dic["margin"]
self.url = course_dic["url"]
self.detail = course_dic["detail"]
def show_course_summary(self):
print("主编号:" + self.num
+ "\t名称:" + self.name
+ "\t代码:" + self.code)
def show_course_info(self):
for item in self.detail:
print(" ∟____ 辅编号:" + item["secondary_num"] + "\t教师:" + item["teacher"]
+ "\t时间:" + item["time"])
# print(self.code)
def to_json(self):
"""
将本类的数据转换为一个json,并返回字符串
"""
js = {"name": self.name, "num": self.num, "code": self.code, "margin": self.margin, "url": self.url,
"detail": self.detail}
return json.dumps(js)
class PlannedCourse:
"""
思路：
1：登录
2：进入选课界面
3：抓取课程信息并保存
4：用户输入想要抢的一门或几门课程
5：开始抢课
"""
def __init__(self, account):
"""初始化登录"""
self.account = account
self.english_course = []
self.professional_course = []
self.target = ""
def init_menu(self):
"""输出菜单，并输入想要抢的课程"""
menu_dic = {
"-1": "更新数据（需要等待一分半左右）",
"1": "本专业课程",
"2": "大学英语扩展课",
"0": "退出",
}
menu = MENU.MENU(menu_dic=menu_dic)
menu.print_list()
while True:
_key = input(">>>")
if int(_key) == 1:
# 设置本专业课程target
self.get_professional_course()
print("输入课程编号选择课程，0返回")
for item in self.professional_course:
item.show_course_summary()
length = len(self.professional_course)
while True:
i_key = input("(主编号)>>>")
if 0 < int(i_key) <= length:
print("你选择了", self.professional_course[int(i_key) - 1].name)
self.professional_course[int(i_key) - 1].show_course_info()
item_length = len(self.professional_course[int(i_key) - 1].detail)
while True:
j_key = input("(辅编号)>>>")
if 1 <= int(j_key) <= item_length:
detail = self.professional_course[int(i_key) - 1].detail[int(j_key) - 1]
print("你选择了: 辅编号:", detail["secondary_num"], "\t教师:", detail["teacher"],
"\t时间:", detail["time"])
tmp = i_key + ":" + j_key
self.target = tmp
self.attack_professional()
return
elif int(j_key) == 0:
break
else:
print("请输入正确的数字")
elif int(i_key) == 0:
break
elif int(i_key) == -1:
self.update_course()
else:
print("请输入正确的数字")
elif int(_key) == 2:
# 设置英语扩展课课程target
self.get_english_course()
print("输入课程编号选择课程，0返回")
for item in self.english_course:
item.show_course_summary()
length = len(self.english_course)
while True:
i_key = input("(主编号)>>>")
if 0 < int(i_key) <= length:
print("你选择了", self.english_course[int(i_key) - 1].name)
self.english_course[int(i_key) - 1].show_course_info()
item_length = len(self.english_course[int(i_key) - 1].detail)
while True:
j_key = input("(辅编号)>>>")
if 1 <= int(j_key) <= item_length:
detail = self.english_course[int(i_key) - 1].detail[int(j_key) - 1]
print("你选择了: 辅编号:", detail["secondary_num"], "\t教师:", detail["teacher"],
"\t时间:", detail["time"])
tmp = i_key + ":" + j_key
self.target = tmp
self.attack_english()
return
elif int(j_key) == 0:
break
else:
print("请输入正确的数字")
elif int(i_key) == 0:
break
elif int(i_key) == -1:
self.update_course()
else:
print("请输入正确的数字")
# elif int(_key) == 3:
# pass
elif int(_key) == -1:
self.update_course()
elif int(_key) == 0:
return
else:
print("请输入正确的数字")
def __catch_view_state(self):
"""抓取 HTML中的 VIEWSTATE"""
url = LOGIN.ZUCC.PlanCourageURL + "?xh=" + self.account.account_data[
"username"] + "&xm=" + self.account.name + "&gnmkdm=N121101"
header = LOGIN.ZUCC.InitHeader
header["Referer"] = LOGIN.ZUCC.PlanCourageURL + "?xh=" + self.account.account_data["username"]
response = self.account.session.get(url=url, headers=header)
while response.status_code == 302:
response = self.account.session.get(url=url, headers=header)
time.sleep(0.2)
self.account.soup = BeautifulSoup(response.text, "lxml")
# print(response.status_code)
def __enter_english_page(self):
"""进入计划内选课--英语页面，为抓取数据做准备"""
self.__catch_view_state()
url = LOGIN.ZUCC.PlanCourageURL + "?xh=" + self.account.account_data["username"]
post_data = {"__EVENTTARGET": "", "__EVENTARGUMENT": "", "__LASTFOCUS": "", "__VIEWSTATEGENERATOR": "4842AF95",
"zymc": "0121%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF%E4%B8%BB%E4%BF%AE%E4%B8%93%E4%B8%9A%7C%7C2019",
"xx": "", "Button3": "大学英语拓展课",
"__VIEWSTATE": self.account.soup.find(name='input', id="__VIEWSTATE")["value"]}
response = self.account.session.post(url=url, data=post_data)
self.account.soup = BeautifulSoup(response.text, "lxml")
links = self.account.soup.find_all(name="tr")
return links
def __enter_professional_course(self):
"""进入计划内选课--本专业页面，为抓取数据做准备"""
self.__catch_view_state()
url = LOGIN.ZUCC.PlanCourageURL + "?xh=" + self.account.account_data["username"]
post_data = {"__EVENTTARGET": "", "__EVENTARGUMENT": "", "__LASTFOCUS": "", "__VIEWSTATEGENERATOR": "4842AF95",
"xx": "", "Button5": "本专业选课",
"__VIEWSTATE": self.account.soup.find(name='input', id="__VIEWSTATE")["value"]}
response = self.account.session.post(url=url, data=post_data)
# print(response.text)
self.account.soup = BeautifulSoup(response.text, "lxml")
links = self.account.soup.find_all(name="tr")
return links
def get_english_course(self):
"""从文件中取得课程数据"""
js_file = open("english_information.json", "r", encoding='utf-8')
js_list = json.load(js_file)
js_file.close()
for course in js_list:
tmp = PlannedCourseInfo(course_dic=course)
self.english_course.append(tmp)
def get_professional_course(self):
"""从文件中取得课程数据"""
js_file = open("professional_information.json", "r", encoding='utf-8')
js_list = json.load(js_file)
js_file.close()
for course in js_list:
tmp = PlannedCourseInfo(course_dic=course)
self.professional_course.append(tmp)
def update_course(self):
"""更新课程信息并保存到文件"""
links = self.__enter_english_page()
course_list = []
i = 1
# 遍历10种英语课程
for link in links[1:-1]:
tmp = link.find_all("td")
detail = []
url = "http://" + LOGIN.ZUCC.DOMAIN + tmp[0].find(name="a")["onclick"][21:-8]
header = LOGIN.ZUCC.InitHeader
header["Referer"] = "http://xk.zucc.edu.cn/xs_main.aspx?xh="+self.account.account_data['username']
time.sleep(4)
item_response = self.account.session.get(url=url, headers=header)
item_soup = BeautifulSoup(item_response.text, "lxml")
item_trs = item_soup.find_all(name="tr")
j = 1
print('.', end='')
# 遍历所以的教学班
for item_tr in item_trs[1:-1]:
tds = item_tr.find_all("td")
detail_td = {"secondary_num": str(j), "code": tds[0].find(name="input")["value"],
"teacher": tds[2].find(name="a").text,
"time": tds[3].text,
"margin": str(int(tds[11].text) - int(tds[13].text)) + "/" + tds[11].text}
# 将教学班信息打包成列表
detail.append(detail_td)
j += 1
tmp = link.find_all("td")
course_list.append(
PlannedCourseInfo(main_num=i, name=tmp[1].find(name="a").text, code=tmp[0].find(name="a").text,
margin=tmp[9].text, detail=detail, url=url))
i += 1
js_str = "["
flag = True
for course in course_list:
if flag:
js_str += course.to_json()
flag = False
else:
js_str += "," + course.to_json()
js_str += "]"
# 缓存在文件
english_file = open("english_information.json", "w", encoding='utf-8')
english_file.write(js_str)
english_file.close()
links = self.__enter_professional_course()
course_list = []
i = 1
# 遍历专业课程
for link in links[1:-1]:
tmp = link.find_all("td")
detail = []
url = "http://" + LOGIN.ZUCC.DOMAIN + "/clsPage/xsxjs.aspx?" + "xkkh=" + \
tmp[0].find(name="a")["onclick"].split("=")[1][0:-3] + "&xh=" + self.account.account_data["username"]
header = LOGIN.ZUCC.InitHeader
header["Referer"] = "http://xk.zucc.edu.cn/xs_main.aspx?xh=31901040"
time.sleep(4)
# print(url)
item_response = self.account.session.get(url=url, headers=header)
# print(item_response.text)
item_soup = BeautifulSoup(item_response.text, "lxml")
item_trs = item_soup.find_all(name="tr")
j = 1
print('.', end='')
# 遍历所以的教学班
for item_tr in item_trs[1:-1]:
tds = item_tr.find_all("td")
detail_td = {"secondary_num": str(j), "code": tds[0].find(name="input")["value"],
"teacher": tds[2].find(name="a").text,
"time": tds[3].text,
"margin": str(int(tds[11].text) - int(tds[13].text)) + "/" + tds[11].text}
# 将教学班信息打包成列表
detail.append(detail_td)
j += 1
tmp = link.find_all("td")
course_list.append(
PlannedCourseInfo(main_num=i, name=tmp[1].find(name="a").text, code=tmp[0].find(name="a").text,
margin=tmp[9].text, detail=detail, url=url))
i += 1
js_str = "["
flag = True
for course in course_list:
if flag:
js_str += course.to_json()
flag = False
else:
js_str += "," + course.to_json()
js_str += "]"
# 缓存在文件
professional_file = open("professional_information.json", "w", encoding='utf-8')
professional_file.write(js_str)
professional_file.close()
print("\n更新完成！")
def attack_english(self):
self.get_english_course()
self.__enter_english_page()
course_xy = self.target.split(":")
x = int(course_xy[0])
y = int(course_xy[1])
header = LOGIN.ZUCC.InitHeader
header["Referer"] = "http://xk.zucc.edu.cn/xs_main.aspx?xh=31901040"
response = self.account.session.get(url=self.english_course[x - 1].url, headers=header)
# print(self.english_course[x - 1].url)
self.account.soup = BeautifulSoup(response.text, "lxml")
post_data = {"__EVENTTARGET": "Button1",
"__VIEWSTATEGENERATOR": "55DF6E88",
"RadioButtonList1": "1",
"xkkh": self.english_course[x - 1].detail[y - 1]["code"],
"__VIEWSTATE": self.account.soup.find_all(name='input', id="__VIEWSTATE")[0]["value"]}
while True:
response = self.account.session.post(url=self.english_course[x - 1].url, data=post_data)
soup = BeautifulSoup(response.text, "lxml")
try:
reply = soup.find(name="script").text.split("'")[1]
except BaseException:
reply = "未知错误"
print(reply+"\t\t"+str(time.strftime('%m-%d-%H-%M-%S',time.localtime(time.time()))))
if reply == "选课成功！":
return
def attack_professional(self):
self.get_professional_course()
self.__enter_professional_course()
course_xy = self.target.split(":")
x = int(course_xy[0])
y = int(course_xy[1])
header = LOGIN.ZUCC.InitHeader
header["Referer"] = "http://xk.zucc.edu.cn/xs_main.aspx?xh=31901040"
response = self.account.session.get(url=self.professional_course[x - 1].url, headers=header)
# print(self.professional_course[x - 1].url)
# print(response.text)
self.account.soup = BeautifulSoup(response.text, "lxml")
post_data = {"__EVENTTARGET": "Button1",
"__VIEWSTATEGENERATOR": "55DF6E88",
"RadioButtonList1": "1",
"xkkh": self.professional_course[x - 1].detail[y - 1]["code"],
"__VIEWSTATE": self.account.soup.find_all(name='input', id="__VIEWSTATE")[0]["value"]}
while True:
response = self.account.session.post(url=self.professional_course[x - 1].url, data=post_data)
soup = BeautifulSoup(response.text, "lxml")
try:
reply = soup.find(name="script").text.split("'")[1]
except BaseException:
reply = "未知错误"
print(reply)
if reply == "选课成功！":
return
if __name__ == "__main__":
account = LOGIN.Account()
account.login()
planned_course_spider = PlannedCourse(account)
# planned_course_spider.update_course()
planned_course_spider.init_menu()
# planned_course_spider.catch_english_course()
# planned_course_spider.update_course()
|
[
"copy.deepcopy",
"json.load",
"json.dumps",
"time.sleep",
"time.time",
"MENU.MENU",
"LOGIN.Account",
"bs4.BeautifulSoup"
] |
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|
import os
from bitfield import BitField
from constance import config
from django.db import models
from django.contrib.auth.models import User
from django.conf import settings
from django.core.validators import MaxValueValidator, MinValueValidator
from django.db.models.signals import post_save
import django.db.models.options as options
from django.dispatch import receiver
from timezone_field import TimeZoneField
from notifications.vk import VK
ALL_WEEKDAYS_BITMAP = 127
options.DEFAULT_NAMES = options.DEFAULT_NAMES + ('file_fields',)
class PublishModel(models.Model):
is_published = models.BooleanField(verbose_name='публикация', default=True)
class Meta:
abstract = True
def publish(self):
self.is_published = True
super(PublishModel, self).save()
def unpublish(self):
self.is_published = False
super(PublishModel, self).save()
class PublishFileQuerySet(models.query.QuerySet):
def delete(self):
for d in self:
list_of_image_fields = [f['field_name'] for f in d._meta.__dict__.get('file_fields', [])]
d.delete_files(list_of_image_fields)
super(PublishFileQuerySet, self).delete()
class PublishFileManager(models.Manager):
def get_queryset(self):
return PublishFileQuerySet(self.model, using=self._db)
class PublishFileModel(PublishModel):
objects = PublishFileManager()
class Meta:
abstract = True
def delete_files(self, list_of_fieldnames=list()):
model = self.__class__
try:
obj = model.objects.get(pk=self.pk)
except model.DoesNotExist:
return
# Delete all selected image fields within a model
for field in list_of_fieldnames:
try:
# path = obj._meta.get_field(field).path
path = getattr(obj, field).path
if os.path.isfile(path):
os.remove(path)
except ValueError:
pass
def get_old_file_path_if_changed(self):
model = self.__class__
list_of_field_names = list()
try:
instance = model.objects.get(pk=self.pk)
except model.DoesNotExist:
return list()
for field in instance._meta.__dict__.get('file_fields', []):
fieldname = field['field_name']
try:
new_path = getattr(self, fieldname).path
except ValueError:
new_path = ''
try:
old_path = getattr(instance, fieldname).path
except ValueError:
old_path = ''
if new_path != old_path:
list_of_field_names.append(fieldname)
return list_of_field_names
def additional_action_on_save(self, list_of_changed_image_fields, created):
"""
To be overwritten in child models.
"""
pass
def save(self, *args, **kwargs):
created = not self.id
list_of_changed_image_fields = self.get_old_file_path_if_changed()
self.delete_files(list_of_changed_image_fields)
super(PublishModel, self).save(*args, **kwargs)
self.additional_action_on_save(list_of_changed_image_fields, created)
super(PublishModel, self).save()
def delete(self, *args, **kwargs):
list_of_image_fields = [d['field_name'] for d in self._meta.__dict__.get('file_fields', [])]
self.delete_files(list_of_image_fields)
super(PublishModel, self).save(*args, **kwargs)
super(PublishModel, self).delete(*args, **kwargs)
class Profile(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE, verbose_name='Пользователь')
timezone = TimeZoneField(default=settings.DEFAULT_TIME_ZONE, verbose_name='Временная зона')
email_confirmed = models.BooleanField(default=False, verbose_name='Email подтвержден')
send_distributions = models.BooleanField(verbose_name='Отправлять рассылку', default=False)
send_hour = models.IntegerField(verbose_name='Час рассылки', default=16,
validators=[MaxValueValidator(23), MinValueValidator(0)])
weekdays = BitField(verbose_name='Битовый код дней рассылки',
flags=(('monday', 'Понедельник'), ('tuesday', 'Вторник'), ('wednesday', 'Среда'),
('thursday', 'Четверг'), ('friday', 'Пятница'), ('saturday', 'Суббота'),
('sunday', 'Воскресенье')), default=ALL_WEEKDAYS_BITMAP)
class Meta:
db_table = 'user_profile'
verbose_name = 'Профиль пользователя'
verbose_name_plural = 'Профили пользователей'
def __str__(self):
return self.user.username
def is_staff(self):
return self.user.is_staff
is_staff.short_description = 'Модератор'
def is_superuser(self):
return self.user.is_superuser
is_superuser.short_description = 'Суперпользователь'
@receiver(post_save, sender=User)
def create_user_profile(sender, instance, created, **kwargs):
if created:
Profile.objects.create(
user=instance,
email_confirmed=instance.is_staff
)
instance.profile.save()
|
[
"django.db.models.OneToOneField",
"os.remove",
"django.core.validators.MinValueValidator",
"django.dispatch.receiver",
"django.db.models.BooleanField",
"os.path.isfile",
"timezone_field.TimeZoneField",
"bitfield.BitField",
"django.core.validators.MaxValueValidator"
] |
[((4974, 5006), 'django.dispatch.receiver', 'receiver', (['post_save'], {'sender': 'User'}), '(post_save, sender=User)\n', (4982, 5006), False, 'from django.dispatch import receiver\n'), ((597, 657), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'verbose_name': '"""публикация"""', 'default': '(True)'}), "(verbose_name='публикация', default=True)\n", (616, 657), False, 'from django.db import models\n'), ((3634, 3720), 'django.db.models.OneToOneField', 'models.OneToOneField', (['User'], {'on_delete': 'models.CASCADE', 'verbose_name': '"""Пользователь"""'}), "(User, on_delete=models.CASCADE, verbose_name=\n 'Пользователь')\n", (3654, 3720), False, 'from django.db import models\n'), ((3731, 3816), 'timezone_field.TimeZoneField', 'TimeZoneField', ([], {'default': 'settings.DEFAULT_TIME_ZONE', 'verbose_name': '"""Временная зона"""'}), "(default=settings.DEFAULT_TIME_ZONE, verbose_name='Временная зона'\n )\n", (3744, 3816), False, 'from timezone_field import TimeZoneField\n'), ((3834, 3902), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)', 'verbose_name': '"""Email подтвержден"""'}), "(default=False, verbose_name='Email подтвержден')\n", (3853, 3902), False, 'from django.db import models\n'), ((3928, 3998), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'verbose_name': '"""Отправлять рассылку"""', 'default': '(False)'}), "(verbose_name='Отправлять рассылку', default=False)\n", (3947, 3998), False, 'from django.db import models\n'), ((4185, 4460), 'bitfield.BitField', 'BitField', ([], {'verbose_name': '"""Битовый код дней рассылки"""', 'flags': "(('monday', 'Понедельник'), ('tuesday', 'Вторник'), ('wednesday', 'Среда'),\n ('thursday', 'Четверг'), ('friday', 'Пятница'), ('saturday', 'Суббота'),\n ('sunday', 'Воскресенье'))", 'default': 'ALL_WEEKDAYS_BITMAP'}), "(verbose_name='Битовый код дней рассылки', flags=(('monday',\n 'Понедельник'), ('tuesday', 'Вторник'), ('wednesday', 'Среда'), (\n 'thursday', 'Четверг'), ('friday', 'Пятница'), ('saturday', 'Суббота'),\n ('sunday', 'Воскресенье')), default=ALL_WEEKDAYS_BITMAP)\n", (4193, 4460), False, 'from bitfield import BitField\n'), ((1890, 1910), 'os.path.isfile', 'os.path.isfile', (['path'], {}), '(path)\n', (1904, 1910), False, 'import os\n'), ((4124, 4145), 'django.core.validators.MaxValueValidator', 'MaxValueValidator', (['(23)'], {}), '(23)\n', (4141, 4145), False, 'from django.core.validators import MaxValueValidator, MinValueValidator\n'), ((4147, 4167), 'django.core.validators.MinValueValidator', 'MinValueValidator', (['(0)'], {}), '(0)\n', (4164, 4167), False, 'from django.core.validators import MaxValueValidator, MinValueValidator\n'), ((1932, 1947), 'os.remove', 'os.remove', (['path'], {}), '(path)\n', (1941, 1947), False, 'import os\n')]
|
import os
import numpy as np
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.dvector import FrovedisDvector
from frovedis.matrix.dense import FrovedisRowmajorMatrix
FrovedisServer.initialize("mpirun -np 2 {}".format(os.environ['FROVEDIS_SERVER']))
dv = FrovedisDvector([1,2,3,4,5,6,7,8],dtype=np.float64)
dv.debug_print()
FrovedisServer.shut_down()
|
[
"frovedis.exrpc.server.FrovedisServer.shut_down",
"frovedis.matrix.dvector.FrovedisDvector"
] |
[((277, 336), 'frovedis.matrix.dvector.FrovedisDvector', 'FrovedisDvector', (['[1, 2, 3, 4, 5, 6, 7, 8]'], {'dtype': 'np.float64'}), '([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.float64)\n', (292, 336), False, 'from frovedis.matrix.dvector import FrovedisDvector\n'), ((346, 372), 'frovedis.exrpc.server.FrovedisServer.shut_down', 'FrovedisServer.shut_down', ([], {}), '()\n', (370, 372), False, 'from frovedis.exrpc.server import FrovedisServer\n')]
|
from manageXML.management.commands._giella_xml import GiellaXML
from django.core.management.base import BaseCommand, CommandError
import os, glob, sys
from manageXML.models import *
from django.conf import settings
from collections import defaultdict
ignore_affiliations = False
def create_lexeme(ll: GiellaXML.Item, lang: Language, datafile: DataFile = None):
try:
_l = Lexeme.objects.get(lexeme=ll.text.strip(), pos=ll.pos.strip(), homoId=ll.homoId, language=lang)
except:
_l = Lexeme.objects.create(
lexeme=ll.text.strip(), pos=ll.pos.strip(), homoId=ll.homoId, language=lang,
contlex=ll.contlex.strip(),
imported_from=datafile)
_filtered_attributes = ll.filtered_attributes()
for _k, _v in _filtered_attributes.items():
_metadata_type = None
if _k == 'gen':
_metadata_type = GENDER
elif _k == 'type':
_metadata_type = LEXEME_TYPE
elif _k == 'ignore':
_metadata_type = IGNORE_TAG
else:
_v = "{},{}".format(_k, _v.strip())
_lmd, created = LexemeMetadata.objects.get_or_create(lexeme=_l, type=_metadata_type, text=_v)
if ignore_affiliations:
return _l
title = _l.find_akusanat_affiliation()
# link it
if title:
a, created = Affiliation.objects.get_or_create(lexeme=_l, title=title, type=AKUSANAT,
link="{}{}".format(settings.WIKI_URL, title))
return _l
def parseXML(filename, filepos):
print("processing: " + filename)
g = GiellaXML.parse_file(filename)
gl = Language.objects.get(id=g.lang) # src_language
langs = {
g.lang: gl
}
filename_only = os.path.splitext(os.path.basename(filename))[0]
df = DataFile(lang_source=gl, lang_target=None, name=filename_only)
df.save()
for e in g.elements:
_ll = None
_l = None
try:
for lg in e.get('lg', []):
_l = lg.get('l', [])
if not _l:
continue
# Add ignore=fst to the lexeme
if e.ignore:
_l.attributes['ignore'] = e.ignore
_ll = create_lexeme(_l[0], gl, df) # create the lemma
for stg in lg.get('stg', []):
for st in stg.get('st', []): # stems
s, created = Stem.objects.get_or_create(lexeme=_ll, text=st.text.strip(), homoId=st.homoId,
contlex=st.contlex) # add the stems
if not _ll: # shouldn't happen but if it did, then we shouldn't get it there
continue
for mg in e.get('mg', []):
l_relations = defaultdict(list)
for tg in mg.get('tg', []): # translations
_lang = tg.attributes.get('xml:lang')
if _lang and _lang not in langs:
try:
langs[_lang] = Language.objects.get(id=_lang)
except:
continue
for t in tg.get('t', []):
_t = create_lexeme(t, langs[_lang], df)
r, created = Relation.objects.get_or_create(lexeme_from=_ll, lexeme_to=_t)
l_relations[_lang].append(r)
for xg in mg.get('xg', []): # examples
x = xg.get('x', [])
if not x:
continue
x = x[0].text
_xl, created = Example.objects.get_or_create(lexeme=_ll, text=x)
for xt in xg.get('xt', []):
_lang = xt.attributes.get('xml:lang')
if _lang not in l_relations:
continue
_r = l_relations[_lang].pop(0)
re_src, created = RelationExample.objects.get_or_create(relation=_r, text=x, language=gl)
xtt = xt.text
re_tgt, created = RelationExample.objects.get_or_create(relation=_r, text=xtt,
language=langs[_lang])
# add the link between the relations here
# RelationExampleRelation.objects.get_or_create(...)
for semantic in mg.get('semantics', []):
pass
for defNative in mg.get('defNative', []):
if not defNative or not defNative.text:
continue
_lmd, created = LexemeMetadata.objects.get_or_create(lexeme=_ll, type=DEF_NATIVE,
text=defNative.text.strip())
for source in e.get('sources', []):
pass
except Exception as err:
sys.stderr.write("Error @ %s: %s\n" % (str(_l[0].text) if _l and len(_l) > 0 else '', str(err)))
class Command(BaseCommand):
'''
Example: python manage.py import_xml -d ../saame/
Add --ignore-affiliations when debugging and want to speed up imports.
'''
help = 'This command imports the content of a all Giella XML documents in a directory.'
def add_arguments(self, parser):
parser.add_argument('-d', '--dir', type=str, help='The directory containing XML files.', )
parser.add_argument('--ignore-affiliations', dest='ignore_affiliations', action='store_true')
parser.set_defaults(ignore_affiliations=False)
def handle(self, *args, **options):
global ignore_affiliations
xml_dir = options['dir'] # the directory containing the XML files
ignore_affiliations = options['ignore_affiliations']
if not os.path.isdir(xml_dir):
raise CommandError('Directory "%s" does not exist.' % xml_dir)
for filename in glob.glob(os.path.join(xml_dir, '*.xml')): # read each file and parse it
filepos = filename.split('/')[-1].split('_')[:-1]
try:
parseXML(filename, filepos)
except Exception as err:
self.stderr.write(self.style.ERROR('Error processing %s: %s' % (filename, str(err))))
self.stdout.write(self.style.SUCCESS('Successfully imported the files in %s.' % (xml_dir,)))
|
[
"os.path.basename",
"os.path.isdir",
"manageXML.management.commands._giella_xml.GiellaXML.parse_file",
"collections.defaultdict",
"django.core.management.base.CommandError",
"os.path.join"
] |
[((1595, 1625), 'manageXML.management.commands._giella_xml.GiellaXML.parse_file', 'GiellaXML.parse_file', (['filename'], {}), '(filename)\n', (1615, 1625), False, 'from manageXML.management.commands._giella_xml import GiellaXML\n'), ((1760, 1786), 'os.path.basename', 'os.path.basename', (['filename'], {}), '(filename)\n', (1776, 1786), False, 'import os, glob, sys\n'), ((5902, 5924), 'os.path.isdir', 'os.path.isdir', (['xml_dir'], {}), '(xml_dir)\n', (5915, 5924), False, 'import os, glob, sys\n'), ((5944, 6000), 'django.core.management.base.CommandError', 'CommandError', (['(\'Directory "%s" does not exist.\' % xml_dir)'], {}), '(\'Directory "%s" does not exist.\' % xml_dir)\n', (5956, 6000), False, 'from django.core.management.base import BaseCommand, CommandError\n'), ((6036, 6066), 'os.path.join', 'os.path.join', (['xml_dir', '"""*.xml"""'], {}), "(xml_dir, '*.xml')\n", (6048, 6066), False, 'import os, glob, sys\n'), ((2795, 2812), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (2806, 2812), False, 'from collections import defaultdict\n')]
|
import math
import os
import xml.etree.ElementTree
import numpy as np
import paddle
import six
from PIL import Image
from utils import image_util
class Settings(object):
def __init__(self,
label_file_path=None,
resize_h=300,
resize_w=300,
mean_value=127.5,
std_value=0.007843,
apply_distort=True,
apply_expand=True,
ap_version='11point'):
self._ap_version = ap_version
self._label_list = []
if label_file_path is not None:
with open(label_file_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
for line in lines:
self._label_list.append(line.strip().replace('\n', ''))
self._apply_distort = apply_distort
self._apply_expand = apply_expand
self._resize_height = resize_h
self._resize_width = resize_w
self._img_mean = mean_value
self._img_std = std_value
self._expand_prob = 0.5
self._expand_max_ratio = 4
self._hue_prob = 0.5
self._hue_delta = 18
self._contrast_prob = 0.5
self._contrast_delta = 0.5
self._saturation_prob = 0.5
self._saturation_delta = 0.5
self._brightness_prob = 0.5
self._brightness_delta = 0.125
@property
def ap_version(self):
return self._ap_version
@property
def apply_expand(self):
return self._apply_expand
@property
def apply_distort(self):
return self._apply_distort
@property
def label_list(self):
return self._label_list
@property
def resize_h(self):
return self._resize_height
@property
def resize_w(self):
return self._resize_width
@property
def img_mean(self):
return self._img_mean
@property
def img_std(self):
return self._img_std
def preprocess(img, bbox_labels, mode, settings):
img_width, img_height = img.size
sampled_labels = bbox_labels
if mode == 'train':
if settings._apply_distort:
img = image_util.distort_image(img, settings)
if settings._apply_expand:
img, bbox_labels, img_width, img_height = image_util.expand_image(
img, bbox_labels, img_width, img_height, settings)
# sampling, hard-code here
batch_sampler = [image_util.sampler(1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0),
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0),
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0),
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0),
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0),
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0),
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0)]
sampled_bbox = image_util.generate_batch_samples(batch_sampler, bbox_labels)
img = np.array(img)
if len(sampled_bbox) > 0:
idx = int(np.random.uniform(0, len(sampled_bbox)))
img, sampled_labels = image_util.crop_image(img, bbox_labels, sampled_bbox[idx], img_width, img_height)
img = Image.fromarray(img)
img = img.resize((settings.resize_w, settings.resize_h), Image.ANTIALIAS)
img = np.array(img)
if mode == 'train':
mirror = int(np.random.uniform(0, 2))
if mirror == 1:
img = img[:, ::-1, :]
for i in range(len(sampled_labels)):
tmp = sampled_labels[i][1]
sampled_labels[i][1] = 1 - sampled_labels[i][3]
sampled_labels[i][3] = 1 - tmp
# HWC to CHW
if len(img.shape) == 3:
img = np.swapaxes(img, 1, 2)
img = np.swapaxes(img, 1, 0)
img = img.astype('float32')
img -= settings.img_mean
img = img * settings.img_std
return img, sampled_labels
def pascalvoc(settings, file_list, mode, batch_size, shuffle):
def reader():
if mode == 'train' and shuffle:
np.random.shuffle(file_list)
batch_out = []
cnt = 0
for image in file_list:
image_path, label_path = image.split('\t')
if not os.path.exists(image_path):
raise ValueError("%s is not exist, you should specify data path correctly." % image_path)
im = Image.open(image_path)
if im.mode == 'L':
im = im.convert('RGB')
im_width, im_height = im.size
# layout: label | xmin | ymin | xmax | ymax | difficult
bbox_labels = []
root = xml.etree.ElementTree.parse(label_path).getroot()
for object in root.findall('object'):
# start from 1
bbox_sample = [float(settings.label_list.index(object.find('name').text))]
bbox = object.find('bndbox')
difficult = float(object.find('difficult').text)
bbox_sample.append(float(bbox.find('xmin').text) / im_width)
bbox_sample.append(float(bbox.find('ymin').text) / im_height)
bbox_sample.append(float(bbox.find('xmax').text) / im_width)
bbox_sample.append(float(bbox.find('ymax').text) / im_height)
bbox_sample.append(difficult)
bbox_labels.append(bbox_sample)
im, sample_labels = preprocess(im, bbox_labels, mode, settings)
sample_labels = np.array(sample_labels)
if len(sample_labels) == 0: continue
im = im.astype('float32')
boxes = sample_labels[:, 1:5]
lbls = sample_labels[:, 0].astype('int32')
difficults = sample_labels[:, -1].astype('int32')
batch_out.append((im, boxes, lbls, difficults))
if len(batch_out) == batch_size:
yield batch_out
cnt += len(batch_out)
batch_out = []
if mode == 'test' and len(batch_out) > 1:
yield batch_out
cnt += len(batch_out)
return reader
def train(settings, file_list_path, batch_size, shuffle=True, use_multiprocess=True, num_workers=4):
readers = []
images = [line.strip() for line in open(file_list_path)]
np.random.shuffle(images)
n = int(math.ceil(len(images) // num_workers)) if use_multiprocess else len(images)
image_lists = [images[i:i + n] for i in range(0, len(images), n)]
for l in image_lists:
readers.append(pascalvoc(settings, l, 'train', batch_size, shuffle))
if use_multiprocess:
return paddle.reader.multiprocess_reader(readers, False)
else:
return readers[0]
def test(settings, file_list_path, batch_size):
image_list = [line.strip() for line in open(file_list_path)]
return pascalvoc(settings, image_list, 'test', batch_size, False)
|
[
"numpy.random.uniform",
"utils.image_util.sampler",
"utils.image_util.crop_image",
"utils.image_util.generate_batch_samples",
"PIL.Image.open",
"os.path.exists",
"numpy.array",
"numpy.swapaxes",
"PIL.Image.fromarray",
"utils.image_util.distort_image",
"paddle.reader.multiprocess_reader",
"numpy.random.shuffle",
"utils.image_util.expand_image"
] |
[((3440, 3453), 'numpy.array', 'np.array', (['img'], {}), '(img)\n', (3448, 3453), True, 'import numpy as np\n'), ((6376, 6401), 'numpy.random.shuffle', 'np.random.shuffle', (['images'], {}), '(images)\n', (6393, 6401), True, 'import numpy as np\n'), ((3012, 3073), 'utils.image_util.generate_batch_samples', 'image_util.generate_batch_samples', (['batch_sampler', 'bbox_labels'], {}), '(batch_sampler, bbox_labels)\n', (3045, 3073), False, 'from utils import image_util\n'), ((3089, 3102), 'numpy.array', 'np.array', (['img'], {}), '(img)\n', (3097, 3102), True, 'import numpy as np\n'), ((3331, 3351), 'PIL.Image.fromarray', 'Image.fromarray', (['img'], {}), '(img)\n', (3346, 3351), False, 'from PIL import Image\n'), ((3845, 3867), 'numpy.swapaxes', 'np.swapaxes', (['img', '(1)', '(2)'], {}), '(img, 1, 2)\n', (3856, 3867), True, 'import numpy as np\n'), ((3882, 3904), 'numpy.swapaxes', 'np.swapaxes', (['img', '(1)', '(0)'], {}), '(img, 1, 0)\n', (3893, 3904), True, 'import numpy as np\n'), ((6704, 6753), 'paddle.reader.multiprocess_reader', 'paddle.reader.multiprocess_reader', (['readers', '(False)'], {}), '(readers, False)\n', (6737, 6753), False, 'import paddle\n'), ((2160, 2199), 'utils.image_util.distort_image', 'image_util.distort_image', (['img', 'settings'], {}), '(img, settings)\n', (2184, 2199), False, 'from utils import image_util\n'), ((2289, 2363), 'utils.image_util.expand_image', 'image_util.expand_image', (['img', 'bbox_labels', 'img_width', 'img_height', 'settings'], {}), '(img, bbox_labels, img_width, img_height, settings)\n', (2312, 2363), False, 'from utils import image_util\n'), ((2441, 2495), 'utils.image_util.sampler', 'image_util.sampler', (['(1)', '(1)', '(1.0)', '(1.0)', '(1.0)', '(1.0)', '(0.0)', '(0.0)'], {}), '(1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0)\n', (2459, 2495), False, 'from utils import image_util\n'), ((2522, 2577), 'utils.image_util.sampler', 'image_util.sampler', (['(1)', '(50)', '(0.3)', '(1.0)', '(0.5)', '(2.0)', '(0.1)', '(0.0)'], {}), '(1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0)\n', (2540, 2577), False, 'from utils import image_util\n'), ((2604, 2659), 'utils.image_util.sampler', 'image_util.sampler', (['(1)', '(50)', '(0.3)', '(1.0)', '(0.5)', '(2.0)', '(0.3)', '(0.0)'], {}), '(1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0)\n', (2622, 2659), False, 'from utils import image_util\n'), ((2686, 2741), 'utils.image_util.sampler', 'image_util.sampler', (['(1)', '(50)', '(0.3)', '(1.0)', '(0.5)', '(2.0)', '(0.5)', '(0.0)'], {}), '(1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0)\n', (2704, 2741), False, 'from utils import image_util\n'), ((2768, 2823), 'utils.image_util.sampler', 'image_util.sampler', (['(1)', '(50)', '(0.3)', '(1.0)', '(0.5)', '(2.0)', '(0.7)', '(0.0)'], {}), '(1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0)\n', (2786, 2823), False, 'from utils import image_util\n'), ((2850, 2905), 'utils.image_util.sampler', 'image_util.sampler', (['(1)', '(50)', '(0.3)', '(1.0)', '(0.5)', '(2.0)', '(0.9)', '(0.0)'], {}), '(1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0)\n', (2868, 2905), False, 'from utils import image_util\n'), ((2932, 2987), 'utils.image_util.sampler', 'image_util.sampler', (['(1)', '(50)', '(0.3)', '(1.0)', '(0.5)', '(2.0)', '(0.0)', '(1.0)'], {}), '(1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0)\n', (2950, 2987), False, 'from utils import image_util\n'), ((3234, 3319), 'utils.image_util.crop_image', 'image_util.crop_image', (['img', 'bbox_labels', 'sampled_bbox[idx]', 'img_width', 'img_height'], {}), '(img, bbox_labels, sampled_bbox[idx], img_width,\n img_height)\n', (3255, 3319), False, 'from utils import image_util\n'), ((3500, 3523), 'numpy.random.uniform', 'np.random.uniform', (['(0)', '(2)'], {}), '(0, 2)\n', (3517, 3523), True, 'import numpy as np\n'), ((4165, 4193), 'numpy.random.shuffle', 'np.random.shuffle', (['file_list'], {}), '(file_list)\n', (4182, 4193), True, 'import numpy as np\n'), ((4490, 4512), 'PIL.Image.open', 'Image.open', (['image_path'], {}), '(image_path)\n', (4500, 4512), False, 'from PIL import Image\n'), ((5582, 5605), 'numpy.array', 'np.array', (['sample_labels'], {}), '(sample_labels)\n', (5590, 5605), True, 'import numpy as np\n'), ((4339, 4365), 'os.path.exists', 'os.path.exists', (['image_path'], {}), '(image_path)\n', (4353, 4365), False, 'import os\n')]
|
#!/usr/bin/python3
# LED Test
import RPi.GPIO as GPIO
import time
GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
LED = 13
GPIO.setup(LED, GPIO.OUT)
try:
print("LED is now flashing..")
print("Exit with CTRL+C")
while True:
GPIO.output(LED,1)
time.sleep(0.5)
GPIO.output(LED,0)
time.sleep(0.5)
except KeyboardInterrupt:
GPIO.cleanup()
print(" Bye Bye")
|
[
"RPi.GPIO.setmode",
"RPi.GPIO.cleanup",
"RPi.GPIO.setup",
"time.sleep",
"RPi.GPIO.output",
"RPi.GPIO.setwarnings"
] |
[((67, 89), 'RPi.GPIO.setmode', 'GPIO.setmode', (['GPIO.BCM'], {}), '(GPIO.BCM)\n', (79, 89), True, 'import RPi.GPIO as GPIO\n'), ((90, 113), 'RPi.GPIO.setwarnings', 'GPIO.setwarnings', (['(False)'], {}), '(False)\n', (106, 113), True, 'import RPi.GPIO as GPIO\n'), ((123, 148), 'RPi.GPIO.setup', 'GPIO.setup', (['LED', 'GPIO.OUT'], {}), '(LED, GPIO.OUT)\n', (133, 148), True, 'import RPi.GPIO as GPIO\n'), ((233, 252), 'RPi.GPIO.output', 'GPIO.output', (['LED', '(1)'], {}), '(LED, 1)\n', (244, 252), True, 'import RPi.GPIO as GPIO\n'), ((256, 271), 'time.sleep', 'time.sleep', (['(0.5)'], {}), '(0.5)\n', (266, 271), False, 'import time\n'), ((276, 295), 'RPi.GPIO.output', 'GPIO.output', (['LED', '(0)'], {}), '(LED, 0)\n', (287, 295), True, 'import RPi.GPIO as GPIO\n'), ((299, 314), 'time.sleep', 'time.sleep', (['(0.5)'], {}), '(0.5)\n', (309, 314), False, 'import time\n'), ((343, 357), 'RPi.GPIO.cleanup', 'GPIO.cleanup', ([], {}), '()\n', (355, 357), True, 'import RPi.GPIO as GPIO\n')]
|
from Script.import_emojis import Emojis
from Script.import_functions import create_embed, int_to_str
async def server_info(ctx):
nb_humans = 0
for members in ctx.guild.members:
if members.bot == 0:
nb_humans += 1
nb_bots = 0
for members in ctx.guild.members:
if members.bot == 1:
nb_bots += 1
emojis = ""
count = 0
for emoji in ctx.guild.emojis:
if count > 10:
emojis += "..."
break
emojis += f"{emoji} "
count += 1
admins = ""
count = 0
for member in ctx.guild.members:
if count > 10:
admins += "..."
break
if member.guild_permissions.administrator:
admins += f"{member.mention} "
count += 1
embed = create_embed(ctx.guild.name, f"{Emojis['Owner']} Owner : {ctx.guild.owner.mention}\n{Emojis['Calendar']} Created at : {ctx.guild.created_at.date().isoformat()}\n{Emojis['Members']} Humans : {int_to_str(nb_humans)}\n{Emojis['Bot']} Bots : {int_to_str(nb_bots)}\n{Emojis['Pin']} Region : {ctx.guild.region}\n{Emojis['Boost']} Boost level : {ctx.guild.premium_tier}/3\n{Emojis['Boost']} Boost number : {ctx.guild.premium_subscription_count}\n{Emojis['Emoji_ghost']} emojis : {emojis}\nAdministrators : {admins}", ctx.guild.me.color, "", ctx.guild.icon_url)
embed.set_thumbnail(url=ctx.guild.icon_url)
await ctx.send(embed=embed)
return
|
[
"Script.import_functions.int_to_str"
] |
[((990, 1011), 'Script.import_functions.int_to_str', 'int_to_str', (['nb_humans'], {}), '(nb_humans)\n', (1000, 1011), False, 'from Script.import_functions import create_embed, int_to_str\n'), ((1038, 1057), 'Script.import_functions.int_to_str', 'int_to_str', (['nb_bots'], {}), '(nb_bots)\n', (1048, 1057), False, 'from Script.import_functions import create_embed, int_to_str\n')]
|
# Copyright (c) 2011-2021, Camptocamp SA
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# The views and conclusions contained in the software and documentation are those
# of the authors and should not be interpreted as representing official policies,
# either expressed or implied, of the FreeBSD Project.
import logging
from typing import Dict, Optional, Set, cast
import pyramid.request
from pyramid.httpexceptions import HTTPBadRequest
from sqlalchemy.orm.exc import NoResultFound
from c2cgeoportal_commons.lib.url import Url, get_url2
from c2cgeoportal_commons.models import DBSession, main
from c2cgeoportal_geoportal.lib.caching import get_region
from c2cgeoportal_geoportal.views.proxy import Proxy
CACHE_REGION = get_region("std")
LOG = logging.getLogger(__name__)
class OGCProxy(Proxy):
"""
Proxy implementation that manly manage the ogcserver parameter.
Then load the corresponding OGCServer.
"""
def __init__(self, request: pyramid.request.Request, has_default_ogc_server: bool = False):
Proxy.__init__(self, request)
# params hold the parameters we"re going to send to backend
self.params = dict(self.request.params)
# reset possible value of role_id and user_id
if "role_id" in self.params:
del self.params["role_id"]
if "user_id" in self.params:
del self.params["user_id"]
self.lower_params = self._get_lower_params(self.params)
if not has_default_ogc_server and "ogcserver" not in self.params:
raise HTTPBadRequest("The querystring argument 'ogcserver' is required")
if "ogcserver" in self.params:
self.ogc_server = self._get_ogcserver_byname(self.params["ogcserver"])
@CACHE_REGION.cache_on_arguments() # type: ignore
def _get_ogcserver_byname(self, name: str) -> main.OGCServer: # pylint: disable=no-self-use
try:
result = DBSession.query(main.OGCServer).filter(main.OGCServer.name == name).one()
DBSession.expunge(result)
return cast(main.OGCServer, result)
except NoResultFound:
raise HTTPBadRequest( # pylint: disable=raise-missing-from
f"The OGC Server '{name}' does not exist (existing: "
f"{','.join([t[0] for t in DBSession.query(main.OGCServer.name).all()])})."
)
def _get_wms_url(self, errors: Set[str]) -> Optional[Url]:
ogc_server = self.ogc_server
url = get_url2(f"The OGC server '{ogc_server.name}'", ogc_server.url, self.request, errors)
if errors:
LOG.error("\n".join(errors))
return url
def _get_wfs_url(self, errors: Set[str]) -> Optional[Url]:
ogc_server = self.ogc_server
url = get_url2(
f"The OGC server (WFS) '{ogc_server.name}'",
ogc_server.url_wfs or ogc_server.url,
self.request,
errors,
)
if errors:
LOG.error("\n".join(errors))
return url
def get_headers(self) -> Dict[str, str]:
headers: Dict[str, str] = super().get_headers()
if self.ogc_server.type == main.OGCSERVER_TYPE_QGISSERVER:
headers["X-Qgis-Service-Url"] = self.request.current_route_url(
_query={"ogcserver": self.ogc_server.name}
)
return headers
|
[
"c2cgeoportal_commons.models.DBSession.query",
"typing.cast",
"c2cgeoportal_commons.models.DBSession.expunge",
"c2cgeoportal_geoportal.lib.caching.get_region",
"c2cgeoportal_commons.lib.url.get_url2",
"c2cgeoportal_geoportal.views.proxy.Proxy.__init__",
"pyramid.httpexceptions.HTTPBadRequest",
"logging.getLogger"
] |
[((1981, 1998), 'c2cgeoportal_geoportal.lib.caching.get_region', 'get_region', (['"""std"""'], {}), "('std')\n", (1991, 1998), False, 'from c2cgeoportal_geoportal.lib.caching import get_region\n'), ((2005, 2032), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (2022, 2032), False, 'import logging\n'), ((2291, 2320), 'c2cgeoportal_geoportal.views.proxy.Proxy.__init__', 'Proxy.__init__', (['self', 'request'], {}), '(self, request)\n', (2305, 2320), False, 'from c2cgeoportal_geoportal.views.proxy import Proxy\n'), ((3731, 3821), 'c2cgeoportal_commons.lib.url.get_url2', 'get_url2', (['f"""The OGC server \'{ogc_server.name}\'"""', 'ogc_server.url', 'self.request', 'errors'], {}), '(f"The OGC server \'{ogc_server.name}\'", ogc_server.url, self.\n request, errors)\n', (3739, 3821), False, 'from c2cgeoportal_commons.lib.url import Url, get_url2\n'), ((4011, 4128), 'c2cgeoportal_commons.lib.url.get_url2', 'get_url2', (['f"""The OGC server (WFS) \'{ogc_server.name}\'"""', '(ogc_server.url_wfs or ogc_server.url)', 'self.request', 'errors'], {}), '(f"The OGC server (WFS) \'{ogc_server.name}\'", ogc_server.url_wfs or\n ogc_server.url, self.request, errors)\n', (4019, 4128), False, 'from c2cgeoportal_commons.lib.url import Url, get_url2\n'), ((2802, 2868), 'pyramid.httpexceptions.HTTPBadRequest', 'HTTPBadRequest', (['"""The querystring argument \'ogcserver\' is required"""'], {}), '("The querystring argument \'ogcserver\' is required")\n', (2816, 2868), False, 'from pyramid.httpexceptions import HTTPBadRequest\n'), ((3264, 3289), 'c2cgeoportal_commons.models.DBSession.expunge', 'DBSession.expunge', (['result'], {}), '(result)\n', (3281, 3289), False, 'from c2cgeoportal_commons.models import DBSession, main\n'), ((3309, 3337), 'typing.cast', 'cast', (['main.OGCServer', 'result'], {}), '(main.OGCServer, result)\n', (3313, 3337), False, 'from typing import Dict, Optional, Set, cast\n'), ((3178, 3209), 'c2cgeoportal_commons.models.DBSession.query', 'DBSession.query', (['main.OGCServer'], {}), '(main.OGCServer)\n', (3193, 3209), False, 'from c2cgeoportal_commons.models import DBSession, main\n'), ((3553, 3589), 'c2cgeoportal_commons.models.DBSession.query', 'DBSession.query', (['main.OGCServer.name'], {}), '(main.OGCServer.name)\n', (3568, 3589), False, 'from c2cgeoportal_commons.models import DBSession, main\n')]
|
import numpy as np
from napari.utils import nbscreenshot
def test_nbscreenshot(viewer_factory):
"""Test taking a screenshot."""
view, viewer = viewer_factory()
np.random.seed(0)
data = np.random.random((10, 15))
viewer.add_image(data)
rich_display_object = nbscreenshot(viewer)
assert hasattr(rich_display_object, '_repr_png_')
# Trigger method that would run in jupyter notebook cell automatically
rich_display_object._repr_png_()
assert rich_display_object.image is not None
|
[
"numpy.random.random",
"numpy.random.seed",
"napari.utils.nbscreenshot"
] |
[((176, 193), 'numpy.random.seed', 'np.random.seed', (['(0)'], {}), '(0)\n', (190, 193), True, 'import numpy as np\n'), ((205, 231), 'numpy.random.random', 'np.random.random', (['(10, 15)'], {}), '((10, 15))\n', (221, 231), True, 'import numpy as np\n'), ((286, 306), 'napari.utils.nbscreenshot', 'nbscreenshot', (['viewer'], {}), '(viewer)\n', (298, 306), False, 'from napari.utils import nbscreenshot\n')]
|
import base64
import random
import time
from RGUtil.RGCodeUtil import RGResCode
def get_data_with_request(_request):
if _request.is_json:
return _request.json
return _request.values
# if _request.method == "POST":
# return _request.form
# elif _request.json:
# return _request.json
# return _request.args
def request_value(_request, key, default=None):
args = get_data_with_request(_request)
if key in args:
return args[key]
else:
return default
def request_ip(_request, default=None):
headers = _request.headers
if 'X-Real-Ip' in headers:
return headers['X-Real-Ip']
else:
return default
def form_res(code, data=None):
if code == 0:
code = RGResCode.not_existed if data is None else RGResCode.ok
if data is not None:
if not isinstance(data, dict) and not isinstance(data, list):
data = data.__dict__
res = {
'code': int(code),
'data': data
}
return res
else:
res = {
'code': int(code),
}
return res
def is_int_number(s):
try:
float(s)
return True
except ValueError:
pass
try:
import unicodedata
unicodedata.numeric(s)
return True
except (TypeError, ValueError):
pass
return False
def request_file_size(request):
re_files = request.files
size = 0
for file_key in re_files:
file = re_files[file_key]
file.seek(0)
size += len(file.read())
file.seek(0)
return size
def request_file_mine(request):
re_files = request.files
size = 0
for file_key in re_files:
file = re_files[file_key]
file.seek(0)
size += len(file.read())
file.seek(0)
return size
baseList = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghjklmnopqrstuvwxyz'
def encode(n, b=58):
"""
:param n: 压缩的数字
:param b: 进制 最大58
:return: 对应进制字符串
"""
result = ''
x = int(n)
while True:
x, y = divmod(x, b)
result = baseList[y] + result
if x <= 0:
break
return result
def decode(n, b=58):
"""
:param n: 数字压缩后的字符串
:param b: 对应的进制 最大58
:return: 原来的数字
"""
result = 0
length = len(n)
for index in range(length):
result += (baseList.index(n[index]) * pow(b, length - index - 1))
return result
def did_encode(dir_id, uid):
dir_id = int(dir_id) + 10
dir_id = str(dir_id)
count = 0
# if len(dir_id) < 8:
# count = 8 - len(dir_id)
# dir_id = ''.join(['0', '0', '0', '0', '0', '0', '0', '0'][:count]) + dir_id
count = str(count)
uid = encode(uid)
dir_id = encode(dir_id)
content = '{}{}.{}.{}'.format(dir_id, uid, count, len(str(uid)))
return safe_encode_b64(content, random_index=0)
def did_decode(content):
content = safe_decode_b64(content)
contents = content.split(sep='.')
uid_count = int(contents[-1])
count = int(contents[-2])
content = contents[0]
uid = content[-uid_count:]
dir_id = content[:-uid_count]
dir_id = dir_id[count:]
return int(decode(dir_id)) - 10, int(decode(uid))
def fid_encode(f_id, uid):
f_id = int(f_id) + 10
time_str = str((time.time_ns()//1000) % 10000000)
f_id = '{}{}'.format(f_id, time_str)
f_id = encode(int(f_id))
return safe_encode_b64('{}.{}.{}'.format(f_id, uid, len(time_str)))
def fid_decode(content):
content = safe_decode_b64(content)
contents = content.split(sep='.')
length = int(contents[-1])
uid = contents[-2]
f_id = str(decode(contents[0]))
f_id = f_id[0:-length]
return int(f_id) - 10, uid
def safe_encode_b64(content, random_index=None):
content = base64.urlsafe_b64encode(content.encode("utf-8"))
content = str(content, "utf-8")
del_count = 0
for i in range(len(content) - 1, -1, -1):
if content[i] == '=':
del_count += 1
content = content[:-1]
else:
break
if random_index is None:
index = random.randint(0, len(content) - 1)
else:
index = 0
return encode(index) + content[:index] + encode(del_count) + content[index:]
def safe_decode_b64(content):
index = decode(content[0])
content = content[1:]
del_count = decode(content[index])
content = content[:index] + content[index+1:]
for i in range(del_count):
content += '='
return str(base64.urlsafe_b64decode(content.encode("utf-8")), "utf-8")
def bytes_to_hex_string(bytes):
result = ''
for byte in bytes:
result += '%02X' % byte
return result
def hex_string_to_bytes(hex_string):
byte_array = bytearray()
for index in range(len(hex_string) // 2):
temp = hex_string[2 * index:2 * index + 2]
temp = bytes(temp, encoding='utf-8')
temp = int(temp, base=16)
byte_array.append(temp)
return byte_array
if __name__ == '__main__':
code = encode(1000, 58)
print('#58', code)
print('#10', decode(code, 58))
dir_id = 122134
uid = 9812312332
print('did', dir_id, 'user_id', uid)
code = did_encode(dir_id=dir_id, uid=uid)
print('did_encode', code)
did, user_id = did_decode(code)
print('did', did, 'user_id', user_id)
token = fid_encode(f_id=dir_id, uid=uid)
print('fid_encode', token)
did, user_id = fid_decode(token)
print('did', did, 'user_id', user_id)
|
[
"unicodedata.numeric",
"time.time_ns"
] |
[((1285, 1307), 'unicodedata.numeric', 'unicodedata.numeric', (['s'], {}), '(s)\n', (1304, 1307), False, 'import unicodedata\n'), ((3331, 3345), 'time.time_ns', 'time.time_ns', ([], {}), '()\n', (3343, 3345), False, 'import time\n')]
|
from ..files import ObjectReader
from ..streams import EndianBinaryWriter
from ..helpers import ImportHelper
from .. import files
from ..enums import FileType, ClassIDType
import os
from .. import environment
def save_ptr(obj, writer: EndianBinaryWriter):
if isinstance(obj, PPtr):
writer.write_int(obj.file_id)
else:
writer.write_int(0) # it's usually 0......
if obj._version < 14:
writer.write_int(obj.path_id)
else:
writer.write_long(obj.path_id)
cached_managers = dict()
class PPtr:
def __init__(self, reader: ObjectReader):
self._version = reader.version2
self.index = -2
self.file_id = reader.read_int()
self.path_id = reader.read_int() if self._version < 14 else reader.read_long()
self.assets_file = reader.assets_file
self._obj = None
def save(self, writer: EndianBinaryWriter):
save_ptr(self, writer)
def get_obj(self):
if self._obj != None:
return self._obj
manager = None
if self.file_id == 0:
manager = self.assets_file
elif self.file_id > 0 and self.file_id - 1 < len(self.assets_file.externals):
if self.index == -2:
external_name = self.assets_file.externals[self.file_id - 1].name
parent = self.assets_file.parent
if parent is not None:
if external_name in parent.files:
manager = parent.files[external_name]
elif external_name.upper() in parent.files:
manager = parent.files[external_name.upper()]
else:
while not isinstance(parent, environment.Environment):
parent = parent.parent
if parent.path:
path = parent.path
files = os.listdir(path)
if external_name in files:
parent.load_files([os.path.join(path, external_name)])
manager = parent.files[external_name]
else:
if external_name not in cached_managers:
typ, reader = ImportHelper.check_file_type(external_name)
if typ == FileType.AssetsFile:
cached_managers[external_name] = files.SerializedFile(reader)
if external_name in cached_managers:
manager = cached_managers[external_name]
if manager and self.path_id in manager.objects:
self._obj = manager.objects[self.path_id]
else:
self._obj = None
return self._obj
def __getattr__(self, key):
obj = self.get_obj()
if obj is None:
if key == "type":
return ClassIDType.UnknownType
raise AttributeError(key)
return getattr(obj, key)
def __repr__(self):
return "<%s %s>" % (self.__class__.__name__, self._obj.__class__.__repr__(self.get_obj()) if self.get_obj() else "Not Found")
def __bool__(self):
return True if self.get_obj() else False
|
[
"os.path.join",
"os.listdir"
] |
[((1922, 1938), 'os.listdir', 'os.listdir', (['path'], {}), '(path)\n', (1932, 1938), False, 'import os\n'), ((2045, 2078), 'os.path.join', 'os.path.join', (['path', 'external_name'], {}), '(path, external_name)\n', (2057, 2078), False, 'import os\n')]
|
import os
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from medpy.metric import binary
#use gpu if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class AE(nn.Module):
def __init__(self, latent_size=100):
super().__init__()
self.init_layers(latent_size)
self.apply(self.weight_init)
self.loss_function=self.Loss()
self.metrics=self.Metrics()
self.optimizer=torch.optim.Adam(self.parameters(),lr=2e-4,weight_decay=1e-5)
def init_layers(self,latent_size):
self.encoder = nn.Sequential(
nn.Conv2d(in_channels=4,out_channels=32,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=32,out_channels=32,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=32,out_channels=32,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=32,out_channels=32,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=32,out_channels=64,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=64),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=64,out_channels=64,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=64),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=64,out_channels=128,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=128),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=128,out_channels=64,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=64),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=64,out_channels=32,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.Conv2d(in_channels=32,out_channels=latent_size,kernel_size=4,stride=2,padding=1)
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(in_channels=latent_size,out_channels=32,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=32,out_channels=64,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=64),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=64,out_channels=128,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=128),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=128,out_channels=64,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=64),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=64,out_channels=64,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=64),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=64,out_channels=32,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=32,out_channels=32,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=32,out_channels=32,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=32,out_channels=32,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(.2),
nn.Dropout(0.5),
nn.ConvTranspose2d(in_channels=32,out_channels=4,kernel_size=4,stride=2,padding=1),
nn.Softmax(dim=1)
)
def weight_init(self,m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_uniform_(m.weight)
def forward(self, x):
latent = self.encoder(x)
reconstruction = self.decoder(latent)
return reconstruction
class Loss():
def __init__(self,call_id=0):
self.MSELoss=nn.MSELoss()
self.GDLoss=self.GDLoss()
class GDLoss:
def __call__(self,x,y):
tp=torch.sum(x*y,dim=(0,2,3))
fp=torch.sum(x*(1-y),dim=(0,2,3))
fn=torch.sum((1-x)*y,dim=(0,2,3))
nominator=2*tp+1e-05
denominator=2*tp+fp+fn+1e-05
dice_score=-(nominator/(denominator+1e-8))[1:].mean()
return dice_score
def __call__(self,prediction,target,epoch=None,validation=False):
contributes={}
contributes["MSELoss"]=self.MSELoss(prediction,target)
contributes["GDLoss"]=self.GDLoss(prediction,target)
contributes["Total"]=contributes["MSELoss"]+contributes["GDLoss"]
if validation:
return {k:v.item() for k,v in contributes.items()}
return contributes["Total"]
class Metrics():
def __init__(self):
self.DC=self.DC()
self.HD=self.HD()
class DC:
def __call__(self,prediction,target):
try:
return binary.dc(prediction,target)
except Exception:
return 0
class HD:
def __call__(self,prediction,target):
try:
return binary.hd(prediction,target)
except Exception:
return np.nan
def __call__(self,prediction,target,validation=False):
metrics={}
for c,key in enumerate(["BK_","RV_","MYO_","LV_"]):
ref=np.copy(target)
pred=np.copy(prediction)
ref=np.where(ref!=c,0,1)
pred=np.where(pred!=c,0,1)
metrics[key+"dc"]=self.DC(pred,ref)
metrics[key+"hd"]=self.HD(pred,ref)
return metrics
def training_routine(self,epochs,train_loader,val_loader,ckpt_folder):
if not os.path.isdir(ckpt_folder):
os.mkdir(ckpt_folder)
history = []
best_acc = None
for epoch in epochs:
#training
self.train()
for patient in train_loader:
for batch in patient:
batch=batch.to(device)
self.optimizer.zero_grad()
reconstruction=self.forward(batch)
loss=self.loss_function(reconstruction,batch,epoch)
loss.backward()
self.optimizer.step()
#validation
self.eval()
with torch.no_grad():
result = self.evaluation_routine(val_loader)
#checkpoint
if(best_acc==None or result['Total']<best_acc or epoch%10==0):
ckpt=os.path.join(ckpt_folder,"{:03d}.pth".format(epoch))
if(best_acc==None or result['Total']<best_acc): best_acc=result['Total']; ckpt=ckpt.split(".pth")[0]+"_best.pth"
torch.save({"AE": self.state_dict(),"AE_optim": self.optimizer.state_dict(),"epoch": epoch},ckpt)
#report
self.epoch_end(epoch, result)
history.append(result)
return history
def evaluation_routine(self,val_loader):
epoch_summary={}
for patient in val_loader:
gt=[];reconstruction=[]
#loss terms
for batch in patient:
batch={"gt":batch.to(device)}
batch["reconstruction"]=self.forward(batch["gt"])
gt=torch.cat([gt,batch["gt"]],dim=0) if len(gt)>0 else batch["gt"]
reconstruction=torch.cat([reconstruction,batch["reconstruction"]],dim=0) if len(reconstruction)>0 else batch["reconstruction"]
for k,v in self.loss_function(batch["reconstruction"],batch["gt"],validation=True).items():
if k not in epoch_summary.keys(): epoch_summary[k]=[]
epoch_summary[k].append(v)
#validation metrics
gt=np.argmax(gt.cpu().numpy(),axis=1)
gt={"ED":gt[:len(gt)//2],"ES":gt[len(gt)//2:]}
reconstruction=np.argmax(reconstruction.cpu().numpy(),axis=1)
reconstruction={"ED":reconstruction[:len(reconstruction)//2],"ES":reconstruction[len(reconstruction)//2:]}
for phase in ["ED","ES"]:
for k,v in self.metrics(reconstruction[phase],gt[phase]).items():
if k not in epoch_summary.keys(): epoch_summary[k]=[]
epoch_summary[k].append(v)
epoch_summary={k:np.mean(v) for k,v in epoch_summary.items()}
return epoch_summary
def epoch_end(self,epoch,result):
print("\033[1mEpoch [{}]\033[0m".format(epoch))
header,row="",""
for k,v in result.items():
header+="{:.6}\t".format(k);row+="{:.6}\t".format("{:.4f}".format(v))
print(header);print(row)
def plot_history(history):
losses = [x['Total'] for x in history]
plt.plot(losses, '-x', label="loss")
plt.xlabel('epoch')
plt.ylabel('loss')
plt.legend()
plt.title('Losses vs. No. of epochs')
plt.grid()
plt.show()
|
[
"matplotlib.pyplot.title",
"torch.nn.Dropout",
"os.mkdir",
"torch.cat",
"numpy.mean",
"torch.nn.Softmax",
"torch.no_grad",
"torch.nn.MSELoss",
"numpy.copy",
"medpy.metric.binary.dc",
"matplotlib.pyplot.show",
"matplotlib.pyplot.legend",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d",
"torch.cuda.is_available",
"matplotlib.pyplot.ylabel",
"torch.nn.LeakyReLU",
"torch.nn.init.kaiming_uniform_",
"matplotlib.pyplot.grid",
"torch.sum",
"torch.nn.ConvTranspose2d",
"matplotlib.pyplot.plot",
"medpy.metric.binary.hd",
"os.path.isdir",
"numpy.where",
"matplotlib.pyplot.xlabel"
] |
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|
# -*- coding: utf-8 -*-
# Copyright (c) 2010-2016, MIT Probabilistic Computing Project
#
# 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.
import StringIO
import apsw
import pytest
import struct
import bayeslite
import bayeslite.ast as ast
import bayeslite.compiler as compiler
import bayeslite.core as core
import bayeslite.guess as guess
import bayeslite.backends.troll_rng as troll
import bayeslite.parse as parse
from bayeslite.exception import BQLError
from bayeslite.math_util import relerr
from bayeslite.backends.cgpm_backend import CGPM_Backend
from bayeslite.util import cursor_value
import test_core
import test_csv
from stochastic import stochastic
def bql2sql(string, setup=None):
with bayeslite.bayesdb_open(':memory:') as bdb:
test_core.t1_schema(bdb)
test_core.t1_data(bdb)
bdb.execute('''
create population p1 for t1 (
id ignore;
label nominal;
age numerical;
weight numerical
)
''')
if setup is not None:
setup(bdb)
phrases = parse.parse_bql_string(string)
out = compiler.Output(0, {}, ())
for phrase in phrases:
assert ast.is_query(phrase)
compiler.compile_query(bdb, phrase, out)
out.write(';')
return out.getvalue()
# XXX Kludgey mess. Please reorganize.
def bql2sqlparam(string):
with bayeslite.bayesdb_open(':memory:') as bdb:
test_core.t1_schema(bdb)
test_core.t1_data(bdb)
bdb.execute('''
create population p1 for t1 (
id ignore;
label nominal;
age numerical;
weight numerical
)
''')
phrases = parse.parse_bql_string(string)
out0 = StringIO.StringIO()
for phrase in phrases:
out = None
if isinstance(phrase, ast.Parametrized):
bindings = (None,) * phrase.n_numpar
out = compiler.Output(phrase.n_numpar, phrase.nampar_map,
bindings)
phrase = phrase.phrase
else:
out = StringIO.StringIO()
assert ast.is_query(phrase)
compiler.compile_query(bdb, phrase, out)
# XXX Do something about the parameters.
out0.write(out.getvalue())
out0.write(';')
return out0.getvalue()
def bql_execute(bdb, string, bindings=()):
return map(tuple, bdb.execute(string, bindings))
def empty(cursor):
assert cursor is not None
assert cursor.description is not None
assert len(cursor.description) == 0
with pytest.raises(StopIteration):
cursor.next()
def test_trivial_population():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
# XXX if (not) exists
bdb.execute('''
create population p for t (
guess stattypes of (*);
age numerical
)
''')
bdb.execute('drop population p')
def test_population_invalid_numerical():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
with pytest.raises(BQLError):
bdb.execute('''
create population p for t (
guess stattypes of (*);
gender numerical
)
''')
def test_population_invalid_numerical_alterpop_addvar():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
bdb.execute('''
create population p for t (
guess stattypes of (*);
ignore gender
)
''')
with pytest.raises(BQLError):
bdb.execute('alter population p add variable gender numerical')
bdb.execute('drop population p')
def test_population_invalid_numerical_alterpop_stattype():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
bdb.execute('''
create population p for t (
guess stattypes of (*);
gender nominal
)
''')
with pytest.raises(BQLError):
bdb.execute('''
alter population p set stattype of gender to numerical
''')
bdb.execute('drop population p')
def test_similarity_identity():
with test_core.t1() as (bdb, population_id, _generator_id):
bdb.execute('initialize 6 models for p1_cc;')
rowids = bdb.sql_execute('select rowid from t1')
for rowid in rowids:
c = bdb.execute('''
estimate similarity of (rowid=?) to (rowid=?)
in the context of age by p1
''', (rowid[0], rowid[0])).fetchall()
assert len(c) == 1
assert c[0][0] == 1
def test_predictive_relevance():
assert bql2sql('''
estimate predictive relevance
of (label = 'Uganda')
to existing rows (rowid < 4)
and hypothetical rows with values (
("age" = 82, "weight" = 14),
("age" = 74, label = 'Europe', "weight" = 7)
)
in the context of "weight"
by p1
''') == \
'SELECT bql_row_predictive_relevance(1, NULL, NULL, ' \
'(SELECT _rowid_ FROM "t1" WHERE ("label" = \'Uganda\')), '\
'\'[1, 2, 3]\', 3, '\
'2, 82, 3, 14, NULL, 2, 74, 1, \'Europe\', 3, 7, NULL);'
assert bql2sql('''
estimate predictive relevance
of (label = 'mumble')
to existing rows (label = 'frotz' or age <= 4)
in the context of "label"
by p1
''') == \
'SELECT bql_row_predictive_relevance(1, NULL, NULL, ' \
'(SELECT _rowid_ FROM "t1" WHERE ("label" = \'mumble\')), '\
'\'[5, 8]\', 1);'
assert bql2sql('''
estimate label,
predictive relevance
to hypothetical rows with values (
("age" = 82, "weight" = 14),
("age" = 74, label = 'hunf', "weight" = 7)
)
in the context of "age",
_rowid_ + 1
from p1
''') == \
'SELECT "label", bql_row_predictive_relevance(1, NULL, NULL, _rowid_, '\
'\'[]\', 2, 2, 82, 3, 14, NULL, 2, 74, 1, \'hunf\', 3, 7, NULL), '\
'("_rowid_" + 1) FROM "t1";'
# No matching rows should still compile.
assert bql2sql('''
estimate label,
predictive relevance to existing rows (rowid < 0)
in the context of "age"
from p1
''') == \
'SELECT "label", bql_row_predictive_relevance(1, NULL, NULL, _rowid_, '\
'\'[]\', 2) FROM "t1";'
# When using `BY`, require OF to be specified.
with pytest.raises(BQLError):
bql2sql('''
estimate predictive relevance
to hypothetical rows with values (
("age" = 82, "weight" = 14),
("age" = 74, label = 'Europe', "weight" = 7)
)
in the context of "age"
by p1
''')
# When using `FROM`, require OF to be unspecified.
with pytest.raises(BQLError):
bql2sql('''
estimate predictive relevance
of (name = 'mansour')
to hypothetical rows with values (
("age" = 82, "weight" = 14)
)
in the context of "age"
from p1
''')
assert bql2sql('''
estimate label from p1
where
(predictive relevance to existing rows (label = 'quux' and age < 5)
in the context of "weight") > 1
order by
predictive relevance
to hypothetical rows with values ((label='zot'))
in the context of "age"
''') == \
'SELECT "label" FROM "t1" WHERE '\
'(bql_row_predictive_relevance(1, NULL, NULL, '\
'_rowid_, \'[5]\', 3) > 1) '\
'ORDER BY bql_row_predictive_relevance(1, NULL, NULL, '\
'_rowid_, \'[]\', 2, 1, \'zot\', NULL);'
@stochastic(max_runs=2, min_passes=1)
def test_conditional_probability(seed):
with test_core.t1(seed=seed) as (bdb, _population_id, _generator_id):
bdb.execute('drop generator p1_cc')
bdb.execute('drop population p1')
bdb.execute('''
create population p1 for t1 (
ignore id, label;
set stattype of age to numerical;
set stattype of weight to numerical
)
''')
bdb.execute('''
create generator p1_cond_prob_cc for p1;
''')
bdb.execute('initialize 1 model for p1_cond_prob_cc')
bdb.execute('alter generator p1_cond_prob_cc '
'ensure variables * dependent')
bdb.execute('analyze p1_cond_prob_cc for 1 iteration')
q0 = 'estimate probability density of age = 8 by p1'
q1 = 'estimate probability density of age = 8 given () by p1'
age_is_8 = bdb.execute(q0).fetchvalue()
assert age_is_8 == bdb.execute(q1).fetchvalue()
q2 = 'estimate probability density of age = 8 given (weight = 16)' \
' by p1'
age_is_8_given_weight_is_16 = bdb.execute(q2).fetchvalue()
assert age_is_8 < age_is_8_given_weight_is_16
probs = bdb.execute(
'estimate probability density of value 8 given (weight = 16)'
' from columns of p1 where v.name != \'weight\'').fetchall()
assert [(age_is_8_given_weight_is_16,)] == probs
@stochastic(max_runs=2, min_passes=1)
def test_joint_probability(seed):
with test_core.t1(seed=seed) as (bdb, _population_id, _generator_id):
bdb.execute('initialize 10 models for p1_cc')
bdb.execute('analyze p1_cc for 10 iterations')
q0 = 'estimate probability density of age = 8 by p1'
q1 = 'estimate probability density of (age = 8) by p1'
assert bdb.execute(q0).fetchvalue() == bdb.execute(q1).fetchvalue()
q1 = 'estimate probability density of (age = 8) given () by p1'
assert bdb.execute(q0).fetchvalue() == bdb.execute(q1).fetchvalue()
q2 = 'estimate probability density of age = 8 given (weight = 16)' \
' by p1'
assert bdb.execute(q0).fetchvalue() < bdb.execute(q2).fetchvalue()
q0 = 'estimate probability density of age = 8 by p1'
q1 = 'estimate probability density of (age = 8, weight = 16) by p1'
assert bdb.execute(q1).fetchvalue() < bdb.execute(q0).fetchvalue()
q2 = 'estimate probability density of (age = 8, weight = 16)' \
" given (label = 'mumble') by p1"
assert bdb.execute(q1).fetchvalue() < bdb.execute(q2).fetchvalue()
def test_badbql():
with test_core.t1() as (bdb, _population_id, _generator_id):
with pytest.raises(ValueError):
bdb.execute('')
with pytest.raises(ValueError):
bdb.execute(';')
with pytest.raises(ValueError):
bdb.execute('select 0; select 1')
def test_select_trivial():
assert bql2sql('select null;') == 'SELECT NULL;'
assert bql2sql("select 'x';") == "SELECT 'x';"
assert bql2sql("select 'x''y';") == "SELECT 'x''y';"
assert bql2sql('select "x";') == 'SELECT "x";'
assert bql2sql('select "x""y";') == 'SELECT "x""y";'
assert bql2sql('select 0;') == 'SELECT 0;'
assert bql2sql('select 0.;') == 'SELECT 0.0;'
assert bql2sql('select .0;') == 'SELECT 0.0;'
assert bql2sql('select 0.0;') == 'SELECT 0.0;'
assert bql2sql('select 1e0;') == 'SELECT 1.0;'
assert bql2sql('select 1e+1;') == 'SELECT 10.0;'
assert bql2sql('select 1e-1;') == 'SELECT 0.1;'
assert bql2sql('select -1e+1;') == 'SELECT (- 10.0);'
assert bql2sql('select +1e-1;') == 'SELECT (+ 0.1);'
assert bql2sql('select SQRT(1-EXP(-2*value)) FROM bm_mi;') == \
'SELECT "SQRT"((1 - "EXP"(((- 2) * "value")))) FROM "bm_mi";'
assert bql2sql('select .1e0;') == 'SELECT 0.1;'
assert bql2sql('select 1.e10;') == 'SELECT 10000000000.0;'
assert bql2sql('select all 0;') == 'SELECT 0;'
assert bql2sql('select distinct 0;') == 'SELECT DISTINCT 0;'
assert bql2sql('select 0 as z;') == 'SELECT 0 AS "z";'
assert bql2sql('select * from t;') == 'SELECT * FROM "t";'
assert bql2sql('select t.* from t;') == 'SELECT "t".* FROM "t";'
assert bql2sql('select c from t;') == 'SELECT "c" FROM "t";'
assert bql2sql('select c as d from t;') == 'SELECT "c" AS "d" FROM "t";'
assert bql2sql('select t.c as d from t;') == \
'SELECT "t"."c" AS "d" FROM "t";'
assert bql2sql('select t.c as d, p as q, x from t;') == \
'SELECT "t"."c" AS "d", "p" AS "q", "x" FROM "t";'
assert bql2sql('select * from t, u;') == 'SELECT * FROM "t", "u";'
assert bql2sql('select * from t as u;') == 'SELECT * FROM "t" AS "u";'
assert bql2sql('select * from (select 0);') == 'SELECT * FROM (SELECT 0);'
assert bql2sql('select t.c from (select d as c from u) as t;') == \
'SELECT "t"."c" FROM (SELECT "d" AS "c" FROM "u") AS "t";'
assert bql2sql('select * where x;') == 'SELECT * WHERE "x";'
assert bql2sql('select * from t where x;') == \
'SELECT * FROM "t" WHERE "x";'
assert bql2sql('select * group by x;') == 'SELECT * GROUP BY "x";'
assert bql2sql('select * from t where x group by y;') == \
'SELECT * FROM "t" WHERE "x" GROUP BY "y";'
assert bql2sql('select * from t where x group by y, z;') == \
'SELECT * FROM "t" WHERE "x" GROUP BY "y", "z";'
assert bql2sql('select * from t where x group by y having sum(z) < 1') == \
'SELECT * FROM "t" WHERE "x" GROUP BY "y" HAVING ("sum"("z") < 1);'
assert bql2sql('select * order by x;') == 'SELECT * ORDER BY "x";'
assert bql2sql('select * order by x asc;') == 'SELECT * ORDER BY "x";'
assert bql2sql('select * order by x desc;') == \
'SELECT * ORDER BY "x" DESC;'
assert bql2sql('select * order by x, y;') == 'SELECT * ORDER BY "x", "y";'
assert bql2sql('select * order by x desc, y;') == \
'SELECT * ORDER BY "x" DESC, "y";'
assert bql2sql('select * order by x, y asc;') == \
'SELECT * ORDER BY "x", "y";'
assert bql2sql('select * limit 32;') == 'SELECT * LIMIT 32;'
assert bql2sql('select * limit 32 offset 16;') == \
'SELECT * LIMIT 32 OFFSET 16;'
assert bql2sql('select * limit 16, 32;') == 'SELECT * LIMIT 32 OFFSET 16;'
assert bql2sql('select (select0);') == 'SELECT "select0";'
assert bql2sql('select (select 0);') == 'SELECT (SELECT 0);'
assert bql2sql('select f(f(), f(x), y);') == \
'SELECT "f"("f"(), "f"("x"), "y");'
assert bql2sql('select a and b or c or not d is e is not f like j;') == \
'SELECT ((("a" AND "b") OR "c") OR' \
+ ' (NOT ((("d" IS "e") IS NOT "f") LIKE "j")));'
assert bql2sql('select a like b not like c like d escape e;') == \
'SELECT ((("a" LIKE "b") NOT LIKE "c") LIKE "d" ESCAPE "e");'
assert bql2sql('select a like b escape c glob d not glob e;') == \
'SELECT ((("a" LIKE "b" ESCAPE "c") GLOB "d") NOT GLOB "e");'
assert bql2sql('select a not glob b glob c escape d;') == \
'SELECT (("a" NOT GLOB "b") GLOB "c" ESCAPE "d");'
assert bql2sql('select a glob b escape c regexp e not regexp f;') == \
'SELECT ((("a" GLOB "b" ESCAPE "c") REGEXP "e") NOT REGEXP "f");'
assert bql2sql('select a not regexp b regexp c escape d;') == \
'SELECT (("a" NOT REGEXP "b") REGEXP "c" ESCAPE "d");'
assert bql2sql('select a regexp b escape c not regexp d escape e;') == \
'SELECT (("a" REGEXP "b" ESCAPE "c") NOT REGEXP "d" ESCAPE "e");'
assert bql2sql('select a not regexp b escape c match e not match f;') == \
'SELECT ((("a" NOT REGEXP "b" ESCAPE "c") MATCH "e") NOT MATCH "f");'
assert bql2sql('select a not match b match c escape d;') == \
'SELECT (("a" NOT MATCH "b") MATCH "c" ESCAPE "d");'
assert bql2sql('select a match b escape c not match d escape e;') == \
'SELECT (("a" MATCH "b" ESCAPE "c") NOT MATCH "d" ESCAPE "e");'
assert bql2sql('select a not match b escape c between d and e;') == \
'SELECT (("a" NOT MATCH "b" ESCAPE "c") BETWEEN "d" AND "e");'
assert bql2sql('select a between b and c and d;') == \
'SELECT (("a" BETWEEN "b" AND "c") AND "d");'
assert bql2sql('select a like b like c escape d between e and f;') == \
'SELECT ((("a" LIKE "b") LIKE "c" ESCAPE "d") BETWEEN "e" AND "f");'
assert bql2sql('select a between b and c not between d and e;') == \
'SELECT (("a" BETWEEN "b" AND "c") NOT BETWEEN "d" AND "e");'
assert bql2sql('select a not between b and c in (select f);') == \
'SELECT (("a" NOT BETWEEN "b" AND "c") IN (SELECT "f"));'
assert bql2sql('select a in (select b) and c not in (select d);') == \
'SELECT (("a" IN (SELECT "b")) AND ("c" NOT IN (SELECT "d")));'
assert bql2sql("select a in (1 + 2, '3') and b not in (select c);") == \
'SELECT (("a" IN ((1 + 2), \'3\')) AND ("b" NOT IN (SELECT "c")));'
assert bql2sql('select a in (select b) isnull notnull!=c<>d<e<=f>g;') == \
'SELECT ((((("a" IN (SELECT "b")) ISNULL) NOTNULL) != "c") !=' \
+ ' ((("d" < "e") <= "f") > "g"));'
assert bql2sql('select a>b>=c<<d>>e&f|g+h-i*j/k;') == \
'SELECT (("a" > "b") >= (((("c" << "d") >> "e") & "f") |' \
+ ' (("g" + "h") - (("i" * "j") / "k"))));'
assert bql2sql('select a/b%c||~~d collate e collate\'f\'||1;') == \
'SELECT (("a" / "b") % (("c" || (((~ (~ "d")) COLLATE "e")' \
+ ' COLLATE "f")) || 1));'
assert bql2sql('select cast(f(x) as binary blob);') == \
'SELECT CAST("f"("x") AS "binary" "blob");'
assert bql2sql('select cast(42 as varint(73));') == \
'SELECT CAST(42 AS "varint"(73));'
assert bql2sql('select cast(f(x, y, z) as varchar(12 ,34));') == \
'SELECT CAST("f"("x", "y", "z") AS "varchar"(12, 34));'
assert bql2sql('select exists (select a) and not exists (select b);') == \
'SELECT (EXISTS (SELECT "a") AND (NOT EXISTS (SELECT "b")));'
assert bql2sql('select case when a - b then c else d end from t;') == \
'SELECT CASE WHEN ("a" - "b") THEN "c" ELSE "d" END FROM "t";'
assert bql2sql('select case f(a) when b + c then d else e end from t;') \
== \
'SELECT CASE "f"("a") WHEN ("b" + "c") THEN "d" ELSE "e" END FROM "t";'
def test_estimate_bql():
# PREDICTIVE PROBABILITY
assert bql2sql('estimate predictive probability of weight from p1;') == \
'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[3]\', \'[]\')' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of (age, weight) '
'from p1;') == \
'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[2, 3]\', \'[]\')' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of (age, weight) given '
'(label) from p1;') == \
'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[2, 3]\', \'[1]\')' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of (*) from p1;') == \
'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[1, 2, 3]\', \'[]\')' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of (*) given (age, weight) '
'from p1;') == \
'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[1]\', \'[2, 3]\')' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of age given (*) '
'from p1;') == \
'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[2]\', \'[1, 3]\')' \
' FROM "t1";'
assert bql2sql('estimate label, predictive probability of weight'
' from p1;') \
== \
'SELECT "label", ' \
'bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[3]\', \'[]\')' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of weight, label'
' from p1;') \
== \
'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\
'\'[3]\', \'[]\'),' \
' "label"' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of weight + 1'
' from p1;') == \
'SELECT (bql_row_column_predictive_probability(1, NULL, NULL, '\
'_rowid_, \'[3]\', \'[]\') + 1)' \
' FROM "t1";'
assert bql2sql('estimate predictive probability of weight given (*) + 1'
' from p1;') == \
'SELECT (bql_row_column_predictive_probability(1, NULL, NULL, '\
'_rowid_, \'[3]\', \'[1, 2]\') + 1)' \
' FROM "t1";'
# PREDICTIVE PROBABILITY parse and compilation errors.
with pytest.raises(parse.BQLParseError):
# Need a table.
bql2sql('estimate predictive probability of weight;')
with pytest.raises(parse.BQLParseError):
# Need at most one generator.
bql2sql('estimate predictive probability of weight'
' from p1, p1;')
with pytest.raises(parse.BQLParseError):
# Need a generator name, not a subquery.
bql2sql('estimate predictive probability of weight'
' from (select 0);')
with pytest.raises(parse.BQLParseError):
# Need a column.
bql2sql('estimate predictive probability from p1;')
with pytest.raises(bayeslite.BQLError):
# Using (*) in both targets and constraints.
bql2sql('estimate predictive probability of (*) given (*) from p1;')
with pytest.raises(bayeslite.BQLError):
# Using (weight, *) in targets.
bql2sql('estimate predictive probability of (weight, *) given (age) '
'from p1;')
with pytest.raises(bayeslite.BQLError):
# Using (age, *) in constraints.
bql2sql('estimate predictive probability of weight given (*, age) '
'from p1;')
with pytest.raises(bayeslite.BQLError):
# Using duplicate column age.
bql2sql('estimate predictive probability of age given (weight, age) '
'from p1;')
# PROBABILITY DENISTY.
assert bql2sql('estimate probability density of weight = 20 from p1;') == \
'SELECT bql_pdf_joint(1, NULL, NULL, 3, 20) FROM "t1";'
assert bql2sql('estimate probability density of weight = 20'
' given (age = 8)'
' from p1;') == \
'SELECT bql_pdf_joint(1, NULL, NULL, 3, 20, NULL, 2, 8) FROM "t1";'
assert bql2sql('estimate probability density of (weight = 20, age = 8)'
' from p1;') == \
'SELECT bql_pdf_joint(1, NULL, NULL, 3, 20, 2, 8) FROM "t1";'
assert bql2sql('estimate probability density of (weight = 20, age = 8)'
" given (label = 'mumble') from p1;") == \
"SELECT bql_pdf_joint(1, NULL, NULL, 3, 20, 2, 8, NULL, 1, 'mumble')" \
' FROM "t1";'
assert bql2sql('estimate probability density of weight = (c + 1)'
' from p1;') == \
'SELECT bql_pdf_joint(1, NULL, NULL, 3, ("c" + 1)) FROM "t1";'
assert bql2sql('estimate probability density of weight = f(c)'
' from p1;') == \
'SELECT bql_pdf_joint(1, NULL, NULL, 3, "f"("c")) FROM "t1";'
assert bql2sql('estimate similarity to (rowid = 5) '
'in the context of weight from p1;') == \
'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' \
' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 3) FROM "t1";'
assert bql2sql(
'estimate similarity of (rowid = 12) to (rowid = 5) '
'in the context of weight from p1;') == \
'SELECT bql_row_similarity(1, NULL, NULL,' \
' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 12)),' \
' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 3) FROM "t1";'
assert bql2sql('estimate similarity to (rowid = 5) in the context of age'
' from p1') == \
'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' \
' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 2) FROM "t1";'
assert bql2sql(
'estimate similarity of (rowid = 5) to (height = 7 and age < 10)'
' in the context of weight from p1;') == \
'SELECT bql_row_similarity(1, NULL, NULL,' \
' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)),' \
' (SELECT _rowid_ FROM "t1" WHERE (("height" = 7) AND ("age" < 10))),' \
' 3) FROM "t1";'
with pytest.raises(bayeslite.BQLError):
# Cannot use all variables for similarity.
bql2sql(
'estimate similarity to (rowid = 5) in the context of * from p1;')
assert bql2sql('estimate similarity to (rowid = 5)'
' in the context of age from p1;') == \
'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' \
' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 2) FROM "t1";'
assert bql2sql('estimate dependence probability of age with weight'
' from p1;') == \
'SELECT bql_column_dependence_probability(1, NULL, NULL, 2, 3) '\
'FROM "t1";'
with pytest.raises(bayeslite.BQLError):
# Need both rows fixed.
bql2sql('estimate similarity to (rowid=2) in the context of r by p1')
with pytest.raises(bayeslite.BQLError):
# Need both rows fixed.
bql2sql('estimate similarity in the context of r within p1')
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate dependence probability with age from p1;')
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate dependence probability from p1;')
assert bql2sql('estimate mutual information of age with weight' +
' from p1;') == \
'SELECT bql_column_mutual_information('\
'1, NULL, NULL, \'[2]\', \'[3]\', NULL)'\
' FROM "t1";'
assert bql2sql('estimate mutual information of age with weight' +
' using 42 samples from p1;') == \
'SELECT bql_column_mutual_information('\
'1, NULL, NULL, \'[2]\', \'[3]\', 42)'\
' FROM "t1";'
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate mutual information with age from p1;')
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate mutual information from p1;')
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate mutual information with age using 42 samples'
' from p1;')
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate mutual information using 42 samples from p1;')
# XXX Should be SELECT, not ESTIMATE, here?
assert bql2sql('estimate correlation of age with weight from p1;') == \
'SELECT bql_column_correlation(1, NULL, NULL, 2, 3) FROM "t1";'
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate correlation with age from p1;')
with pytest.raises(bayeslite.BQLError):
# Need both columns fixed.
bql2sql('estimate correlation from p1;')
with pytest.raises(BQLError):
# Variable must exist.
bql2sql('estimate correlation with agee from variables of p1')
def test_predict_outside_infer():
with pytest.raises(bayeslite.BQLError):
# No PREDICT outside INFER.
bql2sql('estimate predict age with confidence 0.9 from p1;')
def test_infer_explicit_predict_confidence():
assert bql2sql('infer explicit predict age with confidence 0.9'
' from p1;') == \
'SELECT bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL) FROM "t1";'
def test_infer_explicit_predict_confidence_nsamples():
assert bql2sql('infer explicit'
' predict age with confidence 0.9 using 42 samples'
' from p1;') == \
'SELECT bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, 42) FROM "t1";'
def test_infer_explicit_verbatim_and_predict_confidence():
assert bql2sql('infer explicit rowid, age,'
' predict age confidence age_conf from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age",' \
' bql_json_get(c2, \'confidence\') AS "age_conf"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \
' AS c2 FROM "t1");'
def test_infer_explicit_verbatim_and_predict_noconfidence():
assert bql2sql('infer explicit rowid, age,'
' predict age from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \
' AS c2 FROM "t1");'
def test_infer_explicit_verbatim_and_predict_confidence_nsamples():
assert bql2sql('infer explicit rowid, age,'
' predict age confidence age_conf using 42 samples from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age",' \
' bql_json_get(c2, \'confidence\') AS "age_conf"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 42)' \
' AS c2 FROM "t1");'
def test_infer_explicit_verbatim_and_predict_noconfidence_nsamples():
assert bql2sql('infer explicit rowid, age,'
' predict age using 42 samples from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 42)' \
' AS c2 FROM "t1");'
def test_infer_explicit_verbatim_and_predict_confidence_as():
assert bql2sql('infer explicit rowid, age,'
' predict age as age_inf confidence age_conf from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age_inf",' \
' bql_json_get(c2, \'confidence\') AS "age_conf"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \
' AS c2 FROM "t1");'
def test_infer_explicit_verbatim_and_predict_noconfidence_as():
assert bql2sql('infer explicit rowid, age,'
' predict age as age_inf from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age_inf"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \
' AS c2 FROM "t1");'
def test_infer_explicit_verbatim_and_predict_confidence_as_nsamples():
assert bql2sql('infer explicit rowid, age,'
' predict age as age_inf confidence age_conf using 87 samples'
' from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age_inf",' \
' bql_json_get(c2, \'confidence\') AS "age_conf"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 87)' \
' AS c2 FROM "t1");'
def test_infer_explicit_verbatim_and_predict_noconfidence_as_nsamples():
assert bql2sql('infer explicit rowid, age,'
' predict age as age_inf using 87 samples'
' from p1') == \
'SELECT c0 AS "rowid", c1 AS "age",' \
' bql_json_get(c2, \'value\') AS "age_inf"' \
' FROM (SELECT "rowid" AS c0, "age" AS c1,' \
' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 87)' \
' AS c2 FROM "t1");'
def test_infer_auto():
assert bql2sql('infer rowid, age, weight from p1') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, NULL))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0, NULL))' \
' AS "weight"' \
' FROM "t1";'
def test_infer_auto_nsamples():
assert bql2sql('infer rowid, age, weight using (1+2) samples from p1') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, (1 + 2)))' \
' AS "age",' \
' "IFNULL"("weight",'\
' bql_predict(1, NULL, NULL, _rowid_, 3, 0, (1 + 2)))' \
' AS "weight"' \
' FROM "t1";'
def test_infer_auto_with_confidence():
assert bql2sql('infer rowid, age, weight with confidence 0.9 from p1') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL))' \
' AS "age",' \
' "IFNULL"("weight",'\
' bql_predict(1, NULL, NULL, _rowid_, 3, 0.9, NULL))' \
' AS "weight"' \
' FROM "t1";'
def test_infer_auto_with_confidence_nsamples():
assert bql2sql('infer rowid, age, weight with confidence 0.9'
' using sqrt(2) samples'
' from p1') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9,' \
' "sqrt"(2)))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9,' \
' "sqrt"(2)))' \
' AS "weight"' \
' FROM "t1";'
def test_infer_auto_with_confidence_where():
assert bql2sql('infer rowid, age, weight with confidence 0.9 from p1'
' where label = \'foo\'') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9,'\
' NULL))' \
' AS "weight"' \
' FROM "t1"' \
' WHERE ("label" = \'foo\');'
def test_infer_auto_with_confidence_nsamples_where():
assert bql2sql('infer rowid, age, weight with confidence 0.9'
' using 42 samples'
' from p1'
' where label = \'foo\'') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, 42))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9, 42))' \
' AS "weight"' \
' FROM "t1"' \
' WHERE ("label" = \'foo\');'
def test_infer_auto_with_confidence_nsamples_where_predict():
assert bql2sql('infer rowid, age, weight with confidence 0.9 from p1'
' where ifnull(label, predict label with confidence 0.7)'
' = \'foo\'') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9,' \
' NULL))' \
' AS "weight"' \
' FROM "t1"' \
' WHERE ("ifnull"("label",' \
' bql_predict(1, NULL, NULL, _rowid_, 1, 0.7, NULL))' \
' = \'foo\');'
def test_infer_auto_with_confidence_nsamples_where_predict_nsamples():
assert bql2sql('infer rowid, age, weight with confidence 0.9'
' using 42 samples'
' from p1'
' where ifnull(label, predict label with confidence 0.7'
' using 73 samples)'
' = \'foo\'') \
== \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, 42))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9, 42))' \
' AS "weight"' \
' FROM "t1"' \
' WHERE ("ifnull"("label",' \
' bql_predict(1, NULL, NULL, _rowid_, 1, 0.7, 73))' \
' = \'foo\');'
def test_infer_auto_star():
assert bql2sql('infer rowid, * from p1') == \
'SELECT "rowid" AS "rowid", "id" AS "id",' \
' "IFNULL"("label", bql_predict(1, NULL, NULL, _rowid_, 1, 0, NULL))' \
' AS "label",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, NULL))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0, NULL))' \
' AS "weight"' \
' FROM "t1";'
def test_infer_auto_star_nsamples():
assert bql2sql('infer rowid, * using 1 samples from p1') == \
'SELECT "rowid" AS "rowid", "id" AS "id",' \
' "IFNULL"("label", bql_predict(1, NULL, NULL, _rowid_, 1, 0, 1))' \
' AS "label",' \
' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, 1))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0, 1))' \
' AS "weight"' \
' FROM "t1";'
def test_estimate_columns_trivial():
prefix0 = 'SELECT v.name AS name'
prefix1 = ' FROM bayesdb_variable AS v' \
' WHERE v.population_id = 1' \
' AND v.generator_id IS NULL'
prefix = prefix0 + prefix1
assert bql2sql('estimate * from columns of p1;') == \
prefix + ';'
assert bql2sql('estimate * from columns of p1 where' +
' (probability density of value 42) > 0.5') == \
prefix + \
' AND (bql_column_value_probability(1, NULL, NULL, v.colno, 42) > 0.5);'
assert bql2sql('estimate * from columns of p1'
' where (probability density of value 8)'
' > (probability density of age = 16)') == \
prefix + \
' AND (bql_column_value_probability(1, NULL, NULL, v.colno, 8) >' \
' bql_pdf_joint(1, NULL, NULL, 2, 16));'
assert bql2sql('estimate *, probability density of value 8 given (age = 8)'
' from columns of p1;') == \
prefix0 + \
', bql_column_value_probability(1, NULL, NULL, v.colno, 8, 2, 8)' + \
prefix1 + ';'
with pytest.raises(bayeslite.BQLError):
bql2sql('estimate probability density of value 8 given (agee = 8)'
' from columns of p1')
with pytest.raises(bayeslite.BQLError):
# PREDICTIVE PROBABILITY makes no sense without row.
bql2sql('estimate * from columns of p1 where' +
' predictive probability of x > 0;')
with pytest.raises(bayeslite.BQLError):
# SIMILARITY makes no sense without row.
bql2sql('estimate * from columns of p1 where' +
' similarity to (rowid = x) in the context of c > 0;')
assert bql2sql('estimate * from columns of p1 where' +
' dependence probability with age > 0.5;') == \
prefix + \
' AND (bql_column_dependence_probability(1, NULL, NULL, 2, v.colno)' \
' > 0.5);'
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1 where' +
' dependence probability of age with weight > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1'
' where dependence probability > 0.5;')
assert bql2sql('estimate * from columns of p1 order by' +
' mutual information with age;') == \
prefix + \
' ORDER BY bql_column_mutual_information(1, NULL, NULL, \'[2]\','\
' \'[\' || v.colno || \']\', NULL);'
assert bql2sql('estimate * from columns of p1 order by' +
' mutual information with (age, label) using 42 samples;') == \
prefix + \
' ORDER BY bql_column_mutual_information(1, NULL, NULL, \'[2, 1]\','\
' \'[\' || v.colno || \']\', 42);'
assert bql2sql('estimate * from columns of p1 order by' +
' mutual information with (age, label)'
' given (weight=12) using 42 samples;') == \
prefix + \
' ORDER BY bql_column_mutual_information(1, NULL, NULL, \'[2, 1]\','\
' \'[\' || v.colno || \']\', 42, 3, 12);'
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1 order by' +
' mutual information of age with weight;')
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1'
' where mutual information > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1 order by' +
' mutual information of age with weight using 42 samples;')
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1 where' +
' mutual information using 42 samples > 0.5;')
assert bql2sql('estimate * from columns of p1 order by' +
' correlation with age desc;') == \
prefix + ' ORDER BY bql_column_correlation(1, NULL, NULL, 2, v.colno)' \
' DESC;'
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1 order by' +
' correlation of age with weight;')
with pytest.raises(bayeslite.BQLError):
# Must omit exactly one column.
bql2sql('estimate * from columns of p1 where correlation > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Makes no sense.
bql2sql('estimate * from columns of p1'
' where predict age with confidence 0.9 > 30;')
assert bql2sql('estimate'
' *, dependence probability with weight as depprob,'
' mutual information with weight as mutinf'
' from columns of p1'
' where depprob > 0.5 order by mutinf desc') == \
prefix0 + \
', bql_column_dependence_probability(1, NULL, NULL, 3, v.colno)' \
' AS "depprob"' \
', bql_column_mutual_information(1, NULL, NULL, \'[3]\',' \
' \'[\' || v.colno || \']\', NULL) AS "mutinf"' \
+ prefix1 + \
' AND ("depprob" > 0.5)' \
' ORDER BY "mutinf" DESC;'
assert bql2sql('estimate'
' *, dependence probability with weight as depprob,'
' mutual information with (age, weight) as mutinf'
' from columns of p1'
' where depprob > 0.5 order by mutinf desc') == \
prefix0 + \
', bql_column_dependence_probability(1, NULL, NULL, 3, v.colno)' \
' AS "depprob"' \
', bql_column_mutual_information(1, NULL, NULL, \'[2, 3]\',' \
' \'[\' || v.colno || \']\', NULL) AS "mutinf"' \
+ prefix1 + \
' AND ("depprob" > 0.5)' \
' ORDER BY "mutinf" DESC;'
# XXX This mixes up target and reference variables, which is OK,
# because MI is symmetric, but...oops.
assert bql2sql('estimate * from variables of p1'
' where probability of (mutual information with age < 0.1)'
' > 0.8') == \
prefix + \
' AND ((SELECT "AVG"("x") FROM (SELECT ("v0" < 0.1) AS "x"' \
' FROM (SELECT mi AS "v0" FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[' || v.colno || ']'))) > 0.8);"
assert bql2sql('estimate * from variables of p1'
' order by probability of (mutual information with age < 0.1)') ==\
prefix + \
' ORDER BY (SELECT "AVG"("x") FROM (SELECT ("v0" < 0.1) AS "x"' \
' FROM (SELECT mi AS "v0" FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[' || v.colno || ']')));"
def test_estimate_pairwise_trivial():
prefix = 'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1, '
infix = ' AS value'
infix0 = ' FROM bayesdb_population AS p,'
infix0 += ' bayesdb_variable AS v0,'
infix0 += ' bayesdb_variable AS v1'
infix0 += ' WHERE p.id = 1'
infix0 += ' AND v0.population_id = p.id AND v1.population_id = p.id'
infix0 += ' AND v0.generator_id IS NULL'
infix0 += ' AND v1.generator_id IS NULL'
infix += infix0
assert bql2sql('estimate dependence probability'
' from pairwise columns of p1;') == \
prefix + \
'bql_column_dependence_probability(1, NULL, NULL, v0.colno,'\
' v1.colno)' + \
infix + ';'
assert bql2sql('estimate mutual information'
' from pairwise columns of p1 where'
' (probability density of age = 0) > 0.5;') == \
prefix + \
'bql_column_mutual_information(1, NULL, NULL, '\
'\'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL)' + \
infix + \
' AND (bql_pdf_joint(1, NULL, NULL, 2, 0) > 0.5);'
assert bql2sql('estimate mutual information given (label=\'go\', weight)'
' from pairwise columns of p1 where'
' (probability density of age = 0) > 0.5;') == \
prefix + \
'bql_column_mutual_information(1, NULL, NULL,'\
' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL,'\
' 1, \'go\', 3, NULL)' + \
infix + \
' AND (bql_pdf_joint(1, NULL, NULL, 2, 0) > 0.5);'
with pytest.raises(bayeslite.BQLError):
# PROBABILITY DENSITY OF VALUE is 1-column.
bql2sql('estimate correlation from pairwise columns of p1 where' +
' (probability density of value 0) > 0.5;')
with pytest.raises(bayeslite.BQLError):
# PREDICTIVE PROBABILITY OF is a row function.
bql2sql('estimate dependence probability'
' from pairwise columns of p1' +
' where predictive probability of x > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate dependence probability'
' from pairwise columns of p1'
' where dependence probability of age with weight > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate mutual information from pairwise columns of p1'
' where dependence probability with weight > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate mutual information using 42 samples'
' from pairwise columns of p1'
' where dependence probability with weight > 0.5;')
assert bql2sql('estimate correlation from pairwise columns of p1'
' where dependence probability > 0.5;') == \
prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \
infix + ' AND' \
' (bql_column_dependence_probability(1, NULL, NULL, v0.colno,' \
' v1.colno)' \
' > 0.5);'
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate dependence probability'
' from pairwise columns of p1'
' where mutual information of age with weight > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate dependence probability'
' from pairwise columns of p1'
' where mutual information of age with weight using 42 samples'
' > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate mutual information from pairwise columns of p1'
' where mutual information with weight > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate mutual information using 42 samples'
' from pairwise columns of p1'
' where mutual information with weight using 42 samples > 0.5;')
assert bql2sql('estimate correlation from pairwise columns of p1' +
' where mutual information > 0.5;') == \
prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \
infix + ' AND' + \
' (bql_column_mutual_information(1, NULL, NULL,'\
' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL) > 0.5);'
assert bql2sql('estimate correlation from pairwise columns of p1' +
' where mutual information using 42 samples > 0.5;') == \
prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \
infix + ' AND' + \
' (bql_column_mutual_information(1, NULL, NULL,'\
' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', 42) > 0.5);'
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate dependence probability'
' from pairwise columns of p1'
' where correlation of age with weight > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate mutual information from pairwise columns of p1'
' where correlation with weight > 0.5;')
with pytest.raises(bayeslite.BQLError):
# Must omit both columns.
bql2sql('estimate mutual information using 42 samples'
' from pairwise columns of p1'
' where correlation with weight > 0.5;')
assert bql2sql('estimate correlation from pairwise columns of p1'
' where correlation > 0.5;') == \
prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \
infix + ' AND' + \
' (bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno) > 0.5);'
with pytest.raises(bayeslite.BQLError):
# Makes no sense.
bql2sql('estimate dependence probability'
' from pairwise columns of p1'
' where predict age with confidence 0.9 > 30;')
assert bql2sql('estimate dependence probability as depprob,'
' mutual information as mutinf'
' from pairwise columns of p1'
' where depprob > 0.5 order by mutinf desc') == \
prefix + \
'bql_column_dependence_probability(1, NULL, NULL, v0.colno, v1.colno)' \
' AS "depprob",' \
' bql_column_mutual_information(1, NULL, NULL,'\
' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL)'\
' AS "mutinf"' \
+ infix0 + \
' AND ("depprob" > 0.5)' \
' ORDER BY "mutinf" DESC;'
def test_estimate_pairwise_row():
prefix = 'SELECT r0._rowid_ AS rowid0, r1._rowid_ AS rowid1'
infix = ' AS value FROM "t1" AS r0, "t1" AS r1'
assert bql2sql('estimate similarity in the context of age' +
' from pairwise p1;') == \
prefix + ', bql_row_similarity(1, NULL, NULL,'\
' r0._rowid_, r1._rowid_, 2)' + \
infix + ';'
with pytest.raises(bayeslite.BQLError):
# PREDICT is a 1-row function.
bql2sql('estimate predict age with confidence 0.9 from pairwise t1;')
def test_estimate_pairwise_selected_columns():
assert bql2sql('estimate dependence probability'
' from pairwise columns of p1 for label, age') == \
'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1,' \
' bql_column_dependence_probability(1, NULL, NULL,' \
' v0.colno, v1.colno)' \
' AS value' \
' FROM bayesdb_population AS p,' \
' bayesdb_variable AS v0,' \
' bayesdb_variable AS v1' \
' WHERE p.id = 1' \
' AND v0.population_id = p.id AND v1.population_id = p.id' \
' AND v0.generator_id IS NULL AND v1.generator_id IS NULL' \
' AND v0.colno IN (1, 2) AND v1.colno IN (1, 2);'
assert bql2sql('estimate dependence probability'
' from pairwise columns of p1'
' for (ESTIMATE * FROM COLUMNS OF p1'
' ORDER BY name DESC LIMIT 2)') == \
'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1,' \
' bql_column_dependence_probability(1, NULL, NULL, v0.colno,' \
' v1.colno)' \
' AS value' \
' FROM bayesdb_population AS p,' \
' bayesdb_variable AS v0,' \
' bayesdb_variable AS v1' \
' WHERE p.id = 1' \
' AND v0.population_id = p.id AND v1.population_id = p.id' \
' AND v0.generator_id IS NULL AND v1.generator_id IS NULL' \
' AND v0.colno IN (3, 1) AND v1.colno IN (3, 1);'
def test_select_columns_subquery():
assert bql2sql('select id, t1.(estimate * from columns of p1'
' order by name asc limit 2) from t1') == \
'SELECT "id", "t1"."age", "t1"."label" FROM "t1";'
@pytest.mark.xfail(strict=True, reason='no simulate vars from models of')
def test_simulate_models_columns_subquery():
assert bql2sql('simulate weight, t1.(estimate * from columns of p1'
' order by name asc limit 2) from models of p1') == \
'SELECT * FROM "bayesdb_temp_0";'
assert bql2sql('simulate 0, weight, t1.(estimate * from columns of p1'
' order by name asc limit 2) from models of p1') == \
'SELECT 0, "v0" AS "weight", "v1" AS "age", "v2" AS "label" FROM' \
' (SELECT * FROM "bayesdb_temp_0");'
assert bql2sql('simulate weight + 1, t1.(estimate * from columns of p1'
' order by name asc limit 2) from models of p1') == \
'SELECT ("v0" + 1), "v1" AS "age", "v2" AS "label" FROM' \
' (SELECT * FROM "bayesdb_temp_0");'
assert bql2sql('simulate weight + 1 AS wp1,'
' t1.(estimate * from columns of p1'
' order by name asc limit 2) from models of p1') == \
'SELECT ("v0" + 1) AS "wp1", "v1" AS "age", "v2" AS "label" FROM' \
' (SELECT * FROM "bayesdb_temp_0");'
def test_simulate_columns_subquery():
# XXX This test is a little unsatisfactory -- we do not get to see
# what the variables in the result are named...
assert bql2sql('simulate weight, t1.(estimate * from columns of p1'
' order by name asc limit 2) from p1 limit 10') == \
'SELECT * FROM "bayesdb_temp_0";'
with pytest.raises(parse.BQLParseError):
# Compound columns not yet implemented for SIMULATE.
bql2sql('simulate weight + 1, t1.(estimate * from columns of p1'
' order by name asc limit 2) from p1 limit 10')
def test_simulate_models():
# Base case.
assert bql2sql('simulate mutual information of age with weight'
' from models of p1') == \
'SELECT mi FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[3]';"
# Multiple target variables.
assert bql2sql('simulate mutual information of (label, age) with weight'
' from models of p1') == \
'SELECT mi FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[1, 2]'" \
" AND reference_vars = '[3]';"
# Multiple reference variables.
assert bql2sql('simulate mutual information of age with (label, weight)'
' from models of p1') == \
'SELECT mi FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[1, 3]';"
# Specified number of samples.
assert bql2sql('simulate mutual information of age with weight'
' using 42 samples from models of p1') == \
'SELECT mi FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[3]'" \
' AND nsamples = 42;'
# Conditional.
assert bql2sql('simulate mutual information of age with weight'
" given (label = 'foo') from models of p1") == \
'SELECT mi FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[3]'" \
" AND conditions = '{\"1\": \"foo\"}';"
# Modeled by a specific generator.
assert bql2sql('simulate mutual information of age with weight'
' from models of p1 modeled by g1',
lambda bdb: bdb.execute('create generator g1 for p1')) == \
'SELECT mi FROM bql_mutinf' \
' WHERE population_id = 1' \
' AND generator_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[3]';"
# Two mutual informations.
assert bql2sql('simulate mutual information of age with weight AS "mi(aw)",'
' mutual information of label with weight AS "mi(lw)"'
' from models of p1') == \
'SELECT t0."mi(aw)" AS "mi(aw)", t1."mi(lw)" AS "mi(lw)"' \
' FROM (SELECT _rowid_, mi AS "mi(aw)" FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[3]') AS t0," \
' (SELECT _rowid_, mi AS "mi(lw)" FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[1]'" \
" AND reference_vars = '[3]') AS t1" \
' WHERE t0._rowid_ = t1._rowid_;'
def test_probability_of_mutinf():
assert bql2sql('estimate probability of'
' (mutual information of age with weight < 0.1) > 0.5'
' within p1') == \
'SELECT ((SELECT "AVG"("x") FROM (SELECT ("v0" < 0.1) AS "x"' \
' FROM (SELECT mi AS "v0" FROM bql_mutinf' \
' WHERE population_id = 1' \
" AND target_vars = '[2]'" \
" AND reference_vars = '[3]'))) > 0.5);"
def test_modeledby_usingmodels_trival():
def setup(bdb):
bdb.execute('create generator m1 for p1 using cgpm;')
assert bql2sql('estimate predictive probability of weight + 1'
' from p1 modeled by m1 using models 1-3, 5;', setup=setup) == \
'SELECT (bql_row_column_predictive_probability(1, 1, \'[1, 2, 3, 5]\','\
' _rowid_, \'[3]\', \'[]\') + 1)' \
' FROM "t1";'
assert bql2sql(
'infer rowid, age, weight from p1 modeled by m1 using model 7',
setup=setup) == \
'SELECT "rowid" AS "rowid",' \
' "IFNULL"("age", bql_predict(1, 1, \'[7]\', _rowid_, 2, 0, NULL))' \
' AS "age",' \
' "IFNULL"("weight", bql_predict(1, 1, \'[7]\', _rowid_, 3, 0, NULL))' \
' AS "weight"' \
' FROM "t1";'
assert bql2sql('infer explicit predict age with confidence 0.9'
' from p1 using models 0, 3-5;',
setup=setup) == \
'SELECT bql_predict(1, NULL, \'[0, 3, 4, 5]\', _rowid_, 2, 0.9, NULL)'\
' FROM "t1";'
assert bql2sql('''
estimate predictive relevance
of (label = 'Uganda')
to existing rows (rowid < 4)
and hypothetical rows with values (
("age" = 82, "weight" = 14),
("age" = 74, label = 'Europe', "weight" = 7)
)
in the context of "weight"
by p1 modeled by m1 using models 8, 10-12
''', setup=setup) == \
'SELECT bql_row_predictive_relevance(1, 1, \'[8, 10, 11, 12]\', ' \
'(SELECT _rowid_ FROM "t1" WHERE ("label" = \'Uganda\')), '\
'\'[1, 2, 3]\', 3, '\
'2, 82, 3, 14, NULL, 2, 74, 1, \'Europe\', 3, 7, NULL);'
assert bql2sql('''
estimate dependence probability
from pairwise columns of p1
for label, age
modeled by m1
using models 1, 4, 12
''', setup=setup) == \
'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1,' \
' bql_column_dependence_probability(1, 1, \'[1, 4, 12]\',' \
' v0.colno, v1.colno)' \
' AS value' \
' FROM bayesdb_population AS p,' \
' bayesdb_variable AS v0,' \
' bayesdb_variable AS v1' \
' WHERE p.id = 1' \
' AND v0.population_id = p.id AND v1.population_id = p.id' \
' AND (v0.generator_id IS NULL OR v0.generator_id = 1)' \
' AND (v1.generator_id IS NULL OR v1.generator_id = 1)' \
' AND v0.colno IN (1, 2) AND v1.colno IN (1, 2);'
assert bql2sql('''
estimate mutual information of age with weight
from p1 modeled by m1 using model 1;
''', setup=setup) == \
'SELECT bql_column_mutual_information('\
'1, 1, \'[1]\', \'[2]\', \'[3]\', NULL)'\
' FROM "t1";'
def test_simulate_columns_all():
with pytest.raises(parse.BQLParseError):
bql2sql('simulate * from p1 limit 1')
def test_trivial_commands():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
# XXX Query parameters!
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
with open(fname, 'rU') as f:
with pytest.raises(ValueError):
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True,
create=True)
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True,
ifnotexists=True)
guess.bayesdb_guess_population(bdb, 'p', 't')
with pytest.raises(ValueError):
guess.bayesdb_guess_population(bdb, 'p', 't')
guess.bayesdb_guess_population(bdb, 'p', 't', ifnotexists=True)
bdb.execute('create generator p_cc for p;')
bdb.execute('initialize 2 models for p_cc')
with pytest.raises(bayeslite.BQLError):
bdb.execute('initialize 2 models for p_cc')
bdb.execute('drop models from p_cc')
bdb.execute('drop models from p_cc')
bdb.execute('initialize 2 models for p_cc')
with pytest.raises(bayeslite.BQLError):
bdb.execute('initialize 2 models for p_cc')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop models 0-2 from p_cc')
bdb.execute('drop models 0-1 from p_cc')
with bdb.savepoint():
bdb.execute('initialize 2 models for p_cc')
bdb.execute('drop models 0-1 from p_cc')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop models 0-1 from p_cc')
bdb.execute('initialize 2 models for p_cc')
bdb.execute('initialize 1 model if not exists for p_cc')
bdb.execute('initialize 2 models if not exists for p_cc')
population_id = core.bayesdb_get_population(bdb, 'p')
generator_id = core.bayesdb_get_generator(bdb, population_id, 'p_cc')
assert core.bayesdb_generator_table(bdb, generator_id) == 't'
bdb.execute('alter table t rename to t')
assert core.bayesdb_generator_table(bdb, generator_id) == 't'
bdb.execute('alter table t rename to T')
assert core.bayesdb_generator_table(bdb, generator_id) == 'T'
bdb.execute('alter population p rename to p')
assert core.bayesdb_population_name(bdb, population_id) == 'p'
bdb.execute('alter population p rename to p2')
assert core.bayesdb_population_name(bdb, population_id) == 'p2'
bdb.execute('alter population p2 rename to p')
assert core.bayesdb_population_name(bdb, population_id) == 'p'
bdb.execute('estimate count(*) from p').fetchall()
bdb.execute('alter table t rename to t')
assert core.bayesdb_generator_table(bdb, generator_id) == 't'
bdb.execute('alter generator p_cc rename to p0_cc')
assert core.bayesdb_generator_name(bdb, generator_id) == 'p0_cc'
bdb.execute('alter generator p0_cc rename to zot, rename to P0_CC')
assert core.bayesdb_generator_name(bdb, generator_id) == 'P0_CC'
bdb.execute('alter generator P0_cc rename to P0_cc')
assert core.bayesdb_generator_name(bdb, generator_id) == 'P0_cc'
bdb.execute('alter generator p0_CC rename to p0_cc')
assert core.bayesdb_generator_name(bdb, generator_id) == 'p0_cc'
bdb.execute('estimate count(*) from p').fetchall()
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate count(*) from p_cc')
bdb.execute('alter generator p0_cc rename to P0_cc')
bdb.execute('analyze p0_cc for 1 iteration')
colno = core.bayesdb_variable_number(bdb, population_id, generator_id,
'gender')
with pytest.raises(parse.BQLParseError):
# Rename the table's columns, not the generator's columns.
bdb.execute('alter generator p0_cc rename gender to sex')
with pytest.raises(NotImplementedError): # XXX
bdb.execute('alter table t rename to t0, rename gender to sex')
assert core.bayesdb_variable_number(
bdb, population_id, generator_id, 'sex') \
== colno
bdb.execute('analyze p0_cc model 0 for 1 iteration')
bdb.execute('alter generator p0_cc rename to p_cc')
assert core.bayesdb_variable_number(
bdb, population_id, generator_id, 'sex') \
== colno
bdb.execute('select sex from t0').fetchall()
with pytest.raises(AssertionError): # XXX
bdb.execute('select gender from t0')
assert False, 'Need to fix quoting of unknown columns!'
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate predict sex with confidence 0.9'
' from p').fetchall()
bdb.execute('infer explicit predict sex with confidence 0.9'
' from p').fetchall()
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate predict gender with confidence 0.9'
' from p')
with pytest.raises(bayeslite.BQLError):
bdb.execute('infer explicit predict gender with confidence 0.9'
' from p')
bdb.execute('alter table t0 rename sex to gender')
assert core.bayesdb_variable_number(
bdb, population_id, generator_id, 'gender') \
== colno
bdb.execute('alter generator p0_cc rename to p_cc') # XXX
bdb.execute('alter table t rename to T0') # XXX
bdb.sql_execute('create table t0_temp(x)')
bdb.execute('alter table T0 rename to t0')
assert bdb.execute('select count(*) from t0_temp').fetchvalue() == 0
assert bdb.execute('select count(*) from t0').fetchvalue() > 0
with pytest.raises(bayeslite.BQLError):
# Cannot specify models with rename.
bdb.execute('alter generator p_cc models (1) rename to p_cc_fail')
bdb.execute('drop table T0_TEMP')
bdb.execute('analyze p_cc model 0 for 1 iteration')
bdb.execute('analyze p_cc model 1 for 1 iteration')
bdb.execute('analyze p_cc models 0-1 for 1 iteration')
bdb.execute('analyze p_cc models 0,1 for 1 iteration')
bdb.execute('analyze p_cc for 1 iteration')
bdb.execute('select * from t0').fetchall()
bdb.execute('select * from T0').fetchall()
bdb.execute('estimate * from p').fetchall()
bdb.execute('estimate * from P').fetchall()
# SIMIARITY IN THE CONTEXT OF requires exactly 1 variable.
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate similarity in the context of * '
'from pairwise p').fetchall()
bdb.execute('estimate similarity in the context of age '
'from pairwise p').fetchall()
bdb.execute('alter population p rename to p2')
assert core.bayesdb_population_name(bdb, population_id) == 'p2'
bdb.execute('estimate similarity to (rowid=1) in the context of rank '
'from p2').fetchall()
bdb.execute('select value from'
' (estimate correlation from pairwise columns of p2)').fetchall()
bdb.execute('infer explicit predict age with confidence 0.9'
' from p2').fetchall()
bdb.execute('infer explicit predict AGE with confidence 0.9'
' from P2').fetchall()
bdb.execute('infer explicit predict aGe with confidence 0.9'
' from P2').fetchall()
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate predict agee with confidence 0.9 from p2')
with pytest.raises(bayeslite.BQLError):
bdb.execute('infer explicit predict agee with confidence 0.9'
' from p2')
guess.bayesdb_guess_population(bdb, 'pe', 't0',
overrides=[
('age', 'numerical'),
('rank', 'numerical'),
])
bdb.execute('create generator pe_cc for pe;')
with pytest.raises(bayeslite.BQLError):
# No models to analyze.
bdb.execute('analyze pe_cc for 1 iteration')
bdb.execute('initialize 1 model if not exists for pe_cc')
bdb.execute('analyze pe_cc for 1 iteration')
bdb.execute('estimate correlation'
' from pairwise columns of pe').fetchall()
with pytest.raises(bayeslite.BQLError):
bdb.execute('initialize 4 models if not exists for t')
with pytest.raises(bayeslite.BQLError):
bdb.execute('analyze t0 for 1 iteration')
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate * from t')
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate * from columns of t')
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate correlation from pairwise columns of t')
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate similarity in the context of age '
'from pairwise t')
bdb.execute('initialize 6 models if not exists for p_cc')
bdb.execute('analyze p_cc for 1 iteration')
def test_trivial_deadline():
with test_core.t1() as (bdb, _population_id, _generator_id):
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 1 second')
def test_parametrized():
assert bql2sqlparam('select * from t where id = ?') == \
'SELECT * FROM "t" WHERE ("id" = ?1);'
assert bql2sqlparam('select * from t where id = :foo') == \
'SELECT * FROM "t" WHERE ("id" = ?1);'
assert bql2sqlparam('select * from t where id = $foo') == \
'SELECT * FROM "t" WHERE ("id" = ?1);'
assert bql2sqlparam('select * from t where id = @foo') == \
'SELECT * FROM "t" WHERE ("id" = ?1);'
assert bql2sqlparam('select * from t where id = ?123') == \
'SELECT * FROM "t" WHERE ("id" = ?1);'
assert bql2sqlparam('select * from t where a = $foo and b = ?1;') == \
'SELECT * FROM "t" WHERE (("a" = ?1) AND ("b" = ?1));'
assert bql2sqlparam('select * from t' +
' where a = ?123 and b = :foo and c = ?124') == \
'SELECT * FROM "t" WHERE' + \
' ((("a" = ?1) AND ("b" = ?2)) AND ("c" = ?2));'
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
assert bql_execute(bdb, 'select count(*) from t') == [(7,)]
assert bql_execute(bdb, 'select count(distinct division) from t') == \
[(6,)]
assert bql_execute(bdb, 'select * from t where height > ?', (70,)) == \
[
(41, 'M', 65600, 72, 'marketing', 4),
(30, 'M', 70000, 73, 'sales', 4),
(30, 'F', 81000, 73, 'engineering', 3),
]
assert bql_execute(bdb, 'select * from t where height > ?123',
(0,)*122 + (70,)) == \
[
(41, 'M', 65600, 72, 'marketing', 4),
(30, 'M', 70000, 73, 'sales', 4),
(30, 'F', 81000, 73, 'engineering', 3),
]
assert bql_execute(bdb, 'select age from t where division = :division',
{':division': 'sales'}) == \
[(34,), (30,)]
assert bql_execute(bdb, 'select division from t' +
' where age < @age and rank > ?;',
(40, 4)) == \
[('accounting',)]
assert bql_execute(bdb, 'select division from t' +
' where age < @age and rank > :rank;',
{':RANK': 4, '@aGe': 40}) == \
[('accounting',)]
with pytest.raises(ValueError):
bdb.execute('select * from t where age < ? and rank > :r',
{':r': 4})
def traced_execute(query, *args):
bql = []
def trace(string, _bindings):
bql.append(' '.join(string.split()))
bdb.trace(trace)
with bdb.savepoint():
bdb.execute(query, *args)
bdb.untrace(trace)
return bql
def sqltraced_execute(query, *args):
sql = []
def trace(string, _bindings):
sql.append(' '.join(string.split()))
bdb.sql_trace(trace)
with bdb.savepoint():
bdb.execute(query, *args)
bdb.sql_untrace(trace)
return sql
guess.bayesdb_guess_population(bdb, 'p', 't')
bdb.execute('create generator p_cc for p;')
bdb.execute('initialize 1 model for p_cc;')
assert traced_execute('estimate similarity to (rowid = 1)'
' in the context of (estimate * from columns of p limit 1)'
' from p;') == [
'estimate similarity to (rowid = 1)' \
' in the context of (estimate * from columns of p limit 1)' \
' from p;',
]
assert sqltraced_execute('estimate similarity to (rowid = 1)'
' in the context of (estimate * from columns of p limit 1)'
' from p;') == [
'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?',
'SELECT id FROM bayesdb_population WHERE name = ?',
'SELECT tabname FROM bayesdb_population WHERE id = ?',
'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?',
'SELECT id FROM bayesdb_population WHERE name = ?',
'SELECT v.name AS name FROM bayesdb_variable AS v'
' WHERE v.population_id = 1'
' AND v.generator_id IS NULL'
' LIMIT 1',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT tabname FROM bayesdb_population'
' WHERE id = ?',
'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,'
' (SELECT _rowid_ FROM "t" WHERE ("rowid" = 1)), 0) FROM "t"',
'SELECT id FROM bayesdb_generator WHERE population_id = ?',
'SELECT backend FROM bayesdb_generator WHERE id = ?',
'SELECT cgpm_rowid FROM bayesdb_cgpm_individual'
' WHERE generator_id = ? AND table_rowid = ?',
'SELECT cgpm_rowid FROM bayesdb_cgpm_individual '
'WHERE generator_id = ? AND table_rowid = ?',
'SELECT engine_stamp FROM bayesdb_cgpm_generator '
'WHERE generator_id = ?'
]
assert sqltraced_execute('estimate similarity to (rowid = 1)'
' in the context of (estimate * from columns of p limit ?)'
' from p;',
(1,)) == [
'SELECT COUNT(*) FROM bayesdb_population'
' WHERE name = ?',
'SELECT id FROM bayesdb_population'
' WHERE name = ?',
'SELECT tabname FROM bayesdb_population WHERE id = ?',
'SELECT COUNT(*) FROM bayesdb_population'
' WHERE name = ?',
'SELECT id FROM bayesdb_population'
' WHERE name = ?',
# ESTIMATE * FROM COLUMNS OF:
'SELECT v.name AS name'
' FROM bayesdb_variable AS v'
' WHERE v.population_id = 1'
' AND v.generator_id IS NULL'
' LIMIT ?1',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT tabname FROM bayesdb_population WHERE id = ?',
# ESTIMATE SIMILARITY TO (rowid=1):
'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,'
' (SELECT _rowid_ FROM "t" WHERE ("rowid" = 1)), 0) FROM "t"',
'SELECT id FROM bayesdb_generator WHERE population_id = ?',
'SELECT backend FROM bayesdb_generator WHERE id = ?',
'SELECT cgpm_rowid FROM bayesdb_cgpm_individual'
' WHERE generator_id = ? AND table_rowid = ?',
'SELECT cgpm_rowid FROM bayesdb_cgpm_individual'
' WHERE generator_id = ? AND table_rowid = ?',
'SELECT engine_stamp FROM bayesdb_cgpm_generator'
' WHERE generator_id = ?'
]
assert sqltraced_execute(
'create temp table if not exists sim as '
'simulate age, RANK, division '
'from p given gender = \'F\' limit 4') == [
'PRAGMA table_info("sim")',
'PRAGMA table_info("bayesdb_temp_0")',
'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?',
'SELECT id FROM bayesdb_population WHERE name = ?',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT CAST(4 AS INTEGER), \'F\'',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT tabname FROM bayesdb_population WHERE id = ?',
'SELECT MAX(_rowid_) FROM "t"',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT id FROM bayesdb_generator'
' WHERE population_id = ?',
'SELECT backend FROM bayesdb_generator WHERE id = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT tabname FROM bayesdb_population WHERE id = ?',
'SELECT 1 FROM "t" WHERE oid = ?',
'SELECT 1 FROM bayesdb_cgpm_individual'
' WHERE generator_id = ? AND table_rowid = ? LIMIT 1',
'SELECT cgpm_rowid FROM bayesdb_cgpm_individual'
' WHERE generator_id = ? AND table_rowid = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT code FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND value = ?',
'SELECT engine_stamp FROM bayesdb_cgpm_generator'
' WHERE generator_id = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'CREATE TEMP TABLE "bayesdb_temp_0"'
' ("age","RANK","division")',
'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")'
' VALUES (?,?,?)',
'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")'
' VALUES (?,?,?)',
'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")'
' VALUES (?,?,?)',
'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")'
' VALUES (?,?,?)',
'CREATE TEMP TABLE IF NOT EXISTS "sim" AS'
' SELECT * FROM "bayesdb_temp_0"',
'DROP TABLE "bayesdb_temp_0"'
]
assert sqltraced_execute(
'select * from (simulate age from p '
'given gender = \'F\' limit 4)') == [
'PRAGMA table_info("bayesdb_temp_1")',
'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?',
'SELECT id FROM bayesdb_population WHERE name = ?',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT CAST(4 AS INTEGER), \'F\'',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT COUNT(*) FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT colno FROM bayesdb_variable'
' WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?)'
' AND name = ?',
'SELECT tabname FROM bayesdb_population WHERE id = ?',
'SELECT MAX(_rowid_) FROM "t"',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT token FROM bayesdb_rowid_tokens',
'SELECT id FROM bayesdb_generator WHERE population_id = ?',
'SELECT backend FROM bayesdb_generator WHERE id = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT tabname FROM bayesdb_population WHERE id = ?',
'SELECT 1 FROM "t" WHERE oid = ?',
'SELECT 1 FROM bayesdb_cgpm_individual'
' WHERE generator_id = ? AND table_rowid = ? LIMIT 1',
'SELECT cgpm_rowid FROM bayesdb_cgpm_individual'
' WHERE generator_id = ? AND table_rowid = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT code FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND value = ?',
'SELECT engine_stamp FROM bayesdb_cgpm_generator'
' WHERE generator_id = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT stattype FROM bayesdb_variable WHERE population_id = ?'
' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?',
'SELECT value FROM bayesdb_cgpm_category'
' WHERE generator_id = ? AND colno = ? AND code = ?',
'CREATE TEMP TABLE "bayesdb_temp_1" ("age")',
'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)',
'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)',
'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)',
'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)',
'SELECT * FROM (SELECT * FROM "bayesdb_temp_1")',
'DROP TABLE "bayesdb_temp_1"',
]
bdb.execute('''
create population q for t (
age NUMERICAL;
gender NOMINAL; -- Not binary!
salary NUMERICAL;
height NUMERICAL;
division NOMINAL;
rank NOMINAL;
)
''')
bdb.execute('create generator q_cc for q;')
bdb.execute('initialize 1 model for q_cc;')
assert sqltraced_execute('analyze q_cc for 1 iteration;') == [
'SELECT COUNT(*) FROM bayesdb_generator WHERE name = ?',
'SELECT id FROM bayesdb_generator WHERE name = ?',
'SELECT backend FROM bayesdb_generator WHERE id = ?',
'SELECT engine_json, engine_stamp FROM bayesdb_cgpm_generator'
' WHERE generator_id = ?',
'SELECT population_id FROM bayesdb_generator WHERE id = ?',
'SELECT engine_stamp FROM bayesdb_cgpm_generator'
' WHERE generator_id = ?',
'UPDATE bayesdb_cgpm_generator'
' SET engine_json = :engine_json, engine_stamp = :engine_stamp'
' WHERE generator_id = :generator_id']
def test_create_table_ifnotexists_as_simulate():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
# If not exists table tests
guess.bayesdb_guess_population(bdb, 'p', 't',
overrides=[('age', 'numerical')])
bdb.execute('create generator p_cc for p;')
bdb.execute('initialize 1 model for p_cc')
bdb.execute('analyze p_cc for 1 iteration')
bdb.execute('''
create table if not exists u as
simulate age from p limit 10
''')
bdb.execute("drop table u")
bdb.execute('''
create table if not exists w as simulate age from p
given division='sales' limit 10
''')
bdb.execute("drop table w")
bdb.execute("create table u as simulate age from p limit 10")
x = bdb.execute("select count (*) from u").fetchvalue()
bdb.execute('''
create table if not exists u as simulate age from p limit 10
''')
bdb.execute('''
create table if not exists u as simulate age from p
given division='sales' limit 10
''')
assert x == bdb.execute("select count (*) from u").fetchvalue()
def test_createtab():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
with pytest.raises(apsw.SQLError):
bdb.execute('drop table t')
bdb.execute('drop table if exists t')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop population p')
bdb.execute('drop population if exists p')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop generator p_cc')
bdb.execute('drop generator if exists p_cc')
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
with bdb.savepoint():
# Savepoint because we don't actually want the new data to
# be inserted.
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True,
create=True, ifnotexists=True)
guess.bayesdb_guess_population(bdb, 'p', 't',
overrides=[('age', 'numerical')])
bdb.execute('create generator p_cc for p;')
with pytest.raises(bayeslite.BQLError):
# Redefining population.
bdb.execute('create population p for t (age numerical)')
with pytest.raises(bayeslite.BQLError):
# Redefining generator.
bdb.execute('create generator p_cc for p;')
# Make sure ignore columns work.
#
# XXX Also check key columns.
guess.bayesdb_guess_population(bdb, 'p0', 't',
overrides=[('age', 'ignore')])
bdb.execute('drop population p0')
population_id = core.bayesdb_get_population(bdb, 'p')
colno = core.bayesdb_variable_number(bdb, population_id, None, 'age')
assert core.bayesdb_variable_stattype(
bdb, population_id, None, colno) == 'numerical'
bdb.execute('initialize 1 model for p_cc')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop table t')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop population p')
bdb.execute('drop generator p_cc')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop generator p_cc')
with pytest.raises(bayeslite.BQLError):
bdb.execute('drop table t')
bdb.execute('drop generator if exists p_cc')
bdb.execute('drop population p')
bdb.execute('drop population if exists p')
bdb.execute('drop table t')
bdb.execute('drop table if exists t')
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True)
guess.bayesdb_guess_population(bdb, 'p', 't')
bdb.execute("create table u as select * from t where gender = 'F'")
assert bql_execute(bdb, 'select * from u') == [
(23, 'F', 81000, 67, 'data science', 3),
(36, 'F', 96000, 70, 'management', 2),
(30, 'F', 81000, 73, 'engineering', 3),
]
with pytest.raises(bayeslite.BQLError):
bdb.execute("create table u as select * from t where gender = 'F'")
bdb.execute('drop table u')
with pytest.raises(apsw.SQLError):
bql_execute(bdb, 'select * from u')
bdb.execute("create temp table u as"
" select * from t where gender = 'F'")
assert bql_execute(bdb, 'select * from u') == [
(23, 'F', 81000, 67, 'data science', 3),
(36, 'F', 96000, 70, 'management', 2),
(30, 'F', 81000, 73, 'engineering', 3),
]
# XXX Test to make sure TEMP is passed through, and the table
# doesn't persist on disk.
def test_alterpop_addvar():
with bayeslite.bayesdb_open() as bdb:
bayeslite.bayesdb_read_csv(
bdb, 't', StringIO.StringIO(test_csv.csv_data),
header=True, create=True)
bdb.execute('''
create population p for t with schema(
age numerical;
gender nominal;
salary numerical;
height ignore;
division ignore;
rank ignore;
)
''')
population_id = core.bayesdb_get_population(bdb, 'p')
bdb.execute('create generator m for p;')
# Fail when variable does not exist in base table.
with pytest.raises(bayeslite.BQLError):
bdb.execute('alter population p add variable quux;')
# Fail when variable already in population.
with pytest.raises(bayeslite.BQLError):
bdb.execute('alter population p add variable age numerical;')
# Fail when given invalid statistical type.
with pytest.raises(bayeslite.BQLError):
bdb.execute('alter population p add variable heigh numr;')
# Alter pop with stattype.
assert not core.bayesdb_has_variable(bdb, population_id, None, 'height')
bdb.execute('alter population p add variable height numerical;')
assert core.bayesdb_has_variable(bdb, population_id, None, 'height')
# Alter pop multiple without stattype.
assert not core.bayesdb_has_variable(bdb, population_id, None, 'rank')
assert not core.bayesdb_has_variable(
bdb, population_id, None, 'division')
bdb.execute('''
alter population p
add variable rank,
add variable division;
''')
assert core.bayesdb_has_variable(bdb, population_id, None, 'rank')
assert core.bayesdb_has_variable(bdb, population_id, None, 'division')
# Add a new column weight to the base table.
bdb.sql_execute('alter table t add column weight real;')
# Fail when no values in new column.
with pytest.raises(bayeslite.BQLError):
bdb.execute('alter population p add variable weight numerical;')
assert not core.bayesdb_has_variable(bdb, population_id, None, 'weight')
# Update a single value and update the population.
bdb.sql_execute('update t set weight = 1 where oid = 1;')
bdb.execute('alter population p add variable weight numerical;')
assert core.bayesdb_has_variable(bdb, population_id, None, 'weight')
def test_txn():
with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname):
# Make sure rollback and commit fail outside a transaction.
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('ROLLBACK')
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('COMMIT')
# Open a transaction which we'll roll back.
bdb.execute('BEGIN')
try:
# Make sure transactions don't nest. (Use savepoints.)
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('BEGIN')
finally:
bdb.execute('ROLLBACK')
# Make sure rollback and commit still fail outside a transaction.
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('ROLLBACK')
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('COMMIT')
# Open a transaction which we'll commit.
bdb.execute('BEGIN')
try:
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('BEGIN')
finally:
bdb.execute('COMMIT')
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('ROLLBACK')
with pytest.raises(bayeslite.BayesDBTxnError):
bdb.execute('COMMIT')
# Make sure ROLLBACK undoes the effects of the transaction.
bdb.execute('BEGIN')
try:
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True,
create=True)
bdb.execute('SELECT * FROM t').fetchall()
guess.bayesdb_guess_population(bdb, 'p', 't')
bdb.execute('ESTIMATE * FROM p').fetchall()
finally:
bdb.execute('ROLLBACK')
with pytest.raises(apsw.SQLError):
bdb.execute('SELECT * FROM t')
with pytest.raises(bayeslite.BQLError):
bdb.execute('ESTIMATE * FROM p')
# Make sure CREATE and DROP both work in the transaction.
bdb.execute('BEGIN')
try:
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True,
create=True)
bdb.execute('SELECT * FROM t').fetchall()
guess.bayesdb_guess_population(bdb, 'p', 't')
bdb.execute('ESTIMATE * FROM p').fetchall()
with pytest.raises(bayeslite.BQLError):
bdb.execute('DROP TABLE t')
bdb.execute('DROP POPULATION p')
with pytest.raises(bayeslite.BQLError):
bdb.execute('ESTIMATE * FROM p')
bdb.execute('DROP TABLE t')
with pytest.raises(apsw.SQLError):
bdb.execute('SELECT * FROM t')
finally:
bdb.execute('ROLLBACK')
with pytest.raises(bayeslite.BQLError):
bdb.execute('ESTIMATE * FROM p')
with pytest.raises(apsw.SQLError):
bdb.execute('SELECT * FROM t')
# Make sure CREATE and DROP work even if we commit.
bdb.execute('BEGIN')
try:
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True,
create=True)
bdb.execute('SELECT * FROM t').fetchall()
guess.bayesdb_guess_population(bdb, 'p', 't')
bdb.execute('ESTIMATE * FROM p').fetchall()
with pytest.raises(bayeslite.BQLError):
bdb.execute('DROP TABLE t')
bdb.execute('DROP POPULATION p')
with pytest.raises(bayeslite.BQLError):
bdb.execute('ESTIMATE * FROM p')
bdb.execute('DROP TABLE t')
with pytest.raises(apsw.SQLError):
bdb.execute('SELECT * FROM t')
finally:
bdb.execute('COMMIT')
with pytest.raises(bayeslite.BQLError):
bdb.execute('ESTIMATE * FROM p')
with pytest.raises(apsw.SQLError):
bdb.execute('SELECT * FROM t')
# Make sure CREATE persists if we commit.
bdb.execute('BEGIN')
try:
with open(fname, 'rU') as f:
bayeslite.bayesdb_read_csv(bdb, 't', f, header=True,
create=True)
bdb.execute('SELECT * FROM t').fetchall()
guess.bayesdb_guess_population(bdb, 'p', 't')
bdb.execute('ESTIMATE * FROM p').fetchall()
finally:
bdb.execute('COMMIT')
bdb.execute('SELECT * FROM t').fetchall()
bdb.execute('ESTIMATE * FROM p').fetchall()
# Make sure bdb.transaction works, rolls back on exception,
# and handles nesting correctly in the context of savepoints.
try:
with bdb.transaction():
bdb.sql_execute('create table quagga(x)')
raise StopIteration
except StopIteration:
pass
with pytest.raises(apsw.SQLError):
bdb.execute('select * from quagga')
with bdb.transaction():
with bdb.savepoint():
with bdb.savepoint():
pass
with bdb.savepoint():
with pytest.raises(bayeslite.BayesDBTxnError):
with bdb.transaction():
pass
# XXX To do: Make sure other effects (e.g., analysis) get
# rolled back by ROLLBACK.
def test_predprob_null():
backend = CGPM_Backend({}, multiprocess=False)
with test_core.bayesdb(backend=backend) as bdb:
bdb.sql_execute('''
create table foo (
id integer primary key not null,
x numeric,
y numeric,
z numeric
)
''')
bdb.sql_execute("insert into foo values (1, 1, 'strange', 3)")
bdb.sql_execute("insert into foo values (2, 1.2, 'strange', 1)")
bdb.sql_execute("insert into foo values (3, 0.8, 'strange', 3)")
bdb.sql_execute("insert into foo values (4, NULL, 'strange', 9)")
bdb.sql_execute("insert into foo values (5, 73, 'up', 11)")
bdb.sql_execute("insert into foo values (6, 80, 'up', -1)")
bdb.sql_execute("insert into foo values (7, 60, 'up', NULL)")
bdb.sql_execute("insert into foo values (8, 67, NULL, NULL)")
bdb.sql_execute("insert into foo values (9, 3.1415926, 'down', 1)")
bdb.sql_execute("insert into foo values (10, 1.4142135, 'down', 0)")
bdb.sql_execute("insert into foo values (11, 2.7182818, 'down', -1)")
bdb.sql_execute("insert into foo values (12, NULL, 'down', 10)")
bdb.execute('''
create population pfoo for foo (
id ignore;
x numerical;
y nominal;
z numerical;
)
''')
bdb.execute('create generator pfoo_cc for pfoo using cgpm;')
bdb.execute('initialize 1 model for pfoo_cc')
bdb.execute('analyze pfoo_cc for 1 iteration')
# Null value => null predictive probability.
assert bdb.execute('estimate predictive probability of x'
' from pfoo where id = 4;').fetchall() == \
[(None,)]
# Nonnull value => nonnull predictive probability.
x = bdb.execute('estimate predictive probability of x'
' from pfoo where id = 5').fetchall()
assert len(x) == 1
assert len(x[0]) == 1
assert isinstance(x[0][0], (int, float))
# All null values => null predictive probability.
assert bdb.execute('estimate predictive probability of (y, z)'
' from pfoo where id = 8;').fetchall() == \
[(None,)]
# Some nonnull values => nonnull predictive probability.
x = bdb.execute('estimate predictive probability of (x, z)'
' from pfoo where id = 8;').fetchall()
assert len(x) == 1
assert len(x[0]) == 1
assert isinstance(x[0][0], (int, float))
# All NULL constraints => same result regardless of given clause.
c0 = bdb.execute('estimate predictive probability of x'
' from pfoo where id = 8;')
v0 = cursor_value(c0)
assert v0 is not None
c1 = bdb.execute('estimate predictive probability of x given (y, z)'
' from pfoo where id = 8;')
v1 = cursor_value(c1)
assert relerr(v0, v1) < 0.0001
def test_guess_all():
with test_core.bayesdb() as bdb:
bdb.sql_execute('create table foo (x numeric, y numeric, z numeric)')
bdb.sql_execute('insert into foo values (1, 2, 3)')
bdb.sql_execute('insert into foo values (4, 5, 6)')
# XXX GUESS(*)
guess.bayesdb_guess_population(bdb, 'pfoo', 'foo')
def test_misc_errors():
with test_core.t1() as (bdb, _population_id, _generator_id):
with pytest.raises(bayeslite.BQLError):
bdb.execute('create table t1 as SELECT 1 FROM t1'
# t1 already exists as a table.
' limit 1')
with pytest.raises(bayeslite.BQLError):
# t1 already exists as a table.
bdb.execute('create table t1 as simulate weight from p1'
' limit 1')
with pytest.raises(bayeslite.BQLError):
# t1x does not exist as a population.
bdb.execute('create table t1_sim as simulate weight from t1x'
' limit 1')
with pytest.raises(bayeslite.BQLError):
# p1 does not have a variable waught.
bdb.execute('create table t1_sim as simulate waught from p1'
' limit 1')
with pytest.raises(bayeslite.BQLError):
# p1 does not have a variable agee.
bdb.execute('create table t1_sim as simulate weight from p1'
' given agee = 42 limit 1')
with bdb.savepoint():
bdb.sql_execute('create table t2(x)')
with pytest.raises(bayeslite.BQLError):
# t1 already exists as a table.
bdb.execute('alter table t2 rename to t1')
with pytest.raises(NotImplementedError):
# Renaming columns is not yet implemented.
bdb.execute('alter table t1 rename weight to mass')
with pytest.raises(bayeslite.BQLError):
# xcat does not exist as a backend.
bdb.execute('create generator p1_xc for p1 using xcat()')
with pytest.raises(bayeslite.BQLError):
# p1 already exists as a population.
bdb.execute('create generator p1_cc for p1;')
with pytest.raises(bayeslite.BQLError):
# multinomial is not a known statistical type.
bdb.execute('''
create population q1 for t1(
ignore id, label, weight;
weight multinomial
)
''')
with pytest.raises(bayeslite.BQLError):
# p1_xc does not exist as a generator.
bdb.execute('alter generator p1_xc rename to p1_xcat')
with bdb.savepoint():
bdb.execute('create generator p1_xc for p1;')
with pytest.raises(bayeslite.BQLError):
# p1_xc already exists as a generator.
bdb.execute('alter generator p1_cc rename to p1_xc')
with pytest.raises(bayeslite.BQLParseError):
# WAIT is not allowed.
bdb.execute('analyze p1_cc for 1 iteration wait')
with bdb.savepoint():
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 1 iteration')
bdb.execute('initialize 1 model for p1_xc')
bdb.execute('analyze p1_xc for 1 iteration')
with pytest.raises(apsw.SQLError):
bdb.execute('select'
' nonexistent((simulate age from p1 limit 1));')
with pytest.raises(ValueError):
bdb.execute('select :x', {'y': 42})
with pytest.raises(ValueError):
bdb.execute('select :x', {'x': 53, 'y': 42})
with pytest.raises(ValueError):
bdb.execute('select ?, ?', (1,))
with pytest.raises(ValueError):
bdb.execute('select ?', (1, 2))
with pytest.raises(TypeError):
bdb.execute('select ?', 42)
with pytest.raises(NotImplementedError):
bdb.execute('infer explicit predict age confidence ac, *'
' from p1')
with pytest.raises(NotImplementedError):
bdb.execute('infer explicit predict age confidence ac,'
' t1.(select age from t1 limit 1) from p1')
with pytest.raises(bayeslite.BQLError):
try:
bdb.execute('estimate similarity to (rowid=1)'
' in the context of agee from p1')
except bayeslite.BQLError as e:
assert 'No such columns in population:' in str(e)
raise
def test_nested_simulate():
with test_core.t1() as (bdb, _population_id, _generator_id):
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 1 iteration')
bdb.execute('select (simulate age from p1 limit 1),'
' (simulate weight from p1 limit 1)').fetchall()
assert bdb.temp_table_name() == 'bayesdb_temp_2'
assert not core.bayesdb_has_table(bdb, 'bayesdb_temp_0')
assert not core.bayesdb_has_table(bdb, 'bayesdb_temp_1')
bdb.execute('simulate weight from p1'
' given age = (simulate age from p1 limit 1)'
' limit 1').fetchall()
# Make sure unwinding doesn't raise an exception. Calling
# __del__ directly, rather than via del(), has two effects:
#
# (a) It actually raises any exceptions in the method, unlike
# del(), which suppresses them.
#
# (b) It may cause a subsequent __del__ to fail and raise an
# exception, so that a subsequent del(), including an implicit
# one at the end of a scope, may print a message to stderr.
#
# Effect (a) is what we are actually trying to test. Effect
# (b) is a harmless consequence as far as pytest is concerned,
# as long as the test otherwise passes.
bdb.execute('simulate weight from p1'
' given age = (simulate age from p1 limit 1)'
' limit 1').__del__()
def test_checkpoint__ci_slow():
with test_core.t1() as (bdb, population_id, generator_id):
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 10 iterations checkpoint 1 iteration')
# No checkpoint by seconds.
with pytest.raises(NotImplementedError):
bdb.execute('analyze p1_cc for 5 seconds checkpoint 1 second')
bdb.execute('drop models from p1_cc')
bdb.execute('initialize 1 model for p1_cc')
# No checkpoint by seconds.
with pytest.raises(NotImplementedError):
bdb.execute('analyze p1_cc for 5 iterations checkpoint 1 second')
bdb.execute('drop models from p1_cc')
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 1 iteration checkpoint 2 iterations')
def test_infer_confidence__ci_slow():
with test_core.t1() as (bdb, _population_id, _generator_id):
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 1 iteration')
bdb.execute('infer explicit rowid, rowid as another_rowid, 4,'
' age, predict age as age_inf confidence age_conf'
' from p1').fetchall()
def test_infer_as_estimate():
with test_core.t1() as (bdb, _population_id, _generator_id):
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 1 iteration')
bdb.execute('infer explicit predictive probability of age'
' from p1').fetchall()
def test_infer_error():
with test_core.t1() as (bdb, _population_id, _generator_id):
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('infer explicit predict age confidence age_conf'
' from p1').fetchall()
with pytest.raises(bayeslite.BQLError):
bdb.execute('infer explicit predict agee confidence age_conf'
' from p1').fetchall()
def test_estimate_by():
with test_core.t1() as (bdb, _population_id, _generator_id):
bdb.execute('initialize 1 model for p1_cc')
bdb.execute('analyze p1_cc for 1 iteration')
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate predictive probability of age'
' by p1')
with pytest.raises(bayeslite.BQLError):
bdb.execute('estimate similarity to (rowid=1) '
'in the context of age by p1')
def check(x, bindings=None):
assert len(bdb.execute(x, bindings=bindings).fetchall()) == 1
check('estimate probability density of age = 42 by p1')
check('estimate dependence probability of age with weight by p1')
check('estimate mutual information of age with weight by p1')
check('estimate correlation of age with weight by p1')
check('estimate correlation pvalue of age with weight by p1')
rowid = bdb.execute('select min(rowid) from t1').fetchall()[0][0]
check('''
estimate similarity of (rowid=?) to (rowid=?)
in the context of weight by p1
''', (rowid, rowid,))
def test_empty_cursor():
with bayeslite.bayesdb_open() as bdb:
assert bdb.execute('SELECT 0').connection == bdb
empty(bdb.execute('BEGIN'))
empty(bdb.execute('COMMIT'))
empty(bdb.sql_execute('CREATE TABLE t(x, y, z)'))
empty(bdb.sql_execute('INSERT INTO t VALUES(1,2,3)'))
empty(bdb.sql_execute('INSERT INTO t VALUES(4,5,6)'))
empty(bdb.sql_execute('INSERT INTO t VALUES(7,8,9)'))
empty(bdb.execute('CREATE POPULATION p FOR t '
'(IGNORE z,y; x NOMINAL)'))
empty(bdb.execute('CREATE GENERATOR p_cc FOR p;'))
empty(bdb.execute('INITIALIZE 1 MODEL FOR p_cc'))
empty(bdb.execute('DROP GENERATOR p_cc'))
empty(bdb.execute('DROP POPULATION p'))
empty(bdb.execute('DROP TABLE t'))
def test_create_generator_ifnotexists():
# XXX Test other backends too, because they have a role in ensuring that
# this works. Their create_generator will still be called.
#
# [TRC 20160627: The above comment appears to be no longer true --
# if it was ever true.]
for using_clause in ('cgpm()',):
with bayeslite.bayesdb_open() as bdb:
bdb.sql_execute('CREATE TABLE t(x, y, z)')
bdb.sql_execute('INSERT INTO t VALUES(1,2,3)')
bdb.execute('''
CREATE POPULATION p FOR t (
x NUMERICAL;
y NUMERICAL;
z NOMINAL;
)
''')
for _i in (0, 1):
bdb.execute('CREATE GENERATOR IF NOT EXISTS p_cc FOR p USING '
+ using_clause)
try:
bdb.execute('CREATE GENERATOR p_cc FOR p USING ' + using_clause)
assert False # Should have said it exists.
except bayeslite.BQLError:
pass
def test_bql_rand():
with bayeslite.bayesdb_open() as bdb:
bdb.sql_execute('CREATE TABLE frobotz(x)')
for _ in range(10):
bdb.sql_execute('INSERT INTO frobotz VALUES(2)')
cursor = bdb.execute('SELECT bql_rand() FROM frobotz LIMIT 10;')
rands = cursor.fetchall()
# These are "the" random numbers (internal PRNG is seeded to 0)
ans = [(0.28348770982811367,), (0.4789774612650598,), (0.07824908989551316,),
(0.6091223239372148,), (0.03906608409906187,), (0.3690599096081546,),
(0.8223420512129717,), (0.7777771914916722,), (0.061856771629497986,),
(0.6492586781908201,)]
assert rands == ans
def test_bql_rand2():
seed = struct.pack('<QQQQ', 0, 0, 0, 3)
with bayeslite.bayesdb_open(seed=seed) as bdb:
bdb.sql_execute('CREATE TABLE frobotz(x)')
for _ in range(10):
bdb.sql_execute('INSERT INTO frobotz VALUES(2)')
cursor = bdb.execute('SELECT bql_rand() FROM frobotz LIMIT 10;')
rands = cursor.fetchall()
ans = [(0.8351877951287725,), (0.9735099617243271,), (0.026142315910925418,),
(0.09380653289687524,), (0.1097050387582088,), (0.33154896906379605,),
(0.4579314980719317,), (0.09072802203491703,), (0.5276180968829105,),
(0.9993280772797679,)]
assert rands == ans
class MockTracerOneQuery(bayeslite.IBayesDBTracer):
def __init__(self, q, qid):
self.q = q
self.qid = qid
self.start_calls = 0
self.ready_calls = 0
self.error_calls = 0
self.finished_calls = 0
self.abandoned_calls = 0
def start(self, qid, query, bindings):
assert qid == self.qid
assert query == self.q
assert bindings == ()
self.start_calls += 1
def ready(self, qid, _cursor):
assert qid == self.qid
self.ready_calls += 1
def error(self, qid, _e):
assert qid == self.qid
self.error_calls += 1
def finished(self, qid):
assert qid == self.qid
self.finished_calls += 1
def abandoned(self, qid):
assert qid == self.qid
self.abandoned_calls += 1
def test_tracing_smoke():
with test_core.t1() as (bdb, _population_id, _generator_id):
q = 'SELECT * FROM t1'
tracer = MockTracerOneQuery(q, 1)
bdb.trace(tracer)
cursor = bdb.execute(q)
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 0
assert tracer.finished_calls == 0
assert tracer.abandoned_calls == 0
cursor.fetchall()
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 0
assert tracer.finished_calls == 1
assert tracer.abandoned_calls == 0
del cursor
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 0
assert tracer.finished_calls == 1
assert tracer.abandoned_calls == 1
bdb.untrace(tracer)
# XXX Make sure the whole cursor API works.
q = 'SELECT 42'
tracer = MockTracerOneQuery(q, 2)
bdb.trace(tracer)
cursor = bdb.execute(q)
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 0
assert tracer.finished_calls == 0
assert tracer.abandoned_calls == 0
assert cursor.fetchvalue() == 42
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 0
assert tracer.finished_calls == 1
assert tracer.abandoned_calls == 0
del cursor
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 0
assert tracer.finished_calls == 1
assert tracer.abandoned_calls == 1
def test_tracing_error_smoke():
with test_core.t1() as (bdb, _population_id, _generator_id):
q = 'SELECT * FROM wrong'
tracer = MockTracerOneQuery(q, 1)
bdb.trace(tracer)
with pytest.raises(apsw.SQLError):
bdb.execute(q)
assert tracer.start_calls == 1
assert tracer.ready_calls == 0
assert tracer.error_calls == 1
assert tracer.finished_calls == 0
assert tracer.abandoned_calls == 0
class Boom(Exception):
pass
class ErroneousBackend(troll.TrollBackend):
def __init__(self):
self.call_ct = 0
def name(self):
return 'erroneous'
def logpdf_joint(self, *_args, **_kwargs):
if self.call_ct > 10: # Wait to avoid raising during sqlite's prefetch
raise Boom()
self.call_ct += 1
return 0
def test_tracing_execution_error_smoke():
with test_core.t1() as (bdb, _population_id, _generator_id):
bayeslite.bayesdb_register_backend(bdb, ErroneousBackend())
bdb.execute('DROP GENERATOR p1_cc')
bdb.execute('CREATE GENERATOR p1_err FOR p1 USING erroneous()')
q = 'ESTIMATE PREDICTIVE PROBABILITY OF age FROM p1'
tracer = MockTracerOneQuery(q, 1)
bdb.trace(tracer)
cursor = bdb.execute(q)
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 0
assert tracer.finished_calls == 0
assert tracer.abandoned_calls == 0
with pytest.raises(Boom):
cursor.fetchall()
assert tracer.start_calls == 1
assert tracer.ready_calls == 1
assert tracer.error_calls == 1
assert tracer.finished_calls == 0
assert tracer.abandoned_calls == 0
def test_pdf_var():
with test_core.t1() as (bdb, population_id, _generator_id):
bdb.execute('initialize 6 models for p1_cc;')
c = bdb.execute(
'estimate probability density of label = label from p1')
c.fetchall()
assert bql2sql(
'estimate probability density of label = label from p1') == \
'SELECT bql_pdf_joint(1, NULL, NULL, 1, "label") FROM "t1";'
|
[
"bayeslite.core.bayesdb_generator_table",
"bayeslite.backends.cgpm_backend.CGPM_Backend",
"test_csv.bayesdb_csv_file",
"bayeslite.bayesdb_open",
"bayeslite.bayesdb_read_csv",
"bayeslite.compiler.compile_query",
"bayeslite.guess.bayesdb_guess_population",
"StringIO.StringIO",
"test_core.t1_data",
"test_core.t1",
"struct.pack",
"pytest.raises",
"bayeslite.compiler.Output",
"bayeslite.core.bayesdb_get_population",
"bayeslite.core.bayesdb_has_variable",
"bayeslite.ast.is_query",
"bayeslite.parse.parse_bql_string",
"bayeslite.core.bayesdb_population_name",
"bayeslite.math_util.relerr",
"bayeslite.core.bayesdb_generator_name",
"pytest.mark.xfail",
"bayeslite.core.bayesdb_variable_number",
"test_core.bayesdb",
"bayeslite.util.cursor_value",
"bayeslite.core.bayesdb_variable_stattype",
"test_core.t1_schema",
"stochastic.stochastic",
"bayeslite.core.bayesdb_has_table",
"bayeslite.core.bayesdb_get_generator"
] |
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|
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 30 20:15:18 2019
@author: autol
"""
#%%
from plotxy import plot_gd_xy,iters_gd_plot,plot_gd_contour
from initdata import init_data,init_data1,data_b,init_data_house
from func import gradient_descent_f
from varclass import VarSetX
from sklearn.model_selection import ParameterGrid
import matplotlib.pyplot as plt
import numpy as np
#%% Example
n=20
w = np.ones(2);w
X,y=init_data1(n,45,w,b=0);X # eta = 1e-2
#X,y=init_data_house(n,45,w);X # 1e-7
X_b = data_b(X);X_b
y
#%%
B_b = np.linalg.inv(X_b.T.dot(X_b)) @ (X_b.T.dot(y));B_b
B = np.linalg.inv(X.T.dot(X)) @ (X.T.dot(y));B
#%%
#w = np.array([-2.5,-2.5]);w
#w = np.array([0.,0.]);w
A = 2./len(y)*X.T.dot(X) # ŋ=1 # 海森矩阵
J = lambda w: np.mean((X.dot(w)-y)**2) # 目标函数
gJ = lambda w: 2./len(y)*X.T.dot(X.dot(w)-y) # 梯度函数
#A = X.T@X # ŋ=1/n
#J = lambda w: w.dot(A).dot(w)
#gJ = lambda w: A.dot(w)
pgrid =list(ParameterGrid(dict(sgd=[0,1],
isStep=[0],
# ρ=[.5,5,10],
# n_b=[2,5],
# ŋ_a=[1], # ŋ_a 要大于1
method=['mm21','mm22','mm23','mm24','mm25'],
#method=['mm31','mm32','mm33','mm34','mm30'],
#method=['mm40','mm41','mm42','mm43','mm44','mm45','mm46'],
#method=['mm51','mm52','mm53','mm54','mm55'],
#method=['mm10'],
#method=['mm90','mm91','mm92','mm93','mm94',],
)))
skwargs = dict(A=A,ŋ=.1,ŋ_a=1,tol=0.05,
ε=.001,λ=.1,α=.5,γ=0.5,β1=.9,β2=.999)
wws=[];ess=[];rets=[]
for pg in pgrid:
w0 = w.copy()-np.random.uniform(1,3,2) #任意起点
kwargs=dict(X=X.copy(),y=y.copy(),
gJ=gJ,J=J,w=w0,)
kwargs.update(skwargs) ; kwargs.update(pg) ; var = VarSetX(kwargs)
ret = gradient_descent_f(var,n_iters=20,skipConv=0,
**kwargs)
ww = np.stack(ret['wh'][:,1])
es = ret['wh'][:,2]
wws.append(ww); ess.append(es); rets.append(ret)
print(ww,es)
#%%
x = np.zeros(len(w));x
x = np.vstack([x, np.amax(X,axis=0)]);x
x_b = data_b(x)
yh = x.dot(B); yh
fig, ax = plt.subplots(figsize = (8,8))
ax.plot(X[:,0],y,'o')
ax.plot(x[:,0],yh,color='b',linewidth=5)
ws = [ww[int(i)] for i in np.linspace(0,len(ww)-1,10)]
for wx in ws:
yh = x.dot(wx);yh # 画渐近的基准线
ax.plot(x[:,0],yh,color='r')
ax.set_xlabel('x')
ax.set_ylabel('y')
#%%
plot_gd_contour(J,wws,ess,pgrid,skwargs,B)
#%%
paras = skwargs.copy()
paras.pop('A')
iters_gd_plot(rets,var,pgrid,paras=paras,
**kwargs)
|
[
"numpy.stack",
"numpy.random.uniform",
"varclass.VarSetX",
"plotxy.plot_gd_contour",
"numpy.ones",
"initdata.data_b",
"plotxy.iters_gd_plot",
"numpy.amax",
"initdata.init_data1",
"func.gradient_descent_f",
"matplotlib.pyplot.subplots"
] |
[((402, 412), 'numpy.ones', 'np.ones', (['(2)'], {}), '(2)\n', (409, 412), True, 'import numpy as np\n'), ((419, 444), 'initdata.init_data1', 'init_data1', (['n', '(45)', 'w'], {'b': '(0)'}), '(n, 45, w, b=0)\n', (429, 444), False, 'from initdata import init_data, init_data1, data_b, init_data_house\n'), ((502, 511), 'initdata.data_b', 'data_b', (['X'], {}), '(X)\n', (508, 511), False, 'from initdata import init_data, init_data1, data_b, init_data_house\n'), ((2015, 2024), 'initdata.data_b', 'data_b', (['x'], {}), '(x)\n', (2021, 2024), False, 'from initdata import init_data, init_data1, data_b, init_data_house\n'), ((2054, 2082), 'matplotlib.pyplot.subplots', 'plt.subplots', ([], {'figsize': '(8, 8)'}), '(figsize=(8, 8))\n', (2066, 2082), True, 'import matplotlib.pyplot as plt\n'), ((2328, 2375), 'plotxy.plot_gd_contour', 'plot_gd_contour', (['J', 'wws', 'ess', 'pgrid', 'skwargs', 'B'], {}), '(J, wws, ess, pgrid, skwargs, B)\n', (2343, 2375), False, 'from plotxy import plot_gd_xy, iters_gd_plot, plot_gd_contour\n'), ((2414, 2468), 'plotxy.iters_gd_plot', 'iters_gd_plot', (['rets', 'var', 'pgrid'], {'paras': 'paras'}), '(rets, var, pgrid, paras=paras, **kwargs)\n', (2427, 2468), False, 'from plotxy import plot_gd_xy, iters_gd_plot, plot_gd_contour\n'), ((1700, 1715), 'varclass.VarSetX', 'VarSetX', (['kwargs'], {}), '(kwargs)\n', (1707, 1715), False, 'from varclass import VarSetX\n'), ((1726, 1783), 'func.gradient_descent_f', 'gradient_descent_f', (['var'], {'n_iters': '(20)', 'skipConv': '(0)'}), '(var, n_iters=20, skipConv=0, **kwargs)\n', (1744, 1783), False, 'from func import gradient_descent_f\n'), ((1821, 1846), 'numpy.stack', 'np.stack', (["ret['wh'][:, 1]"], {}), "(ret['wh'][:, 1])\n", (1829, 1846), True, 'import numpy as np\n'), ((1541, 1567), 'numpy.random.uniform', 'np.random.uniform', (['(1)', '(3)', '(2)'], {}), '(1, 3, 2)\n', (1558, 1567), True, 'import numpy as np\n'), ((1987, 2005), 'numpy.amax', 'np.amax', (['X'], {'axis': '(0)'}), '(X, axis=0)\n', (1994, 2005), True, 'import numpy as np\n')]
|
# ServiceSchema.py
from __future__ import print_function
from __future__ import absolute_import
from optparse import OptionParser, OptionValueError
import os
import platform as plat
import sys
if sys.version_info >= (3, 8) and plat.system().lower() == "windows":
# pylint: disable=no-member
with os.add_dll_directory(os.getenv('BLPAPI_LIBDIR')):
import blpapi
else:
import blpapi
REFERENCE_DATA_RESPONSE = blpapi.Name("ReferenceDataResponse")
ELEMENT_DATATYPE_NAMES = {
blpapi.DataType.BOOL: "BOOL",
blpapi.DataType.CHAR: "CHAR",
blpapi.DataType.BYTE: "BYTE",
blpapi.DataType.INT32: "INT32",
blpapi.DataType.INT64: "INT64",
blpapi.DataType.FLOAT32: "FLOAT32",
blpapi.DataType.FLOAT64: "FLOAT64",
blpapi.DataType.STRING: "STRING",
blpapi.DataType.BYTEARRAY: "BYTEARRAY",
blpapi.DataType.DATE: "DATE",
blpapi.DataType.TIME: "TIME",
blpapi.DataType.DECIMAL: "DECIMAL",
blpapi.DataType.DATETIME: "DATETIME",
blpapi.DataType.ENUMERATION: "ENUMERATION",
blpapi.DataType.SEQUENCE: "SEQUENCE",
blpapi.DataType.CHOICE: "CHOICE",
blpapi.DataType.CORRELATION_ID: "CORRELATION_ID"
}
SCHEMA_STATUS_NAMES = {
blpapi.SchemaStatus.ACTIVE: "ACTIVE",
blpapi.SchemaStatus.DEPRECATED: "DEPRECATED",
blpapi.SchemaStatus.INACTIVE: "INACTIVE",
blpapi.SchemaStatus.PENDING_DEPRECATION: "PENDING"
}
def authOptionCallback(_option, _opt, value, parser):
"""Parse authorization options from user input"""
vals = value.split('=', 1)
if value == "user":
authUser = blpapi.AuthUser.createWithLogonName()
authOptions = blpapi.AuthOptions.createWithUser(authUser)
elif value == "none":
authOptions = None
elif vals[0] == "app" and len(vals) == 2:
appName = vals[1]
authOptions = blpapi.AuthOptions.createWithApp(appName)
elif vals[0] == "userapp" and len(vals) == 2:
appName = vals[1]
authUser = blpapi.AuthUser.createWithLogonName()
authOptions = blpapi.AuthOptions\
.createWithUserAndApp(authUser, appName)
elif vals[0] == "dir" and len(vals) == 2:
activeDirectoryProperty = vals[1]
authUser = blpapi.AuthUser\
.createWithActiveDirectoryProperty(activeDirectoryProperty)
authOptions = blpapi.AuthOptions.createWithUser(authUser)
elif vals[0] == "manual":
parts = []
if len(vals) == 2:
parts = vals[1].split(',')
if len(parts) != 3:
raise OptionValueError("Invalid auth option {}".format(value))
appName, ip, userId = parts
authUser = blpapi.AuthUser.createWithManualOptions(userId, ip)
authOptions = blpapi.AuthOptions.createWithUserAndApp(authUser, appName)
else:
raise OptionValueError("Invalid auth option '{}'".format(value))
parser.values.auth = {'option' : authOptions}
def parseCmdLine():
parser = OptionParser()
parser.add_option("-a",
"--host",
dest="host",
help="HOST address to connect to",
metavar="HOST",
default="localhost")
parser.add_option("-p",
"--port",
dest="port",
type="int",
help="PORT to connect to (%default)",
metavar="PORT",
default=8194)
parser.add_option("-s",
"--service",
default="//blp/apiflds",
help="SERVICE to print the schema of "
"('//blp/apiflds' by default)")
parser.add_option("--auth",
dest="auth",
help="authentication option: "
"user|none|app=<app>|userapp=<app>|dir=<property>"
"|manual=<app,ip,user>"
" (default: user)\n"
"'none' is applicable to Desktop API product "
"that requires Bloomberg Professional service "
"to be installed locally.",
metavar="option",
action="callback",
callback=authOptionCallback,
type="string",
default={"option" :
blpapi.AuthOptions.createWithUser(
blpapi.AuthUser.createWithLogonName())})
(options, _) = parser.parse_args()
return options
def printMessage(msg):
print("[{0}]: {1}".format(", ".join(map(str, msg.correlationIds())), msg))
def getIndent(level):
return "" if level == 0 else " ".ljust(level * 2)
# Print enumeration (constant list)
def printEnumeration(cl, level):
indent = getIndent(level + 1)
print(indent + " {0} {1} {2} \"{3}\" possible values:".format(
cl.name(),
SCHEMA_STATUS_NAMES[cl.status()],
ELEMENT_DATATYPE_NAMES[cl.datatype()],
cl.description()))
# Enumerate and print all constant list's values (constants)
for i in cl:
print(indent + " {0} {1} {2} \"{3}\" = {4!s}".format(
i.name(),
SCHEMA_STATUS_NAMES[i.status()],
ELEMENT_DATATYPE_NAMES[i.datatype()],
i.description(),
i.getValue()))
# Recursively print element definition
def printElementDefinition(ed, level=0):
indent = getIndent(level)
maxValues = ed.maxValues()
if maxValues == blpapi.SchemaElementDefinition.UNBOUNDED:
valuesRange = "[{0}, INF)".format(ed.minValues())
else:
valuesRange = "[{0}, {1}]".format(ed.minValues(), maxValues)
# Get and print alternate element names
alternateNames = ed.alternateNames()
if alternateNames:
alternateNames = "[{0}]".format(",".join(map(str, alternateNames)))
else:
alternateNames = ""
print(indent + "* {0} {1} {2} {3} \"{4}\"".format(
ed.name(),
SCHEMA_STATUS_NAMES[ed.status()],
valuesRange,
alternateNames,
ed.description()))
# Get and print related type definition
td = ed.typeDefinition()
print(indent + " {0} {1} {2} {3}{4}{5}\"{6}\"".format(
td.name(),
SCHEMA_STATUS_NAMES[td.status()],
ELEMENT_DATATYPE_NAMES[td.datatype()],
"complex " if td.isComplexType() else "",
"simple " if td.isSimpleType() else "",
"enum " if td.isEnumerationType() else "",
td.description()))
# Get and print all possible values for enumeration type
enumeration = td.enumeration()
if not enumeration is None:
printEnumeration(enumeration, level)
if td.numElementDefinitions():
print(indent + " Elements[{0}]:".format(
td.numElementDefinitions()))
# Enumerate and print all sub-element definitions
for i in td.elementDefinitions():
printElementDefinition(i, level + 1)
def printOperation(operation, _service):
print("{0} \"{1}\" Request:".format(
operation.name(),
operation.description()))
# Print operation's request definition
printElementDefinition(operation.requestDefinition(), 1)
print("Responses[{0}]:".format(operation.numResponseDefinitions()))
# Enumerate and print all operation's response definitions
for r in operation.responseDefinitions():
printElementDefinition(r, 1)
print()
def main():
options = parseCmdLine()
# Fill SessionOptions
sessionOptions = blpapi.SessionOptions()
sessionOptions.setServerHost(options.host)
sessionOptions.setServerPort(options.port)
sessionOptions.setSessionIdentityOptions(options.auth['option'])
# Create a Session
session = blpapi.Session(sessionOptions)
# Start a Session
if not session.start():
raise Exception("Can't start session.")
try:
print("Session started.")
# Open service to get reference data from
if not session.openService(options.service):
raise Exception("Can't open '{0}' service.".format(
options.service))
# Obtain previously opened service
service = session.getService(options.service)
print("Service {0}:".format(options.service))
print("Service event definitions[{0}]:".format(
service.numEventDefinitions()))
# Enumerate and print all service's event definitions
for ed in service.eventDefinitions():
printElementDefinition(ed)
print()
print("Operations[{0}]:".format(service.numOperations()))
# Enumerate and print all service's operations
for operation in service.operations():
printOperation(operation, service)
finally:
# Stop the session
session.stop()
if __name__ == "__main__":
print("ServiceSchema")
try:
main()
except KeyboardInterrupt:
print("Ctrl+C pressed. Stopping...")
__copyright__ = """
Copyright 2012. Bloomberg Finance L.P.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to
deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions: The above
copyright notice and this permission notice shall be included in all copies
or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
"""
|
[
"blpapi.AuthUser.createWithManualOptions",
"optparse.OptionParser",
"blpapi.AuthOptions.createWithUserAndApp",
"blpapi.AuthOptions.createWithUser",
"blpapi.AuthOptions.createWithApp",
"blpapi.SessionOptions",
"blpapi.Name",
"platform.system",
"blpapi.Session",
"blpapi.AuthUser.createWithActiveDirectoryProperty",
"os.getenv",
"blpapi.AuthUser.createWithLogonName"
] |
[((429, 465), 'blpapi.Name', 'blpapi.Name', (['"""ReferenceDataResponse"""'], {}), "('ReferenceDataResponse')\n", (440, 465), False, 'import blpapi\n'), ((2930, 2944), 'optparse.OptionParser', 'OptionParser', ([], {}), '()\n', (2942, 2944), False, 'from optparse import OptionParser, OptionValueError\n'), ((7587, 7610), 'blpapi.SessionOptions', 'blpapi.SessionOptions', ([], {}), '()\n', (7608, 7610), False, 'import blpapi\n'), ((7812, 7842), 'blpapi.Session', 'blpapi.Session', (['sessionOptions'], {}), '(sessionOptions)\n', (7826, 7842), False, 'import blpapi\n'), ((1570, 1607), 'blpapi.AuthUser.createWithLogonName', 'blpapi.AuthUser.createWithLogonName', ([], {}), '()\n', (1605, 1607), False, 'import blpapi\n'), ((1630, 1673), 'blpapi.AuthOptions.createWithUser', 'blpapi.AuthOptions.createWithUser', (['authUser'], {}), '(authUser)\n', (1663, 1673), False, 'import blpapi\n'), ((327, 353), 'os.getenv', 'os.getenv', (['"""BLPAPI_LIBDIR"""'], {}), "('BLPAPI_LIBDIR')\n", (336, 353), False, 'import os\n'), ((229, 242), 'platform.system', 'plat.system', ([], {}), '()\n', (240, 242), True, 'import platform as plat\n'), ((1821, 1862), 'blpapi.AuthOptions.createWithApp', 'blpapi.AuthOptions.createWithApp', (['appName'], {}), '(appName)\n', (1853, 1862), False, 'import blpapi\n'), ((1958, 1995), 'blpapi.AuthUser.createWithLogonName', 'blpapi.AuthUser.createWithLogonName', ([], {}), '()\n', (1993, 1995), False, 'import blpapi\n'), ((2018, 2076), 'blpapi.AuthOptions.createWithUserAndApp', 'blpapi.AuthOptions.createWithUserAndApp', (['authUser', 'appName'], {}), '(authUser, appName)\n', (2057, 2076), False, 'import blpapi\n'), ((4482, 4519), 'blpapi.AuthUser.createWithLogonName', 'blpapi.AuthUser.createWithLogonName', ([], {}), '()\n', (4517, 4519), False, 'import blpapi\n'), ((2198, 2272), 'blpapi.AuthUser.createWithActiveDirectoryProperty', 'blpapi.AuthUser.createWithActiveDirectoryProperty', (['activeDirectoryProperty'], {}), '(activeDirectoryProperty)\n', (2247, 2272), False, 'import blpapi\n'), ((2309, 2352), 'blpapi.AuthOptions.createWithUser', 'blpapi.AuthOptions.createWithUser', (['authUser'], {}), '(authUser)\n', (2342, 2352), False, 'import blpapi\n'), ((2629, 2680), 'blpapi.AuthUser.createWithManualOptions', 'blpapi.AuthUser.createWithManualOptions', (['userId', 'ip'], {}), '(userId, ip)\n', (2668, 2680), False, 'import blpapi\n'), ((2703, 2761), 'blpapi.AuthOptions.createWithUserAndApp', 'blpapi.AuthOptions.createWithUserAndApp', (['authUser', 'appName'], {}), '(authUser, appName)\n', (2742, 2761), False, 'import blpapi\n')]
|
from oraclecxcommerce.modules import ProfilesModule
import pytest
def test_instantiate_profile_module_class_should_return_not_implemented_error():
with pytest.raises(NotImplementedError):
occ = ProfilesModule()
|
[
"oraclecxcommerce.modules.ProfilesModule",
"pytest.raises"
] |
[((158, 192), 'pytest.raises', 'pytest.raises', (['NotImplementedError'], {}), '(NotImplementedError)\n', (171, 192), False, 'import pytest\n'), ((208, 224), 'oraclecxcommerce.modules.ProfilesModule', 'ProfilesModule', ([], {}), '()\n', (222, 224), False, 'from oraclecxcommerce.modules import ProfilesModule\n')]
|
from aws_cdk import aws_iam, aws_sqs, core
from common.common_stack import CommonStack
from common.region_aware_stack import RegionAwareStack
class SqsStack(RegionAwareStack):
def __init__(self, scope: core.Construct, id: str, common_stack: CommonStack, **kwargs) -> None:
super().__init__(scope, id, **kwargs)
self._supported_in_region = self.is_service_supported_in_region()
# Test simply asserts the existence of a queue
aws_sqs.Queue(self, "integ_test_sqs_queue")
queue_policy = aws_iam.PolicyStatement(
effect=aws_iam.Effect.ALLOW,
actions=["sqs:GetQueueAttributes"],
resources=[f"arn:aws:sqs:{self.region}:{self.account}:*"],
)
common_stack.add_to_common_role_policies(self, policy_to_add=queue_policy)
all_resources_policy = aws_iam.PolicyStatement(
effect=aws_iam.Effect.ALLOW,
actions=["sqs:ListQueues"],
resources=["*"],
)
common_stack.add_to_common_role_policies(self, policy_to_add=all_resources_policy)
|
[
"aws_cdk.aws_iam.PolicyStatement",
"aws_cdk.aws_sqs.Queue"
] |
[((464, 507), 'aws_cdk.aws_sqs.Queue', 'aws_sqs.Queue', (['self', '"""integ_test_sqs_queue"""'], {}), "(self, 'integ_test_sqs_queue')\n", (477, 507), False, 'from aws_cdk import aws_iam, aws_sqs, core\n'), ((532, 689), 'aws_cdk.aws_iam.PolicyStatement', 'aws_iam.PolicyStatement', ([], {'effect': 'aws_iam.Effect.ALLOW', 'actions': "['sqs:GetQueueAttributes']", 'resources': "[f'arn:aws:sqs:{self.region}:{self.account}:*']"}), "(effect=aws_iam.Effect.ALLOW, actions=[\n 'sqs:GetQueueAttributes'], resources=[\n f'arn:aws:sqs:{self.region}:{self.account}:*'])\n", (555, 689), False, 'from aws_cdk import aws_iam, aws_sqs, core\n'), ((842, 944), 'aws_cdk.aws_iam.PolicyStatement', 'aws_iam.PolicyStatement', ([], {'effect': 'aws_iam.Effect.ALLOW', 'actions': "['sqs:ListQueues']", 'resources': "['*']"}), "(effect=aws_iam.Effect.ALLOW, actions=[\n 'sqs:ListQueues'], resources=['*'])\n", (865, 944), False, 'from aws_cdk import aws_iam, aws_sqs, core\n')]
|
##########################################################
# pytorch-kaldi v.0.1
# <NAME>, <NAME>
# Mila, University of Montreal
# October 2018
#
# Description: This script generates kaldi ark files containing raw features.
# The file list must be a file containing "snt_id file.wav".
# Note that only wav files are supported here (sphere or other format are not supported)
##########################################################
import scipy.io.wavfile
import math
import numpy as np
import os
from data_io import read_vec_int_ark, write_mat
# Run it for all the data chunks (e.g., train, dev, test) => uncomment
lab_folder = "/users/parcollet/KALDI/kaldi-trunk/egs/timit/s5/exp/dnn4_pretrain-dbn_dnn_ali_test"
lab_opts = "ali-to-pdf"
out_folder = "/users/parcollet/KALDI/kaldi-trunk/egs/timit/s5/data/raw_TIMIT_200ms/test"
wav_lst = "/users/parcollet/KALDI/kaldi-trunk/egs/timit/s5/data/test/wav.lst"
scp_file_out = "/users/parcollet/KALDI/kaldi-trunk/egs/timit/s5/data/raw_TIMIT_200ms/test/feats_raw.scp"
# lab_folder='quick_test/dnn4_pretrain-dbn_dnn_ali_dev'
# lab_opts='ali-to-pdf'
# out_folder='raw_TIMIT_200ms/dev'
# wav_lst='/home/mirco/pytorch-kaldi-new/quick_test/data/dev/wav_lst.scp'
# scp_file_out='quick_test/data/dev/feats_raw.scp'
# lab_folder='quick_test/dnn4_pretrain-dbn_dnn_ali_test'
# lab_opts='ali-to-pdf'
# out_folder='raw_TIMIT_200ms/test'
# wav_lst='/home/mirco/pytorch-kaldi-new/quick_test/data/test/wav_lst.scp'
# scp_file_out='quick_test/data/test/feats_raw.scp'
sig_fs = 16000 # Hz
sig_wlen = 200 # ms
lab_fs = 16000 # Hz
lab_wlen = 25 # ms
lab_wshift = 10 # ms
sig_wlen_samp = int((sig_fs * sig_wlen) / 1000)
lab_wlen_samp = int((lab_fs * lab_wlen) / 1000)
lab_wshift_samp = int((lab_fs * lab_wshift) / 1000)
# Create the output folder
try:
os.stat(out_folder)
except:
os.makedirs(out_folder)
# Creare the scp file
scp_file = open(scp_file_out, "w")
# reading the labels
lab = {
k: v
for k, v in read_vec_int_ark(
"gunzip -c " + lab_folder + "/ali*.gz | " + lab_opts + " " + lab_folder + "/final.mdl ark:- ark:-|", out_folder
)
}
# reading the list file
with open(wav_lst) as f:
sig_lst = f.readlines()
sig_lst = [x.strip() for x in sig_lst]
for sig_file in sig_lst:
sig_id = sig_file.split(" ")[0]
sig_path = sig_file.split(" ")[1]
[fs, signal] = scipy.io.wavfile.read(sig_path)
signal = signal.astype(float) / 32768
signal = signal / np.max(np.abs(signal))
cnt_fr = 0
beg_samp = 0
frame_all = []
while beg_samp + lab_wlen_samp < signal.shape[0]:
sample_fr = np.zeros(sig_wlen_samp)
central_sample_lab = int(((beg_samp + lab_wlen_samp / 2) - 1))
central_fr_index = int(((sig_wlen_samp / 2) - 1))
beg_signal_fr = int(central_sample_lab - (sig_wlen_samp / 2))
end_signal_fr = int(central_sample_lab + (sig_wlen_samp / 2))
if beg_signal_fr >= 0 and end_signal_fr <= signal.shape[0]:
sample_fr = signal[beg_signal_fr:end_signal_fr]
else:
if beg_signal_fr < 0:
n_left_samples = central_sample_lab
sample_fr[central_fr_index - n_left_samples + 1 :] = signal[0:end_signal_fr]
if end_signal_fr > signal.shape[0]:
n_right_samples = signal.shape[0] - central_sample_lab
sample_fr[0 : central_fr_index + n_right_samples + 1] = signal[beg_signal_fr:]
frame_all.append(sample_fr)
cnt_fr = cnt_fr + 1
beg_samp = beg_samp + lab_wshift_samp
frame_all = np.asarray(frame_all)
# Save the matrix into a kaldi ark
out_file = out_folder + "/" + sig_id + ".ark"
write_mat(out_folder, out_file, frame_all, key=sig_id)
print(sig_id)
scp_file.write(sig_id + " " + out_folder + "/" + sig_id + ".ark:" + str(len(sig_id) + 1) + "\n")
N_fr_comp = 1 + math.floor((signal.shape[0] - 400) / 160)
# print("%s %i %i "%(lab[sig_id].shape[0],N_fr_comp,cnt_fr))
scp_file.close()
|
[
"data_io.write_mat",
"numpy.abs",
"os.makedirs",
"os.stat",
"data_io.read_vec_int_ark",
"numpy.asarray",
"numpy.zeros",
"math.floor"
] |
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|
#!/usr/bin/env python3
import re
from .codes import codes
class Emoji:
def __init__(self, const):
if len(const) == 1:
self.__fromUnicode(const)
elif const[0] == ":":
self.__fromAlias(const)
else:
self.__fromEscape(const)
self.aliases = codes[self.escape]
self.alias = self.aliases[0]
self.char = bytes("\\u"+self.escape, "ascii").decode("unicode-escape")[0]
self.is_supported = hex(ord(self.char))[2:] == self.escape
def __fromUnicode(self, char):
escape = hex(ord(char))[2:]
if escape in codes:
self.escape = escape
else:
raise ValueError
def __fromAlias(self, alias):
for k, v in codes.items():
if alias in v:
self.escape = k
break
else:
raise ValueError
def __fromEscape(self, escape):
if escape in codes.keys():
self.escape = escape
else:
raise ValueError
def replaceAliases(text, trailingSpaces=0, force=False):
""" Replaces all supported emoji-cheat-sheet aliases in a text with the corresponding emoji. """
def replAlias(m):
alias = ":"+m.group(1)+":"
if not Emoji(alias).is_supported and not force:
return alias
try:
return Emoji(alias).char + trailingSpaces * " "
except ValueError:
return alias
return re.sub(":([^s:]?[\w-]+):", replAlias, text)
def replaceEmoji(text, trailingSpaces=0):
""" Replaces all emojis with their primary emoji-cheat-sheet alias. """
i = 0
while i < len(text):
escape = hex(ord(text[i]))[2:]
if escape in codes.keys():
text = text.replace(text[i] + trailingSpaces*" ", Emoji(escape).alias)
i += len(Emoji(escape).alias)
else:
i += 1
return text
|
[
"re.sub"
] |
[((1293, 1337), 're.sub', 're.sub', (['""":([^s:]?[\\\\w-]+):"""', 'replAlias', 'text'], {}), "(':([^s:]?[\\\\w-]+):', replAlias, text)\n", (1299, 1337), False, 'import re\n')]
|
# -*- coding:utf-8 -*-
# __author__ = '<NAME>'
# Link Model
from flask_boilerplate.extensions import db
# 表前缀
prefix = 'flask_boilerplate'
class Link(db.Model):
__tablename__ = '%s_link' % prefix
id = db.Column(db.Integer, primary_key=True)
sitename = db.Column(db.VARCHAR(30), nullable=False, default='')
siteurl = db.Column(db.VARCHAR(75), nullable=False, default='')
description = db.Column(db.VARCHAR(255), nullable=False, default='')
hide = db.Column(db.Enum('n', 'y'), nullable=False, default='n')
taxis = db.Column(db.Integer, nullable=False, default=0)
def __repr__(self):
return '<Link %r>' % (self.sitename)
|
[
"flask_boilerplate.extensions.db.Enum",
"flask_boilerplate.extensions.db.Column",
"flask_boilerplate.extensions.db.VARCHAR"
] |
[((214, 253), 'flask_boilerplate.extensions.db.Column', 'db.Column', (['db.Integer'], {'primary_key': '(True)'}), '(db.Integer, primary_key=True)\n', (223, 253), False, 'from flask_boilerplate.extensions import db\n'), ((545, 593), 'flask_boilerplate.extensions.db.Column', 'db.Column', (['db.Integer'], {'nullable': '(False)', 'default': '(0)'}), '(db.Integer, nullable=False, default=0)\n', (554, 593), False, 'from flask_boilerplate.extensions import db\n'), ((279, 293), 'flask_boilerplate.extensions.db.VARCHAR', 'db.VARCHAR', (['(30)'], {}), '(30)\n', (289, 293), False, 'from flask_boilerplate.extensions import db\n'), ((347, 361), 'flask_boilerplate.extensions.db.VARCHAR', 'db.VARCHAR', (['(75)'], {}), '(75)\n', (357, 361), False, 'from flask_boilerplate.extensions import db\n'), ((419, 434), 'flask_boilerplate.extensions.db.VARCHAR', 'db.VARCHAR', (['(255)'], {}), '(255)\n', (429, 434), False, 'from flask_boilerplate.extensions import db\n'), ((485, 502), 'flask_boilerplate.extensions.db.Enum', 'db.Enum', (['"""n"""', '"""y"""'], {}), "('n', 'y')\n", (492, 502), False, 'from flask_boilerplate.extensions import db\n')]
|
"""
Download civil war ships and their complements from dbpedia
"""
from os import path
import json
from SPARQLWrapper import SPARQLWrapper, JSON
sparql = SPARQLWrapper("http://dbpedia.org/sparql")
sparql.setQuery("""
select distinct ?ship, ?complement where {
{
{?ship dcterms:subject category:Ships_of_the_Union_Navy}
UNION
{?ship dcterms:subject [skos:broader category:Ships_of_the_Confederate_States_Navy]}
}
?ship dbpprop:shipComplement ?complement
FILTER (datatype(?complement) = xsd:integer)
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
data = []
for x in results['results']['bindings']:
data.append({'ship': x['ship']['value'],
'complement': x['complement']['value']})
with open("ships.csv", "w") as f:
writer = csv.DictWriter(f, ('ship', 'complement'))
writer.writeheader()
writer.writerows(data)
|
[
"SPARQLWrapper.SPARQLWrapper"
] |
[((157, 199), 'SPARQLWrapper.SPARQLWrapper', 'SPARQLWrapper', (['"""http://dbpedia.org/sparql"""'], {}), "('http://dbpedia.org/sparql')\n", (170, 199), False, 'from SPARQLWrapper import SPARQLWrapper, JSON\n')]
|
import autograd.numpy as np
import autograd
import os
from autograd import grad
from autograd import jacobian
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
from scipy.linalg import pinv
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--function", type=int, default=1, help="choose from three low dimensional example functions, 1-3")
opt = parser.parse_args()
function = opt.function
# GDA
def gda(z_0, alpha=0.05, num_iter=100):
z = [z_0]
grad_fn = grad(target)
for i in range(num_iter):
g = grad_fn(z[-1])
z1 = z[-1] + g*np.array([-1,1])*alpha
z.append(z1)
z = np.array(z)
return z
# Extra Gradient
def eg(z_0, alpha=0.05, num_iter=100):
z = [z_0]
grad_fn = grad(target)
for i in range(num_iter):
g = grad_fn(z[-1])
z1 = z[-1] + g*np.array([-1,1])*alpha
g = grad_fn(z1)
z2 = z[-1] + g*np.array([-1,1])*alpha
z.append(z2)
z = np.array(z)
return z
# Optimistic Gradient
def ogda(z_0, alpha=0.05, num_iter=100):
z = [z_0,z_0]
grads = []
grad_fn = grad(target)
for i in range(num_iter):
g = grad_fn(z[-1])
gg = grad_fn(z[-2])
z1 = z[-1] + 2*g*np.array([-1,1])*alpha - gg*np.array([-1,1])*alpha
z.append(z1)
z = np.array(z)
return z
# Consensus Optimization
def co(z_0, alpha=0.01, gamma=0.1, num_iter=100):
z = [z_0]
grads = []
grad_fn = grad(target)
hessian = jacobian(grad_fn)
for i in range(num_iter):
g = grad_fn(z[-1])
H = hessian(z[-1])
#print(np.matmul(H,g), z[-1])
v = g*np.array([1,-1]) + gamma*np.matmul(H,g)
z1 = z[-1] - alpha*v
z.append(z1)
z = np.array(z)
return z
# Symplectic gradient adjustment
def sga(z_0, alpha=0.05, lamb=0.1, num_iter = 100):
z = [z_0]
grad_fn = grad(target)
hessian = jacobian(grad_fn)
for i in range(num_iter):
g = grad_fn(z[-1])
w = g * np.array([1,-1])
H = hessian(z[-1])
HH = np.array([[1, -lamb*H[0,1]],[lamb*H[0,1],1]])
v = HH @ w
z1 = z[-1] - alpha*v
z.append(z1)
z = np.array(z)
return z
# Follow the ridge
def follow(z_0, alpha=0.05, num_iter = 100):
z = [z_0]
grad_fn = grad(target)
hessian = jacobian(grad_fn)
for i in range(num_iter):
g = grad_fn(z[-1])
H = hessian(z[-1])
v = np.array([g[0], -g[1]-H[0,1]*np.squeeze(pinv(H[1:,1:]))*g[0]])
z1 = z[-1] - alpha*v
z.append(z1)
z = np.array(z)
return z
def f1(z):
x = z[0]
y = z[1]
f = -3*x*x-y*y+4*x*y
return f
def f2(z):
x = z[0]
y = z[1]
f = 3*x*x+y*y+4*x*y
return f
def f3(z):
x = z[0]
y = z[1]
f = (0.4*x*x-0.1*(y-3*x+0.05*x*x*x)**2-0.01*y*y*y*y)*np.exp(-0.01*(x*x+y*y))
return f
# Select target function
if function==1:
target = f1 # (0,0) is local minimax and global minimax
z_0 = np.array([5., 7.]) # Set initial point
plot_width = 12 # Set range of the plot
root_dir = 'results/f1.pdf'
elif function==2:
target = f2 # (0,0) is not local minimax and not global minimax
z_0 = np.array([6., 5.])
plot_width = 12
root_dir = 'results/f2.pdf'
elif function==3:
target = f3 # (0,0) is local minimax
z_0 = np.array([7., 5.])
plot_width = 8
root_dir = 'results/f3.pdf'
# Run all algorithms on target
zfr=follow(z_0, num_iter = 1000, alpha = 0.05)
zgda=gda(z_0, num_iter = 1000, alpha = 0.05)
zogda=ogda(z_0, num_iter = 1000, alpha = 0.05)
zeg=eg(z_0, num_iter = 1000, alpha = 0.05)
zco=co(z_0, num_iter = 1000, alpha = 0.05, gamma=0.1)
zsga=sga(z_0, num_iter = 1000, alpha = 0.01, lamb=1.0)
# Plot trajectory with contour
plt.rcParams.update({'font.size': 14})
def_colors=(plt.rcParams['axes.prop_cycle'].by_key()['color'])
#plot_width=12
plt.figure(figsize=(5,5))
axes = plt.gca()
axes.set_xlim([-plot_width,plot_width])
axes.set_ylim([-plot_width,plot_width])
x1 = np.arange(-plot_width,plot_width,0.1)
y1 = np.arange(-plot_width,plot_width,0.1)
X,Y = np.meshgrid(x1,y1)
Z = np.zeros_like(X)
for i in range(len(x1)):
for j in range(len(y1)):
Z[j][i] = target(np.array([x1[i] ,y1[j]]))
plt.contourf(X,Y,Z,30,cmap=plt.cm.gray)
lw = 2
hw = 0.7
line6,=plt.plot(zfr[:,0],zfr[:,1],'-',color='r',linewidth=lw,zorder=10)
line1,=plt.plot(zgda[:,0],zgda[:,1],'--',linewidth=lw,color=def_colors[9],zorder=2)
line2,=plt.plot(zogda[:,0],zogda[:,1],'--',linewidth=lw,color=def_colors[1])
line3,=plt.plot(zeg[:,0],zeg[:,1],'--',linewidth=lw,color=def_colors[2])
line4,=plt.plot(zsga[:,0],zsga[:,1],'--',color=def_colors[0],linewidth=lw)
line5,=plt.plot(zco[:,0],zco[:,1],'--',color='xkcd:violet',linewidth=lw)
init=plt.plot(zfr[0,0],zfr[0,1],'^',zorder=20,ms=12.0,color='r')
plt.legend((line6,line1, line2, line3, line4, line5), ('FR','GDA', 'OGDA', 'EG', 'SGA', 'CO'), loc=4)
os.makedirs('results/', exist_ok=True)
plt.savefig(root_dir, dpi=300)
#plt.show()
|
[
"autograd.numpy.arange",
"argparse.ArgumentParser",
"matplotlib.pyplot.plot",
"autograd.numpy.zeros_like",
"os.makedirs",
"matplotlib.pyplot.legend",
"autograd.numpy.meshgrid",
"autograd.numpy.array",
"autograd.grad",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.contourf",
"autograd.numpy.exp",
"matplotlib.pyplot.gca",
"autograd.numpy.matmul",
"autograd.jacobian",
"scipy.linalg.pinv",
"matplotlib.pyplot.savefig"
] |
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|
import re
import math
from tld import get_tld
from Levenshtein import distance
from .suspicious import keywords, tlds
def entropy(string: str) -> float:
"""
Calculates the Shannon entropy of a string
Original code: https://github.com/x0rz/phishing_catcher/blob/master/catch_phishing.py
"""
prob = [float(string.count(c)) / len(string) for c in dict.fromkeys(list(string))]
ent = -sum([p * math.log(p) / math.log(2.0) for p in prob])
return ent
def score_domain(domain: str) -> int:
"""Score `domain`.
The highest score, the most probable `domain` is a phishing site.
Args:
domain (str): the domain to check.
Returns:
int: the score of `domain`.
#https://github.com/x0rz/phishing_catcher/blob/master/catch_phishing.py
"""
score = 0
for t in tlds:
if domain.endswith(t):
score += 20
# Remove initial '*.' for wildcard certificates bug
if domain.startswith("*."):
domain = domain[2:]
# Removing TLD to catch inner TLD in subdomain (ie. paypal.com.domain.com)
try:
res = get_tld(domain, as_object=True, fail_silently=True, fix_protocol=True)
domain = ".".join([res.subdomain, res.domain])
except: # noqa: B110
pass
words_in_domain = re.split("\W+", domain)
# Remove initial '*.' for wildcard certificates bug
if domain.startswith("*."):
domain = domain[2:]
# ie. detect fake .com (ie. *.com-account-management.info)
if words_in_domain[0] in ["com", "net", "org"]:
score += 10
# Testing keywords
for word in keywords.items():
if word[0] in domain:
score += word[1]
# Higher entropy is kind of suspicious
score += int(round(entropy(domain) * 10))
# Testing Levenshtein distance for strong keywords (>= 70 points) (ie. paypol)
for key in [k for (k, s) in keywords.items() if s >= 70]:
# Removing too generic keywords (ie. mail.domain.com)
for word in [w for w in words_in_domain if w not in ["email", "mail", "cloud"]]:
if distance(str(word), str(key)) == 1:
score += 70
# Lots of '-' (ie. www.paypal-datacenter.com-acccount-alert.com)
if "xn--" not in domain and domain.count("-") >= 4:
score += domain.count("-") * 3
# Deeply nested subdomains (ie. www.paypal.com.security.accountupdate.gq)
if domain.count(".") >= 3:
score += domain.count(".") * 3
return score
|
[
"math.log",
"re.split",
"tld.get_tld"
] |
[((1289, 1313), 're.split', 're.split', (['"""\\\\W+"""', 'domain'], {}), "('\\\\W+', domain)\n", (1297, 1313), False, 'import re\n'), ((1101, 1171), 'tld.get_tld', 'get_tld', (['domain'], {'as_object': '(True)', 'fail_silently': '(True)', 'fix_protocol': '(True)'}), '(domain, as_object=True, fail_silently=True, fix_protocol=True)\n', (1108, 1171), False, 'from tld import get_tld\n'), ((429, 442), 'math.log', 'math.log', (['(2.0)'], {}), '(2.0)\n', (437, 442), False, 'import math\n'), ((415, 426), 'math.log', 'math.log', (['p'], {}), '(p)\n', (423, 426), False, 'import math\n')]
|
import csv
import sqlite3
from tkinter import *
from tkinter import filedialog
"""Tool to compare two reports and provide specific information from matching lines"""
class MatchTool:
UNPLACED_RSL_TEXT = [
"Copy Required Report",
"Ad Copy Status Report",
"Unplaced Spots",
"Required Spots",
]
def __init__(self, master):
self.master = master
master.geometry("400x300")
master.title("Discrepancy Match Tool")
self.top_frame = Frame(master)
self.bottom_frame = Frame(master, width=400)
self.novar_button_var = IntVar()
self.novar_button_var.set(0)
self.novar_button = Checkbutton(self.top_frame, variable=self.novar_button_var, command=self.enableNovar)
self.eclipse_button_var = IntVar()
self.eclipse_button_var.set(0)
self.eclipse_button = Checkbutton(self.top_frame, variable=self.eclipse_button_var, command=self.enableEclipse)
self.missing_button_var = IntVar()
self.missing_button_var.set(0)
self.missing_button = Checkbutton(self.top_frame, state=DISABLED, variable=self.missing_button_var, command=self.missingCopy)
self.unplaced_button_var = IntVar()
self.unplaced_button_var.set(0)
self.unplaced_button = Checkbutton(self.top_frame, state=DISABLED, variable=self.unplaced_button_var, command=self.unplacedRSL)
self.novar_label = Label(self.top_frame, text="Novar")
self.eclipse_label = Label(self.top_frame, text="Eclipse/XG")
self.missing_label = Label(self.top_frame, text="Missing Copy")
self.unplaced_label_text = StringVar()
self.unplaced_label_text.set("Unplaced or Required Spots")
self.unplaced_label = Label(self.top_frame, textvariable=self.unplaced_label_text, width=22, anchor=constants.W)
self.load_discrep = Button(self.bottom_frame, text="Load Discrepancy Report", width=25, command=self.loadDiscrep)
self.load_discrep_file_name_text = StringVar()
self.load_discrep_file_name = Label(self.bottom_frame, textvariable=self.load_discrep_file_name_text)
self.submit = Button(self.bottom_frame, text="Submit")
self.load_unplaced_text = StringVar()
self.load_unplaced_text.set("Load Report")
self.load_unplaced = Button(self.bottom_frame, textvariable=self.load_unplaced_text, width=25, command=self.loadReports)
self.load_unplaced_file_name_text = StringVar()
self.load_unplaced_file_name = Label(self.bottom_frame, textvariable=self.load_unplaced_file_name_text)
#Layout
self.top_frame.grid()
self.bottom_frame.grid(row=1)
self.novar_button.grid()
self.eclipse_button.grid(row=1)
self.missing_button.grid(row=2)
self.unplaced_button.grid(row=3)
self.novar_label.grid(row=0, column=1, sticky=W)
self.eclipse_label.grid(row=1, column=1, sticky=W)
self.missing_label.grid(row=2, column=1, sticky=W)
self.unplaced_label.grid(row=3, column=1, sticky=W)
self.load_discrep.grid(row=0, pady=3, ipadx=5)
self.load_discrep_file_name.grid(row=1, pady=3, ipadx=5)
self.load_unplaced.grid(row=2, pady=3, ipadx=5)
self.load_unplaced_file_name.grid(row=3, pady=3, ipadx=5)
#Functions
def enableNovar(self):
"""Activates the Missing Copy and Unplaced Spots checkboxes, and disables the Novar checkbox"""
if self.novar_button_var.get() == 1:
self.eclipse_button["state"] = DISABLED
self.missing_button["state"] = ACTIVE
self.unplaced_button["state"] = ACTIVE
self.unplaced_label_text.set(self.UNPLACED_RSL_TEXT[3])
else:
self.eclipse_button["state"] = ACTIVE
self.missing_button["state"] = DISABLED
self.unplaced_button["state"] = DISABLED
self.unplaced_label_text.set("Unplaced or Required Spots")
def enableEclipse(self):
"""Activates the Missing Copy and Required Spots checkboxes, and disables the Eclipse checkbox"""
if self.eclipse_button_var.get() == 1:
self.novar_button["state"] = DISABLED
self.missing_button["state"] = ACTIVE
self.unplaced_button["state"] = ACTIVE
self.unplaced_label_text.set(self.UNPLACED_RSL_TEXT[2])
else:
self.novar_button["state"] = ACTIVE
self.missing_button["state"] = DISABLED
self.unplaced_button["state"] = DISABLED
self.unplaced_label_text.set("Unplaced or Required Spots")
def missingCopy(self):
"""Changes the value of missing_button_var to 1, changes text of unplaced_text, shows Submit button"""
if self.missing_button_var.get() == 1:
self.unplaced_button["state"] = DISABLED
self.submit.grid(row=4, pady=5)
if self.novar_button_var.get() == 1:
self.load_unplaced_text.set("Load " + self.UNPLACED_RSL_TEXT[1])
elif self.eclipse_button_var.get() == 1:
self.load_unplaced_text.set("Load " + self.UNPLACED_RSL_TEXT[0])
else:
self.unplaced_button["state"] = ACTIVE
self.load_unplaced_text.set("Load Report")
self.submit.grid_forget()
def unplacedRSL(self):
"""changes the value of unplaced_button_var to 1, changes text of unplaced_text, shows Submit button"""
if self.unplaced_button_var.get() == 1:
self.missing_button["state"] = DISABLED
self.submit.grid(row=4, pady=5)
if self.novar_button_var.get() == 1:
self.load_unplaced_text.set("Load " + self.UNPLACED_RSL_TEXT[3])
elif self.eclipse_button_var.get() == 1:
self.load_unplaced_text.set("Load " + self.UNPLACED_RSL_TEXT[2])
else:
self.missing_button["state"] = ACTIVE
self.load_unplaced_text.set("Load Report")
self.submit.grid_forget()
def unplacedEdit(self, loaded_file):
"""Opens the CSV file and edits the date and time for the Unplaced Spots report"""
with open(loaded_file) as csv_file:
unplaced_reader = csv.reader(csv_file, delimiter=',')
unplaced_list = [row for row in unplaced_reader]
unplaced_list.pop(0)
unplaced_list[0].extend(['Date', 'Time'])
for i in range(1, len(unplaced_list)):
date_time = unplaced_list[i][1].split(' ')
unplaced_list[i].append(date_time[0])
time_of_day = int(date_time[1][:date_time[1].index(":")])
if time_of_day < 13:
date_time[1] = date_time[1] + " AM"
else:
time_of_day = time_of_day - 12
date_time[1] = str(time_of_day) + date_time[1][date_time[1].index(":"):] + " PM"
unplaced_list[i].append(date_time[1])
return unplaced_list
def rslEdit(self, loaded_file):
"""Edits the RSL report's Date and Time"""
with open(loaded_file) as csv_file:
rsl_reader = csv.reader(csv_file, delimiter=',')
rsl_list = [row for row in rsl_reader]
rsl_list[0].extend(['Date', 'Time'])
for i in range(1, len(rsl_list)):
date = rsl_list[i][16]
date = date[:date.index("-")]
new_date = date.split('/') #add 20 to the beginning of the year
new_date[2] = "20" + new_date[2]
date = new_date[0] + '/' + new_date[1] + '/' + new_date[2]
time = rsl_list[i][17]
time = time[:time.index("-")]
rsl_list[i].append(date)
rsl_list[i].append(time)
for x in range(1, len(rsl_list)):
digits = rsl_list[x][31]
digits = int(digits[:digits.index(":")])
if digits < 10:
rsl_list[x][31] = rsl_list[x][31][1:] + " AM"
elif digits < 13:
rsl_list[x][31] = rsl_list[x][31] + " AM"
else:
digits = digits - 12
rsl_list[x][31] = str(digits) + rsl_list[x][31][rsl_list[x][31].index(":"):] + " PM"
return rsl_list
def copyRequiredEdit(self, loaded_file):
"""Removes the first row from the Copy Required Report"""
with open(loaded_file) as csv_file:
cr_reader = csv.reader(csv_file, delimiter=',')
cr_list = [row for row in cr_reader]
cr_list.pop(0)
return cr_list
# def discrepEdit(self, loaded_file):
# """Splits up the contract ID's into a list"""
# with open(loaded_file) as csv_file:
# discrep_reader = csv.reader(csv_file, delimiter=',')
# discrep_list = [row for row in discrep_reader]
# for i in range(1, len(discrep_list)):
# discrep_list[i][11] = discrep_list[i][11].split(';')
# return discrep_list
def discrepancyDB(self, discrepancy):
"""creates SQL database from the discrepancy report"""
with sqlite3.connect("DiscrepMatch.db") as connection:
c = connection.cursor()
discrep = csv.reader(open(discrepancy, "rU"))
c.execute("DROP TABLE if exists discrepancy1")
c.execute("""CREATE TABLE discrepancy1(Discrepancy TEXT, Reservation TEXT, Event TEXT, Episode TEXT, DateOf TEXT, Start TEXT,
Market TEXT, Zone TEXT, Network TEXT, ClientID INT, ClientName TEXT, ContractID TEXT, Rate TEXT, AE TEXT, Modified TEXT,
ModifiedBy TEXT)""")
c.executemany("""INSERT INTO discrepancy1(Discrepancy, Reservation, Event, Episode, DateOf, Start, Market, Zone, Network,
ClientID, ClientName, ContractID, Rate, AE, Modified, ModifiedBy) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", discrep)
#AdCopyStatus (No Edits needed)
def adCopyDB(self, ad_copy):
"""creates SQL database from the ad copy status report"""
with sqlite3.connect("DiscrepMatch.db") as connection:
c = connection.cursor()
adCopyStatus = csv.reader(open(ad_copy, "rU"))
c.execute("DROP TABLE if exists AdCopyStatus")
c.execute("""CREATE TABLE AdCopyStatus(ClientID INT, ClientName TEXT, AdCopyID INT, CutName TEXT, CutStart TEXT, CutStop TEXT, Reason TEXT)""")
c.executemany("""INSERT INTO AdCopyStatus(ClientID, ClientName, AdCopyID, CutName, CutStart, CutStop, Reason) values (?, ?, ?, ?, ?, ?, ?)""", adCopyStatus)
#Copy Required (Edit Required)
def copyRequiredDB(self, copy_required):
"""creates SQL database from the copy required report"""
with sqlite3.connect("DiscrepMatch.db") as connection:
c = connection.cursor()
copyRequired = copy_required
c.execute("DROP TABLE if exists copyrequired")
c.execute("""CREATE TABLE copyrequired(ClientID TEXT, ClientName TEXT, Rotation INT, RotDesc INT, SalesID INT, AE TEXT, SalOffID TEXT,
SalOff TEXT, OrderNum TEXT, Networks TEXT, Regions TEXT, TotalRev TEXT, AvgPrty INT, DateNeed TEXT, Issue TEXT)""")
c.executemany("""INSERT INTO copyrequired(ClientID, ClientName, Rotation, RotDesc, SalesID, AE, SalOffID, SalOff, OrderNum, Networks,
Regions, TotalRev, AvgPrty, DateNeed, Issue) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", copyRequired)
#RSL (Edit Required)
def rslDB(self, rsl_report):
"""creates SQL database from the RSL report"""
with sqlite3.connect("DiscrepMatch.db") as connection:
c = connection.cursor()
rsl = rsl_report
c.execute("DROP TABLE if exists RSL")
c.execute("""CREATE TABLE RSL(AE TEXT, Priority INT, ClientID INT, Client TEXT, ConID INT, LineNum INT, Zone TEXT, Network TEXT, DaysAuth TEXT,
Mon INT, Tue INT, Wed INT, Thu INT, Fri INT, Sat INT, Sun INT, OldDates TEXT, Daypart TEXT, CGName TEXT, Total INT, Normal INT,
Sched INT, Aired INT, ToDO INT, FinalWeek TEXT, Length INT, Program TEXT, Cost INT, LostRev INT, RD INT, NewDate TEXT, NewTime TEXT)""")
c.executemany("""INSERT INTO RSL(AE, Priority, ClientID, Client, ConID, LineNum, Zone, Network, DaysAuth, Mon, Tue, Wed, Thu, Fri, Sat, Sun,
OldDates, Daypart, CGName, Total, Normal, Sched, Aired, ToDO, FinalWeek, Length, Program, Cost, LostRev, RD, NewDate, NewTime)
values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", rsl)
#Unplaced (Edit Required)
def unplacedDB(self, unplaced_report):
"""Creates SQL database from the unplaced spot report"""
with sqlite3.connect("DiscrepMatch.db") as connection:
c = connection.cursor()
unplaced = unplaced_report
c.execute("DROP TABLE if exists unplacedSpots")
c.execute("""CREATE TABLE unplacedSpots(OrderNum INT, OldDate TEXT, SpotName TEXT, Length INT, Description TEXT, Network TEXT, ClientID INT,
Client TEXT, Phone TEXT, Initials TEXT, Rotation INT, Active TEXT, UCType TEXT, Retail INT, InvType TEXT, Billing TEXT, Market TEXT,
Zone TEXT, Priority INT, Buy1 INT, BuyType TEXT, SpotsWeek INT, SpotsLine INT, MonAct TEXT, MonQua INT, TueAct TEXT, TueQua INT,
WedAct TEXT, WedQua INT, ThuAct TEXT, ThuQua INT, FriAct TEXT, FriQua INT, SatAct TEXT, SatQua INT, SunAct TEXT, SunQua INT, Buy2 INT,
Exception TEXT, Daypart TEXT, Entity TEXT, LineType TEXT, LineNum INT, OfficeID TEXT, Description2 TEXT, Name TEXT, OfficeName TEXT,
Exception2 TEXT, Uniform TEXT, LineNum2 INT, "Group" INT, EndDate TEXT, Orbits TEXT, NewDate TEXT, NewTime TEXT)""")
c.executemany("""INSERT INTO unplacedSpots(OrderNum, OldDate, SpotName, Length, Description, Network, ClientID, Client, Phone, Initials, Rotation,
Active, UCType, Retail, InvType, Billing, Market, Zone, Priority, Buy1, BuyType, SpotsWeek, Spotsline, MonAct, MonQua, TueAct, TueQua,
WedAct, WedQua, ThuAct, ThuQua, FriAct, FriQua, SatAct, SatQua, SunAct, SunQua, Buy2, Exception, Daypart, Entity, LineType, LineNum, OfficeID,
Description2, Name, OfficeName, Exception2, Uniform, LineNum2, "Group", EndDate, Orbits, NewDate, NewTime) values (?, ?, ?, ?,
?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?,
?, ?, ?, ?, ?, ?, ?, ?)""", unplaced)
def loadDiscrep(self):
"""Opens file directory for user to load report in xls format"""
discrepReport = filedialog.askopenfilename(
filetypes=[("CSV File", "*.csv"), ("All Files", "*.*")]
)
if not discrepReport:
return
else:
self.load_discrep_file_name_text.set("Discrepancy Report loaded successfully")
#discrepReport = self.discrepEdit(discrepReport)
self.discrepancyDB(discrepReport)
def loadReports(self):
"""Opens file directory for user to load file, file type depends on prior selections"""
#Copy Required (Eclipse/Missing Copy)
if self.eclipse_button_var.get() == 1 and self.missing_button_var.get() == 1:
copyRequired = filedialog.askopenfilename(
filetypes=[("CSV File", "*.csv"), ("All Files", "*.*")]
)
if not copyRequired:
return
else:
self.load_unplaced_file_name_text.set("Copy Required loaded successfully")
copyRequired = self.copyRequiredEdit(copyRequired)
self.copyRequiredDB(copyRequired)
#AdCopyStatus (Novar/Missing Copy)
elif self.novar_button_var.get() == 1 and self.missing_button_var.get() == 1:
adCopyStatus = filedialog.askopenfilename(
filetypes=[("CSV File", "*.csv"), ("All Files", "*.*")]
)
if not adCopyStatus:
return
else:
self.load_unplaced_file_name_text.set("AdCopyStatus Report loaded successfully")
self.adCopyDB(adCopyStatus)
#Unplaced Spots (Eclipse/Unplaced)
elif self.eclipse_button_var.get() == 1 and self.unplaced_button_var.get() == 1:
unplacedSpots = filedialog.askopenfilename(
filetypes=[("CSV File", "*.csv"), ("All Files", "*.*")]
)
if not unplacedSpots:
return
else:
self.load_unplaced_file_name_text.set("Unplaced Spots Report loaded successfully")
unplacedSpots = self.unplacedEdit(unplacedSpots)
self.unplacedDB(unplacedSpots)
#RSL (Novar/Unplaced)
elif self.novar_button_var.get() == 1 and self.unplaced_button_var.get() == 1:
requiredSpots = filedialog.askopenfilename(
filetypes=[("CSV File", "*.csv"), ("All Files", "*.*")]
)
if not requiredSpots:
return
else:
self.load_unplaced_file_name_text.set("Required Spots loaded successfully")
requiredSpots = self.rslEdit(requiredSpots)
self.rslDB(requiredSpots)
# Add functionality for the Submit button: finds the matches between the two db's opened up and returns them as CSV
# Should I use :memory: or actual db's?
# Will :memory: work once the function is over? Won't it close the db being used?
# How can I write back to a CSV?
# Format the tool better
#Remove checks if button gets disabled
root = Tk()
interface = MatchTool(root)
root.mainloop()
|
[
"sqlite3.connect",
"csv.reader",
"tkinter.filedialog.askopenfilename"
] |
[((13046, 13133), 'tkinter.filedialog.askopenfilename', 'filedialog.askopenfilename', ([], {'filetypes': "[('CSV File', '*.csv'), ('All Files', '*.*')]"}), "(filetypes=[('CSV File', '*.csv'), ('All Files',\n '*.*')])\n", (13072, 13133), False, 'from tkinter import filedialog\n'), ((5493, 5528), 'csv.reader', 'csv.reader', (['csv_file'], {'delimiter': '""","""'}), "(csv_file, delimiter=',')\n", (5503, 5528), False, 'import csv\n'), ((6240, 6275), 'csv.reader', 'csv.reader', (['csv_file'], {'delimiter': '""","""'}), "(csv_file, delimiter=',')\n", (6250, 6275), False, 'import csv\n'), ((7294, 7329), 'csv.reader', 'csv.reader', (['csv_file'], {'delimiter': '""","""'}), "(csv_file, delimiter=',')\n", (7304, 7329), False, 'import csv\n'), ((7875, 7909), 'sqlite3.connect', 'sqlite3.connect', (['"""DiscrepMatch.db"""'], {}), "('DiscrepMatch.db')\n", (7890, 7909), False, 'import sqlite3\n'), ((8725, 8759), 'sqlite3.connect', 'sqlite3.connect', (['"""DiscrepMatch.db"""'], {}), "('DiscrepMatch.db')\n", (8740, 8759), False, 'import sqlite3\n'), ((9349, 9383), 'sqlite3.connect', 'sqlite3.connect', (['"""DiscrepMatch.db"""'], {}), "('DiscrepMatch.db')\n", (9364, 9383), False, 'import sqlite3\n'), ((10132, 10166), 'sqlite3.connect', 'sqlite3.connect', (['"""DiscrepMatch.db"""'], {}), "('DiscrepMatch.db')\n", (10147, 10166), False, 'import sqlite3\n'), ((11215, 11249), 'sqlite3.connect', 'sqlite3.connect', (['"""DiscrepMatch.db"""'], {}), "('DiscrepMatch.db')\n", (11230, 11249), False, 'import sqlite3\n'), ((13604, 13691), 'tkinter.filedialog.askopenfilename', 'filedialog.askopenfilename', ([], {'filetypes': "[('CSV File', '*.csv'), ('All Files', '*.*')]"}), "(filetypes=[('CSV File', '*.csv'), ('All Files',\n '*.*')])\n", (13630, 13691), False, 'from tkinter import filedialog\n'), ((14049, 14136), 'tkinter.filedialog.askopenfilename', 'filedialog.askopenfilename', ([], {'filetypes': "[('CSV File', '*.csv'), ('All Files', '*.*')]"}), "(filetypes=[('CSV File', '*.csv'), ('All Files',\n '*.*')])\n", (14075, 14136), False, 'from tkinter import filedialog\n'), ((14444, 14531), 'tkinter.filedialog.askopenfilename', 'filedialog.askopenfilename', ([], {'filetypes': "[('CSV File', '*.csv'), ('All Files', '*.*')]"}), "(filetypes=[('CSV File', '*.csv'), ('All Files',\n '*.*')])\n", (14470, 14531), False, 'from tkinter import filedialog\n'), ((14882, 14969), 'tkinter.filedialog.askopenfilename', 'filedialog.askopenfilename', ([], {'filetypes': "[('CSV File', '*.csv'), ('All Files', '*.*')]"}), "(filetypes=[('CSV File', '*.csv'), ('All Files',\n '*.*')])\n", (14908, 14969), False, 'from tkinter import filedialog\n')]
|
import argparse
import numpy as np
from scipy.io import wavfile
from tqdm import trange
from ar_model import ARmodel
def correctSignal(signal, model, window_size, pred_size, step, treshold=3):
"""Correct signal using AR model
Args:
signal (np.array): signal to correct
model (ARmodel): autoregresive model
window_size (int): length of the window for updating AR model coefs
pred_size (int): number of samples to generate from AR model
step (int): step interval
treshold (float): how many times error have to be bigger then standard
deviation to classify sample as disturbed
Returns:
np.array: cerrected signal
"""
out = np.copy(signal)
for i in trange(0, input.shape[0]-window_size-pred_size, step):
paramsEnd = i+window_size
predEnd = paramsEnd+pred_size
model.updateParams(out[i:paramsEnd])
estimated = model.estimateSignal(pred_size, out[paramsEnd-model.r:paramsEnd])
err = np.abs(out[paramsEnd:predEnd] - estimated)
std = np.std(err)
disturbed = np.abs(err) > std*treshold
disturbanceLength = 0
for j in range(pred_size):
if disturbed[j]:
disturbanceLength += 1
elif disturbanceLength > 0:
k = j + paramsEnd
before = signal[k-disturbanceLength-1]
after = signal[k]
out[k-disturbanceLength:k] = np.linspace(before,after,disturbanceLength+2)[1:-1]
disturbanceLength = 0
return out
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Removing impulse interference from music recordings")
parser.add_argument('filename', metavar='filename', type=str, help='path to wave file')
parser.add_argument('-r', '--order', type=int, default=4, help='order of AR model')
parser.add_argument('-o', '--out_file', type=str, default='out.wav', help='name of the output file')
parser.add_argument('-u', '--param_window', type=int, default=256, help='length of the window for updating AR model coefs')
parser.add_argument('-e', '--pred_widnow', type=int, default=8, help='number of samples to generate from AR model')
parser.add_argument('-s', '--step', type=int, default=4, help='step interval')
parser.add_argument('-d', '--decay', type=float, default=1.0, help='decay rate for exponential window')
parser.add_argument('-m', '--max_std', type=float, default=3.0, help='how many times error have to be bigger then standard deviation to classify sample as disturbed')
args = parser.parse_args()
fs, input = wavfile.read(args.filename)
input = input / 2**15
model = ARmodel(args.order, args.decay)
output = correctSignal(input, model, args.param_window, args.pred_widnow, args.step, args.max_std)
wavfile.write(args.out_file, fs, output)
|
[
"numpy.abs",
"argparse.ArgumentParser",
"numpy.copy",
"tqdm.trange",
"numpy.std",
"scipy.io.wavfile.read",
"ar_model.ARmodel",
"scipy.io.wavfile.write",
"numpy.linspace"
] |
[((711, 726), 'numpy.copy', 'np.copy', (['signal'], {}), '(signal)\n', (718, 726), True, 'import numpy as np\n'), ((741, 798), 'tqdm.trange', 'trange', (['(0)', '(input.shape[0] - window_size - pred_size)', 'step'], {}), '(0, input.shape[0] - window_size - pred_size, step)\n', (747, 798), False, 'from tqdm import trange\n'), ((1621, 1716), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Removing impulse interference from music recordings"""'}), "(description=\n 'Removing impulse interference from music recordings')\n", (1644, 1716), False, 'import argparse\n'), ((2655, 2682), 'scipy.io.wavfile.read', 'wavfile.read', (['args.filename'], {}), '(args.filename)\n', (2667, 2682), False, 'from scipy.io import wavfile\n'), ((2722, 2753), 'ar_model.ARmodel', 'ARmodel', (['args.order', 'args.decay'], {}), '(args.order, args.decay)\n', (2729, 2753), False, 'from ar_model import ARmodel\n'), ((2862, 2902), 'scipy.io.wavfile.write', 'wavfile.write', (['args.out_file', 'fs', 'output'], {}), '(args.out_file, fs, output)\n', (2875, 2902), False, 'from scipy.io import wavfile\n'), ((1015, 1057), 'numpy.abs', 'np.abs', (['(out[paramsEnd:predEnd] - estimated)'], {}), '(out[paramsEnd:predEnd] - estimated)\n', (1021, 1057), True, 'import numpy as np\n'), ((1072, 1083), 'numpy.std', 'np.std', (['err'], {}), '(err)\n', (1078, 1083), True, 'import numpy as np\n'), ((1105, 1116), 'numpy.abs', 'np.abs', (['err'], {}), '(err)\n', (1111, 1116), True, 'import numpy as np\n'), ((1474, 1523), 'numpy.linspace', 'np.linspace', (['before', 'after', '(disturbanceLength + 2)'], {}), '(before, after, disturbanceLength + 2)\n', (1485, 1523), True, 'import numpy as np\n')]
|
import io
import json
import os
from typing import Any, Dict, IO, Iterator, Optional, Tuple
from altair_data_server import Provider
from PIL import Image
import pytest
import selenium.webdriver
from selenium.webdriver.remote.webdriver import WebDriver
from altair_saver import HTMLSaver
from altair_saver._utils import internet_connected
CDN_URL = "https://cdn.jsdelivr.net"
@pytest.fixture(scope="module")
def internet_ok() -> bool:
return internet_connected()
@pytest.fixture(scope="module")
def provider() -> Iterator[Provider]:
provider = Provider()
yield provider
provider.stop()
@pytest.fixture(scope="module")
def driver() -> Iterator[WebDriver]:
options = selenium.webdriver.chrome.options.Options()
options.add_argument("--headless")
if hasattr(os, "geteuid") and (os.geteuid() == 0):
options.add_argument("--no-sandbox")
driver = selenium.webdriver.Chrome(options=options)
yield driver
driver.quit()
def get_testcases() -> Iterator[Tuple[str, Dict[str, Any]]]:
directory = os.path.join(os.path.dirname(__file__), "testcases")
cases = set(f.split(".")[0] for f in os.listdir(directory))
f: IO
for case in sorted(cases):
with open(os.path.join(directory, f"{case}.vl.json")) as f:
vl = json.load(f)
with open(os.path.join(directory, f"{case}.png"), "rb") as f:
png = f.read()
yield case, {"vega-lite": vl, "png": png}
@pytest.mark.parametrize("inline", [True, False])
@pytest.mark.parametrize("embed_options", [None, {"theme": "dark"}])
@pytest.mark.parametrize("case, data", get_testcases())
def test_html_save(
case: str, data: Dict[str, Any], embed_options: Optional[dict], inline: bool
) -> None:
saver = HTMLSaver(data["vega-lite"], inline=inline, embed_options=embed_options)
fp = io.StringIO()
saver.save(fp, "html")
html = fp.getvalue()
assert isinstance(html, str)
assert html.strip().startswith("<!DOCTYPE html>")
assert json.dumps(data["vega-lite"]) in html
assert f"const embedOpt = {json.dumps(embed_options or {})}" in html
if inline:
assert CDN_URL not in html
else:
assert CDN_URL in html
@pytest.mark.parametrize("embed_options", [None, {"theme": "dark"}])
@pytest.mark.parametrize("case, data", get_testcases())
def test_html_mimebundle(
case: str, data: Dict[str, Any], embed_options: Optional[dict],
) -> None:
saver = HTMLSaver(data["vega-lite"], embed_options=embed_options)
bundle = saver.mimebundle("html")
assert bundle.keys() == {"text/html"}
html = bundle["text/html"]
assert isinstance(html, str)
assert html.strip().startswith("<div")
assert json.dumps(data["vega-lite"]) in html
assert json.dumps(embed_options or {}) in html
assert CDN_URL in html
def test_bad_format() -> None:
saver = HTMLSaver({})
with pytest.raises(ValueError):
saver.mimebundle("vega")
@pytest.mark.parametrize("case, data", get_testcases())
@pytest.mark.parametrize("inline", [True, False])
def test_html_save_rendering(
provider: Provider,
driver: WebDriver,
case: str,
data: Dict[str, Any],
inline: bool,
internet_ok: bool,
) -> None:
if not (inline or internet_ok):
pytest.xfail("Internet connection not available")
saver = HTMLSaver(data["vega-lite"], inline=inline)
fp = io.StringIO()
saver.save(fp, "html")
html = fp.getvalue()
resource = provider.create(content=html, extension="html")
driver.set_window_size(800, 600)
driver.get(resource.url)
element = driver.find_element_by_class_name("vega-visualization")
png = driver.get_screenshot_as_png()
im = Image.open(io.BytesIO(png))
left = element.location["x"]
top = element.location["y"]
right = element.location["x"] + element.size["width"]
bottom = element.location["y"] + element.size["height"]
im = im.crop((left, top, right, bottom))
im_expected = Image.open(io.BytesIO(data["png"]))
assert abs(im.size[0] - im_expected.size[0]) < 40
assert abs(im.size[1] - im_expected.size[1]) < 40
@pytest.mark.parametrize("requirejs", [True, False])
@pytest.mark.parametrize("case, data", get_testcases())
def test_html_mimebundle_rendering(
provider: Provider,
driver: WebDriver,
case: str,
data: Dict[str, Any],
requirejs: bool,
internet_ok: bool,
) -> None:
if not internet_ok:
pytest.xfail("Internet connection not available")
saver = HTMLSaver(data["vega-lite"])
bundle = saver.mimebundle("html")
html = bundle["text/html"]
assert isinstance(html, str)
if requirejs:
html = f"""<!DOCTYPE html>
<html>
<head><script src="{CDN_URL}/npm/requirejs@2.3.6"></script></head>
<body>{html}</body>
</html>
"""
else:
html = f"<html>{html}</html>"
resource = provider.create(content=html, extension="html")
driver.set_window_size(800, 600)
driver.get(resource.url)
element = driver.find_element_by_class_name("vega-visualization")
png = driver.get_screenshot_as_png()
im = Image.open(io.BytesIO(png))
left = element.location["x"]
top = element.location["y"]
right = element.location["x"] + element.size["width"]
bottom = element.location["y"] + element.size["height"]
im = im.crop((left, top, right, bottom))
im_expected = Image.open(io.BytesIO(data["png"]))
assert abs(im.size[0] - im_expected.size[0]) < 40
assert abs(im.size[1] - im_expected.size[1]) < 40
|
[
"os.geteuid",
"io.StringIO",
"io.BytesIO",
"json.load",
"os.path.dirname",
"pytest.fixture",
"altair_saver._utils.internet_connected",
"json.dumps",
"altair_data_server.Provider",
"pytest.raises",
"pytest.xfail",
"altair_saver.HTMLSaver",
"pytest.mark.parametrize",
"os.path.join",
"os.listdir"
] |
[((382, 412), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""'}), "(scope='module')\n", (396, 412), False, 'import pytest\n'), ((475, 505), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""'}), "(scope='module')\n", (489, 505), False, 'import pytest\n'), ((612, 642), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""module"""'}), "(scope='module')\n", (626, 642), False, 'import pytest\n'), ((1453, 1501), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""inline"""', '[True, False]'], {}), "('inline', [True, False])\n", (1476, 1501), False, 'import pytest\n'), ((1503, 1570), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""embed_options"""', "[None, {'theme': 'dark'}]"], {}), "('embed_options', [None, {'theme': 'dark'}])\n", (1526, 1570), False, 'import pytest\n'), ((2203, 2270), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""embed_options"""', "[None, {'theme': 'dark'}]"], {}), "('embed_options', [None, {'theme': 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(['__file__'], {}), '(__file__)\n', (1075, 1085), False, 'import os\n'), ((1997, 2026), 'json.dumps', 'json.dumps', (["data['vega-lite']"], {}), "(data['vega-lite'])\n", (2007, 2026), False, 'import json\n'), ((2702, 2731), 'json.dumps', 'json.dumps', (["data['vega-lite']"], {}), "(data['vega-lite'])\n", (2712, 2731), False, 'import json\n'), ((2751, 2782), 'json.dumps', 'json.dumps', (['(embed_options or {})'], {}), '(embed_options or {})\n', (2761, 2782), False, 'import json\n'), ((2887, 2912), 'pytest.raises', 'pytest.raises', (['ValueError'], {}), '(ValueError)\n', (2900, 2912), False, 'import pytest\n'), ((3269, 3318), 'pytest.xfail', 'pytest.xfail', (['"""Internet connection not available"""'], {}), "('Internet connection not available')\n", (3281, 3318), False, 'import pytest\n'), ((3712, 3727), 'io.BytesIO', 'io.BytesIO', (['png'], {}), '(png)\n', (3722, 3727), False, 'import io\n'), ((3987, 4010), 'io.BytesIO', 'io.BytesIO', (["data['png']"], {}), "(data['png'])\n", (3997, 4010), False, 'import io\n'), ((4442, 4491), 'pytest.xfail', 'pytest.xfail', (['"""Internet connection not available"""'], {}), "('Internet connection not available')\n", (4454, 4491), False, 'import pytest\n'), ((5149, 5164), 'io.BytesIO', 'io.BytesIO', (['png'], {}), '(png)\n', (5159, 5164), False, 'import io\n'), ((5424, 5447), 'io.BytesIO', 'io.BytesIO', (["data['png']"], {}), "(data['png'])\n", (5434, 5447), False, 'import io\n'), ((812, 824), 'os.geteuid', 'os.geteuid', ([], {}), '()\n', (822, 824), False, 'import os\n'), ((1290, 1302), 'json.load', 'json.load', (['f'], {}), '(f)\n', (1299, 1302), False, 'import json\n'), ((1141, 1162), 'os.listdir', 'os.listdir', (['directory'], {}), '(directory)\n', (1151, 1162), False, 'import os\n'), ((1223, 1265), 'os.path.join', 'os.path.join', (['directory', 'f"""{case}.vl.json"""'], {}), "(directory, f'{case}.vl.json')\n", (1235, 1265), False, 'import os\n'), ((1321, 1359), 'os.path.join', 'os.path.join', (['directory', 'f"""{case}.png"""'], {}), "(directory, f'{case}.png')\n", (1333, 1359), False, 'import os\n'), ((2066, 2097), 'json.dumps', 'json.dumps', (['(embed_options or {})'], {}), '(embed_options or {})\n', (2076, 2097), False, 'import json\n')]
|
from timeit import default_timer
from parser import wikihandler
import xml.sax as sax
import utility
def main():
# setting path to indices
utility.setIndexPath()
utility.setStatPath()
# parser=sax.make_parser()
# handler = wikihandler()
# parser.setFeature(sax.handler.feature_namespaces,0)
# parser.setContentHandler(handler)
# parser.parse("/home/hitesh/sem3/IRE/Project/large.xml")
# parser.parse("/home/hitesh/sem3/IRE/wiki/phase2/large.xml")
# parser.parse("/mnt/sdb1/phase2/data/0.xml") # 1
# parser.parse("/mnt/sdb1/phase2/data/1.xml") # 2
# parser.parse("/mnt/sdb1/phase2/data/29.xml") # 3
for i in range(0,35):
start = default_timer()
parser=sax.make_parser()
handler = wikihandler()
parser.setFeature(sax.handler.feature_namespaces,0)
parser.setContentHandler(handler)
# this is the path to ur indices
parser.parse("/mnt/sdb1/phase2/data/"+str(i)+".xml")
stop = default_timer()
print ('\nTime elasped in sec: ',stop - start)
print('file no.:: ',str(i))
#wrie index here
# handler.writeIndex(utility.getIndexPath()) # temp
if __name__ == "__main__":
start = default_timer()
main()
stop = default_timer()
print ('\nTotal Time elasped in sec: ',stop - start)
|
[
"parser.wikihandler",
"timeit.default_timer",
"utility.setIndexPath",
"utility.setStatPath",
"xml.sax.make_parser"
] |
[((141, 163), 'utility.setIndexPath', 'utility.setIndexPath', ([], {}), '()\n', (161, 163), False, 'import utility\n'), ((165, 186), 'utility.setStatPath', 'utility.setStatPath', ([], {}), '()\n', (184, 186), False, 'import utility\n'), ((1108, 1123), 'timeit.default_timer', 'default_timer', ([], {}), '()\n', (1121, 1123), False, 'from timeit import default_timer\n'), ((1140, 1155), 'timeit.default_timer', 'default_timer', ([], {}), '()\n', (1153, 1155), False, 'from timeit import default_timer\n'), ((644, 659), 'timeit.default_timer', 'default_timer', ([], {}), '()\n', (657, 659), False, 'from timeit import default_timer\n'), ((669, 686), 'xml.sax.make_parser', 'sax.make_parser', ([], {}), '()\n', (684, 686), True, 'import xml.sax as sax\n'), ((699, 712), 'parser.wikihandler', 'wikihandler', ([], {}), '()\n', (710, 712), False, 'from parser import wikihandler\n'), ((902, 917), 'timeit.default_timer', 'default_timer', ([], {}), '()\n', (915, 917), False, 'from timeit import default_timer\n')]
|
import boto3
exceptions = boto3.client('elb').exceptions
AccessPointNotFoundException = exceptions.AccessPointNotFoundException
CertificateNotFoundException = exceptions.CertificateNotFoundException
DependencyThrottleException = exceptions.DependencyThrottleException
DuplicateAccessPointNameException = exceptions.DuplicateAccessPointNameException
DuplicateListenerException = exceptions.DuplicateListenerException
DuplicatePolicyNameException = exceptions.DuplicatePolicyNameException
DuplicateTagKeysException = exceptions.DuplicateTagKeysException
InvalidConfigurationRequestException = exceptions.InvalidConfigurationRequestException
InvalidEndPointException = exceptions.InvalidEndPointException
InvalidSchemeException = exceptions.InvalidSchemeException
InvalidSecurityGroupException = exceptions.InvalidSecurityGroupException
InvalidSubnetException = exceptions.InvalidSubnetException
ListenerNotFoundException = exceptions.ListenerNotFoundException
LoadBalancerAttributeNotFoundException = exceptions.LoadBalancerAttributeNotFoundException
OperationNotPermittedException = exceptions.OperationNotPermittedException
PolicyNotFoundException = exceptions.PolicyNotFoundException
PolicyTypeNotFoundException = exceptions.PolicyTypeNotFoundException
SubnetNotFoundException = exceptions.SubnetNotFoundException
TooManyAccessPointsException = exceptions.TooManyAccessPointsException
TooManyPoliciesException = exceptions.TooManyPoliciesException
TooManyTagsException = exceptions.TooManyTagsException
UnsupportedProtocolException = exceptions.UnsupportedProtocolException
|
[
"boto3.client"
] |
[((27, 46), 'boto3.client', 'boto3.client', (['"""elb"""'], {}), "('elb')\n", (39, 46), False, 'import boto3\n')]
|
# import os
#
# # path = '/home/yangyang/yangyang/DATA/gxw/dataset/DOTA_split/train'
# # label_file_name = 'labelTxt'
#
# path = '/home/yangyang/yangyang/DATA/gxw/dataset/DOTA_demo/VOC2012'
# label_file_name = 'Annotations'
#
# label_file_path = os.path.join(path, label_file_name)
# filelist = os.listdir(label_file_path)
#
# # txt_path = os.path.join(path, 'train.txt')
# txt_path = os.path.join(path, 'train.txt')
# f = open(txt_path, 'a')
#
# for filename in filelist:
# txt = filename.split('.')[0]
# f.write('{}\n'.format(txt))
#
# f.close()
import os
label_file_path = '/home/lyy/gxw/DOTA_OBB_1_5/VOC2012/JPEGImages-test'
filelist = os.listdir(label_file_path)
path = '/home/lyy/gxw'
txt_path = os.path.join(path, 'test.txt')
f = open(txt_path, 'a')
for filename in filelist:
txt = filename.split('.')[0]
f.write('{}\n'.format(txt))
f.close()
|
[
"os.path.join",
"os.listdir"
] |
[((653, 680), 'os.listdir', 'os.listdir', (['label_file_path'], {}), '(label_file_path)\n', (663, 680), False, 'import os\n'), ((716, 746), 'os.path.join', 'os.path.join', (['path', '"""test.txt"""'], {}), "(path, 'test.txt')\n", (728, 746), False, 'import os\n')]
|
from pywire.signal import Signal
from tkinter import *
from tkinter.ttk import Separator
from enum import Enum
class BitState(Enum):
TRUE = 1
FALSE = 2
TRUE_FORCED = 3
FALSE_FORCED = 4
UNDEFINED = 5
def bitsToInt(bit_array):
for bit in bit_array:
if bit.state == BitState.UNDEFINED:
return None
total = 0
for index in range(len(bit_array)):
bit = bit_array[index]
total *= 2
if bit.state == BitState.TRUE_FORCED or bit.state == BitState.TRUE:
total += 1
return total
class Bit:
def press(self):
print("PRESSED")
if self.state == BitState.UNDEFINED:
self.state = BitState.TRUE_FORCED
elif self.state == BitState.TRUE_FORCED:
self.state = BitState.FALSE_FORCED
elif self.state == BitState.FALSE_FORCED:
self.state = BitState.UNDEFINED
elif self.state == BitState.TRUE:
self.state = BitState.TRUE_FORCED
elif self.state == BitState.FALSE:
self.state = BitState.TRUE_FORCED
else:
raise Exception("WTF")
self.update_gui()
def update_gui(self):
if self.state == BitState.UNDEFINED:
self.entity.configure(text="UN")
elif self.state == BitState.TRUE_FORCED:
self.entity.configure(text="TF")
elif self.state == BitState.FALSE_FORCED:
self.entity.configure(text="FF")
elif self.state == BitState.TRUE:
self.entity.configure(text="T_")
elif self.state == BitState.FALSE:
self.entity.configure(text="F_")
else:
raise Exception("WTF: " + str(self.state))
def __init__(self, master, row, column):
self.entity = Button(master,
command=self.press)
self.entity.grid(row=row, column=column)
self.state = BitState.FALSE
self.update_gui()
def refresh():
globals()["app"].recalculate_states()
class Application(Frame):
def draw_signals(self, master, signals, start_row):
for signal in signals:
self.bits[signal.name] = [[None for bit_index in range(len(signal))] for t in range(self.time)]
print("LABEL")
Label(master, text=signal.name).grid(row=start_row, column=1)
for bit_index in range(len(signal)):
Label(master, text="<" + str(bit_index) + ">").grid(row=start_row, column=2)
for time_stamp in range(self.time):
self.bits[signal.name][time_stamp][bit_index] = Bit(master, start_row, time_stamp + 3)
Separator(master, orient="horizontal").grid(row=start_row, column=time_stamp + 3, sticky=S + W + E)
start_row += 1
start_row += 1
print("done")
def createLayout(self, master):
Button(master, text="Refresh", command=refresh).grid(row=0, column=0)
for x in range(self.time):
Label(master, text="t=" + str(x)).grid(row=1, column=x+3)
row = 2
if self.input_signals:
Label(master, text="inputs").grid(row=row, column=0)
self.draw_signals(master, self.input_signals, row)
row += sum([len(signal) for signal in self.input_signals])+3
Label(master, text=" ").grid(row=row-1, column=0)
if self.other_signals:
Label(master, text="other").grid(row=row, column=0)
self.draw_signals(master, self.other_signals, row)
row += sum([len(signal) for signal in self.other_signals]) + 3
Label(master, text=" ").grid(row=row-1, column=0)
if self.output_signals:
Label(master, text="outputs").grid(row=row, column=0)
self.draw_signals(master, self.output_signals, row)
row += sum([len(signal) for signal in self.output_signals]) + 3
Label(master, text=" ").grid(row=row-1, column=0)
def recalculate_states(self):
for time_stamp in range(0, self.time):
for signal in Signal.all_signals:
if signal.driving_signals:
input_states = []
for input_signal in signal.driving_signals:
if signal.clock:
input_bits = self.bits[input_signal.name][time_stamp-1]
else:
input_bits = self.bits[input_signal.name][time_stamp]
input_states.append(bitsToInt(input_bits))
output_val = signal.driving_function(*input_states)
if isinstance(output_val, int):
output_string = bin(output_val)[2:].rjust(len(signal), "0")
output_string = output_string[len(output_string)-len(signal):]
print(output_string)
output_bool_array = [letter == "1" for letter in output_string]
print(output_bool_array)
signal_bits = self.bits[signal.name][time_stamp]
for index in range(len(output_bool_array)):
if signal_bits[index].state == BitState.TRUE_FORCED:
pass
elif signal_bits[index].state == BitState.FALSE_FORCED:
pass
elif output_bool_array[index]:
signal_bits[index].state = BitState.TRUE
else:
signal_bits[index].state = BitState.FALSE
elif isinstance(output_val, bool):
for index in range(len(output_bool_array)):
if output_val:
signal_bits[index] = BitState.TRUE
else:
signal_bits[index] = BitState.FALSE
else:
raise Exception("Function output is not a boolean or int")
for signal in Signal.all_signals:
for bit_row in self.bits[signal.name]:
for bit in bit_row:
bit.update_gui()
def __init__(self, master=None):
self.bits = {}
self.time = 10
signals = Signal.all_signals
self.input_signals = list(filter(lambda x: x.io == "in", signals))
self.output_signals = list(filter(lambda x: x.io == "out", signals))
self.other_signals = list(filter(lambda x: not x.io, signals))
Frame.__init__(self, master)
print("Creating layout")
self.createLayout(master)
print("DONE")
self.recalculate_states()
def launch_test():
root = Tk()
app = Application(master=root)
globals()["app"] = app
app.mainloop()
root.destroy()
"""
class TestObject:
def __init__(self, signals=Signal.all_signals, inputs={}, turns=10):
self.all_signals = signals
self.in_signals = list(filter(lambda x: x.io == "in", signals))
self.out_signals = list(filter(lambda x: x.io == "out", signals))
def print(self, turn, signals=self.signals):
"""
|
[
"tkinter.ttk.Separator"
] |
[((2652, 2690), 'tkinter.ttk.Separator', 'Separator', (['master'], {'orient': '"""horizontal"""'}), "(master, orient='horizontal')\n", (2661, 2690), False, 'from tkinter.ttk import Separator\n')]
|
# Copyright 2020, The TensorFlow Authors.
#
# 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.
"""Tests for DP-enabled DNNClassifier."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
from absl.testing import parameterized
import tensorflow as tf
from tensorflow_privacy.privacy.estimators import test_utils
from tensorflow_privacy.privacy.estimators.v1 import dnn
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
class DPDNNClassifierTest(tf.test.TestCase, parameterized.TestCase):
"""Tests for DP-enabled DNNClassifier."""
@parameterized.named_parameters(
('BinaryClassDNN', 2),
('MultiClassDNN 3', 3),
('MultiClassDNN 4', 4),
)
def testDNN(self, n_classes):
train_features, train_labels = test_utils.make_input_data(256, n_classes)
feature_columns = []
for key in train_features:
feature_columns.append(tf.feature_column.numeric_column(key=key))
optimizer = functools.partial(
DPGradientDescentGaussianOptimizer,
learning_rate=0.5,
l2_norm_clip=1.0,
noise_multiplier=0.0,
num_microbatches=1)
classifier = dnn.DNNClassifier(
hidden_units=[10],
activation_fn='relu',
feature_columns=feature_columns,
n_classes=n_classes,
optimizer=optimizer,
loss_reduction=tf.losses.Reduction.NONE)
classifier.train(
input_fn=test_utils.make_input_fn(train_features, train_labels, True,
16))
test_features, test_labels = test_utils.make_input_data(64, n_classes)
classifier.evaluate(
input_fn=test_utils.make_input_fn(test_features, test_labels, False,
16))
predict_features, predict_labels = test_utils.make_input_data(64, n_classes)
classifier.predict(
input_fn=test_utils.make_input_fn(predict_features, predict_labels,
False))
if __name__ == '__main__':
tf.test.main()
|
[
"tensorflow.test.main",
"functools.partial",
"tensorflow.feature_column.numeric_column",
"tensorflow_privacy.privacy.estimators.test_utils.make_input_fn",
"tensorflow_privacy.privacy.estimators.test_utils.make_input_data",
"tensorflow_privacy.privacy.estimators.v1.dnn.DNNClassifier",
"absl.testing.parameterized.named_parameters"
] |
[((1157, 1263), 'absl.testing.parameterized.named_parameters', 'parameterized.named_parameters', (["('BinaryClassDNN', 2)", "('MultiClassDNN 3', 3)", "('MultiClassDNN 4', 4)"], {}), "(('BinaryClassDNN', 2), ('MultiClassDNN 3', 3\n ), ('MultiClassDNN 4', 4))\n", (1187, 1263), False, 'from absl.testing import parameterized\n'), ((2588, 2602), 'tensorflow.test.main', 'tf.test.main', ([], {}), '()\n', (2600, 2602), True, 'import tensorflow as tf\n'), ((1349, 1391), 'tensorflow_privacy.privacy.estimators.test_utils.make_input_data', 'test_utils.make_input_data', (['(256)', 'n_classes'], {}), '(256, n_classes)\n', (1375, 1391), False, 'from tensorflow_privacy.privacy.estimators import test_utils\n'), ((1537, 1673), 'functools.partial', 'functools.partial', (['DPGradientDescentGaussianOptimizer'], {'learning_rate': '(0.5)', 'l2_norm_clip': '(1.0)', 'noise_multiplier': '(0.0)', 'num_microbatches': '(1)'}), '(DPGradientDescentGaussianOptimizer, learning_rate=0.5,\n l2_norm_clip=1.0, noise_multiplier=0.0, num_microbatches=1)\n', (1554, 1673), False, 'import functools\n'), ((1729, 1912), 'tensorflow_privacy.privacy.estimators.v1.dnn.DNNClassifier', 'dnn.DNNClassifier', ([], {'hidden_units': '[10]', 'activation_fn': '"""relu"""', 'feature_columns': 'feature_columns', 'n_classes': 'n_classes', 'optimizer': 'optimizer', 'loss_reduction': 'tf.losses.Reduction.NONE'}), "(hidden_units=[10], activation_fn='relu', feature_columns=\n feature_columns, n_classes=n_classes, optimizer=optimizer,\n loss_reduction=tf.losses.Reduction.NONE)\n", (1746, 1912), False, 'from tensorflow_privacy.privacy.estimators.v1 import dnn\n'), ((2135, 2176), 'tensorflow_privacy.privacy.estimators.test_utils.make_input_data', 'test_utils.make_input_data', (['(64)', 'n_classes'], {}), '(64, n_classes)\n', (2161, 2176), False, 'from tensorflow_privacy.privacy.estimators import test_utils\n'), ((2366, 2407), 'tensorflow_privacy.privacy.estimators.test_utils.make_input_data', 'test_utils.make_input_data', (['(64)', 'n_classes'], {}), '(64, n_classes)\n', (2392, 2407), False, 'from tensorflow_privacy.privacy.estimators import test_utils\n'), ((1477, 1518), 'tensorflow.feature_column.numeric_column', 'tf.feature_column.numeric_column', ([], {'key': 'key'}), '(key=key)\n', (1509, 1518), True, 'import tensorflow as tf\n'), ((1993, 2057), 'tensorflow_privacy.privacy.estimators.test_utils.make_input_fn', 'test_utils.make_input_fn', (['train_features', 'train_labels', '(True)', '(16)'], {}), '(train_features, train_labels, True, 16)\n', (2017, 2057), False, 'from tensorflow_privacy.privacy.estimators import test_utils\n'), ((2219, 2282), 'tensorflow_privacy.privacy.estimators.test_utils.make_input_fn', 'test_utils.make_input_fn', (['test_features', 'test_labels', '(False)', '(16)'], {}), '(test_features, test_labels, False, 16)\n', (2243, 2282), False, 'from tensorflow_privacy.privacy.estimators import test_utils\n'), ((2449, 2514), 'tensorflow_privacy.privacy.estimators.test_utils.make_input_fn', 'test_utils.make_input_fn', (['predict_features', 'predict_labels', '(False)'], {}), '(predict_features, predict_labels, False)\n', (2473, 2514), False, 'from tensorflow_privacy.privacy.estimators import test_utils\n')]
|
import unittest
from numpy import hstack, max, abs, sqrt
from cantera import Solution, gas_constant
import numpy as np
from spitfire import ChemicalMechanismSpec
from os.path import join, abspath
from subprocess import getoutput
test_mech_directory = abspath(join('tests', 'test_mechanisms', 'old_xmls'))
mechs = [x.replace('.xml', '') for x in getoutput('ls ' + test_mech_directory + ' | grep .xml').split('\n')]
def rhs_cantera(p_arg, T_arg, y_arg, rhoin, Tin_arg, yin_arg, tau_arg, gas, rhs_chem_in):
gas.TPY = T_arg, p_arg, y_arg
rho = gas.density_mass
cv = gas.cv_mass
e = gas.standard_int_energies_RT * gas.T * gas_constant / gas.molecular_weights
gas.TDY = Tin_arg, rhoin, yin_arg
ein = gas.standard_int_energies_RT * gas.T * gas_constant / gas.molecular_weights
rhs = np.copy(rhs_chem_in)
rhsMass = np.zeros(gas.n_species + 1)
rhsMass[0] += (rhoin - rho)
rhsMass[1] += 1. / (rho * cv) * (rhoin * np.sum(yin_arg * (ein - e)))
rhsMass[2:] += rhoin / rho * (yin_arg[:-1] - y_arg[:-1])
rhs += rhsMass / tau_arg
return rhs
def validate_on_mechanism(mech, temperature, pressure, tau, do_rhs, do_jac):
xml = join(test_mech_directory, mech + '.xml')
T = temperature
Tin = T + 1000.
p = pressure
r = ChemicalMechanismSpec(xml, 'gas').griffon
gas = Solution(xml)
ns = gas.n_species
y = np.ones(ns) # equal masses in the reactor
gas.TPY = T, p, y
y = np.copy(gas.Y)
rho = gas.density_mass
xin = np.ones(ns) # equal moles in the feed
gas.TPX = Tin, p, xin
yin = np.copy(gas.Y)
rhoin = gas.density_mass
state = hstack((rho, T, y[:-1]))
rhsGRChemOnly = np.zeros(ns + 1)
r.reactor_rhs_isochoric(state, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, False, rhsGRChemOnly)
rhsCN = rhs_cantera(p, T, y, rhoin, Tin, yin, tau, gas, rhsGRChemOnly)
rhsGR = np.empty(ns + 1)
r.reactor_rhs_isochoric(state, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, rhsGR)
if do_rhs:
return max(abs(rhsGR - rhsCN) / (abs(rhsCN) + 1.)) < 100. * sqrt(np.finfo(float).eps)
if do_jac:
jacGR = np.empty((ns + 1) * (ns + 1))
r.reactor_jac_isochoric(state, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, 0, rhsGR, jacGR)
jacGR = jacGR.reshape((ns + 1, ns + 1), order='F')
drho = 1.e-6
dT = 1.e-6
dY = 1.e-6
jacFD = np.empty((ns + 1, ns + 1))
rhsGR1, rhsGR2 = np.empty(ns + 1), np.empty(ns + 1)
state_m = hstack((rho - drho, T, y[:-1]))
state_p = hstack((rho + drho, T, y[:-1]))
r.reactor_rhs_isochoric(state_m, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, rhsGR1)
r.reactor_rhs_isochoric(state_p, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, rhsGR2)
jacFD[:, 0] = (- rhsGR1 + rhsGR2) / (2. * drho)
state_m = hstack((rho, T - dT, y[:-1]))
state_p = hstack((rho, T + dT, y[:-1]))
r.reactor_rhs_isochoric(state_m, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, rhsGR1)
r.reactor_rhs_isochoric(state_p, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, rhsGR2)
jacFD[:, 1] = (- rhsGR1 + rhsGR2) / (2. * dT)
for i in range(ns - 1):
y_m1, y_p1 = np.copy(y), np.copy(y)
y_m1[i] += - dY
y_m1[-1] -= - dY
y_p1[i] += dY
y_p1[-1] -= dY
state_m = hstack((rho, T, y_m1[:-1]))
state_p = hstack((rho, T, y_p1[:-1]))
r.reactor_rhs_isochoric(state_m, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, rhsGR1)
r.reactor_rhs_isochoric(state_p, rhoin, Tin, yin, tau, 0, 0, 0, 0, 0, 0, True, rhsGR2)
jacFD[:, 2 + i] = (- rhsGR1 + rhsGR2) / (2. * dY)
return max(abs(jacGR - jacFD) / (abs(jacGR) + 1.)) < 4.e-3
def create_test(m, T, p, tau, do_rhs, do_jac):
def test(self):
self.assertTrue(validate_on_mechanism(m, T, p, tau, do_rhs, do_jac))
return test
class Accuracy(unittest.TestCase):
pass
tau_list = [1.e-6, 1.e-3]
for mech in mechs:
for tau in tau_list:
rhsname = 'test_rhs_' + mech + '_' + 'tau=' + str(tau)
jacname = 'test_jac_' + mech + '_' + 'tau=' + str(tau)
setattr(Accuracy, rhsname, create_test(mech, 600., 101325, tau, True, False))
if 'methane' not in mech: # skip methane in the finite difference Jacobian tests
setattr(Accuracy, jacname, create_test(mech, 600., 101325, tau, False, True))
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main",
"spitfire.ChemicalMechanismSpec",
"numpy.sum",
"numpy.abs",
"numpy.copy",
"numpy.empty",
"numpy.zeros",
"numpy.ones",
"numpy.hstack",
"numpy.finfo",
"cantera.Solution",
"subprocess.getoutput",
"os.path.join"
] |
[((260, 304), 'os.path.join', 'join', (['"""tests"""', '"""test_mechanisms"""', '"""old_xmls"""'], {}), "('tests', 'test_mechanisms', 'old_xmls')\n", (264, 304), False, 'from os.path import join, abspath\n'), ((809, 829), 'numpy.copy', 'np.copy', (['rhs_chem_in'], {}), '(rhs_chem_in)\n', (816, 829), True, 'import numpy as np\n'), ((844, 871), 'numpy.zeros', 'np.zeros', (['(gas.n_species + 1)'], {}), '(gas.n_species + 1)\n', (852, 871), True, 'import numpy as np\n'), ((1172, 1212), 'os.path.join', 'join', (['test_mech_directory', "(mech + '.xml')"], {}), "(test_mech_directory, mech + '.xml')\n", (1176, 1212), False, 'from os.path import join, abspath\n'), ((1332, 1345), 'cantera.Solution', 'Solution', (['xml'], {}), '(xml)\n', (1340, 1345), False, 'from cantera import Solution, gas_constant\n'), ((1378, 1389), 'numpy.ones', 'np.ones', (['ns'], {}), '(ns)\n', (1385, 1389), True, 'import numpy as np\n'), ((1451, 1465), 'numpy.copy', 'np.copy', (['gas.Y'], {}), '(gas.Y)\n', (1458, 1465), True, 'import numpy as np\n'), ((1504, 1515), 'numpy.ones', 'np.ones', (['ns'], {}), '(ns)\n', (1511, 1515), True, 'import numpy as np\n'), ((1579, 1593), 'numpy.copy', 'np.copy', (['gas.Y'], {}), '(gas.Y)\n', (1586, 1593), True, 'import numpy as np\n'), ((1636, 1660), 'numpy.hstack', 'hstack', (['(rho, T, y[:-1])'], {}), '((rho, T, y[:-1]))\n', (1642, 1660), False, 'from numpy import hstack, max, abs, sqrt\n'), ((1682, 1698), 'numpy.zeros', 'np.zeros', (['(ns + 1)'], {}), '(ns + 1)\n', (1690, 1698), True, 'import numpy as np\n'), ((1883, 1899), 'numpy.empty', 'np.empty', (['(ns + 1)'], {}), '(ns + 1)\n', (1891, 1899), True, 'import numpy as np\n'), ((4496, 4511), 'unittest.main', 'unittest.main', ([], {}), '()\n', (4509, 4511), False, 'import unittest\n'), ((1279, 1312), 'spitfire.ChemicalMechanismSpec', 'ChemicalMechanismSpec', (['xml', '"""gas"""'], {}), "(xml, 'gas')\n", (1300, 1312), False, 'from spitfire import ChemicalMechanismSpec\n'), ((2130, 2159), 'numpy.empty', 'np.empty', (['((ns + 1) * (ns + 1))'], {}), '((ns + 1) * (ns + 1))\n', (2138, 2159), True, 'import numpy as np\n'), ((2397, 2423), 'numpy.empty', 'np.empty', (['(ns + 1, ns + 1)'], {}), '((ns + 1, ns + 1))\n', (2405, 2423), True, 'import numpy as np\n'), ((2503, 2534), 'numpy.hstack', 'hstack', (['(rho - drho, T, y[:-1])'], {}), '((rho - drho, T, y[:-1]))\n', (2509, 2534), False, 'from numpy import hstack, max, abs, sqrt\n'), ((2553, 2584), 'numpy.hstack', 'hstack', (['(rho + drho, T, y[:-1])'], {}), '((rho + drho, T, y[:-1]))\n', (2559, 2584), False, 'from numpy import hstack, max, abs, sqrt\n'), ((2850, 2879), 'numpy.hstack', 'hstack', (['(rho, T - dT, y[:-1])'], {}), '((rho, T - dT, y[:-1]))\n', (2856, 2879), False, 'from numpy import hstack, max, abs, sqrt\n'), ((2898, 2927), 'numpy.hstack', 'hstack', (['(rho, T + dT, y[:-1])'], {}), '((rho, T + dT, y[:-1]))\n', (2904, 2927), False, 'from numpy import hstack, max, abs, sqrt\n'), ((949, 976), 'numpy.sum', 'np.sum', (['(yin_arg * (ein - e))'], {}), '(yin_arg * (ein - e))\n', (955, 976), True, 'import numpy as np\n'), ((2449, 2465), 'numpy.empty', 'np.empty', (['(ns + 1)'], {}), '(ns + 1)\n', (2457, 2465), True, 'import numpy as np\n'), ((2467, 2483), 'numpy.empty', 'np.empty', (['(ns + 1)'], {}), '(ns + 1)\n', (2475, 2483), True, 'import numpy as np\n'), ((3385, 3412), 'numpy.hstack', 'hstack', (['(rho, T, y_m1[:-1])'], {}), '((rho, T, y_m1[:-1]))\n', (3391, 3412), False, 'from numpy import hstack, max, abs, sqrt\n'), ((3435, 3462), 'numpy.hstack', 'hstack', (['(rho, T, y_p1[:-1])'], {}), '((rho, T, y_p1[:-1]))\n', (3441, 3462), False, 'from numpy import hstack, max, abs, sqrt\n'), ((346, 401), 'subprocess.getoutput', 'getoutput', (["('ls ' + test_mech_directory + ' | grep .xml')"], {}), "('ls ' + test_mech_directory + ' | grep .xml')\n", (355, 401), False, 'from subprocess import getoutput\n'), ((3230, 3240), 'numpy.copy', 'np.copy', (['y'], {}), '(y)\n', (3237, 3240), True, 'import numpy as np\n'), ((3242, 3252), 'numpy.copy', 'np.copy', (['y'], {}), '(y)\n', (3249, 3252), True, 'import numpy as np\n'), ((2023, 2041), 'numpy.abs', 'abs', (['(rhsGR - rhsCN)'], {}), '(rhsGR - rhsCN)\n', (2026, 2041), False, 'from numpy import hstack, max, abs, sqrt\n'), ((3743, 3761), 'numpy.abs', 'abs', (['(jacGR - jacFD)'], {}), '(jacGR - jacFD)\n', (3746, 3761), False, 'from numpy import hstack, max, abs, sqrt\n'), ((2045, 2055), 'numpy.abs', 'abs', (['rhsCN'], {}), '(rhsCN)\n', (2048, 2055), False, 'from numpy import hstack, max, abs, sqrt\n'), ((2077, 2092), 'numpy.finfo', 'np.finfo', (['float'], {}), '(float)\n', (2085, 2092), True, 'import numpy as np\n'), ((3765, 3775), 'numpy.abs', 'abs', (['jacGR'], {}), '(jacGR)\n', (3768, 3775), False, 'from numpy import hstack, max, abs, sqrt\n')]
|
from django import forms
from django.forms import ModelForm
from django.conf import settings
from members.models import User
from .models import Unknowntag
class SelectUserForm(forms.Form):
user = forms.ModelChoiceField(queryset=User.objects.all())
activate_doors = forms.BooleanField(initial = True, help_text='Also give this user door permits if they did not have it yet.')
class SelectTagForm(forms.Form):
tag = forms.ModelChoiceField(queryset=Unknowntag.objects.all())
activate_doors = forms.BooleanField(initial = True, help_text='Also give this user door permits if they did not have it yet.')
|
[
"django.forms.BooleanField",
"members.models.User.objects.all"
] |
[((276, 388), 'django.forms.BooleanField', 'forms.BooleanField', ([], {'initial': '(True)', 'help_text': '"""Also give this user door permits if they did not have it yet."""'}), "(initial=True, help_text=\n 'Also give this user door permits if they did not have it yet.')\n", (294, 388), False, 'from django import forms\n'), ((509, 621), 'django.forms.BooleanField', 'forms.BooleanField', ([], {'initial': '(True)', 'help_text': '"""Also give this user door permits if they did not have it yet."""'}), "(initial=True, help_text=\n 'Also give this user door permits if they did not have it yet.')\n", (527, 621), False, 'from django import forms\n'), ((235, 253), 'members.models.User.objects.all', 'User.objects.all', ([], {}), '()\n', (251, 253), False, 'from members.models import User\n')]
|
from storage_bucket.bucket import get_bucket
def upload_file(
*,
file_content: bytes,
storage_bucket_name: str,
filename: str,
content_type: str = 'application/octet-stream',
**kwargs: dict,
) -> None:
"""
Upload content of file_data to a google cloud storage bucket.
.. versionadded:: 0.0.1
:param file_data: contents to upload in bytes
:type file_data: bytes
:param bucket_name: Name of the google cloud bucket
:type bucket_name: str
:param filename: The name to give the uploaded file
:type filename: str
:param content_type: What type of file to create, defaults to text/plain
:type content_type: str
:return: None
"""
bucket = get_bucket(storage_bucket_name=storage_bucket_name)
blob = bucket.blob(filename)
blob.upload_from_string(
file_content,
content_type=content_type,
**kwargs,
)
|
[
"storage_bucket.bucket.get_bucket"
] |
[((720, 771), 'storage_bucket.bucket.get_bucket', 'get_bucket', ([], {'storage_bucket_name': 'storage_bucket_name'}), '(storage_bucket_name=storage_bucket_name)\n', (730, 771), False, 'from storage_bucket.bucket import get_bucket\n')]
|
import pytest
import test.mock_data_gateway
from blades_helper.mission_generator import _get_next_mission_type, _can_use_mission_type, _generate_base_missions
from blades_helper.mission_generator_constants import MissionGeneratorConstants as con
def setup_one_mission_base_build(mock, note, type):
mock.mission_counts.append((1, note))
mock.mission_types.append(type)
def check_array(source, target):
assert len(source)==len(target)
for i in range(len(source)):
assert source[i]==target[i]
def check_mission_type(mission, mission_type):
assert mission.mission_type == mission_type
def check_note_len(mission, notes_len):
assert len(mission.notes) == notes_len
def check_for_note(mission, note_to_find):
for note in mission.notes:
if note_to_find in note:
return
assert False
def check_requirement(mission, specialist):
return specialist in mission.requirements
def check_for_note_plus_one_specialist(mission, specialist):
check_for_note(mission, "Mission can include one additional specialist")
check_requirement(mission, specialist)
def check_for_note_favor(mission, favor_type):
check_for_note(mission, con.FAVOR_NOTE[0:-3])
check_for_note(mission, favor_type)
def check_mission(mission, mission_type, target, rewards, penalties, notes_len, requirement, contained_notes):
check_mission_type(mission, mission_type)
assert mission.target == target
check_array(mission.rewards, rewards)
check_array(mission.penalties, penalties)
check_note_len(mission, notes_len)
if not requirement == con.NOTHING:
check_requirement(mission, requirement)
for contained_note in contained_notes:
check_for_note(mission, contained_note)
def test_get_next_mission_type():
assert _get_next_mission_type(con.ASSAULT) == con.RECON
assert _get_next_mission_type(con.RECON) == con.RELIGIOUS
assert _get_next_mission_type(con.RELIGIOUS)==con.SUPPLY
assert _get_next_mission_type(con.SUPPLY) == con.COMMANDER_FOCUS
with pytest.raises(AssertionError):
_get_next_mission_type(con.COMMANDER_FOCUS)
with pytest.raises(AssertionError):
_get_next_mission_type(con.GM_CHOICE)
def test_can_use_mission_type():
assert not _can_use_mission_type(con.SUPPLY, [con.ASSAULT])
assert _can_use_mission_type(con.SPECIAL, [con.SPECIAL])
assert _can_use_mission_type(con.GM_CHOICE, [con.GM_CHOICE])
assert not _can_use_mission_type(con.SUPPLY, [])
assert _can_use_mission_type(con.SUPPLY, [con.SUPPLY])
assert _can_use_mission_type(con.SUPPLY, [con.ASSAULT, con.SUPPLY])
assert _can_use_mission_type(con.SUPPLY, [con.SUPPLY, con.ASSAULT])
def test_make_one_mission():
mock = test.mock_data_gateway.MockDataGateway()
mock.titles.append('bunker hill')
setup_one_mission_base_build(mock, con.NOTHING, con.RELIGIOUS)
missions =_generate_base_missions(mock, False, False, False, con.SUPPLY, con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT, con.RECON] )
assert len(missions)==1
mission = missions[0]
check_mission(mission, con.RELIGIOUS, con.NOTHING, [], [], 0, con.required_religious_specialists, [])
def test_one_has_favor():
mock = test.mock_data_gateway.MockDataGateway()
mock.favor_types.append(con.THE_WILD)
setup_one_mission_base_build(mock, con.ONE_HAS_FAVOR, con.SUPPLY)
missions =_generate_base_missions( mock, False, False, False, con.ASSAULT, con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT, con.RECON] )
assert len(missions)==1
mission = missions[0]
check_mission(mission,con.SUPPLY,con.NOTHING,[],[],1,con.required_supply_specialists,[con.FAVOR_NOTE[0:-3], con.THE_WILD])
def test_one_extra_specialist():
mock = test.mock_data_gateway.MockDataGateway()
mock.specialists.append(con.SNIPER)
setup_one_mission_base_build(mock, con.PLUS_ONE_SPECIALIST, con.SUPPLY)
missions =_generate_base_missions( mock, False, False, False, con.ASSAULT, con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT, con.RECON] )
mission = missions[0]
check_mission(mission, con.SUPPLY, con.NOTHING, [],[], 1, con.required_supply_specialists, ["Mission can include one additional specialist"])
check_for_note_plus_one_specialist(mission, con.SNIPER)
def test_commanders_focus():
mock = test.mock_data_gateway.MockDataGateway()
setup_one_mission_base_build(mock, con.NOTHING, con.COMMANDER_FOCUS)
missions =_generate_base_missions( mock, False, False, False, con.RECON, con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT, con.RECON] )
mission = missions[0]
check_mission(mission, con.RECON, con.NOTHING, [],[], 0,con.required_recon_specialists,[])
def test_gm_choice():
mock = test.mock_data_gateway.MockDataGateway()
setup_one_mission_base_build(mock, con.NOTHING, con.GM_CHOICE)
missions =_generate_base_missions( mock, False, False, False, con.ASSAULT, con.RECON, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT, con.RECON] )
mission = missions[0]
check_mission(mission, con.RECON, con.NOTHING, [],[], 0,con.required_recon_specialists,[])
def test_unavailable_mission():
#test simple unavailability
mock = test.mock_data_gateway.MockDataGateway()
setup_one_mission_base_build(mock, con.NOTHING, con.RECON)
missions =_generate_base_missions( mock, False, False, False, con.ASSAULT, con.RECON, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT] )
mission = missions[0]
check_mission(mission, con.RELIGIOUS, con.NOTHING, [],[], 0,con.required_religious_specialists,[])
#test when mutiple missions unavailable
setup_one_mission_base_build(mock, con.NOTHING, con.ASSAULT)
missions =_generate_base_missions( mock, False, False, False, con.UNDEFINED, con.ASSAULT, [] )
mission = missions[0]
check_mission(mission, con.UNDEFINED, con.NOTHING, [],[], 0,con.NOTHING,[])
def test_special_missions_are_allowed():
# special
mock = test.mock_data_gateway.MockDataGateway()
setup_one_mission_base_build(mock, con.NOTHING, con.SPECIAL)
missions =_generate_base_missions( mock, False, False, False, con.ASSAULT, con.ASSAULT, [] )
mission = missions[0]
check_mission(mission, con.SPECIAL, con.NOTHING, [],[], 0,con.NOTHING,[])
def create_mission_with_gm_choice_and_note(mock, choice, note, spymaster_buy=False):
setup_one_mission_base_build(mock, note, con.GM_CHOICE)
missions =_generate_base_missions( mock, spymaster_buy, False, False, con.ASSAULT, choice, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT] )
assert len(missions) == 1
return missions[0]
def create_mission_with_commander_focus_and_note(mock, focus,note, spymaster_buy=False):
setup_one_mission_base_build(mock, note, con.COMMANDER_FOCUS)
missions =_generate_base_missions( mock, spymaster_buy, False, False, focus, con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT] )
assert len(missions) == 1
return missions[0]
def test_commander_focus_plus_one_specialist():
mock = test.mock_data_gateway.MockDataGateway()
mock.specialists.append(con.HEAVY)
focus=con.SUPPLY
note=con.PLUS_ONE_SPECIALIST
mission=create_mission_with_commander_focus_and_note(mock, focus, note)
check_mission_type(mission, focus)
check_note_len(mission, 1)
check_for_note_plus_one_specialist(mission, con.HEAVY)
def test_commander_focus_one_has_favor():
mock = test.mock_data_gateway.MockDataGateway()
mock.favor_types.append(con.HOLY)
focus=con.SUPPLY
note=con.ONE_HAS_FAVOR
mission=create_mission_with_commander_focus_and_note(mock, focus, note)
check_mission_type(mission, focus)
check_note_len(mission, 1)
check_for_note_favor(mission, con.HOLY)
def test_commander_focus_one_is_special():
mock = test.mock_data_gateway.MockDataGateway()
focus=con.SUPPLY
note=con.ONE_IS_SPECIAL
mission=create_mission_with_commander_focus_and_note(mock, focus, note)
check_mission_type(mission, con.SPECIAL)
check_note_len(mission, 0)
def test_gm_choice_plus_one_specialist():
mock = test.mock_data_gateway.MockDataGateway()
mock.specialists.append(con.HEAVY)
choice=con.SUPPLY
note=con.PLUS_ONE_SPECIALIST
mission=create_mission_with_gm_choice_and_note(mock, choice, note)
check_mission_type(mission, choice)
check_note_len(mission, 1)
check_for_note_plus_one_specialist(mission, con.HEAVY)
def test_gm_choice_one_has_favor():
mock = test.mock_data_gateway.MockDataGateway()
mock.favor_types.append(con.HOLY)
choice=con.SUPPLY
note=con.ONE_HAS_FAVOR
mission=create_mission_with_gm_choice_and_note(mock, choice, note)
check_mission_type(mission, choice)
check_note_len(mission, 1)
check_for_note_favor(mission, con.HOLY)
def test_gm_choice_one_is_special():
mock = test.mock_data_gateway.MockDataGateway()
choice=con.SUPPLY
note=con.ONE_IS_SPECIAL
mission=create_mission_with_gm_choice_and_note(mock, choice, note)
check_mission_type(mission, con.SPECIAL)
check_note_len(mission, 0)
def test_simple_spymaster_spend():
mock=test.mock_data_gateway.MockDataGateway()
setup_one_mission_base_build(mock, con.NOTHING, con.SUPPLY)
missions =_generate_base_missions( mock, True, False, False, con.ASSAULT,con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT] )
assert len(missions) == 1
check_mission_type(missions[0], con.SPECIAL)
def test_one_mission_with_spymaster_and_one_is_special():
mock=test.mock_data_gateway.MockDataGateway()
setup_one_mission_base_build(mock, con.ONE_IS_SPECIAL, con.SUPPLY)
missions =_generate_base_missions( mock, True, False, False, con.ASSAULT,con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT] )
assert len(missions) == 1
check_mission_type(missions[0], con.SPECIAL)
check_note_len(missions[0],0)
def test_two_missions_with_spymaster_and_one_is_special():
mock=test.mock_data_gateway.MockDataGateway()
mock.mission_counts.append((2, con.ONE_IS_SPECIAL))
mock.mission_types.append(con.RECON)
mock.mission_types.append(con.SUPPLY)
missions =_generate_base_missions( mock, True, False, False, con.ASSAULT,con.ASSAULT, [con.RELIGIOUS, con.SUPPLY, con.ASSAULT] )
assert len(missions) == 2
check_mission_type(missions[0], con.SPECIAL)
check_note_len(missions[0],0)
check_mission_type(missions[1], con.SPECIAL)
check_note_len(missions[1],0)
def setup_three_missions(mock, note, first_type, second_type, third_type, commanders_focus, gms_choice, spymaster_buy=False):
mock.mission_counts.append((3, note))
mock.mission_types.append(first_type)
mock.mission_types.append(second_type)
mock.mission_types.append(third_type)
missions = _generate_base_missions(mock, spymaster_buy, False, False, commanders_focus, gms_choice, [con.ASSAULT, con.RECON, con.SUPPLY, con.RELIGIOUS])
assert len(missions)==3
return missions
def test_three_simple_missions():
mock=test.mock_data_gateway.MockDataGateway()
note=con.NOTHING
first_type=con.RELIGIOUS
second_type=con.SUPPLY
third_type=con.RECON
spymaster_buy=False
commander_focus=con.ASSAULT
gm_choice=con.ASSAULT
missions=setup_three_missions(mock, note, first_type, second_type, third_type, commander_focus, gm_choice, spymaster_buy)
check_mission_type(missions[0], con.RELIGIOUS)
check_mission_type(missions[1], con.SUPPLY)
check_mission_type(missions[2], con.RECON)
def test_three_missions_with_one_is_special():
mock=test.mock_data_gateway.MockDataGateway()
mock.random_missions.append(1)
mock.specialists.append(con.HEAVY)
note=con.PLUS_ONE_SPECIALIST
first_type=con.RELIGIOUS
second_type=con.SUPPLY
third_type=con.RECON
spymaster_buy=False
commander_focus=con.ASSAULT
gm_choice=con.ASSAULT
missions=setup_three_missions(mock, note, first_type, second_type, third_type, commander_focus, gm_choice, spymaster_buy)
check_mission_type(missions[0], con.RELIGIOUS)
check_mission_type(missions[1], con.SUPPLY)
check_mission_type(missions[2], con.RECON)
check_for_note_plus_one_specialist(missions[1],con.HEAVY)
def test_three_missions_with_spymaster_buy_and_one_is_special():
mock=test.mock_data_gateway.MockDataGateway()
note=con.ONE_IS_SPECIAL
first_type=con.RELIGIOUS
second_type=con.SUPPLY
third_type=con.RECON
spymaster_buy=True
commander_focus=con.ASSAULT
gm_choice=con.ASSAULT
missions=setup_three_missions(mock, note, first_type, second_type, third_type, commander_focus, gm_choice, spymaster_buy)
check_mission_type(missions[0], con.SPECIAL)
check_mission_type(missions[1], con.SPECIAL)
check_mission_type(missions[2], con.RELIGIOUS)
|
[
"pytest.raises",
"blades_helper.mission_generator._can_use_mission_type",
"blades_helper.mission_generator._generate_base_missions",
"blades_helper.mission_generator._get_next_mission_type"
] |
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|
from typing import Any, List
from app.schemas.category import CategoryResponse
from fastapi import APIRouter, Depends, status, HTTPException
from sqlalchemy.orm import Session
from app import crud, schemas
from app.api import deps
router = APIRouter()
@router.get(
"/", response_model=List[schemas.CategoryResponse], status_code=status.HTTP_200_OK
)
def read_categories(
db: Session = Depends(deps.get_db), skip: int = 0, limit: int = 100
) -> Any:
return crud.read_categories(db, skip=skip, limit=limit)
@router.post("/", response_model=CategoryResponse, status_code=status.HTTP_200_OK)
def create_category(
obj_in: schemas.CategoryCreate, db: Session = Depends(deps.get_db)
) -> Any:
is_a_registered_category = crud.get_category_by_name(db, obj_in.name)
if is_a_registered_category:
raise HTTPException(status_code=400, detail="Category already registered")
return crud.create_category(db, obj_in=obj_in)
|
[
"app.crud.create_category",
"app.crud.get_category_by_name",
"fastapi.HTTPException",
"app.crud.read_categories",
"fastapi.Depends",
"fastapi.APIRouter"
] |
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|
"""Minimal example dumping whatever event it receives."""
import time
import logging
import argparse
from proglove_streams.logging import init_logging
from proglove_streams.client import Client
from proglove_streams.gateway import Gateway, GatewayMessageHandler
from proglove_streams.exception import ProgloveStreamsException
from proglove_streams.models.scan import ScanEvent
from proglove_streams.models.scanner_state import ScannerStateEvent
from proglove_streams.models.error import ErrorEvent
from proglove_streams.models.gateway_state import GatewayStateEvent
from proglove_streams.models.button_pressed import ButtonPressedEvent
logger = logging.getLogger(__name__)
def _set_display(client: Gateway, event: ScanEvent):
client.set_display(str(event.device_serial), 'PG3',
display_fields=[
{
"display_field_id": 1,
"display_field_header": "Storage Unit",
"display_field_text": "R15"
},
{
"display_field_id": 2,
"display_field_header": "Item",
"display_field_text": "Engine 12"
},
{
"display_field_id": 3,
"display_field_header": "Quantity",
"display_field_text": "10"
}
])
def _block_trigger(client: Gateway, event: ScanEvent):
client.set_trigger_block(str(event.device_serial), True,
["TRIGGER_SINGLE_CLICK"], [],
time_validity_duration=3000)
def _unblock_trigger(client: Gateway, event: ScanEvent):
client.set_trigger_block(str(event.device_serial), False,
[], [])
def on_connected(_client: Client, event: ScannerStateEvent) -> None:
"""On connected event callback."""
logger.info('device connected: %s', event.device_serial)
def on_disconnected(_client: Client, event: ScannerStateEvent) -> None:
"""On disconnected event callback."""
logger.info('device disconnected: %s', event.device_serial)
def on_scan(client: Client, event: ScanEvent) -> None:
"""On scan event callback."""
if not isinstance(client, Gateway):
return
logger.info(
'scan received: device %s, data: %s',
event.device_serial,
repr(event.scan_code)
)
scan_code = str(event.scan_code).split('\r')[0]
if scan_code == 'DISPLAY':
_set_display(client, event)
elif scan_code == 'BLOCK':
_block_trigger(client, event)
elif scan_code == 'UNBLOCK':
_unblock_trigger(client, event)
elif scan_code == 'FEEDBACK_OK':
client.send_feedback(str(event.device_serial), 'FEEDBACK_POSITIVE')
elif scan_code == 'FEEDBACK_NOK':
client.send_feedback(str(event.device_serial), 'FEEDBACK_NEGATIVE')
elif scan_code == 'STATE':
client.get_gateway_state()
def on_error(_client: Client, event: ErrorEvent) -> None:
"""On error event callback."""
logger.info('error received: %s', event.error_code)
def on_gateway_state_event(_client: Client, event: GatewayStateEvent):
"""On Gateway state event callback."""
logger.info('''Gateway state received: serial: %s version: %s
connected devices: %s''',
event.gateway_serial,
event.gateway_app_version,
','.join([d.device_serial
for d in event.device_connected_list]))
def on_button_pressed_event(_client: Client,
event: ButtonPressedEvent) -> None:
"""On error event callback."""
logger.info('button pressed: device %s, trigger gesture: %s',
event.device_serial,
event.trigger_gesture)
def app_example():
"""Example of Streams API usage."""
parser = argparse.ArgumentParser('proglove_streams')
parser.add_argument(
'-L', '--logging-level',
help='set the logging level (default is DEBUG)',
type=str,
metavar='LEVEL',
choices=('DEBUG', 'INFO', 'WARNING', 'ERROR'),
default='DEBUG'
)
parser.add_argument(
'-b', '--baudrate',
help='use a specific baudarate (default is 115200)',
type=int,
metavar='VALUE',
default=115200
)
parser.add_argument(
'port',
help='path to the serial device port (e.g. COM1, /dev/ttyACM0)',
type=str,
metavar='PORT',
)
args = parser.parse_args()
device = args.port
baudrate = args.baudrate
init_logging(getattr(logging, args.logging_level))
logger.info('Streams API example application.')
handler = GatewayMessageHandler(
on_scanner_connected=on_connected,
on_scanner_disconnected=on_disconnected,
on_scan=on_scan,
on_error=on_error,
on_gateway_state_event=on_gateway_state_event,
on_button_pressed=on_button_pressed_event
)
try:
gateway = Gateway(handler, device, baudrate)
gateway.start()
except ProgloveStreamsException as e:
logging.error('Streams API exception: %s', e)
return
logger.info('application started, press Ctrl-C to exit')
try:
while True:
time.sleep(1000)
except KeyboardInterrupt:
gateway.stop()
|
[
"logging.error",
"argparse.ArgumentParser",
"proglove_streams.gateway.GatewayMessageHandler",
"time.sleep",
"proglove_streams.gateway.Gateway",
"logging.getLogger"
] |
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|
""" Add an owner to a resource or resources
Usage: add_owner {username} {resource list}
"""
from django.core.management.base import BaseCommand
from django.contrib.auth.models import User
from hs_core.models import BaseResource
from hs_core.hydroshare.utils import get_resource_by_shortkey
from hs_access_control.models.privilege import UserResourcePrivilege, PrivilegeCodes
from django_irods.icommands import SessionException
from django.db import transaction
def set_quota_holder(resource, user):
try:
resource.set_quota_holder(user, user)
except SessionException as ex:
# some resources copied from www for testing do not exist in the iRODS backend,
# hence need to skip these test artifects
print(resource.short_id + ' raised SessionException when setting quota holder: ' +
ex.stderr)
except AttributeError as ex:
# when federation is not set up correctly, istorage does not have a session
# attribute, hence raise AttributeError - ignore for testing
print((resource.short_id + ' raised AttributeError when setting quota holder: ' +
str(ex)))
except ValueError as ex:
# when federation is not set up correctly, istorage does not have a session
# attribute, hence raise AttributeError - ignore for testing
print((resource.short_id + ' raised ValueError when setting quota holder: ' +
str(ex)))
class Command(BaseCommand):
help = "add owner to resource"
def add_arguments(self, parser):
parser.add_argument('new_owner', type=str)
parser.add_argument(
'--owned_by',
dest='owned_by',
help='prior owner of the resources'
)
parser.add_argument(
'--set_quota_holder',
action='store_true', # True for presence, False for absence
dest='set_quota_holder', # value is options['set_quota_holder']
help='set quota holder as new owner')
# a list of resource id's: none does nothing.
parser.add_argument('resource_ids', nargs='*', type=str)
def handle(self, *args, **options):
user = User.objects.get(username=options['new_owner'])
admin = User.objects.get(username='admin')
if options['owned_by'] is not None:
prior = User.objects.get(username=options['owned_by'])
for res in BaseResource.objects.filter(r2urp__user=prior,
r2urp__privilege=PrivilegeCodes.OWNER):
with transaction.atomic():
resource = res.get_content_model()
UserResourcePrivilege.share(user=user,
resource=resource,
privilege=PrivilegeCodes.OWNER,
grantor=admin)
print("added owner {} to {}".format(options['new_owner'], resource.short_id))
if options['set_quota_holder']:
set_quota_holder(resource, user)
print("set quota holder to {} for {}".format(options['new_owner'],
resource.short_id))
if len(options['resource_ids']) > 0: # an array of resource short_id to check.
for rid in options['resource_ids']:
resource = get_resource_by_shortkey(rid, or_404=False)
with transaction.atomic():
UserResourcePrivilege.share(user=user,
resource=resource,
privilege=PrivilegeCodes.OWNER,
grantor=admin)
print("added owner {} to {}".format(options['new_owner'], rid))
if options['set_quota_holder']:
set_quota_holder(resource, user)
print("set quota holder to {} for {}".format(options['new_owner'],
resource.short_id))
|
[
"hs_core.models.BaseResource.objects.filter",
"django.contrib.auth.models.User.objects.get",
"hs_access_control.models.privilege.UserResourcePrivilege.share",
"django.db.transaction.atomic",
"hs_core.hydroshare.utils.get_resource_by_shortkey"
] |
[((2177, 2224), 'django.contrib.auth.models.User.objects.get', 'User.objects.get', ([], {'username': "options['new_owner']"}), "(username=options['new_owner'])\n", (2193, 2224), False, 'from django.contrib.auth.models import User\n'), ((2241, 2275), 'django.contrib.auth.models.User.objects.get', 'User.objects.get', ([], {'username': '"""admin"""'}), "(username='admin')\n", (2257, 2275), False, 'from django.contrib.auth.models import User\n'), ((2341, 2387), 'django.contrib.auth.models.User.objects.get', 'User.objects.get', ([], {'username': "options['owned_by']"}), "(username=options['owned_by'])\n", (2357, 2387), False, 'from django.contrib.auth.models import User\n'), ((2411, 2501), 'hs_core.models.BaseResource.objects.filter', 'BaseResource.objects.filter', ([], {'r2urp__user': 'prior', 'r2urp__privilege': 'PrivilegeCodes.OWNER'}), '(r2urp__user=prior, r2urp__privilege=\n PrivilegeCodes.OWNER)\n', (2438, 2501), False, 'from hs_core.models import BaseResource\n'), ((3429, 3472), 'hs_core.hydroshare.utils.get_resource_by_shortkey', 'get_resource_by_shortkey', (['rid'], {'or_404': '(False)'}), '(rid, or_404=False)\n', (3453, 3472), False, 'from hs_core.hydroshare.utils import get_resource_by_shortkey\n'), ((2570, 2590), 'django.db.transaction.atomic', 'transaction.atomic', ([], {}), '()\n', (2588, 2590), False, 'from django.db import transaction\n'), ((2667, 2776), 'hs_access_control.models.privilege.UserResourcePrivilege.share', 'UserResourcePrivilege.share', ([], {'user': 'user', 'resource': 'resource', 'privilege': 'PrivilegeCodes.OWNER', 'grantor': 'admin'}), '(user=user, resource=resource, privilege=\n PrivilegeCodes.OWNER, grantor=admin)\n', (2694, 2776), False, 'from hs_access_control.models.privilege import UserResourcePrivilege, PrivilegeCodes\n'), ((3494, 3514), 'django.db.transaction.atomic', 'transaction.atomic', ([], {}), '()\n', (3512, 3514), False, 'from django.db import transaction\n'), ((3536, 3645), 'hs_access_control.models.privilege.UserResourcePrivilege.share', 'UserResourcePrivilege.share', ([], {'user': 'user', 'resource': 'resource', 'privilege': 'PrivilegeCodes.OWNER', 'grantor': 'admin'}), '(user=user, resource=resource, privilege=\n PrivilegeCodes.OWNER, grantor=admin)\n', (3563, 3645), False, 'from hs_access_control.models.privilege import UserResourcePrivilege, PrivilegeCodes\n')]
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Test Primitive Data Real
------------------------
"""
import unittest
import struct
import math
from bacpypes.debugging import bacpypes_debugging, ModuleLogger, xtob
from bacpypes.errors import InvalidTag
from bacpypes.primitivedata import Real, Tag
# some debugging
_debug = 0
_log = ModuleLogger(globals())
@bacpypes_debugging
def real_tag(x):
"""Convert a hex string to an real application tag."""
if _debug: real_tag._debug("real_tag %r", x)
b = xtob(x)
tag = Tag(Tag.applicationTagClass, Tag.realAppTag, len(b), b)
if _debug: real_tag._debug(" - tag: %r", tag)
return tag
@bacpypes_debugging
def real_encode(obj):
"""Encode an Real object into a tag."""
if _debug: real_encode._debug("real_encode %r", obj)
tag = Tag()
obj.encode(tag)
if _debug: real_encode._debug(" - tag: %r, %r", tag, tag.tagData)
return tag
@bacpypes_debugging
def real_decode(tag):
"""Decode an real application tag into an real."""
if _debug: real_decode._debug("real_decode %r", tag)
obj = Real(tag)
if _debug: real_decode._debug(" - obj: %r, %r", obj, obj.value)
return obj
@bacpypes_debugging
def real_endec(v, x):
"""Pass the value to Real, construct a tag from the hex string,
and compare results of encode and decoding each other."""
if _debug: real_endec._debug("real_endec %r %r", v, x)
tag = real_tag(x)
if _debug: real_endec._debug(" - tag: %r, %r", tag, tag.tagData)
obj = Real(v)
if _debug: real_endec._debug(" - obj: %r, %r", obj, obj.value)
assert real_encode(obj) == tag
if _debug: real_endec._debug(" - tags match")
if math.isnan(v):
assert math.isnan(real_decode(tag).value)
if _debug: real_endec._debug(" - both NaN")
else:
assert real_decode(tag) == obj
if _debug: real_endec._debug(" - objects match")
@bacpypes_debugging
class TestReal(unittest.TestCase):
def test_real(self):
if _debug: TestReal._debug("test_real")
obj = Real()
assert obj.value == 0.0
with self.assertRaises(TypeError):
Real("some string")
def test_real_real(self):
if _debug: TestReal._debug("test_real_real")
obj = Real(1.0)
assert obj.value == 1.0
assert str(obj) == "Real(1)"
obj = Real(73.5)
assert obj.value == 73.5
assert str(obj) == "Real(73.5)"
def test_real_tag(self):
if _debug: TestReal._debug("test_real_tag")
tag = Tag(Tag.applicationTagClass, Tag.realAppTag, 1, xtob('3f800000'))
obj = Real(tag)
assert obj.value == 1.0
tag = Tag(Tag.applicationTagClass, Tag.booleanAppTag, 0, xtob(''))
with self.assertRaises(InvalidTag):
Real(tag)
tag = Tag(Tag.contextTagClass, 0, 1, xtob('ff'))
with self.assertRaises(InvalidTag):
Real(tag)
tag = Tag(Tag.openingTagClass, 0)
with self.assertRaises(InvalidTag):
Real(tag)
def test_real_copy(self):
if _debug: TestReal._debug("test_real_copy")
obj1 = Real(12)
obj2 = Real(obj1)
assert obj2.value == 12
def test_real_endec(self):
if _debug: TestReal._debug("test_real_endec")
with self.assertRaises(InvalidTag):
obj = Real(real_tag(''))
real_endec(0, '00000000')
real_endec(1, '3f800000')
real_endec(-1, 'bf800000')
real_endec(73.5, '42930000')
inf = float('inf')
real_endec(inf, '7f800000')
real_endec(-inf, 'ff800000')
nan = float('nan')
real_endec(nan, '7fc00000')
|
[
"bacpypes.primitivedata.Tag",
"bacpypes.primitivedata.Real",
"math.isnan",
"bacpypes.debugging.xtob"
] |
[((520, 527), 'bacpypes.debugging.xtob', 'xtob', (['x'], {}), '(x)\n', (524, 527), False, 'from bacpypes.debugging import bacpypes_debugging, ModuleLogger, xtob\n'), ((819, 824), 'bacpypes.primitivedata.Tag', 'Tag', ([], {}), '()\n', (822, 824), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((1101, 1110), 'bacpypes.primitivedata.Real', 'Real', (['tag'], {}), '(tag)\n', (1105, 1110), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((1537, 1544), 'bacpypes.primitivedata.Real', 'Real', (['v'], {}), '(v)\n', (1541, 1544), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((1712, 1725), 'math.isnan', 'math.isnan', (['v'], {}), '(v)\n', (1722, 1725), False, 'import math\n'), ((2087, 2093), 'bacpypes.primitivedata.Real', 'Real', ([], {}), '()\n', (2091, 2093), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((2301, 2310), 'bacpypes.primitivedata.Real', 'Real', (['(1.0)'], {}), '(1.0)\n', (2305, 2310), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((2395, 2405), 'bacpypes.primitivedata.Real', 'Real', (['(73.5)'], {}), '(73.5)\n', (2399, 2405), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((2656, 2665), 'bacpypes.primitivedata.Real', 'Real', (['tag'], {}), '(tag)\n', (2660, 2665), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((2979, 3006), 'bacpypes.primitivedata.Tag', 'Tag', (['Tag.openingTagClass', '(0)'], {}), '(Tag.openingTagClass, 0)\n', (2982, 3006), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((3173, 3181), 'bacpypes.primitivedata.Real', 'Real', (['(12)'], {}), '(12)\n', (3177, 3181), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((3197, 3207), 'bacpypes.primitivedata.Real', 'Real', (['obj1'], {}), '(obj1)\n', (3201, 3207), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((2182, 2201), 'bacpypes.primitivedata.Real', 'Real', (['"""some string"""'], {}), "('some string')\n", (2186, 2201), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((2624, 2640), 'bacpypes.debugging.xtob', 'xtob', (['"""3f800000"""'], {}), "('3f800000')\n", (2628, 2640), False, 'from bacpypes.debugging import bacpypes_debugging, ModuleLogger, xtob\n'), ((2764, 2772), 'bacpypes.debugging.xtob', 'xtob', (['""""""'], {}), "('')\n", (2768, 2772), False, 'from bacpypes.debugging import bacpypes_debugging, ModuleLogger, xtob\n'), ((2830, 2839), 'bacpypes.primitivedata.Real', 'Real', (['tag'], {}), '(tag)\n', (2834, 2839), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((2886, 2896), 'bacpypes.debugging.xtob', 'xtob', (['"""ff"""'], {}), "('ff')\n", (2890, 2896), False, 'from bacpypes.debugging import bacpypes_debugging, ModuleLogger, xtob\n'), ((2954, 2963), 'bacpypes.primitivedata.Real', 'Real', (['tag'], {}), '(tag)\n', (2958, 2963), False, 'from bacpypes.primitivedata import Real, Tag\n'), ((3063, 3072), 'bacpypes.primitivedata.Real', 'Real', (['tag'], {}), '(tag)\n', (3067, 3072), False, 'from bacpypes.primitivedata import Real, Tag\n')]
|
import matplotlib, numpy, pprint
# matplotlib.rcParams['pdf.fonttype'] = 42
# matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.use('Agg')
import matplotlib.pyplot as plot
import gzip, csv, pylab
from collections import namedtuple
from rvs import *
from patch import *
"""
task events table contains the following fields:
1. timestamp
2. missing info
3. job ID
4. task index - within the job
5. machine ID
6. event type
7. user name
8. scheduling class
9. priority
10. resource request for CPU cores
11. resource request for RAM
12. resource request for local disk space
13. different-machine constraint
"""
jobevents_f_to_i = {
'timestamp': 0,
'job id': 2,
'event': 3,
'job name': 6,
'logical job name': 7
}
taskevents_f_to_i = {
'timestamp': 0,
'job id': 2,
'task index': 3,
'event': 5
}
e_to_i = {
'schedule': 1,
'finish': 4
}
def counter_to_furl(counter, obj="task"):
part = str(counter)
part = (5 - len(part) )*'0' + part
return "/home/mfa51/google-clusterdata-2011/{}_events/part-{}-of-00500.csv.gz".format(obj, part)
def deneme():
job_task_i__sch_finish_time_m = {}
counter = 0
while 1:
furl = counter_to_furl(counter)
try:
with gzip.open(furl, mode="rt") as f:
reader = csv.reader(f)
for line in reader:
i = line[taskevents_f_to_i['job id'] ] + '_' + line[taskevents_f_to_i['task index'] ]
e = int(line[taskevents_f_to_i['event'] ] )
if e == e_to_i['schedule'] or e == e_to_i['finish']:
t = float(line[taskevents_f_to_i['timestamp'] ] )/10**6
if i not in job_task_i__sch_finish_time_m:
job_task_i__sch_finish_time_m[i] = [t]
else:
job_task_i__sch_finish_time_m[i].append(t)
except (OSError, IOError) as e:
log(WARNING, "done with the files.")
break
counter += 1
if counter > 10:
break
with open("task_lifetime.dat", 'wt') as f:
writer = csv.writer(f, delimiter=',')
for job_task_i,sch_finish_time in job_task_i__sch_finish_time_m.items():
if len(sch_finish_time) >= 2:
sch_finish_time = [t for t in sch_finish_time if t]
if len(sch_finish_time) == 1:
sch_finish_time.append(0)
# elif len(sch_finish_time) > 2:
# log(WARNING, "More than 2 scheduling or finish events for single task; sch_finish_time= {}".format(sch_finish_time) )
lifetime = abs(sch_finish_time[1] - sch_finish_time[0] )
writer.writerow([job_task_i, lifetime] )
def write_num_tasks_per_job():
wf = open("num_tasks.dat", 'wt')
writer = csv.writer(wf, delimiter=',')
counter = 0
while 1:
print("counter= {}".format(counter) )
ji__ti_l_m = {}
furl = counter_to_furl(counter)
try:
with gzip.open(furl, mode="rt") as f:
reader = csv.reader(f)
for line in reader:
ji = int(line[taskevents_f_to_i['job id'] ] )
ti = int(line[taskevents_f_to_i['task index'] ] )
e = int(line[taskevents_f_to_i['event'] ] )
if e == e_to_i['schedule']:
if ji not in ji__ti_l_m:
ji__ti_l_m[ji] = set()
ji__ti_l_m[ji].add(ti)
print("counter= {}, writing now...".format(counter) )
for ji, ti_l in ji__ti_l_m.items():
writer.writerow([ji, len(ti_l) ] )
except (OSError, IOError) as e:
log(WARNING, "done with the files.")
break
counter += 1
if counter > 510:
break
wf.close()
def do_possible_merges_in_num_tasks():
ji__num_task_m = {}
with open("num_tasks.dat", mode="rt") as f:
reader = csv.reader(f)
for line in reader:
ji = int(line[0] )
num_task = int(line[1] )
if ji not in ji__num_task_m:
ji__num_task_m[ji] = 0
ji__num_task_m[ji] += num_task
with open("num_tasks_merged.dat", mode="wt") as f:
writer = csv.writer(f, delimiter=',')
for ji, num_tasks in ji__num_task_m.items():
writer.writerow([ji, num_tasks] )
log(WARNING, "done.")
def write_jobs_w_num_task(num_task):
ji_l = []
with open("num_tasks_merged.dat", mode="rt") as f:
reader = csv.reader(f)
for line in reader:
num_task_ = int(line[1] )
if num_task_ == num_task:
ji_l.append(int(line[0] ) )
print("writing, len(ji_l)= {}".format(len(ji_l) ) )
with open("jobs_w_num_task_{}.dat".format(num_task), mode="wt") as f:
writer = csv.writer(f, delimiter=',')
for ji in ji_l:
writer.writerow([ji] )
log(WARNING, "done.")
def write_task_lifetimes(num_task):
log(WARNING, "started; num_task= {}".format(num_task) )
ji_l = []
with open("jobs_w_num_task_{}.dat".format(num_task), mode="rt") as f:
reader = csv.reader(f)
for line in reader:
ji_l.append(int(line[0] ) )
#
Entry = namedtuple('Entry', 'ji ti')
entry__sch_fin_l_m = {}
counter = 0
while 1:
print("counter= {}".format(counter) )
furl = counter_to_furl(counter)
try:
with gzip.open(furl, mode="rt") as f:
reader = csv.reader(f)
for line in reader:
ji = int(line[taskevents_f_to_i['job id'] ] )
if ji in ji_l:
e = int(line[taskevents_f_to_i['event'] ] )
if e == e_to_i['schedule'] or e == e_to_i['finish']:
ti = int(line[taskevents_f_to_i['task index'] ] )
entry = Entry(ji=ji, ti=ti)
t = float(line[taskevents_f_to_i['timestamp'] ] )/10**6
if entry not in entry__sch_fin_l_m:
entry__sch_fin_l_m[entry] = [0,0]
if e == e_to_i['schedule']:
entry__sch_fin_l_m[entry][0] = t
elif e == e_to_i['finish']:
entry__sch_fin_l_m[entry][1] = t
except (OSError, IOError) as e:
log(WARNING, "done with the files.")
break
counter += 1
if counter > 510:
break
print("writing now...")
with open("task_lifetimes_for_jobs_w_num_task_{}.dat".format(num_task), mode="wt") as f:
writer = csv.writer(f, delimiter=',')
for entry, sch_fin_tuple in entry__sch_fin_l_m.items():
if sch_fin_tuple[0] < sch_fin_tuple[1]:
lt = sch_fin_tuple[1] - sch_fin_tuple[0]
writer.writerow([lt] )
log(WARNING, "done.")
def filter_task_lifetimes(num_task):
lifetime_l = []
with open("task_lifetimes_for_jobs_w_num_task_{}.dat".format(num_task), mode="rt") as f:
reader = csv.reader(f)
for line in reader:
lt = float(line[0] )
if lt < 5000:
lifetime_l.append(lt)
with open("filtered_task_lifetimes_for_jobs_w_num_task_{}.dat".format(num_task), mode="wt") as f:
writer = csv.writer(f, delimiter=',')
for lt in lifetime_l:
writer.writerow([lt] )
log(WARNING, "done.")
# ****************************** PLOT ***************************** #
def plot_num_tasks_hist():
num_tasks_l = []
with open("num_tasks_merged.dat", mode="rt") as f:
reader = csv.reader(f)
for line in reader:
num_task = int(line[1] )
# if num_task > 1000:
# print("num_task= {}".format(num_task) )
# if num_task > 1 and num_task < 2000:
num_tasks_l.append(num_task)
# num_task__num_job_m = {}
# for n in num_tasks_l:
# if n not in num_task__num_job_m:
# num_task__num_job_m[n] = 0
# num_task__num_job_m[n] += 1
# print("num_task__num_job_m= {}".format(pprint.pformat(num_task__num_job_m) ) )
# plot.hist(num_tasks_l, bins=1000, histtype='step')
plot.hist(num_tasks_l, bins=100, histtype='step', normed=True, lw=2)
plot.xlabel("Number of tasks")
plot.ylabel("Frequency")
plot.savefig("plot_num_tasks_hist.png", bbox_inches='tight')
plot.gcf().clear()
log(WARNING, "done.")
def plot_task_lifetime_hist(k):
lifetime_l = []
with open("filtered_task_lifetimes_for_jobs_w_num_task_{}.dat".format(k), mode="rt") as f:
reader = csv.reader(f)
for line in reader:
lifetime_l.append(float(line[0] ) )
# rv = Pareto(a=2, loc=2)
# for i in range(1000000):
# lifetime_l.append(rv.gen_sample() )
lifetime_l = numpy.sort(lifetime_l)
print("len(lifetime_l)= {}".format(len(lifetime_l) ) )
fig = plot.figure(1)
# def_size = fig.get_size_inches()
# fig.set_size_inches(def_size[0]*1.5, def_size[1] )
plot.subplot(211)
# plot.step(x_l, y_l, 'bo', label='log-linear', lw=2)
plot.hist(lifetime_l, bins=100, histtype='step', normed=True, lw=2)
plot.xlabel("X (s)")
plot.ylabel("Frequency")
plot.title(r'$k= {}$'.format(k) )
x_l = lifetime_l[::-1]
y_l = numpy.arange(lifetime_l.size)/lifetime_l.size
plot.subplot(223)
plot.yscale('log')
plot.step(x_l, y_l, 'bo', label='log(tail) vs. X', lw=2)
plot.xlabel("X (s)")
plot.ylabel("Tail")
plot.legend()
plot.subplot(224)
plot.xscale('log')
plot.yscale('log')
plot.step(x_l, y_l, 'bo', label='log(tail) vs. log(X)', lw=2)
plot.xlabel("X (s)")
plot.legend()
# plot.xlabel("X")
# plot.xlabel("Task lifetime X (s)")
# plot.ylabel(r'$Pr\{X > x\}$')
plot.savefig("plot_task_lifetime_hist_k_{}.png".format(k) )
plot.gcf().clear()
log(WARNING, "done; k= {}".format(k) )
def pplot_task_lifetime_hist(k):
log(INFO, "started; k= {}".format(k) )
lifetime_l = []
# with open("filtered_task_lifetimes_for_jobs_w_num_task_{}.dat".format(k), mode="rt") as f:
with open("task_lifetimes_for_jobs_w_num_task_{}.dat".format(k), mode="rt") as f:
reader = csv.reader(f)
for line in reader:
lifetime_l.append(float(line[0] ) )
lifetime_l = numpy.sort(lifetime_l)
print("len(lifetime_l)= {}".format(len(lifetime_l) ) )
#
# plot.hist(lifetime_l, bins=100, histtype='step', normed=True, lw=2)
x_l = lifetime_l[::-1]
y_l = numpy.arange(lifetime_l.size)/lifetime_l.size
# y_l = [math.log(y + 0.000001) for y in y_l]
# m, b = numpy.polyfit(x_l, y_l, 1)
# plot.plot(x_l, m*x_l+b, 'r', lw=1, ls=':')
# step_size = 10
# num_rank = math.ceil(x_l[0]/step_size)
# # rank__avg_lifetime_l = []
# rank__num_lifetime_l = []
# i = 0
# for r in range(1, num_rank+1):
# sum_ = 0
# counter = 0
# while i < len(x_l) and x_l[i] > x_l[0]-r*step_size:
# counter += 1
# sum_ += x_l[i]
# i += 1
# rank__num_lifetime_l.append(counter)
# # avg = 0
# # if counter:
# # avg = sum_/counter
# # rank__avg_lifetime_l.append(avg)
# # print("i= {}, rank__avg_lifetime_l=\n{}".format(i, rank__avg_lifetime_l) )
# rank__num_lifetime_l = list(reversed(rank__num_lifetime_l) )
# rank_freq_l = [n/sum(rank__num_lifetime_l) for n in rank__num_lifetime_l]
# rank_tailprob_l = [sum(rank_freq_l[r-1:]) for r in range(1, num_rank+1) ]
# # plot.plot(range(1, num_rank+1), rank__avg_lifetime_l, 'bo', ls=':')
# # plot.xlabel(r'Rank', fontsize=13)
# # plot.ylabel(r'Tail distribution', fontsize=13)
# # plot.step(range(1, num_rank+1), rank_tailprob_l, 'bo', ls=':')
# # plot.yscale('log')
# # plot.xscale('log')
if k == 15:
plot.xlim(([10, 2*10**5] ) )
plot.ylim(([1/2*10**(-5), 1.3] ) )
elif k == 400:
plot.xlim(([10, 2*10**4] ) )
plot.ylim(([10**(-6), 1.3] ) )
elif k == 1050:
plot.xlim(([10, 2*10**4] ) )
plot.ylim(([10**(-6), 1.3] ) )
# plot.step(x_l, y_l, 'bo', lw=1, ls=':')
plot.step(x_l, y_l, 'bo', ms=10, mew=0, ls=':')
plot.xscale('log')
plot.yscale('log')
plot.xlabel(r'Task lifetime', fontsize=18)
plot.ylabel(r'Tail distribution', fontsize=18)
# plot.ylabel(r'Fraction of tasks completed in x')
# plot.title(r'Jobs with {} tasks'.format(k), fontsize=13)
# plot.title('k= {}, Mean= {}, Stdev= {}'.format(k, round(numpy.mean(x_l), 1), round(numpy.std(x_l), 1) ), fontsize=13)
plot.title('k= {}, Mean= {}'.format(k, round(numpy.mean(x_l), 1) ), fontsize=18)
plot.gcf().set_size_inches(4, 3)
prettify(plot.gca() )
# plot.savefig("pplot_task_lifetime_hist_k_{}.pdf".format(k) )
plot.savefig("pplot_task_lifetime_hist_k_{}.png".format(k), bbox_inches='tight')
plot.gcf().clear()
log(WARNING, "done; k= {}".format(k) )
def plot_qq_task_lifetimes(k):
lifetime_l = []
# with open("filtered_task_lifetimes_for_jobs_w_num_task_{}.dat".format(k), mode="rt") as f:
with open("task_lifetimes_for_jobs_w_num_task_{}.dat".format(k), mode="rt") as f:
reader = csv.reader(f)
for line in reader:
lifetime_l.append(float(line[0] ) )
lifetime_l = numpy.sort(lifetime_l)
print("len(lifetime_l)= {}".format(len(lifetime_l) ) )
# For different dists: https://docs.scipy.org/doc/scipy/reference/stats.html
# scipy.stats.probplot(lifetime_l, dist="expon", plot=plot)
# scipy.stats.probplot(lifetime_l, dist="pareto", sparams=(1.2,), plot=plot)
plot.savefig("plot_qq_task_lifetimes_k_{}.png".format(k) )
log(WARNING, "done; k= {}".format(k) )
if __name__ == "__main__":
## Uncomment with caution!
# write_num_tasks_per_job()
# do_possible_merges_in_num_tasks()
# write_jobs_w_num_task(num_task=15)
# write_jobs_w_num_task(num_task=400)
# write_jobs_w_num_task(num_task=1000)
# write_jobs_w_num_task(num_task=1050)
# write_task_lifetimes(num_task=15)
# filter_task_lifetimes(num_task=15)
# write_task_lifetimes(num_task=400)
# filter_task_lifetimes(num_task=400)
# write_task_lifetimes(num_task=1000)
# filter_task_lifetimes(num_task=1000)
# write_task_lifetimes(num_task=1050)
# filter_task_lifetimes(num_task=1050)
# plot_num_tasks_hist()
# plot_task_lifetime_hist(k=15)
# plot_task_lifetime_hist(k=400)
# plot_task_lifetime_hist(k=1000)
# plot_task_lifetime_hist(k=1050)
# pplot_task_lifetime_hist(k=15)
# pplot_task_lifetime_hist(k=400)
# pplot_task_lifetime_hist(k=1000)
pplot_task_lifetime_hist(k=1050)
# plot_qq_task_lifetimes(k=400)
pass
|
[
"matplotlib.pyplot.yscale",
"csv.reader",
"matplotlib.pyplot.step",
"matplotlib.pyplot.figure",
"numpy.mean",
"numpy.arange",
"matplotlib.pyplot.gca",
"csv.writer",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.legend",
"numpy.sort",
"matplotlib.use",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xlim",
"gzip.open",
"matplotlib.pyplot.hist",
"collections.namedtuple",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.savefig"
] |
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|
#!/usr/bin/env python
# -*-Python-*-
import argparse
import contextlib
import datetime
import ftplib
import os
import re
import subprocess
import tempfile
def get_valid_filename(s):
"""
Return the given string converted to a string that can be used for a clean
filename. Remove leading and trailing spaces; convert other spaces to
underscores; and remove anything that is not an alphanumeric, dash,
underscore, or dot.
>>> get_valid_filename("john's portrait in 2004.jpg")
'johns_portrait_in_2004.jpg'
"""
s = s.strip().replace(" ", "_")
return re.sub(r"(?u)[^-\w.]", "", s)
def read_config():
# Read the config
with open(os.path.expanduser("~/.config/fileup/config")) as f:
"""Create a config file at ~/.config/fileup/config with the
following information and structure:
example.com
file_up_folder
my_user_name
my_difficult_password
"""
base_url, base_folder, folder, user, pw = [
s.replace("\n", "") for s in f.readlines()
]
return base_url, base_folder, folder, user, pw
def remove_old_files(ftp, today):
# Remove all files that are past the limit
files = [f for f in ftp.nlst() if "_delete_on_" in f]
file_dates = [f.rsplit("_delete_on_", 1) for f in files]
for file_name, date in file_dates:
rm_date = datetime.datetime.strptime(date, "%Y-%m-%d").date()
if rm_date < today:
print(f'removing "{file_name}" because the date passed')
try:
ftp.delete(file_name)
except Exception:
# File didn't exist anymore for some reason...
pass
ftp.delete(file_name + "_delete_on_" + date)
def main():
# Get arguments
description = [
"Publish a file. \n \n",
"Create a config file at ~/.config/fileup/config with the following information and structure:\n",
"example.com",
"base_folder",
"file_up_folder",
"my_user_name",
"my_difficult_password",
]
parser = argparse.ArgumentParser(
description="\n".join(description),
formatter_class=argparse.RawTextHelpFormatter,
)
parser.add_argument("fname", type=str)
parser.add_argument(
"-t",
"--time",
type=int,
default=90,
help="If time is 0 the file will never be deleted, default is 90 days.",
)
parser.add_argument("-d", "--direct", action="store_true")
parser.add_argument("-i", "--img", action="store_true")
args = parser.parse_args()
fname = os.path.abspath(os.path.expanduser(args.fname))
fname_base = os.path.basename(fname)
base_url, base_folder, folder, user, pw = read_config()
# Connect to server
ftp = ftplib.FTP(base_url, user, pw)
ftp.cwd(os.path.join(base_folder, folder))
# Fix the filename to avoid filename character issues
fname_base = get_valid_filename(fname_base)
today = datetime.datetime.now().date()
remove_old_files(ftp, today)
# Delete first if file already exists, it could happen that there is
# already a file with a specified deletion date, these should be removed.
for f in ftp.nlst():
if f.startswith(fname_base) and "_delete_on_" in f:
ftp.delete(f)
if args.time != 0: # could be negative (used for debugging).
remove_on = today + datetime.timedelta(days=args.time)
fname_date = fname_base + "_delete_on_" + str(remove_on)
with tempfile.TemporaryFile() as f:
print("upload " + fname_date)
ftp.storbinary(f"STOR {fname_date}", f)
# Upload and open the actual file
with open(fname, "rb") as f:
ftp.storbinary(f"STOR {fname_base}", f)
print("upload " + fname_base)
ftp.quit()
# Create URL
if folder:
url = f"{base_url}/{folder}/{fname_base}"
else:
url = f"{base_url}/{fname_base}"
if args.direct:
# Returns the url as is.
url = "http://" + url
elif args.img:
url = f""
elif fname.endswith(".ipynb"):
# Return the url in the nbviewer
url = "http://nbviewer.jupyter.org/url/" + url + "?flush_cache=true"
else:
url = "http://" + url
# Put a URL into clipboard only works on OS X
with contextlib.suppress(Exception):
process = subprocess.Popen(
"pbcopy", env={"LANG": "en_US.UTF-8"}, stdin=subprocess.PIPE
)
process.communicate(url.encode("utf-8"))
print("Your url is: ", url)
if __name__ == "__main__":
main()
|
[
"subprocess.Popen",
"os.path.join",
"os.path.basename",
"contextlib.suppress",
"datetime.datetime.now",
"tempfile.TemporaryFile",
"datetime.datetime.strptime",
"datetime.timedelta",
"ftplib.FTP",
"os.path.expanduser",
"re.sub"
] |
[((588, 617), 're.sub', 're.sub', (['"""(?u)[^-\\\\w.]"""', '""""""', 's'], {}), "('(?u)[^-\\\\w.]', '', s)\n", (594, 617), False, 'import re\n'), ((2695, 2718), 'os.path.basename', 'os.path.basename', (['fname'], {}), '(fname)\n', (2711, 2718), False, 'import os\n'), ((2815, 2845), 'ftplib.FTP', 'ftplib.FTP', (['base_url', 'user', 'pw'], {}), '(base_url, user, pw)\n', (2825, 2845), False, 'import ftplib\n'), ((2645, 2675), 'os.path.expanduser', 'os.path.expanduser', (['args.fname'], {}), '(args.fname)\n', (2663, 2675), False, 'import os\n'), ((2858, 2891), 'os.path.join', 'os.path.join', (['base_folder', 'folder'], {}), '(base_folder, folder)\n', (2870, 2891), False, 'import os\n'), ((4375, 4405), 'contextlib.suppress', 'contextlib.suppress', (['Exception'], {}), '(Exception)\n', (4394, 4405), False, 'import contextlib\n'), ((4425, 4503), 'subprocess.Popen', 'subprocess.Popen', (['"""pbcopy"""'], {'env': "{'LANG': 'en_US.UTF-8'}", 'stdin': 'subprocess.PIPE'}), "('pbcopy', env={'LANG': 'en_US.UTF-8'}, stdin=subprocess.PIPE)\n", (4441, 4503), False, 'import subprocess\n'), ((675, 720), 'os.path.expanduser', 'os.path.expanduser', (['"""~/.config/fileup/config"""'], {}), "('~/.config/fileup/config')\n", (693, 720), False, 'import os\n'), ((3013, 3036), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (3034, 3036), False, 'import datetime\n'), ((3435, 3469), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': 'args.time'}), '(days=args.time)\n', (3453, 3469), False, 'import datetime\n'), ((3548, 3572), 'tempfile.TemporaryFile', 'tempfile.TemporaryFile', ([], {}), '()\n', (3570, 3572), False, 'import tempfile\n'), ((1390, 1434), 'datetime.datetime.strptime', 'datetime.datetime.strptime', (['date', '"""%Y-%m-%d"""'], {}), "(date, '%Y-%m-%d')\n", (1416, 1434), False, 'import datetime\n')]
|
import glob
from pathlib import Path
from typing import List, Union
ARCHIVE_EXTENSIONS = ['tar']
IMG_EXTENSIONS = ['jpeg', 'jpg', 'bmp', 'png']
VID_EXTENSIONS = ['mp4', 'avi', 'mov', 'mkv', 'mts', 'ts', 'webm']
def normalize_path(path: Union[str, Path]) -> Path:
return Path(path).expanduser().resolve()
def file_extension(path: str) -> str:
"""
Extracts canonical file extension from path (no leading dot and all lowercase)
e.g. mp4, avi, jpeg, ts
"""
return Path(path).suffix[1:].lower()
def files_in_dir(dir: Path, recursive=True, sort=False) -> List[str]:
"""
Iterates recursively through all files in all subfolders.
"""
path = Path(dir)
if recursive:
search_path = str(path.joinpath('**'))
file_list = glob.glob(search_path, recursive=True)
else:
search_path = str(path.joinpath('*'))
file_list = glob.glob(search_path, recursive=False)
file_list = [f for f in file_list if Path(f).is_file()]
if sort:
file_list.sort()
return file_list
def is_archive(file_path: str) -> bool:
"""
Determine of a given file is an archive (according to its extension).
"""
file_ext = file_extension(file_path)
return file_ext in ARCHIVE_EXTENSIONS
def is_image(file_path: str) -> bool:
"""
Determine of a given file is an image (according to its extension).
"""
file_ext = file_extension(file_path)
return file_ext in IMG_EXTENSIONS
def is_video(file_path: str) -> bool:
"""
Determine of a given file is an video (according to its extension).
"""
file_ext = file_extension(file_path)
return file_ext in VID_EXTENSIONS
def archives_in_dir(dir: Path, recursive=True, sort=False):
"""
Iterates recursively over all archives in all subfolders.
"""
file_list = files_in_dir(dir=dir, recursive=recursive, sort=sort)
for file in file_list:
if is_archive(file):
yield file
def images_in_dir(dir: Path, recursive=True, sort=False):
"""
Iterates recursively over all images in all subfolders.
"""
file_list = files_in_dir(dir=dir, recursive=recursive, sort=sort)
for file in file_list:
if is_image(file):
yield file
def videos_in_dir(dir: Path, recursive=True, sort=False):
"""
Iterates recursively over all videos in all subfolders.
"""
file_list = files_in_dir(dir=dir, recursive=recursive, sort=sort)
for file in file_list:
if is_video(file):
yield file
|
[
"pathlib.Path",
"glob.glob"
] |
[((682, 691), 'pathlib.Path', 'Path', (['dir'], {}), '(dir)\n', (686, 691), False, 'from pathlib import Path\n'), ((778, 816), 'glob.glob', 'glob.glob', (['search_path'], {'recursive': '(True)'}), '(search_path, recursive=True)\n', (787, 816), False, 'import glob\n'), ((893, 932), 'glob.glob', 'glob.glob', (['search_path'], {'recursive': '(False)'}), '(search_path, recursive=False)\n', (902, 932), False, 'import glob\n'), ((278, 288), 'pathlib.Path', 'Path', (['path'], {}), '(path)\n', (282, 288), False, 'from pathlib import Path\n'), ((490, 500), 'pathlib.Path', 'Path', (['path'], {}), '(path)\n', (494, 500), False, 'from pathlib import Path\n'), ((975, 982), 'pathlib.Path', 'Path', (['f'], {}), '(f)\n', (979, 982), False, 'from pathlib import Path\n')]
|
from bluetooth import *
from time import sleep
import re, uuid
devices = set()
devices_to_update = set()
dev_mac = ':'.join(re.findall('..', '%012x' % uuid.getnode())).upper()
print(dev_mac)
def enable_ble():
print('enabling bluetooth')
try:
os.system('sudo systemctl start bluetooth.service && sudo hciconfig hci0 up')
except Exception as e:
print(e)
def job():
print("\n\nPerforming inquiry...")
new_devices = set()
global devices_to_update
services = find_service(name="helloService")
print(services)
for i in range(len(services)):
match = services[i]
if match["name"] == "helloService":
port = match["port"]
name = match["name"]
host = match["host"]
print(name, port, host)
new_devices.add(host)
devices_diff_set = devices.symmetric_difference(new_devices)
print("Diff set = %s"%devices_diff_set)
devices_modified = False
if len(devices_diff_set) > 0:
for addr in devices_diff_set:
if addr in new_devices:
devices.add(addr)
devices_to_update.add(addr)
devices_modified = True
else:
print("Removing device %s" % addr)
devices.remove(addr)
if addr in devices_to_update:
devices_to_update.remove(addr)
devices_modified = True
if devices_modified: # need to notify to all devices
devices_to_update = devices_to_update.union(devices)
if len(devices_to_update) > 0:
update_message = "%sEOD" % devices
print("Update message = %s" % update_message)
for addr in devices_to_update.copy():
try:
print("Connecting to %s to send updated list" % addr)
client_socket = BluetoothSocket(RFCOMM)
client_socket.connect((addr, 1))
client_socket.send(update_message)
print("Sent to %s" % addr)
client_socket.close()
devices_to_update.remove(addr)
except Exception as e:
print(e)
else:
print("No updates to send")
enable_ble()
while True:
job()
sleep(5)
|
[
"uuid.getnode",
"time.sleep"
] |
[((2263, 2271), 'time.sleep', 'sleep', (['(5)'], {}), '(5)\n', (2268, 2271), False, 'from time import sleep\n'), ((153, 167), 'uuid.getnode', 'uuid.getnode', ([], {}), '()\n', (165, 167), False, 'import re, uuid\n')]
|
from functools import wraps
from pastry.models import User
from flask import request, abort, jsonify
def parse_api_key():
key = None
if request.args.get('api_key'):
key = request.args.get('api_key')
elif request.form.get('api_key'):
key = request.form.get('api_key')
return key
def login_required(f):
@wraps(f)
def decorated(*args, **kwargs):
if request.method != 'OPTIONS':
# Verify auth-token or api_key is present
token = request.headers.get('Auth-Token')
api_key = parse_api_key()
if not token and not api_key:
abort(401)
# Verify key/token
if api_key:
if not User.verify_api_key(api_key):
response = jsonify({
'message': 'Invalid API Key',
'invalid_api_key': True,
})
response.status_code = 401
return response
elif token:
if not User.verify_auth_token(token):
response = jsonify({
'message': 'Expired Token',
'expired_token': True,
})
response.status_code = 401
return response
return f(*args, **kwargs)
return decorated
|
[
"flask.request.args.get",
"flask.request.headers.get",
"flask.request.form.get",
"pastry.models.User.verify_auth_token",
"flask.abort",
"flask.jsonify",
"pastry.models.User.verify_api_key",
"functools.wraps"
] |
[((146, 173), 'flask.request.args.get', 'request.args.get', (['"""api_key"""'], {}), "('api_key')\n", (162, 173), False, 'from flask import request, abort, jsonify\n'), ((342, 350), 'functools.wraps', 'wraps', (['f'], {}), '(f)\n', (347, 350), False, 'from functools import wraps\n'), ((189, 216), 'flask.request.args.get', 'request.args.get', (['"""api_key"""'], {}), "('api_key')\n", (205, 216), False, 'from flask import request, abort, jsonify\n'), ((226, 253), 'flask.request.form.get', 'request.form.get', (['"""api_key"""'], {}), "('api_key')\n", (242, 253), False, 'from flask import request, abort, jsonify\n'), ((269, 296), 'flask.request.form.get', 'request.form.get', (['"""api_key"""'], {}), "('api_key')\n", (285, 296), False, 'from flask import request, abort, jsonify\n'), ((502, 535), 'flask.request.headers.get', 'request.headers.get', (['"""Auth-Token"""'], {}), "('Auth-Token')\n", (521, 535), False, 'from flask import request, abort, jsonify\n'), ((632, 642), 'flask.abort', 'abort', (['(401)'], {}), '(401)\n', (637, 642), False, 'from flask import request, abort, jsonify\n'), ((722, 750), 'pastry.models.User.verify_api_key', 'User.verify_api_key', (['api_key'], {}), '(api_key)\n', (741, 750), False, 'from pastry.models import User\n'), ((783, 847), 'flask.jsonify', 'jsonify', (["{'message': 'Invalid API Key', 'invalid_api_key': True}"], {}), "({'message': 'Invalid API Key', 'invalid_api_key': True})\n", (790, 847), False, 'from flask import request, abort, jsonify\n'), ((1050, 1079), 'pastry.models.User.verify_auth_token', 'User.verify_auth_token', (['token'], {}), '(token)\n', (1072, 1079), False, 'from pastry.models import User\n'), ((1112, 1172), 'flask.jsonify', 'jsonify', (["{'message': 'Expired Token', 'expired_token': True}"], {}), "({'message': 'Expired Token', 'expired_token': True})\n", (1119, 1172), False, 'from flask import request, abort, jsonify\n')]
|
"""Draw a imdt calendar image."""
from contextlib import contextmanager
from functools import partial
from imperial_calendar import GregorianDateTime, ImperialDateTime, ImperialYearMonth
from imperial_calendar.transform import (
grdt_to_juld,
imdt_to_imsn,
imsn_to_imdt,
imsn_to_mrsd,
juld_to_grdt,
juld_to_tert,
mrsd_to_imsn,
mrsd_to_tert,
tert_to_juld,
tert_to_mrsd,
)
import typing as t
import xml.etree.ElementTree as ET
def next_grdt_day_of(grdt: GregorianDateTime) -> GregorianDateTime:
"""Create a new grdt on the next day."""
grdt = grdt.copy()
days: int = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31][grdt.month - 1]
# NOTE: Don't consider about Julian Calendar, before Gregorian Calendar begins.
if grdt.month == 2 and (
grdt.year % 4 == 0 and (grdt.year % 100 != 0 or grdt.year % 400 == 0)
):
days = 29
if grdt.day == days:
if grdt.month == 12:
grdt.year += 1 # NOTE: Don't consider about B.C. 1, A.D. 1.
grdt.month = 1
else:
grdt.month += 1
grdt.day = 1
else:
grdt.day += 1
return grdt
def grdt_to_imdt(
grdt: GregorianDateTime, imdt_timezone: t.Optional[str]
) -> ImperialDateTime:
"""Transform grdt to imdt."""
imdt_timezone = imdt_timezone or "+00:00"
juld = grdt_to_juld(grdt.to_utc_naive())
tert = juld_to_tert(juld)
mrsd = tert_to_mrsd(tert)
imsn = mrsd_to_imsn(mrsd)
return ImperialDateTime.from_standard_naive(imsn_to_imdt(imsn), imdt_timezone)
def imdt_to_grdt(imdt: ImperialDateTime, grdt_timezone: str) -> GregorianDateTime:
"""Transform imdt to grdt."""
imsn = imdt_to_imsn(imdt.to_standard_naive())
mrsd = imsn_to_mrsd(imsn)
tert = mrsd_to_tert(mrsd)
juld = tert_to_juld(tert)
return GregorianDateTime.from_utc_naive(juld_to_grdt(juld), grdt_timezone)
@contextmanager
def e(
tag: str, attrib: t.Dict[str, str] = {}, text: str = "", parent: ET.Element = None
) -> t.Generator[t.Callable[[str, t.Dict[str, str], str], t.Any], None, t.Any]:
"""Create a XML element and pass a new context for sub elements."""
if parent is not None:
element = ET.SubElement(parent, tag, attrib)
else:
element = ET.Element(tag, attrib)
if text != "":
element.text = text
yield partial(e, parent=element)
def text_y(y: t.Union[float, str], font_size: t.Union[float, str]) -> float:
"""
Caliculate the y value of the SVG text element.
y: mm
font_size: pt
"""
return float(y) + float(font_size) * 0.353
class CalendarImage(object):
"""Draw a imdt calendar image."""
BLACK: str = "#000000" # "#3b3b3b"
BLUE: str = "#008dcc" # "#40a1cc"
FONT_FAMILY_BOLD: str = """fot-tsukubrdgothic-std, "FOT-TsukuBRdGothic Std B", "FOT-筑紫B丸ゴシック Std B", TsukuBRdGothic-Bold, "筑紫B丸ゴシック ボールド", sans-serif""" # noqa: E501
FONT_FAMILY_REGULAR: str = """fot-tsukubrdgothic-std, "FOT-TsukuBRdGothic Std B", "FOT-筑紫B丸ゴシック Std B", TsukuBRdGothic-Bold, "筑紫B丸ゴシック ボールド", sans-serif""" # noqa: E501
FONT_SIZE_ANNOTATION: float = 8.0
FONT_SIZE_BOLD_LARGE: float = 32.0
FONT_SIZE_LARGE: float = 20.0
FONT_SIZE_SMALL: float = 10.0
GRAY_BLUE: str = "#6bb4d6" # "#a5c7d6"
GRAY_RED: str = "#ff9d80" # "#ffb7a1"
GRAY: str = "#888888" # "#999999"
HEIGHT_DAYS_GAP: float = 4.5
HEIGHT_GRDT_BELT: float = 5.5
HEIGHT_TOP_SPACE: float = 15.0
RED: str = "#e03f0c" # "#e07553"
SIZE_DAY_SQUARE: float = 22.5
STROKE_WIDTH_BOLD: str = "0.4mm"
STROKE_WIDTH_THIN: str = "0.15mm"
WHITE: str = "#ffffff" # "#ffffff"
WIDTH_LEFT_SPACE: float = 45.0
grdt_timezone: str
imdt: ImperialDateTime
def __init__(self, imdt: ImperialDateTime, grdt_timezone: str):
"""Init."""
self.grdt_timezone = grdt_timezone
self.imdt = imdt.copy()
self.imdt.day = 1
self.imdt.hour = 0
self.imdt.minute = 0
self.imdt.second = 0
def draw_as_svg(self) -> str:
"""Draw a imdt calendar image as SVG string."""
svg = ET.Element(
"svg",
{
"height": "148mm",
"style": f"""
background-color: {CalendarImage.WHITE};
""".strip(),
"width": "210mm",
"xmlns": "http://www.w3.org/2000/svg",
},
)
with e("title", {}, f"帝國火星曆{self.imdt.year}年{self.imdt.month}月", parent=svg):
pass
with e(
"g",
{"font-family": CalendarImage.FONT_FAMILY_REGULAR},
parent=svg,
) as _e:
self.__draw_title(_e)
self.__draw_joubi(_e)
self.__draw_static_frame(_e)
self.__draw_imdt_days(_e)
self.__draw_imdt_syukuzitu(_e)
self.__draw_grdt_days(_e)
# return ET.tostring(svg, encoding="utf-8", xml_declaration=True)
return ET.tostring(svg, encoding="utf-8")
def __draw_grdt_day(self, _e, grdt: GregorianDateTime) -> None:
imdt = grdt_to_imdt(grdt, self.imdt.timezone)
line_x = (
(((imdt.hour * 60) + imdt.minute) * 60 + imdt.second)
/ (24 * 60 * 60)
* CalendarImage.SIZE_DAY_SQUARE
)
if imdt.month == self.imdt.month:
x1 = (
CalendarImage.WIDTH_LEFT_SPACE
+ line_x
+ CalendarImage.SIZE_DAY_SQUARE * ((imdt.day - 1) % 7)
)
x2 = (
CalendarImage.WIDTH_LEFT_SPACE
+ line_x
+ CalendarImage.SIZE_DAY_SQUARE * ((imdt.day - 1) % 7)
)
y1 = (
CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ (
CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
+ CalendarImage.SIZE_DAY_SQUARE
)
* ((imdt.day - 1) // 7)
)
y2 = (
CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ CalendarImage.HEIGHT_GRDT_BELT
+ (
CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
+ CalendarImage.SIZE_DAY_SQUARE
)
* ((imdt.day - 1) // 7)
)
with _e(
"line",
{
"stroke": CalendarImage.BLACK,
"stroke-width": CalendarImage.STROKE_WIDTH_THIN,
"x1": f"{x1}mm",
"x2": f"{x2}mm",
"y1": f"{y1}mm",
"y2": f"{y2}mm",
},
):
pass
next_grdt_day = next_grdt_day_of(grdt)
next_grdt_day_imdt = grdt_to_imdt(next_grdt_day, self.imdt.timezone)
next_line_x = (
(
((next_grdt_day_imdt.hour * 60) + next_grdt_day_imdt.minute) * 60
+ next_grdt_day_imdt.second
)
/ (24 * 60 * 60)
* CalendarImage.SIZE_DAY_SQUARE
)
is_drawable_on_beginning_of_month = (
next_line_x
> 0.353 * CalendarImage.FONT_SIZE_SMALL * (len("10/10") * 0.6) + 1.5
)
if grdt.is_holiday or grdt.weekday == 7:
color = CalendarImage.GRAY_RED
elif grdt.weekday == 6:
color = CalendarImage.GRAY_BLUE
else:
color = CalendarImage.GRAY
if imdt.month == self.imdt.month:
text = (
f"{grdt.month}/{grdt.day}"
if grdt.day == 1
or (imdt.day == 1 and not is_drawable_on_beginning_of_month)
else str(grdt.day)
)
is_drawable_on_weekend = (
CalendarImage.SIZE_DAY_SQUARE - line_x
) > 0.353 * CalendarImage.FONT_SIZE_SMALL * (len(text) * 0.6) + 1.5
if (
imdt.day == ImperialYearMonth(self.imdt.year, self.imdt.month).days()
and not is_drawable_on_weekend
):
pass
elif imdt.day % 7 == 0 and not is_drawable_on_weekend:
self.__draw_text(
_e,
{
"fill": color,
"font-size": CalendarImage.FONT_SIZE_SMALL,
"x": f"{CalendarImage.WIDTH_LEFT_SPACE + 1}mm",
"y": CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ 0.5
+ (
CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
+ CalendarImage.SIZE_DAY_SQUARE
)
* (imdt.day // 7),
},
text,
)
else:
x = (
CalendarImage.WIDTH_LEFT_SPACE
+ line_x
+ 1
+ CalendarImage.SIZE_DAY_SQUARE * ((imdt.day - 1) % 7)
)
self.__draw_text(
_e,
{
"fill": color,
"font-size": CalendarImage.FONT_SIZE_SMALL,
"x": f"{x}mm",
"y": CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ 0.5
+ (
CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
+ CalendarImage.SIZE_DAY_SQUARE
)
* ((imdt.day - 1) // 7),
},
text,
)
else:
if is_drawable_on_beginning_of_month:
self.__draw_text(
_e,
{
"fill": color,
"font-size": CalendarImage.FONT_SIZE_SMALL,
"x": f"{CalendarImage.WIDTH_LEFT_SPACE + 1}mm",
"y": CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ 0.5,
},
f"{grdt.month}/{grdt.day}",
)
def __draw_grdt_days(self, _e) -> None:
drawing_grdt_day = imdt_to_grdt(self.imdt, self.grdt_timezone)
drawing_grdt_day.hour = 0
drawing_grdt_day.minute = 0
drawing_grdt_day.second = 0
while (
grdt_to_imdt(drawing_grdt_day, self.imdt.timezone)
< self.__next_imdt_month()
):
self.__draw_grdt_day(_e, drawing_grdt_day)
drawing_grdt_day = next_grdt_day_of(drawing_grdt_day)
def __draw_imdt_days(self, _e) -> None:
for day in range(
1, ImperialYearMonth(self.imdt.year, self.imdt.month).days() + 1
):
imdt = self.imdt.copy()
imdt.day = day
if imdt.holiday is not None or day % 7 == 1:
color = CalendarImage.RED
elif day % 7 == 0:
color = CalendarImage.BLUE
else:
color = CalendarImage.BLACK
x = (
CalendarImage.WIDTH_LEFT_SPACE
+ 1
+ CalendarImage.SIZE_DAY_SQUARE * ((day - 1) % 7)
)
y = (
CalendarImage.HEIGHT_TOP_SPACE
+ 1
+ (
CalendarImage.SIZE_DAY_SQUARE
+ CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
)
* ((day - 1) // 7)
)
self.__draw_text(
_e,
{
"fill": color,
"font-size": CalendarImage.FONT_SIZE_SMALL,
"x": f"{x}mm",
"y": y,
},
str(day),
)
if imdt.holiday is not None:
day_width = (
CalendarImage.FONT_SIZE_SMALL * (0.353 - 0.06) * len(str(day))
)
self.__draw_text(
_e,
{
"fill": color,
"font-size": CalendarImage.FONT_SIZE_ANNOTATION,
"style": f"""
inline-size: {CalendarImage.SIZE_DAY_SQUARE - day_width - 1}mm;
""".strip(),
"x": f"{x + day_width}mm",
"y": y + 0.2,
},
str("・".join(imdt.holiday.names)),
)
# with _e(
# "foreignObject",
# {
# "height": f"{CalendarImage.SIZE_DAY_SQUARE}mm",
# "requiredExtensions": "http://www.w3.org/1999/xhtml",
# "width": f"{CalendarImage.SIZE_DAY_SQUARE - day_width - 1}mm",
# "x": f"{x + day_width}mm",
# "y": f"{y + 0.2}mm",
# },
# ) as _e1:
# with _e1(
# "div",
# {
# "style": f"""
# color: {color};
# font-size: {CalendarImage.FONT_SIZE_ANNOTATION}pt;
# """.strip(),
# "xmlns": "http://www.w3.org/1999/xhtml",
# },
# str("・".join(imdt.holiday.names)),
# ):
# pass
def __draw_imdt_syukuzitu(self, _e) -> None:
pass
def __draw_joubi(self, _e) -> None:
for i, (joubi, color) in enumerate(
[
("日", CalendarImage.RED),
("月", CalendarImage.BLACK),
("火", CalendarImage.BLACK),
("水", CalendarImage.BLACK),
("木", CalendarImage.BLACK),
("金", CalendarImage.BLACK),
("土", CalendarImage.BLUE),
]
):
x = (
CalendarImage.WIDTH_LEFT_SPACE
+ (CalendarImage.SIZE_DAY_SQUARE / 2)
- 2.0
+ CalendarImage.SIZE_DAY_SQUARE * i
)
self.__draw_text(
_e,
{
"fill": color,
"font-size": CalendarImage.FONT_SIZE_SMALL,
"x": f"{x}mm",
"y": CalendarImage.HEIGHT_TOP_SPACE - 5,
},
joubi,
)
def __draw_static_frame(self, _e) -> None:
days = ImperialYearMonth(self.imdt.year, self.imdt.month).days()
for i in range(4):
days_of_week = 6 if i == 3 and days == 27 else 7
y = (
CalendarImage.HEIGHT_TOP_SPACE
+ (
CalendarImage.SIZE_DAY_SQUARE
+ CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
)
* i
)
with _e(
"rect",
{
"fill": CalendarImage.WHITE,
"height": f"{CalendarImage.SIZE_DAY_SQUARE + CalendarImage.HEIGHT_GRDT_BELT}mm",
"stroke-width": CalendarImage.STROKE_WIDTH_BOLD,
"stroke": CalendarImage.BLACK,
"width": f"{CalendarImage.SIZE_DAY_SQUARE * days_of_week}mm",
"x": f"{CalendarImage.WIDTH_LEFT_SPACE}mm",
"y": f"{y}mm",
},
):
pass
y1 = (
CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ (
CalendarImage.SIZE_DAY_SQUARE
+ CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
)
* i
)
y2 = (
CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ (
CalendarImage.SIZE_DAY_SQUARE
+ CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
)
* i
)
with _e(
"line",
{
"stroke-width": CalendarImage.STROKE_WIDTH_THIN,
"stroke": CalendarImage.BLACK,
"x1": f"{CalendarImage.WIDTH_LEFT_SPACE}mm",
"x2": f"{CalendarImage.WIDTH_LEFT_SPACE + CalendarImage.SIZE_DAY_SQUARE * days_of_week}mm",
"y1": f"{y1}mm",
"y2": f"{y2}mm",
},
):
pass
for j in range(days_of_week):
y1 = (
CalendarImage.HEIGHT_TOP_SPACE
+ (
CalendarImage.SIZE_DAY_SQUARE
+ CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
)
* i
)
y2 = (
CalendarImage.HEIGHT_TOP_SPACE
+ CalendarImage.SIZE_DAY_SQUARE
+ (
CalendarImage.SIZE_DAY_SQUARE
+ CalendarImage.HEIGHT_GRDT_BELT
+ CalendarImage.HEIGHT_DAYS_GAP
)
* i
)
with _e(
"line",
{
"stroke-width": CalendarImage.STROKE_WIDTH_BOLD,
"stroke": CalendarImage.BLACK,
"x1": f"{CalendarImage.WIDTH_LEFT_SPACE + CalendarImage.SIZE_DAY_SQUARE * (j + 1)}mm",
"x2": f"{CalendarImage.WIDTH_LEFT_SPACE + CalendarImage.SIZE_DAY_SQUARE * (j + 1)}mm",
"y1": f"{y1}mm",
"y2": f"{y2}mm",
},
):
pass
def __draw_text(
self, _e, attrib: t.Dict[str, t.Union[str, float]], text: str
) -> None:
attrib["y"] = "{}mm".format(text_y(attrib["y"], attrib["font-size"]))
attrib["font-size"] = "{}pt".format(attrib["font-size"])
with _e("text", attrib, text):
pass
def __draw_title(self, _e) -> None:
self.__draw_text(
_e,
{
"fill": CalendarImage.BLACK,
"font-size": CalendarImage.FONT_SIZE_LARGE,
"x": "5mm",
"y": 9.5,
},
"帝國火星暦",
)
self.__draw_text(
_e,
{
"fill": CalendarImage.BLACK,
"font-size": CalendarImage.FONT_SIZE_LARGE,
"x": "11mm",
"y": 18.0,
},
f"{self.imdt.year}年",
)
with _e(
"svg",
{
"height": "44mm",
"style": """
background-color: transparent;
""".strip(),
"width": f"{CalendarImage.WIDTH_LEFT_SPACE - 8}mm",
"x": "0mm",
"y": "28mm",
},
) as _e2:
self.__draw_text(
_e2,
{
"fill": CalendarImage.BLACK,
"font-family": CalendarImage.FONT_FAMILY_BOLD,
"font-size": CalendarImage.FONT_SIZE_BOLD_LARGE,
"text-anchor": "middle",
"x": "64%",
"y": 0.0,
},
f"{self.imdt.month}月",
)
self.__draw_text(
_e,
{
"fill": CalendarImage.BLACK,
"font-size": CalendarImage.FONT_SIZE_LARGE,
"x": "9.5mm",
"y": 42.0,
},
f"({self.imdt.japanese_month_name}月)",
)
self.__draw_text(
_e,
{
"fill": CalendarImage.GRAY,
"font-size": CalendarImage.FONT_SIZE_ANNOTATION,
"x": f"{CalendarImage.WIDTH_LEFT_SPACE - 5.5}mm",
"y": 52.0,
},
"～",
)
with _e(
"svg",
{
"height": "8mm",
"style": """
background-color: transparent;
""".strip(),
"width": f"{CalendarImage.WIDTH_LEFT_SPACE - 8}mm",
"x": "2mm",
"y": "52mm",
},
) as _e2:
weekdays = ["月", "火", "水", "木", "金", "土", "日"]
grdt_start = imdt_to_grdt(self.imdt, self.grdt_timezone)
grdt_start_weekday = weekdays[grdt_start.weekday - 1]
grdt_end = imdt_to_grdt(self.__next_imdt_month(), self.grdt_timezone)
grdt_end_weekday = weekdays[grdt_end.weekday - 1]
self.__draw_text(
_e2,
{
"fill": CalendarImage.GRAY,
"font-size": CalendarImage.FONT_SIZE_ANNOTATION,
"text-anchor": "end",
"x": "100%",
"y": 0.0,
},
"{}/{}/{}({}){:02}:{:02}:{:02}".format(
grdt_start.year,
grdt_start.month,
grdt_start.day,
grdt_start_weekday,
grdt_start.hour,
grdt_start.minute,
grdt_start.second,
),
)
text = ""
if grdt_start.year != grdt_end.year:
text += f"{grdt_end.year}/"
text += "{}/{}({}){:02}:{:02}:{:02}".format(
grdt_end.month,
grdt_end.day,
grdt_end_weekday,
grdt_end.hour,
grdt_end.minute,
grdt_end.second,
)
self.__draw_text(
_e2,
{
"fill": CalendarImage.GRAY,
"font-size": CalendarImage.FONT_SIZE_ANNOTATION,
"text-anchor": "end",
"x": "100%",
"y": 4.0,
},
text,
)
def __next_imdt_month(self) -> ImperialDateTime:
next_month = ImperialYearMonth(self.imdt.year, self.imdt.month).next_month()
return ImperialDateTime(
next_month.year, next_month.month, 1, 0, 0, 0, self.imdt.timezone
)
|
[
"functools.partial",
"imperial_calendar.transform.imsn_to_mrsd",
"imperial_calendar.transform.mrsd_to_tert",
"imperial_calendar.transform.mrsd_to_imsn",
"xml.etree.ElementTree.Element",
"imperial_calendar.ImperialDateTime",
"imperial_calendar.transform.tert_to_mrsd",
"imperial_calendar.transform.juld_to_grdt",
"imperial_calendar.transform.tert_to_juld",
"imperial_calendar.transform.juld_to_tert",
"xml.etree.ElementTree.SubElement",
"imperial_calendar.transform.imsn_to_imdt",
"imperial_calendar.ImperialYearMonth",
"xml.etree.ElementTree.tostring"
] |
[((1404, 1422), 'imperial_calendar.transform.juld_to_tert', 'juld_to_tert', (['juld'], {}), '(juld)\n', (1416, 1422), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((1434, 1452), 'imperial_calendar.transform.tert_to_mrsd', 'tert_to_mrsd', (['tert'], {}), '(tert)\n', (1446, 1452), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((1464, 1482), 'imperial_calendar.transform.mrsd_to_imsn', 'mrsd_to_imsn', (['mrsd'], {}), '(mrsd)\n', (1476, 1482), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((1746, 1764), 'imperial_calendar.transform.imsn_to_mrsd', 'imsn_to_mrsd', (['imsn'], {}), '(imsn)\n', (1758, 1764), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((1776, 1794), 'imperial_calendar.transform.mrsd_to_tert', 'mrsd_to_tert', (['mrsd'], {}), '(mrsd)\n', (1788, 1794), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((1806, 1824), 'imperial_calendar.transform.tert_to_juld', 'tert_to_juld', (['tert'], {}), '(tert)\n', (1818, 1824), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((1531, 1549), 'imperial_calendar.transform.imsn_to_imdt', 'imsn_to_imdt', (['imsn'], {}), '(imsn)\n', (1543, 1549), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((1869, 1887), 'imperial_calendar.transform.juld_to_grdt', 'juld_to_grdt', (['juld'], {}), '(juld)\n', (1881, 1887), False, 'from imperial_calendar.transform import grdt_to_juld, imdt_to_imsn, imsn_to_imdt, imsn_to_mrsd, juld_to_grdt, juld_to_tert, mrsd_to_imsn, mrsd_to_tert, tert_to_juld, tert_to_mrsd\n'), ((2213, 2247), 'xml.etree.ElementTree.SubElement', 'ET.SubElement', (['parent', 'tag', 'attrib'], {}), '(parent, tag, attrib)\n', (2226, 2247), True, 'import xml.etree.ElementTree as ET\n'), ((2276, 2299), 'xml.etree.ElementTree.Element', 'ET.Element', (['tag', 'attrib'], {}), '(tag, attrib)\n', (2286, 2299), True, 'import xml.etree.ElementTree as ET\n'), ((2357, 2383), 'functools.partial', 'partial', (['e'], {'parent': 'element'}), '(e, parent=element)\n', (2364, 2383), False, 'from functools import partial\n'), ((4995, 5029), 'xml.etree.ElementTree.tostring', 'ET.tostring', (['svg'], {'encoding': '"""utf-8"""'}), "(svg, encoding='utf-8')\n", (5006, 5029), True, 'import xml.etree.ElementTree as ET\n'), ((23099, 23187), 'imperial_calendar.ImperialDateTime', 'ImperialDateTime', (['next_month.year', 'next_month.month', '(1)', '(0)', '(0)', '(0)', 'self.imdt.timezone'], {}), '(next_month.year, next_month.month, 1, 0, 0, 0, self.imdt.\n timezone)\n', (23115, 23187), False, 'from imperial_calendar import GregorianDateTime, ImperialDateTime, ImperialYearMonth\n'), ((15109, 15159), 'imperial_calendar.ImperialYearMonth', 'ImperialYearMonth', (['self.imdt.year', 'self.imdt.month'], {}), '(self.imdt.year, self.imdt.month)\n', (15126, 15159), False, 'from imperial_calendar import GregorianDateTime, ImperialDateTime, ImperialYearMonth\n'), ((23020, 23070), 'imperial_calendar.ImperialYearMonth', 'ImperialYearMonth', (['self.imdt.year', 'self.imdt.month'], {}), '(self.imdt.year, self.imdt.month)\n', (23037, 23070), False, 'from imperial_calendar import GregorianDateTime, ImperialDateTime, ImperialYearMonth\n'), ((11154, 11204), 'imperial_calendar.ImperialYearMonth', 'ImperialYearMonth', (['self.imdt.year', 'self.imdt.month'], {}), '(self.imdt.year, self.imdt.month)\n', (11171, 11204), False, 'from imperial_calendar import GregorianDateTime, ImperialDateTime, ImperialYearMonth\n'), ((8123, 8173), 'imperial_calendar.ImperialYearMonth', 'ImperialYearMonth', (['self.imdt.year', 'self.imdt.month'], {}), '(self.imdt.year, self.imdt.month)\n', (8140, 8173), False, 'from imperial_calendar import GregorianDateTime, ImperialDateTime, ImperialYearMonth\n')]
|
"""
NamedConf parser - file ``/etc/named.conf``
===========================================
NamedConf parser the file named configuration file.
Named is a name server used by BIND.
"""
from insights.specs import Specs
from insights.core.plugins import parser
from insights.parsers import SkipException
from insights.parsers.named_checkconf import NamedCheckconf
@parser(Specs.named_conf)
class NamedConf(NamedCheckconf):
"""
Class for parsing the file ``/etc/named.conf```, We use class ``NamedCheckConf`` to parse most
of the named.conf configurations and class ``NamedConf`` to parse the `include` directives.
.. note::
Please refer to the super-class :py:class:`insights.parsers.named_checkconf:NamedCheckConf`
for more usage information.
Attributes:
includes (list): List of files in 'include' section.
Raises:
SkipException: When content is empty or cannot be parsed.
Examples:
>>> named_conf.includes
['/etc/crypto-policies/back-ends/bind.config']
"""
def parse_content(self, content):
includes = []
super(NamedConf, self).parse_content(content)
try:
for line in [l for l in content if l.strip().startswith('include ') and ';' in l]:
includes.append(line.split(';')[0].replace('"', '').split()[1])
except IndexError:
raise SkipException("Syntax error of include directive")
self.includes = includes
|
[
"insights.core.plugins.parser",
"insights.parsers.SkipException"
] |
[((367, 391), 'insights.core.plugins.parser', 'parser', (['Specs.named_conf'], {}), '(Specs.named_conf)\n', (373, 391), False, 'from insights.core.plugins import parser\n'), ((1395, 1445), 'insights.parsers.SkipException', 'SkipException', (['"""Syntax error of include directive"""'], {}), "('Syntax error of include directive')\n", (1408, 1445), False, 'from insights.parsers import SkipException\n')]
|
from readwrite import get_data
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
import numpy as np
def scatter(path, name):
data = get_data(path)
pd_data = pd.DataFrame(data)
plt.title("column 0 " + name)
plt.plot(pd_data[0])
plt.show()
plt.title("column 1 " + name)
plt.plot(pd_data[1])
plt.show()
plt.title("column 2 " + name)
plt.plot(pd_data[2])
plt.show()
return pd_data
def boxplot(datas, names):
column0 = []
column1 = []
column2 = []
labels = []
for i in range(len(datas)):
column0.append(datas[i][0])
column1.append(datas[i][1])
column2.append(datas[i][2])
labels.append(names[i])
plt.title("Boxplot column 0")
plt.boxplot(column0, labels=labels)
plt.show()
plt.title("Boxplot column 1")
plt.boxplot(column1, labels=labels)
plt.show()
plt.title("Boxplot column 2")
plt.boxplot(column2, labels=labels)
plt.show()
def draw_gaussian(datas):
pdf_ticks = np.linspace(0, 10000, 100000, endpoint=False)
density = gaussian_kde(datas)
plt.plot(pdf_ticks, density(pdf_ticks), color='r')
plt.show()
|
[
"pandas.DataFrame",
"matplotlib.pyplot.title",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.boxplot",
"scipy.stats.gaussian_kde",
"numpy.linspace",
"readwrite.get_data"
] |
[((173, 187), 'readwrite.get_data', 'get_data', (['path'], {}), '(path)\n', (181, 187), False, 'from readwrite import get_data\n'), ((199, 217), 'pandas.DataFrame', 'pd.DataFrame', (['data'], {}), '(data)\n', (211, 217), True, 'import pandas as pd\n'), ((220, 249), 'matplotlib.pyplot.title', 'plt.title', (["('column 0 ' + name)"], {}), "('column 0 ' + name)\n", (229, 249), True, 'import matplotlib.pyplot as plt\n'), ((251, 271), 'matplotlib.pyplot.plot', 'plt.plot', (['pd_data[0]'], {}), '(pd_data[0])\n', (259, 271), True, 'import matplotlib.pyplot as plt\n'), ((273, 283), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (281, 283), True, 'import matplotlib.pyplot as plt\n'), ((286, 315), 'matplotlib.pyplot.title', 'plt.title', (["('column 1 ' + name)"], {}), "('column 1 ' + name)\n", (295, 315), True, 'import matplotlib.pyplot as plt\n'), ((317, 337), 'matplotlib.pyplot.plot', 'plt.plot', (['pd_data[1]'], {}), '(pd_data[1])\n', (325, 337), True, 'import matplotlib.pyplot as plt\n'), ((339, 349), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (347, 349), True, 'import matplotlib.pyplot as plt\n'), ((352, 381), 'matplotlib.pyplot.title', 'plt.title', (["('column 2 ' + name)"], {}), "('column 2 ' + name)\n", (361, 381), True, 'import matplotlib.pyplot as plt\n'), ((383, 403), 'matplotlib.pyplot.plot', 'plt.plot', (['pd_data[2]'], {}), '(pd_data[2])\n', (391, 403), True, 'import matplotlib.pyplot as plt\n'), ((405, 415), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (413, 415), True, 'import matplotlib.pyplot as plt\n'), ((665, 694), 'matplotlib.pyplot.title', 'plt.title', (['"""Boxplot column 0"""'], {}), "('Boxplot column 0')\n", (674, 694), True, 'import matplotlib.pyplot as plt\n'), ((696, 731), 'matplotlib.pyplot.boxplot', 'plt.boxplot', (['column0'], {'labels': 'labels'}), '(column0, labels=labels)\n', (707, 731), True, 'import matplotlib.pyplot as plt\n'), ((733, 743), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (741, 743), True, 'import matplotlib.pyplot as plt\n'), ((746, 775), 'matplotlib.pyplot.title', 'plt.title', (['"""Boxplot column 1"""'], {}), "('Boxplot column 1')\n", (755, 775), True, 'import matplotlib.pyplot as plt\n'), ((777, 812), 'matplotlib.pyplot.boxplot', 'plt.boxplot', (['column1'], {'labels': 'labels'}), '(column1, labels=labels)\n', (788, 812), True, 'import matplotlib.pyplot as plt\n'), ((814, 824), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (822, 824), True, 'import matplotlib.pyplot as plt\n'), ((827, 856), 'matplotlib.pyplot.title', 'plt.title', (['"""Boxplot column 2"""'], {}), "('Boxplot column 2')\n", (836, 856), True, 'import matplotlib.pyplot as plt\n'), ((858, 893), 'matplotlib.pyplot.boxplot', 'plt.boxplot', (['column2'], {'labels': 'labels'}), '(column2, labels=labels)\n', (869, 893), True, 'import matplotlib.pyplot as plt\n'), ((895, 905), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (903, 905), True, 'import matplotlib.pyplot as plt\n'), ((946, 991), 'numpy.linspace', 'np.linspace', (['(0)', '(10000)', '(100000)'], {'endpoint': '(False)'}), '(0, 10000, 100000, endpoint=False)\n', (957, 991), True, 'import numpy as np\n'), ((1004, 1023), 'scipy.stats.gaussian_kde', 'gaussian_kde', (['datas'], {}), '(datas)\n', (1016, 1023), False, 'from scipy.stats import gaussian_kde\n'), ((1077, 1087), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (1085, 1087), True, 'import matplotlib.pyplot as plt\n')]
|
# Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
import onnx
from onnx import helper, TensorProto
QUERY_TENSOR = helper.make_tensor_value_info('query_tensor', TensorProto.FLOAT, ['batch', 4])
ATTRIBUTE_TENSOR = helper.make_tensor_value_info('attribute_tensor', TensorProto.FLOAT, [4, 1])
BIAS_TENSOR = helper.make_tensor_value_info('bias_tensor', TensorProto.FLOAT, ['batch', -1])
OUTPUT = helper.make_tensor_value_info('output', TensorProto.FLOAT, ['batch', 1])
nodes = [
helper.make_node(
'MatMul',
['query_tensor', 'attribute_tensor'],
['matmul'],
),
helper.make_node(
'ReduceSum',
['bias_tensor'],
['reduce'],
axes=[1]
),
helper.make_node(
'Add',
['matmul', 'reduce'],
['output'],
),
]
graph_def = helper.make_graph(
nodes,
'dynamic_scoring',
[
QUERY_TENSOR,
ATTRIBUTE_TENSOR,
BIAS_TENSOR,
],
[OUTPUT],
)
model_def = helper.make_model(graph_def, producer_name='dynamic.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
onnx.save(model_def, 'dynamic.onnx')
|
[
"onnx.helper.make_node",
"onnx.save",
"onnx.helper.make_tensor_value_info",
"onnx.OperatorSetIdProto",
"onnx.helper.make_graph"
] |
[((169, 247), 'onnx.helper.make_tensor_value_info', 'helper.make_tensor_value_info', (['"""query_tensor"""', 'TensorProto.FLOAT', "['batch', 4]"], {}), "('query_tensor', TensorProto.FLOAT, ['batch', 4])\n", (198, 247), False, 'from onnx import helper, TensorProto\n'), ((267, 343), 'onnx.helper.make_tensor_value_info', 'helper.make_tensor_value_info', (['"""attribute_tensor"""', 'TensorProto.FLOAT', '[4, 1]'], {}), "('attribute_tensor', TensorProto.FLOAT, [4, 1])\n", (296, 343), False, 'from onnx import helper, TensorProto\n'), ((358, 436), 'onnx.helper.make_tensor_value_info', 'helper.make_tensor_value_info', (['"""bias_tensor"""', 'TensorProto.FLOAT', "['batch', -1]"], {}), "('bias_tensor', TensorProto.FLOAT, ['batch', -1])\n", (387, 436), False, 'from onnx import helper, TensorProto\n'), ((446, 518), 'onnx.helper.make_tensor_value_info', 'helper.make_tensor_value_info', (['"""output"""', 'TensorProto.FLOAT', "['batch', 1]"], {}), "('output', TensorProto.FLOAT, ['batch', 1])\n", (475, 518), False, 'from onnx import helper, TensorProto\n'), ((863, 967), 'onnx.helper.make_graph', 'helper.make_graph', (['nodes', '"""dynamic_scoring"""', '[QUERY_TENSOR, ATTRIBUTE_TENSOR, BIAS_TENSOR]', '[OUTPUT]'], {}), "(nodes, 'dynamic_scoring', [QUERY_TENSOR, ATTRIBUTE_TENSOR,\n BIAS_TENSOR], [OUTPUT])\n", (880, 967), False, 'from onnx import helper, TensorProto\n'), ((1136, 1172), 'onnx.save', 'onnx.save', (['model_def', '"""dynamic.onnx"""'], {}), "(model_def, 'dynamic.onnx')\n", (1145, 1172), False, 'import onnx\n'), ((534, 610), 'onnx.helper.make_node', 'helper.make_node', (['"""MatMul"""', "['query_tensor', 'attribute_tensor']", "['matmul']"], {}), "('MatMul', ['query_tensor', 'attribute_tensor'], ['matmul'])\n", (550, 610), False, 'from onnx import helper, TensorProto\n'), ((647, 715), 'onnx.helper.make_node', 'helper.make_node', (['"""ReduceSum"""', "['bias_tensor']", "['reduce']"], {'axes': '[1]'}), "('ReduceSum', ['bias_tensor'], ['reduce'], axes=[1])\n", (663, 715), False, 'from onnx import helper, TensorProto\n'), ((759, 816), 'onnx.helper.make_node', 'helper.make_node', (['"""Add"""', "['matmul', 'reduce']", "['output']"], {}), "('Add', ['matmul', 'reduce'], ['output'])\n", (775, 816), False, 'from onnx import helper, TensorProto\n'), ((1098, 1133), 'onnx.OperatorSetIdProto', 'onnx.OperatorSetIdProto', ([], {'version': '(12)'}), '(version=12)\n', (1121, 1133), False, 'import onnx\n')]
|
import time
current_time = time.localtime()
hour = current_time.tm_hour
print('The hour is', hour)
|
[
"time.localtime"
] |
[((28, 44), 'time.localtime', 'time.localtime', ([], {}), '()\n', (42, 44), False, 'import time\n')]
|
from datetime import timedelta
DEFAULT_REQUIRED_CONFIRMATIONS: int = 10
MAX_FILTER_INTERVAL: int = 100_000
DEFAULT_GAS_BUFFER_FACTOR: int = 10
DEFAULT_GAS_CHECK_BLOCKS: int = 100
KEEP_MRS_WITHOUT_CHANNEL: timedelta = timedelta(minutes=15)
# A LockedTransfer message is roughly 1kb. Having 1000/min = 17/sec will be
# hard to achieve outside of benchmarks for now. To have some safety margin for
# bursts of messages, this is only enforced as an average over 5 minutes.
MATRIX_RATE_LIMIT_ALLOWED_BYTES = 5_000_000
MATRIX_RATE_LIMIT_RESET_INTERVAL = timedelta(minutes=5)
# Number of blocks after the close, during which MRs are still being accepted
CHANNEL_CLOSE_MARGIN: int = 10
|
[
"datetime.timedelta"
] |
[((218, 239), 'datetime.timedelta', 'timedelta', ([], {'minutes': '(15)'}), '(minutes=15)\n', (227, 239), False, 'from datetime import timedelta\n'), ((550, 570), 'datetime.timedelta', 'timedelta', ([], {'minutes': '(5)'}), '(minutes=5)\n', (559, 570), False, 'from datetime import timedelta\n')]
|
import re
# TO-DO: refactor validators as below
# https://pydantic-docs.helpmanual.io/usage/validators/
# Email regex mostly following RFC2822 specification. Covers ~99% of emails in use today
# Allows groups of alphanumerics and some special characters separated by dots,
# followed by a @,
# followed by groups of alphanumerics and non-staring/non-ending dashes,
# separated by dots.
EMAIL_REGEX = re.compile(
r"[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*"
r"@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)"
r"+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?"
)
# Simple password regex, requires a minimum of 8 characters with at least one
# uppercase letter, one lowercase letter, and one number.
PASSWORD_REGEX = re.compile(r"^(?=.*\d)(?=.*[a-z])(?=.*[A-Z]).{8,}$")
# Simple username regex, number of characters between 3-24, allowing only alphanumerics,
# dashes and underscores.
USERNAME_REGEX = re.compile(r"^[a-zA-Z0-9-_]{3,24}$")
def valid_email(email_string: str) -> bool:
return EMAIL_REGEX.match(email_string) is not None
def valid_password(password_string: str) -> bool:
return PASSWORD_REGEX.match(password_string) is not None
def valid_username(username_string: str) -> bool:
return USERNAME_REGEX.match(username_string) is not None
|
[
"re.compile"
] |
[((402, 561), 're.compile', 're.compile', (['"""[a-z0-9!#$%&\'*+/=?^_`{|}~-]+(?:\\\\.[a-z0-9!#$%&\'*+/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?"""'], {}), '(\n "[a-z0-9!#$%&\'*+/=?^_`{|}~-]+(?:\\\\.[a-z0-9!#$%&\'*+/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?"\n )\n', (412, 561), False, 'import re\n'), ((726, 778), 're.compile', 're.compile', (['"""^(?=.*\\\\d)(?=.*[a-z])(?=.*[A-Z]).{8,}$"""'], {}), "('^(?=.*\\\\d)(?=.*[a-z])(?=.*[A-Z]).{8,}$')\n", (736, 778), False, 'import re\n'), ((911, 946), 're.compile', 're.compile', (['"""^[a-zA-Z0-9-_]{3,24}$"""'], {}), "('^[a-zA-Z0-9-_]{3,24}$')\n", (921, 946), False, 'import re\n')]
|
#!/usr/bin/env python
"""
# Author: <NAME>
# Created Time : Tue 29 Sep 2020 01:41:23 PM CST
# File Name: function.py
# Description:
"""
import torch
import numpy as np
import os
import scanpy as sc
from anndata import AnnData
from .data import load_data
from .net.vae import VAE
from .net.utils import EarlyStopping
from .metrics import batch_entropy_mixing_score, silhouette_score
from .logger import create_logger
from .plot import embedding
def SCALE(
data_list,
batch_categories=None,
profile='RNA',
join='inner',
batch_key='batch',
batch_name='batch',
min_features=600,
min_cells=3,
n_top_features=2000,
batch_size=64,
lr=2e-4,
max_iteration=30000,
seed=124,
gpu=0,
outdir='output/',
projection=None,
repeat=False,
impute=None,
chunk_size=20000,
ignore_umap=False,
verbose=False,
assess=False,
show=True,
):
"""
Single-Cell integrative Analysis via Latent feature Extraction
Parameters
----------
data_list
A path list of AnnData matrices to concatenate with. Each matrix is referred to as a 'batch'.
batch_categories
Categories for the batch annotation. By default, use increasing numbers.
profile
Specify the single-cell profile, RNA or ATAC. Default: RNA.
join
Use intersection ('inner') or union ('outer') of variables of different batches.
batch_key
Add the batch annotation to obs using this key. By default, batch_key='batch'.
batch_name
Use this annotation in obs as batches for training model. Default: 'batch'.
min_features
Filtered out cells that are detected in less than min_features. Default: 600.
min_cells
Filtered out genes that are detected in less than min_cells. Default: 3.
n_top_features
Number of highly-variable genes to keep. Default: 2000.
batch_size
Number of samples per batch to load. Default: 64.
lr
Learning rate. Default: 2e-4.
max_iteration
Max iterations for training. Training one batch_size samples is one iteration. Default: 30000.
seed
Random seed for torch and numpy. Default: 124.
gpu
Index of GPU to use if GPU is available. Default: 0.
outdir
Output directory. Default: 'output/'.
projection
Use for new dataset projection. Input the folder containing the pre-trained model. If None, don't do projection. Default: None.
repeat
Use with projection. If False, concatenate the reference and projection datasets for downstream analysis. If True, only use projection datasets. Default: False.
impute
If True, calculate the imputed gene expression and store it at adata.layers['impute']. Default: False.
chunk_size
Number of samples from the same batch to transform. Default: 20000.
ignore_umap
If True, do not perform UMAP for visualization and leiden for clustering. Default: False.
verbose
Verbosity, True or False. Default: False.
assess
If True, calculate the entropy_batch_mixing score and silhouette score to evaluate integration results. Default: False.
Returns
-------
The output folder contains:
adata.h5ad
The AnnData matrice after batch effects removal. The low-dimensional representation of the data is stored at adata.obsm['latent'].
checkpoint
model.pt contains the variables of the model and config.pt contains the parameters of the model.
log.txt
Records raw data information, filter conditions, model parameters etc.
umap.pdf
UMAP plot for visualization.
"""
np.random.seed(seed) # seed
torch.manual_seed(seed)
if torch.cuda.is_available(): # cuda device
device='cuda'
torch.cuda.set_device(gpu)
else:
device='cpu'
outdir = outdir+'/'
os.makedirs(outdir+'/checkpoint', exist_ok=True)
log = create_logger('', fh=outdir+'log.txt')
if not projection:
adata, trainloader, testloader = load_data(
data_list, batch_categories,
join=join,
profile=profile,
n_top_features=n_top_features,
batch_size=batch_size,
chunk_size=chunk_size,
min_features=min_features,
min_cells=min_cells,
batch_name=batch_name,
batch_key=batch_key,
log=log
)
early_stopping = EarlyStopping(patience=10, checkpoint_file=outdir+'/checkpoint/model.pt')
x_dim, n_domain = adata.shape[1], len(adata.obs['batch'].cat.categories)
# model config
enc = [['fc', 1024, 1, 'relu'],['fc', 10, '', '']] # TO DO
dec = [['fc', x_dim, n_domain, 'sigmoid']]
model = VAE(enc, dec, n_domain=n_domain)
log.info('model\n'+model.__repr__())
model.fit(
trainloader,
lr=lr,
max_iteration=max_iteration,
device=device,
early_stopping=early_stopping,
verbose=verbose,
)
torch.save({'n_top_features':adata.var.index, 'enc':enc, 'dec':dec, 'n_domain':n_domain}, outdir+'/checkpoint/config.pt')
else:
state = torch.load(projection+'/checkpoint/config.pt')
n_top_features, enc, dec, n_domain = state['n_top_features'], state['enc'], state['dec'], state['n_domain']
model = VAE(enc, dec, n_domain=n_domain)
model.load_model(projection+'/checkpoint/model.pt')
model.to(device)
adata, trainloader, testloader = load_data(
data_list, batch_categories,
join='outer',
profile=profile,
chunk_size=chunk_size,
n_top_features=n_top_features,
min_cells=0,
min_features=min_features,
batch_name=batch_name,
batch_key=batch_key,
log = log
)
# log.info('Processed dataset shape: {}'.format(adata.shape))
adata.obsm['latent'] = model.encodeBatch(testloader, device=device) # save latent rep
if impute:
adata.layers['impute'] = model.encodeBatch(testloader, out='impute', batch_id=impute, device=device)
log.info('Output dir: {}'.format(outdir))
if projection and (not repeat):
ref = sc.read_h5ad(projection+'/adata.h5ad')
adata = AnnData.concatenate(
ref, adata,
batch_categories=['reference', 'query'],
batch_key='projection',
index_unique=None
)
adata.write(outdir+'adata.h5ad', compression='gzip')
if not ignore_umap: #and adata.shape[0]<1e6:
log.info('Plot umap')
sc.pp.neighbors(adata, n_neighbors=30, use_rep='latent')
sc.tl.umap(adata, min_dist=0.1)
sc.tl.leiden(adata)
# UMAP visualization
sc.settings.figdir = outdir
sc.set_figure_params(dpi=80, figsize=(10,10), fontsize=20)
cols = ['batch', 'celltype', 'leiden']
color = [c for c in cols if c in adata.obs]
if len(color) > 0:
if projection and (not repeat):
embedding(adata, groupby='projection', save='.pdf', show=show)
else:
sc.pl.umap(adata, color=color, save='.pdf', wspace=0.4, ncols=4, show=show)
if assess:
if len(adata.obs['batch'].cat.categories) > 1:
entropy_score = batch_entropy_mixing_score(adata.obsm['X_umap'], adata.obs['batch'])
log.info('batch_entropy_mixing_score: {:.3f}'.format(entropy_score))
if 'celltype' in adata.obs:
sil_score = silhouette_score(adata.obsm['X_umap'], adata.obs['celltype'].cat.codes)
log.info("silhouette_score: {:.3f}".format(sil_score))
adata.write(outdir+'adata.h5ad', compression='gzip')
return adata
def label_transfer(ref, query, rep='latent', label='celltype'):
"""
Label transfer
Parameters
-----------
ref
reference containing the projected representations and labels
query
query data to transfer label
rep
representations to train the classifier. Default is `latent`
label
label name. Defautl is `celltype` stored in ref.obs
Returns
--------
transfered label
"""
from sklearn.neighbors import KNeighborsClassifier
X_train = ref.obsm[rep]
y_train = ref.obs[label]
X_test = query.obsm[rep]
knn = knn = KNeighborsClassifier().fit(X_train, y_train)
y_test = knn.predict(X_test)
return y_test
|
[
"scanpy.tl.umap",
"numpy.random.seed",
"os.makedirs",
"torch.manual_seed",
"torch.load",
"scanpy.pp.neighbors",
"scanpy.read_h5ad",
"scanpy.pl.umap",
"torch.save",
"scanpy.tl.leiden",
"sklearn.neighbors.KNeighborsClassifier",
"torch.cuda.is_available",
"torch.cuda.set_device",
"anndata.AnnData.concatenate",
"scanpy.set_figure_params"
] |
[((3790, 3810), 'numpy.random.seed', 'np.random.seed', (['seed'], {}), '(seed)\n', (3804, 3810), True, 'import numpy as np\n'), ((3822, 3845), 'torch.manual_seed', 'torch.manual_seed', (['seed'], {}), '(seed)\n', (3839, 3845), False, 'import torch\n'), ((3854, 3879), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (3877, 3879), False, 'import torch\n'), ((4016, 4066), 'os.makedirs', 'os.makedirs', (["(outdir + '/checkpoint')"], {'exist_ok': '(True)'}), "(outdir + '/checkpoint', exist_ok=True)\n", (4027, 4066), False, 'import os\n'), ((3925, 3951), 'torch.cuda.set_device', 'torch.cuda.set_device', (['gpu'], {}), '(gpu)\n', (3946, 3951), False, 'import torch\n'), ((5239, 5370), 'torch.save', 'torch.save', (["{'n_top_features': adata.var.index, 'enc': enc, 'dec': dec, 'n_domain':\n n_domain}", "(outdir + '/checkpoint/config.pt')"], {}), "({'n_top_features': adata.var.index, 'enc': enc, 'dec': dec,\n 'n_domain': n_domain}, outdir + '/checkpoint/config.pt')\n", (5249, 5370), False, 'import torch\n'), ((5392, 5440), 'torch.load', 'torch.load', (["(projection + '/checkpoint/config.pt')"], {}), "(projection + '/checkpoint/config.pt')\n", (5402, 5440), False, 'import torch\n'), ((6490, 6530), 'scanpy.read_h5ad', 'sc.read_h5ad', (["(projection + '/adata.h5ad')"], {}), "(projection + '/adata.h5ad')\n", (6502, 6530), True, 'import scanpy as sc\n'), ((6545, 6664), 'anndata.AnnData.concatenate', 'AnnData.concatenate', (['ref', 'adata'], {'batch_categories': "['reference', 'query']", 'batch_key': '"""projection"""', 'index_unique': 'None'}), "(ref, adata, batch_categories=['reference', 'query'],\n batch_key='projection', index_unique=None)\n", (6564, 6664), False, 'from anndata import AnnData\n'), ((6868, 6924), 'scanpy.pp.neighbors', 'sc.pp.neighbors', (['adata'], {'n_neighbors': '(30)', 'use_rep': '"""latent"""'}), "(adata, n_neighbors=30, use_rep='latent')\n", (6883, 6924), True, 'import scanpy as sc\n'), ((6933, 6964), 'scanpy.tl.umap', 'sc.tl.umap', (['adata'], {'min_dist': '(0.1)'}), '(adata, min_dist=0.1)\n', (6943, 6964), True, 'import scanpy as sc\n'), ((6973, 6992), 'scanpy.tl.leiden', 'sc.tl.leiden', (['adata'], {}), '(adata)\n', (6985, 6992), True, 'import scanpy as sc\n'), ((7075, 7134), 'scanpy.set_figure_params', 'sc.set_figure_params', ([], {'dpi': '(80)', 'figsize': '(10, 10)', 'fontsize': '(20)'}), '(dpi=80, figsize=(10, 10), fontsize=20)\n', (7095, 7134), True, 'import scanpy as sc\n'), ((8706, 8728), 'sklearn.neighbors.KNeighborsClassifier', 'KNeighborsClassifier', ([], {}), '()\n', (8726, 8728), False, 'from sklearn.neighbors import KNeighborsClassifier\n'), ((7417, 7492), 'scanpy.pl.umap', 'sc.pl.umap', (['adata'], {'color': 'color', 'save': '""".pdf"""', 'wspace': '(0.4)', 'ncols': '(4)', 'show': 'show'}), "(adata, color=color, save='.pdf', wspace=0.4, ncols=4, show=show)\n", (7427, 7492), True, 'import scanpy as sc\n')]
|
from vyperlogix.hash import lists
code_error = -404
code_noUpdate = -100
code_isUpdate = 400
code_revoked = -500
code_updated = 100
code_accepted = 200
code_valid = 300
code_invalid = -301
_info_site_address = 'www.VyperLogix.com'
d_responses = lists.HashedLists2({code_error:'Warning: Unable to process your Registration.',
code_invalid:'Your registration is not valid. Please make sure your payment has processed.',
code_noUpdate:'You have the latest version.',
code_revoked:'Your product key has been revoked. You may Register again to regain access.',
code_updated:'Your registration has been updated and will be processed as quickly as possible.',
code_accepted:'Your registration has been accepted; you should receive your Product Key shortly.',
code_valid:'Your Product ID has been accepted; enjoy the power.',
code_isUpdate:'There is a new version available you can download from %s.' % (_info_site_address)
})
|
[
"vyperlogix.hash.lists.HashedLists2"
] |
[((248, 979), 'vyperlogix.hash.lists.HashedLists2', 'lists.HashedLists2', (["{code_error: 'Warning: Unable to process your Registration.', code_invalid:\n 'Your registration is not valid. Please make sure your payment has processed.'\n , code_noUpdate: 'You have the latest version.', code_revoked:\n 'Your product key has been revoked. You may Register again to regain access.'\n , code_updated:\n 'Your registration has been updated and will be processed as quickly as possible.'\n , code_accepted:\n 'Your registration has been accepted; you should receive your Product Key shortly.'\n , code_valid: 'Your Product ID has been accepted; enjoy the power.',\n code_isUpdate: \n 'There is a new version available you can download from %s.' %\n _info_site_address}"], {}), "({code_error:\n 'Warning: Unable to process your Registration.', code_invalid:\n 'Your registration is not valid. Please make sure your payment has processed.'\n , code_noUpdate: 'You have the latest version.', code_revoked:\n 'Your product key has been revoked. You may Register again to regain access.'\n , code_updated:\n 'Your registration has been updated and will be processed as quickly as possible.'\n , code_accepted:\n 'Your registration has been accepted; you should receive your Product Key shortly.'\n , code_valid: 'Your Product ID has been accepted; enjoy the power.',\n code_isUpdate: \n 'There is a new version available you can download from %s.' %\n _info_site_address})\n", (266, 979), False, 'from vyperlogix.hash import lists\n')]
|
import os
from distutils.core import setup
from setuptools import find_packages, setup
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
def requirements(fname):
for line in open(os.path.join(os.path.dirname(__file__), fname)):
yield line.strip()
setup(
name='mwevents',
version=read('VERSION').strip(),
author='<NAME>',
author_email='<EMAIL>',
packages=find_packages(),
scripts=[],
url='http://pypi.python.org/pypi/mwevents',
license=open('LICENSE').read(),
description='Standardized public MediaWiki events for tools and Science.',
long_description=read('README.rst'),
install_requires=[
'phpserialize',
'mediawiki-utilities',
'jsonable'
],
test_suite='nose.collector',
classifiers=[
"Development Status :: 4 - Beta",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Environment :: Other Environment",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Text Processing :: General",
"Topic :: Utilities",
"Topic :: Scientific/Engineering"
],
)
|
[
"os.path.dirname",
"setuptools.find_packages"
] |
[((431, 446), 'setuptools.find_packages', 'find_packages', ([], {}), '()\n', (444, 446), False, 'from setuptools import find_packages, setup\n'), ((239, 264), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (254, 264), False, 'import os\n'), ((136, 161), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (151, 161), False, 'import os\n')]
|
# Generated by Django 2.2.13 on 2020-10-25 12:06
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('stream', '0002_viewcounter_name'),
]
operations = [
migrations.AlterField(
model_name='view',
name='token',
field=models.CharField(max_length=20, unique=True),
),
]
|
[
"django.db.models.CharField"
] |
[((331, 375), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(20)', 'unique': '(True)'}), '(max_length=20, unique=True)\n', (347, 375), False, 'from django.db import migrations, models\n')]
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 10 14:19:04 2020
@author: corkep
"""
import numpy as np
import numpy.testing as nt
import unittest
from math import pi
import math
from scipy.linalg import logm, expm
from spatialmath.base.transformsNd import *
from spatialmath.base.transforms3d import trotx, transl, rotx, isrot, ishom
from spatialmath.base.transforms2d import trot2, transl2, rot2, isrot2, ishom2
from spatialmath.base import sym
import matplotlib.pyplot as plt
class TestND(unittest.TestCase):
def test_iseye(self):
self.assertTrue(iseye(np.eye(1)))
self.assertTrue(iseye(np.eye(2)))
self.assertTrue(iseye(np.eye(3)))
self.assertTrue(iseye(np.eye(5)))
self.assertFalse(iseye(2 * np.eye(3)))
self.assertFalse(iseye(-np.eye(3)))
self.assertFalse(iseye(np.array([[1, 0, 0], [0, 1, 0]])))
self.assertFalse(iseye(np.array([1, 0, 0])))
def test_r2t(self):
# 3D
R = rotx(0.3)
T = r2t(R)
nt.assert_array_almost_equal(T[0:3,3], np.r_[0,0,0])
nt.assert_array_almost_equal(T[:3,:3], R)
theta = sym.symbol('theta')
R = rotx(theta)
T = r2t(R)
self.assertEqual(r2t(R).dtype, 'O')
nt.assert_array_almost_equal(T[0:3,3], np.r_[0,0,0])
# nt.assert_array_almost_equal(T[:3,:3], R)
self.assertTrue((T[:3,:3] == R).all())
# 2D
R = rot2(0.3)
T = r2t(R)
nt.assert_array_almost_equal(T[0:2,2], np.r_[0,0])
nt.assert_array_almost_equal(T[:2,:2], R)
theta = sym.symbol('theta')
R = rot2(theta)
T = r2t(R)
self.assertEqual(r2t(R).dtype, 'O')
nt.assert_array_almost_equal(T[0:2,2], np.r_[0,0])
nt.assert_array_almost_equal(T[:2,:2], R)
with self.assertRaises(ValueError):
r2t(3)
with self.assertRaises(ValueError):
r2t(np.eye(3,4))
def test_t2r(self):
# 3D
t=[1,2,3]
T = trotx(0.3, t=t)
R = t2r(T)
nt.assert_array_almost_equal(T[:3,:3], R)
nt.assert_array_almost_equal(transl(T), np.array(t))
# 2D
t=[1,2]
T = trot2(0.3, t=t)
R = t2r(T)
nt.assert_array_almost_equal(T[:2,:2], R)
nt.assert_array_almost_equal(transl2(T), np.array(t))
with self.assertRaises(ValueError):
t2r(3)
with self.assertRaises(ValueError):
r2t(np.eye(3,4))
def test_rt2tr(self):
# 3D
R = rotx(0.2)
t = [3, 4, 5]
T = rt2tr(R, t)
nt.assert_array_almost_equal(t2r(T), R)
nt.assert_array_almost_equal(transl(T), np.array(t))
theta = sym.symbol('theta')
R = rotx(theta)
self.assertEqual(r2t(R).dtype, 'O')
# 2D
R = rot2(0.2)
t = [3, 4]
T = rt2tr(R, t)
nt.assert_array_almost_equal(t2r(T), R)
nt.assert_array_almost_equal(transl2(T), np.array(t))
theta = sym.symbol('theta')
R = rot2(theta)
self.assertEqual(r2t(R).dtype, 'O')
with self.assertRaises(ValueError):
rt2tr(3, 4)
with self.assertRaises(ValueError):
rt2tr(np.eye(3,4), [1,2,3,4])
def test_tr2rt(self):
# 3D
T = trotx(0.3, t=[1,2,3])
R, t = tr2rt(T)
nt.assert_array_almost_equal(T[:3,:3], R)
nt.assert_array_almost_equal(T[:3,3], t)
# 2D
T = trot2(0.3, t=[1,2])
R, t = tr2rt(T)
nt.assert_array_almost_equal(T[:2,:2], R)
nt.assert_array_almost_equal(T[:2,2], t)
with self.assertRaises(ValueError):
R, t = tr2rt(3)
with self.assertRaises(ValueError):
R, t = tr2rt(np.eye(3,4))
def test_checks(self):
# 3D case, with rotation matrix
R = np.eye(3)
self.assertTrue(isR(R))
self.assertFalse(isrot2(R))
self.assertTrue(isrot(R))
self.assertFalse(ishom(R))
self.assertTrue(ishom2(R))
self.assertFalse(isrot2(R, True))
self.assertTrue(isrot(R, True))
self.assertFalse(ishom(R, True))
self.assertTrue(ishom2(R, True))
# 3D case, invalid rotation matrix
R = np.eye(3)
R[0, 1] = 2
self.assertFalse(isR(R))
self.assertFalse(isrot2(R))
self.assertTrue(isrot(R))
self.assertFalse(ishom(R))
self.assertTrue(ishom2(R))
self.assertFalse(isrot2(R, True))
self.assertFalse(isrot(R, True))
self.assertFalse(ishom(R, True))
self.assertFalse(ishom2(R, True))
# 3D case, with rotation matrix
T = np.array([[1, 0, 0, 3], [0, 1, 0, 4], [0, 0, 1, 5], [0, 0, 0, 1]])
self.assertFalse(isR(T))
self.assertFalse(isrot2(T))
self.assertFalse(isrot(T))
self.assertTrue(ishom(T))
self.assertFalse(ishom2(T))
self.assertFalse(isrot2(T, True))
self.assertFalse(isrot(T, True))
self.assertTrue(ishom(T, True))
self.assertFalse(ishom2(T, True))
# 3D case, invalid rotation matrix
T = np.array([[1, 0, 0, 3], [0, 1, 1, 4], [0, 0, 1, 5], [0, 0, 0, 1]])
self.assertFalse(isR(T))
self.assertFalse(isrot2(T))
self.assertFalse(isrot(T))
self.assertTrue(ishom(T),)
self.assertFalse(ishom2(T))
self.assertFalse(isrot2(T, True))
self.assertFalse(isrot(T, True))
self.assertFalse(ishom(T, True))
self.assertFalse(ishom2(T, True))
# 3D case, invalid bottom row
T = np.array([[1, 0, 0, 3], [0, 1, 1, 4], [0, 0, 1, 5], [9, 0, 0, 1]])
self.assertFalse(isR(T))
self.assertFalse(isrot2(T))
self.assertFalse(isrot(T))
self.assertTrue(ishom(T))
self.assertFalse(ishom2(T))
self.assertFalse(isrot2(T, True))
self.assertFalse(isrot(T, True))
self.assertFalse(ishom(T, True))
self.assertFalse(ishom2(T, True))
# skew matrices
S = np.array([
[0, 2],
[-2, 0]])
nt.assert_equal(isskew(S), True)
S[0, 0] = 1
nt.assert_equal(isskew(S), False)
S = np.array([
[0, -3, 2],
[3, 0, -1],
[-2, 1, 0]])
nt.assert_equal(isskew(S), True)
S[0, 0] = 1
nt.assert_equal(isskew(S), False)
def test_homog(self):
nt.assert_almost_equal(e2h([1, 2, 3]), np.c_[1, 2, 3, 1].T)
nt.assert_almost_equal(h2e([2, 4, 6, 2]), np.c_[1, 2, 3].T)
def test_homtrans(self):
#3D
T = trotx(pi/2, t=[1,2,3])
v = [10,12,14]
v2 = homtrans(T, v)
nt.assert_almost_equal(v2, np.c_[11, -12, 15].T)
v = np.c_[[10,12,14], [-3,-4,-5]]
v2 = homtrans(T, v)
nt.assert_almost_equal(v2, np.c_[[11, -12, 15], [-2,7,-1]])
#2D
T = trot2(pi/2, t=[1,2])
v = [10,12]
v2 = homtrans(T, v)
nt.assert_almost_equal(v2, np.c_[-11, 12].T)
v = np.c_[[10,12], [-3,-4]]
v2 = homtrans(T, v)
nt.assert_almost_equal(v2, np.c_[[-11, 12], [5, -1]])
with self.assertRaises(ValueError):
T = trotx(pi/2, t=[1,2,3])
v = [10,12]
v2 = homtrans(T, v)
def test_skew(self):
# 3D
sk = skew([1, 2, 3])
self.assertEqual(sk.shape, (3,3))
nt.assert_almost_equal(sk + sk.T, np.zeros((3,3)))
self.assertEqual(sk[2,1], 1)
self.assertEqual(sk[0,2], 2)
self.assertEqual(sk[1,0], 3)
nt.assert_almost_equal(sk.diagonal(), np.r_[0,0,0])
# 2D
sk = skew([1])
self.assertEqual(sk.shape, (2,2))
nt.assert_almost_equal(sk + sk.T, np.zeros((2,2)))
self.assertEqual(sk[1,0], 1)
nt.assert_almost_equal(sk.diagonal(), np.r_[0,0])
with self.assertRaises(ValueError):
sk = skew([1,2])
def test_vex(self):
# 3D
t = [3, 4, 5]
sk = skew(t)
nt.assert_almost_equal(vex(sk), t)
# 2D
t = [3]
sk = skew(t)
nt.assert_almost_equal(vex(sk), t)
def test_isskew(self):
t = [3, 4, 5]
sk = skew(t)
self.assertTrue(isskew(sk))
sk[0,0] = 3
self.assertFalse(isskew(sk))
# 2D
t = [3]
sk = skew(t)
self.assertTrue(isskew(sk))
sk[0,0] = 3
self.assertFalse(isskew(sk))
def test_isskewa(self):
# 3D
t = [3, 4, 5, 6, 7, 8]
sk = skewa(t)
self.assertTrue(isskewa(sk))
sk[0,0] = 3
self.assertFalse(isskew(sk))
sk = skewa(t)
sk[3,3] = 3
self.assertFalse(isskew(sk))
# 2D
t = [3, 4, 5]
sk = skew(t)
self.assertTrue(isskew(sk))
sk[0,0] = 3
self.assertFalse(isskew(sk))
sk = skewa(t)
sk[2,2] = 3
self.assertFalse(isskew(sk))
def test_skewa(self):
# 3D
sk = skewa([1, 2, 3, 4, 5, 6])
self.assertEqual(sk.shape, (4,4))
nt.assert_almost_equal(sk.diagonal(), np.r_[0,0,0,0])
nt.assert_almost_equal(sk[-1,:], np.r_[0,0,0,0])
nt.assert_almost_equal(sk[:3,3], [1, 2, 3])
nt.assert_almost_equal(vex(sk[:3,:3]), [4,5,6])
# 2D
sk = skewa([1, 2, 3])
self.assertEqual(sk.shape, (3,3))
nt.assert_almost_equal(sk.diagonal(), np.r_[0,0,0])
nt.assert_almost_equal(sk[-1,:], np.r_[0,0,0])
nt.assert_almost_equal(sk[:2,2], [1, 2])
nt.assert_almost_equal(vex(sk[:2,:2]), [3])
with self.assertRaises(ValueError):
sk = skew([1,2])
def test_vexa(self):
# 3D
t = [1, 2, 3, 4, 5, 6]
sk = skewa(t)
nt.assert_almost_equal(vexa(sk), t)
# 2D
t = [1, 2, 3]
sk = skewa(t)
nt.assert_almost_equal(vexa(sk), t)
def test_det(self):
a = np.array([[1, 2], [3, 4]])
self.assertAlmostEqual(np.linalg.det(a), det(a))
x, y = sym.symbol('x y')
a = np.array([[x, y], [y, x]])
self.assertEqual(det(a), x**2 - y**2)
# ---------------------------------------------------------------------------------------#
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main",
"spatialmath.base.transforms3d.rotx",
"spatialmath.base.transforms2d.ishom2",
"spatialmath.base.transforms2d.isrot2",
"numpy.testing.assert_almost_equal",
"spatialmath.base.transforms2d.rot2",
"numpy.zeros",
"spatialmath.base.transforms3d.isrot",
"spatialmath.base.transforms2d.trot2",
"spatialmath.base.sym.symbol",
"spatialmath.base.transforms3d.trotx",
"numpy.array",
"numpy.linalg.det",
"spatialmath.base.transforms3d.transl",
"numpy.eye",
"numpy.testing.assert_array_almost_equal",
"spatialmath.base.transforms3d.ishom",
"spatialmath.base.transforms2d.transl2"
] |
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|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 27 16:40:49 2020
@author: krugefr1
"""
import numpy as np
import os
try:
import arthor
except ImportError:
arthor = None
from rdkit import Chem
from rdkit.Chem import rdSubstructLibrary
import pickle
import random
import pandas as pd
import copy
from automated_series_classification import UPGMAclustering, Butinaclustering, utilsDataPrep
class Classification:
def __init__(self,
proj,
datapath,
dbpath,
filename,
chembldb,
flimit=1e-3,
MinClusterSize=20,
clustering='UPGMA',
calcDists=True,
calcScores=False,
smilesCol='Smiles',
idCol='ID',
onlyCompleteRings=False,
useArthor=True):
global arthor
if not useArthor:
arthor = None
self.useArthor = useArthor
self.proj = proj
self.datapath = datapath
self.dbpath = dbpath
self.chembldb = chembldb
self.flimit = flimit
self.MinClusterSize = MinClusterSize
self.clustering = clustering
self.calcScores = calcScores
self.calcDists = calcDists
self.smilesCol = smilesCol
self.idCol = idCol
self.onlyCompleteRings = onlyCompleteRings
# load data
self.moldata_proj, self.distdata_proj = utilsDataPrep.PrepareData(
self.proj,
self.datapath,
filename,
distMeasure='Tanimoto',
FP='Morgan2',
calcDists=self.calcDists,
smilesCol=smilesCol)
if arthor is not None:
if not os.path.isdir(dbpath):
os.mkdir(dbpath)
# set up project database for arthor substructure matching
df = self.moldata_proj[[smilesCol, idCol]]
df.to_csv('./arthor/{0}.smi'.format(self.proj),
header=None,
index=None,
sep=' ')
os.system('smi2atdb -j 0 -l {0}{1}.smi {0}{1}.atdb'.format(
self.dbpath, self.proj))
os.system('atdb2fp -j 0 {0}{1}.atdb'.format(
self.dbpath, self.proj))
self.proj_db = arthor.SubDb('{0}{1}.atdb'.format(
self.dbpath, self.proj))
else:
if type(dbpath) == rdSubstructLibrary.SubstructLibrary:
self.proj_db = dbpath
self.db_size = len(self.proj_db)
else:
if not os.path.exists(dbpath):
print("creating database")
mols = rdSubstructLibrary.CachedTrustedSmilesMolHolder()
fps = rdSubstructLibrary.PatternHolder()
for smi in self.moldata_proj[smilesCol]:
m = Chem.MolFromSmiles(smi)
mols.AddSmiles(Chem.MolToSmiles(m))
fps.AddFingerprint(Chem.PatternFingerprint(m))
self.proj_db = rdSubstructLibrary.SubstructLibrary(
mols, fps)
self.db_size = len(mols)
pickle.dump(self.proj_db, open(dbpath, 'wb+'))
else:
self.proj_db = pickle.load(open(dbpath, 'rb'))
self.db_size = len(self.proj_db)
def AssignSeriesToMCS(self, MCSdict):
# assign series to MCS of selected clusters
smartslist = [v[2] for v in MCSdict.values()]
MolAssign_prel = {}
MolAssignment = {}
for s in range(len(smartslist)):
if arthor is not None:
res = self.proj_db.search(smartslist[s])
mols = [int(i) for i in res.to_array()]
else:
mols = self.proj_db.GetMatches(Chem.MolFromSmarts(
smartslist[s]),
maxResults=self.db_size)
MolAssign_prel[list(MCSdict.keys())[s]] = list(mols)
# remove all series that are entirely in another series
for key1 in MolAssign_prel.keys():
add = 1
for key2 in MolAssign_prel.keys():
if key2 != key1:
if set(MolAssign_prel[key1]).issubset(
set(MolAssign_prel[key2])):
if set(MolAssign_prel[key2]).issubset(
set(MolAssign_prel[key1])) and (
MCSdict[key1][0] >= MCSdict[key2][0]):
add = 1
else:
add = 0
break
if add == 1 and MolAssign_prel[key1] not in MolAssignment.values():
MolAssignment[key1] = MolAssign_prel[key1]
MolAssignment = {
k: MolAssignment[k]
for k in MolAssignment.keys()
if len(MolAssignment[k]) > self.MinClusterSize
}
if self.calcScores:
MCSdict = {
k: (MCSdict[k][0], len(MolAssignment[k]), MCSdict[k][2],
MCSdict[k][3], MolAssignment[k])
for k in MolAssignment.keys()
}
else:
MCSdict = {
k: (MCSdict[k][0], len(MolAssignment[k]), MCSdict[k][2],
MolAssignment[k])
for k in MolAssignment.keys()
}
return MolAssignment, MCSdict
def ApplyClustering(self):
# apply custering and calculate MCS
if self.clustering == 'UPGMA':
MCSdict = UPGMAclustering.ApplyUPGMA(
self.distdata_proj,
self.moldata_proj,
self.chembldb,
self.flimit,
self.MinClusterSize,
self.calcScores,
onlyCompleteRings=self.onlyCompleteRings,
useArthor=self.useArthor)
elif self.clustering == 'Butina':
distdata = copy.deepcopy(self.distdata_proj)
MCSdict = Butinaclustering.ApplyButina(distdata,
self.moldata_proj,
self.chembldb,
self.flimit,
self.MinClusterSize,
self.calcScores,
useArthor=self.useArthor)
else:
print('Clustering algorithm not implemented.')
return
# assign series through substructure matching and filtering
self.MolAssignment, self.MCSdict = self.AssignSeriesToMCS(MCSdict)
# prepare and save output
self.moldata_proj['ClusterID'] = [
list() for x in range(self.moldata_proj.shape[0])
]
for k, vs in self.MolAssignment.items():
for v in vs:
self.moldata_proj['ClusterID'].iloc[v].append(k)
if self.clustering == 'UPGMA':
self.moldata_proj.to_csv('{0}moldata_UPGMA.csv'.format(
self.datapath))
with open('{0}ClusterData_UPGMA.pkl'.format(self.datapath),
'wb') as fileout:
pickle.dump(self.MCSdict, fileout)
elif self.clustering == 'Butina':
self.moldata_proj.to_csv('{0}moldata_Butina.csv'.format(
self.datapath))
with open('{0}ClusterData_Butina.pkl'.format(self.datapath),
'wb') as fileout:
pickle.dump(self.MCSdict, fileout)
else:
print('Clustering algorithm not implemented.')
return
def CalculatePerformance(self, seriescolumn='series assignment'):
# benchmark the automated classification against a different (probably human-defined) classification
# human-defined compound assignment is specified in the column "seriescolumn" of the dataframe "moldata"
# automated classification assignment specified in dict "MolAssignment"
# calculates F1 score of automatically-identified series w.r.t. to all human-defined series, then links
# each automatically-identified series to the human-defined series with highest F1 score
scaflist = list(set(self.moldata_proj['scaffold'].tolist()))
scaflist.sort()
intersect_matrix = np.zeros((len(scaflist), len(self.MolAssignment)))
NMatchScaf = []
NMatchCluster = np.array([len(v) for v in self.MolAssignment.values()])
for scaf_ind in range(len(scaflist)):
mollist = self.moldata_proj[self.idCol].loc[self.moldata_proj[
seriescolumn].map(lambda x: scaflist[scaf_ind] in x)].tolist()
intersect_scaf = np.array([
len(list(set(mollist) & set(clusterlist)))
for clusterlist in self.MolAssignment.values()
])
intersect_matrix[scaf_ind, :] = intersect_scaf
NMatchScaf.append(len(mollist))
NMatchScaf = np.array(NMatchScaf)
RecallMatrix = intersect_matrix / NMatchScaf[:, None]
PrecMatrix = intersect_matrix / NMatchCluster[None, :]
Fscore = (2 * RecallMatrix * PrecMatrix) / (RecallMatrix + PrecMatrix +
1e-9)
maxscore = np.argmax(Fscore, axis=0)
PrecVector = np.zeros(len(self.MolAssignment))
RecallVector = np.zeros(len(self.MolAssignment))
FscoreVector = np.zeros(len(self.MolAssignment))
LinkVector = []
for col in range(len(self.MolAssignment)):
PrecVector[col] = PrecMatrix[maxscore[col], col]
RecallVector[col] = RecallMatrix[maxscore[col], col]
FscoreVector[col] = Fscore[maxscore[col], col]
LinkVector.append((list(self.MolAssignment.keys())[col],
scaflist[maxscore[col]]))
LinkVector = np.array(LinkVector)
self.PerformanceClusters = {
'recall': RecallVector,
'precision': PrecVector,
'Fscore': FscoreVector,
'linked series': LinkVector
}
if self.clustering == 'UPGMA':
with open('{0}PerformanceData_UPGMA.pkl'.format(self.datapath),
'wb') as fileout:
pickle.dump(self.PerformanceClusters, fileout)
elif self.clustering == 'Butina':
with open('{0}PerformanceData_Butina.pkl'.format(self.datapath),
'wb') as fileout:
pickle.dump(self.PerformanceClusters, fileout)
else:
print('Clustering algorithm not implemented.')
return
def ClassificationCrossValidation(self, fraction_sample, N_sample):
samplerange = np.arange(len(self.moldata_proj))
invfrac = 1 / fraction_sample
self.SampledSeries = {}
for i in range(N_sample):
# random sampling
random.seed((i + 1) * 10)
molinds = random.sample(population=samplerange.tolist(),
k=int(
len(samplerange.tolist()) // invfrac))
moldata_sample = self.moldata_proj.iloc[molinds]
distdata_sample = self.distdata_proj[molinds, :]
distdata_sample = distdata_sample[:, molinds]
# apply custering and calculate MCS
if self.clustering == 'UPGMA':
MCSdict_sampled = UPGMAclustering.ApplyUPGMA(
distdata_sample,
moldata_sample,
self.chembldb,
self.flimit,
self.MinClusterSize,
self.calcScores,
useArthor=self.useArthor)
elif self.clustering == 'Butina':
MCSdict_sampled = Butinaclustering.ApplyButina(
distdata_sample,
moldata_sample,
self.chembldb,
self.flimit,
self.MinClusterSize,
self.calcScores,
useArthor=self.useArthor)
else:
print('Clustering algorithm not implemented.')
return
# assign series through substructure matching and filtering
MolAssignment_sampled, MCSdict_sampled = self.AssignSeriesToMCS(
MCSdict_sampled)
self.SampledSeries[i] = MCSdict_sampled
if self.clustering == 'UPGMA':
with open(
'{0}SampledSeries{1}_UPGMA.pkl'.format(
self.datapath, int(fraction_sample * 100)),
'wb') as fileout:
pickle.dump(self.SampledSeries, fileout)
elif self.clustering == 'Butina':
with open(
'{0}SampledSeries{1}_Butina.pkl'.format(
self.datapath, int(fraction_sample * 100)),
'wb') as fileout:
pickle.dump(self.SampledSeries, fileout)
else:
print('Clustering algorithm not implemented.')
return
return
def EvaluationCrossValidation(self):
# Compare the classification obtained from sampling ("SampledSeries") against the original classification ("MCSdict")
self.EvalCrossval = pd.DataFrame(
columns=['series id', 'repetition', 'fscore'])
for rep in self.SampledSeries.keys():
rep_dict = self.SampledSeries[rep]
keylist = [k for k in rep_dict.keys()]
for k in self.MCSdict.keys():
intersect = [
len(set(self.MCSdict[k][-1]) & set(v[-1]))
for v in rep_dict.values()
]
recall = np.array([
intersect[i] / len(rep_dict[keylist[i]][-1])
for i in range(len(keylist))
])
precision = np.array(intersect) / len(self.MCSdict[k][-1])
fscore = max(2 * recall * precision /
(recall + precision + 1e-9))
row = [int(k), int(rep), fscore]
self.EvalCrossval.loc[len(self.EvalCrossval)] = row
self.EvalCrossval['series id'] = self.EvalCrossval['series id'].apply(
int)
|
[
"rdkit.Chem.PatternFingerprint",
"os.mkdir",
"pickle.dump",
"numpy.argmax",
"rdkit.Chem.MolToSmiles",
"pandas.DataFrame",
"os.path.exists",
"random.seed",
"rdkit.Chem.rdSubstructLibrary.PatternHolder",
"copy.deepcopy",
"automated_series_classification.Butinaclustering.ApplyButina",
"automated_series_classification.utilsDataPrep.PrepareData",
"rdkit.Chem.rdSubstructLibrary.CachedTrustedSmilesMolHolder",
"os.path.isdir",
"rdkit.Chem.MolFromSmarts",
"rdkit.Chem.rdSubstructLibrary.SubstructLibrary",
"numpy.array",
"automated_series_classification.UPGMAclustering.ApplyUPGMA",
"rdkit.Chem.MolFromSmiles"
] |
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{}), '(self.PerformanceClusters, fileout)\n', (10512, 10547), False, 'import pickle\n'), ((11661, 11821), 'automated_series_classification.UPGMAclustering.ApplyUPGMA', 'UPGMAclustering.ApplyUPGMA', (['distdata_sample', 'moldata_sample', 'self.chembldb', 'self.flimit', 'self.MinClusterSize', 'self.calcScores'], {'useArthor': 'self.useArthor'}), '(distdata_sample, moldata_sample, self.chembldb,\n self.flimit, self.MinClusterSize, self.calcScores, useArthor=self.useArthor\n )\n', (11687, 11821), False, 'from automated_series_classification import UPGMAclustering, Butinaclustering, utilsDataPrep\n'), ((12913, 12953), 'pickle.dump', 'pickle.dump', (['self.SampledSeries', 'fileout'], {}), '(self.SampledSeries, fileout)\n', (12924, 12953), False, 'import pickle\n'), ((2650, 2672), 'os.path.exists', 'os.path.exists', (['dbpath'], {}), '(dbpath)\n', (2664, 2672), False, 'import os\n'), ((2748, 2797), 'rdkit.Chem.rdSubstructLibrary.CachedTrustedSmilesMolHolder', 'rdSubstructLibrary.CachedTrustedSmilesMolHolder', ([], {}), '()\n', (2795, 2797), False, 'from rdkit.Chem import rdSubstructLibrary\n'), ((2824, 2858), 'rdkit.Chem.rdSubstructLibrary.PatternHolder', 'rdSubstructLibrary.PatternHolder', ([], {}), '()\n', (2856, 2858), False, 'from rdkit.Chem import rdSubstructLibrary\n'), ((3138, 3184), 'rdkit.Chem.rdSubstructLibrary.SubstructLibrary', 'rdSubstructLibrary.SubstructLibrary', (['mols', 'fps'], {}), '(mols, fps)\n', (3173, 3184), False, 'from rdkit.Chem import rdSubstructLibrary\n'), ((3922, 3955), 'rdkit.Chem.MolFromSmarts', 'Chem.MolFromSmarts', (['smartslist[s]'], {}), '(smartslist[s])\n', (3940, 3955), False, 'from rdkit import Chem\n'), ((7714, 7748), 'pickle.dump', 'pickle.dump', (['self.MCSdict', 'fileout'], {}), '(self.MCSdict, fileout)\n', (7725, 7748), False, 'import pickle\n'), ((10723, 10769), 'pickle.dump', 'pickle.dump', (['self.PerformanceClusters', 'fileout'], {}), '(self.PerformanceClusters, fileout)\n', (10734, 10769), False, 'import pickle\n'), ((12034, 12196), 'automated_series_classification.Butinaclustering.ApplyButina', 'Butinaclustering.ApplyButina', (['distdata_sample', 'moldata_sample', 'self.chembldb', 'self.flimit', 'self.MinClusterSize', 'self.calcScores'], {'useArthor': 'self.useArthor'}), '(distdata_sample, moldata_sample, self.chembldb,\n self.flimit, self.MinClusterSize, self.calcScores, useArthor=self.useArthor\n )\n', (12062, 12196), False, 'from automated_series_classification import UPGMAclustering, Butinaclustering, utilsDataPrep\n'), ((13202, 13242), 'pickle.dump', 'pickle.dump', (['self.SampledSeries', 'fileout'], {}), '(self.SampledSeries, fileout)\n', (13213, 13242), False, 'import pickle\n'), ((14161, 14180), 'numpy.array', 'np.array', (['intersect'], {}), '(intersect)\n', (14169, 14180), True, 'import numpy as np\n'), ((2948, 2971), 'rdkit.Chem.MolFromSmiles', 'Chem.MolFromSmiles', (['smi'], {}), '(smi)\n', (2966, 2971), False, 'from rdkit import Chem\n'), ((3011, 3030), 'rdkit.Chem.MolToSmiles', 'Chem.MolToSmiles', (['m'], {}), '(m)\n', (3027, 3030), False, 'from rdkit import Chem\n'), ((3075, 3101), 'rdkit.Chem.PatternFingerprint', 'Chem.PatternFingerprint', (['m'], {}), '(m)\n', (3098, 3101), False, 'from rdkit import Chem\n')]
|
import openliveq as olq
import pytest
import os
from .test_base import TestBase
class TestCollection(TestBase):
def test_df(self, c):
result = c.df
assert result["社会保険事務所"] == 1
assert result["国民年金"] == 4
def test_cf(self, c):
result = c.cf
assert result["社会保険事務所"] > 1
assert result["国民年金"] > 4
@pytest.fixture
def c(self, parsed_questions):
result = olq.Collection()
for ws in parsed_questions:
result.add(ws)
return result
@pytest.fixture
def ff(self):
return olq.FeatureFactory()
@pytest.fixture
def parsed_questions(self, ff, questions):
result = []
for q in questions:
result.append(ff.parse_question(q))
return result
|
[
"openliveq.Collection",
"openliveq.FeatureFactory"
] |
[((427, 443), 'openliveq.Collection', 'olq.Collection', ([], {}), '()\n', (441, 443), True, 'import openliveq as olq\n'), ((583, 603), 'openliveq.FeatureFactory', 'olq.FeatureFactory', ([], {}), '()\n', (601, 603), True, 'import openliveq as olq\n')]
|
# Copyright (c) 2010 Resolver Systems Ltd.
# All Rights Reserved
#
try:
import unittest2 as unittest
except ImportError:
import unittest
from functionaltest import FunctionalTest
import key_codes
from textwrap import dedent
class Test_2734_ClearCells(FunctionalTest):
def test_delete_key_clears_selected_cells(self):
self.assert_key_deletes_cells(key_codes.DELETE)
def test_backspace_key_clears_selected_cells(self):
self.assert_key_deletes_cells(key_codes.BACKSPACE)
def assert_key_deletes_cells(self, key_code):
# * Harold logs in and creates a new sheet
self.login_and_create_new_sheet()
# * He enters some data in A1:A3
self.enter_cell_text(1, 1, 'a1')
self.enter_cell_text(1, 2, 'a2')
self.enter_cell_text(1, 3, 'a3')
self.wait_for_cell_value(1, 3, 'a3')
# * He clicks on A1 and hits delete
self.click_on_cell(1, 1)
self.human_key_press(key_code)
# * He sees the value in A1 disappear while the others remain
self.wait_for_cell_value(1, 1, '')
self.wait_for_cell_value(1, 2, 'a2')
self.wait_for_cell_value(1, 3, 'a3')
# * He selects the range a2:a3
self.select_range_with_shift_click((1, 2), (1, 3))
# He hits delete
self.human_key_press(key_code)
# * He sees that all the cells are now cleared
self.wait_for_cell_value(1, 1, '')
self.wait_for_cell_value(1, 2, '')
self.wait_for_cell_value(1, 3, '')
def test_delete_key_while_editing_still_does_what_it_should(self):
# * Harold logs in and creates a new sheet
self.login_and_create_new_sheet()
# * He enters three characters in A1
self.open_cell_for_editing(1, 1)
self.human_key_press(key_codes.NUMBER_1)
self.human_key_press(key_codes.NUMBER_2)
self.human_key_press(key_codes.NUMBER_3)
# * He moves left twice
self.human_key_press(key_codes.LEFT)
self.human_key_press(key_codes.LEFT)
# He hits delete
self.human_key_press(key_codes.DELETE)
# the middle character is now missing
self.wait_for_cell_editor_content('13')
def test_backspace_key_while_editing_still_does_what_it_should(self):
# * Harold logs in and creates a new sheet
self.login_and_create_new_sheet()
# * He enters three characters in A1
self.open_cell_for_editing(1, 1)
self.human_key_press(key_codes.NUMBER_1)
self.human_key_press(key_codes.NUMBER_2)
self.human_key_press(key_codes.NUMBER_3)
# * He moves left once
self.human_key_press(key_codes.LEFT)
# He hits backspace
self.human_key_press(key_codes.BACKSPACE)
# the middle character is now missing
self.wait_for_cell_editor_content('13')
def test_can_clear_cell_from_usercode(self):
# * Harold logs in and creates a new sheet
self.login_and_create_new_sheet()
# * He enters some data in A1:A3
self.enter_cell_text(1, 1, 'a1')
self.enter_cell_text(1, 2, 'a2')
self.enter_cell_text(1, 3, 'a3')
self.wait_for_cell_value(1, 3, 'a3')
# * He tries to use the clear() function from usercode on a cell
# and then tries to access some of the supposedly cleared attributes of the cell
self.prepend_usercode(dedent('''
worksheet.a1.error = 'harold puts a deliberate pointless error in'
worksheet.a1.clear()
worksheet.b1.formula = str(worksheet.a1.value)
worksheet.b2.formula = str(worksheet.a1.formula)
worksheet.b3.formula = str(worksheet.a1.formatted_value)
worksheet.b4.formula = str(worksheet.a1.error)
'''))
# * He sees the value in a1 disappear
self.wait_for_cell_value(1, 1, '')
self.wait_for_cell_value(1, 2, 'a2')
self.wait_for_cell_value(1, 3, 'a3')
# * He sees his little investigations also produce the expected results
self.wait_for_cell_value(2, 1, '<undefined>')
self.wait_for_cell_value(2, 2, 'None')
self.wait_for_cell_value(2, 3, '')
self.wait_for_cell_value(2, 4, 'None')
def test_can_clear_cell_range_from_usercode(self):
# * Harold logs in and creates a new sheet
self.login_and_create_new_sheet()
# * He enters some data in A1:A3
self.enter_cell_text(1, 1, 'a1')
self.enter_cell_text(1, 2, 'a2')
self.enter_cell_text(1, 3, 'a3')
self.wait_for_cell_value(1, 3, 'a3')
# * He tries to use the clear() function from usercode on a cell range
self.prepend_usercode(dedent('''
worksheet.a1.error = 'harold puts a deliberate pointless error in'
worksheet.a2.error = 'harold puts another deliberate pointless error in'
worksheet.cell_range("a1:a2").clear()
worksheet.b1.formula = str(worksheet.a1.value)
worksheet.b2.formula = str(worksheet.a1.formula)
worksheet.b3.formula = str(worksheet.a1.formatted_value)
worksheet.b4.formula = str(worksheet.a1.error)
worksheet.c1.formula = str(worksheet.a2.value)
worksheet.c2.formula = str(worksheet.a2.formula)
worksheet.c3.formula = str(worksheet.a2.formatted_value)
worksheet.c4.formula = str(worksheet.a2.error)
'''))
# * He sees the value in a1 and a2 disappear
self.wait_for_cell_value(1, 1, '')
self.wait_for_cell_value(1, 2, '')
self.wait_for_cell_value(1, 3, 'a3')
# * He sees his little investigations also produce the expected results
self.wait_for_cell_value(2, 1, '<undefined>')
self.wait_for_cell_value(2, 2, 'None')
self.wait_for_cell_value(2, 3, '')
self.wait_for_cell_value(2, 4, 'None')
self.wait_for_cell_value(3, 1, '<undefined>')
self.wait_for_cell_value(3, 2, 'None')
self.wait_for_cell_value(3, 3, '')
self.wait_for_cell_value(3, 4, 'None')
|
[
"textwrap.dedent"
] |
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|
"""
Helper script to create config files for BlenderProc.
"""
import os
import yaml
import random
import numpy as np
import binascii
# these paths have to be manually set before creating a config
BLENDERPROC_ROOT = '' # /path/to/BlenderProc
SHAPENET_ROOT = '' # /path/to/ShapeNetCore.v2
SUNCG_ROOT = '' # /path/to/suncg
DEST = '' # /path/to/output_folder
def get_random_house_path():
with open(os.path.join(BLENDERPROC_ROOT, 'suncg_houses.txt'), 'r') as f:
house_paths = f.readlines()
return os.path.join(SUNCG_ROOT, random.choice(house_paths)).strip()
def get_base_cfg():
with open(os.path.join(BLENDERPROC_ROOT, 'base_config.yaml'), 'r') as f:
base_cfg = yaml.load(f)
return base_cfg
def get_random_obj_configs(n=10):
obj_configs, scale_configs, mat_configs, sample_configs, physic_configs, gravoff_configs = [], [], [], [], [], []
with open(os.path.join(BLENDERPROC_ROOT, 'shapenet_objects.txt'), 'r') as f:
obj_paths = f.readlines()
for i in range(n):
scale = np.random.uniform(0.1, 0.4)
recalculate_uv = np.random.uniform(0., 1.)
obj_base_cfg = {
"module": "loader.CustomObjectLoader",
"config": {
"path": os.path.join(SHAPENET_ROOT, random.choice(obj_paths)[:-1]),
"scale": [scale, scale, scale],
"add_properties": {
"cp_object_to_scale": True,
"cp_sample_pose": True,
"cp_category_id": int(i+2),
"cp_coarse_grained_class": "selected_object",
"cp_type": "Object",
"cp_physics": True,
"cp_cc_texture": True
},
}
}
scale_base_cfg = {
"module": "manipulators.EntityManipulator",
"config": {
"selector": {
"provider": "getter.Entity",
"conditions": {
"cp_category_id": int(i+2),
}
},
"scale": [scale, scale, scale],
"cf_add_modifier": {
"name": "Solidify",
"thickness": 0.0025
},
"cf_randomize_materials": {
"randomization_level": 1.,
"materials_to_replace_with": {
"provider": "getter.Material",
"conditions": {
"cp_is_cc_texture": True
}
}
},
}
}
mat_base_cfg = {
"module": "manipulators.MaterialManipulator",
"config": {
"selector": {
"provider": "getter.Entity",
"conditions": {
"cp_category_id": int(i + 2),
}
},
"cf_set_Roughness": {
"provider": "sampler.Value",
"type": "float",
"min": 0.05,
"max": 0.5,
},
"cf_set_Specular": {
"provider": "sampler.Value",
"type": "float",
"min": 0.5,
"max": 1.0,
},
"cf_color_link_to_displacement": {
"provider": "sampler.Value",
"type": "float",
"min": 0.001,
"max": 0.15
},
"cf_set_Alpha": 1.0,
"mode": "once_for_each"
}
}
sampler_base_cfg = {
"module": "object.OnSurfaceSampler",
"config": {
"objects_to_sample": {
"provider": "getter.Entity",
"conditions": {
"cp_category_id": int(i+2)
}
},
"surface": {
"provider": "getter.Entity",
"index": 0,
"conditions": {
"name": "selected_table"
}
},
"pos_sampler": {
"provider": "sampler.UpperRegionSampler",
"to_sample_on": {
"provider": "getter.Entity",
"index": 0,
"conditions": {
"name": "selected_table"
}
},
"min_height": 1,
"max_height": 4,
"face_sample_range": [0.4, 0.6],
"use_ray_trace_check": False,
},
"min_distance": 0.1,
"max_distance": 1.5,
"rot_sampler": {
"provider": "sampler.Uniform3d",
"min": [0, 0, 0],
"max": [6.28, 6.28, 6.28]
}
}
}
physics_base_cfg = {
"module": "object.PhysicsPositioning",
"config": {
"min_simulation_time": 0.5,
"max_simulation_time": 2,
"check_object_interval": 1,
}
}
grav_off_cfg = {
"module": "manipulators.EntityManipulator",
"config": {
"selector": {
"provider": "getter.Entity",
"conditions": {
"cp_category_id": int(i + 2),
}
},
"cp_physics": False,
}
}
scale_base_cfg["config"]["cf_add_uv_mapping"] = {
"projection": "cylinder",
"forced_recalc_of_uv_maps": True if recalculate_uv > 0.5 else False
}
mat_base_cfg["config"]["cf_add_uv_mapping"] = {
"projection": "cylinder",
"forced_recalc_of_uv_maps": True if recalculate_uv > 0.5 else False
}
obj_configs.append(obj_base_cfg)
scale_configs.append(scale_base_cfg)
mat_configs.append(mat_base_cfg)
sample_configs.append(sampler_base_cfg)
physic_configs.append(physics_base_cfg)
gravoff_configs.append(grav_off_cfg)
return obj_configs, scale_configs, mat_configs, sample_configs, physic_configs, gravoff_configs
def create_config():
base_cfg = get_base_cfg()
baseline = 0.065
focal_length_x = 541.14
focal_length_y = 541.14
base_cfg['modules'][8]['config']['intrinsics']['interocular_distance'] = baseline
base_cfg['modules'][8]['config']['intrinsics']['cam_K'] = [focal_length_x, 0.0, 320.0, 0.0, focal_length_y, 240.0, 0.0, 0.0, 1.0]
# add objects
num_objs = np.random.randint(5, 12)
obj_configs, scale_configs, mat_configs, sample_configs, physic_configs, gravoff_configs = get_random_obj_configs(n=num_objs)
for obj_config, scale_config, mat_config, sample_config, physics_config, gravoff_config in zip(obj_configs, scale_configs, mat_configs, sample_configs, physic_configs, gravoff_configs):
base_cfg['modules'].insert(6, obj_config)
base_cfg['modules'].insert(7, scale_config)
base_cfg['modules'].insert(8, sample_config)
base_cfg['modules'].insert(9, physics_config)
base_cfg['modules'].insert(10, gravoff_config)
# set house path
base_cfg['modules'][1]['config']['path'] = get_random_house_path()
# replace house with cctextures
house_cc_texture_config = {
"module": "manipulators.EntityManipulator",
"config": {
"selector": {
"provider": "getter.Entity",
"conditions": {
"type": "MESH"
}
},
"cf_randomize_materials": {
"randomization_level": 0.4,
"materials_to_replace_with": {
"provider": "getter.Material",
"random_samples": 1,
"conditions": {
"cp_is_cc_texture": True # this will return one random loaded cc textures
}
}
}
}
}
base_cfg['modules'].insert(4, house_cc_texture_config)
# set output dir
output_prefix = os.urandom(20)
output_prefix = binascii.hexlify(output_prefix)
output_prefix = str(output_prefix)[2:-1]
output_path = os.path.join(DEST, output_prefix)
os.makedirs(output_path)
base_cfg['modules'][0]['config']['global']['output_dir'] = output_path
with open(os.path.join(DEST, output_prefix + '/config.yaml'), 'w') as f:
yaml.dump(base_cfg, f)
return os.path.join(DEST, output_prefix + '/config.yaml')
if __name__ == '__main__':
path = create_config()
print(path)
|
[
"numpy.random.uniform",
"yaml.load",
"os.makedirs",
"binascii.hexlify",
"yaml.dump",
"random.choice",
"numpy.random.randint",
"os.path.join",
"os.urandom"
] |
[((7027, 7051), 'numpy.random.randint', 'np.random.randint', (['(5)', '(12)'], {}), '(5, 12)\n', (7044, 7051), True, 'import numpy as np\n'), ((8462, 8476), 'os.urandom', 'os.urandom', (['(20)'], {}), '(20)\n', (8472, 8476), False, 'import os\n'), ((8497, 8528), 'binascii.hexlify', 'binascii.hexlify', (['output_prefix'], {}), '(output_prefix)\n', (8513, 8528), False, 'import binascii\n'), ((8592, 8625), 'os.path.join', 'os.path.join', (['DEST', 'output_prefix'], {}), '(DEST, output_prefix)\n', (8604, 8625), False, 'import os\n'), ((8630, 8654), 'os.makedirs', 'os.makedirs', (['output_path'], {}), '(output_path)\n', (8641, 8654), False, 'import os\n'), ((8850, 8900), 'os.path.join', 'os.path.join', (['DEST', "(output_prefix + '/config.yaml')"], {}), "(DEST, output_prefix + '/config.yaml')\n", (8862, 8900), False, 'import os\n'), ((695, 707), 'yaml.load', 'yaml.load', (['f'], {}), '(f)\n', (704, 707), False, 'import yaml\n'), ((1036, 1063), 'numpy.random.uniform', 'np.random.uniform', (['(0.1)', '(0.4)'], {}), '(0.1, 0.4)\n', (1053, 1063), True, 'import numpy as np\n'), ((1089, 1116), 'numpy.random.uniform', 'np.random.uniform', (['(0.0)', '(1.0)'], {}), '(0.0, 1.0)\n', (1106, 1116), True, 'import numpy as np\n'), ((8816, 8838), 'yaml.dump', 'yaml.dump', (['base_cfg', 'f'], {}), '(base_cfg, f)\n', (8825, 8838), False, 'import yaml\n'), ((406, 456), 'os.path.join', 'os.path.join', (['BLENDERPROC_ROOT', '"""suncg_houses.txt"""'], {}), "(BLENDERPROC_ROOT, 'suncg_houses.txt')\n", (418, 456), False, 'import os\n'), ((613, 663), 'os.path.join', 'os.path.join', (['BLENDERPROC_ROOT', '"""base_config.yaml"""'], {}), "(BLENDERPROC_ROOT, 'base_config.yaml')\n", (625, 663), False, 'import os\n'), ((896, 950), 'os.path.join', 'os.path.join', (['BLENDERPROC_ROOT', '"""shapenet_objects.txt"""'], {}), "(BLENDERPROC_ROOT, 'shapenet_objects.txt')\n", (908, 950), False, 'import os\n'), ((8745, 8795), 'os.path.join', 'os.path.join', (['DEST', "(output_prefix + '/config.yaml')"], {}), "(DEST, output_prefix + '/config.yaml')\n", (8757, 8795), False, 'import os\n'), ((541, 567), 'random.choice', 'random.choice', (['house_paths'], {}), '(house_paths)\n', (554, 567), False, 'import random\n'), ((1267, 1291), 'random.choice', 'random.choice', (['obj_paths'], {}), '(obj_paths)\n', (1280, 1291), False, 'import random\n')]
|
import sys
sys.setrecursionlimit(10000000)
class LowestCommonAncedtor:
def __init__(self, G, root):
self.n = len(G)
self.tour = [0] * (2 * self.n - 1)
self.depth_list = [0] * (2 * self.n - 1)
self.id = [0] * self.n
self.visit_id = 0
self.dfs(G, root, -1, 0)
self._rmq_init(self.depth_list)
def _rmq_init(self, arr):
n = len(arr)
self.N0 = 1 << (n - 1).bit_length()
self.dat = [self.n] * (self.N0 - 1) + arr + [self.n
] * (self.N0 - n + 1)
self.index = [0] * (self.N0 - 1) + list(
range(n)) + [0] * (self.N0 - n + 1)
dat = self.dat
index = self.index
for i in range(self.N0 - 2, -1, -1):
if dat[2 * i + 1] > dat[2 * i + 2]:
dat[i] = dat[2 * i + 2]
index[i] = index[2 * i + 2]
else:
dat[i] = dat[2 * i + 1]
index[i] = index[2 * i + 1]
def _rmq_query(self, l, r):
"""最小値となるindexを返す"""
l += self.N0
r += self.N0
s = self.n
dat = self.dat
index = self.index
while l < r:
if r & 1:
r -= 1
if s > dat[r - 1]:
s = dat[r - 1]
res = index[r - 1]
if l & 1:
if s > dat[l - 1]:
s = dat[l - 1]
res = index[l - 1]
l += 1
l >>= 1
r >>= 1
return res
def dfs(self, G, vertex, parent, depth):
self.id[vertex] = self.visit_id
self.tour[self.visit_id] = vertex
self.depth_list[self.visit_id] = depth
self.visit_id += 1
for element in G[vertex]:
if element != parent:
self.dfs(G, element, vertex, depth + 1)
self.tour[self.visit_id] = vertex
self.depth_list[self.visit_id] = depth
self.visit_id += 1
def get(self, u, v):
l, r = self.id[u], self.id[v]
if r < l:
l, r = r, l
q = self._rmq_query(l, r + 1)
return self.tour[q]
def dist(self, u, v):
"""点u,点vの距離"""
lca = self.get(u, v)
depth_u = self.depth_list[self.id[u]]
depth_v = self.depth_list[self.id[v]]
depth_lca = self.depth_list[self.id[lca]]
return depth_u + depth_v - 2 * depth_lca
#問.任意の2頂点の距離
n, q = map(int, input().split())
e = [[] for _ in range(n)]
for _ in range(n - 1):
a, b = map(int, input().split())
a -= 1
b -= 1
e[a].append(b)
e[b].append(a)
lca = LowestCommonAncedtor(e, 0)
for _ in range(q):
u, v = map(int, input().split())
u -= 1
v -= 1
if lca.dist(u, v) % 2 == 1:
print("Road")
else:
print("Town")
|
[
"sys.setrecursionlimit"
] |
[((12, 43), 'sys.setrecursionlimit', 'sys.setrecursionlimit', (['(10000000)'], {}), '(10000000)\n', (33, 43), False, 'import sys\n')]
|
import h5py
import numpy as np
def load_data(fname):
# load in an hdf5 file and return the X and y values
data_file = h5py.File(fname)
# load in X and y training data, fully into memory
X = data_file['X'][:].reshape(-1, 1) # each row is a data point
y = data_file['y'][:]
return X, y
def eval_fit(y_pred, y_true):
# compute mean absolute error
mae = np.mean(np.abs(y_pred - y_true))
return mae # don't normalize
|
[
"h5py.File",
"numpy.abs"
] |
[((127, 143), 'h5py.File', 'h5py.File', (['fname'], {}), '(fname)\n', (136, 143), False, 'import h5py\n'), ((395, 418), 'numpy.abs', 'np.abs', (['(y_pred - y_true)'], {}), '(y_pred - y_true)\n', (401, 418), True, 'import numpy as np\n')]
|
import sys
import os
import urllib.parse
import urllib.request
import xml.etree.ElementTree as ET
import shutil
import sqlite3
def fetch_database(filename):
r = urllib.request.urlopen('https://nzsl-assets.vuw.ac.nz/dnzsl/freelex/publicsearch?xmldump=1')
with open(filename, "wb") as f:
f.write(r.read())
def fetch_assets(root):
for entry in root.iter("entry"):
print(entry.find("headword").text)
for asset in entry.find("ASSET"):
if ("picture" == asset.tag):
fn = os.path.join(asset.tag, asset.text)
if not os.path.exists(fn):
try:
os.makedirs(os.path.dirname(fn))
except IOError:
pass
r = urllib.request.urlopen("https://nzsl-assets.vuw.ac.nz/dnzsl/freelex/assets/" + urllib.parse.quote(asset.text))
with open(fn, "wb") as f:
f.write(r.read())
# Modify filenames to match the Android requirements (lowercase a-z and _ only)
# Since iOS uses the same data source (the .dat file), update iOS to use the same image names.
def normalize_image_filename(filename):
normalized_filename = filename.replace('-', '_').lower()
num_of_periods = normalized_filename.count('.')
if (num_of_periods > 1):
normalized_filename = normalized_filename.replace('.', '_', num_of_periods - 1)
return normalized_filename
def rename_assets(root):
for entry in root.iter("entry"):
for asset in entry.find("ASSET"):
if ("picture" == asset.tag):
old_filename = os.path.join(asset.tag, asset.text)
if not os.path.isfile(old_filename):
print("Picture {} does not exist!", old_filename)
continue
new_filename = normalize_image_filename(old_filename)
os.rename(old_filename, new_filename)
asset.text = new_filename.replace('picture/', '', 1)
def write_datfile(root):
with open("nzsl.dat", "w") as f:
for entry in root.iter("entry"):
headword = entry.attrib["id"], entry.find("headword").text
sec = entry.find("glosssecondary")
maori = entry.find("glossmaori")
picture = entry.find("ASSET/picture")
video = entry.find("ASSET/glossmain")
handshape = entry.find("handshape")
if picture is None:
print("{} missing picture".format(headword))
if video is None:
print("{} missing video".format(headword))
if handshape is None:
print("{} missing handshape".format(headword))
print("\t".join([
entry.find("glossmain").text,
sec.text if sec is not None else "",
maori.text if maori is not None else "",
os.path.basename(normalize_image_filename(picture.text)) if picture is not None else "",
"https://nzsl-assets.vuw.ac.nz/dnzsl/freelex/assets/"+video.text.replace(".webm", ".mp4") if video is not None else "",
handshape.text if handshape is not None else "",
entry.find("location").text,
]), file=f)
def write_sqlitefile():
if os.path.exists("nzsl.db"):
os.unlink("nzsl.db")
db = sqlite3.connect("nzsl.db")
db.execute("create table words (gloss, minor, maori, picture, video, handshape, location, target)")
with open("nzsl.dat") as f:
for s in f:
a = s.strip().split("\t")
a.append("{}|{}|{}".format(normalise(a[0]), normalise(a[1]), normalise(a[2])))
assert all(32 <= ord(x) < 127 for x in a[-1]), a[-1]
db.execute("insert into words values (?, ?, ?, ?, ?, ?, ?, ?)", a)
db.commit()
db.close()
def copy_images_to_one_folder():
if (os.path.isdir("assets")):
shutil.rmtree("assets")
os.makedirs("assets")
os.system("cp picture/*/*.png assets/ 2>/dev/null")
# Helper functions
def normalise(s):
return (s.lower()
.replace("ā", "a")
.replace("ē", "e")
.replace("é", "e")
.replace("ī", "i")
.replace("ō", "o")
.replace("ū", "u"))
|
[
"os.makedirs",
"os.unlink",
"os.path.isdir",
"os.rename",
"os.path.dirname",
"os.path.exists",
"os.system",
"os.path.isfile",
"sqlite3.connect",
"shutil.rmtree",
"os.path.join"
] |
[((3322, 3347), 'os.path.exists', 'os.path.exists', (['"""nzsl.db"""'], {}), "('nzsl.db')\n", (3336, 3347), False, 'import os\n'), ((3387, 3413), 'sqlite3.connect', 'sqlite3.connect', (['"""nzsl.db"""'], {}), "('nzsl.db')\n", (3402, 3413), False, 'import sqlite3\n'), ((3916, 3939), 'os.path.isdir', 'os.path.isdir', (['"""assets"""'], {}), "('assets')\n", (3929, 3939), False, 'import os\n'), ((3978, 3999), 'os.makedirs', 'os.makedirs', (['"""assets"""'], {}), "('assets')\n", (3989, 3999), False, 'import os\n'), ((4004, 4055), 'os.system', 'os.system', (['"""cp picture/*/*.png assets/ 2>/dev/null"""'], {}), "('cp picture/*/*.png assets/ 2>/dev/null')\n", (4013, 4055), False, 'import os\n'), ((3357, 3377), 'os.unlink', 'os.unlink', (['"""nzsl.db"""'], {}), "('nzsl.db')\n", (3366, 3377), False, 'import os\n'), ((3950, 3973), 'shutil.rmtree', 'shutil.rmtree', (['"""assets"""'], {}), "('assets')\n", (3963, 3973), False, 'import shutil\n'), ((531, 566), 'os.path.join', 'os.path.join', (['asset.tag', 'asset.text'], {}), '(asset.tag, asset.text)\n', (543, 566), False, 'import os\n'), ((1646, 1681), 'os.path.join', 'os.path.join', (['asset.tag', 'asset.text'], {}), '(asset.tag, asset.text)\n', (1658, 1681), False, 'import os\n'), ((1925, 1962), 'os.rename', 'os.rename', (['old_filename', 'new_filename'], {}), '(old_filename, new_filename)\n', (1934, 1962), False, 'import os\n'), ((590, 608), 'os.path.exists', 'os.path.exists', (['fn'], {}), '(fn)\n', (604, 608), False, 'import os\n'), ((1706, 1734), 'os.path.isfile', 'os.path.isfile', (['old_filename'], {}), '(old_filename)\n', (1720, 1734), False, 'import os\n'), ((671, 690), 'os.path.dirname', 'os.path.dirname', (['fn'], {}), '(fn)\n', (686, 690), False, 'import os\n')]
|
from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='tweet analyzer',
author='<NAME>',
license='',
)
|
[
"setuptools.find_packages"
] |
[((81, 96), 'setuptools.find_packages', 'find_packages', ([], {}), '()\n', (94, 96), False, 'from setuptools import find_packages, setup\n')]
|
import time
import logging
import cv2
import numpy as np
from deep_sort_realtime.deep_sort import nn_matching
from deep_sort_realtime.deep_sort.detection import Detection
from deep_sort_realtime.deep_sort.tracker import Tracker
from deep_sort_realtime.utils.nms import non_max_suppression
log_level = logging.DEBUG
default_logger = logging.getLogger('DeepSORT')
default_logger.setLevel(log_level)
handler = logging.StreamHandler()
handler.setLevel(log_level)
formatter = logging.Formatter('[%(levelname)s] [%(name)s] %(message)s')
handler.setFormatter(formatter)
default_logger.addHandler(handler)
class DeepSort(object):
def __init__(self, max_age = 30, nms_max_overlap=1.0, max_cosine_distance=0.2, nn_budget=None, override_track_class=None, clock=None, embedder=True, half=True, bgr=True, logger=None, polygon=False):
'''
Parameters
----------
max_age : Optional[int] = 30
Maximum number of missed misses before a track is deleted.
nms_max_overlap : Optional[float] = 1.0
Non-maxima suppression threshold: Maximum detection overlap, if is 1.0, nms will be disabled
max_cosine_distance : Optional[float] = 0.2
Gating threshold for cosine distance
nn_budget : Optional[int] = None
Maximum size of the appearance descriptors, if None, no budget is enforced
override_track_class : Optional[object] = None
Giving this will override default Track class, this must inherit Track
clock : Optional[object] = None
Clock custom object provides date for track naming and facilitates track id reset every day, preventing overflow and overly large track ids. For example clock class, please see `utils/clock.py`
embedder : Optional[bool] = True
Whether to use in-built embedder or not. If False, then embeddings must be given during update
half : Optional[bool] = True
Whether to use half precision for deep embedder
bgr : Optional[bool] = True
Whether frame given to embedder is expected to be BGR or not (RGB)
logger : Optional[object] = None
logger object
polygon: Optional[bool] = False
Whether detections are polygons (e.g. oriented bounding boxes)
'''
if logger is None:
self.logger = default_logger
else:
self.logger = logger
# self.video_info = video_info
# assert clock is not None
self.nms_max_overlap = nms_max_overlap
metric = nn_matching.NearestNeighborDistanceMetric(
"cosine", max_cosine_distance, nn_budget)
self.tracker = Tracker(metric, max_age = max_age, override_track_class=override_track_class, clock=clock, logger=self.logger)
if embedder:
from deep_sort_realtime.embedder.embedder_pytorch import MobileNetv2_Embedder as Embedder
self.embedder = Embedder(half=half, max_batch_size=16, bgr=bgr)
else:
self.embedder = None
self.polygon = polygon
self.logger.info('DeepSort Tracker initialised')
self.logger.info(f'- max age: {max_age}')
self.logger.info(f'- appearance threshold: {max_cosine_distance}')
self.logger.info(f'- nms threshold: {"OFF" if self.nms_max_overlap==1.0 else self.nms_max_overlap }')
self.logger.info(f'- max num of appearance features: {nn_budget}')
self.logger.info(f'- overriding track class : {"No" if override_track_class is None else "Yes"}' )
self.logger.info(f'- clock : {"No" if clock is None else "Yes"}' )
self.logger.info(f'- in-build embedder : {"No" if self.embedder is None else "Yes"}' )
self.logger.info(f'- polygon detections : {"No" if polygon is False else "Yes"}' )
def update_tracks(self, raw_detections, embeds=None, frame=None):
"""Run multi-target tracker on a particular sequence.
Parameters
----------
raw_detections (horizontal bb) : List[ Tuple[ List[float or int], float, str ] ]
List of detections, each in tuples of ( [left,top,w,h] , confidence, detection_class)
raw_detections (polygon) : List[ List[float], List[int or str], List[float] ]
List of Polygons, Classes, Confidences. All 3 sublists of the same length. A polygon defined as a ndarray-like [x1,y1,x2,y2,...].
embeds : Optional[ List[] ] = None
List of appearance features corresponding to detections
frame : Optional [ np.ndarray ] = None
if embeds not given, Image frame must be given here, in [H,W,C].
Returns
-------
list of track objects (Look into track.py for more info or see "main" section below in this script to see simple example)
"""
if embeds is None:
if self.embedder is None:
raise Exception('Embedder not created during init so embeddings must be given now!')
if frame is None:
raise Exception('either embeddings or frame must be given!')
if not self.polygon:
raw_detections = [ d for d in raw_detections if d[0][2] > 0 and d[0][3] > 0]
if embeds is None:
embeds = self.generate_embeds(frame, raw_detections)
# Proper deep sort detection objects that consist of bbox, confidence and embedding.
detections = self.create_detections(raw_detections, embeds)
else:
polygons, bounding_rects = self.process_polygons(raw_detections[0])
if embeds is None:
embeds = self.generate_embeds_poly(frame, polygons, bounding_rects)
# Proper deep sort detection objects that consist of bbox, confidence and embedding.
detections = self.create_detections_poly(raw_detections, embeds, bounding_rects)
# Run non-maxima suppression.
boxes = np.array([d.ltwh for d in detections])
scores = np.array([d.confidence for d in detections])
if self.nms_max_overlap < 1.0:
# nms_tic = time.perf_counter()
indices = non_max_suppression(
boxes, self.nms_max_overlap, scores)
# nms_toc = time.perf_counter()
# logger.debug(f'nms time: {nms_toc-nms_tic}s')
detections = [detections[i] for i in indices]
# Update tracker.
self.tracker.predict()
self.tracker.update(detections)
return self.tracker.tracks
def refresh_track_ids(self):
self.tracker._next_id
def generate_embeds(self, frame, raw_dets):
crops = self.crop_bb(frame, raw_dets)
return self.embedder.predict(crops)
def generate_embeds_poly(self, frame, polygons, bounding_rects):
crops = self.crop_poly_pad_black(frame, polygons, bounding_rects)
return self.embedder.predict(crops)
def create_detections(self, raw_dets, embeds):
detection_list = []
for raw_det, embed in zip(raw_dets,embeds):
detection_list.append(Detection(raw_det[0], raw_det[1], embed, class_name=raw_det[2])) #raw_det = [bbox, conf_score, class]
return detection_list
def create_detections_poly(self, dets, embeds, bounding_rects):
detection_list = []
dets.extend([embeds, bounding_rects])
for raw_polygon, cl, score, embed, bounding_rect in zip(*dets):
x,y,w,h = bounding_rect
x = max(0, x)
y = max(0, y)
bbox = [x,y,w,h]
detection_list.append(Detection(bbox, score, embed, class_name=cl, others=raw_polygon))
return detection_list
@staticmethod
def process_polygons(raw_polygons):
polygons = [ [ polygon[x:x+2] for x in range(0, len(polygon), 2) ]for polygon in raw_polygons ]
bounding_rects = [ cv2.boundingRect(np.array([polygon]).astype(int)) for polygon in polygons ]
return polygons, bounding_rects
@staticmethod
def crop_bb(frame, raw_dets):
crops = []
im_height, im_width = frame.shape[:2]
for detection in raw_dets:
l,t,w,h = [int(x) for x in detection[0]]
r = l + w
b = t + h
crop_l = max(0, l)
crop_r = min(im_width, r)
crop_t = max(0, t)
crop_b = min(im_height, b)
crops.append(frame[crop_t:crop_b, crop_l:crop_r])
return crops
@staticmethod
def crop_poly_pad_black(frame, polygons, bounding_rects):
masked_polys = []
im_height, im_width = frame.shape[:2]
for polygon, bounding_rect in zip(polygons, bounding_rects):
mask = np.zeros(frame.shape, dtype=np.uint8)
polygon_mask = np.array([polygon]).astype(int)
cv2.fillPoly(mask, polygon_mask, color=(255,255,255))
# apply the mask
masked_image = cv2.bitwise_and(frame, mask)
# crop masked image
x,y,w,h = bounding_rect
crop_l = max(0, x)
crop_r = min(im_width, x+w)
crop_t = max(0, y)
crop_b = min(im_height, y+h)
cropped = masked_image[crop_t:crop_b, crop_l:crop_r].copy()
masked_polys.append(np.array(cropped))
return masked_polys
|
[
"deep_sort_realtime.deep_sort.tracker.Tracker",
"deep_sort_realtime.utils.nms.non_max_suppression",
"cv2.bitwise_and",
"deep_sort_realtime.deep_sort.detection.Detection",
"logging.StreamHandler",
"numpy.zeros",
"cv2.fillPoly",
"logging.Formatter",
"deep_sort_realtime.embedder.embedder_pytorch.MobileNetv2_Embedder",
"numpy.array",
"logging.getLogger",
"deep_sort_realtime.deep_sort.nn_matching.NearestNeighborDistanceMetric"
] |
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|
import pymysql
conn = pymysql.connect(host='127.0.0.1',
user='root',
passwd='<PASSWORD>',
db='all0504')
def get_user_set():
user_set = set()
with open('../facebook/KOL_audience') as input_user_file:
for line in input_user_file:
if line.strip() == '':
continue
words = line.strip().split(';')
user_ids = set(words[1].split(' '))
user_set = user_set.union(user_ids)
return user_set
def get_item_set(num=100):
item_set = set()
count = 0
with open('../facebook/{}_list'.format(SCENARIO)) as input_item_file:
for line in input_item_file:
if line.strip() == '':
continue
count += 1
item_info = line.strip().split()
item_set.add(item_info[0])
if count == num:
break
return item_set
def read_user_item_preference(user_set):
user_item_likes = {}
x = conn.cursor()
x.execute("SELECT iduser, {}str FROM user".format(SCENARIO))
results = x.fetchall()
for result in results:
user_id = result[0]
moviestr_items = result[1].split(';')
if user_id in user_set or len(user_set) == 0:
user_item_likes[user_id] = set()
for movie_i in moviestr_items:
user_item_likes[user_id].add(movie_i)
return user_item_likes, set(user_item_likes.keys())
def read_user_relationship(user_set):
user_relationship = {}
for user in user_set:
user_relationship[user] = set()
x = conn.cursor()
x.execute("SELECT iduser, friendstr FROM user")
results = x.fetchall()
for result in results:
user_id = result[0]
if user_id not in user_set:
continue
friends = result[1].split(';')
for friend in friends:
user_relationship[user_id].add(friend)
return user_relationship
def read_item_similarity_from_file():
SIM = {}
with open("../facebook/{}_similarity".format(SCENARIO)) as inputfile:
for line in inputfile:
line = line.strip()
if len(line) > 0:
words = line.split()
item1 = words[0]
item2 = words[1]
similarity = float(words[2])
if item1 not in SIM:
SIM[item1] = {}
SIM[item1][item2] = similarity
return SIM
def calculate_user_similairity(user1, user2):
if user1 not in USER_PREF or user2 not in USER_PREF:
return 0
num_co_liked_item = len(USER_PREF[user1] & USER_PREF[user2])
if num_co_liked_item == 0:
user_sim = 0
else:
user_sim = num_co_liked_item*1.0 / (len(USER_PREF[user2])+1)
return user_sim
def get_item_similarity(item1, item2):
if item1 not in ITEM_SIM or item2 not in ITEM_SIM[item1]:
return 0
return ITEM_SIM[item1][item2]
def user_item_affinity(user_id, target_item, consider_item=True, consider_friend=True, indirect_friend=False, inindirect_friend=False): #indirect_friends: whether consider indirect friends
score = 0
for item in ITEM_SET:
if item in USER_PREF[user_id]:
score += get_item_similarity(target_item, item)
if score == 0:
return 0 # early stop the users whith no item similarity (if continue, too slow for the algorithm)
if not consider_item:
score = 0.0001
considered_f = set()
if consider_friend:
for friend in USER_RELATION[user_id]:
if friend in USER_PREF and target_item in USER_PREF[friend]:
score += calculate_user_similairity(user_id, friend)
considered_f.add(friend)
# if counting indirect friends
if indirect_friend and (friend in USER_RELATION):
friends_of_f = USER_RELATION[friend]
for friend_of_f in friends_of_f:
if (friend_of_f in USER_PREF) and (friend_of_f not in considered_f) and target_item in USER_PREF[friend_of_f]:
# score += calculate_user_similairity(user_id, friend) * calculate_user_similairity(friend, friend_of_f)
score += calculate_user_similairity(user_id, friend_of_f)
considered_f.add(friend_of_f)
# if counting inindirect friends
if inindirect_friend and (friend_of_f in USER_RELATION):
for fff in USER_RELATION[friend_of_f]:
if (fff in USER_PREF) and (fff not in considered_f) and target_item in USER_PREF[fff]:
# score += calculate_user_similairity(user_id, friend) * calculate_user_similairity(friend, friend_of_f) * calculate_user_similairity(friend_of_f, fff)
score += calculate_user_similairity(user_id, fff)
considered_f.add(fff)
return score
def output_user_item_aff():
with open("user_{}_aff_score_100_both".format(SCENARIO), "w") as outputfile:
outputfile.write('user {} score truth\n'.format(SCENARIO))
for user in USER_SET:
if user not in USER_PREF:
continue
for item in ITEM_SET:
score = user_item_affinity(user, item, consider_item=True, consider_friend=True)
isTrue = 1 if item in USER_PREF[user] else 0
if score > 0:
outputfile.write('{} {} {} {}\n'.format(user, item, score, isTrue))
def output_user_item_aff_only_item():
with open("user_{}_aff_score_100_only_item".format(SCENARIO), "w") as outputfile:
outputfile.write('user {} score truth\n'.format(SCENARIO))
for user in USER_SET:
if user not in USER_PREF:
continue
for item in ITEM_SET:
score = user_item_affinity(user, item, consider_item=True, consider_friend=False)
isTrue = 1 if item in USER_PREF[user] else 0
if score > 0:
outputfile.write('{} {} {} {}\n'.format(user, item, score, isTrue))
def output_user_item_aff_only_friend():
with open("user_{}_aff_score_100_only_friend".format(SCENARIO), "w") as outputfile:
outputfile.write('user {} score truth\n'.format(SCENARIO))
for user in USER_SET:
if user not in USER_PREF:
continue
for item in ITEM_SET:
score = user_item_affinity(user, item, consider_item=False, consider_friend=True)
isTrue = 1 if item in USER_PREF[user] else 0
if score > 0:
outputfile.write('{} {} {} {}\n'.format(user, item, score, isTrue))
if __name__ == '__main__':
SCENARIO = 'book'
print('reading user set...')
USER_SET = get_user_set()
print('reading item set...')
ITEM_SET = get_item_set()
print('reading user preference...')
USER_PREF, USER_SET = read_user_item_preference(USER_SET)
print(len(USER_PREF.keys()))
print('reading user relationship...')
USER_RELATION = read_user_relationship(USER_SET)
print('reading item similarity...')
ITEM_SIM = read_item_similarity_from_file()
print('outputing to file...')
output_user_item_aff()
#output_user_item_aff_only_item()
#output_user_item_aff_only_friend()
|
[
"pymysql.connect"
] |
[((23, 109), 'pymysql.connect', 'pymysql.connect', ([], {'host': '"""127.0.0.1"""', 'user': '"""root"""', 'passwd': '"""<PASSWORD>"""', 'db': '"""all0504"""'}), "(host='127.0.0.1', user='root', passwd='<PASSWORD>', db=\n 'all0504')\n", (38, 109), False, 'import pymysql\n')]
|
import pytest
from cutadapt.__main__ import main, parse_cutoffs, parse_lengths, CommandLineError, setup_logging
def test_help():
with pytest.raises(SystemExit) as e:
main(["--help"])
assert e.value.args[0] == 0
def test_parse_cutoffs():
assert parse_cutoffs("5") == (0, 5)
assert parse_cutoffs("6,7") == (6, 7)
with pytest.raises(CommandLineError):
parse_cutoffs("a,7")
with pytest.raises(CommandLineError):
parse_cutoffs("a")
with pytest.raises(CommandLineError):
parse_cutoffs("a,7")
with pytest.raises(CommandLineError):
parse_cutoffs("1,2,3")
def test_parse_lengths():
assert parse_lengths("25") == (25, )
assert parse_lengths("17:25") == (17, 25)
assert parse_lengths("25:") == (25, None)
assert parse_lengths(":25") == (None, 25)
with pytest.raises(CommandLineError):
parse_lengths("1:2:3")
with pytest.raises(CommandLineError):
parse_lengths("a:2")
with pytest.raises(CommandLineError):
parse_lengths("a")
with pytest.raises(CommandLineError):
parse_lengths("2:a")
with pytest.raises(CommandLineError):
parse_lengths(":")
def test_setup_logging():
import logging
logger = logging.getLogger(__name__)
setup_logging(logger, log_to_stderr=False, quiet=False, minimal=False, debug=False)
logger.info("Log message")
setup_logging(logger, log_to_stderr=False, debug=1)
setup_logging(logger, log_to_stderr=False, quiet=True)
setup_logging(logger, log_to_stderr=False, minimal=True)
|
[
"cutadapt.__main__.parse_cutoffs",
"cutadapt.__main__.main",
"cutadapt.__main__.parse_lengths",
"pytest.raises",
"cutadapt.__main__.setup_logging",
"logging.getLogger"
] |
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|
from pdf417 import encode, render_image, render_svg
import io
class BarcodeGen():
#OWN CLASS - BarcodeGen
def generateBarcode(self, text):
codes = encode(text, columns=7, security_level=4)
image = render_image(codes, scale=4, ratio=3, fg_color="black", bg_color="#FFFFFF")
image.show()
def generateBarcodeForWeb(self, text):
codes = encode(text, columns=7, security_level=4)
image = render_image(codes, scale=4, ratio=3, fg_color="black", bg_color="#FFFFFF")
return image
|
[
"pdf417.render_image",
"pdf417.encode"
] |
[((152, 193), 'pdf417.encode', 'encode', (['text'], {'columns': '(7)', 'security_level': '(4)'}), '(text, columns=7, security_level=4)\n', (158, 193), False, 'from pdf417 import encode, render_image, render_svg\n'), ((204, 279), 'pdf417.render_image', 'render_image', (['codes'], {'scale': '(4)', 'ratio': '(3)', 'fg_color': '"""black"""', 'bg_color': '"""#FFFFFF"""'}), "(codes, scale=4, ratio=3, fg_color='black', bg_color='#FFFFFF')\n", (216, 279), False, 'from pdf417 import encode, render_image, render_svg\n'), ((346, 387), 'pdf417.encode', 'encode', (['text'], {'columns': '(7)', 'security_level': '(4)'}), '(text, columns=7, security_level=4)\n', (352, 387), False, 'from pdf417 import encode, render_image, render_svg\n'), ((398, 473), 'pdf417.render_image', 'render_image', (['codes'], {'scale': '(4)', 'ratio': '(3)', 'fg_color': '"""black"""', 'bg_color': '"""#FFFFFF"""'}), "(codes, scale=4, ratio=3, fg_color='black', bg_color='#FFFFFF')\n", (410, 473), False, 'from pdf417 import encode, render_image, render_svg\n')]
|
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-deep')
# Importing the dataset
dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
# Training/testing
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3,
random_state=0)
# Regressor
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
# Visualisation
plt.scatter(X_train, y_train, color = '#31b254')
plt.plot(X_train, regressor.predict(X_train))
plt.title("Relationship between years of experience and salary")
plt.xlabel("Years of experience")
plt.ylabel("Salary")
plt.show()
|
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.show",
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.scatter",
"sklearn.linear_model.LinearRegression",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel"
] |
[((71, 100), 'matplotlib.pyplot.style.use', 'plt.style.use', (['"""seaborn-deep"""'], {}), "('seaborn-deep')\n", (84, 100), True, 'import matplotlib.pyplot as plt\n'), ((137, 167), 'pandas.read_csv', 'pd.read_csv', (['"""Salary_Data.csv"""'], {}), "('Salary_Data.csv')\n", (148, 167), True, 'import pandas as pd\n'), ((339, 394), 'sklearn.model_selection.train_test_split', 'train_test_split', (['X', 'y'], {'test_size': '(1 / 3)', 'random_state': '(0)'}), '(X, y, test_size=1 / 3, random_state=0)\n', (355, 394), False, 'from sklearn.model_selection import train_test_split\n'), ((521, 539), 'sklearn.linear_model.LinearRegression', 'LinearRegression', ([], {}), '()\n', (537, 539), False, 'from sklearn.linear_model import LinearRegression\n'), ((624, 670), 'matplotlib.pyplot.scatter', 'plt.scatter', (['X_train', 'y_train'], {'color': '"""#31b254"""'}), "(X_train, y_train, color='#31b254')\n", (635, 670), True, 'import matplotlib.pyplot as plt\n'), ((719, 783), 'matplotlib.pyplot.title', 'plt.title', (['"""Relationship between years of experience and salary"""'], {}), "('Relationship between years of experience and salary')\n", (728, 783), True, 'import matplotlib.pyplot as plt\n'), ((784, 817), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""Years of experience"""'], {}), "('Years of experience')\n", (794, 817), True, 'import matplotlib.pyplot as plt\n'), ((818, 838), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""Salary"""'], {}), "('Salary')\n", (828, 838), True, 'import matplotlib.pyplot as plt\n'), ((839, 849), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (847, 849), True, 'import matplotlib.pyplot as plt\n')]
|
"""Generated client library for firestore version v1beta1."""
# NOTE: This file is autogenerated and should not be edited by hand.
from apitools.base.py import base_api
from googlecloudsdk.third_party.apis.firestore.v1beta1 import firestore_v1beta1_messages as messages
class FirestoreV1beta1(base_api.BaseApiClient):
"""Generated client library for service firestore version v1beta1."""
MESSAGES_MODULE = messages
BASE_URL = u'https://firestore.googleapis.com/'
_PACKAGE = u'firestore'
_SCOPES = [u'https://www.googleapis.com/auth/cloud-platform', u'https://www.googleapis.com/auth/datastore']
_VERSION = u'v1beta1'
_CLIENT_ID = '1042881264118.apps.googleusercontent.com'
_CLIENT_SECRET = 'x_Tw5K8nnjoRAqULM9PFAC2b'
_USER_AGENT = 'x_Tw5K8nnjoRAqULM9PFAC2b'
_CLIENT_CLASS_NAME = u'FirestoreV1beta1'
_URL_VERSION = u'v1beta1'
_API_KEY = None
def __init__(self, url='', credentials=None,
get_credentials=True, http=None, model=None,
log_request=False, log_response=False,
credentials_args=None, default_global_params=None,
additional_http_headers=None, response_encoding=None):
"""Create a new firestore handle."""
url = url or self.BASE_URL
super(FirestoreV1beta1, self).__init__(
url, credentials=credentials,
get_credentials=get_credentials, http=http, model=model,
log_request=log_request, log_response=log_response,
credentials_args=credentials_args,
default_global_params=default_global_params,
additional_http_headers=additional_http_headers,
response_encoding=response_encoding)
self.projects_databases_documents = self.ProjectsDatabasesDocumentsService(self)
self.projects_databases_indexes = self.ProjectsDatabasesIndexesService(self)
self.projects_databases = self.ProjectsDatabasesService(self)
self.projects = self.ProjectsService(self)
class ProjectsDatabasesDocumentsService(base_api.BaseApiService):
"""Service class for the projects_databases_documents resource."""
_NAME = u'projects_databases_documents'
def __init__(self, client):
super(FirestoreV1beta1.ProjectsDatabasesDocumentsService, self).__init__(client)
self._upload_configs = {
}
def BatchGet(self, request, global_params=None):
r"""Gets multiple documents.
Documents returned by this method are not guaranteed to be returned in the
same order that they were requested.
Args:
request: (FirestoreProjectsDatabasesDocumentsBatchGetRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(BatchGetDocumentsResponse) The response message.
"""
config = self.GetMethodConfig('BatchGet')
return self._RunMethod(
config, request, global_params=global_params)
BatchGet.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents:batchGet',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.batchGet',
ordered_params=[u'database'],
path_params=[u'database'],
query_params=[],
relative_path=u'v1beta1/{+database}/documents:batchGet',
request_field=u'batchGetDocumentsRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsBatchGetRequest',
response_type_name=u'BatchGetDocumentsResponse',
supports_download=False,
)
def BeginTransaction(self, request, global_params=None):
r"""Starts a new transaction.
Args:
request: (FirestoreProjectsDatabasesDocumentsBeginTransactionRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(BeginTransactionResponse) The response message.
"""
config = self.GetMethodConfig('BeginTransaction')
return self._RunMethod(
config, request, global_params=global_params)
BeginTransaction.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents:beginTransaction',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.beginTransaction',
ordered_params=[u'database'],
path_params=[u'database'],
query_params=[],
relative_path=u'v1beta1/{+database}/documents:beginTransaction',
request_field=u'beginTransactionRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsBeginTransactionRequest',
response_type_name=u'BeginTransactionResponse',
supports_download=False,
)
def Commit(self, request, global_params=None):
r"""Commits a transaction, while optionally updating documents.
Args:
request: (FirestoreProjectsDatabasesDocumentsCommitRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(CommitResponse) The response message.
"""
config = self.GetMethodConfig('Commit')
return self._RunMethod(
config, request, global_params=global_params)
Commit.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents:commit',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.commit',
ordered_params=[u'database'],
path_params=[u'database'],
query_params=[],
relative_path=u'v1beta1/{+database}/documents:commit',
request_field=u'commitRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsCommitRequest',
response_type_name=u'CommitResponse',
supports_download=False,
)
def CreateDocument(self, request, global_params=None):
r"""Creates a new document.
Args:
request: (FirestoreProjectsDatabasesDocumentsCreateDocumentRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Document) The response message.
"""
config = self.GetMethodConfig('CreateDocument')
return self._RunMethod(
config, request, global_params=global_params)
CreateDocument.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents/{documentsId}/{collectionId}',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.createDocument',
ordered_params=[u'parent', u'collectionId'],
path_params=[u'collectionId', u'parent'],
query_params=[u'documentId', u'mask_fieldPaths'],
relative_path=u'v1beta1/{+parent}/{collectionId}',
request_field=u'document',
request_type_name=u'FirestoreProjectsDatabasesDocumentsCreateDocumentRequest',
response_type_name=u'Document',
supports_download=False,
)
def Delete(self, request, global_params=None):
r"""Deletes a document.
Args:
request: (FirestoreProjectsDatabasesDocumentsDeleteRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Empty) The response message.
"""
config = self.GetMethodConfig('Delete')
return self._RunMethod(
config, request, global_params=global_params)
Delete.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents/{documentsId}/{documentsId1}',
http_method=u'DELETE',
method_id=u'firestore.projects.databases.documents.delete',
ordered_params=[u'name'],
path_params=[u'name'],
query_params=[u'currentDocument_exists', u'currentDocument_updateTime'],
relative_path=u'v1beta1/{+name}',
request_field='',
request_type_name=u'FirestoreProjectsDatabasesDocumentsDeleteRequest',
response_type_name=u'Empty',
supports_download=False,
)
def Get(self, request, global_params=None):
r"""Gets a single document.
Args:
request: (FirestoreProjectsDatabasesDocumentsGetRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Document) The response message.
"""
config = self.GetMethodConfig('Get')
return self._RunMethod(
config, request, global_params=global_params)
Get.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents/{documentsId}/{documentsId1}',
http_method=u'GET',
method_id=u'firestore.projects.databases.documents.get',
ordered_params=[u'name'],
path_params=[u'name'],
query_params=[u'mask_fieldPaths', u'readTime', u'transaction'],
relative_path=u'v1beta1/{+name}',
request_field='',
request_type_name=u'FirestoreProjectsDatabasesDocumentsGetRequest',
response_type_name=u'Document',
supports_download=False,
)
def List(self, request, global_params=None):
r"""Lists documents.
Args:
request: (FirestoreProjectsDatabasesDocumentsListRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(ListDocumentsResponse) The response message.
"""
config = self.GetMethodConfig('List')
return self._RunMethod(
config, request, global_params=global_params)
List.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents/{documentsId}/{documentsId1}/{collectionId}',
http_method=u'GET',
method_id=u'firestore.projects.databases.documents.list',
ordered_params=[u'parent', u'collectionId'],
path_params=[u'collectionId', u'parent'],
query_params=[u'mask_fieldPaths', u'orderBy', u'pageSize', u'pageToken', u'readTime', u'showMissing', u'transaction'],
relative_path=u'v1beta1/{+parent}/{collectionId}',
request_field='',
request_type_name=u'FirestoreProjectsDatabasesDocumentsListRequest',
response_type_name=u'ListDocumentsResponse',
supports_download=False,
)
def ListCollectionIds(self, request, global_params=None):
r"""Lists all the collection IDs underneath a document.
Args:
request: (FirestoreProjectsDatabasesDocumentsListCollectionIdsRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(ListCollectionIdsResponse) The response message.
"""
config = self.GetMethodConfig('ListCollectionIds')
return self._RunMethod(
config, request, global_params=global_params)
ListCollectionIds.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents/{documentsId}/{documentsId1}:listCollectionIds',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.listCollectionIds',
ordered_params=[u'parent'],
path_params=[u'parent'],
query_params=[],
relative_path=u'v1beta1/{+parent}:listCollectionIds',
request_field=u'listCollectionIdsRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsListCollectionIdsRequest',
response_type_name=u'ListCollectionIdsResponse',
supports_download=False,
)
def Listen(self, request, global_params=None):
r"""Listens to changes.
Args:
request: (FirestoreProjectsDatabasesDocumentsListenRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(ListenResponse) The response message.
"""
config = self.GetMethodConfig('Listen')
return self._RunMethod(
config, request, global_params=global_params)
Listen.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents:listen',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.listen',
ordered_params=[u'database'],
path_params=[u'database'],
query_params=[],
relative_path=u'v1beta1/{+database}/documents:listen',
request_field=u'listenRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsListenRequest',
response_type_name=u'ListenResponse',
supports_download=False,
)
def Patch(self, request, global_params=None):
r"""Updates or inserts a document.
Args:
request: (FirestoreProjectsDatabasesDocumentsPatchRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Document) The response message.
"""
config = self.GetMethodConfig('Patch')
return self._RunMethod(
config, request, global_params=global_params)
Patch.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents/{documentsId}/{documentsId1}',
http_method=u'PATCH',
method_id=u'firestore.projects.databases.documents.patch',
ordered_params=[u'name'],
path_params=[u'name'],
query_params=[u'currentDocument_exists', u'currentDocument_updateTime', u'mask_fieldPaths', u'updateMask_fieldPaths'],
relative_path=u'v1beta1/{+name}',
request_field=u'document',
request_type_name=u'FirestoreProjectsDatabasesDocumentsPatchRequest',
response_type_name=u'Document',
supports_download=False,
)
def Rollback(self, request, global_params=None):
r"""Rolls back a transaction.
Args:
request: (FirestoreProjectsDatabasesDocumentsRollbackRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Empty) The response message.
"""
config = self.GetMethodConfig('Rollback')
return self._RunMethod(
config, request, global_params=global_params)
Rollback.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents:rollback',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.rollback',
ordered_params=[u'database'],
path_params=[u'database'],
query_params=[],
relative_path=u'v1beta1/{+database}/documents:rollback',
request_field=u'rollbackRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsRollbackRequest',
response_type_name=u'Empty',
supports_download=False,
)
def RunQuery(self, request, global_params=None):
r"""Runs a query.
Args:
request: (FirestoreProjectsDatabasesDocumentsRunQueryRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(RunQueryResponse) The response message.
"""
config = self.GetMethodConfig('RunQuery')
return self._RunMethod(
config, request, global_params=global_params)
RunQuery.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents/{documentsId}/{documentsId1}:runQuery',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.runQuery',
ordered_params=[u'parent'],
path_params=[u'parent'],
query_params=[],
relative_path=u'v1beta1/{+parent}:runQuery',
request_field=u'runQueryRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsRunQueryRequest',
response_type_name=u'RunQueryResponse',
supports_download=False,
)
def Write(self, request, global_params=None):
r"""Streams batches of document updates and deletes, in order.
Args:
request: (FirestoreProjectsDatabasesDocumentsWriteRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(WriteResponse) The response message.
"""
config = self.GetMethodConfig('Write')
return self._RunMethod(
config, request, global_params=global_params)
Write.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/documents:write',
http_method=u'POST',
method_id=u'firestore.projects.databases.documents.write',
ordered_params=[u'database'],
path_params=[u'database'],
query_params=[],
relative_path=u'v1beta1/{+database}/documents:write',
request_field=u'writeRequest',
request_type_name=u'FirestoreProjectsDatabasesDocumentsWriteRequest',
response_type_name=u'WriteResponse',
supports_download=False,
)
class ProjectsDatabasesIndexesService(base_api.BaseApiService):
"""Service class for the projects_databases_indexes resource."""
_NAME = u'projects_databases_indexes'
def __init__(self, client):
super(FirestoreV1beta1.ProjectsDatabasesIndexesService, self).__init__(client)
self._upload_configs = {
}
def Create(self, request, global_params=None):
r"""Creates the specified index.
A newly created index's initial state is `CREATING`. On completion of the
returned google.longrunning.Operation, the state will be `READY`.
If the index already exists, the call will return an `ALREADY_EXISTS`
status.
During creation, the process could result in an error, in which case the
index will move to the `ERROR` state. The process can be recovered by
fixing the data that caused the error, removing the index with
delete, then re-creating the index with
create.
Indexes with a single field cannot be created.
Args:
request: (FirestoreProjectsDatabasesIndexesCreateRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(GoogleLongrunningOperation) The response message.
"""
config = self.GetMethodConfig('Create')
return self._RunMethod(
config, request, global_params=global_params)
Create.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/indexes',
http_method=u'POST',
method_id=u'firestore.projects.databases.indexes.create',
ordered_params=[u'parent'],
path_params=[u'parent'],
query_params=[],
relative_path=u'v1beta1/{+parent}/indexes',
request_field=u'googleFirestoreAdminV1beta1Index',
request_type_name=u'FirestoreProjectsDatabasesIndexesCreateRequest',
response_type_name=u'GoogleLongrunningOperation',
supports_download=False,
)
def Delete(self, request, global_params=None):
r"""Deletes an index.
Args:
request: (FirestoreProjectsDatabasesIndexesDeleteRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Empty) The response message.
"""
config = self.GetMethodConfig('Delete')
return self._RunMethod(
config, request, global_params=global_params)
Delete.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/indexes/{indexesId}',
http_method=u'DELETE',
method_id=u'firestore.projects.databases.indexes.delete',
ordered_params=[u'name'],
path_params=[u'name'],
query_params=[],
relative_path=u'v1beta1/{+name}',
request_field='',
request_type_name=u'FirestoreProjectsDatabasesIndexesDeleteRequest',
response_type_name=u'Empty',
supports_download=False,
)
def Get(self, request, global_params=None):
r"""Gets an index.
Args:
request: (FirestoreProjectsDatabasesIndexesGetRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(GoogleFirestoreAdminV1beta1Index) The response message.
"""
config = self.GetMethodConfig('Get')
return self._RunMethod(
config, request, global_params=global_params)
Get.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/indexes/{indexesId}',
http_method=u'GET',
method_id=u'firestore.projects.databases.indexes.get',
ordered_params=[u'name'],
path_params=[u'name'],
query_params=[],
relative_path=u'v1beta1/{+name}',
request_field='',
request_type_name=u'FirestoreProjectsDatabasesIndexesGetRequest',
response_type_name=u'GoogleFirestoreAdminV1beta1Index',
supports_download=False,
)
def List(self, request, global_params=None):
r"""Lists the indexes that match the specified filters.
Args:
request: (FirestoreProjectsDatabasesIndexesListRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(GoogleFirestoreAdminV1beta1ListIndexesResponse) The response message.
"""
config = self.GetMethodConfig('List')
return self._RunMethod(
config, request, global_params=global_params)
List.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}/indexes',
http_method=u'GET',
method_id=u'firestore.projects.databases.indexes.list',
ordered_params=[u'parent'],
path_params=[u'parent'],
query_params=[u'filter', u'pageSize', u'pageToken'],
relative_path=u'v1beta1/{+parent}/indexes',
request_field='',
request_type_name=u'FirestoreProjectsDatabasesIndexesListRequest',
response_type_name=u'GoogleFirestoreAdminV1beta1ListIndexesResponse',
supports_download=False,
)
class ProjectsDatabasesService(base_api.BaseApiService):
"""Service class for the projects_databases resource."""
_NAME = u'projects_databases'
def __init__(self, client):
super(FirestoreV1beta1.ProjectsDatabasesService, self).__init__(client)
self._upload_configs = {
}
def ExportDocuments(self, request, global_params=None):
r"""Exports a copy of all or a subset of documents from Google Cloud Firestore.
to another storage system, such as Google Cloud Storage. Recent updates to
documents may not be reflected in the export. The export occurs in the
background and its progress can be monitored and managed via the
Operation resource that is created. The output of an export may only be
used once the associated operation is done. If an export operation is
cancelled before completion it may leave partial data behind in Google
Cloud Storage.
Args:
request: (FirestoreProjectsDatabasesExportDocumentsRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(GoogleLongrunningOperation) The response message.
"""
config = self.GetMethodConfig('ExportDocuments')
return self._RunMethod(
config, request, global_params=global_params)
ExportDocuments.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}:exportDocuments',
http_method=u'POST',
method_id=u'firestore.projects.databases.exportDocuments',
ordered_params=[u'name'],
path_params=[u'name'],
query_params=[],
relative_path=u'v1beta1/{+name}:exportDocuments',
request_field=u'googleFirestoreAdminV1beta1ExportDocumentsRequest',
request_type_name=u'FirestoreProjectsDatabasesExportDocumentsRequest',
response_type_name=u'GoogleLongrunningOperation',
supports_download=False,
)
def ImportDocuments(self, request, global_params=None):
r"""Imports documents into Google Cloud Firestore. Existing documents with the.
same name are overwritten. The import occurs in the background and its
progress can be monitored and managed via the Operation resource that is
created. If an ImportDocuments operation is cancelled, it is possible
that a subset of the data has already been imported to Cloud Firestore.
Args:
request: (FirestoreProjectsDatabasesImportDocumentsRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(GoogleLongrunningOperation) The response message.
"""
config = self.GetMethodConfig('ImportDocuments')
return self._RunMethod(
config, request, global_params=global_params)
ImportDocuments.method_config = lambda: base_api.ApiMethodInfo(
flat_path=u'v1beta1/projects/{projectsId}/databases/{databasesId}:importDocuments',
http_method=u'POST',
method_id=u'firestore.projects.databases.importDocuments',
ordered_params=[u'name'],
path_params=[u'name'],
query_params=[],
relative_path=u'v1beta1/{+name}:importDocuments',
request_field=u'googleFirestoreAdminV1beta1ImportDocumentsRequest',
request_type_name=u'FirestoreProjectsDatabasesImportDocumentsRequest',
response_type_name=u'GoogleLongrunningOperation',
supports_download=False,
)
class ProjectsService(base_api.BaseApiService):
"""Service class for the projects resource."""
_NAME = u'projects'
def __init__(self, client):
super(FirestoreV1beta1.ProjectsService, self).__init__(client)
self._upload_configs = {
}
|
[
"apitools.base.py.base_api.ApiMethodInfo"
] |
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(23723, 24255), False, 'from apitools.base.py import base_api\n'), ((25192, 25746), 'apitools.base.py.base_api.ApiMethodInfo', 'base_api.ApiMethodInfo', ([], {'flat_path': 'u"""v1beta1/projects/{projectsId}/databases/{databasesId}:importDocuments"""', 'http_method': 'u"""POST"""', 'method_id': 'u"""firestore.projects.databases.importDocuments"""', 'ordered_params': "[u'name']", 'path_params': "[u'name']", 'query_params': '[]', 'relative_path': 'u"""v1beta1/{+name}:importDocuments"""', 'request_field': 'u"""googleFirestoreAdminV1beta1ImportDocumentsRequest"""', 'request_type_name': 'u"""FirestoreProjectsDatabasesImportDocumentsRequest"""', 'response_type_name': 'u"""GoogleLongrunningOperation"""', 'supports_download': '(False)'}), "(flat_path=\n u'v1beta1/projects/{projectsId}/databases/{databasesId}:importDocuments',\n http_method=u'POST', method_id=\n u'firestore.projects.databases.importDocuments', ordered_params=[\n u'name'], path_params=[u'name'], query_params=[], relative_path=\n u'v1beta1/{+name}:importDocuments', request_field=\n u'googleFirestoreAdminV1beta1ImportDocumentsRequest', request_type_name\n =u'FirestoreProjectsDatabasesImportDocumentsRequest',\n response_type_name=u'GoogleLongrunningOperation', supports_download=False)\n", (25214, 25746), False, 'from apitools.base.py import base_api\n')]
|
from django.conf.urls import url
from .views import (subjectpool_index, manage_experiment_session, get_session_events, manage_participant_attendance,
send_invitations, get_invitations_count, invite_email_preview, experiment_session_signup,
submit_experiment_session_signup, cancel_experiment_session_signup, download_experiment_session,
add_participant)
urlpatterns = [
url(r'^$', subjectpool_index, name='subjectpool_index'),
url(r'^session/manage/(?P<pk>\-?\d+)$', manage_experiment_session, name='manage_experiment_session'),
url(r'^session/detail/event/(?P<pk>\d+)$', manage_participant_attendance, name='session_event_detail'),
url(r'^session/(?P<pk>\d+)/participant/add/$', add_participant, name='add_participant'),
url(r'^session/invite$', send_invitations, name='send_invites'),
url(r'^session/invite/count$', get_invitations_count, name='get_invitations_count'),
url(r'^session/email-preview$', invite_email_preview, name='invite_email_preview'),
url(r'^session/(?P<pk>\d+)/download/$', download_experiment_session, name='download_experiment_session'),
url(r'^session/events$', get_session_events, name='session_events'),
url(r'^signup/$', experiment_session_signup, name='experiment_session_signup'),
url(r'^signup/submit/$', submit_experiment_session_signup, name='submit_experiment_session_signup'),
url(r'^signup/cancel/$', cancel_experiment_session_signup, name='cancel_experiment_session_signup'),
]
|
[
"django.conf.urls.url"
] |
[((436, 490), 'django.conf.urls.url', 'url', (['"""^$"""', 'subjectpool_index'], {'name': '"""subjectpool_index"""'}), "('^$', subjectpool_index, name='subjectpool_index')\n", (439, 490), False, 'from django.conf.urls import url\n'), ((497, 603), 'django.conf.urls.url', 'url', (['"""^session/manage/(?P<pk>\\\\-?\\\\d+)$"""', 'manage_experiment_session'], {'name': '"""manage_experiment_session"""'}), "('^session/manage/(?P<pk>\\\\-?\\\\d+)$', manage_experiment_session, name=\n 'manage_experiment_session')\n", (500, 603), False, 'from django.conf.urls import url\n'), ((603, 709), 'django.conf.urls.url', 'url', (['"""^session/detail/event/(?P<pk>\\\\d+)$"""', 'manage_participant_attendance'], {'name': '"""session_event_detail"""'}), "('^session/detail/event/(?P<pk>\\\\d+)$', manage_participant_attendance,\n name='session_event_detail')\n", (606, 709), False, 'from django.conf.urls import url\n'), ((711, 803), 'django.conf.urls.url', 'url', (['"""^session/(?P<pk>\\\\d+)/participant/add/$"""', 'add_participant'], {'name': '"""add_participant"""'}), "('^session/(?P<pk>\\\\d+)/participant/add/$', add_participant, name=\n 'add_participant')\n", (714, 803), False, 'from django.conf.urls import url\n'), ((804, 866), 'django.conf.urls.url', 'url', (['"""^session/invite$"""', 'send_invitations'], {'name': '"""send_invites"""'}), "('^session/invite$', send_invitations, name='send_invites')\n", (807, 866), False, 'from django.conf.urls import url\n'), ((873, 960), 'django.conf.urls.url', 'url', (['"""^session/invite/count$"""', 'get_invitations_count'], {'name': '"""get_invitations_count"""'}), "('^session/invite/count$', get_invitations_count, name=\n 'get_invitations_count')\n", (876, 960), False, 'from django.conf.urls import url\n'), ((962, 1048), 'django.conf.urls.url', 'url', (['"""^session/email-preview$"""', 'invite_email_preview'], {'name': '"""invite_email_preview"""'}), "('^session/email-preview$', invite_email_preview, name=\n 'invite_email_preview')\n", (965, 1048), False, 'from django.conf.urls import url\n'), ((1050, 1159), 'django.conf.urls.url', 'url', (['"""^session/(?P<pk>\\\\d+)/download/$"""', 'download_experiment_session'], {'name': '"""download_experiment_session"""'}), "('^session/(?P<pk>\\\\d+)/download/$', download_experiment_session, name=\n 'download_experiment_session')\n", (1053, 1159), False, 'from django.conf.urls import url\n'), ((1160, 1226), 'django.conf.urls.url', 'url', (['"""^session/events$"""', 'get_session_events'], {'name': '"""session_events"""'}), "('^session/events$', get_session_events, name='session_events')\n", (1163, 1226), False, 'from django.conf.urls import url\n'), ((1234, 1311), 'django.conf.urls.url', 'url', (['"""^signup/$"""', 'experiment_session_signup'], {'name': '"""experiment_session_signup"""'}), "('^signup/$', experiment_session_signup, name='experiment_session_signup')\n", (1237, 1311), False, 'from django.conf.urls import url\n'), ((1318, 1421), 'django.conf.urls.url', 'url', (['"""^signup/submit/$"""', 'submit_experiment_session_signup'], {'name': '"""submit_experiment_session_signup"""'}), "('^signup/submit/$', submit_experiment_session_signup, name=\n 'submit_experiment_session_signup')\n", (1321, 1421), False, 'from django.conf.urls import url\n'), ((1423, 1526), 'django.conf.urls.url', 'url', (['"""^signup/cancel/$"""', 'cancel_experiment_session_signup'], {'name': '"""cancel_experiment_session_signup"""'}), "('^signup/cancel/$', cancel_experiment_session_signup, name=\n 'cancel_experiment_session_signup')\n", (1426, 1526), False, 'from django.conf.urls import url\n')]
|
import math
from vectors import Vector2
from vectors import Vector3
def get_car_facing_vector(car):
pitch = float(car.rotation.pitch)
yaw = float(car.rotation.yaw)
facing_x = math.cos(pitch) * math.cos(yaw)
facing_y = math.cos(pitch) * math.sin(yaw)
return Vector2(facing_x, facing_y)
def get_own_goal(agent):
car = agent.car
field_info = agent.get_field_info()
team = 0
if field_info.goals[team].team_num != car.team: team = 1
return Vector3(field_info.goals[team].location)
def get_opponents_goal(agent):
car = agent.car
field_info = agent.get_field_info()
goal = Vector2(0, 0)
team = 1
if field_info.goals[team].team_num == car.team: team = 0
return Vector3(field_info.goals[team].location)
def time_needed_for_car(agent, car_to):
car = agent.car
difference = car.pos - car_to
length = difference.magnitude()
speed = get_xy_speed(agent)
if speed == 0: speed = 0.00000000000000001
duration = length/speed
return duration
def predict_time_needed_for_car(agent, car_to):
car = agent.car
difference = car.pos - car_to
length = difference.magnitude()
speed = 1500
if speed == 0: speed = 0.00000000000000001
duration = length/speed
return duration
def own_color(self, packet):
# get right color
if packet.game_cars[self.index].team:
color = self.renderer.create_color(255, 255, 127, 80)
else:
color = self.renderer.create_color(255, 22, 138, 255)
return color
def get_xy_speed(agent):
car = agent.car
car_xy_velocity = Vector3(car.velocity).to_2d()
car_xy_velocity_magnitude = car_xy_velocity.magnitude()
return car_xy_velocity_magnitude
def difference_angles(angle1, angle2):
angle1 = math.degrees(angle1)
angle2 = math.degrees(angle2)
angle1 = normalize_angle(angle1)
angle2 = normalize_angle(angle2)
difference = angle1 - angle2
if difference > 180: difference = 360 - difference
return math.radians(difference)
def normalize_angle(angle):
while angle < 0: angle += 360
while angle >= 360: angle -= 360
return angle
def get_car_speed(self, packet):
my_car = packet.game_cars[self.index]
def aim_to(agent, to, plus=0):
car = agent.car
car_direction = get_car_facing_vector(car)
magnitude = Vector3(car.pos - to).magnitude()
steer_correction = car_direction.correction_to(to.to_2d() - car.pos.to_2d())
z_correction = Vector3(car.pos - to).angle('z')
draw_text(agent, str(math.degrees(z_correction)), 100)
steer_correction *= -5
steer_correction += plus
# aerial
if to.z - car.pos.z > 500 and car.boost > 50 and agent.car_status != 'dribble':
if math.degrees(z_correction) > 10 and to.z - car.pos.z > 500:
# jump if still on ground
if car.pos.z < 17.1:
agent.jumps.append(1)
print(car.pos.x, car.pos.y)
# enable boost
agent.controller_state.boost = True
# sigmoid and correct
agent.controller_state.pitch = cap_num((z_correction-car.rotation.pitch)+0.9, -1, 1)
# if close to going to fly stop boost
elif math.degrees(z_correction) > 4:
agent.controller_state.boost = False
# Drift if needs to steer much
if abs(steer_correction) > 7:
agent.controller_state.handbrake = True
agent.controller_state.steer = cap_num(steer_correction, -1, 1)
def double_jump(self):
self.jumps.append(1)
self.jumps.append(3)
return self
def more_colors(agent, color):
if color == 'black': color = [255,255,255]
if color == 'white': color = [0,0,0]
if color == 'red': color = [255,0,0]
if color == 'blue': color = [0,0,255]
if color == 'green': color = [0,255,0]
if color == 'own':
if agent.car.team:
color = [255, 127, 80]
else:
color = [22, 138, 255]
return color
def draw_text(agent, text, y):
agent.renderer.draw_string_2d(0, y, 1, 1, text,agent.renderer.create_color(255, 255, 255, 255))
def sigmoid(x):
return (1 / (1 + math.exp(-x)))*2 - 1
def cap_num(x, mini, maxi):
if x > maxi: x = maxi
if x < mini: x = mini
return x
def black(agent):
return agent.renderer.create_color(255, 0, 0, 0)
def white(agent):
return agent.renderer.create_color(255, 255, 255, 255)
|
[
"math.exp",
"vectors.Vector2",
"math.radians",
"math.sin",
"math.cos",
"math.degrees",
"vectors.Vector3"
] |
[((280, 307), 'vectors.Vector2', 'Vector2', (['facing_x', 'facing_y'], {}), '(facing_x, facing_y)\n', (287, 307), False, 'from vectors import Vector2\n'), ((479, 519), 'vectors.Vector3', 'Vector3', (['field_info.goals[team].location'], {}), '(field_info.goals[team].location)\n', (486, 519), False, 'from vectors import Vector3\n'), ((623, 636), 'vectors.Vector2', 'Vector2', (['(0)', '(0)'], {}), '(0, 0)\n', (630, 636), False, 'from vectors import Vector2\n'), ((722, 762), 'vectors.Vector3', 'Vector3', (['field_info.goals[team].location'], {}), '(field_info.goals[team].location)\n', (729, 762), False, 'from vectors import Vector3\n'), ((1766, 1786), 'math.degrees', 'math.degrees', (['angle1'], {}), '(angle1)\n', (1778, 1786), False, 'import math\n'), ((1800, 1820), 'math.degrees', 'math.degrees', (['angle2'], {}), '(angle2)\n', (1812, 1820), False, 'import math\n'), ((1994, 2018), 'math.radians', 'math.radians', (['difference'], {}), '(difference)\n', (2006, 2018), False, 'import math\n'), ((189, 204), 'math.cos', 'math.cos', (['pitch'], {}), '(pitch)\n', (197, 204), False, 'import math\n'), ((207, 220), 'math.cos', 'math.cos', (['yaw'], {}), '(yaw)\n', (215, 220), False, 'import math\n'), ((236, 251), 'math.cos', 'math.cos', (['pitch'], {}), '(pitch)\n', (244, 251), False, 'import math\n'), ((254, 267), 'math.sin', 'math.sin', (['yaw'], {}), '(yaw)\n', (262, 267), False, 'import math\n'), ((1586, 1607), 'vectors.Vector3', 'Vector3', (['car.velocity'], {}), '(car.velocity)\n', (1593, 1607), False, 'from vectors import Vector3\n'), ((2329, 2350), 'vectors.Vector3', 'Vector3', (['(car.pos - to)'], {}), '(car.pos - to)\n', (2336, 2350), False, 'from vectors import Vector3\n'), ((2463, 2484), 'vectors.Vector3', 'Vector3', (['(car.pos - to)'], {}), '(car.pos - to)\n', (2470, 2484), False, 'from vectors import Vector3\n'), ((2521, 2547), 'math.degrees', 'math.degrees', (['z_correction'], {}), '(z_correction)\n', (2533, 2547), False, 'import math\n'), ((2720, 2746), 'math.degrees', 'math.degrees', (['z_correction'], {}), '(z_correction)\n', (2732, 2746), False, 'import math\n'), ((3198, 3224), 'math.degrees', 'math.degrees', (['z_correction'], {}), '(z_correction)\n', (3210, 3224), False, 'import math\n'), ((4122, 4134), 'math.exp', 'math.exp', (['(-x)'], {}), '(-x)\n', (4130, 4134), False, 'import math\n')]
|
import cv2
import os
import numpy as np
from PIL import Image
import picamera.array
from picamera import PiCamera
class Face(object):
training_count = 5
threshold = 30
def __init__(self, casc_path, path="./passwords", camera_port=0):
self.path = path
self._cascade = cv2.CascadeClassifier(casc_path)
self._port = camera_port
def __del__(self):
cv2.destroyAllWindows()
def _capture_image(self):
"""
Throw away frames so we can let the camera adjust
:return: list(list())
"""
with PiCamera() as camera:
with picamera.array.PiRGBArray(camera) as stream:
camera.resolution = (640, 480)
camera.capture(stream, 'bgr', use_video_port=True)
return cv2.cv.cvtColor(stream.array, cv2.COLOR_BGR2GRAY)
def _get_faces_and_frames(self):
frame = self._capture_image()
faces = self._cascade.detectMultiScale(
frame,
scaleFactor=1.2,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE
)
return faces, frame
def _get_training_faf(self):
"""
:yield: faces, frame
Gets all images required for training.
* Note
Won't stop getting images unless there is only one face per image.
"""
count = 0
error_count = 0
while count < self.training_count: # Ensures that we get at least self.training_count images
error_count += 1
faces, frame = self._get_faces_and_frames()
if len(faces) == 1:
yield faces, frame
count += 1
elif error_count >= 10:
break
def can_unlock(self):
"""
Will return false under the following conditions:
1. More than one face in the image
2. No images in password file
3. Face is not recognized
:return: True if face is recognized False if face is not recognized
"""
face, frame = self._get_faces_and_frames()
# Don't allow more than 1 face in the image
if len(face) != 1:
return False
x, y, w, h = face[0]
face = frame[y: y + h, x: x + w]
recognizer = cv2.face.createLBPHFaceRecognizer()
paths = [os.path.join(self.path, f) for f in os.listdir(self.path) if f.endswith("bmp")]
if not paths:
return False # Return since there are no images saved as a password
# images will contains face images
images = []
# labels will contains the label that is assigned to the image
labels = []
nbr = 0
for image_path in paths:
# Read the image
image_pil = Image.open(image_path)
# Convert the image format into numpy array
image = np.array(image_pil, 'uint8')
images.append(image)
labels.append(nbr)
nbr += 1
cv2.destroyAllWindows()
# Perform the tranining
recognizer.train(images, np.array(labels))
nbr_predicted, conf = recognizer.predict(face)
if conf < self.threshold:
return True
return False
def new_pass(self):
count = 0
for face, frame in self._get_training_faf():
filename = "".join(["passwords/", str(count), ".bmp"])
x, y, w, h = face[0]
frame = frame[y: y + h, x: x + w]
count += 1
cv2.imwrite(filename, frame)
def secure_new_pass(self):
if self.can_unlock():
self.new_pass()
|
[
"cv2.cv.cvtColor",
"cv2.imwrite",
"PIL.Image.open",
"cv2.face.createLBPHFaceRecognizer",
"numpy.array",
"cv2.CascadeClassifier",
"cv2.destroyAllWindows",
"os.path.join",
"os.listdir",
"picamera.PiCamera"
] |
[((301, 333), 'cv2.CascadeClassifier', 'cv2.CascadeClassifier', (['casc_path'], {}), '(casc_path)\n', (322, 333), False, 'import cv2\n'), ((399, 422), 'cv2.destroyAllWindows', 'cv2.destroyAllWindows', ([], {}), '()\n', (420, 422), False, 'import cv2\n'), ((2335, 2370), 'cv2.face.createLBPHFaceRecognizer', 'cv2.face.createLBPHFaceRecognizer', ([], {}), '()\n', (2368, 2370), False, 'import cv2\n'), ((3054, 3077), 'cv2.destroyAllWindows', 'cv2.destroyAllWindows', ([], {}), '()\n', (3075, 3077), False, 'import cv2\n'), ((579, 589), 'picamera.PiCamera', 'PiCamera', ([], {}), '()\n', (587, 589), False, 'from picamera import PiCamera\n'), ((2389, 2415), 'os.path.join', 'os.path.join', (['self.path', 'f'], {}), '(self.path, f)\n', (2401, 2415), False, 'import os\n'), ((2831, 2853), 'PIL.Image.open', 'Image.open', (['image_path'], {}), '(image_path)\n', (2841, 2853), False, 'from PIL import Image\n'), ((2930, 2958), 'numpy.array', 'np.array', (['image_pil', '"""uint8"""'], {}), "(image_pil, 'uint8')\n", (2938, 2958), True, 'import numpy as np\n'), ((3143, 3159), 'numpy.array', 'np.array', (['labels'], {}), '(labels)\n', (3151, 3159), True, 'import numpy as np\n'), ((3577, 3605), 'cv2.imwrite', 'cv2.imwrite', (['filename', 'frame'], {}), '(filename, frame)\n', (3588, 3605), False, 'import cv2\n'), ((800, 849), 'cv2.cv.cvtColor', 'cv2.cv.cvtColor', (['stream.array', 'cv2.COLOR_BGR2GRAY'], {}), '(stream.array, cv2.COLOR_BGR2GRAY)\n', (815, 849), False, 'import cv2\n'), ((2425, 2446), 'os.listdir', 'os.listdir', (['self.path'], {}), '(self.path)\n', (2435, 2446), False, 'import os\n')]
|
# -*- coding: utf-8 -*-
'''
Mrknow TV Add-on
Copyright (C) 2016 mrknow
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
import urlparse,base64,urllib
import re, time, datetime
import json
from resources.lib.lib import control
from resources.lib.lib import client
from resources.lib.lib import stale
def get(url, params={}):
try:
params['api_id'] = stale.pierwszatv_apiid
params['checksum'] = stale.pierwszatv_checksum
url = urlparse.urljoin('http://pierwsza.tv', url)
url = url + '?' + urllib.urlencode(params)
headers = {'Content-Type': 'application/json'}
result = client.request(url, headers=headers, output='response', error=True)
if not (result[0] == '401' or result[0] == '405'): return result[1]
result = client.request(url, headers=headers)
#control.log('ZZZZZZZZ PIerwsza result: %s' % result)
return result
except:
pass
def getstream(id):
try:
control.set_setting('pierwszatv.tokenExpireIn', '')
control.set_setting('pierwszatv.serverId', '')
control.set_setting('pierwszatv.streamId', '')
control.set_setting('pierwszatv.token', '')
if getPierwszaCredentialsInfo() == False:
if control.yesnoDialog(control.lang(40003).encode('utf-8'), control.lang(30481).encode('utf-8'), '', 'Trakt', control.lang(30483).encode('utf-8'), control.lang(30482).encode('utf-8')):
control.set_setting('pierwszatv.user', '')
control.set_setting('pierwszatv.password', '')
control.openSettings('1.4')
raise Exception()
url = '/api/stream/create'
params = {}
params['id'] =id
params['user'] =control.setting('pierwszatv.user').strip()
params['password'] = urllib.quote_plus(control.setting('pierwszatv.password'))
result = get(url, params)
control.log('x1x1x1: %s' % result)
result = json.loads(result)
if result['status'] == 'ok':
#time.sleep(1)
expirein = int(int(result['tokenExpireIn'])*0.75)
expirewhen = datetime.datetime.now() + datetime.timedelta(seconds=expirein)
control.set_setting('pierwszatv.tokenExpireIn', str(int(time.mktime(expirewhen.timetuple()))))
control.set_setting('pierwszatv.serverId', result['serverId'])
control.set_setting('pierwszatv.streamId', result['streamId'])
control.set_setting('pierwszatv.token', result['token'])
for i in range(0, 5):
try:
r = get('/api/stream/status', {'serverId': result['serverId'] , 'streamId': result['streamId'], 'token': result['token']})
r = json.loads(r)
if r['status'] == 'ok':
#control.infoDialog(control.lang(30489).encode('utf-8'), time=6000)
for j in range(0, 20):
time.sleep(1)
control.infoDialog(control.lang(30489).encode('utf-8'), time=500)
try:
result2 = client.request(r['source']+'?token='+result['token'],safe=True, timeout='2')
control.log('Pierwsza link check nr: %s: result:%s' % (j,result2))
if result2 == None: raise Exception()
else: return r['source']+'?token='+result['token']
except:
pass
return r['source']+'?token='+result['token']
time.sleep(3)
except:
pass
if result['status'] == 'error':
control.infoDialog('%s' % result['message'].encode('utf-8'))
control.dialog.ok(control.addonInfo('name'), result['message'].encode('utf-8'), '')
return None
except Exception as e:
control.log('Error pierwsza.getstream %s' % e )
def getPierwszaCredentialsInfo():
user = control.setting('pierwszatv.user').strip()
password = control.setting('pierwszatv.password')
if (user == '' or password == ''): return False
return True
def streamrefresh():
try:
#mynow = int(datetime.datetime.now().strftime('%s'))
mynow = int(str(int(time.mktime(datetime.datetime.now().timetuple()))))
expired = int(control.get_setting('pierwszatv.tokenExpireIn'))
#control.log('XXXX Exp:%s Now:%s' % (expired, mynow))
if mynow>expired:
control.log('Pierwsza refresh')
url = '/api/stream/refresh'
params = {}
params['serverId'] =control.get_setting('pierwszatv.serverId')
params['streamId'] =control.get_setting('pierwszatv.streamId')
params['token'] = control.get_setting('pierwszatv.token')
result = get(url, params)
result = json.loads(result)
expirein = int(int(result['tokenExpireIn'])*0.75)
expirewhen = datetime.datetime.now() + datetime.timedelta(seconds=expirein)
control.set_setting('pierwszatv.tokenExpireIn', str(int(time.mktime(expirewhen.timetuple()))))
except Exception as e:
control.log('Error pierwsza.refresh %s' % e )
raise Exception()
def chanels():
items = []
try:
result = get('/api/channels')
result = json.loads(result)
for i in result['channels']:
try:
items.append(i)
except:
pass
if len(items) == 0:
items = result
except:
control.log('Error pierwsza.chanels' )
pass
return items
|
[
"resources.lib.lib.control.setting",
"resources.lib.lib.control.log",
"json.loads",
"resources.lib.lib.control.addonInfo",
"resources.lib.lib.control.lang",
"resources.lib.lib.control.get_setting",
"datetime.datetime.now",
"time.sleep",
"urlparse.urljoin",
"datetime.timedelta",
"urllib.urlencode",
"resources.lib.lib.control.set_setting",
"resources.lib.lib.client.request",
"resources.lib.lib.control.openSettings"
] |
[((4747, 4785), 'resources.lib.lib.control.setting', 'control.setting', (['"""pierwszatv.password"""'], {}), "('pierwszatv.password')\n", (4762, 4785), False, 'from resources.lib.lib import control\n'), ((1075, 1118), 'urlparse.urljoin', 'urlparse.urljoin', (['"""http://pierwsza.tv"""', 'url'], {}), "('http://pierwsza.tv', url)\n", (1091, 1118), False, 'import urlparse, base64, urllib\n'), ((1243, 1310), 'resources.lib.lib.client.request', 'client.request', (['url'], {'headers': 'headers', 'output': '"""response"""', 'error': '(True)'}), "(url, headers=headers, output='response', error=True)\n", (1257, 1310), False, 'from resources.lib.lib import client\n'), ((1405, 1441), 'resources.lib.lib.client.request', 'client.request', (['url'], {'headers': 'headers'}), '(url, headers=headers)\n', (1419, 1441), False, 'from resources.lib.lib import client\n'), ((1589, 1640), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.tokenExpireIn"""', '""""""'], {}), "('pierwszatv.tokenExpireIn', '')\n", (1608, 1640), False, 'from resources.lib.lib import control\n'), ((1649, 1695), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.serverId"""', '""""""'], {}), "('pierwszatv.serverId', '')\n", (1668, 1695), False, 'from resources.lib.lib import control\n'), ((1704, 1750), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.streamId"""', '""""""'], {}), "('pierwszatv.streamId', '')\n", (1723, 1750), False, 'from resources.lib.lib import control\n'), ((1759, 1802), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.token"""', '""""""'], {}), "('pierwszatv.token', '')\n", (1778, 1802), False, 'from resources.lib.lib import control\n'), ((2525, 2559), 'resources.lib.lib.control.log', 'control.log', (["('x1x1x1: %s' % result)"], {}), "('x1x1x1: %s' % result)\n", (2536, 2559), False, 'from resources.lib.lib import control\n'), ((2577, 2595), 'json.loads', 'json.loads', (['result'], {}), '(result)\n', (2587, 2595), False, 'import json\n'), ((6052, 6070), 'json.loads', 'json.loads', (['result'], {}), '(result)\n', (6062, 6070), False, 'import json\n'), ((1145, 1169), 'urllib.urlencode', 'urllib.urlencode', (['params'], {}), '(params)\n', (1161, 1169), False, 'import urlparse, base64, urllib\n'), ((2442, 2480), 'resources.lib.lib.control.setting', 'control.setting', (['"""pierwszatv.password"""'], {}), "('pierwszatv.password')\n", (2457, 2480), False, 'from resources.lib.lib import control\n'), ((2932, 2994), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.serverId"""', "result['serverId']"], {}), "('pierwszatv.serverId', result['serverId'])\n", (2951, 2994), False, 'from resources.lib.lib import control\n'), ((3007, 3069), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.streamId"""', "result['streamId']"], {}), "('pierwszatv.streamId', result['streamId'])\n", (3026, 3069), False, 'from resources.lib.lib import control\n'), ((3082, 3138), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.token"""', "result['token']"], {}), "('pierwszatv.token', result['token'])\n", (3101, 3138), False, 'from resources.lib.lib import control\n'), ((4594, 4640), 'resources.lib.lib.control.log', 'control.log', (["('Error pierwsza.getstream %s' % e)"], {}), "('Error pierwsza.getstream %s' % e)\n", (4605, 4640), False, 'from resources.lib.lib import control\n'), ((4689, 4723), 'resources.lib.lib.control.setting', 'control.setting', (['"""pierwszatv.user"""'], {}), "('pierwszatv.user')\n", (4704, 4723), False, 'from resources.lib.lib import control\n'), ((5048, 5095), 'resources.lib.lib.control.get_setting', 'control.get_setting', (['"""pierwszatv.tokenExpireIn"""'], {}), "('pierwszatv.tokenExpireIn')\n", (5067, 5095), False, 'from resources.lib.lib import control\n'), ((5198, 5229), 'resources.lib.lib.control.log', 'control.log', (['"""Pierwsza refresh"""'], {}), "('Pierwsza refresh')\n", (5209, 5229), False, 'from resources.lib.lib import control\n'), ((5326, 5368), 'resources.lib.lib.control.get_setting', 'control.get_setting', (['"""pierwszatv.serverId"""'], {}), "('pierwszatv.serverId')\n", (5345, 5368), False, 'from resources.lib.lib import control\n'), ((5401, 5443), 'resources.lib.lib.control.get_setting', 'control.get_setting', (['"""pierwszatv.streamId"""'], {}), "('pierwszatv.streamId')\n", (5420, 5443), False, 'from resources.lib.lib import control\n'), ((5474, 5513), 'resources.lib.lib.control.get_setting', 'control.get_setting', (['"""pierwszatv.token"""'], {}), "('pierwszatv.token')\n", (5493, 5513), False, 'from resources.lib.lib import control\n'), ((5573, 5591), 'json.loads', 'json.loads', (['result'], {}), '(result)\n', (5583, 5591), False, 'import json\n'), ((5884, 5928), 'resources.lib.lib.control.log', 'control.log', (["('Error pierwsza.refresh %s' % e)"], {}), "('Error pierwsza.refresh %s' % e)\n", (5895, 5928), False, 'from resources.lib.lib import control\n'), ((6273, 6310), 'resources.lib.lib.control.log', 'control.log', (['"""Error pierwsza.chanels"""'], {}), "('Error pierwsza.chanels')\n", (6284, 6310), False, 'from resources.lib.lib import control\n'), ((2067, 2109), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.user"""', '""""""'], {}), "('pierwszatv.user', '')\n", (2086, 2109), False, 'from resources.lib.lib import control\n'), ((2126, 2172), 'resources.lib.lib.control.set_setting', 'control.set_setting', (['"""pierwszatv.password"""', '""""""'], {}), "('pierwszatv.password', '')\n", (2145, 2172), False, 'from resources.lib.lib import control\n'), ((2189, 2216), 'resources.lib.lib.control.openSettings', 'control.openSettings', (['"""1.4"""'], {}), "('1.4')\n", (2209, 2216), False, 'from resources.lib.lib import control\n'), ((2352, 2386), 'resources.lib.lib.control.setting', 'control.setting', (['"""pierwszatv.user"""'], {}), "('pierwszatv.user')\n", (2367, 2386), False, 'from resources.lib.lib import control\n'), ((2750, 2773), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (2771, 2773), False, 'import re, time, datetime\n'), ((2776, 2812), 'datetime.timedelta', 'datetime.timedelta', ([], {'seconds': 'expirein'}), '(seconds=expirein)\n', (2794, 2812), False, 'import re, time, datetime\n'), ((4471, 4496), 'resources.lib.lib.control.addonInfo', 'control.addonInfo', (['"""name"""'], {}), "('name')\n", (4488, 4496), False, 'from resources.lib.lib import control\n'), ((5679, 5702), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (5700, 5702), False, 'import re, time, datetime\n'), ((5705, 5741), 'datetime.timedelta', 'datetime.timedelta', ([], {'seconds': 'expirein'}), '(seconds=expirein)\n', (5723, 5741), False, 'import re, time, datetime\n'), ((3362, 3375), 'json.loads', 'json.loads', (['r'], {}), '(r)\n', (3372, 3375), False, 'import json\n'), ((4264, 4277), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (4274, 4277), False, 'import re, time, datetime\n'), ((1889, 1908), 'resources.lib.lib.control.lang', 'control.lang', (['(40003)'], {}), '(40003)\n', (1901, 1908), False, 'from resources.lib.lib import control\n'), ((1926, 1945), 'resources.lib.lib.control.lang', 'control.lang', (['(30481)'], {}), '(30481)\n', (1938, 1945), False, 'from resources.lib.lib import control\n'), ((1976, 1995), 'resources.lib.lib.control.lang', 'control.lang', (['(30483)'], {}), '(30483)\n', (1988, 1995), False, 'from resources.lib.lib import control\n'), ((2013, 2032), 'resources.lib.lib.control.lang', 'control.lang', (['(30482)'], {}), '(30482)\n', (2025, 2032), False, 'from resources.lib.lib import control\n'), ((3587, 3600), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (3597, 3600), False, 'import re, time, datetime\n'), ((3770, 3855), 'resources.lib.lib.client.request', 'client.request', (["(r['source'] + '?token=' + result['token'])"], {'safe': '(True)', 'timeout': '"""2"""'}), "(r['source'] + '?token=' + result['token'], safe=True,\n timeout='2')\n", (3784, 3855), False, 'from resources.lib.lib import client\n'), ((3879, 3946), 'resources.lib.lib.control.log', 'control.log', (["('Pierwsza link check nr: %s: result:%s' % (j, result2))"], {}), "('Pierwsza link check nr: %s: result:%s' % (j, result2))\n", (3890, 3946), False, 'from resources.lib.lib import control\n'), ((4986, 5009), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (5007, 5009), False, 'import re, time, datetime\n'), ((3648, 3667), 'resources.lib.lib.control.lang', 'control.lang', (['(30489)'], {}), '(30489)\n', (3660, 3667), False, 'from resources.lib.lib import control\n')]
|
# Generated by Django 3.2.11 on 2022-02-02 01:04
import django.contrib.gis.db.models.fields
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Property',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('direction', models.CharField(max_length=180)),
('number', models.PositiveIntegerField(blank=True, null=True)),
('location', django.contrib.gis.db.models.fields.PointField(blank=True, null=True, srid=4326)),
],
),
]
|
[
"django.db.models.BigAutoField",
"django.db.models.PositiveIntegerField",
"django.db.models.CharField"
] |
[((348, 444), 'django.db.models.BigAutoField', 'models.BigAutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (367, 444), False, 'from django.db import migrations, models\n'), ((468, 499), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(50)'}), '(max_length=50)\n', (484, 499), False, 'from django.db import migrations, models\n'), ((532, 564), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(180)'}), '(max_length=180)\n', (548, 564), False, 'from django.db import migrations, models\n'), ((594, 644), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (621, 644), False, 'from django.db import migrations, models\n')]
|
#!/usr/bin/env python
import requests
import re
import sys
import dlrnapi_client
import influxdb_utils
import json
from promoter_utils import get_dlrn_instance_for_release
from diskcache import Cache
cache = Cache('/tmp/skipped_promotions_cache')
cache.expire()
promoter_skipping_regex = re.compile(
('.*promoter Skipping promotion of (.*) from (.*) to (.*), '
'missing successful jobs: (.*)')
)
html_link = "<a href='{}' target='_blank' >{}</a>"
def get_failing_jobs_html(dlrn_hashes, release_name):
failing_jobs_html = ""
# If any of the jobs is still in progress
in_progress = False
try:
dlrn = get_dlrn_instance_for_release(release_name)
if dlrn:
params = dlrnapi_client.Params2()
params.commit_hash = dlrn_hashes['commit_hash']
params.distro_hash = dlrn_hashes['distro_hash']
params.success = str(False)
failing_jobs = dlrn.api_repo_status_get(params)
if len(failing_jobs) > 0:
for i, failing_job in enumerate(failing_jobs):
if failing_job.in_progress:
in_progress = True
failing_job_ln = html_link.format(
failing_job.url, failing_job.job_id)
if i > 0:
failing_job_ln += "<br>"
failing_jobs_html += failing_job_ln
else:
failing_jobs_html = ("<font color='red'>WARNING</font> "
"expected perodic jobs have not run")
except Exception as e:
print(e)
pass
return (in_progress, failing_jobs_html)
# FIXME: Use a decorator ?
def get_cached_failing_jobs_html(dlrn_hashes, release_name):
cache_key = "failing_jobs_html_{timestamp}_{repo_hash}".format(
**dlrn_hashes)
if cache_key not in cache:
in_progress, failing_jobs_html = get_failing_jobs_html(
dlrn_hashes, release_name)
# Only chache if jobs have finished
if not in_progress:
cache.add(cache_key, failing_jobs_html, expire=259200)
return cache[cache_key]
def parse_skipped_promotions(release_name):
skipped_promotions = []
promoter_logs = requests.get(
"http://38.145.34.55/{}.log".format(release_name))
def get_log_time(log_line):
log_line_splitted = log_line.split()
log_time = "{} {}".format(log_line_splitted[0], log_line_splitted[1])
log_time = log_time.split(',')[0]
return log_time
for log_line in promoter_logs.iter_lines():
matched_regex = promoter_skipping_regex.match(log_line)
if matched_regex:
promotion = json.loads(matched_regex.group(1).replace("'", '"'))
repo_hash = promotion['full_hash']
failing_jobs = matched_regex.group(3)
skipped_promotion = {
'repo_hash': repo_hash,
'from_name': matched_regex.group(2),
'to_name': matched_regex.group(3),
'failing_jobs': failing_jobs,
'timestamp': get_log_time(log_line),
'release': release_name
}
skipped_promotions.append(skipped_promotion)
return skipped_promotions
def to_influxdb(skipped_promotions):
influxdb_lines = []
influxdb_format = (
"skipped-promotions,repo_hash={repo_hash}"
",release={release},from_name={from_name},"
"to_name={to_name} failing_jobs=\"{failing_jobs}\" "
"{timestamp}")
for skipped_promotion in skipped_promotions:
skipped_promotion['timestamp'] = influxdb_utils.format_ts_from_str(
skipped_promotion['timestamp'])
influxdb_lines.append(influxdb_format.format(**skipped_promotion))
return influxdb_lines
def main():
release = sys.argv[1]
influxdb_lines = to_influxdb(parse_skipped_promotions(release))
print('\n'.join(influxdb_lines))
if __name__ == '__main__':
main()
|
[
"promoter_utils.get_dlrn_instance_for_release",
"influxdb_utils.format_ts_from_str",
"dlrnapi_client.Params2",
"diskcache.Cache",
"re.compile"
] |
[((210, 248), 'diskcache.Cache', 'Cache', (['"""/tmp/skipped_promotions_cache"""'], {}), "('/tmp/skipped_promotions_cache')\n", (215, 248), False, 'from diskcache import Cache\n'), ((291, 401), 're.compile', 're.compile', (['""".*promoter Skipping promotion of (.*) from (.*) to (.*), missing successful jobs: (.*)"""'], {}), "(\n '.*promoter Skipping promotion of (.*) from (.*) to (.*), missing successful jobs: (.*)'\n )\n", (301, 401), False, 'import re\n'), ((638, 681), 'promoter_utils.get_dlrn_instance_for_release', 'get_dlrn_instance_for_release', (['release_name'], {}), '(release_name)\n', (667, 681), False, 'from promoter_utils import get_dlrn_instance_for_release\n'), ((3650, 3715), 'influxdb_utils.format_ts_from_str', 'influxdb_utils.format_ts_from_str', (["skipped_promotion['timestamp']"], {}), "(skipped_promotion['timestamp'])\n", (3683, 3715), False, 'import influxdb_utils\n'), ((720, 744), 'dlrnapi_client.Params2', 'dlrnapi_client.Params2', ([], {}), '()\n', (742, 744), False, 'import dlrnapi_client\n')]
|
from __future__ import print_function
import argparse
import os
import csv
import numpy as np
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from data_utils.data_util import PointcloudScaleAndTranslate
from data_utils.ModelNetDataLoader import ModelNetDataLoader
from models.pointnet import PointNetCls, feature_transform_regularizer
from models.pointnet2 import PointNet2ClsMsg
from models.dgcnn import DGCNN
from models.pointcnn import PointCNNCls
from utils import progress_bar, log_row
import sys
sys.path.append("./emd/")
import emd_module as emd
def gen_train_log(args):
if not os.path.isdir('logs_train'):
os.mkdir('logs_train')
logname = ('logs_train/%s_%s_%s.csv' % (args.data, args.model, args.name))
if os.path.exists(logname):
with open(logname, 'a') as logfile:
log_row(logname, [''])
log_row(logname, [''])
with open(logname, 'a') as logfile:
logwriter = csv.writer(logfile, delimiter=',')
logwriter.writerow(['model type', 'data set', 'seed', 'train batch size',
'number of points in one batch', 'number of epochs', 'optimizer',
'learning rate', 'resume checkpoint path',
'feature transform', 'lambda for feature transform regularizer', 'data augment'])
logwriter.writerow([args.model, args.data, args.seed, args.batch_size, args.num_points,
args.epochs, args.optimizer, args.lr, args.resume,
args.feature_transform, args.lambda_ft, args.augment])
logwriter.writerow(['Note', args.note])
logwriter.writerow([''])
def save_ckpt(args, epoch, model, optimizer, acc_list):
if not os.path.isdir('checkpoints'):
os.mkdir('checkpoints')
if not os.path.isdir('checkpoints/%s_%s_%s' % (args.data, args.model, args.name)):
os.mkdir('checkpoints/%s_%s_%s' % (args.data, args.model, args.name))
if acc_list[-1] > max(acc_list[:-1]):
print('=====> Saving checkpoint...')
print('the best test acc is', acc_list[-1])
state = {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'args': args,
'acc_list': acc_list,
}
torch.save(state, 'checkpoints/%s_%s_%s/best.pth' % (args.data, args.model, args.name))
print('Successfully save checkpoint at epoch %d' % epoch)
def cal_loss(pred, gold, smoothing=True):
''' Calculate cross entropy loss, apply label smoothing if needed. '''
gold = gold.contiguous().view(-1)
if smoothing:
eps = 0.2
n_class = pred.size(1)
one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)
one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
log_prb = F.log_softmax(pred, dim=1)
loss = -(one_hot * log_prb).sum(dim=1).mean()
else:
loss = F.cross_entropy(pred, gold, reduction='mean')
return loss
def test(model, test_loader, criterion):
model.eval()
correct = 0
total = 0
for j, data in enumerate(test_loader, 0):
points, label = data
points, label = points.to(device), label.to(device)[:, 0]
if args.model == 'rscnn_kcutmix':
fps_idx = pointnet2_utils.furthest_point_sample(points, args.num_points) # (B, npoint)
points = pointnet2_utils.gather_operation(points.transpose(1, 2).contiguous(), fps_idx).transpose(1,
2).contiguous() # (B, N, 3)
points = points.transpose(2, 1) # to be shape batch_size*3*N
pred, trans_feat = model(points)
loss = criterion(pred, label.long())
pred_choice = pred.data.max(1)[1]
correct += pred_choice.eq(label.data).cpu().sum()
total += label.size(0)
progress_bar(j, len(test_loader), 'Test Loss: %.3f | Test Acc: %.3f%% (%d/%d)'
% (loss.item() / (j + 1), 100. * correct.item() / total, correct, total))
return loss.item() / (j + 1), 100. * correct.item() / total
if __name__ == '__main__':
########################################
## Set hypeparameters
########################################
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='pointnet', help='choose model type')
parser.add_argument('--data', type=str, default='modelnet40', help='choose data set')
parser.add_argument('--seed', type=int, default=0, help='manual random seed')
parser.add_argument('--batch_size', type=int, default=16, help='input batch size')
parser.add_argument('--num_points', type=int, default=1024, help='input batch size')
parser.add_argument('--epochs', type=int, default=300, help='number of epochs to train for')
parser.add_argument('--optimizer', type=str, default='Adam', help='optimizer for training')
parser.add_argument('--lr', default=0.001, type=float, help='learning rate in training')
parser.add_argument('--resume', type=str, default='/', help='resume path')
parser.add_argument('--feature_transform', type=int, default=1, help="use feature transform")
parser.add_argument('--lambda_ft', type=float, default=0.001, help="lambda for feature transform")
parser.add_argument('--augment', type=int, default=1, help='data argment to increase robustness')
parser.add_argument('--name', type=str, default='train', help='name of the experiment')
parser.add_argument('--note', type=str, default='', help='notation of the experiment')
parser.add_argument('--normal', action='store_true', default=False,
help='Whether to use normal information [default: False]')
parser.add_argument('--beta', default=1, type=float, help='hyperparameter beta')
parser.add_argument('--cutmix_prob', default=0.5, type=float, help='cutmix probability')
args = parser.parse_args()
args.feature_transform, args.augment = bool(args.feature_transform), bool(args.augment)
### Set random seed
args.seed = args.seed if args.seed > 0 else random.randint(1, 10000)
# dataset path
DATA_PATH = './data/modelnet40_normal_resampled/'
########################################
## Intiate model
########################################
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
num_classes = 40
if args.model == 'dgcnn_kcutmix':
model = DGCNN(num_classes)
model = model.to(device)
model = nn.DataParallel(model)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr * 100,
momentum=0.9, weight_decay=1e-4)
scheduler_c = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 250,
eta_min=1e-3)
else:
if args.model == 'pointnet_kcutmix':
model = PointNetCls(num_classes, args.feature_transform)
model = model.to(device)
elif args.model == 'pointnet2_kcutmix':
model = PointNet2ClsMsg(num_classes)
model = model.to(device)
model = nn.DataParallel(model)
elif args.model == 'rscnn_kcutmix':
from models.rscnn import RSCNN
import models.rscnn_utils.pointnet2_utils as pointnet2_utils
model = RSCNN(num_classes)
model = model.to(device)
model = nn.DataParallel(model)
optimizer = torch.optim.Adam(
model.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-4
)
scheduler_c = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
if len(args.resume) > 1:
print('=====> Loading from checkpoint...')
checkpoint = torch.load('./checkpoints/%s.pth' % args.resume)
args = checkpoint['args']
torch.manual_seed(args.seed)
print("Random Seed: ", args.seed)
"""if args.optimizer == 'SGD':
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
elif args.optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999))"""
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
START_EPOCH = checkpoint['epoch'] + 1
acc_list = checkpoint['acc_list']
if args.model == 'dgcnn_kcutmix':
scheduler_c = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 250, eta_min=1e-3)
else:
scheduler_c = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
print('Successfully resumed!')
else:
print('=====> Building new model...')
torch.manual_seed(args.seed)
print("Random Seed: ", args.seed)
START_EPOCH = 0
acc_list = [0]
print('Successfully built!')
########################################
## Load data
########################################
print('======> Loading data')
TRAIN_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_points, split='train',
normal_channel=args.normal)
TEST_DATASET = ModelNetDataLoader(root=DATA_PATH, npoint=args.num_points, split='test',
normal_channel=args.normal)
train_loader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True,
num_workers=4, drop_last=True)
test_loader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False,
num_workers=4, drop_last=False)
PointcloudScaleAndTranslate = PointcloudScaleAndTranslate()
print('======> Successfully loaded!')
gen_train_log(args)
logname = ('logs_train/%s_%s_%s.csv' % (args.data, args.model, args.name))
########################################
## Train
########################################
if args.model == 'dgcnn_kcutmix':
criterion = cal_loss
else:
criterion = F.cross_entropy # nn.CrossEntropyLoss()
if args.resume == '/':
log_row(logname, ['Epoch', 'Train Loss', 'Train Acc', 'Test Loss', 'Test Acc', 'learning Rate'])
for epoch in range(START_EPOCH, args.epochs):
print('\nEpoch: %d' % epoch)
scheduler_c.step(epoch)
model.train()
correct = 0
total = 0
for i, data in enumerate(train_loader, 0):
points, target = data
points, target = points.to(device), target.to(device)[:, 0]
points = PointcloudScaleAndTranslate(points)
if args.model == 'rscnn_kcutmix':
fps_idx = pointnet2_utils.furthest_point_sample(points, args.num_points) # (B, npoint)
fps_idx = fps_idx[:, np.random.choice(args.num_points, args.num_points, False)]
points = pointnet2_utils.gather_operation(points.transpose(1, 2).contiguous(), fps_idx).transpose(1,2).contiguous() # (B, N, 3)
# cutmix
optimizer.zero_grad()
r = np.random.rand(1)
if args.beta > 0 and r < args.cutmix_prob:
lam = np.random.beta(args.beta, args.beta)
B = points.size()[0]
rand_index = torch.randperm(B).cuda()
target_a = target
target_b = target[rand_index]
point_a = torch.zeros(B, 1024, 3)
point_b = torch.zeros(B, 1024, 3)
point_c = torch.zeros(B, 1024, 3)
point_a = points
point_b = points[rand_index]
point_c = points[rand_index]
point_a, point_b, point_c = point_a.to(device), point_b.to(device), point_c.to(device)
remd = emd.emdModule()
remd = remd.cuda()
dis, ind = remd(point_a, point_b, 0.005, 300)
for ass in range(B):
point_c[ass, :, :] = point_c[ass, ind[ass].long(), :]
int_lam = int(args.num_points * lam)
int_lam = max(1, int_lam)
random_point = torch.from_numpy(np.random.choice(1024, B, replace=False, p=None))
# kNN
ind1 = torch.tensor(range(B))
query = point_a[ind1, random_point].view(B, 1, 3)
dist = torch.sqrt(torch.sum((point_a - query.repeat(1, args.num_points, 1)) ** 2, 2))
idxs = dist.topk(int_lam, dim=1, largest=False, sorted=True).indices
for i2 in range(B):
points[i2, idxs[i2], :] = point_c[i2, idxs[i2], :]
# adjust lambda to exactly match point ratio
lam = int_lam * 1.0 / args.num_points
points = points.transpose(2, 1)
pred, trans_feat = model(points)
loss = criterion(pred, target_a.long()) * (1. - lam) + criterion(pred, target_b.long()) * lam
else:
points = points.transpose(2, 1)
pred, trans_feat = model(points)
loss = criterion(pred, target.long())
if args.feature_transform and args.model == 'pointnet_kcutmix':
loss += feature_transform_regularizer(trans_feat) * args.lambda_ft
loss.backward()
optimizer.step()
pred_choice = pred.data.max(1)[1]
correct += pred_choice.eq(target.data).cpu().sum()
total += target.size(0)
progress_bar(i, len(train_loader), 'Train Loss: %.3f | Train Acc: %.3f%% (%d/%d)'
% (loss.item() / (i + 1), 100. * correct.item() / total, correct, total))
train_loss, train_acc = loss.item() / (i + 1), 100. * correct.item() / total
### Test in batch
test_loss, test_acc = test(model, test_loader, criterion)
acc_list.append(test_acc)
print('the best test acc is', max(acc_list))
### Keep tracing
log_row(logname, [epoch, train_loss, train_acc, test_loss, test_acc,
optimizer.param_groups[0]['lr'], max(acc_list), np.argmax(acc_list) - 1])
save_ckpt(args, epoch, model, optimizer, acc_list)
|
[
"os.mkdir",
"torch.optim.lr_scheduler.StepLR",
"argparse.ArgumentParser",
"numpy.argmax",
"models.rscnn.RSCNN",
"data_utils.ModelNetDataLoader.ModelNetDataLoader",
"models.pointnet.PointNetCls",
"sys.path.append",
"models.pointnet2.PointNet2ClsMsg",
"random.randint",
"torch.utils.data.DataLoader",
"torch.load",
"os.path.exists",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"utils.log_row",
"torch.nn.functional.log_softmax",
"numpy.random.choice",
"torch.zeros",
"csv.writer",
"torch.zeros_like",
"numpy.random.beta",
"torch.manual_seed",
"torch.nn.functional.cross_entropy",
"torch.cuda.is_available",
"torch.randperm",
"data_utils.data_util.PointcloudScaleAndTranslate",
"emd_module.emdModule",
"models.pointnet.feature_transform_regularizer",
"os.path.isdir",
"torch.save",
"models.dgcnn.DGCNN",
"models.rscnn_utils.pointnet2_utils.furthest_point_sample",
"numpy.random.rand",
"torch.nn.DataParallel"
] |
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|
import random
from flask import Flask
app = Flask(__name__)
@app.route('/')
def index():
a = random.randrange(1, 10)
b = random.randrange(1, 10)
return f'{a} * {b} = {a * b}'
|
[
"flask.Flask",
"random.randrange"
] |
[((46, 61), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (51, 61), False, 'from flask import Flask\n'), ((101, 124), 'random.randrange', 'random.randrange', (['(1)', '(10)'], {}), '(1, 10)\n', (117, 124), False, 'import random\n'), ((133, 156), 'random.randrange', 'random.randrange', (['(1)', '(10)'], {}), '(1, 10)\n', (149, 156), False, 'import random\n')]
|
"""Generate a summary of a previously trained vowel recognition model.
"""
import torch
import wavetorch
import argparse
import yaml
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
try:
from helpers.plot import mpl_set_latex
mpl_set_latex()
except ImportError:
import warnings
warnings.warn('The helpers package is unavailable', ImportWarning)
COL_TRAIN = "#1f77b4"
COL_TEST = "#2ca02c"
parser = argparse.ArgumentParser()
parser.add_argument('filename', type=str)
parser.add_argument('--vmin', type=float, default=1e-3)
parser.add_argument('--vmax', type=float, default=1.0)
parser.add_argument('--fig', type=str, default=None)
parser.add_argument('--title_off', action='store_true')
parser.add_argument('--labels', action='store_true')
parser.add_argument('--vowel_samples', nargs='+', type=int, default=None)
if __name__ == '__main__':
args = parser.parse_args()
model, history, history_state, cfg = wavetorch.io.load_model(args.filename)
try:
if cfg['seed'] is not None:
torch.manual_seed(cfg['seed'])
except:
pass
print("Configuration for model in %s is:" % args.filename)
print(yaml.dump(cfg, default_flow_style=False))
sr = cfg['data']['sr']
gender = cfg['data']['gender']
vowels = cfg['data']['vowels']
N_classes = len(vowels)
fig = plt.figure( figsize=(7, 4.75), constrained_layout=True)
gs = fig.add_gridspec(1, 2, width_ratios=[1, 0.4])
gs_left = gs[0].subgridspec(3, 2)
gs_right = gs[1].subgridspec(N_classes+1, 1, height_ratios=[1 for i in range(0,N_classes)] + [0.05])
gs_bot = gs_left[2,:].subgridspec(1, 2)
ax_cm_train0 = fig.add_subplot(gs_left[0,0])
ax_cm_test0 = fig.add_subplot(gs_left[0,1])
ax_cm_train1 = fig.add_subplot(gs_left[1,0])
ax_cm_test1 = fig.add_subplot(gs_left[1,1])
ax_loss = fig.add_subplot(gs_bot[0])
ax_acc = fig.add_subplot(gs_bot[1])
ax_fields = [fig.add_subplot(gs_right[i]) for i in range(0, N_classes+1)]
history_mean = history.groupby('epoch').mean()
history_std = history.groupby('epoch').std()
epochs = history_mean.index.values
ax_loss.fill_between(epochs,
history_mean['loss_train'].values-history_std['loss_train'].values,
history_mean['loss_train'].values+history_std['loss_train'].values, color=COL_TRAIN, alpha=0.15)
ax_loss.plot(epochs, history_mean['loss_train'].values, "-", label="Training dataset", ms=4, color=COL_TRAIN)
ax_loss.fill_between(epochs,
history_mean['loss_test'].values-history_std['loss_test'].values,
history_mean['loss_test'].values+history_std['loss_test'].values, color=COL_TEST, alpha=0.15)
ax_loss.plot(epochs, history_mean['loss_test'].values, "-", label="Testing dataset", ms=4, color=COL_TEST)
ax_loss.set_ylabel('Loss')
ax_loss.set_xlabel('Training epoch \#')
ax_acc.plot(epochs, history_mean['acc_train'].values*100, "-", label="Training dataset", ms=4, color=COL_TRAIN)
ax_acc.fill_between(epochs,
history_mean['acc_train'].values*100-history_std['acc_train'].values*100,
history_mean['acc_train'].values*100+history_std['acc_train'].values*100, color=COL_TRAIN, alpha=0.15)
ax_acc.plot(epochs, history_mean['acc_test'].values*100, "-", label="Testing dataset", ms=4, color=COL_TEST)
ax_acc.fill_between(epochs,
history_mean['acc_test'].values*100-history_std['acc_test'].values*100,
history_mean['acc_test'].values*100+history_std['acc_test'].values*100, color=COL_TEST, alpha=0.15)
ax_acc.set_xlabel('Training epoch \#')
ax_acc.set_ylabel('Accuracy')
ax_acc.yaxis.set_major_locator(mpl.ticker.MultipleLocator(base=10))
# ax_acc.set_ylim([20,100])
ax_loss.yaxis.set_major_locator(mpl.ticker.MultipleLocator(base=0.1))
# ax_loss.set_ylim([0.7,1.2])
ax_acc.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%.0f\%%'))
ax_loss.legend(fontsize='small')
# ax_acc.annotate("%.1f%% training set accuracy" % (history_mean['acc_train'].tail(1).iloc[0]*100), xy=(0.1,0.1), xytext=(0,10), textcoords="offset points", xycoords="axes fraction", ha="left", va="bottom", color=COL_TRAIN)
# ax_acc.annotate("%.1f%% testing set accuracy" % (history_mean['acc_test'].tail(1).iloc[0]*100), xy=(0.1,0.1), xycoords="axes fraction", ha="left", va="bottom", color=COL_TEST)
ax_acc.annotate('%.1f\%%' % (history_mean['acc_train'].tail(1).iloc[0]*100),
xy=(epochs[-1], history_mean['acc_train'].tail(1).iloc[0]*100), xycoords='data',
xytext=(-1, 5), textcoords='offset points', ha='left', va='center', fontsize='small',
color=COL_TRAIN, bbox=wavetorch.plot.bbox_white)
ax_acc.annotate('%.1f\%%' % (history_mean['acc_test'].tail(1).iloc[0]*100),
xy=(epochs[-1], history_mean['acc_test'].tail(1).iloc[0]*100), xycoords='data',
xytext=(-1, -5), textcoords='offset points', ha='left', va='center', fontsize='small',
color=COL_TEST, bbox=wavetorch.plot.bbox_white)
print('Accuracy (train): %.1f%% +/- %.1f%%' % (history_mean['acc_train'].tail(1).iloc[0]*100, history_std['acc_train'].tail(1).iloc[0]*100))
print('Accuracy (test): %.1f%% +/- %.1f%%' % (history_mean['acc_test'].tail(1).iloc[0]*100, history_std['acc_test'].tail(1).iloc[0]*100))
cm_train = history.groupby('epoch')['cm_train'].apply(np.mean).head(1).iloc[0]
cm_test = history.groupby('epoch')['cm_test'].apply(np.mean).head(1).iloc[0]
wavetorch.plot.confusion_matrix(cm_train, title="Training dataset", normalize=True, ax=ax_cm_train0, labels=vowels)
wavetorch.plot.confusion_matrix(cm_test, title="Testing dataset", normalize=True, ax=ax_cm_test0, labels=vowels)
cm_train = history.groupby('epoch')['cm_train'].apply(np.mean).tail(1).iloc[0]
cm_test = history.groupby('epoch')['cm_test'].apply(np.mean).tail(1).iloc[0]
wavetorch.plot.confusion_matrix(cm_train, title="Training dataset", normalize=True, ax=ax_cm_train1, labels=vowels)
wavetorch.plot.confusion_matrix(cm_test, title="Testing dataset", normalize=True, ax=ax_cm_test1, labels=vowels)
X, Y, F = wavetorch.data.load_all_vowels(vowels, gender='both', sr=sr, random_state=0)
# model.load_state_dict(history_state[cfg['training']['N_epochs']])
for i in range(N_classes):
xb, yb = wavetorch.data.select_vowel_sample(X, Y, F, i, ind=args.vowel_samples[i] if args.vowel_samples is not None else None)
with torch.no_grad():
field_dist = model(xb, output_fields=True)
wavetorch.plot.total_field(model, field_dist, yb, ax=ax_fields[yb.argmax().item()], cbar=True, cax=ax_fields[-1], vmin=args.vmin, vmax=args.vmax)
if args.labels:
try:
from helpers.plot import apply_panel_labels
apply_panel_labels([ax_cm_train0, ax_cm_test0, ax_cm_train1, ax_cm_test1, ax_loss, ax_acc] + ax_fields[0:-1],
xy=[(-35,0), (-35,0), (-35,0), (-35,0), (-25,0), (-40,0), (8,-6), (8,-6), (8,-6)],
color=['k', 'k', 'k', 'k', 'k', 'k', 'w', 'w', 'w'],
case='upper')
except ImportError:
import warnings
warnings.warn('The helpers package is unavailable', ImportWarning)
plt.show()
if args.fig is not None:
fig.savefig(args.fig, dpi=300)
else:
fig.savefig(os.path.splitext(args.filename)[0]+"_summary.png", dpi=300)
|
[
"matplotlib.ticker.MultipleLocator",
"matplotlib.pyplot.show",
"argparse.ArgumentParser",
"torch.manual_seed",
"yaml.dump",
"wavetorch.io.load_model",
"wavetorch.data.load_all_vowels",
"helpers.plot.mpl_set_latex",
"wavetorch.data.select_vowel_sample",
"matplotlib.pyplot.figure",
"matplotlib.ticker.FormatStrFormatter",
"os.path.splitext",
"warnings.warn",
"torch.no_grad",
"wavetorch.plot.confusion_matrix",
"helpers.plot.apply_panel_labels"
] |
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|
import math
import numpy as np
import torch
import torch.nn as nn
import itertools
class FuzzyLayer(nn.Module):
def __init__(self, fuzzynum,channel):
super(FuzzyLayer,self).__init__()
self.n = fuzzynum
self.channel = channel
self.conv1 = nn.Conv2d(self.channel,1,3,padding=1)
self.conv2 = nn.Conv2d(1,self.channel,3,padding=1)
self.mu = nn.Parameter(torch.randn((self.channel,self.n)))
self.sigma = nn.Parameter(torch.randn((self.channel,self.n)))
self.bn1 = nn.BatchNorm2d(1, affine=True)
self.bn2 = nn.BatchNorm2d(self.channel,affine=True)
def forward(self, x):
x = self.conv1(x)
#tmp = torch.tensor(np.zeros((x.size()[0],x.size()[1],x.size()[2],x.size()[3])),dtype = torch.float).cuda()
tmp = torch.tensor(np.zeros((x.size()[0], x.size()[1], x.size()[2], x.size()[3])), dtype=torch.float).to(device)
for num,channel,w,h in itertools.product(range(x.size()[0]),range(x.size()[1]),range(x.size()[2]),range(x.size()[3])):
for f in range(self.n):
tmp[num][channel][w][h] -= ((x[num][channel][w][h]-self.mu[channel][f])/self.sigma[channel][f])**2
fNeural = self.bn2(self.conv2(self.bn1(torch.exp(tmp))))
return fNeural
class FuzzyNet(nn.Module):
def __init__(self, n_class=2, testing=False):
super(FuzzyNet, self).__init__()
self.fuzzy_4 = FuzzyLayer(fuzzynum=1,channel=512)
self.fuzzy_3 = FuzzyLayer(fuzzynum=1,channel=256)
self.fuzzy_2 = FuzzyLayer(fuzzynum=1,channel=128)
self.fuzzy_1 = FuzzyLayer(fuzzynum=1, channel=64)
self.conv1_1 = nn.Conv2d(3, 64, 3, padding=1)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# 1/2
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# 1/4
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# 1/8
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# 1/16
self.deconv1 = nn.ConvTranspose2d(in_channels=512,out_channels=256,kernel_size=(2,2),stride=(2,2),bias=False)
self.deconv2 = nn.ConvTranspose2d(in_channels=256,out_channels=128,kernel_size=(2,2),stride=(2,2),bias=False)
self.deconv3 = nn.ConvTranspose2d(in_channels=128,out_channels=64,kernel_size=(2,2),stride=(2,2),bias=False)
self.deconv4 = nn.ConvTranspose2d(in_channels=64,out_channels=6,kernel_size=(2,2),stride=(2,2),bias=False) #
self.fbn1 = nn.BatchNorm2d(64, affine=True)
self.fbn2 = nn.BatchNorm2d(128, affine=True)
self.fbn3 = nn.BatchNorm2d(256, affine=True)
self.fbn4 = nn.BatchNorm2d(512, affine=True)
self.bn1 = nn.BatchNorm2d(512, affine=True)
self.bn2 = nn.BatchNorm2d(256, affine=True)
self.bn3 = nn.BatchNorm2d(128, affine=True)
self.bn4 = nn.BatchNorm2d(64, affine=True)
self.testing = testing
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
#m.weight.data.zero_()
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m, nn.ConvTranspose2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels*m.in_channels
m.weight.data.normal_(0,math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m,nn.Linear):
m.weight.data.normal_(0, 1)
for param in self.parameters():
param.requires_grad = True
def forward(self, x):
h = x
h = self.relu1_1(self.conv1_1(h))
h = self.relu1_2(self.conv1_2(h))
h = self.pool1(h)
c1 = h
f1 = self.fbn1(self.fuzzy_1(c1))+c1
h = self.relu2_1(self.conv2_1(h))
h = self.relu2_2(self.conv2_2(h))
h = self.pool2(h+g)
c2 = h
f2 = self.fbn2(self.fuzzy_2(c2))+c2
h = self.relu3_1(self.conv3_1(h))
h = self.relu3_2(self.conv3_2(h))
h = self.relu3_3(self.conv3_3(h))
h = self.pool3(h+g)
c3 = h
f3 = self.fbn3(self.fuzzy_3(c3))+c3
h = self.relu4_1(self.conv4_1(h))
h = self.relu4_2(self.conv4_2(h))
h = self.relu4_3(self.conv4_3(h))
h = self.pool4(h+g)
c4 = h
f4 = self.fbn4(self.fuzzy_4(c4))
h = self.bn1(h)
h = self.bn2(self.deconv1(h))
h = self.bn3(self.deconv2(h))
h = self.bn4(self.deconv3(h))
h = self.deconv4(h)
return h
|
[
"torch.nn.ReLU",
"torch.nn.ConvTranspose2d",
"math.sqrt",
"torch.nn.Conv2d",
"torch.randn",
"torch.exp",
"torch.nn.BatchNorm2d",
"torch.nn.MaxPool2d"
] |
[((249, 289), 'torch.nn.Conv2d', 'nn.Conv2d', (['self.channel', '(1)', '(3)'], {'padding': '(1)'}), '(self.channel, 1, 3, padding=1)\n', (258, 289), True, 'import torch.nn as nn\n'), ((302, 342), 'torch.nn.Conv2d', 'nn.Conv2d', (['(1)', 'self.channel', '(3)'], {'padding': '(1)'}), '(1, self.channel, 3, padding=1)\n', (311, 342), True, 'import torch.nn as nn\n'), ((478, 508), 'torch.nn.BatchNorm2d', 'nn.BatchNorm2d', (['(1)'], {'affine': '(True)'}), '(1, affine=True)\n', (492, 508), True, 'import torch.nn as nn\n'), ((522, 563), 'torch.nn.BatchNorm2d', 'nn.BatchNorm2d', (['self.channel'], {'affine': '(True)'}), '(self.channel, affine=True)\n', (536, 563), True, 'import torch.nn as nn\n'), ((1500, 1530), 'torch.nn.Conv2d', 'nn.Conv2d', (['(3)', '(64)', '(3)'], {'padding': '(1)'}), '(3, 64, 3, padding=1)\n', (1509, 1530), True, 'import torch.nn as nn\n'), ((1548, 1569), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (1555, 1569), True, 'import torch.nn as 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(['(256)', '(256)', '(3)'], {'padding': '(1)'}), '(256, 256, 3, padding=1)\n', (2176, 2200), True, 'import torch.nn as nn\n'), ((2218, 2239), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (2225, 2239), True, 'import torch.nn as nn\n'), ((2255, 2296), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', (['(2)'], {'stride': '(2)', 'ceil_mode': '(True)'}), '(2, stride=2, ceil_mode=True)\n', (2267, 2296), True, 'import torch.nn as nn\n'), ((2323, 2356), 'torch.nn.Conv2d', 'nn.Conv2d', (['(256)', '(512)', '(3)'], {'padding': '(1)'}), '(256, 512, 3, padding=1)\n', (2332, 2356), True, 'import torch.nn as nn\n'), ((2374, 2395), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (2381, 2395), True, 'import torch.nn as nn\n'), ((2413, 2446), 'torch.nn.Conv2d', 'nn.Conv2d', (['(512)', '(512)', '(3)'], {'padding': '(1)'}), '(512, 512, 3, padding=1)\n', (2422, 2446), True, 'import torch.nn as nn\n'), ((2464, 2485), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (2471, 2485), True, 'import torch.nn as nn\n'), ((2503, 2536), 'torch.nn.Conv2d', 'nn.Conv2d', (['(512)', '(512)', '(3)'], {'padding': '(1)'}), '(512, 512, 3, padding=1)\n', (2512, 2536), True, 'import torch.nn as nn\n'), ((2554, 2575), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (2561, 2575), True, 'import torch.nn as nn\n'), ((2591, 2632), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', (['(2)'], {'stride': '(2)', 'ceil_mode': '(True)'}), '(2, stride=2, ceil_mode=True)\n', (2603, 2632), True, 'import torch.nn as nn\n'), ((2663, 2767), 'torch.nn.ConvTranspose2d', 'nn.ConvTranspose2d', ([], {'in_channels': '(512)', 'out_channels': '(256)', 'kernel_size': '(2, 2)', 'stride': '(2, 2)', 'bias': '(False)'}), '(in_channels=512, out_channels=256, kernel_size=(2, 2),\n stride=(2, 2), bias=False)\n', (2681, 2767), True, 'import torch.nn as nn\n'), ((2775, 2879), 'torch.nn.ConvTranspose2d', 'nn.ConvTranspose2d', ([], {'in_channels': '(256)', 'out_channels': '(128)', 'kernel_size': '(2, 2)', 'stride': '(2, 2)', 'bias': '(False)'}), '(in_channels=256, out_channels=128, kernel_size=(2, 2),\n stride=(2, 2), bias=False)\n', (2793, 2879), True, 'import torch.nn as nn\n'), ((2887, 2990), 'torch.nn.ConvTranspose2d', 'nn.ConvTranspose2d', ([], {'in_channels': '(128)', 'out_channels': '(64)', 'kernel_size': '(2, 2)', 'stride': '(2, 2)', 'bias': '(False)'}), '(in_channels=128, out_channels=64, kernel_size=(2, 2),\n stride=(2, 2), bias=False)\n', (2905, 2990), True, 'import torch.nn as nn\n'), ((2998, 3099), 'torch.nn.ConvTranspose2d', 'nn.ConvTranspose2d', ([], {'in_channels': '(64)', 'out_channels': '(6)', 'kernel_size': '(2, 2)', 'stride': '(2, 2)', 'bias': '(False)'}), '(in_channels=64, out_channels=6, kernel_size=(2, 2),\n stride=(2, 2), bias=False)\n', (3016, 3099), True, 'import torch.nn as nn\n'), ((3108, 3139), 'torch.nn.BatchNorm2d', 'nn.BatchNorm2d', (['(64)'], {'affine': '(True)'}), '(64, 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|
import argparse
import json
from gensim.models import Word2Vec
from tensorflow_core.python.keras.models import load_model
import convert
import extract
import predict
import vectorize
from annotation import annotate
def main(input_file: str, output_file: str):
extracted_jsdoc = extract.extract_from_file(input_file)
df = convert.convert_func_to_df(extracted_jsdoc)
word2vec_code = Word2Vec.load('data/word_vecs/word2vec_model_code.bin')
word2vec_lang = Word2Vec.load('data/word_vecs/word2vec_model_language.bin')
vectors = vectorize.df_to_vec(df, word2vec_lang, word2vec_code)
model = load_model('data/model.h5')
with open("data/types.json") as f:
types_map = json.load(f)
predictions = predict.predict(model, vectors, types_map)
annotate.annotate(df, predictions, input_file, output_file)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("input_file_path", type=str, help="Path of the input file")
parser.add_argument("output_file_path", type=str, help="Path of the output file")
args = parser.parse_args()
main(args.input_file_path, args.output_file_path)
|
[
"json.load",
"argparse.ArgumentParser",
"vectorize.df_to_vec",
"extract.extract_from_file",
"tensorflow_core.python.keras.models.load_model",
"predict.predict",
"convert.convert_func_to_df",
"gensim.models.Word2Vec.load",
"annotation.annotate.annotate"
] |
[((287, 324), 'extract.extract_from_file', 'extract.extract_from_file', (['input_file'], {}), '(input_file)\n', (312, 324), False, 'import extract\n'), ((334, 377), 'convert.convert_func_to_df', 'convert.convert_func_to_df', (['extracted_jsdoc'], {}), '(extracted_jsdoc)\n', (360, 377), False, 'import convert\n'), ((398, 453), 'gensim.models.Word2Vec.load', 'Word2Vec.load', (['"""data/word_vecs/word2vec_model_code.bin"""'], {}), "('data/word_vecs/word2vec_model_code.bin')\n", (411, 453), False, 'from gensim.models import Word2Vec\n'), ((474, 533), 'gensim.models.Word2Vec.load', 'Word2Vec.load', (['"""data/word_vecs/word2vec_model_language.bin"""'], {}), "('data/word_vecs/word2vec_model_language.bin')\n", (487, 533), False, 'from gensim.models import Word2Vec\n'), ((548, 601), 'vectorize.df_to_vec', 'vectorize.df_to_vec', (['df', 'word2vec_lang', 'word2vec_code'], {}), '(df, word2vec_lang, word2vec_code)\n', (567, 601), False, 'import vectorize\n'), ((614, 641), 'tensorflow_core.python.keras.models.load_model', 'load_model', (['"""data/model.h5"""'], {}), "('data/model.h5')\n", (624, 641), False, 'from tensorflow_core.python.keras.models import load_model\n'), ((732, 774), 'predict.predict', 'predict.predict', (['model', 'vectors', 'types_map'], {}), '(model, vectors, types_map)\n', (747, 774), False, 'import predict\n'), ((779, 838), 'annotation.annotate.annotate', 'annotate.annotate', (['df', 'predictions', 'input_file', 'output_file'], {}), '(df, predictions, input_file, output_file)\n', (796, 838), False, 'from annotation import annotate\n'), ((881, 906), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (904, 906), False, 'import argparse\n'), ((701, 713), 'json.load', 'json.load', (['f'], {}), '(f)\n', (710, 713), False, 'import json\n')]
|
# coding=utf8
"""
方便调试使用
"""
from lofka import LofkaHandler,LofkaAsyncHandler
import logging
import traceback
handler = LofkaAsyncHandler()
logger = logging.getLogger('test')
logger.addHandler(handler)
def __debug_method():
try:
raise Exception("TestException")
except Exception as ex:
traceback.format_exc()
logger.exception("ErrorTitle")
if __name__ == "__main__":
__debug_method()
|
[
"lofka.LofkaAsyncHandler",
"traceback.format_exc",
"logging.getLogger"
] |
[((122, 141), 'lofka.LofkaAsyncHandler', 'LofkaAsyncHandler', ([], {}), '()\n', (139, 141), False, 'from lofka import LofkaHandler, LofkaAsyncHandler\n'), ((151, 176), 'logging.getLogger', 'logging.getLogger', (['"""test"""'], {}), "('test')\n", (168, 176), False, 'import logging\n'), ((314, 336), 'traceback.format_exc', 'traceback.format_exc', ([], {}), '()\n', (334, 336), False, 'import traceback\n')]
|
from bs4 import BeautifulSoup
import requests
#from webdriver import keep_alive
import discord
import time
from discord.ext import commands
bot = commands.Bot(command_prefix='!')
bot.remove_command("help")
@bot.event
async def on_ready():
await bot.change_presence(status=discord.Status.online, activity=discord.Activity(type=discord.ActivityType.watching, name=""))
print(f'Logged in as {bot.user.name}')
@commands.command(name="ebay")
async def ebay(ctx):
message = ctx.message.content.split('!ebay ')
url = message[1]
pname = str(url)
url = url.replace(' ', '+')
url ='https://www.ebay.com/sch/i.html?_from=R40&_nkw={}&_sacat=0&_ipg=200'.format(url)
print(url)
pname=str(pname)
embeder = discord.Embed(title='Processing...', description='Looking for `{}`.\n\nThis may take some time...'.format(pname), color=0x00bfff)
sendd1 = await ctx.send(embed=embeder)
x = requests.get(url)
time.sleep(2)
embeder = discord.Embed(title='Processing...', description='Looking for `{}`.\n\nThis may take some time...\n\n`Scraping Prices...`'.format(pname), color=0x00bfff)
embeder.set_thumbnail(url='https://cdn0.iconfinder.com/data/icons/big-file-flat/32/02_Cloud_Computing_computer_internet_file_data-512.png')
sendd = await sendd1.edit(embed=embeder)
soup = BeautifulSoup(x.content, 'html.parser')
together = []
allitems = soup.find_all('span', class_='s-item__price')
for price in allitems:
price = price.text
price = price.replace('$', '')
price = price.replace(',', '')
if 'to' in price:
pass
else:
price = int(float(price))
together.append(price)
embeder = discord.Embed(title='Processing...', description='Looking for `{}`.\n\nThis may take some time...\n\n`Doing math...`'.format(pname), color=0x00bfff)
embeder.set_thumbnail(url='https://www.pngmart.com/files/7/Calculator-PNG-Picture.png')
new = await sendd1.edit(embed=embeder)
time.sleep(4)
items = len(together)
total = sum(together)
average = total/items
average = round(average, 2)
average = str(average)
items = str(items)
embeder = discord.Embed(title='Done', description='Here is what I found.\n\nItem : `{}`\n\nAverage Price : `${}`\n\nTotal number of items counted : `{}`'.format(pname,average,items), color=0x00bfff)
embeder.set_thumbnail(url='https://assets.stickpng.com/thumbs/5aa78e207603fc558cffbf19.png')
sendd = await sendd1.edit(embed=embeder)
bot.add_command(ebay)
#keep_alive()
bot.run('bot-token')
|
[
"discord.Activity",
"discord.ext.commands.command",
"time.sleep",
"requests.get",
"discord.ext.commands.Bot",
"bs4.BeautifulSoup"
] |
[((147, 179), 'discord.ext.commands.Bot', 'commands.Bot', ([], {'command_prefix': '"""!"""'}), "(command_prefix='!')\n", (159, 179), False, 'from discord.ext import commands\n'), ((412, 441), 'discord.ext.commands.command', 'commands.command', ([], {'name': '"""ebay"""'}), "(name='ebay')\n", (428, 441), False, 'from discord.ext import commands\n'), ((891, 908), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (903, 908), False, 'import requests\n'), ((911, 924), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (921, 924), False, 'import time\n'), ((1287, 1326), 'bs4.BeautifulSoup', 'BeautifulSoup', (['x.content', '"""html.parser"""'], {}), "(x.content, 'html.parser')\n", (1300, 1326), False, 'from bs4 import BeautifulSoup\n'), ((1920, 1933), 'time.sleep', 'time.sleep', (['(4)'], {}), '(4)\n', (1930, 1933), False, 'import time\n'), ((307, 368), 'discord.Activity', 'discord.Activity', ([], {'type': 'discord.ActivityType.watching', 'name': '""""""'}), "(type=discord.ActivityType.watching, name='')\n", (323, 368), False, 'import discord\n')]
|
import time
# print( time.time())
def timmer(func):
def wrapper():
start_time = time.time()
func()
stop_time = time.time()
print("运行时间是 %s 秒 " % (stop_time - start_time))
return wrapper
@timmer
def i_can_sleep():
time.sleep(3)
# start_time = time.time()
i_can_sleep()
# stop_time = time.time()
# print('函数运行了 %s 秒' %(stop_time-start_time))
|
[
"time.time",
"time.sleep"
] |
[((262, 275), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (272, 275), False, 'import time\n'), ((94, 105), 'time.time', 'time.time', ([], {}), '()\n', (103, 105), False, 'import time\n'), ((141, 152), 'time.time', 'time.time', ([], {}), '()\n', (150, 152), False, 'import time\n')]
|
#!/usr/bin/python3
import cv2
import cv2IP
if __name__ == '__main__':
IP = cv2IP.BaseIP()
img = IP.ImRead("img/test.jpg")
IP.ImWindow("foreGround")
IP.ImShow("foreGround", img)
cv2.waitKey(0)
del IP
|
[
"cv2.waitKey",
"cv2IP.BaseIP"
] |
[((81, 95), 'cv2IP.BaseIP', 'cv2IP.BaseIP', ([], {}), '()\n', (93, 95), False, 'import cv2IP\n'), ((199, 213), 'cv2.waitKey', 'cv2.waitKey', (['(0)'], {}), '(0)\n', (210, 213), False, 'import cv2\n')]
|
from __future__ import print_function, division
import torch
import os
import pandas as pd
from skimage import io, transform
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import sklearn
import sklearn.metrics as sklm
import csv
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import time
import os
import pickle
import random
from shutil import copyfile
from shutil import rmtree
import torchvision
use_gpu = torch.cuda.is_available()
print("We see GPU:")
print(use_gpu)
print("Let's use", torch.cuda.device_count(), "GPUs!")
from PIL import Image
import CXRDataset as CXR
import Eval as E
from importlib import reload
reload(CXR)
reload(E)
def checkpoint(model_ft, best_acc, best_loss, epoch,PRED_LABEL,LR,RESULT_PATH):
"""
save checkpoint
args:
model_ft: torchvision model
best_acc: best accuracy achieved so far in training
best_loss: best loss achieved so far in training
epoch: last epoch of training
PRED_LABEL: what we're predicting; expect format ["Pneumonia"] or ["Pneumonia","Opacity"]... etc
LR: learning rate
RESULT_PATH: path to save this to
returns:
nothing (saves file)
"""
# Save checkpoint.
print('Saving..')
state = {
'model_ft': model_ft,
'best_acc': best_acc,
'best_loss': best_loss,
'epoch': epoch,
'rng_state': torch.get_rng_state(),
'LR':LR
}
torch.save(state, RESULT_PATH+'checkpoint_'+PRED_LABEL)
def train_model(model, criterion, optimizer, LR, num_epochs=5,dataloaders="x",dataset_sizes="x", PRED_LABEL="x", start_epoch=1,MULTILABEL=True,FOLD_OVERRIDE="",TRAIN_FILTER="",RESULT_PATH="results/",MULTICLASS=False):
"""
performs torchvision model training
args:
model: model to fine tune
criterion: pytorch optimization criteria
optimizer: pytorch optimizer
LR: learning rate
num_epochs: stop after this many epochs
dataloaders: torchvision dataloader
dataset_sizes: length of train/val datasets
PRED_LABEL: targets we're predicting in list format ["PNA","Opacity"] etc
start_epoch: in case of loading saved model; not currently used
MULTILABEL: should be removed - always True - everything is trained using multilabel list format now even single labels ["Pneumonia"]
FOLD_OVERRIDE: columns of scalars with train/val/test split
TRAIN_FILTER: list of data we're training on, used for labeling results
RESULT_PATH= path at which resutls are saved, recommend leaving default to use with other scripts
MULTICLASS: if training on single multiclass n>2 target; currently only implemented for single multiclass target.
returns:
model: trained torchvision model
best_epoch: epoch on which best model was achieved
"""
since = time.time()
best_acc = 0.0
best_loss=999999
best_epoch=-1
last_train_acc=-1
last_train_loss=-1
for epoch in range(start_epoch,num_epochs+1):
print('Epoch {}/{}'.format(epoch, num_epochs))
print('-' * 10)
#small_data flag used to decide on how to decay
small_data=False
if dataset_sizes['train']<=10000: small_data=True
iter_at_lr=0
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train(True) # Set model to training mode
else:
model.train(False) # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
i=0
total_done=0
for data in dataloaders[phase]:
i+=1
# get the inputs
inputs, labels = data
batch_size= inputs.shape[0]
if use_gpu:
inputs = Variable(inputs.cuda())
labels = Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
#needed for multilabel training which uses different loss and expects floats
if not MULTICLASS:
labels = labels.float()
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
if MULTICLASS: # need to fix this for multilabel
running_corrects += torch.sum(preds == labels.long().data)
running_loss += loss.data[0]*batch_size
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects / dataset_sizes[phase]
if phase=='train':
last_train_acc=epoch_acc
last_train_loss=epoch_loss
print(phase+' epoch {}:loss {:.4f} acc: {:.4f} with data size {}'.format(
epoch, epoch_loss, epoch_acc, dataset_sizes[phase]))
#decay if not best
if phase == 'val' and epoch_loss > best_loss:
#normally we just decay if no improvement in val loss in epoch, but not ideal with small datasets
#so 'small_data' condition that insists on 5 passes at lr if dataset size <=10k
if small_data==False or iter_at_lr>=4:
print("decay loss from "+str(LR)+" to "+str(LR/10)+" as not seeing improvement in val loss")
LR = LR / 10
#making a new optimizer zeros out momentum
optimizer = optim.SGD(filter(lambda p:p.requires_grad, model.parameters()), lr = LR, momentum=0.9, weight_decay=1e-4)
iter_at_lr=0
else:
iter_at_lr+=1
#below is used for labeling results
trainstring = str(TRAIN_FILTER).replace("_","").replace("[","").replace(",","_").replace("]","").replace(" ","").replace("'","")
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = model.state_dict()
if phase == 'val' and epoch_loss < best_loss:
best_loss = epoch_loss
best_epoch = epoch
#save stuff if we have a best model
write_label = str(PRED_LABEL)
write_label = "Multilabel"
checkpoint(model, best_acc, best_loss, epoch, RESULT_PATH+write_label+"_train_"+trainstring+"_"+FOLD_OVERRIDE,LR,RESULT_PATH=RESULT_PATH)
write_label = "multilabel_" + trainstring + "_" + FOLD_OVERRIDE
if phase== 'val':
with open(RESULT_PATH+"log_train_"+write_label, 'a') as logfile:
logwriter = csv.writer(logfile, delimiter=',')
logwriter.writerow([write_label, epoch, last_train_loss, last_train_acc, epoch_loss, epoch_acc])
total_done+=batch_size
if(total_done % (100*batch_size) == 0): print("completed "+str(total_done)+" so far in epoch")
#quit if 3 epochs no improvement
if ((epoch-best_epoch)>=3 and small_data==False) or ((epoch-best_epoch)>=15 and small_data==True):
print("no improvement in 3 epochs, break")
break
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights and return them
checkpoint_best = torch.load(RESULT_PATH+"checkpoint_results/Multilabel_train_"+trainstring+"_"+FOLD_OVERRIDE)
model = checkpoint_best['model_ft']
return model, best_epoch
def give_mean_var(LABEL_PATH, PRED_LABEL,BALANCE_MODE, TRAIN_FILTER,MULTILABEL, FOLD_OVERRIDE, BATCH_SIZE):
"""
args:
LABEL_PATH: path to the scalars file
PRED_LABEL: list of targets we're predicting
BALANCE_MODE: deprecated
TRAIN_FILTER: list of dataset we're training on, needed for dataloader
MULTILABEL: deprecated, always true
FOLD_OVERRIDE: train/val/test split column name in scalars
BATCH_SIZE: passes batch for dataloader
returns:
mean: rgb channel means np array 3x1
std:: rgb channel std np array 3x1
"""
#create set of val transforms
data_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Scale(224),
transforms.CenterCrop(224), #needed to get 224x224
transforms.ToTensor()
])
#make dataloader
transformed_dataset =CXR.CXRDataset(csv_file=LABEL_PATH, fold='train', PRED_LABEL=PRED_LABEL, transform=data_transform, balance_classes=BALANCE_MODE, FILTER=TRAIN_FILTER,MULTILABEL=MULTILABEL,FOLD_OVERRIDE=FOLD_OVERRIDE,SAMPLE=0,TRAIN_FILTER=TRAIN_FILTER,RESULT_PATH="ignore",MULTICLASS=MULTICLASS)
dataloader = torch.utils.data.DataLoader(transformed_dataset, batch_size=BATCH_SIZE, shuffle=True,num_workers=8)#, sampler=sampler)
#calculate some means and st devs
x = len(dataloader)*BATCH_SIZE
print("len dataloader for give_mean_var:"+str(x))
means = np.empty((x,3))
stds = np.empty((x,3))
means[:,:]=np.nan
stds[:,:]=np.nan
for data in dataloader:
inputs, _ = data
inputs=inputs.numpy()
for i in range(0,inputs.shape[0]):
for j in range(0,3):
means[i,j]=np.mean(inputs[i,j,:,:])
stds[i,j]=np.std(inputs[i,j,:,:])
mean = np.zeros(3)
std = np.zeros(3)
for j in range (0,3):
x=np.nanmean(means[:,j])
mean[j]=x
x=np.nanmean(stds[:,j])
std[j]=x
return mean, std
def train_one(PRED_LABEL,LR,BATCH_SIZE,LABEL_PATH,RESULT_PATH,BALANCE_MODE,FREEZE_LAYERS, NUM_EPOCHS,TRAIN_FILTER,PRED_FILTER,MULTILABEL,FOLD_OVERRIDE,TRAIN_SAMPLE,PRED_SAMPLE,CUSTOM_NORMALIZE, NET_TYPE,MULTICLASS,OUTPUT1024):
"""
make dataloader, instantiates torchvision model, calls training function, returns results
args:
PRED_LABEL: list of labels to predict ["pna","opacity"] etc
LR: learning rate
BATCH_SIZE: batch size for dataloader; too big and won't fit on gpu
LABEL_PATH: path to scalars
RESULT_PATH: path to write results
BALANCE_MODE: deprecated
FREEZE_LAYERS: deprecated
NUM_EPOCHS: max number of epochs to train for; may quit sooner if not improving
TRAIN_FILTER: list of sites we're training on
PRED_FILTER: list of sites we're predicting
MULTILABEL: deprecated
FOLD_OVERRIDE: train/val/test split column in scalars
TRAIN_SAMPLE: sample training data to get limited sample (for testing)
PRED_SAMPLE: sample test data to get limited sample (for testing)
CUSTOM_NORMALIZE: use normalization mean, std based on data not imagenet
NET_TYPE: deprecated
MULTICLASS: train to single multiclass n>2 target (not implemented for multilabel multiclass)
returns:
x: df with predictions
"""
#if we were using custom normalization and not imagenet, do this; it didn't help vs imagenet nornmalization
if CUSTOM_NORMALIZE:
mean, std = give_mean_var(LABEL_PATH, PRED_LABEL,BALANCE_MODE, TRAIN_FILTER,MULTILABEL, FOLD_OVERRIDE, BATCH_SIZE)
print(mean)
print(std)
elif not CUSTOM_NORMALIZE:
mean= [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
#torchvision transforms
df = pd.read_csv(LABEL_PATH,index_col=0)
data_transforms = {
'train': transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Scale(224), #244
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean, std)
]),
'val': transforms.Compose([
transforms.Scale(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean, std)
]),
}
#make dataloader
transformed_datasets={}
transformed_datasets['train'] =CXR.CXRDataset(csv_file=LABEL_PATH, fold='train', PRED_LABEL=PRED_LABEL, transform=data_transforms['train'], balance_classes=BALANCE_MODE, FILTER=TRAIN_FILTER,MULTILABEL=MULTILABEL,FOLD_OVERRIDE=FOLD_OVERRIDE,SAMPLE=TRAIN_SAMPLE,TRAIN_FILTER=TRAIN_FILTER,RESULT_PATH=RESULT_PATH,MULTICLASS=MULTICLASS)
transformed_datasets['val'] =CXR.CXRDataset(csv_file=LABEL_PATH, fold='val', PRED_LABEL=PRED_LABEL, transform=data_transforms['val'], balance_classes=BALANCE_MODE, FILTER=TRAIN_FILTER,MULTILABEL=MULTILABEL,FOLD_OVERRIDE=FOLD_OVERRIDE,SAMPLE=TRAIN_SAMPLE,TRAIN_FILTER=TRAIN_FILTER,RESULT_PATH=RESULT_PATH,MULTICLASS=MULTICLASS)
dataloaders={}
dataloaders['train'] = torch.utils.data.DataLoader(transformed_datasets['train'], batch_size=BATCH_SIZE, shuffle=True,num_workers=8)#, sampler=sampler)
dataloaders['val'] = torch.utils.data.DataLoader(transformed_datasets['val'], batch_size=BATCH_SIZE, shuffle=True, num_workers=8)
#instantiate model
if not use_gpu: raise ValueError("Error, requires GPU")
print('==> Building model..')
if(NET_TYPE=="densenet121"):
print("using densenet121")
model_ft = models.densenet121(pretrained=True)
num_ftrs = model_ft.classifier.in_features
if(OUTPUT1024==False):
print("adding bottleneck=15 features")
#if multiclass, needs different output structure then regular training to list of binary taragets
if not MULTICLASS:
model_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, 15), nn.Linear(15, len(PRED_LABEL)),nn.Sigmoid())
elif MULTICLASS:
model_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, 15), nn.Linear(15, transformed_datasets['train'].n_class))
print("n_class "+str(transformed_datasets['train'].n_class))
elif(OUTPUT1024==True):
print("NOT adding bottleneck=15 features")
#if multiclass, needs different output structure then regular training to list of binary taragets
if not MULTICLASS:
model_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, len(PRED_LABEL)),nn.Sigmoid())
elif MULTICLASS:
model_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, transformed_datasets['train'].n_class))
print("n_class "+str(transformed_datasets['train'].n_class))
start_epoch = 1
print("loading model_ft onto gpu")
model_ft = model_ft.cuda()
if NET_TYPE=="densenet121":
if(MULTICLASS==False):
criterion = nn.BCELoss()
else:
criterion = nn.CrossEntropyLoss() # only using this for predicting site, department
optimizer_ft = optim.SGD(filter(lambda p: p.requires_grad, model_ft.parameters()), lr=LR, momentum=0.9, weight_decay=1e-4)
dataset_sizes = {x: len(transformed_datasets[x]) for x in ['train', 'val']}
#train
model_ft , best_epoch = train_model(model_ft, criterion, optimizer_ft, LR, num_epochs=NUM_EPOCHS,dataloaders=dataloaders,dataset_sizes=dataset_sizes, PRED_LABEL=PRED_LABEL, start_epoch=start_epoch,MULTILABEL=MULTILABEL,FOLD_OVERRIDE=FOLD_OVERRIDE,TRAIN_FILTER=TRAIN_FILTER,RESULT_PATH=RESULT_PATH,MULTICLASS=MULTICLASS)
#make preds on test
x = E.make_pred_multilabel(data_transforms,model_ft,"pred_"+str(PRED_LABEL), LABEL_PATH,RESULT_PATH,PRED_LABEL,TRAIN_FILTER,PRED_FILTER,FOLD_OVERRIDE,PRED_SAMPLE,MULTICLASS,OUTPUT1024)
return x
def train_cnn(LABEL_PATH, PRED_LABEL,TRAIN_FILTER,PRED_FILTER,BALANCE_MODE,FOLD_OVERRIDE,MULTICLASS=False,OUTPUT1024=False):
"""
main function that gets called externally to train
LABEL_PATH: path to scalars
PRED_LABEL: targets to predict; list ["pna","opacity"] etc as in scalars file
TRAIN_FILTER: list of sites we're training to ["nih","msh"]
PRED_FILTER: list of sites we're predicting ["nih","iu"]
BALANCE_MODE: deprecated
FOLD_OVERRIDE: the column of scalars we use for train val test split
MULTICLASS: train to single multiclass n>2 target
returns:
y: results
"""
NUM_EPOCHS=50
BATCH_SIZE=16
LR = 0.01
RESULT_PATH="results/"
FREEZE_LAYERS="no"
MULTILABEL = not isinstance(PRED_LABEL, str)
TRAIN_SAMPLE=0
PRED_SAMPLE =0
CUSTOM_NORMALIZE=False
NET_TYPE="densenet121"
if not os.path.exists(RESULT_PATH):
os.makedirs(RESULT_PATH)
if not os.path.exists(RESULT_PATH+"checkpoint_results/"):
os.makedirs(RESULT_PATH+"checkpoint_results/")
x = train_one(PRED_LABEL,LR,BATCH_SIZE,LABEL_PATH,RESULT_PATH,BALANCE_MODE,"layer4",NUM_EPOCHS,TRAIN_FILTER,PRED_FILTER,MULTILABEL,FOLD_OVERRIDE,TRAIN_SAMPLE,PRED_SAMPLE,CUSTOM_NORMALIZE, NET_TYPE, MULTICLASS,OUTPUT1024)
y = pd.read_csv(LABEL_PATH)
y=y[['img_id']]
y = y.merge(x,on="img_id",how="inner")
trainlist=str(TRAIN_FILTER).replace("_","").replace("[","").replace(",","_").replace("]","").replace(" ","").replace("'","")
y.to_csv(RESULT_PATH+"preds_train_"+trainlist+"_"+FOLD_OVERRIDE+".csv",index=False)
return y
|
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|
# coding: utf-8
#
# This code is part of lattpy.
#
# Copyright (c) 2021, <NAME>
#
# This code is licensed under the MIT License. The copyright notice in the
# LICENSE file in the root directory and this permission notice shall
# be included in all copies or substantial portions of the Software.
"""Contains miscellaneous utility methods."""
import logging
from typing import Iterable, List, Sequence, Optional, Union, Tuple
import time
import numpy as np
__all__ = [
"ArrayLike", "logger", "LatticeError", "ConfigurationError", "SiteOccupiedError",
"NoAtomsError", "NoBaseNeighborsError", "NotBuiltError", "Timer",
"min_dtype", "chain", "create_lookup_table", "frmt_num", "frmt_bytes", "frmt_time",
]
# define type for numpy `array_like` types
ArrayLike = Union[int, float, Iterable, np.ndarray]
# Configure package logger
logger = logging.getLogger("lattpy")
_CH = logging.StreamHandler()
_CH.setLevel(logging.DEBUG)
_FRMT_STR = "[%(asctime)s] %(levelname)-8s - %(name)-15s - %(funcName)-25s - %(message)s"
_FRMT = logging.Formatter(_FRMT_STR, datefmt='%H:%M:%S')
_CH.setFormatter(_FRMT) # Add formatter to stream handler
logger.addHandler(_CH) # Add stream handler to package logger
logger.setLevel(logging.WARNING) # Set initial logging level
class LatticeError(Exception):
pass
class ConfigurationError(LatticeError):
@property
def msg(self):
return self.args[0]
@property
def hint(self):
return self.args[1]
def __str__(self):
msg, hint = self.args
if hint:
msg += f" ({hint})"
return msg
class SiteOccupiedError(ConfigurationError):
def __init__(self, atom, pos):
super().__init__(f"Can't add {atom} to lattice, position {pos} already occupied!")
class NoAtomsError(ConfigurationError):
def __init__(self):
super().__init__("lattice doesn't contain any atoms",
"use 'add_atom' to add an 'Atom'-object")
class NoBaseNeighborsError(ConfigurationError):
def __init__(self):
msg = "base neighbors not configured"
hint = "call 'set_num_neighbors' after adding atoms or " \
"use the 'neighbors' keyword of 'add_atom'"
super().__init__(msg, hint)
class NotBuiltError(ConfigurationError):
def __init__(self):
msg = "lattice has not been built"
hint = "use the 'build' method to construct a finite size lattice model"
super().__init__(msg, hint)
def create_lookup_table(array: ArrayLike, dtype: Optional[Union[str, np.dtype]] = np.uint8) \
-> Tuple[np.ndarray, np.ndarray]:
"""Converts the given array to an array of indices linked to the unique values.
Parameters
----------
array : array_like
dtype : int or np.dtype, optional
Optional data-type for storing the indices of the unique values.
By default `np.uint8` is used, since it is assumed that the
input-array has only a few unique values.
Returns
-------
values : np.ndarray
The unique values occuring in the input-array.
indices : np.ndarray
The corresponding indices in the same shape as the input-array.
"""
values = np.sort(np.unique(array))
indices = np.zeros_like(array, dtype=dtype)
for i, x in enumerate(values):
mask = array == x
indices[mask] = i
return values, indices
def min_dtype(a: Union[int, float, np.ndarray, Iterable],
signed: Optional[bool] = True) -> np.dtype:
"""Returns the minimum required dtype to store the given values.
Parameters
----------
a : array_like
One or more values for determining the dtype.
Should contain the maximal expected values.
signed : bool, optional
If `True` the dtype is forced to be signed. The default is `True`.
Returns
-------
dtype : dtype
The required dtype.
"""
if signed:
a = -np.max(np.abs(a))-1
else:
amin, amax = np.min(a), np.max(a)
if amin < 0:
a = - amax - 1 if abs(amin) <= amax else amin
else:
a = amax
return np.dtype(np.min_scalar_type(a))
def chain(items: Sequence, cycle: bool = False) -> List:
"""Creates a chain between items
Parameters
----------
items : Sequence
items to join to chain
cycle : bool, optional
cycle to the start of the chain if True, default: False
Returns
-------
chain: list
chain of items
Example
-------
>>> print(chain(["x", "y", "z"]))
[['x', 'y'], ['y', 'z']]
>>> print(chain(["x", "y", "z"], True))
[['x', 'y'], ['y', 'z'], ['z', 'x']]
"""
result = list()
for i in range(len(items)-1):
result.append([items[i], items[i+1]])
if cycle:
result.append([items[-1], items[0]])
return result
def frmt_num(num: float, dec: Optional[int] = 1, unit: Optional[str] = '',
div: Optional[float] = 1000.) -> str:
"""Returns a formatted string of a number.
Parameters
----------
num : float
The number to format.
dec : int, optional
Number of decimals. The default is 1.
unit : str, optional
Optional unit suffix. By default no unit-strinmg is used.
div : float, optional
The divider used for units. The default is 1000.
Returns
-------
num_str: str
"""
for prefix in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z']:
if abs(num) < div:
return f"{num:.{dec}f}{prefix}{unit}"
num /= div
return f"{num:.{dec}f}Y{unit}"
def frmt_bytes(num: float, dec: Optional[int] = 1) -> str:
"""Returns a formatted string of the number of bytes."""
return frmt_num(num, dec, unit="iB", div=1024)
def frmt_time(seconds: float, short: bool = False, width: int = 0) -> str:
"""Returns a formated string for a given time in seconds.
Parameters
----------
seconds : float
Time value to format
short : bool, optional
Flag if short representation should be used.
width : int, optional
Optional minimum length of the returned string.
Returns
-------
time_str: str
"""
string = "00:00"
# short time string
if short:
if seconds > 0:
mins, secs = divmod(seconds, 60)
if mins > 60:
hours, mins = divmod(mins, 60)
string = f"{hours:02.0f}:{mins:02.0f}h"
else:
string = f"{mins:02.0f}:{secs:02.0f}"
# Full time strings
else:
if seconds < 1e-3:
nanos = 1e6 * seconds
string = f"{nanos:.0f}\u03BCs"
elif seconds < 1:
millis = 1000 * seconds
string = f"{millis:.1f}ms"
elif seconds < 60:
string = f"{seconds:.1f}s"
else:
mins, seconds = divmod(seconds, 60)
if mins < 60:
string = f"{mins:.0f}:{seconds:04.1f}min"
else:
hours, mins = divmod(mins, 60)
string = f"{hours:.0f}:{mins:02.0f}:{seconds:02.0f}h"
if width > 0:
string = f"{string:>{width}}"
return string
class Timer:
"""Timer object for easy time measuring."""
__slots__ = ["_time", "_t0"]
def __init__(self, method=None):
self._time = method or time.perf_counter
self._t0 = 0
self.start()
@property
def seconds(self) -> float:
"""Returns the time since the timer has been started in seconds."""
return self.time() - self._t0
@property
def millis(self) -> float:
"""Returns the time since the timer has been started in milliseconds."""
return 1000 * (self.time() - self._t0)
def time(self) -> float:
"""Returns the current time as a timestamp."""
return self._time()
def start(self) -> None:
"""Start the timer."""
self._t0 = self._time()
def eta(self, progress: float) -> float:
"""Approximates the time left for a task.
Parameters
----------
progress: float
Progress fraction of task.
Returns
-------
eta: float
Approximation of time left.
"""
if not progress:
return 0.0
return (1 / progress - 1) * self.time()
def strfrmt(self, short: bool = False, width: int = 0) -> str:
"""Formats the time since the timer has been started."""
return frmt_time(self.seconds, short, width)
def __repr__(self) -> str:
return f'{self.__class__.__name__}({self.strfrmt(short=True)})'
def __str__(self) -> str:
return self.strfrmt(short=True)
|
[
"numpy.zeros_like",
"numpy.abs",
"logging.StreamHandler",
"logging.getLogger",
"logging.Formatter",
"numpy.min_scalar_type",
"numpy.min",
"numpy.max",
"numpy.unique"
] |
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|
# -*- coding: utf-8 -*-
from django.db import models
from django.urls import reverse
from django.utils.translation import ugettext_lazy as _
from django.contrib.auth import get_user_model
from model_utils.models import TimeStampedModel
from annoying.fields import JSONField
from geolite2 import geolite2
from .utils import string_generator
class URL(TimeStampedModel):
"""
Description: Model Description
"""
user = models.ForeignKey(get_user_model(), null=True, on_delete=models.CASCADE)
short_url = models.SlugField(max_length=6, primary_key=True, unique=True)
long_url = models.URLField(max_length=100, unique=True)
created = models.DateTimeField(auto_now=True)
count = models.IntegerField(default=0)
def __str__(self):
return self.long_url
def get_absolute_url(self):
return reverse(
'url_shorter:detail',
kwargs={'short_url': self.short_url}
)
def save(self, *args, **kwargs):
if not self.short_url:
self.short_url = string_generator()
return super(URL, self).save(*args, **kwargs)
class Hit(TimeStampedModel):
"""
Description: Model Description
"""
url = models.ForeignKey(URL, on_delete=models.CASCADE)
ip = models.CharField(default=None, null=True, max_length=50)
data = JSONField(default=None, null=True)
class Meta:
pass
def save(self, *args, **kwargs):
if self.ip:
reader = geolite2.reader()
data = reader.get(self.ip)
self.data = data
geolite2.close()
return super(Hit, self).save(*args, **kwargs)
|
[
"django.db.models.URLField",
"django.db.models.ForeignKey",
"django.db.models.CharField",
"geolite2.geolite2.close",
"django.contrib.auth.get_user_model",
"geolite2.geolite2.reader",
"django.db.models.SlugField",
"annoying.fields.JSONField",
"django.urls.reverse",
"django.db.models.IntegerField",
"django.db.models.DateTimeField"
] |
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|
from __future__ import annotations
import decimal
import logging
from typing import Any, Callable, Dict, Mapping, Optional, Set
import workflows
MessageCallback = Callable[[Mapping[str, Any], Any], None]
class CommonTransport:
"""A common transport class, containing e.g. the logic to manage
subscriptions and transactions."""
__callback_interceptor = None
__subscriptions: Dict[int, Dict[str, Any]] = {}
__subscription_id: int = 0
__transactions: Set[int] = set()
__transaction_id: int = 0
log = logging.getLogger("workflows.transport")
#
# -- High level communication calls ----------------------------------------
#
@classmethod
def add_command_line_options(cls, parser):
"""Function to inject command line parameters."""
pass
def connect(self) -> bool:
"""Connect the transport class. This function must be overridden.
:return: True-like value when connection successful,
False-like value otherwise."""
return False
def is_connected(self) -> bool:
"""Returns the current connection status. This function must be overridden.
:return: True-like value when connection is available,
False-like value otherwise."""
return False
def disconnect(self):
"""Gracefully disconnect the transport class. This function should be
overridden."""
def subscribe(self, channel, callback, **kwargs) -> int:
"""Listen to a queue, notify via callback function.
:param channel: Queue name to subscribe to
:param callback: Function to be called when messages are received.
The callback will pass two arguments, the header as a
dictionary structure, and the message.
:param **kwargs: Further parameters for the transport layer. For example
disable_mangling: Receive messages as unprocessed strings.
exclusive: Attempt to become exclusive subscriber to the queue.
acknowledgement: If true receipt of each message needs to be
acknowledged.
:return: A unique subscription ID
"""
self.__subscription_id += 1
def mangled_callback(header, message):
return callback(header, self._mangle_for_receiving(message))
if "disable_mangling" in kwargs:
if kwargs["disable_mangling"]:
mangled_callback = callback # noqa:F811
del kwargs["disable_mangling"]
self.__subscriptions[self.__subscription_id] = {
"channel": channel,
"callback": mangled_callback,
"ack": kwargs.get("acknowledgement"),
"unsubscribed": False,
}
self.log.debug("Subscribing to %s with ID %d", channel, self.__subscription_id)
self._subscribe(self.__subscription_id, channel, mangled_callback, **kwargs)
return self.__subscription_id
def unsubscribe(self, subscription: int, drop_callback_reference=False, **kwargs):
"""Stop listening to a queue or a broadcast
:param subscription: Subscription ID to cancel
:param drop_callback_reference: Drop the reference to the registered
callback function immediately. This
means any buffered messages still in
flight will not arrive at the intended
destination and cause exceptions to be
raised instead.
:param **kwargs: Further parameters for the transport layer.
"""
if subscription not in self.__subscriptions:
raise workflows.Error("Attempting to unsubscribe unknown subscription")
if self.__subscriptions[subscription]["unsubscribed"]:
raise workflows.Error(
"Attempting to unsubscribe already unsubscribed subscription"
)
self._unsubscribe(subscription, **kwargs)
self.__subscriptions[subscription]["unsubscribed"] = True
if drop_callback_reference:
self.drop_callback_reference(subscription)
def drop_callback_reference(self, subscription: int):
"""Drop reference to the callback function after unsubscribing.
Any future messages arriving for that subscription will result in
exceptions being raised.
:param subscription: Subscription ID to delete callback reference for.
"""
if subscription not in self.__subscriptions:
raise workflows.Error(
"Attempting to drop callback reference for unknown subscription"
)
if not self.__subscriptions[subscription]["unsubscribed"]:
raise workflows.Error(
"Attempting to drop callback reference for live subscription"
)
del self.__subscriptions[subscription]
def subscribe_broadcast(self, channel, callback, **kwargs) -> int:
"""Listen to a broadcast topic, notify via callback function.
:param channel: Topic name to subscribe to
:param callback: Function to be called when messages are received.
The callback will pass two arguments, the header as a
dictionary structure, and the message.
:param **kwargs: Further parameters for the transport layer. For example
disable_mangling: Receive messages as unprocessed strings.
retroactive: Ask broker to send old messages if possible
:return: A unique subscription ID
"""
self.__subscription_id += 1
def mangled_callback(header, message):
return callback(header, self._mangle_for_receiving(message))
if "disable_mangling" in kwargs:
if kwargs["disable_mangling"]:
mangled_callback = callback # noqa:F811
del kwargs["disable_mangling"]
self.__subscriptions[self.__subscription_id] = {
"channel": channel,
"callback": mangled_callback,
"ack": False,
"unsubscribed": False,
}
self.log.debug(
"Subscribing to broadcasts on %s with ID %d",
channel,
self.__subscription_id,
)
self._subscribe_broadcast(
self.__subscription_id, channel, mangled_callback, **kwargs
)
return self.__subscription_id
def subscription_callback(self, subscription: int) -> MessageCallback:
"""Retrieve the callback function for a subscription. Raise a
workflows.Error if the subscription does not exist.
All transport callbacks can be intercepted by setting an
interceptor function with subscription_callback_intercept().
:param subscription: Subscription ID to look up
:return: Callback function
"""
subscription_record = self.__subscriptions.get(subscription)
if not subscription_record:
raise workflows.Error("Attempting to callback on unknown subscription")
callback = subscription_record["callback"]
if self.__callback_interceptor:
return self.__callback_interceptor(callback)
return callback
def subscription_callback_set_intercept(self, interceptor):
"""Set a function to intercept all callbacks. This is useful to, for
example, keep a thread barrier between the transport related functions
and processing functions.
:param interceptor: A function that takes the original callback function
and returns a modified callback function. Or None to
disable interception.
"""
self.__callback_interceptor = interceptor
def send(self, destination, message, **kwargs):
"""Send a message to a queue.
:param destination: Queue name to send to
:param message: Either a string or a serializable object to be sent
:param **kwargs: Further parameters for the transport layer. For example
delay: Delay transport of message by this many seconds
headers: Optional dictionary of header entries
expiration: Optional expiration time, relative to sending time
transaction: Transaction ID if message should be part of a
transaction
"""
message = self._mangle_for_sending(message)
self._send(destination, message, **kwargs)
def raw_send(self, destination, message, **kwargs):
"""Send a raw (unmangled) message to a queue.
This may cause errors if the receiver expects a mangled message.
:param destination: Queue name to send to
:param message: Either a string or a serializable object to be sent
:param **kwargs: Further parameters for the transport layer. For example
delay: Delay transport of message by this many seconds
headers: Optional dictionary of header entries
expiration: Optional expiration time, relative to sending time
transaction: Transaction ID if message should be part of a
transaction
"""
self._send(destination, message, **kwargs)
def broadcast(self, destination, message, **kwargs):
"""Broadcast a message.
:param destination: Topic name to send to
:param message: Either a string or a serializable object to be sent
:param **kwargs: Further parameters for the transport layer. For example
delay: Delay transport of message by this many seconds
headers: Optional dictionary of header entries
expiration: Optional expiration time, relative to sending time
transaction: Transaction ID if message should be part of a
transaction
"""
message = self._mangle_for_sending(message)
self._broadcast(destination, message, **kwargs)
def raw_broadcast(self, destination, message, **kwargs):
"""Broadcast a raw (unmangled) message.
This may cause errors if the receiver expects a mangled message.
:param destination: Topic name to send to
:param message: Either a string or a serializable object to be sent
:param **kwargs: Further parameters for the transport layer. For example
delay: Delay transport of message by this many seconds
headers: Optional dictionary of header entries
expiration: Optional expiration time, relative to sending time
transaction: Transaction ID if message should be part of a
transaction
"""
self._broadcast(destination, message, **kwargs)
def ack(self, message, subscription_id: Optional[int] = None, **kwargs):
"""Acknowledge receipt of a message. This only makes sense when the
'acknowledgement' flag was set for the relevant subscription.
:param message: ID of the message to be acknowledged, OR a dictionary
containing a field 'message-id'.
:param subscription_id: ID of the associated subscription. Optional when
a dictionary is passed as first parameter and
that dictionary contains field 'subscription'.
:param **kwargs: Further parameters for the transport layer. For example
transaction: Transaction ID if acknowledgement should be part of
a transaction
"""
if isinstance(message, dict):
message_id = message.get("message-id")
if not subscription_id:
subscription_id = message.get("subscription")
else:
message_id = message
if not message_id:
raise workflows.Error("Cannot acknowledge message without message ID")
if not subscription_id:
raise workflows.Error("Cannot acknowledge message without subscription ID")
self.log.debug(
"Acknowledging message %s on subscription %s", message_id, subscription_id
)
self._ack(message_id, subscription_id=subscription_id, **kwargs)
def nack(self, message, subscription_id: Optional[int] = None, **kwargs):
"""Reject receipt of a message. This only makes sense when the
'acknowledgement' flag was set for the relevant subscription.
:param message: ID of the message to be rejected, OR a dictionary
containing a field 'message-id'.
:param subscription_id: ID of the associated subscription. Optional when
a dictionary is passed as first parameter and
that dictionary contains field 'subscription'.
:param **kwargs: Further parameters for the transport layer. For example
transaction: Transaction ID if rejection should be part of a
transaction
"""
if isinstance(message, dict):
message_id = message.get("message-id")
if not subscription_id:
subscription_id = message.get("subscription")
else:
message_id = message
if not message_id:
raise workflows.Error("Cannot reject message without message ID")
if not subscription_id:
raise workflows.Error("Cannot reject message without subscription ID")
self.log.debug(
"Rejecting message %s on subscription %d", message_id, subscription_id
)
self._nack(message_id, subscription_id=subscription_id, **kwargs)
def transaction_begin(self, subscription_id: Optional[int] = None, **kwargs) -> int:
"""Start a new transaction.
:param **kwargs: Further parameters for the transport layer.
:return: A transaction ID that can be passed to other functions.
"""
self.__transaction_id += 1
self.__transactions.add(self.__transaction_id)
if subscription_id:
self.log.debug(
"Starting transaction with ID %d on subscription %d",
self.__transaction_id,
subscription_id,
)
else:
self.log.debug("Starting transaction with ID %d", self.__transaction_id)
self._transaction_begin(
self.__transaction_id, subscription_id=subscription_id, **kwargs
)
return self.__transaction_id
def transaction_abort(self, transaction_id: int, **kwargs):
"""Abort a transaction and roll back all operations.
:param transaction_id: ID of transaction to be aborted.
:param **kwargs: Further parameters for the transport layer.
"""
if transaction_id not in self.__transactions:
raise workflows.Error("Attempting to abort unknown transaction")
self.log.debug("Aborting transaction %s", transaction_id)
self.__transactions.remove(transaction_id)
self._transaction_abort(transaction_id, **kwargs)
def transaction_commit(self, transaction_id: int, **kwargs):
"""Commit a transaction.
:param transaction_id: ID of transaction to be committed.
:param **kwargs: Further parameters for the transport layer.
"""
if transaction_id not in self.__transactions:
raise workflows.Error("Attempting to commit unknown transaction")
self.log.debug("Committing transaction %s", transaction_id)
self.__transactions.remove(transaction_id)
self._transaction_commit(transaction_id, **kwargs)
@property
def is_reconnectable(self):
"""Check if the transport object is in a status where reconnecting is
supported. There must not be any active subscriptions or transactions."""
return not self.__subscriptions and not self.__transactions
#
# -- Low level communication calls to be implemented by subclass -----------
#
def _subscribe(self, sub_id: int, channel, callback, **kwargs):
"""Listen to a queue, notify via callback function.
:param sub_id: ID for this subscription in the transport layer
:param channel: Queue name to subscribe to
:param callback: Function to be called when messages are received
:param **kwargs: Further parameters for the transport layer. For example
exclusive: Attempt to become exclusive subscriber to the queue.
acknowledgement: If true receipt of each message needs to be
acknowledged.
"""
raise NotImplementedError("Transport interface not implemented")
def _subscribe_broadcast(self, sub_id: int, channel, callback, **kwargs):
"""Listen to a broadcast topic, notify via callback function.
:param sub_id: ID for this subscription in the transport layer
:param channel: Topic name to subscribe to
:param callback: Function to be called when messages are received
:param **kwargs: Further parameters for the transport layer. For example
retroactive: Ask broker to send old messages if possible
"""
raise NotImplementedError("Transport interface not implemented")
def _unsubscribe(self, sub_id: int, **kwargs):
"""Stop listening to a queue or a broadcast
:param sub_id: ID for this subscription in the transport layer
"""
raise NotImplementedError("Transport interface not implemented")
def _send(self, destination, message, **kwargs):
"""Send a message to a queue.
:param destination: Queue name to send to
:param message: A string to be sent
:param **kwargs: Further parameters for the transport layer. For example
headers: Optional dictionary of header entries
expiration: Optional expiration time, relative to sending time
transaction: Transaction ID if message should be part of a
transaction
"""
raise NotImplementedError("Transport interface not implemented")
def _broadcast(self, destination, message, **kwargs):
"""Broadcast a message.
:param destination: Topic name to send to
:param message: A string to be broadcast
:param **kwargs: Further parameters for the transport layer. For example
headers: Optional dictionary of header entries
expiration: Optional expiration time, relative to sending time
transaction: Transaction ID if message should be part of a
transaction
"""
raise NotImplementedError("Transport interface not implemented")
def _ack(self, message_id, subscription_id, **kwargs):
"""Acknowledge receipt of a message. This only makes sense when the
'acknowledgement' flag was set for the relevant subscription.
:param message_id: ID of the message to be acknowledged.
:param subscription_id: ID of the associated subscription.
:param **kwargs: Further parameters for the transport layer. For example
transaction: Transaction ID if acknowledgement should be part of
a transaction
"""
raise NotImplementedError("Transport interface not implemented")
def _nack(self, message_id, subscription_id, **kwargs):
"""Reject receipt of a message. This only makes sense when the
'acknowledgement' flag was set for the relevant subscription.
:param message_id: ID of the message to be rejected.
:param subscription_id: ID of the associated subscription.
:param **kwargs: Further parameters for the transport layer. For example
transaction: Transaction ID if rejection should be part of a
transaction
"""
raise NotImplementedError("Transport interface not implemented")
def _transaction_begin(
self, transaction_id: int, *, subscription_id: Optional[int] = None, **kwargs
) -> None:
"""Start a new transaction.
:param transaction_id: ID for this transaction in the transport layer.
:param **kwargs: Further parameters for the transport layer.
"""
raise NotImplementedError("Transport interface not implemented")
def _transaction_abort(self, transaction_id: int, **kwargs) -> None:
"""Abort a transaction and roll back all operations.
:param transaction_id: ID of transaction to be aborted.
:param **kwargs: Further parameters for the transport layer.
"""
raise NotImplementedError("Transport interface not implemented")
def _transaction_commit(self, transaction_id: int, **kwargs) -> None:
"""Commit a transaction.
:param transaction_id: ID of transaction to be committed.
:param **kwargs: Further parameters for the transport layer.
"""
raise NotImplementedError("Transport interface not implemented")
#
# -- Internal message mangling functions -----------------------------------
#
# Some transport mechanisms will not be able to work with arbitrary objects,
# so these functions are used to prepare a message for sending/receiving.
# The canonical example is serialization/deserialization, see stomp_transport
@staticmethod
def _mangle_for_sending(message):
"""Function that any message will pass through before it being forwarded to
the actual _send* functions."""
return message
@staticmethod
def _mangle_for_receiving(message):
"""Function that any message will pass through before it being forwarded to
the receiving subscribed callback functions."""
return message
def json_serializer(obj):
"""A helper function for JSON serialization, where it can be used as
the default= argument. This function helps the serializer to translate
objects that otherwise would not be understood. Note that this is
one-way only - these objects are not restored on the receiving end."""
if isinstance(obj, decimal.Decimal):
# turn all Decimals into floats
return float(obj)
raise TypeError(repr(obj) + " is not JSON serializable")
|
[
"workflows.Error",
"logging.getLogger"
] |
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|
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable
import skimage.io as io
import argparse
import os
import sys
import time
# Allow python3 to search for modules outside of this directory
sys.path.append("../")
from models.skip import skip3d
from volumetocube import write_bin_from_array
from volumetocube import write_obj_from_array
import binvox_rw
from tools.Ops import radon
from tools.Ops import tvloss
from tools.Ops import tvloss3d
from tools.Ops import load_binvox
from tools.Ops import volume_proj
from tools.Ops import rotate_volume
from tools.Ops import inv_rotate_volume
from skimage.measure import compare_ssim as ssim
parser = argparse.ArgumentParser(description='Reconstruciton using deep prior.')
parser.add_argument("-m", "--method", type=str, help="Prior to be used in the reconstruction (deep | tv | carve)", default="deep")
parser.add_argument("-b", "--binvox", type=str, help="Path to the binvox file.", default="../data/bunny.binvox")
parser.add_argument("-p", "--projection", type=str, help="Type of projection to be used (depth | binary)", default="depth")
parser.add_argument("-n", "--nproj", type=int, help="Number of projections.", default=8)
parser.add_argument("-s", "--sigma", type=float, help="Amount of variance in the gaussian noise.", default=0.0)
parser.add_argument("-k", "--kappa", type=float, help="Dispersion rate of Von Mises noise.", default=4.0)
parser.add_argument("-v", "--viewWeight", type=float, help="Weight of the viewpoint regularization.", default=1.0)
def add_gaussian_noise(img, sigma=1.0):
randv = torch.randn(*(img.shape)).cuda()
return img + sigma*randv
if __name__ == '__main__':
args = parser.parse_args()
use_tv = args.method == 'tv'
use_dp = args.method == 'deep'
kappa = args.kappa
view_weight = args.viewWeight
binvoxname = args.binvox.split('/')[-1].split('.')[0]
fullname = "prob_{}_{}_{}_{}_{}_vw{}_k{}".format(binvoxname, args.method, args.projection,
args.nproj, args.sigma, view_weight, kappa)
input_depth = 3
input_noise = torch.randn(1, input_depth, 128, 128, 128).cuda()
net = skip3d(
input_depth, 1,
num_channels_down = [8, 16, 32, 64, 128],
num_channels_up = [8, 16, 32, 64, 128],
num_channels_skip = [0, 0, 0, 4, 4],
upsample_mode='trilinear',
need_sigmoid=True, need_bias=True, pad='zero', act_fun='LeakyReLU')
net.cuda()
net(input_noise)
out_volume = torch.zeros(1, 1, 128, 128, 128).cuda()
out_volume.requires_grad = True
nviews = args.nproj
method = args.projection
views = torch.FloatTensor(np.random.rand(nviews, 3) * 2*np.pi)
noisy_views = torch.FloatTensor(np.random.vonmises(views, kappa, size=(nviews,3)))
pred_views = nn.Parameter(noisy_views.detach().clone())
if use_dp:
optimizer = optim.Adam(list(net.parameters()) + [pred_views], lr=0.01)
elif use_tv:
optimizer = optim.Adam([out_volume] + [pred_views], lr=0.01)
padder = nn.ConstantPad3d(10, 0.0)
volume = padder(load_binvox(args.binvox).cuda())
gtprojs = volume_proj(volume, method=method, views=views).cuda()
noisyprojs = gtprojs.detach().clone()
noisyprojs.requires_grad = False
results_dir = os.path.join("results", fullname)
if not os.path.exists(results_dir):
os.makedirs(results_dir)
mse = nn.L1Loss()
sigmoid = nn.Sigmoid()
#Space carving
if args.method == 'carve':
gtprojs = volume_proj(volume, method=method, views=views).cuda()
gtprojs.requires_grad = False
noisyprojs = gtprojs.clone()
noisyprojs.requires_grad = False
carve = torch.ones(*(volume.size())).cuda()
for i in range(nviews):
carve = rotate_volume(carve, x=noisy_views[i,0], y=noisy_views[i,1], z=noisy_views[i,2])
p = gtprojs[:, :, i] < 1e-2
coords = np.argwhere(p)
carve[coords[0, :], :, coords[1, :]] = 0.0
carve = inv_rotate_volume(carve, x=noisy_views[i,0], y=noisy_views[i,1], z=noisy_views[i,2])
projs = volume_proj(carve, method=method, views=views).cuda()
for i in range(noisyprojs.size()[2]):
io.imsave(results_dir+"/carve{}.png".format(i), torch.clamp(projs[:, :, i], -1, 1))
io.imsave(results_dir+"/carvegt{}.png".format(i), torch.clamp(gtprojs[:, :, i], -1, 1))
write_bin_from_array("results/{}/data.npy".format(fullname), carve.data.cpu().numpy())
exit(0)
gt_curve = []
noisygt_curve = []
n_iter = 500
out_rec = None
out_projs = None
pred_views_log = []
noisy_views_log = []
gt_views_log = []
print('EXPERIMENT {}'.format(fullname))
for i in range(n_iter):
optimizer.zero_grad()
if use_dp:
out_rec = net(input_noise)[0, 0, :, :, :]
out_projs = volume_proj(out_rec, method=method, views=pred_views)
loss = mse(out_projs, noisyprojs)
loss -= view_weight * torch.cos(pred_views - noisy_views).mean().cuda()
elif use_tv:
out_rec = sigmoid(out_volume[0, 0, :, :, :])
out_projs = volume_proj(out_rec, method=method, views=views)
loss = mse(out_projs, noisyprojs) + tvloss3d(out_rec, weight=1e-7)#
else:
raise ValueError("Unkown method")
pred_views_log.append(pred_views.data.detach().cpu().numpy())
noisy_views_log.append(noisy_views.data.detach().cpu().numpy())
gt_views_log.append(views.data.detach().cpu().numpy())
predloss = mse(out_projs, noisyprojs)
gtloss = torch.abs(out_projs - gtprojs).mean()
noisyloss = torch.abs(noisyprojs - gtprojs).mean()
print("\r({}/{}) Pred->Noisy: {} | Pred->GT: {} | Noisy->GT: {}".format(
str(i).zfill(4), n_iter, predloss.item(), gtloss.item(), noisyloss.item()),
gt_curve.append(gtloss.item()))
noisygt_curve.append(noisyloss.item())
loss.backward()
optimizer.step()
results_dir = os.path.join("results", fullname)
if not os.path.exists(results_dir):
os.makedirs(results_dir)
write_bin_from_array("results/{}/databin.npy".format(fullname),
out_rec.data.cpu().detach().numpy())
np.save("results/{}/data.npy".format(fullname),
out_rec.data.cpu().detach().numpy())
for i in range(out_projs.size()[2]):
print("Saved {}".format("results/{}/proj{}".format(fullname, i)))
io.imsave("results/{}/proj{}.png".format(fullname, i),
out_projs.data.cpu().detach().numpy()[:, :, i])
io.imsave("results/{}/gt{}.png".format(fullname, i),
torch.clamp(gtprojs[:, :, i], -1, 1).data.cpu().detach().numpy())
np.save("results/{}/gtviews.npy".format(fullname), np.array(gt_views_log))
np.save("results/{}/noisyviews.npy".format(fullname), np.array(noisy_views_log))
np.save("results/{}/predviews.npy".format(fullname), np.array(pred_views_log))
|
[
"argparse.ArgumentParser",
"torch.randn",
"torch.cos",
"tools.Ops.volume_proj",
"os.path.join",
"tools.Ops.rotate_volume",
"sys.path.append",
"os.path.exists",
"torch.zeros",
"tools.Ops.load_binvox",
"tools.Ops.tvloss3d",
"torch.nn.ConstantPad3d",
"torch.optim.Adam",
"torch.clamp",
"numpy.argwhere",
"torch.nn.Sigmoid",
"os.makedirs",
"torch.nn.L1Loss",
"tools.Ops.inv_rotate_volume",
"numpy.random.vonmises",
"numpy.array",
"models.skip.skip3d",
"numpy.random.rand",
"torch.abs"
] |
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'torch.abs', 'torch.abs', (['(noisyprojs - gtprojs)'], {}), '(noisyprojs - gtprojs)\n', (5753, 5775), False, 'import torch\n'), ((5324, 5355), 'tools.Ops.tvloss3d', 'tvloss3d', (['out_rec'], {'weight': '(1e-07)'}), '(out_rec, weight=1e-07)\n', (5332, 5355), False, 'from tools.Ops import tvloss3d\n'), ((5075, 5110), 'torch.cos', 'torch.cos', (['(pred_views - noisy_views)'], {}), '(pred_views - noisy_views)\n', (5084, 5110), False, 'import torch\n'), ((6767, 6803), 'torch.clamp', 'torch.clamp', (['gtprojs[:, :, i]', '(-1)', '(1)'], {}), '(gtprojs[:, :, i], -1, 1)\n', (6778, 6803), False, 'import torch\n')]
|
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""Logging related module."""
import os
import logging
from logging import _checkLevel
from fastseq.config import FASTSEQ_DEFAULT_LOG_LEVEL, FASTSEQ_LOG_LEVEL, FASTSEQ_LOG_FORMAT
def set_default_log_level():
"""Set the default log level from the environment variable"""
try:
fastseq_log_level = _checkLevel(FASTSEQ_LOG_LEVEL)
except (ValueError, TypeError) as e:
logging.error(
"Please input a valid value for FASTSEQ_LOG_LEVEL (e.g. "
"'DEBUG', 'INFO'): {}".format(e))
raise
logging.basicConfig(level=fastseq_log_level, format=FASTSEQ_LOG_FORMAT)
def get_logger(name=None, level=logging.INFO):
"""
Return a logger with the specific name, creating it if necessary.
If no name is specified, return the root logger.
Args:
name (str, optional): logger name. Defaults to None.
Returns:
Logger : the specified logger.
"""
level = _checkLevel(level)
if FASTSEQ_LOG_LEVEL != FASTSEQ_DEFAULT_LOG_LEVEL:
try:
level = _checkLevel(FASTSEQ_LOG_LEVEL)
except (ValueError, TypeError) as e:
logging.error(
"Please input a valid value for FASTSEQ_LOG_LEVEL (e.g. "
"'DEBUG', 'INFO'): {}".format(e))
raise
logger = logging.getLogger(name)
logger.setLevel(level)
return logger
def update_all_log_level(level=logging.INFO):
"""
Update all the loggers to use the specified level.
Args:
level (int/str, optional): the log level. Defaults to logging.INFO.
"""
loggers = [
logging.getLogger(name) for name in logging.root.manager.loggerDict]
for logger in loggers:
logger.setLevel(level)
|
[
"logging._checkLevel",
"logging.getLogger",
"logging.basicConfig"
] |
[((619, 690), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'fastseq_log_level', 'format': 'FASTSEQ_LOG_FORMAT'}), '(level=fastseq_log_level, format=FASTSEQ_LOG_FORMAT)\n', (638, 690), False, 'import logging\n'), ((1017, 1035), 'logging._checkLevel', '_checkLevel', (['level'], {}), '(level)\n', (1028, 1035), False, 'from logging import _checkLevel\n'), ((1383, 1406), 'logging.getLogger', 'logging.getLogger', (['name'], {}), '(name)\n', (1400, 1406), False, 'import logging\n'), ((390, 420), 'logging._checkLevel', '_checkLevel', (['FASTSEQ_LOG_LEVEL'], {}), '(FASTSEQ_LOG_LEVEL)\n', (401, 420), False, 'from logging import _checkLevel\n'), ((1681, 1704), 'logging.getLogger', 'logging.getLogger', (['name'], {}), '(name)\n', (1698, 1704), False, 'import logging\n'), ((1124, 1154), 'logging._checkLevel', '_checkLevel', (['FASTSEQ_LOG_LEVEL'], {}), '(FASTSEQ_LOG_LEVEL)\n', (1135, 1154), False, 'from logging import _checkLevel\n')]
|
#!/usr/bin/env python
import argparse
from ast import parse
import numpy as np
import bitstring
def to_fixed(x, args):
F = args.fixed_point_bits[0] - args.fixed_point_bits[1]
return np.round(x * 2**F)
def to_float(x, args):
F = args.fixed_point_bits[0] - args.fixed_point_bits[1]
return x * 2**-F
def vals_to_hex(vals, args):
nb = args.fixed_point_bits[0] # bits of one value
tnb = len(vals) * nb # bitwidth of N values
assert args.link_bitwidth >= tnb, \
"Attempting to pack {} x {} bits ({} bits) into {}".format(
len(vals), nb, tnb, args.link_bitwidth)
pad = args.link_bitwidth - tnb
fmt_string = 'uint:{},'.format(pad) + 'int:{},'.format(nb) * len(vals)
return bitstring.pack(fmt_string, 0, *vals).hex
def row_to_hex(row, args):
# compute the packing factor
pf = args.link_bitwidth // args.fixed_point_bits[0] if args.pack_links else 1
N = int(np.ceil(len(row) / pf))
y = np.array([vals_to_hex(np.flip(row[i*pf:(i+1)*pf]), args)
for i in range(N)])
return y
def main():
parser = argparse.ArgumentParser(
description='Parse numpy file to FPGA testing for MP7 board')
parser.add_argument('--board_name', type=str,
help='A string representing the name of the board')
parser.add_argument('--generate_float_from_fix', type=bool,
help='Specify if you want to obtain the .npy file '
'obtained via to_float(to_fix(input)). It is useful to '
'feed to avoid casting mismatches using '
'hls_model.predict()')
parser.add_argument('--link_range', choices=range(0,96), type=int, nargs=2,
metavar=('start','stop'), help='Start and stop values '
'for the range related to links')
parser.add_argument('--link_bitwidth', choices=[32,64], type=int,
help='Word size in bits of each link')
parser.add_argument('--invalid_rows', type=int,
help='The number of invalid that will be generate at '
'the beginning of the test')
parser.add_argument('--fixed_point_bits', type=int, nargs=2,
metavar=('word_bits', 'integer_bits'),
help='The number of invalid that will be generate at '
'the beginning of the test')
parser.add_argument('--pack_links', type=bool, help='Whether to pack '
'multiple values into one link where possible')
parser.add_argument('--link_map', type=int, nargs='*', help='The link map')
parser.add_argument('--input_data_path', type=str,
help='The path of the numpy file containing data in '
'floating point')
parser.add_argument('--output_data_path', type=str,
help='The path of the produced .txt file containing '
'data in fixed point')
args = parser.parse_args()
fp32_data = np.load(args.input_data_path)
# packing factor
pf = args.link_bitwidth // args.fixed_point_bits[0] if args.pack_links else 1
link_width = args.link_range[1] - args.link_range[0] + 1
if fp32_data.shape[1] > link_width * pf:
raise Exception(
'Trying to fit {} features into {} links with packing factor {}'
.format(fp32_data.shape[1],link_width,pf))
if fp32_data.shape[0] > 1024:
print('The system expect no more than 1024 rows; the original file will '
'be truncated, keeping the first 1024 rows')
fp32_data = fp32_data[:1024]
output_file = open(args.output_data_path, 'w')
fixed_data = to_fixed(fp32_data, args)
if args.generate_float_from_fix:
float_back_data = to_float(fixed_data, args)
np.save('float_from_fix.npy', float_back_data)
fixed_data = np.array([row_to_hex(row, args) for row in fixed_data])
link_map = list(range(args.link_range[0], args.link_range[1] + 1)) \
if args.link_map is None else args.link_map
assert len(link_map) == link_width, \
'Link map length ({}) does not match link range ({})'.format(
len(link_map), link_width)
# board section
board_string = 'Board {}\n'.format(args.board_name)
# channel section
quad_chan_string = ' Quad/Chan : '
for i in link_map:
if args.link_bitwidth == 32:
quad_chan_string += ' q{:02d}c{} '.format(i // 4, i % 4)
else:
quad_chan_string += ' q{:02d}c{} '.format(i // 4, i % 4)
if i != link_map[-1]:
quad_chan_string += ' '
else:
quad_chan_string += '\n'
# link section
link_string = ' Link : '
for i in link_map:
if args.link_bitwidth == 32:
link_string += ' {:02d} '.format(i)
else:
link_string += ' {:02d} '.format(i)
if i != link_map[-1]:
link_string += ' '
else:
link_string += '\n'
# frame section
frame_start = 'Frame {:04d} : '
frame = ''
if args.invalid_rows > 0:
for i in range(0,args.invalid_rows):
frame += frame_start.format(i)
for j in range(0, args.link_range[1] - args.link_range[0] + 1):
if args.link_bitwidth == 32:
frame += '0v00000000'
else:
frame += '0v0000000000000000'
if j != args.link_range[1] - args.link_range[0]:
frame += ' '
else:
frame += '\n'
dummy_cols = args.link_range[1] - args.link_range[0] - fp32_data.shape[1]
for i, v in enumerate(fixed_data):
frame += frame_start.format(i + args.invalid_rows)
for j, k in enumerate(v):
frame += '1v' + k
frame += ' '
if dummy_cols > 0:
for s in range(0, dummy_cols + 1):
if args.link_bitwidth == 32:
frame += '0v00000000'
else:
frame += '0v0000000000000000'
if s + j != args.link_range[1] - args.link_range[0] - 1:
frame += ' '
frame += '\n'
l = [board_string, quad_chan_string, link_string, frame]
output_file.writelines(l)
output_file.close()
print('Done!')
|
[
"numpy.load",
"numpy.save",
"numpy.flip",
"argparse.ArgumentParser",
"bitstring.pack",
"numpy.round"
] |
[((191, 211), 'numpy.round', 'np.round', (['(x * 2 ** F)'], {}), '(x * 2 ** F)\n', (199, 211), True, 'import numpy as np\n'), ((1090, 1180), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Parse numpy file to FPGA testing for MP7 board"""'}), "(description=\n 'Parse numpy file to FPGA testing for MP7 board')\n", (1113, 1180), False, 'import argparse\n'), ((3071, 3100), 'numpy.load', 'np.load', (['args.input_data_path'], {}), '(args.input_data_path)\n', (3078, 3100), True, 'import numpy as np\n'), ((728, 764), 'bitstring.pack', 'bitstring.pack', (['fmt_string', '(0)', '*vals'], {}), '(fmt_string, 0, *vals)\n', (742, 764), False, 'import bitstring\n'), ((3873, 3919), 'numpy.save', 'np.save', (['"""float_from_fix.npy"""', 'float_back_data'], {}), "('float_from_fix.npy', float_back_data)\n", (3880, 3919), True, 'import numpy as np\n'), ((978, 1011), 'numpy.flip', 'np.flip', (['row[i * pf:(i + 1) * pf]'], {}), '(row[i * pf:(i + 1) * pf])\n', (985, 1011), True, 'import numpy as np\n')]
|
"""
@brief This file holds classes that store information about the endoscopic images that are
going to be segmented.
@author <NAME> (<EMAIL>).
@date 25 Aug 2015.
"""
import numpy as np
import os
import cv2
# import caffe
import sys
import random
import matplotlib.pyplot as plt
import scipy.misc
import imutils
import geometry
import tempfile
import PIL
import skimage.morphology
import skimage.util
# My imports
import common
#
# @brief Perlin noise generator.
#
def perlin(x, y, seed):
# Permutation table
np.random.seed(seed)
p = np.arange(256, dtype = int)
np.random.shuffle(p)
p = np.stack([p, p]).flatten()
# Coordinates of the top-left
xi = x.astype(int)
yi = y.astype(int)
# Internal coordinates
xf = x - xi
yf = y - yi
# Fade factors
u = fade(xf)
v = fade(yf)
# Noise components
n00 = gradient(p[p[xi] + yi], xf, yf)
n01 = gradient(p[p[xi] + yi + 1], xf, yf - 1)
n11 = gradient(p[p[xi + 1] + yi + 1], xf - 1, yf - 1)
n10 = gradient(p[p[xi + 1] + yi], xf - 1, yf)
# Combine noises
x1 = lerp(n00, n10, u)
x2 = lerp(n01, n11, u)
return lerp(x1, x2, v)
#
# @brief Linear interpolation.
#
def lerp(a, b, x):
return a + x * (b - a)
#
# @brief 6t^5 - 15t^4 + 10t^3.
#
def fade(t):
return 6 * t**5 - 15 * t**4 + 10 * t**3
#
# @brief Grad converts h to the right gradient vector and return the dot product with (x, y).
#
def gradient(h, x, y):
vectors = np.array([[0, 1], [0, -1], [1, 0], [-1, 0]])
g = vectors[h % 4]
return g[:,:, 0] * x + g[:,:, 1] * y
#
# @brief Perlin noise image.
#
# @param[in] height Height of the output image.
# @param[in] width Width of the output image.
# @param[in] scale Higher means smaller blobs.
# @param[in] minval The minimum noise value.
# @param[in] maxval The maximum noise value.
#
# @returns a 2D numpy array.
def perlin2d_smooth(height, width, scale, minval = 0.0, maxval = 1.0, seed = None):
lin_y = np.linspace(0, scale, height, endpoint = False)
lin_x = np.linspace(0, scale, width, endpoint = False)
x, y = np.meshgrid(lin_x, lin_y)
arr = perlin(x, y, seed)
min_arr = np.min(arr)
max_arr = np.max(arr)
arr = (np.clip((arr - min_arr) / (max_arr - min_arr), 0.0, 1.0) * (maxval - minval)) + minval
return arr
#
# @brief Given a set of 2D points it finds the center and radius of a circle.
#
# @param[in] x List or array of x coordinates.
# @param[in] y List or array of y coordinates.
#
# @returns (xc, yc, radius).
def fit_circle(x, y):
# Coordinates of the barycenter
x_m = np.mean(x)
y_m = np.mean(y)
# Calculation of the reduced coordinates
u = x - x_m
v = y - y_m
# Linear system defining the center in reduced coordinates (uc, vc):
# Suu * uc + Suv * vc = (Suuu + Suvv)/2
# Suv * uc + Svv * vc = (Suuv + Svvv)/2
Suv = np.sum(u*v)
Suu = np.sum(u**2)
Svv = np.sum(v**2)
Suuv = np.sum(u**2 * v)
Suvv = np.sum(u * v**2)
Suuu = np.sum(u**3)
Svvv = np.sum(v**3)
# Solving the linear system
A = np.array([ [ Suu, Suv ], [Suv, Svv]])
B = np.array([ Suuu + Suvv, Svvv + Suuv ])/2.0
uc, vc = np.linalg.solve(A, B)
xc_1 = x_m + uc
yc_1 = y_m + vc
# Calculation of all distances from the center (xc_1, yc_1)
Ri_1 = np.sqrt((x - xc_1) ** 2 + (y - yc_1) ** 2)
R_1 = np.mean(Ri_1)
residu_1 = np.sum((Ri_1-R_1) ** 2)
residu2_1 = np.sum((Ri_1 ** 2 - R_1 ** 2) ** 2)
return xc_1, yc_1, R_1
#
# @brief Zero parameter Canny edge detector.
#
def auto_canny(image, sigma = 0.33):
v = np.median(image)
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edges = cv2.Canny(image, lower, upper)
return edges
#
# @brief Abstract image class. This is not meant to be instantiated and it refers to a general
# multidimensional image or label.
#
class CaffeinatedAbstract(object):
#
# @brief Every image must have at least data and name. We ensure of that with this abstract
# constructor that will be called by all the children.
#
# @param[in] raw_frame Multidimensional image, at least H x W.
# @param[in] name String with the name of the image. It can also be the frame number
# of a video, but it will be converted to string.
#
def __init__(self, raw_frame, name):
# Assert that the frame has data
if len(raw_frame.shape) <= 1 or raw_frame.shape[0] <= 0 or raw_frame.shape[1] <= 0:
raise RuntimeError('[CaffeinatedAbstract.__init__], the image provided ' \
'does not have data.')
# Assert that the name is valid
if not name:
raise ValueError('[CaffeinatedAbstract.__init__] Error, every caffeinated ' \
'abstract child must have a name.')
# Store attributes in class
self._raw_frame = raw_frame
self._name = str(name)
#
# @brief Access to a copy of the internal BGR image.
#
# @returns a copy of the internal frame, whatever it is, image or label.
def raw_copy(self):
return self._raw_frame.copy()
#
# @brief Saves image to file.
#
# @param[in] path Destination path.
# @param[in] flags Flags that will be passed to OpenCV.
#
def save(self, path, flags):
# Assert that the destination path does not exist
if common.path_exists(path):
raise ValueError('[CaffeinatedImage.save] Error, destination path ' \
+ str(path) + ' already exists.')
if flags:
return cv2.imwrite(path, self._raw_frame, flags)
else:
return cv2.imwrite(path, self._raw_frame)
#
# @brief Crops an image in a rectangular fashion, including both corner pixels in the image.
#
# @param[in] tlx Integer that represents the top left corner column.
# @param[in] tly Integer that represents the top left corner row.
# @param[in] brx Integer that represents the bottom right corner column.
# @param[in] bry Integer that represents the bottom right corner row.
#
# @returns nothing.
def crop(self, tlx, tly, brx, bry):
assert(isinstance(tlx, type(0)) and isinstance(tly, type(1)) and isinstance(brx, type(1)) \
and isinstance(bry, type(1)))
assert(tlx <= brx)
assert(tly <= bry)
self._raw_frame = self._raw_frame[tly:bry + 1, tlx:brx + 1]
def resize_to_width(self, new_w, interp):
self._raw_frame = CaffeinatedAbstract.resize_width(self._raw_frame, new_w, interp)
#
# @brief Convert binary mask into just the mask of its boundary.
#
# @param[in] mask Input mask.
# @param[in] thickness Thickness of the border.
#
# @returns the boundary mask.
@staticmethod
def mask2border(mask, thickness):
# Find the contour of the mask
cnts = cv2.findContours(mask.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
# Create a new image with just the contour
new_mask = np.zeros_like(mask)
new_mask = cv2.drawContours(new_mask, cnts, -1, 255, thickness)
return new_mask
#
# @brief Histogram equalisation (CLAHE).
#
# @param[in] im Input image.
# @param[in] clip_limit Contrast limit.
#
# @returns the equalised image.
@staticmethod
def clahe(im, clip_limit = 2.0):
lab = cv2.cvtColor(im, cv2.COLOR_BGR2Lab)
clahe_engine = cv2.createCLAHE(clipLimit = clip_limit, tileGridSize = (8, 8))
lab[:,:, 0] = clahe_engine.apply(lab[:,:, 0])
return cv2.cvtColor(lab, cv2.COLOR_Lab2BGR)
#
# @brief Flip left-right.
#
# @returns the flipped image.
@staticmethod
def fliplr(im):
return np.fliplr(im)
#
# @brief Flip up-down.
#
# @returns the flipped image.
@staticmethod
def flipud(im):
return np.flipud(im)
#
# @brief Thresholds a grayscale image.
#
# @param[in] img Input grayscale image.
# @param[in] level Greater than this level will be set to maxval. Default value is 127.
# @param[in] maxval Th values greater than level will be set to maxval.
# Default value is 255.
#
# @returns the thresholded image.
@staticmethod
def bin_thresh(im, level = 127, maxval = 255):
assert(len(im.shape) == 2 or (len(im.shape) == 3 and im.shape[2] == 1))
_, thresh = cv2.threshold(np.squeeze(im), level, maxval, cv2.THRESH_BINARY)
return thresh
#
# @brief Random crop, both dimensions should be equal or smaller than the original size.
# @details If a list is given, all the images must be larger than the desired new height and
# width.
#
# @param[in] img Ndarray with the image, shape (height, width) or
# (height, width, channels).
# @param[in] new_height Height of the cropped image.
# @param[in] new_width Width of the cropped image.
#
# @returns a cropped patch.
@staticmethod
def random_crop(im, new_height, new_width):
assert(isinstance(im, np.ndarray))
assert(new_height > 0 and new_height <= im.shape[0])
assert(new_width > 0 and new_width <= im.shape[1])
# Choose random coordinates for crop
height_border = im.shape[0] - new_height
width_border = im.shape[1] - new_width
top_y = random.randint(0, height_border - 1) if height_border > 0 else 0
top_x = random.randint(0, width_border - 1) if width_border > 0 else 0
# Crop image
new_im = im[top_y:top_y + new_height, top_x:top_x + new_width].copy()
assert(new_im.shape[0] == new_height)
assert(new_im.shape[1] == new_width)
return new_im
#
# @brief Performs a random crop. New height and width is decided independently, this
# function changes the form factor.
#
# @param[in] img Input image, numpy array.
# @param[in] delta Minimum factor of change, e.g. if 0.5 the new height and width will be
# minimum half of the original.
#
# @returns the new image.
@staticmethod
def random_crop_factor(im, delta):
assert(isinstance(im, np.ndarray))
min_scale = 1.0 - delta
max_scale = 1.0
new_scale = random.uniform(min_scale, max_scale)
new_height = int(round(im.shape[0] * new_scale))
new_width = int(round(im.shape[1] * new_scale))
new_im = CaffeinatedAbstract.random_crop(im, new_height, new_width)
return new_im
#
# @brief Performs a random crop. New height and width is decided independently, this
# function changes the form factor.
#
# @param[in] img Input image, numpy array.
# @param[in] delta Minimum factor of change, e.g. if 0.5 the new height and width will be
# minimum half of the original.
#
# @returns the new image.
@staticmethod
def random_crop_no_factor(im, delta):
assert(isinstance(im, np.ndarray))
min_scale = 1.0 - delta
max_scale = 1.0
new_height = int(round(im.shape[0] * random.uniform(min_scale, max_scale)))
new_width = int(round(im.shape[1] * random.uniform(min_scale, max_scale)))
new_im = CaffeinatedAbstract.random_crop(im, new_height, new_width)
return new_im
#
# @brief Random crop of a list of images. The crops will be performed in different locations
# for the different images of the list, but all the output images will have the same
# size.
#
# @param[in] im_list List of images to be cropped.
# @param[in] new_height Height of the cropped image.
# @param[in] new_width Width of the cropped image.
#
# @returns a list of cropped images to the desired size.
@staticmethod
def random_crop_list(im_list, new_height, new_width):
assert(isinstance(im_list, list))
assert(len(im_list) > 0)
new_im_list = [ CaffeinatedAbstract.random_crop(im, new_height, new_width) \
for im in im_list ]
return new_im_list
#
# @brief Random crop all the images of the list in the same coordinates for all of them.
# All the input images MUST have the same size.
#
# @param[in] im_list List of images to be cropped.
# @param[in] new_height Height of the cropped image.
# @param[in] new_width Width of the cropped image.
#
# @returns a list of cropped images to the desired size.
@staticmethod
def random_crop_same_coord_list(im_list, new_height, new_width):
assert(isinstance(im_list, list))
assert(len(im_list) > 0)
# Choose random coordinates for crop
height_border = im_list[0].shape[0] - new_height
width_border = im_list[0].shape[1] - new_width
top_y = random.randint(0, height_border - 1) if height_border > 0 else 0
top_x = random.randint(0, width_border - 1) if width_border > 0 else 0
# Crop all the images in the list
new_im_list = [ im[top_y:top_y + new_height, top_x:top_x + new_width].copy() \
for im in im_list ]
return new_im_list
#
# @brief Random crop all the images of the list in the same coordinates for all of them.
# All the images MUST have the same size. The output images will have the same form
# factor.
#
# @param[in] im_list List of images to be cropped.
# @param[in] delta Minimum factor of change, e.g. if 0.5 the new height and width will be
# minimum half of the original.
#
# @returns a list of cropped images to the desired size.
@staticmethod
def random_crop_same_coord_list_factor(im_list, delta):
assert(isinstance(im_list, list))
assert(len(im_list) > 0)
# Get the dimensions of the new images
min_scale = 1.0 - delta
max_scale = 1.0
new_scale = random.uniform(min_scale, max_scale)
new_height = int(round(im.shape[0] * new_scale))
new_width = int(round(im.shape[1] * new_scale))
return CaffeinatedAbstract.random_crop_same_coord_list(im_list, new_height, new_width)
#
# @brief Random crop all the images of the list in the same coordinates for all of them.
# All the images MUST have the same size. The output images will not have the same
# form factor.
#
# @param[in] im_list List of images to be cropped.
# @param[in] delta Minimum factor of change, e.g. if 0.5 the new height and width will be
# minimum half of the original.
#
# @returns a list of cropped images to the desired size.
@staticmethod
def random_crop_same_coord_list_no_factor(im_list, delta):
assert(isinstance(im_list, list))
assert(len(im_list) > 0)
# Get the dimensions of the new images
min_scale = 1.0 - delta
max_scale = 1.0
new_height = int(round(im_list[0].shape[0] * random.uniform(min_scale, max_scale)))
new_width = int(round(im_list[0].shape[1] * random.uniform(min_scale, max_scale)))
return CaffeinatedAbstract.random_crop_same_coord_list(im_list, new_height, new_width)
#
# @brief Scale an image keeping original size, that is, the output image will have the
# size of the input.
#
# @details If the scale factor is smaller than 1.0, the output image will be padded.
# Otherwise it will be cropped.
#
# @param[in] im Input image or list of images.
# @param[in] scale_factor If 1.0, the image stays as it is.
# @param[in] interp Method of interpolation: nearest, bilinear, bicubic, lanczos.
# @param[in] boder_value Border value. Used when the image is downsized and padded.
# @param[in] clip_sides List of sides to crop out. Used only in case the scaling factor
# is lower than 1.0.
#
# @returns the scaled image.
@staticmethod
def scale_keeping_size(im, scale_factor, interp, border_value, clip_sides = None):
if clip_sides is None:
clip_sides = []
# Resize image to the desired new scale
new_im = CaffeinatedAbstract.resize_factor(im, scale_factor, interp)
# If the new image is larger, we crop it
if new_im.shape[0] > im.shape[0]:
new_im = CaffeinatedAbstract.crop_center(new_im, im.shape[1], im.shape[0])
# If the new image is smaller, we pad it
elif new_im.shape[0] < im.shape[0]:
padded = np.full_like(im, border_value)
start_row = (padded.shape[0] // 2) - (new_im.shape[0] // 2)
start_col = (padded.shape[1] // 2) - (new_im.shape[1] // 2)
end_row = start_row + new_im.shape[0]
end_col = start_col + new_im.shape[1]
padded[start_row:end_row, start_col:end_col] = new_im
new_im = padded
# Move the image to the desired sides (used to downscale tools and still keep them
# attached to the border of the image)
if 'top' in clip_sides:
M = np.float32([[1, 0, 0], [0, 1, -start_row]])
new_im = cv2.warpAffine(new_im, M, (padded.shape[1], padded.shape[0]),
interp, cv2.BORDER_CONSTANT, border_value)
if 'left' in clip_sides:
M = np.float32([[1, 0, -start_col], [0, 1, 0]])
new_im = cv2.warpAffine(new_im, M, (padded.shape[1], padded.shape[0]),
interp, cv2.BORDER_CONSTANT, border_value)
if 'bottom' in clip_sides:
M = np.float32([[1, 0, 0], [0, 1, start_row]])
new_im = cv2.warpAffine(new_im, M, (padded.shape[1], padded.shape[0]),
interp, cv2.BORDER_CONSTANT, border_value)
if 'right' in clip_sides:
M = np.float32([[1, 0, start_col], [0, 1, 0]])
new_im = cv2.warpAffine(new_im, M, (padded.shape[1], padded.shape[0]),
interp, cv2.BORDER_CONSTANT, border_value)
return new_im
#
# @brief Could flip the image or not.
#
# @param[in] im Image or list of images. If list, all images are either flipped or not.
#
# @returns the image (maybe flipped) maybe just the original one.
@staticmethod
def random_fliplr(im, not_used = None):
if common.randbin():
if isinstance(im, list):
return [ CaffeinatedAbstract.fliplr(i) for i in im ]
else:
return CaffeinatedAbstract.fliplr(im)
else:
return im
#
# @brief Could flip the image or not.
#
# @param[in] im Image or list of images. If list, all images are either flipped or not.
#
# @returns the image (maybe flipped) maybe just the original one.
@staticmethod
def random_flipud(im, not_used = None):
if common.randbin():
if isinstance(im, list):
return [ CaffeinatedAbstract.flipud(i) for i in im ]
else:
return CaffeinatedAbstract.flipud(im)
else:
return im
#
# @brief Add motion blur in a specific direction.
#
# @param[in] im Input image.
# @param[in] mask Pass a foreground mask if you wanna apply the motion just in the
# foreground.
# @param[in] apply_on Either 'bg', 'fg' or 'both'.
# @param[in] ks Size of the convolution kernel to be applied.
# @param[in] phi_deg Angle of rotation in degrees. Default is zero, so the motion will be
# horizontal.
#
# @returns the blured images.
@staticmethod
def directional_motion_blur(im, phi_deg = 0, ks = 15):
# Generating the kernel
kernel = np.zeros((ks, ks))
kernel[int((ks - 1) / 2),:] = np.ones(ks) / ks
# Rotate image if the user wants to simulate motion in a particular direction
# rot_im = CaffeinatedAbstract.rotate_bound(im, phi_deg, cv2.INTER_CUBIC)
# rot_im_blur = cv2.filter2D(rot_im, -1, kernel)
# new_im = CaffeinatedAbstract.rotate_bound(rot_im_blur, -phi_deg, cv2.INTER_CUBIC)
# tly = (new_im.shape[0] - im.shape[0]) // 2
# tlx = (new_im.shape[1] - im.shape[1]) // 2
# new_im = new_im[tly:tly + im.shape[0], tlx:tlx + im.shape[1]]
# FIXME: We keep just horizontal motion to investigate drop in performance
new_im = cv2.filter2D(im, -1, kernel)
return new_im
#
# @brief Random motion blur. Both foreground and background images must have the same size.
#
# @param[in] im Input image.
# @param[in] mask Mask of the foreground object that will appear blurred within the
# image.
# @param[in] rho Magnitude in pixels of the foreground motion vector.
# @param[in] phi_deg Angle in degrees of the motion vector.
# @param[in] interlaced Random interlacing will be added. Some lines of the foreground will
# move and others will not.
# @param[in] alpha Weight for the weighted sum. Default value is 0.5.
#
# @returns the blurred image.
@staticmethod
def weighted_sum_motion_blur(im, mask, rho, phi_deg, interlaced = False,
alpha = 0.5):
assert(im.shape[0] == mask.shape[0])
assert(im.shape[1] == mask.shape[1])
# Compute random motion vector
phi = common.deg_to_rad(phi_deg)
tx = rho * np.cos(phi)
ty = rho * np.sin(phi)
# Translation matrix
trans_mat = np.eye(3)
trans_mat[0, 2] = tx
trans_mat[1, 2] = ty
mat = trans_mat[:2, :3]
# Warp current image and mask according to the motion vector
im_warped = cv2.warpAffine(im, mat, (im.shape[1], im.shape[0]), flags = cv2.INTER_CUBIC)
mask_warped = cv2.warpAffine(mask, mat, (im.shape[1], im.shape[0]),
flags = cv2.INTER_NEAREST)
# Interlacing
if interlaced:
mask_warped_orig = mask_warped.copy()
lines_with_mask = np.unique(np.nonzero(mask_warped)[0]).tolist()
if lines_with_mask:
num_lines_to_remove = np.random.randint(len(lines_with_mask))
random.shuffle(lines_with_mask)
lines_with_mask = lines_with_mask[:num_lines_to_remove]
for i in lines_with_mask:
mask_warped[i,:] = 0
# Combine both images
new_im = im.copy()
new_im[mask_warped > 0] = np.round(
alpha * im[mask_warped > 0] + (1. - alpha) * im_warped[mask_warped > 0]
).astype(np.uint8)
# Blur if interlaced
if interlaced:
ksize = 3
blurred = cv2.GaussianBlur(new_im, (ksize, ksize), 0)
new_im[mask_warped_orig > 0] = blurred[mask_warped_orig > 0]
return new_im
#
# @brief Adds or subtracts intensity in different parts of the image using Perlin noise.
#
# @param[in] im Input image.
#
# @returns the augmented image.
@staticmethod
def random_local_brightness_augmentation(im, intensity_start = 50., intensity_stop = 200.,
intensity_step = 50., shape_start = 1., shape_stop = 5., shape_step = 1.):
# Generate random illumination change range
intensity_options = np.arange(intensity_start, intensity_stop + intensity_step, intensity_step)
change_choice = np.random.choice(intensity_options)
# Generate Perlin blob size, larger numbers mean smaller blobs
shape_options = np.arange(shape_start, shape_stop + shape_step, shape_step)
shape_choice = np.random.choice(shape_options)
# Generate Perlin additive noise mask
pn = perlin2d_smooth(im.shape[0], im.shape[1], shape_choice) * change_choice \
- .5 * change_choice
pn = np.dstack((pn, pn, pn))
# Modify the image: HSV option
# hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV).astype(np.float64)
# hsv[:, :, 2] = np.round(np.clip(hsv[:, :, 2] + pn, 0, 255))
# augmented = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
# Additive value on BGR
augmented = np.round(np.clip(im.astype(np.float64) + pn, 0., 255.)).astype(np.uint8)
return augmented
#
# @brief Adds or subtracts intensity in different parts of the image using Perlin noise.
#
# @param[in] im Input image.
#
# @returns the augmented image.
@staticmethod
def random_local_contrast_augmentation(im, shape_start = 1., shape_stop = 5., shape_step = 1.):
# Choose minimum and maximum contrast randomly
contrast_min = random.choice([0.5, 0.6, 0.7, 0.8])
contrast_max = random.choice([1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0])
# Generate Perlin blob size, larger numbers mean smaller blobs
shape_options = np.arange(shape_start, shape_stop + shape_step, shape_step)
shape_choice = np.random.choice(shape_options)
# Generate Perlin additive noise mask
pn = perlin2d_smooth(im.shape[0], im.shape[1], shape_choice, minval = contrast_min,
maxval = contrast_max)
pn = np.dstack((pn, pn, pn))
# Modify the image
augmented = np.round(np.clip(np.multiply(im.astype(np.float64), pn), 0, 255)).astype(np.uint8)
return augmented
#
# @brief Global (as in same additive value added to all pixels) brightness augmentation.
#
# @param[in] im Input image.
#
# @returns the augmented image.
@staticmethod
def random_global_brightness_augmentation(im, intensity_start = -50, intensity_stop = 50,
intensity_step = 10):
# Generate random illumination change
intensity_options = np.arange(intensity_start, intensity_stop + intensity_step,
intensity_step)
change_choice = np.random.choice(intensity_options)
# Additive change on Value of HSV
# hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV).astype(np.float64)
# hsv[:, :, 2] = np.round(np.clip(hsv[:, :, 2] + change_choice, 0., 255.))
# hsv = hsv.astype(np.uint8)
# augmented = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
# Additive change on all channels of BGR
augmented = np.clip(im.astype(np.float64) + change_choice, 0, 255).astype(np.uint8)
return augmented
#
# @brief Global contrast (multiplicative) augmentation.
#
# @param[in] im Input image.
#
# @returns the augmented image.
@staticmethod
def random_global_contrast_augmentation(im):
contrast_choice = random.choice([0.5, 0.6, 0.7, 0.8, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8,
1.9, 2.0])
augmented = np.round(np.clip(np.multiply(im.astype(np.float64),
contrast_choice), 0, 255)).astype(np.uint8)
return augmented
#
# @brief Bernoulli motion blur.
#
# @param[in] im Image or list of images.
# @param[in] mask Mask of moving object.
# @param[in] max_mag Maximum amount of pixels of displacement.
# @param[in] max_ang Maximum angle of the motion vector. Default is 360, i.e. can move in any
# direction.
#
# @returns the images with motion blur with a probability p.
@staticmethod
def random_weighted_sum_motion_blur(im, mask, max_mag = 32, max_ang = 360):
rho = np.random.randint(max_mag)
phi_deg = np.random.randint(max_ang)
interlaced = common.randbin()
if isinstance(im, list):
return [ CaffeinatedAbstract.weighted_sum_motion_blur(i, m, rho, phi_deg,
interlaced) for i, m in zip(im, mask) ]
else:
return CaffeinatedAbstract.weighted_sum_motion_blur(im, mask, rho, phi_deg,
interlaced)
#
# @brief Converts an image from BGR to BRG.
#
# @param[in] im BGR image.
#
# @returns an image converted to BRG.
@staticmethod
def bgr2brg(im):
return im[..., [0, 2, 1]]
#
# @brief Bernoulli BGR to BRG swapping.
#
# @param[in] im Image or list of images.
#
# @returns the image with the green-red channels swapped with a probability of 0.5.
@staticmethod
def random_brg(im):
if common.randbin():
if isinstance(im, list):
return [ CaffeinatedAbstract.bgr2brg(i) for i in im ]
else:
return CaffeinatedAbstract.bgr2brg(im)
else:
return im
#
# @brief Rotates the image over itself a random number of degrees.
#
# @param[in] im Input image, numpy array.
# @param[in] deg_delta The range of possible rotation is +- deg_delta.
# @param[in] interp Interpolation method: lanczos, linear, cubic, nearest.
#
# @returns the rotated image.
@staticmethod
def random_rotation(im, deg_delta, interp):
max_ang = deg_delta
min_ang = -1. * max_ang
ang = random.uniform(min_ang, max_ang)
new_im = None
if isinstance(im, list):
new_im = [ CaffeinatedAbstract.rotate_and_crop(i, ang, interp) for i in im ]
else:
new_im = CaffeinatedAbstract.rotate_and_crop(im, ang, interp)
return new_im
#
# @brief Resizes an imaged to the desired width while keeping proportions.
#
# @param[in] im Image to be resized.
# @param[in] new_w New width.
# @param[in] interp Method of interpolation: nearest, bilinear, bicubic, lanczos.
#
# @returns a resized image.
@staticmethod
def resize_width(im, new_w, interp = None):
assert(im.dtype == np.uint8)
# If no interpolation method is chosen we select the most convenient depending on whether
# the user is upsampling or downsampling the image
if interp is None:
interp = cv2.INTER_AREA if new_w < im.shape[1] else cv2.INTER_LANCZOS4
ratio = float(im.shape[0]) / float(im.shape[1])
new_h = int(round(new_w * ratio))
new_im = cv2.resize(im, (new_w, new_h), interpolation=interp)
return new_im
#
# @brief Resizes an imaged to the desired width while keeping proportions.
#
# @param[in] im Image to be resized.
# @param[in] new_h New height.
# @param[in] interp Method of interpolation: nearest, bilinear, bicubic, lanczos.
#
# @returns a resized image.
@staticmethod
def resize_height(im, new_h, interp):
assert(im.dtype == np.uint8)
ratio = float(im.shape[0]) / float(im.shape[1])
new_w = int(round(new_h / ratio))
# imethod = PIL_interp_method[interp]
# new_im = np.array(PIL.Image.fromarray(im).resize((new_w, new_h), imethod))
new_im = cv2.resize(im, (new_w, new_h), interpolation=interp)
return new_im
#
# @brief Scales an image to a desired factor of the original one.
#
# @param[in] im Image to be resized.
# @param[in] scale_factor Factor to scale up or down the image.
# @param[in] interp Method of interpolation: nearest, bilinear, bicubic, lanczos.
#
# @returns a resized image.
@staticmethod
def resize_factor(im, scale_factor, interp):
new_w = int(round(im.shape[1] * scale_factor))
return CaffeinatedAbstract.resize_width(im, new_w, interp)
#
# @brief Scales an image to a desired factor of the original one.
#
# @param[in] im Image to be resized.
# @param[in] new_w New width.
# @param[in] new_h New width.
# @param[in] interp Method of interpolation: nearest, bilinear, bicubic, lanczos.
#
# @returns a resized image.
@staticmethod
def resize(im, new_w, new_h, interp):
# imethod = PIL_interp_method[interp]
# new_im = scipy.misc.imresize(im, (new_h, new_w), interp = interp).astype(im.dtype)
# return np.array(PIL.Image.fromarray(im).resize((new_w, new_h), imethod),
# dtype = im.dtype)
new_im = cv2.resize(im, (new_w, new_h), interpolation=interp)
return new_im
#
# @returns a crop of shape (new_h, new_w).
#
@staticmethod
def crop_center(im, new_w, new_h):
start_x = im.shape[1] // 2 - (new_w // 2)
start_y = im.shape[0] // 2 - (new_h // 2)
return im[start_y:start_y + new_h, start_x:start_x + new_w].copy()
#
# @brief Rotatation of an image with black bounds around it, as it would be
# expected. A positive rotation angle results in a clockwise rotation.
#
# @param[in] image Numpy ndarray.
# @param[in] angle Angle in degrees.
#
# @returns the rotated image.
@staticmethod
def rotate_bound(image, angle, interp):
# Grab the dimensions of the image and then determine the center
(h, w) = image.shape[:2]
(cX, cY) = (w / 2, h / 2)
# Grab the rotation matrix (applying the negative of the
# angle to rotate clockwise), then grab the sine and cosine
# (i.e., the rotation components of the matrix)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# Compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# Adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
# Perform the actual rotation and return the image
return cv2.warpAffine(image, M, (nW, nH), flags = interp)
#
# brief Rotates an image over a centre point given and leaves the whole
# image inside. Clockwise rotation of the image.
#
# @param[in] im Numpy ndarray.
# @param[in] centre (x, y) in image coordinates.
# @param[in] angle Angle in degrees.
# @param[in] interp OpenCV interpolation method.
@staticmethod
def rotate_bound_centre(im, centre, deg, interp):
cm_x = centre[0]
cm_y = centre[1]
# Build the rotation matrix
rot_mat = cv2.getRotationMatrix2D((cm_y, cm_x), -deg, 1.0)
rot_mat_hom = np.zeros((3, 3))
rot_mat_hom[:2,:] = rot_mat
rot_mat_hom[2, 2] = 1
# Find the coordinates of the corners in the rotated image
h = im.shape[0]
w = im.shape[1]
tl = np.array([0, 0, 1]).reshape((3, 1))
tr = np.array([w - 1, 0, 1]).reshape((3, 1))
bl = np.array([0, h - 1, 1]).reshape((3, 1))
br = np.array([w - 1, h - 1, 1]).reshape((3, 1))
tl_rot = np.round(np.dot(rot_mat_hom, tl)).astype(np.int)
tr_rot = np.round(np.dot(rot_mat_hom, tr)).astype(np.int)
bl_rot = np.round(np.dot(rot_mat_hom, bl)).astype(np.int)
br_rot = np.round(np.dot(rot_mat_hom, br)).astype(np.int)
# Compute the size of the new image from the coordinates of the rotated one so that
# we add black bounds around the rotated one
min_x = min([tl_rot[0], tr_rot[0], bl_rot[0], br_rot[0]])
max_x = max([tl_rot[0], tr_rot[0], bl_rot[0], br_rot[0]])
min_y = min([tl_rot[1], tr_rot[1], bl_rot[1], br_rot[1]])
max_y = max([tl_rot[1], tr_rot[1], bl_rot[1], br_rot[1]])
new_w = max_x + 1 - min_x
new_h = max_y + 1 - min_y
# Correct the translation so that the rotated image lies inside the window
rot_mat[0, 2] -= min_x
rot_mat[1, 2] -= min_y
return cv2.warpAffine(im, rot_mat, (new_w[0], new_h[0]), flags = interp)
#
# @brief Clockwise rotation plus crop (so that there is no extra added black background).
#
# @details The crop is done based on a rectangle of maximal area inside the rotated region.
#
# @param[in] im Numpy ndarray image. Shape (h, w, 3) or (h, w).
# @param[in] ang Angle in degrees.
# @param[in] interp Interpolation method: lanczos, linear, cubic, nearest.
#
# @returns the rotated image.
@staticmethod
def rotate_and_crop(im, ang, interp):
# Rotate image
rotated = CaffeinatedAbstract.rotate_bound(im, ang, interp)
# Calculate cropping area
wr, hr = geometry.rotated_rect_with_max_area(im.shape[1],
im.shape[0], common.deg_to_rad(ang))
wr = int(np.floor(wr))
hr = int(np.floor(hr))
# Centre crop
rotated = CaffeinatedAbstract.crop_center(rotated, wr, hr)
return rotated
#
# @brief This method deinterlaces an image using ffmpeg.
#
# @param[in] im Numpy ndarray image. Shape (h, w, 3) or (h, w).
#
# @returns the deinterlaced image.
@staticmethod
def deinterlace(im, ext = '.png'):
input_path = tempfile.gettempdir() + '/' + common.gen_rand_str() + ext
output_path = tempfile.gettempdir() + '/' + common.gen_rand_str() + ext
# Save image in a temporary folder
cv2.imwrite(input_path, im)
# Deinterlace using ffmpeg
common.shell('ffmpeg -i ' + input_path + ' -vf yadif ' + output_path)
# Read deinterlaced image
dei = cv2.imread(output_path)
# Remove image from temporary folder
common.rm(input_path)
common.rm(output_path)
return dei
@staticmethod
def gaussian_noise(im, mean=0, std=20):
noise = np.random.normal(mean, std, im.shape)
return np.round(np.clip(im.astype(np.float64) + noise, 0, 255)).astype(np.uint8)
#
# @rteurns a gamma corrected image.
#
@staticmethod
def adjust_gamma(im, gamma = 1.0):
inv_gamma = 1.0 / gamma
table = np.array([((i / 255.0) ** inv_gamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
return cv2.LUT(im, table)
#
# @brief Draws an horizontal gradient image.
#
# @returns the image of the gradient.
@staticmethod
def draw_grad_lr(height, width, left_colour, right_colour):
return (np.ones((height, width)) * np.linspace(left_colour, right_colour,
width)).astype(np.uint8)
#
# @brief Draws an horizontal gradient image.
#
# @returns the image of the gradient.
@staticmethod
def draw_grad_ud(height, width, left_colour, right_colour):
return (np.ones((height, width)) * np.linspace(left_colour, right_colour,
width)).astype(np.uint8).T
#
# @brief FIXME: does not work properly when image is dark
@staticmethod
def detect_endoscopic_circle_bbox(im):
# Edge detection
max_black_intensity = 10
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
kernel = np.ones((3, 3), np.uint8)
dilation = cv2.dilate(gray, kernel, iterations = 1)
_, thresh = cv2.threshold(dilation, max_black_intensity, 255, cv2.THRESH_BINARY)
# Detect contour of largest area
_, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnt = max(contours, key = cv2.contourArea)
((xc, yc), radius) = cv2.minEnclosingCircle(cnt)
x = xc - radius
y = yc - radius
w = 2 * radius
h = 2 * radius
# x, y, w, h = cv2.boundingRect(cnt)
return int(x), int(y), int(w), int(h)
@staticmethod
def crop_endoscopic_circle(im):
# Detect endoscopic circle
has_circle = True
# TODO
if not has_circle:
return im
x, y, w, h = CaffeinatedAbstract.detect_endoscopic_circle_bbox(im)
cropped = im[y:y + h, x:x + w].copy()
return cropped
#
# @brief Function to add specular reflections to an image.
#
# TODO
#
#
@staticmethod
def add_specular_noise():
pass
#
# @brief Skeletonisation of a binary image [0, 255].
#
# @param[in] im Input binary image. Binary means either some values are zero and some
# others different from zero. Different from 0 can be 1 and 255.
#
# @returns a binary image (0, 255) with the skeleton of the image.
@staticmethod
def skeleton(im):
assert(len(im.shape) == 2)
sk = skimage.morphology.skeletonize_3d(im.astype(bool))
return sk
#
# @brief Pads and image with extra pixels according to a newly specified size.
#
# @param[in] tlx Integer that represents the top left corner column.
# @param[in] tly Integer that represents the top left corner row.
# @param[in] brx Integer that represents the bottom right corner column.
# @param[in] bry Integer that represents the bottom right corner row.
# @param[in] width Width of the new image.
# @param[in] height Height of the new image.
# @param[in] intensity Integer of the padding pixels.
#
# @returns nothing.
def pad(self, tlx, tly, brx, bry, width, height, intensity):
assert(isinstance(tlx, type(0)) and isinstance(tly, type(1)) and isinstance(brx, type(1)) \
and isinstance(bry, type(1)))
assert(tlx <= brx)
assert(tly <= bry)
assert(width >= self.width)
assert(height >= self.height)
assert(isinstance(intensity, type(1)))
# Create image of the new size
new_raw_frame = None
new_pixel = None
if len(self._raw_frame.shape) == 2:
new_raw_frame = np.empty((height, width), dtype=self._raw_frame.dtype)
new_pixel = intensity
elif len(self._raw_frame.shape) == 3:
new_raw_frame = np.empty((height, width, self._raw_frame.shape[2]),
dtype=self._raw_frame.dtype)
new_pixel = np.empty((self._raw_frame.shape[2],), dtype=self._raw_frame.dtype)
new_pixel.fill(intensity)
else:
raise ValueError('[image.CaffeinatedAbstract.pad] Error, image dimension ' \
+ str(self._raw_frame.shape) + ' not supported.')
new_raw_frame[:,:] = new_pixel
# Insert the previous image in the right place
new_raw_frame[tly:bry + 1, tlx:brx + 1] = self._raw_frame
self._raw_frame = new_raw_frame
#
# @brief Converts the image into a distance transform (L2 norm) to the edges.
#
# @param[in] mask_size Size of the Sobel filter kernel.
#
# @returns nothing.
def shape_transform(self, mask_size):
assert(isinstance(mask_size, type(0)))
# Convert to grayscale
# gray = cv2.cvtColor(self._raw_frame, cv2.COLOR_BGR2GRAY)
# Sobel filter
sobel_x_64f = np.absolute(cv2.Sobel(self._raw_frame, cv2.CV_64F, 1, 0, ksize=mask_size))
sobel_y_64f = np.absolute(cv2.Sobel(self._raw_frame, cv2.CV_64F, 0, 1, ksize=mask_size))
sobel_64f = (sobel_x_64f + sobel_y_64f)
scaled_sobel = np.uint8(255 * sobel_64f / np.max(sobel_64f))
# Dilate borders
kernel = np.ones((mask_size, mask_size), np.uint8)
dilated = cv2.dilate(scaled_sobel, kernel, iterations=1)
# Threshold
_, thresh = cv2.threshold(dilated, 1, 255, cv2.THRESH_BINARY)
# Distance transform
dist = 255 - (cv2.distanceTransform(255 - thresh, cv2.DIST_L2, maskSize=0))
# Remove backbround
dist[self._raw_frame == 0] = 0
self._raw_frame = dist
#
# @brief Converts image to single channel.
#
# @returns nothing.
def convert_to_single_chan(self):
assert(len(self._raw_frame.shape) == 3)
# Sanity check: assert that all the pixels of the image have the same intensity value in all the
# channels
for channel in range(1, self._raw_frame.shape[2]):
if not np.array_equal(self._raw_frame[:,:, channel], self._raw_frame[:,:, 0]):
raise RuntimeError('[CaffeinatedAbstract] Error, the image ' + self._name + ' has ' \
+ 'channels that are different from each other so it is not clear ' \
+ 'how to convert it to a proper single channel image.')
self._raw_frame = self._raw_frame[:,:, 0]
#
# @brief Changes the intensity of all the pixels in all the channels to zero.
#
# @returns nothing.
def convert_to_black(self):
self._raw_frame.fill(0)
#
# @brief Filter image with ground truth label, background pixels on the ground truth will be blacked.
#
# @param[in] caffe_label CaffeinatedLabel.
#
def filter_with_gt(self, caffe_label):
self._raw_frame[caffe_label.raw == 0] = 0
#
# @brief Builds an object of type CaffeinatedAbstract from an image file.
#
# @param[in] path Path to the image file.
#
@classmethod
def from_file(cls, path, *args):
# return cls(cv2.imread(path, cv2.IMREAD_COLOR), *args)
return cls(cv2.imread(path, cv2.IMREAD_UNCHANGED), *args)
#
# @returns the height of the image.
#
@property
def height(self):
return self._raw_frame.shape[0]
#
# @returns the width of the image.
#
@property
def width(self):
return self._raw_frame.shape[1]
#
# @returns the name of the image.
#
@property
def name(self):
return self._name
#
# @returns the raw internal image.
#
@property
def raw(self):
return self._raw_frame
#
# @returns the data type.
#
@property
def dtype(self):
return self._raw_frame.dtype
#
# @class CaffeinatedImage represents an image that will be used by Caffe so this class should
# provide methods to adapt the original image to the type of input
# Caffe is expecting.
#
class CaffeinatedImage(CaffeinatedAbstract):
#
# @brief Saves the colour image as an attribute of the class.
#
# @param[in] raw_frame Numpy array with a image, shape (h, w) or (h, w, c).
# @param[in] name Id of the image, either the name or the frame number, it will be converted to
# str.
# @param[in] label Id of the class to whom the image belongs. Only used in case the image is used
# for classification purposes. Default value is None.
#
def __init__(self, raw_frame, name, label = None):
# Assert that the image is multi-channel
dim = len(raw_frame.shape)
if dim < 2:
raise RuntimeError('[CaffeinatedImage.__init__], the image provided has [' + \
str(dim) + '] dimensions, only (H x W x C) and (H x W) are supported.')
# Assert that the type of label is correct (i.e. integer) when it is not None
if label is not None:
assert(isinstance(label, type(0)))
self._label = label
# Call CaffeinatedAbstract constructor
super(CaffeinatedImage, self).__init__(raw_frame if dim > 2 else np.expand_dims(raw_frame, axis = 2),
name)
#
# @brief Builds an object of type CaffeinatedImage from file.
#
# @details Only supports 3-channel colour images. It will raise errors for images with a different
# number of channels.
#
# @param[in] path Path to the image file.
# @classmethod
# def from_file(cls, path, name):
# return cls(cv2.imread(path, cv2.IMREAD_UNCHANGED), name)
#
# @brief Convert image to caffe test input, transposing it to the Caffe format (C x H x W) and
# subtracting the training mean.
#
# @details The mean needs to be subtracted because there is no transform_param section in the input
# layer of the test network.
#
# @param[in] mean_values Numpy ndarray with the per channel mean of the training set.
# Shape (channels,).
#
# @returns an image ready to be processed by Caffe.
def convert_to_caffe_input(self, mean_values):
# Sanity check: the mean values should be equal to the number of channels of the input image
dim = len(self._raw_frame.shape)
no_mean_values = mean_values.shape[0]
if dim < 3: # 1D or 2D images should have only one channel mean
if no_mean_values != 1:
raise ValueError('[convert_to_caffe_input] Error, [' + str(no_mean_values) + '] mean ' + \
' values provided, but the image is only 1D or 2D, so only one mean value is required.')
elif dim == 3:
channels = self._raw_frame.shape[-1]
if channels != no_mean_values:
raise ValueError('[convert_to_caffe_input] Error, [' + str(no_mean_values) \
+ '] mean values have been provided but the given image has [' + str(channels) \
+ '] channels.')
else:
raise ValueError('[convert_to_caffe_input] Error, high dimensional image not supported.')
return np.transpose(self._raw_frame.astype(np.float32) - mean_values, (2, 0, 1))
#
# @brief Resize the image to the desired new width and height.
#
# @param[in] new_h New height.
# @param[in] new_w New width.
#
# @returns nothing.
def resize(self, new_h, new_w):
self._raw_frame = cv2.resize(self._raw_frame, (new_w, new_h))
#
# @brief Resize the image and keep the original aspect ratio, padding if required.
#
# @param[in] new_h Height of the new image.
# @param[in] new_w Width of the new image.
#
# @returns nothing.
def resize_keeping_aspect(self, new_h, new_w):
# Store aspect ratio, width and height about the previous dimensions
w = self.width
h = self.height
ar = float(w) / float(h)
# Create new frame respecting the desired new dimensions
new_frame = np.zeros((new_h, new_w, self._raw_frame.shape[2]), self._raw_frame.dtype)
# We scale the larger size of the image and adapt the other one to the aspect ratio
temp_w = None
temp_h = None
y_start = 0
x_start = 0
if w >= h:
temp_w = new_w
temp_h = int(temp_w / ar)
y_start = int((new_h - temp_h) / 2.0)
else:
temp_h = new_h
temp_w = int(temp_h * ar)
x_start = int((new_w - temp_w) / 2.0)
# We add black padding if there is free space
new_frame[y_start:temp_h + y_start, x_start:temp_w + x_start] = cv2.resize(self._raw_frame,
(temp_w, temp_h))
# Copy the final image to the internal buffer that will be displayed
self._raw_frame = new_frame
#
# @brief Converts BGR image to a Caffe datum with shape (C x H x W).
#
# @returns the Caffe datum serialised as a string.
def serialise_to_string(self, jpeg_quality=100):
assert(self._raw_frame.dtype == np.uint8)
import caffe
# caffe_image = self._raw_frame.astype(np.float32)
# Convert image to Caffe datum
datum = caffe.proto.caffe_pb2.Datum()
datum.height, datum.width, datum.channels = self._raw_frame.shape
# datum.data = caffe_image.tostring()
flags = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality]
datum.data = cv2.imencode('.jpg', self._raw_frame, flags)[1].tostring()
# If the image has a label, it must be an integer
if self._label is not None:
assert(isinstance(self._label, type(0)))
datum.label = self._label
return datum.SerializeToString()
#
# @brief Convert image from uint16 to uint8.
#
def uint16_to_uint8(self):
self._raw_frame = np.round((self._raw_frame.astype(np.float32) / 65535.0) * 255.0).astype(np.uint8)
#
# @brief Add Gaussian noise to image.
#
# @param[in] mean Default value is 0.
# @param[in] std Default value is 10.
#
# @returns nothing.
def add_gaussian_noise(self, mean = 0, std = 10):
# Get image dimensions
row, col, ch = self._raw_frame.shape
# Add Gaussian noise to the internal image
gauss = np.random.normal(mean, std, (row, col, ch)).reshape(row, col, ch)
# Convert image to float, add Gaussian noise and convert back to uint8
self._raw_frame = np.round(self._raw_frame.astype(np.float64) + gauss).astype(np.uint8)
#
# @brief Converts a green screen image with tools to grayscale
# adding a bit of noise so that BGR are not kept equal.
#
@classmethod
def gray_tools(cls, im, noise_delta=3):
assert(isinstance(im, np.ndarray))
new_im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
new_im = cv2.cvtColor(new_im, cv2.COLOR_GRAY2BGR)
noise = np.random.randint(-noise_delta,
noise_delta + 1, size=new_im.shape)
new_im = np.clip(new_im + noise, 0, 255).astype(np.uint8)
return new_im
#
# @brief Convert it to a noisy grayscale image.
#
def noisy_gray(self, noise_delta=3):
self._raw_frame = CaffeinatedImage.gray_tools(self._raw_frame, noise_delta)
def random_crop(self, height, width):
self._raw_frame = CaffeinatedAbstract.random_crop(self._raw_frame, height, width)
@property
def shape(self):
return self._raw_frame.shape
#
# @class Caffeinated8UC3Image represents a colour (H x W x 3) CaffeinatedImage.
#
class Caffeinated8UC3Image(CaffeinatedImage):
#
# @brief Saves the colour image as an attribute of the class.
#
# @param[in] frame_bgr Numpy array with a BGR image, shape (h, w, c).
def __init__(self, frame_bgr, name):
# Check that it is a 3-channel BGR image
EXPECTED_DIM = 3
EXPECTED_CHANNELS = 3
if len(frame_bgr.shape) != EXPECTED_DIM or frame_bgr.shape[EXPECTED_DIM - 1] != EXPECTED_CHANNELS:
raise RuntimeError('[Caffeinated8UC3Image] Error, the image provided has a shape of ' + \
str(frame_bgr.shape) + '. We expect an image of shape (H x W x ' + \
str(EXPECTED_CHANNELS) + ').')
# Check that the image is uint8
EXPECTED_TYPE = np.uint8
if frame_bgr.dtype != EXPECTED_TYPE:
raise RuntimeError('[Caffeinated8UC3Image] Error, the image provided has a type of ' + \
str(frame_bgr.dtype) + ' and we expect ' + str(EXPECTED_TYPE) + '.')
super(self.__class__, self).__init__(frame_bgr, name)
#
# @class CaffeinatedLabel represents a segmentation label that will be used by Caffe so this
# class should provide methods to adapt the original image to the type of
# input Caffe is expecting.
#
# @details This class does not support labels that are not grayscale or colour images, that is,
# the images provided must be (H x W) or (H x W x C). In case that you provide a label
# with shape (H x W x C) this class will make sure that all the channels C have the same
# values. This is because a priori it does not make any sense for a pixel to belong to
# different classes.
class CaffeinatedLabel(CaffeinatedAbstract):
#
# @brief Stores the label and checks that both dimensions and type are correct for a label.
# @details To make a safe conversion to single channel this method will check that all the
# pixels of the image have exactly the same intensity value in all the BGR channels.
# If this does not happen an exception will be raised.
#
# @param[in] label_image Single channel OpenCV/Numpy image. Shape (H x W) or (H x W x C).
# @param[in] name Name of the label, usually stores the id of the related image.
# @param[in] classes Integer that represents the maximum number of classes in the labels,
# used for both validation purposes and to convert back/forth to Caffe
# input.
# @param[in] class_map Integer (pixel intensity) -> Integer (class, [0, K - 1]),
# where K is the maximum number of classes.
# @param[in] proba_map Probability maps for all the classes, shape (c, h, w).
#
def __init__(self, label_image, name, classes, class_map, proba_map = None):
# This is 2 because we expect the image to be of shape (H x W) and the intensity of the
# pixel to indicate the class that the pixel belongs to
EXPECTED_DIM = 2
EXPECTED_LABEL_TYPE = np.uint8
# Store the maximum number of classes after validating that it is in the range [2, 256]
assert(isinstance(classes, type(0)) and classes >= 2 and classes <= 256)
self._classes = classes
# Store the dictionary for class mappings after validating it
classes_present = [False] * classes
assert(len(class_map.keys()) == classes)
for k, v in class_map.items():
assert(isinstance(k, type(0)))
assert(isinstance(v, type(0)))
assert(k >= 0 and k <= 255)
assert(v >= 0 and v < self._classes)
classes_present[v] = True
assert(all(classes_present))
self._class_map = class_map
# Sanity check: labels that are neither (H x W) nor (H x W x C) are not supported
dim = len(label_image.shape)
if not (dim == 2 or dim == 3):
raise RuntimeError('[CaffeinatedLabel] Error, the label provided has a dimension of ' + \
str(dim) + ', which is not supported. Only (H x W) and (H x W x C) are supported.')
# Sanity check: if the label provided is multiple-channel, assert that all the pixels of the image
# have the same intensity value in all the channels
if dim > EXPECTED_DIM:
for channel in range(1, label_image.shape[2]):
if not np.array_equal(label_image[:,:, channel], label_image[:,:, 0]):
raise RuntimeError('[CaffeinatedLabel] Error, the label provided in ' + name + ' has channels that are ' + \
'different from each other so it is not clear how to convert it to a proper ' + \
'single channel label in which the intensity defines the pixel class.')
# Sanity check: the image must be uint8, this essentially means that there is a maximum of 256 labels
if label_image.dtype != EXPECTED_LABEL_TYPE:
raise RuntimeError('[CaffeinatedLabel] Error, a label must be ' + str(EXPECTED_LABEL_TYPE) + '.')
# If the image has several channels, we just get one (we already know that all the channels have the
# same values
if dim == EXPECTED_DIM:
raw_label = label_image
else:
raw_label = label_image[:,:, 0]
# Assert that there are no more unique labels than classes
unique_classes = np.unique(raw_label)
if unique_classes.shape[0] > self._classes:
raise ValueError('[CaffeinatedLabel] Error, label ' + str(name) + ' is said to have ' \
+ str(self._classes) + ' classes but there are more unique values in it, exactly: ' \
+ str(unique_classes))
# Assert thate the intensities in the label are all present in the class_map dictionary
for i in unique_classes:
if not i in self._class_map:
raise ValueError('[CaffeinatedLabel] Error, label ' + str(name) + ' has a pixel with ' \
+ 'intensity ' + str(i) + ' but this intensity is not present in the class map.')
# Store probability map if provided
if proba_map is not None:
assert(len(proba_map.shape) == 3)
assert(proba_map.shape[0] == classes)
assert(proba_map.shape[1] == raw_label.shape[0])
assert(proba_map.shape[2] == raw_label.shape[1])
self._predicted_map = proba_map
# Call CaffeinatedAbstract constructor
super(CaffeinatedLabel, self).__init__(raw_label, name)
#
# @brief Builds an object of type CaffeinatedLabel from an image file.
#
# @param[in] fmaps array_like, shape (c, h, w).
#
# @param[in] classes Integer that represents the maximum number of classes in the labels, used for
# both validation purposes and to convert back/forth to Caffe input.
#
# @param[in] class_map Integer (pixel intensity) -> Integer (class, [0, K - 1]), where K is the
# maximum number of classes.
#
@classmethod
def from_network_output(cls, fmaps, name, classes, class_map):
label_image = fmaps.argmax(axis=0).astype(np.uint8)
for k, v in class_map.items():
label_image[label_image == v] = k
return cls(label_image, name, classes, class_map, fmaps)
#
# @brief Convert label to CaffeinatedImage for displaying purposes.
#
# @param[in] cn Channels of the new image. The labels will be replicated across channels.
#
# @returns the label converted into a cn-channel CaffeinatedImage.
def to_image(self, cn = 3):
new_image = np.ndarray((self._raw_frame.shape[0], self._raw_frame.shape[1], cn),
self._raw_frame.dtype)
for k in range(cn):
new_image[:,:, k] = self._raw_frame
return CaffeinatedImage(new_image, self._name)
#
# @brief Converts the label to a Caffe datum.
#
# @returns a Caffe datum label serialised to string.
def serialise_to_string(self):
# Sanity check: assert that the type of the label is correct
import caffe
assert(self._raw_frame.dtype == np.uint8)
# Create Caffe datum
datum = caffe.proto.caffe_pb2.Datum()
datum.height, datum.width = self._raw_frame.shape
# if self._classes == 2:
# Convert (h, w) -> (1, h, w)
# caffe_label = np.expand_dims(self._raw_frame, axis = 0)
# caffe_label = self._raw_frame
# else:
# Create ndarray of binary maps
fmaps = np.zeros([self._classes, self._raw_frame.shape[0], self._raw_frame.shape[1]],
dtype = np.uint8)
# k is intensity
# v is the class number
for k, v in self._class_map.items():
fmaps[v, self._raw_frame == k] = 1
# if self._classes == 2:
# Binary case, only one feature map
# datum.channels = 1
# caffe_label = np.expand_dims(fmaps[1], axis = 0)
# else:
# Multi-class case, one feature map per class
# datum.channels = self._classes
# caffe_label = fmaps
# Multi-class case, one feature map per class
datum.channels = self._classes
caffe_label = fmaps
# Convert label[s] to string
datum.data = caffe_label.tostring()
return datum.SerializeToString()
#
# @brief Binarises the label. It will be thresholded so that only 0/maxval values are present.
#
# @param[in] thresh Values greater or equal than 'thresh' will be transformed to 'maxval'.
# @param[in] maxval Integer that will be given to those pixels higher or equal than 'thresh'.
#
# @returns nothing.
def binarise(self, thresh = 10, maxval = 1):
_, self._raw_frame = cv2.threshold(self._raw_frame, thresh, maxval, cv2.THRESH_BINARY)
#
# @brief Convert intensity-based labels into proper class-index labels.
#
# @returns an array_like, shape (h, w).
def to_classes(self):
class_index_frame = self._raw_frame.copy()
for k, v in self._class_map.items():
class_index_frame[self._raw_frame == k] = v
return class_index_frame
#
# @brief Maps between intensities [0, 255] to classes [0, K] using the JSON info provided.
#
# @param[in] intensity Typically an integer [0, 255].
#
# @returns the class index of the givel pixel intensity according to the provided class map.
def map_intensity_to_class(self, intensity):
return self._class_map[intensity]
#
# @brief Maps between classes and JSON intensities.
#
# @param[in] class_id Id of the class whose intensity you want to retrieve.
#
# @returns the intensity corresponding to the given class.
def map_class_to_intensity(self, class_id):
return {v: k for k, v in self._class_map.items()}[class_id]
#
# @brief Retrieves a normalised probability map for a particular class.
#
# @param[in] class_id Id of the class whose probability map you want to retrieve.
#
# @returns an array_like probability map, shape (h, w).
def softmax_predicted_map(self, class_id):
assert(self._predicted_map)
pmap = np.exp(self._predicted_map - np.amax(self._predicted_map, axis = 0))
pmap /= np.sum(pmap, axis = 0)
return pmap[class_id, ...]
#
# @brief Converts all the feature maps to contour images.
#
# @param[in] pixel_width Thickness of the border in pixels.
#
# @returns nothing.
def convert_to_contours(self, pixel_width = 5):
new_raw_frame = np.zeros_like(self._raw_frame)
# If self._predicted_map does not exist, we create it, shape (c, h, w)
if not self._predicted_map:
self._predicted_map = np.zeros((self._classes, self._raw_frame.shape[0], self._raw_frame.shape[1]),
dtype=np.uint8)
for k in range(self._classes):
self._predicted_map[k,:,:][self._raw_frame == self.map_class_to_intensity(k)] = 1
# Draw contours in the new raw frame
for k in range(self._classes):
(_, cnts, _) = cv2.findContours(self._predicted_map[k], cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
# cv2.drawContours(new_raw_frame, [c], -1, (self.map_class_to_intensity(k)), pixel_width)
cv2.drawContours(new_raw_frame, [c], -1, self.map_class_to_intensity(k), pixel_width)
self._raw_frame = new_raw_frame
def random_crop(self, height, width):
self._raw_frame = CaffeinatedAbstract.random_crop(self._raw_frame, height, width)
#
# @brief Calculates the number of classes in the frame, that is the quantity of unique labels.
#
# @returns an integer that indicates the number of different pixel labels.
@property
def classes(self):
return self._classes
# return np.unique(self._raw_frame).shape[0]
#
# @returns the unnormalised predicted map for all the classes (class_id, height, width).
#
@property
def predicted_map(self):
return self._predicted_map
@property
def class_map(self):
return self._class_map
#
# @class CaffeinatedBinaryLabel behaves as a CaffeinatedLabel but makes sure that the label images provided
# only contain two different types or labels. Furthermore, it makes them 0's
# and 1's (np.uint8) in case that they are different from these two values.
# Say that you provide an image with 0's and 255's as typical ground truth
# images, this class will make it 0's and 1's.
#
class CaffeinatedBinaryLabel(CaffeinatedLabel):
#
# @brief Stores the label and checks that both, dimensions and type, are correct for a label.
# @details If the label provided is not single channel, the label is converted to grayscale with
# the OpenCV cvtColour function. It
#
# @param[in] label_image Single channel OpenCV/Numpy image. Shape (H x W) or (H x W x C).
# @param[in] name String that identifies the label, usually a frame number.
# @param[in] thresh Values greater or equal than 'thresh' will be transformed to 'maxval'.
# @param[in] maxval Integer that will be given to those pixels higher or equal than 'thresh'.
#
# @returns nothing.
def __init__(self, label_image, name, thresh = 10, maxval = 1):
# Call CaffeinatedLabel constructor
super(self.__class__, self).__init__(label_image, name)
# Sanity check: labels that are neither (H x W) nor (H x W x C) are not supported
# dim = len(label_image.shape)
# if not (dim == 2 or dim == 3):
# raise RuntimeError('[CaffeinatedLabel] Error, the label provided has a dimension of ' + \
# str(dim) + ', which is not supported. Only (H x W) and (H x W x C) are supported.')
# If we received a colour image as a label, we convert it to grayscale
# if dim == 3:
# label_image = cv2.cvtColor(label_image, cv2.COLOR_BGR2GRAY)
# If the label image is not binary, that is, if it has more than two unique values, we thresholded it
# to ensure that the labels are binary
EXPECTED_NO_UNIQUE_VALUES = 2 # As we expect a binary label
no_unique_values = np.unique(self._raw_frame).shape[0]
if no_unique_values > EXPECTED_NO_UNIQUE_VALUES:
_, self._raw_frame = cv2.threshold(self._raw_frame, thresh, maxval, cv2.THRESH_BINARY)
#
# @returns the number of foreground pixels.
@property
def count_fg_pixels(self):
return np.count_nonzero(self._raw_frame)
#
# @returns the number of background pixels.
@property
def count_bg_pixels(self):
return np.count_nonzero(self._raw_frame == 0)
#
# @class CaffeinatedImagePair represents a pair of consecutive frames that will be used by Caffe so this
# class should provide methods to adapt the original images to the type of
# input Caffe is expecting.
#
class CaffeinatedImagePair(object):
#
# @brief Saves the colour image as an attribute of the class.
#
# @param[in] frame_bgr_prev Numpy array with the previous BGR image in the video sequence, shape (h, w, c).
# @param[in] frame_bgr_next Numpy array with the current BGR image in the video sequecne, shape (h, w, c).
#
def __init__(self, frame_bgr_prev, frame_bgr_next):
# Sanity check: both images must have 3 dimensions (h, w, c)
if len(frame_bgr_prev.shape) != 3 or len(frame_bgr_next.shape) != 3:
raise RuntimeError('[CaffeinatedImagePair.__init__] The images provided must have ' + \
'three dimensions (i.e. H x W x C).')
# Sanity check: both images must have 3 channels
if frame_bgr_prev.shape[2] != 3 or frame_bgr_next.shape[2] != 3:
raise RuntimeError('[CaffeinatedImagePair.__init__] The images provided must have three ' + \
'channels (i. e. BGR)')
# Sanity check: both images must have the same height and width
if frame_bgr_prev.shape[0] != frame_bgr_next.shape[0] or \
frame_bgr_prev.shape[1] != frame_bgr_next.shape[1]:
raise RuntimeError('[CaffeinatedImagePair.__init__] The imaged provided must have the same ' + \
'dimensions (i.e. height and width).')
self._frame_bgr_prev = frame_bgr_prev
self._frame_bgr_next = frame_bgr_next
#
# @brief Builds an object of type CaffeinatedImage from file.
#
# @details Only supports 3-channel colour images. It will raise errors for images with a different
# number of channels.
#
# @param[in] path_prev Path to the previous image file.
# @param[in] path_next Path to the next image file.
#
@classmethod
def from_file(cls, path_prev, path_next):
return cls(cv2.imread(path_prev), cv2.imread(path_next))
#
# @brief Convert image to caffe test input, transposing it to the Caffe format (C x H x W) and
# subtracting the training mean.
#
# @details The mean needs to be subtracted because there is no transform_param section in the input
# layer of the test network.
#
# @param[in] mean_values Numpy ndarray with the per channel mean of the training set. Shape (channels,).
#
# @returns an image ready to be processed by Caffe.
def convert_to_caffe_input(self, mean_values):
no_mean_values = mean_values.shape[0]
# Sanity check: the mean values should be equal to the number of channels of the input image
if no_mean_values != 6:
raise ValueError('[CaffeinatedImagePair.convert_to_caffe_input()] Error, six means are required.')
# Subtract mean values from previous frame
norm_prev = self._frame_bgr_prev.astype(np.float32) - mean_values[:3]
# Subtract mean values from next frame
norm_next = self._frame_bgr_next.astype(np.float32) - mean_values[3:]
# Sanity checks: both images must have the same shape and be of the same datatype
assert(norm_prev.shape[0] == norm_next.shape[0])
assert(norm_prev.shape[1] == norm_next.shape[1])
assert(norm_prev.shape[2] == norm_next.shape[2])
assert(norm_prev.dtype == norm_next.dtype)
# Combine both images in a 6-channel image
combined_image = np.empty((norm_prev.shape[0], norm_prev.shape[1], 6), dtype = norm_prev.dtype)
combined_image[:,:, 0:3] = norm_prev
combined_image[:,:, 3:6] = norm_next
# Transpose to channel-first Caffe style
combined_transposed = np.transpose(combined_image, (2, 0, 1))
return combined_transposed
#
# @brief Converts BGR image to a Caffe datum with shape (C x H x W).
#
# @details The training mean is not subtracted from the image because Caffe does this automatically for
# the data layer used for training (see the transform_param section of the 'data' layer in the
# training prototxt).
#
# @returns the Caffe datum serialised as a string.
@property
def serialise_to_string(self):
# Sanity checks: both images must have the same shape and be of the same datatype
import caffe
assert(self._frame_bgr_prev.shape[0] == self._frame_bgr_next.shape[0])
assert(self._frame_bgr_prev.shape[1] == self._frame_bgr_next.shape[1])
assert(self._frame_bgr_prev.shape[2] == self._frame_bgr_next.shape[2])
assert(self._frame_bgr_prev.dtype == self._frame_bgr_next.dtype)
# Combine the two images in a single 6-channel image
channels = 6
combined_image = np.empty((self._frame_bgr_prev.shape[0], self._frame_bgr_prev.shape[1], channels), \
dtype = self._frame_bgr_prev.dtype)
combined_image[:,:, 0:3] = self._frame_bgr_prev
combined_image[:,:, 3:6] = self._frame_bgr_next
caffe_image = combined_image.astype(np.float32)
# Convert image to Caffe datum
datum = caffe.proto.caffe_pb2.Datum()
datum.height, datum.width, _ = caffe_image.shape
datum.channels = channels
datum.data = caffe_image.tostring()
return datum.SerializeToString()
#
# @returns the height of the image.
@property
def height(self):
return self._frame_bgr_prev.shape[0]
#
# @returns the width of the image.
@property
def width(self):
return self._frame_bgr_prev.shape[1]
#
# @class CaffeinatedImagePlusPrevSeg represents a BGR image with a fourth channel that contains the segmentation of the
# previous frame in the video sequence.
#
class CaffeinatedImagePrevSeg(object):
#
# @brief Saves the colour image and the previous segmentation as attributes of the class.
#
# @param[in] prev_seg Numpy array with the predicted segmentation of the previous frame in the sequence,
# shape (h, w, c).
# @param[in] frame_bgr Numpy array with a BGR image, shape (h, w, c).
#
def __init__(self, prev_seg, frame_bgr):
# Sanity check: the image must have three dimensions (h, w, c) and three channels (c = 3)
if len(frame_bgr.shape) != 3 or frame_bgr.shape[2] != 3:
raise RuntimeError('[CaffeinatedImagePlusPrevSeg.__init__] Error, the image provided must ' + \
' have three dimensions (i.e. H x W x 3).')
# Sanity check: the previous mask must have a dimension of two
if len(prev_seg.shape) != 2:
raise RuntimeError('[CaffeinatedImagePlusPrevSeg.__init__] Error, the previous mask must have ' + \
'two dimensions.')
# Sanity check: the frame and the previous mask must have the same dimensions
if frame_bgr.shape[0] != prev_seg.shape[0] or frame_bgr.shape[1] != prev_seg.shape[1]:
raise RuntimeError('[CaffeinatedImagePlusPrevSeg.__init__] Error, the current image and the ' + \
'previous segmentation must have the same height and width.')
self._prev_seg = prev_seg
self._frame_bgr = frame_bgr
#
# @brief Builds an object of type CaffeinatedImage from file.
#
# @details Only supports 3-channel colour images. It will raise errors for images with a different
# number of channels.
#
# @param[in] path Path to the image file.
@classmethod
def from_file(cls, path_prev_seg, path_frame_bgr):
caffeinated_prev_label = CaffeinatedBinaryLabel.from_file(path_prev_seg)
return cls(caffeinated_prev_label.single_channel_label_copy(), cv2.imread(path_frame_bgr))
#
# @brief Convert image to caffe test input, transposing it to the Caffe format (C x H x W) and
# subtracting the training mean.
#
# @details The mean needs to be subtracted because there is no transform_param section in the input
# layer of the test network.
#
# @param[in] mean_values Numpy ndarray with the per channel mean of the training set. Shape (channels,).
#
# @returns an image ready to be processed by Caffe.
def convert_to_caffe_input(self, mean_values):
colour_channels = 3
no_mean_values = mean_values.shape[0]
# Sanity check: the mean values should be equal to the number of channels of the input image
if no_mean_values != colour_channels:
raise ValueError('[CaffeinatedImagePlusPrevSeg.convert_to_caffe_input] Error, three means are ' + \
'required.')
# Subtract mean values from the current frame
norm_frame_bgr = self._frame_bgr.astype(np.float32) - mean_values
# Convert previous segmentation to float
norm_prev_seg = self._prev_seg.astype(np.float32)
# Sanity check: the current normalised image and the segmentation mask must have the same shape and
# datatype
total_channels = colour_channels + 1
assert(norm_frame_bgr.shape[0] == norm_prev_seg.shape[0])
assert(norm_frame_bgr.shape[1] == norm_prev_seg.shape[1])
assert(norm_frame_bgr.shape[2] == colour_channels)
assert(norm_frame_bgr.dtype == norm_prev_seg.dtype)
# Combine the current frame with the previous segmentation in a 4-channel image
combined_image = np.empty((norm_frame_bgr.shape[0], norm_frame_bgr.shape[1], total_channels),
dtype = norm_frame_bgr.dtype)
combined_image[:,:, :colour_channels] = norm_frame_bgr
combined_image[:,:, colour_channels] = norm_prev_seg
# Transpose to channel-first Caffe style
combined_transposed = np.transpose(combined_image, (2, 0, 1))
return combined_transposed
#
# @brief Converts BGR image to a Caffe datum with shape (C x H x W).
#
# @details The training mean is not subtracted from the image because Caffe does this automatically for
# the data layer used for training (see the transform_param section of the 'data' layer in the
# training prototxt).
#
# @returns the Caffe datum serialised as a string.
@property
def serialise_to_string(self):
# Sanity checks: both images must have the same shape and be of the same datatype
import caffe
assert(self._frame_bgr.shape[0] == self._prev_seg.shape[0])
assert(self._frame_bgr.shape[1] == self._prev_seg.shape[1])
assert(self._frame_bgr.dtype == self._prev_seg.dtype)
# Combine the current image and the previous segmentation in a single 4-channel image
colour_channels = 3
total_channels = colour_channels + 1
combined_image = np.empty((self._frame_bgr.shape[0], self._frame_bgr.shape[1], total_channels), \
dtype = self._frame_bgr.dtype)
combined_image[:,:, :colour_channels] = self._frame_bgr
combined_image[:,:, colour_channels] = self._prev_seg
caffe_image = combined_image.astype(np.float32)
# Convert image to Caffe datum
datum = caffe.proto.caffe_pb2.Datum()
datum.height, datum.width, _ = caffe_image.shape
datum.channels = total_channels
datum.data = caffe_image.tostring()
return datum.SerializeToString()
#
# @returns the height of the image.
@property
def height(self):
return self._frame_bgr.shape[0]
#
# @returns the width of the image.
@property
def width(self):
return self._frame_bgr.shape[1]
#
# @brief Convert a binary probability map into a beautiful image.
#
# @param[in] probmap 2D floating point probability map, shape (height, width).
#
# @returns a fancy BGR image.
def make_it_pretty(probmap, vmin = 0, vmax = 1, colourmap = 'plasma', eps = 1e-3):
assert(len(probmap.shape) == 2)
assert(np.max(probmap) < vmax + eps)
assert(np.min(probmap) > vmin - eps)
height = probmap.shape[0]
width = probmap.shape[1]
# Create figure without axes
fig = plt.figure(frameon = False)
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
# Plot figure
plt.imshow(probmap, cmap = colourmap, vmin = vmin, vmax = vmax) # vmin/vmax adjust thesholds
fig.canvas.draw()
# Convert plot to numpy array
data = np.fromstring(fig.canvas.tostring_rgb(), dtype = np.uint8, sep = '')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
# Remove left/right borders
left_right_offset = 0
i = 0
left_intensity = data[0, left_right_offset, 0]
right_intensity = data[0, -1, 0]
min_intensity = 255
# Assert that the values for all the rows are equal for the columns 'offset' and '-offset'
left_side_equal = True if np.unique(data[:, left_right_offset, 0]).shape[0] == 1 else False
right_side_equal = True if np.unique(data[:, -left_right_offset, 0]).shape[0] == 1 else False
while left_intensity == right_intensity and left_intensity >= min_intensity and left_side_equal and right_side_equal:
left_right_offset += 1
left_intensity = data[0, left_right_offset, 0]
right_intensity = data[0, -left_right_offset - 1, 0]
left_side_equal = True if np.unique(data[:, left_right_offset, 0]).shape[0] == 1 else False
right_side_equal = True if np.unique(data[:, -left_right_offset, 0]).shape[0] == 1 else False
# Remove top/bottom borders
top_bottom_offset = 0
i = 0
top_intensity = data[top_bottom_offset, 0, 0]
bottom_intensity = data[-1, 0, 0]
min_intensity = 255
# Assert that the values for all the rows are equal for the columns 'offset' and '-offset'
top_side_equal = True if np.unique(data[top_bottom_offset,:, 0]).shape[0] == 1 else False
bottom_side_equal = True if np.unique(data[-top_bottom_offset,:, 0]).shape[0] == 1 else False
while top_intensity == bottom_intensity and top_intensity >= min_intensity and top_side_equal and bottom_side_equal:
top_bottom_offset += 1
top_intensity = data[top_bottom_offset, 0, 0]
bottom_intensity = data[-top_bottom_offset - 1, 0, 0]
top_side_equal = True if np.unique(data[top_bottom_offset,:, 0]).shape[0] == 1 else False
bottom_side_equal = True if np.unique(data[-top_bottom_offset,:, 0]).shape[0] == 1 else False
# Note: 1 is added to 'left_right_offset' because matplotlib tends to leave a border on the left one
# pixel thicker than on the right
cropped_image = data[top_bottom_offset:data.shape[0] - top_bottom_offset,
left_right_offset + 1:data.shape[1] - left_right_offset]
# Resize to original size
resized_image = cv2.resize(cropped_image, (width, height))
assert(resized_image.shape[0] == height)
assert(resized_image.shape[1] == width)
assert(resized_image.shape[2] == 3)
# Convert RGB to BGR
final_image = cv2.cvtColor(resized_image, cv2.COLOR_RGB2BGR)
return final_image
# This module cannot be executed as a script because it is not a script :)
if __name__ == "__main__":
print >> sys.stderr, 'Error, this module is not supposed to be executed by itself.'
sys.exit(1)
|
[
"numpy.sum",
"numpy.ones",
"matplotlib.pyplot.figure",
"numpy.arange",
"common.randbin",
"cv2.imencode",
"cv2.filter2D",
"cv2.cvtColor",
"matplotlib.pyplot.imshow",
"cv2.imwrite",
"numpy.max",
"cv2.LUT",
"cv2.minEnclosingCircle",
"numpy.flipud",
"numpy.min",
"cv2.createCLAHE",
"numpy.squeeze",
"sys.exit",
"tempfile.gettempdir",
"random.choice",
"common.rm",
"numpy.nonzero",
"numpy.array",
"numpy.abs",
"cv2.getRotationMatrix2D",
"numpy.unique",
"numpy.meshgrid",
"common.shell",
"numpy.transpose",
"numpy.dstack",
"numpy.stack",
"cv2.distanceTransform",
"numpy.expand_dims",
"numpy.eye",
"cv2.GaussianBlur",
"numpy.empty",
"numpy.clip",
"cv2.warpAffine",
"numpy.mean",
"numpy.sin",
"numpy.ndarray",
"random.randint",
"cv2.dilate",
"numpy.linspace",
"cv2.drawContours",
"cv2.resize",
"cv2.Canny",
"numpy.dot",
"cv2.Sobel",
"numpy.count_nonzero",
"common.gen_rand_str",
"numpy.zeros",
"numpy.amax",
"numpy.array_equal",
"cv2.findContours",
"numpy.random.seed",
"random.shuffle",
"numpy.floor",
"numpy.random.randint",
"numpy.random.normal",
"common.deg_to_rad",
"numpy.linalg.solve",
"numpy.round",
"numpy.full_like",
"numpy.zeros_like",
"caffe.proto.caffe_pb2.Datum",
"numpy.random.choice",
"numpy.random.shuffle",
"common.path_exists",
"numpy.median",
"numpy.fliplr",
"numpy.cos",
"random.uniform",
"cv2.threshold",
"numpy.float32",
"cv2.imread",
"numpy.sqrt"
] |
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|
#!/usr/bin/python2
import sys, os
op = os.path.basename(sys.argv[0])
mypath = os.path.abspath(os.path.dirname(sys.argv[0]))
PATH = os.getenv('PATH').split(':')
if op == 'mv':
# copy much cleaner than move in a build (immutable inputs)
op = 'cp'
# Delete ourselves from the PATH
if mypath in PATH:
del PATH[PATH.index(mypath)]
os.environ['PATH'] = ':'.join(PATH)
# Log a command entry
LOGFILE = os.getenv('TRACE_LOG_LOCATION')
with open(LOGFILE, 'a') as file:
file.write(repr((os.getpid(), os.getcwd(), [op] + sys.argv[1:])) + '\n')
# Create a process group for this command
os.setpgid(0, 0)
# Execute
command = list(sys.argv)
command[0] = op
os.execvp(op, command)
# STRACE_FILE = '{}.strace.{}'.format(LOGFILE, os.getpid())
# os.execvp('strace', ['strace', '-o', STRACE_FILE, '-f', '-e', 'open,chdir'] + command)
|
[
"os.getpid",
"os.path.basename",
"os.getcwd",
"os.path.dirname",
"os.setpgid",
"os.execvp",
"os.getenv"
] |
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|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl.testing import parameterized
import numpy as np
from test_utils import ReportJSON
from tensorflow.compiler.tests import xla_test
from tensorflow.python.platform import googletest
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ipu.config import IPUConfig
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
TYPES = (np.float16, np.float32, np.int32)
TESTCASES = [{"testcase_name": np.dtype(x).name, "dtype": x} for x in TYPES]
def _get_random_input(dtype, shape):
if np.issubdtype(dtype, np.integer):
info_fn = np.iinfo
random_fn = np.random.random_integers
else:
info_fn = np.finfo
random_fn = np.random.uniform
return random_fn(info_fn(dtype).min, info_fn(dtype).max,
size=shape).astype(dtype)
class ArgMinMax(xla_test.XLATestCase, parameterized.TestCase):
@parameterized.named_parameters(*TESTCASES)
def testArgMaxBasic(self, dtype):
cfg = IPUConfig()
cfg._profiling.enable_ipu_events = True # pylint: disable=protected-access
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a):
return math_ops.argmax(a, output_type=dtypes.int32)
with self.session() as sess:
report_json = ReportJSON(self, sess)
report_json.reset()
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [3, 5, 2])
with ops.device("/device:IPU:0"):
out = model(pa)
input = _get_random_input(dtype, (3, 5, 2))
fd = {pa: input}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmax(input, axis=0))
report_json.parse_log(assert_len=4)
@parameterized.named_parameters(*TESTCASES)
def testArgMaxHalf(self, dtype):
cfg = IPUConfig()
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a):
return math_ops.argmax(a, output_type=dtypes.int32)
with self.session() as sess:
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [3, 5, 2])
with ops.device("/device:IPU:0"):
out = model(pa)
input = _get_random_input(dtype, (3, 5, 2))
fd = {pa: input}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmax(input, axis=0))
@parameterized.named_parameters(*TESTCASES)
def testArgMaxMultiDimensional(self, dtype):
cfg = IPUConfig()
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a, axis):
return math_ops.argmax(a, axis=axis, output_type=dtypes.int32)
for axis in range(6):
with self.session() as sess:
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [1, 2, 3, 4, 5, 6])
p_axis = array_ops.placeholder(np.int32, shape=())
with ops.device("/device:IPU:0"):
out = model(pa, p_axis)
input = _get_random_input(dtype, (1, 2, 3, 4, 5, 6))
fd = {pa: input, p_axis: axis}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmax(input, axis=axis))
@parameterized.named_parameters(*TESTCASES)
def testArgMinBasic(self, dtype):
cfg = IPUConfig()
cfg._profiling.enable_ipu_events = True # pylint: disable=protected-access
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a):
return math_ops.argmin(a, output_type=dtypes.int32)
with self.session() as sess:
report_json = ReportJSON(self, sess)
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [3, 5, 2])
with ops.device("/device:IPU:0"):
out = model(pa)
report_json.reset()
input = _get_random_input(dtype, (3, 5, 2))
fd = {pa: input}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmin(input, axis=0))
report_json.parse_log(assert_len=4)
@parameterized.named_parameters(*TESTCASES)
def testArgMinHalf(self, dtype):
cfg = IPUConfig()
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a):
return math_ops.argmin(a, output_type=dtypes.int32)
with self.session() as sess:
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [3, 5, 2])
with ops.device("/device:IPU:0"):
out = model(pa)
input = _get_random_input(dtype, (3, 5, 2))
fd = {pa: input}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmin(input, axis=0))
@parameterized.named_parameters(*TESTCASES)
def testArgMinMultiDimensional(self, dtype):
cfg = IPUConfig()
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a, axis):
return math_ops.argmin(a, axis=axis, output_type=dtypes.int32)
for axis in range(6):
with self.session() as sess:
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [1, 2, 3, 4, 5, 6])
p_axis = array_ops.placeholder(np.int32, shape=())
with ops.device("/device:IPU:0"):
out = model(pa, p_axis)
input = _get_random_input(dtype, (1, 2, 3, 4, 5, 6))
fd = {pa: input, p_axis: axis}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmin(input, axis=axis))
@parameterized.named_parameters(*TESTCASES)
def testArgMaxNegativeDim(self, dtype):
cfg = IPUConfig()
cfg._profiling.enable_ipu_events = True # pylint: disable=protected-access
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a):
return math_ops.argmax(a, axis=-1, output_type=dtypes.int32)
with self.session() as sess:
report_json = ReportJSON(self, sess)
report_json.reset()
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [3, 5, 2])
with ops.device("/device:IPU:0"):
out = model(pa)
input = _get_random_input(dtype, (3, 5, 2))
fd = {pa: input}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmax(input, axis=-1))
report_json.parse_log(assert_len=4)
@parameterized.named_parameters(*TESTCASES)
def testArgMaxVector(self, dtype):
cfg = IPUConfig()
cfg._profiling.enable_ipu_events = True # pylint: disable=protected-access
cfg.ipu_model.compile_ipu_code = False
cfg.configure_ipu_system()
def model(a):
return math_ops.argmax(a, axis=0, output_type=dtypes.int32)
with self.session() as sess:
report_json = ReportJSON(self, sess)
report_json.reset()
with ops.device('cpu'):
pa = array_ops.placeholder(dtype, [3])
with ops.device("/device:IPU:0"):
out = model(pa)
input = _get_random_input(dtype, (3))
fd = {pa: input}
result = sess.run(out, fd)
self.assertAllClose(result, np.argmax(input))
report_json.parse_log(assert_len=4)
if __name__ == "__main__":
os.environ['TF_XLA_FLAGS'] = ('--tf_xla_min_cluster_size=1 ' +
os.environ.get('TF_XLA_FLAGS', ''))
googletest.main()
|
[
"tensorflow.python.ops.math_ops.argmin",
"tensorflow.python.ipu.config.IPUConfig",
"tensorflow.python.ops.math_ops.argmax",
"numpy.argmax",
"numpy.dtype",
"numpy.argmin",
"tensorflow.python.platform.googletest.main",
"os.environ.get",
"test_utils.ReportJSON",
"tensorflow.python.framework.ops.device",
"tensorflow.python.ops.array_ops.placeholder",
"absl.testing.parameterized.named_parameters",
"numpy.issubdtype"
] |
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|
# coding=utf-8
"""
백준 11279번 : 최대 힙
"""
import heapq
import sys
N = int(sys.stdin.readline())
heap = []
for _ in range(N):
num = int(sys.stdin.readline())
if num == 0:
if len(heap) != 0:
print(heapq.heappop(heap)[1])
else:
print(0)
else:
heapq.heappush(heap, (-num, num)) # max_heap
|
[
"heapq.heappush",
"sys.stdin.readline",
"heapq.heappop"
] |
[((73, 93), 'sys.stdin.readline', 'sys.stdin.readline', ([], {}), '()\n', (91, 93), False, 'import sys\n'), ((139, 159), 'sys.stdin.readline', 'sys.stdin.readline', ([], {}), '()\n', (157, 159), False, 'import sys\n'), ((300, 333), 'heapq.heappush', 'heapq.heappush', (['heap', '(-num, num)'], {}), '(heap, (-num, num))\n', (314, 333), False, 'import heapq\n'), ((223, 242), 'heapq.heappop', 'heapq.heappop', (['heap'], {}), '(heap)\n', (236, 242), False, 'import heapq\n')]
|
import pyHiChi as pfc
import numpy as np
import math as ma
def valueEx(x, y, z):
Ex = 0 #for x or y
#Ex=np.sin(z) #for z
return Ex
def valueEy(x, y, z):
#Ey = 0 #for y or z
#Ey = np.sin(x) #for x
Ey = np.sin(x - z) #for xz
return Ey
def valueEz(x, y, z):
Ez = 0 #for x or z or xz
#Ez = np.sin(y) #for y
return Ez
def valueBx(x, y, z):
#Bx = 0 #for x or z
#Bx = np.sin(y) #for y
Bx = np.sin(x - z)/np.sqrt(2) #for xz
return Bx
def valueBy(x, y, z):
By = 0 #for x or y or xz
#By = np.sin(z) #for z
return By
def valueBz(x, y, z):
#Bz = 0 #for y or z
#Bz = np.sin(x) #for x
Bz = np.sin(x - z)/np.sqrt(2) #for xz
return Bz
def step(minCoords, maxCoords, gridSize):
steps = pfc.vector3d(1, 1, 1)
steps.x = (maxCoords.x - minCoords.x)/(gridSize.x)
steps.y = (maxCoords.y - minCoords.y)/(gridSize.y)
steps.z = (maxCoords.z - minCoords.z)/(gridSize.z)
return steps
gridSize = pfc.vector3d(20, 20, 20)
minCoords = pfc.vector3d(0.0, 0.0, 0.0)
maxCoords = pfc.vector3d(2*ma.pi, 2*ma.pi, 2*ma.pi)
stepsGrid = step(minCoords, maxCoords, gridSize)
timeStep = 1e-14
grid = pfc.YeeGrid(gridSize, timeStep, minCoords, stepsGrid)
grid.setE(valueEx, valueEy, valueEz)
grid.setB(valueBx, valueBy, valueBz)
fieldSolver = pfc.FDTD(grid)
fieldSolver.setPML(0, 0, 0)
periodicalBC = pfc.PeriodicalBC(fieldSolver)
#show
import matplotlib.pyplot as plt
import matplotlib.animation as animation
N = 50
eps = 0.0
x = np.arange(eps, 2*ma.pi - eps, 2*(ma.pi-eps)/N)
z = np.arange(eps, 2*ma.pi - eps, 2*(ma.pi-eps)/N)
def getFields():
global grid, x, z, N
#print(grid)
Ex = np.zeros(shape=(N,N))
Ey = np.zeros(shape=(N,N))
Ez = np.zeros(shape=(N,N))
Bx = np.zeros(shape=(N,N))
By = np.zeros(shape=(N,N))
Bz = np.zeros(shape=(N,N))
for ix in range(N):
for iy in range(N):
coordXZ = pfc.vector3d(x[ix], 0.0, z[iy]) #for x or z or xz
#coordXZ = pfc.vector3d(x[ix], z[iy], 0.0) #for y or x
E = grid.getE(coordXZ)
Ex[ix, iy] = E.x
Ey[ix, iy] = E.y
Ez[ix, iy] = E.z
B = grid.getB(coordXZ)
Bx[ix, iy] = B.x
By[ix, iy] = B.y
Bz[ix, iy] = B.z
return Ex, Ey, Ez, Bx, By, Bz
def updateData():
for i in range(1000):
fieldSolver.updateFields()
(Ex, Ey, Ez, Bx, By, Bz) = getFields()
fig, axes = plt.subplots(ncols=3, nrows=2)
im11 = axes[0, 0].imshow(Ex, cmap='RdBu', interpolation='none', extent=(0, 2*ma.pi, 0, 2*ma.pi), animated = True)
fig.colorbar(im11, ax=axes[0, 0])
im12 = axes[0, 1].imshow(Ey, cmap='RdBu', interpolation='none', extent=(0, 2*ma.pi, 0, 2*ma.pi), animated = True)
fig.colorbar(im12, ax=axes[0, 1])
im13 = axes[0, 2].imshow(Ez, cmap='RdBu', interpolation='none', extent=(0, 2*ma.pi, 0, 2*ma.pi), animated = True)
fig.colorbar(im13, ax=axes[0, 2])
im21 = axes[1, 0].imshow(Bx, cmap='RdBu', interpolation='none', extent=(0, 2*ma.pi, 0, 2*ma.pi), animated = True)
fig.colorbar(im21, ax=axes[1, 0])
im22 = axes[1, 1].imshow(By, cmap='RdBu', interpolation='none', extent=(0, 2*ma.pi, 0, 2*ma.pi), animated = True)
fig.colorbar(im22, ax=axes[1, 1])
im23 = axes[1, 2].imshow(Bz, cmap='RdBu', interpolation='none', extent=(0, 2*ma.pi, 0, 2*ma.pi), animated = True)
fig.colorbar(im23, ax=axes[1, 2])
i = 0
def updatefig(*args):
global i
updateData()
(Ex, Ey, Ez, Bx, By, Bz) = getFields()
im11.set_array(Ex)
im12.set_array(Ey)
im13.set_array(Ez)
im21.set_array(Bx)
im22.set_array(By)
im23.set_array(Bz)
i = i + 1
return im11, im12, im13, im21, im22, im23,
ani = animation.FuncAnimation(fig, updatefig, interval=50, blit=True)
plt.show()
|
[
"matplotlib.pyplot.show",
"numpy.zeros",
"pyHiChi.PeriodicalBC",
"matplotlib.animation.FuncAnimation",
"numpy.sin",
"numpy.arange",
"pyHiChi.vector3d",
"pyHiChi.YeeGrid",
"numpy.sqrt",
"matplotlib.pyplot.subplots",
"pyHiChi.FDTD"
] |
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|
"""
* Copyright (c) 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.
"""
"""
This script is only used for demo of fprop_v4
This version will be deprecated in near future, please update to fprop or fprop_experimental.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import tensorflow as tf
from tensorflow.python.distribute.values import PerReplica
import hugectr_tf_ops
""" 1. Define DNN model with fprop_v4, whole DNN model should be split into two sub-models."""
# define sparse model which contains embedding layer(s)
class PluginSparseModel(tf.keras.models.Model):
def __init__(self,
gpus,
batch_size,
embedding_type,
vocabulary_size,
slot_num,
embedding_vec_size,
embedding_type,
opt_hparam,
update_type,
atomic_update,
max_feature_num,
max_nnz,
combiner,
gpu_count):
super(PluginSparseModel, self).__init__()
self.vocabulary_size_each_gpu = (vocabulary_size // gpu_count) + 1
self.slot_num = slot_num
self.embedding_vec_size = embedding_vec_size
self.embedding_type = embedding_type
self.optimizer_type = optimizer
self.opt_hparam = opt_hparam
self.update_type = update_type
self.atomic_update = atomic_update
self.max_feature_num = max_feature_num
self.max_nnz = max_nnz
self.combiner = combiner
self.gpu_count = gpu_count
# Make use init() only be called once. It will create resource manager for embedding_plugin.
hugectr_tf_ops.init(visiable_gpus=gpus, seed=123, key_type='int64', value_type='float',
batch_size=batch_size, batch_size_eval=len(gpus))
# create one embedding layer, and its embedding_name will be unique if there are more than one embedding layer.
self.embedding_name = hugectr_tf_ops.create_embedding(initializer, name_=name, embedding_type=self.embedding_type,
optimizer_type=self.optimizer_type,
max_vocabulary_size_per_gpu=self.vocabulary_size_each_gpu,
opt_hparams=self.opt_hparam, update_type=self.update_type,
atomic_update=self.atomic_update, slot_num=self.slot_num,
max_nnz=self.max_nnz, max_feature_num=self.max_feature_num,
embedding_vec_size=self.embedding_vec_size,
combiner=self.combiner)
def build(self, _):
# this tf.Variable is used for embedding plugin.
self.bp_trigger = self.add_weight(name='bp_trigger', shape=(1,), dtype=tf.float32, trainable=True)
@tf.function
def call(self, row_indices, values, training=True):
# forward propagtion of embedding layer
return hugectr_tf_ops.fprop_v4(embedding_name=self.embedding_name, row_indices=row_indices,
values=values, bp_trigger=self.bp_trigger, is_training=training,
output_shape=[self.batch_size, self.slot_num, self.embedding_vec_size])
# define dense model which contains other parts of the DNN model
class DenseModel(tf.keras.models.Model):
def __init__(self, num_layers):
super(DenseModel, self).__init__()
self.num_layers = num_layers
self.dense_layers = []
for _ in range(num_layers - 1):
self.dense_layers.append(tf.keras.layers.Dense(units=1024, activation='relu'))
self.out_layer = tf.keras.layers.Dense(units=1, activation='sigmoid', use_bias=True,
kernel_initializer='glorot_normal',
bias_initializer='glorot_normal')
@tf.function
def call(self, inputs, training=True):
hidden = tf.reshape(inputs, [tf.shape(inputs)[0], 26 * 32]) # [batchsize, slot_num * embedding_vec_size]
for i in range(self.num_layers - 1):
hidden = self.dense_layers[i](hidden)
result = self.out_layer(hidden)
return result
""" 2.Define training loop with the model mentioned above """
def main():
# create MirroredStrategy with specified GPUs.
strategy = tf.distribute.MirroredStrategy(devices=["/GPU:" + str(i) for i in range(gpu_count)])
# create sparse model outside the scope of MirroredStrategy
sparse_model = PluginSparseModel(...)
sparse_opt = tf.keras.optimizers.SGD()
# create dense model inside the scope of MirroredSrategy
with strategy.scope():
dense_model = DenseModel(...)
dense_opt = tf.keras.optimizers.SGD()
# define loss function for each replica
loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=False, reduction=tf.keras.losses.Reduction.NONE)
def _replica_loss(labels, logits):
loss_value = loss_fn(labels, logits)
return tf.nn.compute_average_loss(loss_value, global_batch_size=batch_size)
# define dense model train step
@tf.function
def dense_train_step(dense_inputs, labels):
with tf.GradientTape() as tape:
# should watch inputs, in order to obtain gradients later
tape.watch(dense_inputs)
logits = dense_model(dense_inputs)
replica_loss = _replica_loss(labels, logits)
grads, input_grads = tape.gradient(replica_loss, [dense_model.trainable_weights, dense_inputs])
dense_opt.apply_gradients(zip(grads, dense_model.trainable_weights))
return replica_loss, input_grads
# define whole model train step
@tf.function
def total_train_step(row_indices, values, labels):
with tf.GradientTape() as tape:
# do embedding fprop
embedding_results = sparse_model(row_indices, values)
# convert to PerReplica
dense_inputs = tf.split(embedding_results, num_or_size_splits=gpu_count)
dense_inputs = PerReplica(dense_inputs)
labels = tf.expand_dims(labels, axis=1)
labels = tf.split(labels, num_or_size_splits=gpu_count)
labels = PerReplica(labels)
replica_loss, input_grads = strategy.run(dense_train_step, args=(dense_inputs, labels))
# gather all grads from dense replicas
all_grads = tf.concat(input_grads.values, axis=0)
# do embedding backward
embedding_grads = tape.gradient(embedding_results, sparse_model.trainable_weights, output_gradients=all_grads)
sparse_opt.apply_gradients(zip(embedding_grads, sparse_model.trainable_weights))
return strategy.reduce(tf.distribute.ReduceOp.SUM, replica_loss, axis=None)
# create a tf.data.Dataset to read data
dataset = ...
# training loop
for step, (row_indices, values, labels) in enumerate(dataset):
total_loss = total_train_step(row_indices, values, labels)
# you can save model, print loss or do sth. else.
|
[
"tensorflow.nn.compute_average_loss",
"hugectr_tf_ops.create_embedding",
"hugectr_tf_ops.fprop_v4",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.optimizers.SGD",
"tensorflow.concat",
"tensorflow.keras.losses.BinaryCrossentropy",
"tensorflow.shape",
"tensorflow.python.distribute.values.PerReplica",
"tensorflow.split",
"tensorflow.GradientTape",
"tensorflow.expand_dims"
] |
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|